Monday, June 18, 2012

5300.txt

date: Wed, 29 Sep 1999 12:09:02 +0200 (MET DST)
from: GIORGI FILIPPO <giorgiatXYZxyzp.trieste.it>
subject: chapter 10, draft 1
to: tar 10 site <tar10atXYZxyz.uct.ac.za>

Dear All

in the attachment, please find the entire chapter as it now stands. You
should consider this as the going official draft. Anything else you might
have in your computers (ESPECIALLY IN WORD) does not count.

This is now the going plan:

1) You should all read the whole thing (if possible). Especially read
your individual sections, but also pay special attention to the "common"
sections, i.e. 10.1, 10.2, 10.8. In particular make sure that all that is
said there is in tune with what is said in the individual sections.

2) Do not worry about little editorial issues. I and Bruce ar going to go
over the text carefully to fix those without changing the meaning of
things. At this point there is no time for
major "structural" changes. If you have some significant changes you want
to make, indicate the original paragraph and the modifications to
it. At this point there should not be many of them (but you never know).
Direct the changes to all, but I and Bruce will be in charge of putting
them in.

3) Let everybody know if there are major issues you want to raise.

4) If you know of work that is not included here but will likely be
included in the next draft, make a list and we will include it within
the text at the end of the appropriate section (please indicate that too).

what is missing, needed etc.

6) Section coordinators, collect all figure captions for your section
and please send them to me so I can put them together.

7) Section coordinators, please complete all missing tables and other
relevant material and send to Bruce in some ps format.

8) Section coordinators please check with Bruce about figures that he has
or that are missing.

9) Give any other comment you might feel is pertinent.

All the points 1) through 9) should be completed by end of day
thursday (Trieste time). Then on friday I and Bruce will coordinate
to produce the final offical draft and put it on the website for
the TSU.

Couple more days and we should be done. Thansk a lot for your effort in
the last weeks.

Cheers, Filippo

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10.1 Introduction

This Chapter is a new addition compared to previous IPCC assessment
reports. It stems from the increasing need to evaluate regional climate
change information for use in impact studies and policy planning.
To date, regional climate change information has been characterized by a
relatively high level of uncertainty. This is due to the complexity of
processes that determine regional climate change, which span a wide
range of spatial and temporal scales, and to the difficulty of extracting
fine scale regional information from coarse resolution AOGCMs.

Coupled AOGCMs are the modeling tools traditionally used for generating
projections of climatic changes due to anthropogenic forcings.
The horizontal atmospheric resolution of present day AOGCMs is still
relatively coarse, order of 300-500 km, due to the
centennial to millenial timescales associated with the
ocean circulation and the computing requirements that these imply.
However, regional climate is often affected by forcings and circulations
that occur at the sub-AOGCM horizontal grid scale (e.g. Giorgi and Mearns
1991). As a result, AOGCMs cannot explicitly capture the fine scale structure
that characterizes climatic variables in many regions of the world and
that is needed for impact assessment studies (see Chapter 13).

Therefore, a number of techniques have been developed with the goal of
enhancing the regional information provided by coupled AOGCMs
and providing fine scale climate information.
here these are referred to as "regionalization" techniques and classify them
into three categories:
1) high resolution and variable resolution time-slice" AGCM experiments;
2) nested limited area (or regional) climate models;
3) empirical/statistical and statistical/dynamical methods.
Since the SAR report, a substantial
development has been achieved in all these areas of research.

This chapter has two fundamental objectives. The first is
to assess whether the scientific
community has been able to increase the confidence which can be placed
in the projection of regional climate change caused by anthropogenic
forcings since the SAR report. The second is to evaluate progress
in regional climate research and to provide guidelines for the
use of different methods. It is not the purpose of this chapter
to provide actual regional climate change information for direct use
in impact work, although the material discussed in this chapter serves
most often for the formation of climate change scenarios.
Climate scenario development is discussed in Chapter 13.

Our assessment is based on an analysis of
studies employing all the different modeling tools that are today
available to obtain regional climate information.
The analysis includes: a) an evaluation of the
performance, strengths and weaknesses of different techniques in
reproducing present day climate
characteristics and in simulating processes of importance for
regional climate; and b) an evaluation of the confidence and uncertainties in
the simulation of climate change at the regional scale.
In fact, even though a good simulation of present day climate
does not necessarily imply a more accurate simulations of future climate
confidence in the realism of a model's response to an anomalous
climate forcing can be expected to be higher when the model is capable of
reproducing observed climate. Also, interpretation of the
response is often facilitated by understanding the behaviour of the model in
simulating the current climate.

Based on this premise, the chapter is organized
as follows. In the remainder of this section we present a summary
of the conclusions reached in the SAR report concerning regional climate
change and then briefly discuss in general terms the regional climate
problem. In section 10.2 we examine the principles behind
different approaches to the generation of regional climate information.
Regional attributes of coupled AOGCM simulations are discussed in
section 10.3. This discussion is important for two reasons: first,
because AOGCMs are the
starting point in the generation of regional climate change scenarios;
and second, because many climate impact assessment studies still make
use of output from coupled AOGCM experiments without utilizing any
regionalization tool.
Sections 10.4, 10.5 and 10.6 are devoted to the analysis
of experiments using high resolution and variable resolution AGCMs,
regional climate models and empirical/statistical and statistical/dynamical
methods, respectively. In section 10.7 we then discuss studies in which
different regionalization techniques have been compared, and
in section 10.8 we summarize our main conclusions.

10.1.1 Summary of SAR

The analysis of regional climate information in the SAR
(section 6.6) consisted of two primary segments. In the first, results
were analysed from an intercomparison of a number of coupled AOGCM
experiments over 7 regions of the world.
The intercomparison included coupled AOGCMs with
and without ocean flux correction and focused on summer and winter
precipitation and surface air temperature. Biases in the simulation
of present day climate with respect to
observations and sensitivities at time of CO$_2$ doubling were
analyzed. A wide intermodel range of both biases and sensitivities was
found, with marked inter-regional variability. Temperature
biases were mostly in the range of +/- 5 C, with several instances
of larger biases. Precipitation biases were mostly in the range of
+/- 50%, but with a few instances of
biases exceeding 100%. The range of sensitivities
was lower for both variables.

The second segment of the analysis mostly focused on results from nested
regional models and downscaling experiments. Both these techniques were
still at the early stages of their development and application, so that
only a limited set of studies was available for the SAR. The primary
conclusions from these studies were: a) both regional modeling and
downscaling techniques showed a promising performance in reproducing the
regional detail in surface climate characteristics as forced by topography,
lake, coastlines and land use distributions; b) high resolution surface
forcings significantly modify the surface climate change signal at
the sub-AOGCM grid scale.

Overall, the SAR still placed low confidence in the simulation of
regional climate change
produced by available modeling tools, primarily because of three
factors: 1) Errors in the reproduction of present day regional
climate characteristics; 2) wide inter-model variability in the simulated
climatic changes; 3) sub-AOGCM grid scale structure of the climate change
signal suggested by available regionalization studies.
Other points raised in the SAR
were the need of better datasets for model validation at the regional scale
and the need to examine higher order climate statistics.

\vskip .5cm
\item{\it 10.1.2} {\it The regional climate problem}
\vskip .3cm

A definition of regional scale is difficult, as different definitions are
often implied in different contexts. For example, definitions can be based
on geographical, political or physiographic considerations, considerations
of climate homogeneity, or considerations of model resolution. Because of
this difficulty, in this chapter we adopt an operational definition
based on the range of "regional scale" adopted in the available
literature. From this perspective, we here define
regional scale as describing the range of 10**4--10**7 km**2.
The upper end of the range (10**7 km**2) is also often referred to as
sub-continental scale. Circulations occurring at larger scales are
clearly dominated by general circulation processes and interactions.
Note that marked climatic inhomogeneity
can occur within a region of 10**7 km**2 size in many areas of the globe.
We refer to scales greater than 10**7 km**2 as large scale".
The lower end of the range (10**4 km**2) is representative of the
smallest scales resolved by current regional climate models. Scales smaller
than 10**4 km**2 are here referred to as local scale".

Given these definitions, the climate of a given region is determined by the
interaction of forcings and circulations that occur at the large, regional
and local spatial scales, and at a wide range of temporal scales,
from sub-daily to multi-decadal. Large scale forcings regulate the
general circulation of the global atmosphere. This in turn determines the
sequence and characteristics of weather events and weather regimes
which characterize the climate of a region. Embedded within the
large scale circulation regimes, regional and local forcings and mesoscale
circulations modulate the spatial and temporal structure of the
regional climate signal, with an effect that can in turn
influence large scale circulation features. Examples of regional and local
scale forcings are those due to complex topography,
land use characteristics, inland bodies of water, land-ocean contrasts,
atmospheric aerosol,
radiatively active gases, snow and sea ice distributions.
Moreover, climatic variability of a region
can be strongly influenced through teleconnection
patterns originated by forcing anomalies in distant regions, such as
in the El Nino Southern Oscillation (ENSO) and
North Atlantic Oscillation (NAO) phenomena.

The difficulty of simulating regional climate change is therefore evident.
The effects of forcings at the global, regional and local scale need to
be properly represented, along with the teleconnection effects of
regional forcing anomalies. These processes are characterized by
a range of temporal variability scales, and can be highly non-linear.
Moreover, similarly to what happens for the global Earth system,
climate at the regional scale is also modulated by
interactions among different components of the climate system,
such as the atmosphere, hydrosphere, cryosphere,
biosphere and chemosphere.

Therefore, a cross-disciplinary and multi-scale approach
is necessary for a full understanding of regional
climate change processes. This is based on the use of
coupled AOGCMs to simulate the global climate system
response to large scale
forcings and the variability patterns associated with broad regional forcing
anomalies. The information provided by the AOGCMs can then be enhanced
via a suitable use of the regionalization techniques discussed in this chapter.

---------------------------

10.2 Deriving Regional Information: Principles, objectives and assumptions

It is useful to present an overall discussion of the principles,
objectives and assumptions underlying the different techniques today
available for deriving regional climate change information.
For some applications, the regional information provided by AOGCMs
may suffice (10.2.1), while in other cases
regionalization techniques can be used to enhance the regional information
provided by coupled AOGCMs.
The basic principles behind the
regionalization methods we identified are discussed in sections 10.2.2,
high resolution and variable resolution time slice" AGCM
experiments; 10.2.3, regional climate models; and
10.2.4, empirical/statistical and statistical/dynamical models.

The latter two techniques are often referred to as "downscaling"
methods which use large-scale AOGCM information to derive
consistent and detailed information at the regional and local scale. The
concept of "downscaling" implies that the regional climate is
conditioned but not completely determined by the large-scale state.
In fact, regional states associated with similar large-scale states may
vary substantially (e.g. Starr, 1942; Roebber and Bosart, 1998).

The use of regionalization tools is advisable only when
this enhances the information of AOGCMs at the regional and local
scale. The "added value" provided by regionalization techniques
depends on the spatial and temporal scales of interest as well as on
the variable and climate statistics.
This aspect of the regional climate problem is discussed in 10.2.5.
Finally, the section closes with a brief overarching discussion
of different sources of uncertaintiy present in the production of
regional climate change information.

10.2.1 Coupled AOGCMs

The majority of climate change impact studies have made use of
raw climate information provided by transient runs with coupled AOGCMs
without any further regionalization processing. The primary reason for this
is twofold, i.e. the ready availability of this information, which is global
in nature and is routinely stored by major laboratories, and the only
recent development of regionalization techniques.
Data can be easily drawn from the full range of currently available
GCM experiments of the various modelling centres for any region of
the World and uncertainty due to inter-model (or inter-run) differences can
thus be evaluated (e.g. Hulme and Brown 1998).
Also, data can be obtained for a
large range of variables down to very short (sub-daily) time scales.
In particular, spatially coherent climatic variability at short time
scales is routinely simulated.

>From the theoretical viewpoint,
the major advantage of obtaining regional climate information
directly from AOGCMs is the knowledge that internal physical consistency is
maintained. The feedback resulting from climate change in a particular
region on large scale climate and the climate of other regions is allowed
for through physical and dynamical processes in the model.
This may be an important consideration when the simulation of regional
climate or climate change is compared across regions.

The limitations of coupled AOGCM regional information are however well
known. By definition, coupled AOGCMs cannot provide direct
information at scales smaller than their resolution (order of several
hundred km), neither can they
capture the detailed effects of forcings acting at sub-grid scales (unless
parameterized). Biases in the climate simulation at the AOGCM resolution
can thus be introduced by the absence of subgrid scale variations in forcing.
As an example, a narrow (subgrid scale) mountain range can be
Many important aspects of the climate of a region (e.g. climatic
means in areas of complex topography or extreme weather systems such
as tropical cyclones) can only be
directly simulated at much finer resolution than that of current AOGCMs.
Analysis relevant to these aspects is
undertaken with AOGCM output, but various qualifications need to be
considered in the interpretation of the results.

Past analyses have indicated that even at their
smallest resolvable scales, which still fall under our definition of
regional, coupled AOGCMs have substantial problems in reproducing present
day climate characteristics. Many scientists maintain that the
minimum skillful scale of a model is of several grid lengths, since these
are necessary to describe the smallest wavelengths in the model and since
numerical truncation errors
are most severe for the smallest resolved spatial scales.
Also, non-linear interactions are poorly represented for those scales
closest to the truncation of a model because of the damping of dissipation
terms and because only the contribution of larger scale (and not smaller
scale) eddies is accounted for (e.g. von Storch, 1995).

studies can weigh-up differently
depending on the region and variables of interest.
For example, in instances for which sub-grid scale variation is weak (e.g.
for mean sea level pressure)
the practical advantages of using direct AOGCM data may predominate.
Chapter 13 discusses the use of AOGCM information for climate change
scenario development.

Even if resolution factors limit the feasibility of using regional
information from coupled AOGCM for impact work, coupled AOGCMs are the
starting point of any regionalization technique presently used. Therefore,
it is of utmost importance that coupled AOGCMs show a good performance in
simulating circulation and climatic features that affect regional climates,
such as jet streams and storm tracks. Indeed, most indications are that,
in this regard, the performance of coupled AOGCMs is generally improving,
because of both, increased resolution and improvements in the
representation of physical processes (see chapter 8 of this report).

10.2.2 High resolution and variable resolution time-slice AGCM experiments

Though simulations of many centuries are required to fully integrate the
global climate system, for many applications regional
information on climate or climate change is required for
at most several decades. Over these time scales
atmospheric GCM (AGCM) simulations are feasible at resolutions
of the order of 100 km globally, or 50 km locally with variable
resolution models. This suggests identifying periods of
interest (or "time-slices") within AOGCM transient simulations and
modeling these with a higher resolution or variable
resolution AGCM to provide
additional spatial detail (e.g. Bengtsson et.
al., 1995; Cubasch et al., 1995).

Such an AGCM can then be used to simulate the climate
response to an anomalous forcing (e.g. changes in greenhouse gas (GHG) and
sulphate aerosols) by direct inclusion of the forcing along with a
consistent set of initial conditions and ocean surface
boundary conditions. For the control simulation these
could be derived from observations, an AOGCM control
simulation or a transient AOGCM simulation using observed
changes in atmospheric trace gases and aerosols. For the
anomaly experiment there are several possibilities:
Values could be used directly from an AOGCM anomaly
simulation (equilibrium or transient)
or derived from various perturbations of the
control. The latter could take the form of an idealised
uniform change in SSTs or spatially
and seasonally varying changes derived from
AOGCM simulations with matching radiative forcing changes.

In a typical experiment (e.g. May 1999), two time
slices, say 1961-1990 and 2071-2100, are selected from a
transient AOGCM simulation. Time-dependent fields of SST
and sea ice distribution are extracted and used as lower
boundary conditions for a high resolution (or variable
resolution) AGCM. Also,
time-dependent GHG and aerosol concentrations
(or aerosol forcing) in the AGCM experiments are the same
as in the corresponding coupled AOGCM time slice. Initial
atmospheric and land surface conditions for the AGCM
experiments are also interpolated from the AOGCM fields.
The philosophy behind the use of time-slice AGCM
simulations is that, given the SST, sea-ice, trace gas
and aerosol forcing, relatively high resolution
information can be obtained globally or regionally
without having to perform the whole transient simulation
with high resolution models.

The approach is based on two major assumptions.
The first is that the large scale
circulation patterns in the coarse and high resolution
GCMs are not markedly different from each other, otherwise the
consistency between the high resolution AGCM climate and
the SST, sea ice and aerosol forcing from the coarse
resolution AOGCM would be questionable. The other
assumption is that the state of the atmosphere may be
considered as being in equilibrium with its lower
boundary conditions provided by the slower-evolving ocean
and sea ice components.

The main theoretical advantage of this approach is that
the resulting simulations are globally consistent,
capturing remote responses to the impact of higher
resolution. Conversely this also allows the AGCM to
evolve its own large scale climatology, possibly violating
the first of the above assumptions. Thus it is important
to consider the degree of convergence obtained at
the standard and high resolutions. As resolution increases it
is assumed that model simulations of the resolved
large scale variables would asymptote to a solution. This
implies there will be a threshold resolution greater than
which the solution will not change fundamentally in
character but just add extra detail at the finer scales.
There is evidence that this has not been reached at the
current resolution of AOGCMs which may add uncertainty
to the value of regional information derived from AGCM
timeslice experiments.

A practical weakness of high resolution models is that
they generally use the same formulations as at the coarse
resolution at which they have been optimized
to give accurate simulations of the current climate. The
representation of some processes may thus be less
accurate when finer scales are resolved so that
some model formulations may need to be "re-tuned" for use
at higher resolution. Experience with high resolution
GCMs is still limited, so that
presently increasing the resolution of an AGCM
generally both enhances and degrades aspects of the simulations.
With global variable resolution models this issue is
further complicated as the model physics parameterizations
have to be designed in a way that they can be valid and
function correctly over the range of resolutions covered
by the model.

Another issue concerning the use of variable resolution models
is that feedback effects from fine scales to large scales
are represented only as generated by the region of interest.
Conversely, in the real atmosphere feedbacks derive from
different regions and interact with each other so that a variable
resolution model based on a single high resolution region
might give an improper description of fine-to-coarse scale feedbacks.
In addition, a sufficient minimal resolution must be retained outside
the high resolution area of interest in order to prevent
a degradation of the simulation of the whole global system.

Use of high resolution and variable resolution global
models is computationally very demanding, which poses
limits on the increase in resolution obtainable with this method.
However, it has
been suggested that high resolution AGCMs could be used
to obtain forcing fields for higher resolution regional model experiments
or statistical downscaling, thus effectively providing an
intermediate step between coarse coupled AOGCMs and
regional and empirical models.

10.2.3 Regional climate models

What is commonly referred to as nested regional climate modeling technique
consists of using output from global model simulations
to provide initial conditions and time-dependent lateral
meteorological conditions to drive high-resolution regional climate model (RCM)
(or limited area model) simulations
for selected time periods of the global model run
(e.g. Dickinson et al. 1989; Giorgi 1990).
SST, sea ice, GHG and aerosol forcing, as well as
initial soil conditions, are also provided by the driving AOGCM.
Some variations of this technique include forcing of the large scale
component of the solution throughout the entire RCM domain (e.g. Kida et al.
1991; vonStorch et al. 1999)

To date, this
technique has been used only in one-way mode, i.e. with no feedback from
the RCM simulation to the driving GCM. The basic strategy
underlying this one-way nesting approach is that the GCM is used to
simulate the response of the global circulation to large scale forcings and
the RCM is used to account for sub-GCM grid scale forcings (e.g.
complex topographical features and land cover inhomogeneity) in a
physically-based way and to enhance the simulation of atmospheric
circulations and climatic variables at fine spatial scales.

The nested regional modeling technique essentially originated from
numerical weather prediction, but is by now extensively used in a wide
range of climate applications, going from paleoclimate
to anthropogenic climate change studies. Over the last
decade, regional climate models have proven to be flexible tools, capable
of reaching high resolution (up to 10-20 km or less) and multi-decadal
simulation times and capable of describing climate feedback mechanisms
acting at the regional scale.
A number of widely used limited area modeling systems have been
adapted to, or developed for, climate application.

On the other hand, the fundamental theoretical
limitations of this technique are by now
well known: lack of two-way interactions between global and regional
climate; and effects of systematic errors in the driving large scale fields
provided by global models. In addition, for each application careful
consideration needs to be given to some aspects of model configuration,
such as physics parameterizations, model domain size and resolution,
technique for assimilation of large scale meteorological forcing
(e.g. Giorgi and Mearns 1991, 1999).
Recent studies have also
shown that regional models exhibit internal variability due to non-linear
internal dynamics not associated to the boundary forcing, which adds a
further element to be considered in regional climate change simulations (Ji
and Vernekar, 1997).
Outstanding issues related to the above aspects of nested RCM modeling
are discussed in section 10.5.

>From the practical viewpoint, depending on the domain size and resolution,
RCM simulations can be computationally demanding, which has limited
the length of many experiments to date. An additional consideration is
that in order to run an RCM experiment high frequency (e.g. 6-hourly)
time dependent GCM fields are needed. These are not routinely stored
because of the implied mass-storage requirements, so that careful
coordination between global and regional modelers is needed to design
nested RCM experiments. In this regard an aspect which should be
considered for a specific demand of regional
information is whether this can be obtained by simpler disaggregation methods.
For instance, specification of topographically induced spatial detail in
near-surface temperature may be possible with the use of GIS-based
disaggregation schemes without having to rely on complex physical models
(Agnew and Palutikof, 1999).

Of particular interest is the direction taken by recent RCM modeling efforts
towards the
coupling of atmospheric models with other climate process models, such
as hydrology, ocean, sea-ice, chemistry/aerosol and ecosystem models. The
possibility of developing
coupled "regional climate system models" will certainly open the use of
RCMs to many new areas of global change research.

10.2.4 Empirical/statistical and statistical/dynamical downscaling

Statistical downscaling is based on the view that regional climate may be
thought of as being conditioned by two factors: the large scale
climatic state, and regional/local physiographic features (e.g.
topography, land-sea distribution and landuse; von Storch, 1995, 1999).
>From this viewpoint, regional or local climate information is
derived by first determining a statistical model which relates large-scale
climate variables (or "predictors") to regional and local variables (or
"predictands"). Then the large-scale output of an AOGCM simulation is fed
into this statistical model to estimate the corresponding local and
regional climate characteristics.

A range of statistical downscaling models, from regressions to neural
network and analogues, have been developed for regions where sufficiently
good datasets are available for model calibration. In a particular type of
statistical downscaling methods, called statistical-dynamical
downscaling (see 10.6.3.3), use is made of atmospheric mesoscale
models to develop the statistical models.
Statistical downscaling techniques have their roots in synoptic climatology
(Growetterlagen; e.g., Baur et al., 1944; Lamb 1972) and numerical weather
prediction (Klein and Glahn, 1974), but they are also currently used for
a wide range of climate applications, from historical reconstruction
(e.g. Appenzeller et al., 1998, Luterbacher et al., 1999), to regional
climate change problems (see section 10.6).
A number of review papers have dealt with
downscaling concepts, prospects and limitations: von Storch (1995),
Hewitson and Crane (1996) and Wilby and Wigley (1998), Gyalistras et
al. (1998), Murphy (1999a,b), Zorita (1999).

One of the primary advantages of these techniques is that they are
computationally inexpensive, and thus can be easily applied to output from
different GCM experiments. Another advantage is that they can be used to
provide local information, which can be most needed in many climate change
impact applications. The applications of downscaling
techniques vary widely with respect to regions, spatial and temporal scales,
type of predictors and predictands, and climate statistics
(from average temperature and precipitation to more episodic quantities such
as storm interarrival times or frequency of strong wind events).

The major theoretical weakness of statistical downscaling methods is
that their basic assumption is
often not verifiable, i.e. that the statistical relationships developed
for present day climate also hold under the different forcing conditions of
possible future climates. (TUNING SENTENCE)
Another caveat is that these empirically
based techniques cannot account for possible systematic changes
in regional forcing conditions or feedback processes.

The possibility of tailoring the statistical model to the
requesetd regional or local information is a distinct advantage.
However, it has the drawback that a systematice assessment of the
uncertainty of this type of technique, as well as a comparison with
other techniques, is difficult and may need to be carried out on a
case-by-case basis. A number of examples are presented
in section 10.6.

An interesting by-product of empirical downscaling methods
is that they offer a framework for testing the ability of physical
models to simulate the empirically found links between large-scale and
small-scale climate (Busuioc et al., 1999; Murphy, 1999a; Osborn et
al., 1999; von Storch et al., 1993; Noguer, 1994).

10.2.5 The "Added Value" of regionalization techniques.

The issue of "added value" of regionalization techniques is a
difficult and much debated one. This is because it essentially
depends on, and thus needs to be carefully formulated for,
the specific scientific problem of interest.
AOGCMs are designed to generate information at the
large scale but, due to their resolution limitations, in many circumstances
they are not expected to provide accurate regional and local climate detail.
A fundamental question is therefore,
whether it is possible to use regionalization techniques
scales and their interaction with the climate system taking as input the large
scale information from AOGCMs.
The use of a regionalization tool for climate change simulation
compared to the AOGCM.

One of the reasons for developing regionalization
techniques is to capture the effect of fine scale forcings in areas
characterized by fine spatial variability of features such as
topography and land surface conditions. In fact, in many
regions topography and land use affect the spatial distribution of climate
variables and generate (or modulate) atmospheric circulations at
scales that are not explicitly described by AOGCMs. A regionalization
method is thus needed to capture these effects and research
has for example shown that the simulation of
the spatial patterns of precipitation and temperature over complex terrain
is generally improved with the increasing resolution obtained with
regionalization techniques (see remainder of the chapter).

The increased spatial resolution of regionalization tools
also allows an improved description of
regional and local atmospheric circulations such as synoptic and frontal
extratropical systems, narrow jet cores, cyclogenetic processes, gravity
waves, mesoscale convective systems, sea-breeze type circulations and
extreme weather systems (e.g. tropical storms). Sub-grid scale processes
that are parameterized in AOGCMs, such as cloud and precipitation
formation, can also benefit from increased spatial resolution.

Because spatial and temporal scales in atmospheric phenomena are often
related, regionalization techniques can also be expected to improve
the AOGCM information at high frequency
temporal scales, such as daily or sub-daily.
This is despite the fact that AOGCMs
do provide high resolution temporal information. Therefore, for example,
regionalization models can be used to improve the simulation of quantities
such as daily
precipitation frequency and intensity distributions, surface wind speed
variability, storm inter-arrival times, monsoon front onset and transition
times.

>From a philosophical point of view, regionalization techniques are not intended
to strongly modify the large scale circulations produced by the forcing
AOGCMs, as this would result in inconsistencies between large scale forcing
fields and high resolution simulated fields whose effects and implications
would be difficult to evaluate. The assumption underlying this approach is
that the effects of fine scale processes on the large scale fields is
sufficiently well "parameterized" in the AOGCMs. In practice, the high
resolution forcing described by some regionalization models, such as
high resolution and variable resolution AGCMs and RCMs with sufficiently
large domains, can yield significant modification of the large scale flows
(e.g. storm tracks), possibly leading to an improved simulation of them.
This has the important by-product of providing valuable information for
the future development of higher resolution AOGCMs.

10.2.6 Uncertainties in the generation of regional climate change information

There are several levels of uncertainty in the generation of regional
climate change information. The first level, which is not dealt with in
this chapter, is associated with emission and corresponding concentration
scenarios (see Chapter 13). The second level of
uncertainty is related to the simulation of the transient climate response
by coupled AOGCMs for a given emission scenario. This uncertainty has
a global aspect, related to the model global sensitivity to forcing,
as well as a regional aspect, more tied to the model simulation
of general circulation features. This uncertainty is
important both, when AOGCM information is used for impact work
without the intermediate step of a regionalization tool, and when AOGCM
fields are used to drive a regionalization technique. The final level of
uncertainty occurs when the
AOGCM data are processed through a regionalization method.

Sources of uncertainty in producing regional climate information are of
different nature. On the modeling and statistical downscaling side,
uncertainties are associated with imperfect knowledge and/or representation
of physical processes, limitations due to the numerical approximation of
the model's equations, simplifications and assumptions in the models and/or
approaches, internal model variability, and inter-model or inter-method
differences in the simulation of climate response to given forcings. It is
also important to recognize that regional climate observations are
sometimes characterized by a high level of uncertainty, especially in
remote regions and in regions of complex topography.
Finally, the internal variability of the global
and regional climate system adds a further level of uncertainty
in the evaluation of a climate change simulation.

It is difficult to find unambiguous criteria to evaluate the level of
confidence of a regional climate prediction, say for the 21st century,
since this prediction is not
directly verifiable. In general, a model's (or method's) capability of
providing a good simulation of observed historical climate and climatic
variability is an indication of increased confidence in the climate change
simulation. Based on this criterion, a measure of uncertainty could
be associated with the deviation of the model simulation from observed climate.
This should however be viewed within the context that some
model parameters are often optimized to reproduce present day climate
and that, as mentioned, a good simulation of present day climate
is not a sufficient condition for accurate simulation of climate
change if the relevant processes and feedbacks that lead to the change
are not well described.

Another measure of confidence in the simulation of climate change
is the model's ability to reproduce known climate conditions
different from present, such as paleoclimates. A third measure
of confidence can be related to the convergence of
simulations by different models (or methods). Based on this criterion,
a measure of uncertainty could be the spread of model (or method)
results. Within this context, however,
a convergence in model simulations might also indicate a commonality
of basic flaws among models, since fundamental modeling assumptions
are shared by most models.
The emerging activity of seasonal to interannual climate forecasting
may also give valuable insights into the capability of models to
simulate climatic changes and may provide methodologies for evaluating
the long term prediction performance of climate models.

---------------------------

10.3 Regional Attributes of A/OGCMs

Since the SAR, GCM-based regional studies have been undertaken for North
America (eg, Risbey and Stone, 1996, Fyfe and Flato 1999, Doherty and
Mearns, 1999), South America (eg. Carril, Menendez and Nunez 1997, Labraga
and Lopez 1997), Europe (eg. Barrow et al. 1996, Osborn et al. 1999,
Raisanen 1998, Hulme and Brown 1998, Osborn and Hulme 1998, Benestad 1999),
Australasia (e.g., Kidson and Watterson, 1995, Whetton et al. 1996a, Whetton
et al. 1999), Southern Asia (e.g, Lal et al. 1998a,b, Dumenil 1998, Lal and
Harasawa 1999a,b), Southeast Asia (e.g, Smith et al. 1998), Africa (eg,
Joubert and Tyson, 1996, Fyfe 1998) and the South Pacific (Pittock, 1996,
Jones et al. 1999). In addition, there have also been a number of studies
which have considered multiple regions (e.g., Whetton et al 1996 b, Giorgi
and Francisco 1999a,b Boer et al.1999a, Flato et al. 1999).

These regional studies vary considerably with regard to:
� Regional domains used.
� Consideration given to within-region patterns. In some studies (e.g.
Labraga and Lopez, 1997) this is a major focus.
� Variables considered. Temperature and precipitation are most commonly
considered, although often MSLP is considered as well (e.g. Schubert 1998).
� The relative attention given to current climate validation as opposed to
enhanced GHG changes.
� Whether climatic variability and extremes are considered as well as
climatic means.
� The range of GCM experiments considered. This may be a single run only, a
number of runs from a modeling center in which different forcing is used
(e.g. GHG only versus GHG with sulphates - Boer et al. 1999a) and ensemble
of runs with the same forcing and model (e.g. Hulme et al. 1999, Giorgi and
Francisco 1999a), or runs with similar forcing from different models (e.g.
Lal and Harasawa, 1999b, Giorgi and Francisco 1999b).
� The age of the model runs. Some of the above studies include, amongst
newer runs, some relatively old simulations.
� Type of model run. Some use equilibrium 1xCO2 and 2xCO2 slab ocean GCMs
(which are know to differ systematically from AOGCMs in their simulated
pattern of regional climate change- Whetton et al. 1996a).

This variation has allowed a range of relevant issues to be addressed and in
the following sections studies such as those listed above will be drawn upon
to illustrate various aspects of regional climate simulation with AGCMs.
However, the large variety in study regions and methods also means that a
comprehensive and consistent region by region analysis cannot be readily
undertaken within the scope of this assessment.

10.3.1 Current Climate

10.3.1.1 Regional evaluation

In this section we consider simulation of current regional climate using
AOGCMs. Assessment of current climate simulation against observations
(regional evaluation) is essential if the enhanced GHG regional results of
models are to be correctly interpreted. Various issues associated with model
evaluation at the global scale are discussed in Chapter 8 (section 8.2). In
regional evaluation, the need to assess model errors arising due to the
coarse horizontal resolution of current models is a major concern.

Comparing model results against observations in regional studies can serve
two objectives. The first is to assess the capability of the model to
simulate the climatic features that are to be the focus of study under
enhanced GHG conditions. Knowledge of this capability is important for the
interpretation of the climate change results. For example, if the tropical
low pressure systems simulated by models do not resemble tropical cyclones
in key respects (e.g. insufficient intensity), the relevance of simulated
changes in these systems for assessing changes in tropical cyclone behavior
is problematic and needs careful interpretation. Many of the shortcomings
revealed in comparisons of this sort will relate to the coarse horizontal
resolution of the model, so that the regional evaluation can also be
considered as an exercise in determining the skillful spatial scale of the
model.

The second objective is to assess how reliably the model simulates processes
contributing to changes in key regional climatic features. Comparison of the
climatic feature of interest against observations does contribute to this
assessment, but there is a need to consider model climate more broadly. For
example, if one's interest is in precipitation change, it would be
appropriate to consider the model's simulation of synoptic circulation
patterns associated with rainfall occurrence. Where regional precipitation
is sensitive to sea-surface temperature patterns, it could be argued that
regional oceanic processes require validation. Choosing appropriate
variables for evaluation, determining the appropriate domain, weighing-up
performance of one variable against another, and drawing overall conclusions
on whether a model is performing acceptably well are difficult issues for
which there is as yet no generally accepted methodology.

10.3.1.2 Climatic means

Although current AOGCMs simulate well the observed global pattern of surface
temperature (see Chapter 8), at the regional scale substantial biases are
evident. To give an overview of the regional performance of current models,
results are presented of Giorgi and Francisco (1999b) who compared model and
observed seasonal mean temperature and precipitation averaged for each of
the regions indicated in Figure 10.3.1. The AOGCM experiments they
considered were a selection of those available through the IPCC Data
Distribution Centre and included single simulations using the CSIRO, CCSR
and MPI models, a three-member ensemble of CCC simulations and a four-member
ensemble of HADCM2 simulations (see Table 9.4.1 for further model details).
Figure 10.3.2 shows the biases in regionally averaged seasonal mean
temperature and precipitation for 1961-1990 as analysed by Giorgi and
Francisco (1999b). Temperature biases are typically within the range of +/-
3 K but exceed +/- 5 K in some regions, particularly in DJF. Precipitation
biases are mostly between -20 and +50%, but exceed 100% in some regions,
particularly in DJF. These regional biases are, in general terms, smaller
than those of a similar analysis presented in the SAR (see also Kittel et
al. 1998). For example, in the previous analysis regional temperature
biases as high as 10-15 K were present in some models and regions. Given
that the current analysis also includes many more regions, this difference
in general performance strongly suggests that simulation of regional climate
is significantly improved in current generation AOGCMs.

Figure 10.3.3 illustrates model-to-model differences in the simulation of
the annual cycle of regional precipitation and temperature for the examples
of the Indian subcontinent and central Asia. These results are taken from
the analysis of Lal and Harasawa (1999a) who also used a set of current
AOGCMs runs available from the DDC (Table 9.4.1). Although in both regions
all models generally reproduce the observed annual cycle of temperature, for
some models and months errors as large as 5 K are present. The strong
seasonal peak in precipitation over India from June to September is well
simulated by all models, but the annual cycle in precipitation over Central
Asia is poorly captured in a number of the simulations.

Current generation AOGCM simulations in which historical changes in climate
forcing over the 20th century are used enable simulated regional climatic
trends to be assessed against observations. This was done by Boer et al.
(1999a) for temperature and precipitation for the regions of southern
Europe, North America, Southeast Asia, Sahel and Australia (defined as in
the SAR). Simulated and observed regional linear temperature trends were in
agreement for all regions except the Sahel for the runs where sulphate
forcing was included. Little could be said about agreement in observed and
model precipitation trends which were weak over the period in both the model
and the observations.

It should be stressed that assessments of model regional performance which
are based on area-averaging of AOGCM output over large regularly-shaped
regions (as was done in the studies reported above) should not be assumed to
apply to all areas within these regions. Many of the regions considered
contain a number of distinct climate regimes, and model performance may vary
considerably from regime to regime. Climate change results may similarly
differ. For the purposes of assessing model performance in a particular
region, more detailed analysis is usually appropriate.

Where studies have examined spatial patterns within regions (e.g. Joubert
and Tyson, 1996; Labraga and Lopez, 1997), reasonable correspondence with
observations were found, especially for temperature and MSLP. Most studies
focus on seasonal mean conditions, but models can be analysed so as to focus
on simulation of specific climate features. For example, Arritt and Goering
(1999) examined circulation and precipitation patterns associated with the
onset of the North American monsoon in simulations with the HADCM2 model and
found this feature to be well simulated. It must be noted that some studies
have identified important errors in current simulations of regional MSLP,
such as the tendency for pressure to be too low over Europe and too high
north and south of this area noted by Machenhauer et al. (1998). Such errors
contribute significantly to local temperature and precipitation biases both
in the global climate model and in nested high resolution RCM simulations
(Risbey and Stone, 1996; Noguer et al. 1998, Machenhauer et al. 1998).

As would be expected, GCM simulations of current climate are poorest at the
local scale, particularly in areas of strong topographical controls (e.g.
Schubert 1998). Widman and Bretherton (1999) concluded that means (and
interannual variability) of precipitation can be well simulated in a
mountainous areas only down to a resolution of three grid squares. However,
in areas without complex topography, it is possible for the model results at
individual gridpoints to compare well with observations, although it is
necessary that the observations be averaged appropriately over the model
grid boxes (Osborn et al. 1999).

10.3.1.3 Variability and extremes

Interannual variability in temperature was assessed regionally, as well as
globally, in a long control simulation with the HADCM2 model (Tett et al.
1997). Many aspects model variability compared well against observations,
although there was a tendency for temperature variability to be too high
over land. In the multi-regional study of Giorgi and Francisco (1999a),
which used the ensemble HADCM2 simulations (Table 9.4.1), both regional
temperature and precipitation variability were found to be overestimated.
However, in a 200 year control simulation with the CCC model (Table 9.4.1),
Flato et al (1999) noted that simulated interannual temperature and
precipitation variability compared well with observations both globally and
in five selected study regions (Sahel, North America, Australia, southern
Europe and Southeast Asia).

Validation of daily precipitation variability as simulated at gridboxes in
GCMs is problematic because the corresponding variability in the real world
operates at much finer spatial scale (e.g. Hennessy et al. 1997). A
significant development in this area has been the work of Osborn and Hulme
(1997) who devised a method of calculating grid box average observed daily
precipitation data that corrected for biases commonly introduced due to
insufficient station density. When they applied their method to output from
the CSIRO GCM, daily precipitation performance was found to be better than
that from a comparison based on simpler, and less appropriate, methods.
Osborn and Hulme (1998) used this approach to assess the performance of a
range of AGCMs in simulating rainfall variability over Europe. Models
commonly simulated precipitation in winter to be more frequent and less
intense than observed, although performance in some models was good in
summer.

Synoptic circulation variability at daily and longer time scales operates at
a spatial scale which GCMs can simulate directly and there has been work
focussed on GCM performance in this area at the regional scale (e.g. Huth,
1997, Joubert 1996, Katzfey and McInnes 1996, Osborn et al. 1999, Fyfe,
1998, Schubert 1998, Wilby et al. 1998a). Regions studied include North
America, Europe, Southern Africa, Australia and East Asia. Although in many
respects model performance is good, some studies have noted synoptic
variability to be less than in the observations and the more extreme
deviations from the mean flow to be less intense or less frequent than
observed. For example, Osborn et al (1998) noted that HadCM2 output
underestimated the frequency of the most intense flow situations over the
UK. Finer scale circulation systems such as tropical cyclones can only be
studied indirectly using GCMs (see Chapter 9).

Osborn et al. (1999) also examined the relationship between the circulation
anomalies and grid box average temperature and precipitation anomalies and
found this to be well represented by the model. A similar investigation has
been conducted by Busuioc et al. (1999) with regard to the representation of
precipitation variability over Romania in versions of the ECHAM3 AGCM and
model performance was found to be good in most seasons. Establishing the
existence of appropriate links between variables in this way increases
confidence in model performance under climate change conditions.

10.3.2 Future climate change

10.3.2.1 Climatic means

With the exception of some restricted oceanic regions in some models, all
regions of the globe show warming under enhanced GHG conditions. Typically
the simulated regional warming is greater than the global average over land
and over the northern higher latitudes in winter, and less than the global
average in the tropics, the high southern latitudes, over the ocean, and in
regions with strong local increase in sulphate forcing (see Chapter 9).
Giorgi and Francisco (1999b) analysed regional temperature change in current
AOGCMs under a range of forcing scenarios. In all regions warming depended
strongly on the forcing scenario used. It was also noted that inter-model
differences in simulated warming were large compared to differences between
ensemble members from a single model. To considerable extent these
inter-model differences would reflect differences in the global climate
sensitivities of the models concerned. In some locations, additional
regional factors can be identified as influencing the regional temperature
response in a systematic way; for example Fyfe and Flato (1999) using the
CCC model noted a tendency for greater warming at high elevations in the
Rocky Mountains due to a snow-albedo feedback. To illustrate regional
temperature change as simulated by current AOGCMs, Figure 10.3.4 presents
some results from Giorgi and Francisco (1999b). The regions are as indicated
in Figure 10.3.1, the range of simulations are from the same set of models
as described for Figure 10.3.2 (including some ensembles), scenarios of 1%
pa increasing CO2 with and without changes in sulphate aerosols are
considered, and changes are for 2071-2100 compared to 1961-1990.

In line with the globally averaged precipitation increase given by all
models (see Chapter 9), precipitation is also simulated to increase
regionally in the majority of cases. However, regions of precipitation
decrease are also simulated. Figure 10.3.5 presents an analysis of simulated
changes in regional precipitation equivalent to that for temperature
presented in Figure 10.3.4. Note that although the figure includes some
ensemble results, the following discussion will focus mainly on comparing
the results of different models. Where CO2 only is increased (Figure
10.3.4a), most or all models show increased DJF precipitation for regions in
the mid to high latitudes of the northern hemisphere. There is also some
consistency on precipitation increase in the regions affected by the ITCZ at
this time of the year. Simulated regional precipitation decreases in DJF are
common in subtropical latitudes, but only for central America (CAM) and
northern Australia (NAU) are decreases indicated by most or all models. The
pattern is broadly similar in JJA, although with some features shifting
northwards. Only the northernmost regions (ALA, GRL, NAS) show consistent
increase, and simulated regional decreases are now common in the northern
midlatitudes and the subtropics. Most models show decrease in the
Mediterranean (MED) and central America (CAM) regions. Some regions along
the ITCZ show increase (AMZ, SAS, SEA), but this is not true for the
relevant African regions. The southern hemisphere midlatitude regions show
inconsistent change (SSA, SAF) or decrease (NAU, SAU). When increased
sulphates are included in the forcing scenario (Figure 10.3.4b) the results
are similar, although there is some increase in frequency of simulated
precipitation decrease in Africa (SAF, EAF) and southeast Asia (SAS, EAS,
and SEA) in DJF. The magnitude of regional precipitation change varies
considerably amongst models with the typical range being around zero to 40%
where the direction of change is strongly indicated and around -20% to +20%
where it is not. Larger ranges occur in some regions (e.g. -30% to +60% in
southern Africa in JJA), but this occurs mainly in regions of low seasonal
precipitation where the implied range in absolute terms would not be large.

To illustrate further inter-model variations in simulated regional
precipitation change we examine results obtained in model intercomparison
studies for the Australian, Indian, North American and European regions. All
of these regions have been extensively studied over the years using
equilibrium 2xCO2 experiments (such as those featured in IPCC (1990)), first
generation transient coupled AOGCMs (as in IPCC (1995) and more recent
AOGCMs available in the DDC (Table 9.4.1). They are representative of a
broad range of climatic regimes. This comparison also enables us to make
some assessment of how the regional precipitation projections have changed
as the models have evolved.

In the Australian region, the pattern of simulated precipitation change in
winter (JJA) has remained broadly similar across these three groups of
experiments and consists of rainfall decrease in subtropical latitudes and
rainfall increase south of 35-40 S (Whetton et al. 1996a, Whetton et al.
1999). However, as the latitude of the boundary between these two zones
varied between models, southernmost parts of the Australia lay in the zone
where the direction of precipitation change was inconsistent amongst models.
The subtropical decreases ranged from around zero to minus 10% per degree of
global warming with the larger decreases being more commonly found amongst
the more recent coupled AOGCMs; the precipitation changes in the continental
areas south of 35 S are typically in the range of plus or minus 5% per
degree of global warming (Whetton et al. 1999). In summer (DJF) the
equilibrium 2xCO2 experiments showed a strong tendency for precipitation
increase over Australia, particularly in the northwest of the continent
where changes as large +30% per degree of global warming were present in
some models. This tendency was replaced in the first coupled AOGCMs by one
of little change or precipitation decrease. This has remained the case when
the most recent coupled models are considered. Whetton et al. (1996a) was
able to partly attribute the contrast in the regional precipitation response
of the two types of experiments to contrasts in their hemispheric patterns
of warming. Differences in the precipitation change in the Australian region
between simulations with and without sulphate forcing have not been
thoroughly examined, although the results of Giorgi and Francisco (1999b)
indicate little effect of sulfates.

Together, Lal et al (1998b) and Lal and Harasawa (1999b) surveyed the
results for the Indian subcontinent of seventeen climate change experiments
including both equilibrium 2xCO2 and transient AOGCM simulations with and
without sulfate aerosol forcing. In the simulations forced by GHG increases
most models show wet season (JJA) rainfall increases over the region,
although these increases are mostly less than 5% per degree of global
warming. A minority of experiments show rainfall decreases. The experiments
including sulphate forcing all showed reduced rainfall increases, or
stronger rainfall decreases than their corresponding GHG only experiments.

For North America we focus on the central Plains of the continent, which was
established as one of the IPCC regions in the 1990 Report (Houghton et al.,
1990). In the equilibrium 2xCO2 experiments reviewed in that report, there
was a good deal of similarity of model response, with precipitation
decreases prevailing in the summer and increases in the winter. Decreases
and increases ranged within plus or minus 10%. In the second group of
experiments (9 transient runs with AOGCMs) reviewed primarily in Houghton et
al. (1996), a wider range of responses was found. In winter changes in
precipitation ranged from about -12% to + 20% for the time of CO2 doubling,
and most of the models (6 out of 9) exhibited increases. In summer the
range of change was narrower, all within + and - 10%, but there was no clear
majority response towards increases or decreases. Two of the most recent
transient runs including aerosol forcing, the CCC AOGCM and the HADCM2 have
been used in the first National United States Assessment Program, and were
evaluated over North America by Doherty and Mearns (1999). The models
simulated opposite changes in precipitation in both seasons. The CCC model
simulated precipitation decreases (0.5 mm/day or 20%) in winter, and the
HADCM2 increases of 0.5 mm/day, while in summer the CCC model simulated
small decreases (-0.5 mm/day or 10%) and the HADCM2 mainly increases of the
same magnitude. While all these results considered together indicate
overall a tendency for more decreases to be simulated in the summer and more
increases in the winter for the central US, there doesn't seem to be a
striking reduction in the uncertainty for this region regarding changes in
precipitation through the progression of climate models.

For the European region, the evolution over the years of GCM simulations of
enhanced GHG climate change has been examined by Hulme et al. (2000). They
consider twenty-three climate change simulations (forced by CO2 change only)
produced between the years 1983 and 1998 and including mixed-layer 1x and
2xCO2 equilibrium experiments as well as transient experiments. Figure
10.3.5 shows their results for simulated change in annual precipitation,
averaged by latitude and normalised to percentage change per degree of
global warming (to remove the effect of differences in forcing and model
sensitivity). It may be seen that the consensus amongst current models for
drying in southern Europe and wetter conditions in northern Europe
represents a continuation of a pattern established amongst the earlier
simulations. The effect of model development has primarily been to
intensify this pattern of response.

Variations from simulation to simulation in the regional enhanced GHG
results of GCMs, which are particularly evident for precipitation,
represents a major uncertainty in any assessment of regional climate change.
Such variation may arise due to differences in forcing, systematic model to
model differences in the regional response to a given forcing or differences
Giorgi and Francisco (1999a,b) using the ensemble HADCM2 runs for various
transient scenarios showed that uncertainty in forcing was clearly very
important for regional temperature change, but less important, relative to
other uncertainties, for precipitation change. Giorgi and Francisco (1999b)
compared inter-ensemble differences in regional climate change (which can be
viewed as representing the effect of the model's natural internal
variability) with inter-model differences and concluded that the
contribution of inter-ensemble differences to uncertainty in regional
climate change was relatively small (this can be seen for precipitation
change in Figure 10.3.4). However, it should be noted that Giorgi and
Francisco (1999) used long (thirty year) means and large
(sub-continental-scale) regions and that the uncertainty due to simulated
natural variability would be larger when shorter averaging periods, or
smaller regions, are used. The results of Hulme et al. (1999) also suggest
that low frequency natural climatic variability is quite important at the
subregional scale in Europe and can mask the enhanced GHG signal.

Regional changes in the mean pattern of atmospheric circulation have been
noted in various studies although typically the changes are not marked (e.g.
Huth 1997, Schubert 1998). Indeed the work Conway (1998) and Wilby et al.
(1998) suggests that the contribution of changes in synoptic circulation to
regional climate change may be relatively small compared to that of
non-synoptic processes.

10.3.2.2 Variability and extremes

Giorgi and Francisco (1999a) found a tendency for interannual variability in
regional precipitation to increase in HADCM2 under enhanced GHG conditions,
but for temperature the response was less consistent across regions. Boer et
al. (1999b) using the CCC AOGCM obtained marked decreases in interannual
temperature variability over North America and Europe in DJF. They also
noted a tendency for precipitation variability to increase. Beersma and
Buishand (1999) analyzed monthly temperature and precipitation variability
in a transient Hadley simulation over southern Europe, northern Europe and
central North America. Between control and enhanced GHG samples of ten
years duration few changes of statistical significance were found, although
these included an increase in the standard deviation of precipitation of
around 25% in winter, summer and autumn in northern Europe at the time of
effective CO2 doubling. Furthermore they found that across the three
regions and four seasons all substantial changes in precipitation variance
were increases. It should also be noted that in many regions interannual
climatic variability is strongly related to ENSO, and thus will be affected
by changes in ENSO behavior (see Chapter 9).

As noted in the SAR, many global climate models simulate increases in daily
precipitation intensity and in the magnitude of extreme daily precipitation
events. Kharin and Zwiers (1998) and Zwiers and Kharin (1999) have recently
demonstrated this tendency at the global scale using the current version of
the CCC GCM. Hennessy et al. (1997) addressed this topic regionally using
both the CSIRO and Hadley centre models and found that under 2xCO2
conditions the one-year return period events in Europe, Australia, India and
the USA increased in intensity by 10-25%. McGuffie et al. (1999) undertook
similar analysis for the Sahel, North America, South Asia, Southern Europe
and Australia using the BMRC model and various versions of the CCM model and
also obtained decreased return periods for extreme precipitation events and
increases in precipitation intensity.

Daily temperature variability over Europe has been examined by Buishand and
Beersma (1996) using the ECHAM/LSG model. They obtained statistically
significant decreases of 15-30% around the time of CO2 tripling in the
standard deviation of winter and spring temperature.

Fewer studies have considered changes in variability and extremes of
synoptic circulation under enhanced GHG conditions. Huth (1997) noted
little change in synoptic circulation variability under equilibrium 2xCO2
conditions over North America and Europe. Katzfey and McInnes (1996) found
that the intense cut-off lows off the Australian east coast became less
common under equilibrium 2xCO2 conditions in the CSIRO model, although they
had limited confidence in this result.

10.3.3 Summary

Analysis of transient simulations with AOGCMs indicates that average
climatic features are generally well simulated at the large and continental
scale. At the regional scale, area-average biases in the simulation of
present day climate are highly variable from region-to-region and among
models. Temperature biases are typically within the range of +/- 3 K but
exceed +/- 5 K in some regions, particularly in DJF. Precipitation biases
are mostly between -20 and +50%, but exceed 100% in some regions. These
regional biases are, in general terms, significantly smaller than those of a
similar analysis presented in the SAR. Many aspects model variability
compare well against observations, although there is a tendency for
temperature variability to be too high over land.

Simulated changes in mean climatic conditions for the late decades of the
21st century (compared to present day climate) vary substantially among
models and among regions. All land regions undergo warming in all seasons,
with the warming being generally more pronounced over cold climate regions
and seasons. Average precipitation increases over most regions, especially
in the cold season, as a result of an intensified hydrologic cycle. However,
some exceptions occur in which most models concur in simulating decreases in
precipitation. These include broad regions of Central America, Australia,
Southern Africa and in the Mediterranean region in JJA. The magnitude of
regional precipitation change varies considerably amongst models with the
typical range being around zero to 40% where the direction of change is
strongly indicated and around -20% to +20% where it is not. There is
strong tendency for models to simulate regional increases in precipitation
variability with associated reductions in the return period of extreme
rainfall events. Increased interannual precipitation variability is also
commonly simulated. However, changes in regional temperature variability
vary considerably from model to model and region to region.

---------------------------

10.4 AGCMs with variable and increased horizontal resolution

This section deals with the relatively new idea of deriving
regional climate information from AGCMs with increased horizontal
resolution. Although the basic methodology is suggested in the
work of Bengtsson et al. (1995), where a high resolution GCM was
used to predict changes in tropical cyclones in a warmer climate,
it is only in the last few years that such models have been used
more widely to predict regional aspects of climate change. Even
so, only a limited number of experiments have been conducted to
date and hence what follows is not a definitive evaluation of the
technique but an initial exploration of its potential. For
climate change applications, only AGCMs have been used
to date. These consist of two versions of the Max Planck
Institut�s ECHAM spectral AGCM, the Meteo-France/CNRM Arpege
variable resolution spectral AGCM (run at both uniform high and
variable resolution), two versions of the UKMO/Hadley Centre�s
HadAM gridpoint AGCM, the LMD (Paris) variable resolution
gridpoint AGCM and the MRI (Tokyo) JMA spectral AGCM (table
10.4.1).
Instituti Model Horizonta Control Anomaly Region of
on l Forcing Forcing interest
Resolutio
n
MPI ECHAM3 T42 ECHAM/LSG ECHAM/LSG Euro/Glob
al
MPI ECHAM3/4 T106 Obs ECHAM/OPY Euro/Glob
C al
2 al
al
LMD LMD-GCM 100-700km Obs Antarctic
a
MRI JMA T106 Obs MRI/GFDL/ Tropics
+2K

Table 10.4.1: High and variable resolution GCM control and anomaly
simulations

10.4.1 Simulations of current climate
Analysis of the current climate simulations of timeslice models
has considered both deviations from the observed climate and
effects on the model's climatology due to changes in
resolution. Validation is generally performed on the sub-
continental to global scale with high resolution information only
considered for a particular region of interest. Most studies have
considered just the mean climate and some measures of variability.
The only extreme behaviour studied in any detail was the
simulation of tropical cyclones. Even for mean climate, no comprehensive
assessment of the surface climatology of variable or high
resolution models has been attempted. Europe has been the most common
area of study to date, although south Asia and Antarctica have also
received attention. Thus, what follows is only indicative of the potential
of the method and only raises selected concerns.

10.4.1.1 Seasonal mean climate

The mean circulation is generally well simulated by AOGCMs, even though
relatively large regional scale biases can still be present. Many features of
the large scale climate are retained at higher resolution (Deque and
Piedelievre, 1995, Stendel and Roeckner, 1998, Stratton, 1999a,
May, 1999). A common deviation which has been found between
ciarse and high resolution AGCMs is a poleward shift in the extra-tropical
storm-track regions. It is suggested by
Stratton (1999a) that this is linked to a general deepening of
cyclones noted as a common feature in high resolution climate
models by Machenhauer et al. (1996). More intense activity is also
seen at higher resolution in the tropics, with a stronger Hadley
circulation in ECHAM4 and HadAM3 that worsened the agreement with
observations in both cases (Stendel and Roeckner, 1998 and
Stratton, 1999b).

The repositioning of the storm tracks generally improves the simulation in
the northern hemisphere, resulting in a reduced positive
polar surface pressure bias seen in the models at standard
improvements in northern hemisphere low level flow in winter (fig.
10.4.1). In the southern hemisphere, the impact is not consistently
positive in all models, with the ECHAM and Arpege T106 simulations degrading
features of circumpolar flow and HadAM3 and LMD models showins improvements
(fig. 10.4.1, and Krinner et al., 1997). For surface pressure, in
the tropics resolution appears to have little impact on the negative
biases observed in both ECHAM4 and HadAM3. Increased resolution,
however, improves
the low-level south Asian monsoon flow in both models (Lal et al., 1997
and Stratton, 1999b).

The fact that the above responses to increased resolution have common
features amongst different models is an indication that they likely result
from improved representation of the resolved variables. In
contrast, an increase in the intensity of subtropical anti-
cyclones observed in ECHAM4 results from a tropospheric warming promoted by
excessive cirrus clouds. this was attributed to a scale-dependent
response in the relevant parametrization (Stendel and Roeckner, 1998).

AOGCMs generally perform worse in their simulations of surface
climatology. This was one motivation for the first study employing a timeslice
AGCM (Cubash et al., 1995), in which a T42 version of ECHAM3 was
driven by the T21 ECHAM3/LSG AOGCM. The time-slice simulation
provided a reasonable representation of the seasonal cycle
of surface temperature over seven regions spanning
the continents, but overall surface temperature was
too high (by 2-5K), especially in summer.
Precipitation was generally underestimated in summer, sometimes
severely. Later experiments with the same model at T106 resolution
(Cubasch et al., 1996) found that, over southern Europe, the
winter temperature simulation did not improve with resolution and
in summer the patterns improved but the positive biases became
larger. As before, precipitation was still severely underestimated
(by a factor of 2) in summer and the spatial precipitation patterns were
improved at T106 resolution in summer but degraded in winter.
Wild et al. (1995)
showed that the summer warming in this simulation
resulted from excessive insolation associated
to reduced cloud cover. Updating the physics used for ECHAM4
improved some of these underlying biases but still the T106
simulation gave large negative precipitation biases and positive
temperature biases in summer (Stendel and Roeckner, 1998).

In simulations of European climate with the Arpege
model, increasing resolution to T106 improved surface temperature
simulation in both summer and winter due to reductions in the
strength of a positive zonal flow bias. These biases were further reduced
when using a stretched grid version T63s (stretching
factor 3.5) with a resolution of T150 over most of Europe.
Precipitation biases were also generally reduced with increasing resolution
except for the too low values in south eastern Europe. The experience
with HadAM2/3 was more mixed. As with Arpege, an improvement in
the westerly flow at high resolution lead to improved temperatures
and precipitation in winter throughout Europe and lso in summer
over the north and
west of Europe (Stratton, 1999 and Jones, 1999). However, a small
warm and dry bias in the south east of Europe at standard resolution was
greatly increased at higher resolution. This
was caused by increased vertical activity at the higher resolution,
promoting condensation and reducing a positive tropospheric
humidity bias. This had the secondary impact of reducing cloud
cover, increasing insolation, and
reducing soil moisture in many areas of Europe to values which severely
limited evapotranspiration (Jones, 1999).

The same warming and drying in summer is seen over all
extratropical continents in HadAM3 (Stratton, 1999) and clearly
demonstrates a potential drawback of increasing the resolution of
a model without comprehensively retuning the physics. Krinner et
al. (1997) showed that to obtain a reasonable simulation of the
surface climatology of the Antarctic with the LMD variable
resolution AGCM many modifications to the model physics were
required. With these modifications,
the model was able to simulate surface
temperatures to within 2-4K of observations and to provide a good
simulation of the ice mass balance (snow accumulation), with both
aspects being better than at standard resolution.

10.4.1.2 Variability and extreme events

Many elements of the AGCMs flow-field intraseasonal variability,
both intermediate frequency (or band-pass filtered) and low
frequency, are simulated better at high resolution. Stendel and
Roeckner (1998) show that in Echam4 low frequency variability of
geopotential height and some eddy fluxes are simulated better at
T106 than T42 (Gibson et al.,
1997). In other cases, values underestimated at T42 are
overestimated at T106. A similar picture in seen for HadAM2/3
(Stratton, 1999a,b) and in the Arpege model. In Arpege, at
T106 there are also improvements in intermediate frequencies compared
with the T63s. In contrast, Martin (1999) found little change with
resolution in
either the interannual or intraseasonal variability of circulation
and precipitation of the south Asian monsoon in HadAM3.

In studies with two high resolution AGCMs McDonald (1999) for
HadAM3 and Yoshimura et al. (1999) for JMA have shown that they
both produce realistic simulation of the location and frequency of
tropical cyclones. In HadAM3 the frequency is somewhat
overestimated in some areas but the annual cycle agrees well with
observations. In addition McDonald (1999) demonstrates that whilst
both standard and high resolution models capture large-scale
features associated with tropical cyclones, the intensity and
inner detail of cyclones is much more realistic at high resolution.

10.4.2 Responses to climate change

Climate change studies with high resolution AGCMs have been limited
to an even smaller selection of atmospheric models (table 10.4.1).
The following results therefore cannot be regarded as
fully representative, although some of the conclusions which
are drawn from these studies point to
important methodological strengths and weaknesses.

10.4.2.1 Applying anomalous atmosphere forcing

When using a high resolution AGCM to simulate
a climate change response consistent with that of an AOGCM experiment
both the anomalous atmospheric forcing (GHG, sulphate aerosols
etc.) and the accumulated effect of this on the ocean SST
have to be provided as forcings to the AGCM. For the
atmospheric forcing, GHG concentrations
can be provided to the AGCM if their radiative effect is
calculated by the model, or alternatively an equivalent amount of CO2
can be prescribed to give the same column integrated radiative forcing.
For sulphate aerosols, if prescribed concentrations are used in the
AOGCMs then these can be applied directly to the AGCMS. If the
AOGCMs calculate the aerosol concentrations from prescribed sources
then the AGCM can
either use the same method or derive its concentrations from the
AOGCMs. If the former method is used then the radiative forcing
due to the aerosols may be different in response to possible
scale dependencies in the sulphur cycle model or in the
atmospheric circulations.
If the latter method is employed and the large scale
flow changes in the AGCm compared to the AOGCm,
then the forcing will be inconsistent with the flow field.

The most direct way of applying the oceanic surface forcing for an
AGCM climate change experiment is to use AOGCM control values of
SST and sea ice in the AGCM control experiment and anomaly
AOGCM values in the AGCM anomaly experiment. This has the
advantage of providing equivalent ocean surface forcing to the
AGCM experiment. However, if there are substantial systematic errors
in the control values, these could induce large biases in the
atmospheric climatology of the AGCM. An alternative method is to
use observed SSTs and sea-ice distribution
for the control AGCM and then apply
the changes in these values computed in the AOGCM anomaly experiment to
provide anomalous AGCM values. This method provides forcing
in the two model simulations which is consistent for SST changes,
but not necessarily so for sea ice changes.

10.4.2.2 Changes in the mean climate

The ECHAM3 timeslice climate change simulations reported in Cubash
et al. 1996 predicted substantially different responses for
southern Europe at T42 and T106 resolutions. For example, surface temperature
in summer increased by over 4K over much of the region
at T106 resolution, whereas at T42 resolution the response was generally less
than +2K. Also, winter precipitation increased more in the T106 than
the T42 experiments. In
these cases, substantial differences in the control simulation were
seen between the two resolutions, which would be an important factor in
generating the response. For
example, Wild et al. (1997) showed a large summer surface temperature
positive bias at T106 resolution, implying a tendency towards soil drying
which minimises soil moisture available for evaporative cooling in
a warmer climate and hence enhances the response.

Focusing over the whole of Europe, Deque et al. (1998) report a
variable grid AGCM climate change experiment using SST
forcing from HadCM2 control and GHG anomaly
integrations. Their experiment predicts a moderate warming of
maxima 2.5K in winter and 3.5K in summer over southern Europe
and 1.5K and 1K, respectively, over northern Europe (fig. 10.4.2).
In contrast,
the driving AOGCM predicted greater warmings and a larger north-south
(fig. 10.4.2). The reason for these differences result mainly from
systematic errors in the control run Arpege large-scale flow,
which is too zonal and too strong over mainland Europe.
These errors, which are not present in HadCM2,
enhance the moderating influence of the ocean SST.
The precipitation
especially in summer, when both
models predict a general decrease of up to 30% over most of
Europe, a maximum decrease in southern Europe and small increases over
north east Europe. However, the differences in the control simulations
imply that confidence in this prediction is still low.

In a similar experiment, HadAM3 was integrated at
1.25�x0.83� resolution with
observed SSTs and sea-ice for the control and anomaly forcing
from an HadCM3 GHG simulation. Globally, Johns (1999)
found that in the annual mean at the largest scales many aspects
of the timeslice response were similar to that in HadCM3. However,
regionally or seasonally many differences are evident, notable
examples being the land-sea contrasts and monsoon precipitation. Many
circulation changes are also different in the two models,
as could be expected given
the differences in the control runs (see 10.4.1). In these experiments,
the cloud feedbacks were found to be
substantially different at the two resolutions,
implying that changes in the parametrized
processes are probably important in determining the responses.
In contrast to this and the
Arpege results, Jones (1999) showed many similarities in the large-
scale patterns of the surface temperature and precipitation
responses over Europe. Precipitation reduced everywhere in summer
except the north west and increased almost everywhere in winter.
Surface temperature increases during summer had a maximum in
the south of Europe. The main difference in the response
calculated by the two models is that surface warming
in winter increases northwards in HadCM3 whereas
it has a maximum east of the Baltic in the high resolution
experiment. This is due
to differences in the control simulations. HadCM3 predicts sea-ice
which extends to the north Scandinavian coast in winter whereas in
the HadAM3 simulation this area is ice-free. In the anomaly
integration the ice-edge migrates polewards giving a
much larger warming over this region in HadCM3.

is complicated by the use of observed sea-ice in the time slice
control and model-derived sea ice changes in the anomaly run.
A cleaner experimental design is
used in an ECHAM4/OPYC simulations described by May (1999). In
this experiment, the AGCM (ECHAM4) is run at T106 resolution and
is driven by SSTs and
sea-ice from a T42 AOGCM simulation. Two 30
year timeslices are simulated, 1970-99 and 2060-89, with
with GHG and sulfate forcing from the IPCC IS92a scenario.
The main inference from this
study is that, as the future climate simulations are more similar
to each other than the present day simulations,
differences in the responses are due mainly to deviations in the
control simulations. This conclusion largely
rests on differences in extra-tropical circulations and
precipitation over tropical land masses.

Work currently in progress indicates that the positioning of the
extra-tropical storm tracks in coarse and high resolution AGCMs is
sensitive to the distribution of sea-ice. This may help explain
the convergence of anomaly integrations noted above and help in
the assessment of confidence in the responses in timeslice
experiments. [DETAILS TO FOLLOW IN SECOND DRAFT]

10.4.2.3 Changes in variability and extremes
In a T106 ECHAM3 simulation Bengtsson et al. (1996, 1997) found
that under emhanced GHG conditions, the number of tropical cyclones
decreased slightly in the
Northern Hemisphere, and decreased by more than a factor of 2
in the Southern Hemisphere.
This large difference in response for the two hemispheres
raised questions about the model's ability to properly
represent tropical cyclones
at the resolution emplyed in the experiments. The tropical climate of
ECHAM3 is quite sensitive to horizontal resolution, and
methodological concerns were raised regarding the design of the
experiment (Landsea 1997).

Yoshimura et al. (1999) used a
GCM of similar horizontal resolution to reexamine this issue.
Under enhanced greenhouse conditions, they
simulated a reduction in total tropical cyclone-like vortex
formation in both hemispheres. This was despite the GCM simulation
displaying an increase in rainfall in the tropics. Using a rather
lower-resolution GCM (T42 NCAR CCM2), Tsutsui et al. (1999) built
upon the previous work of Tsutsui and Kasahara (1996) to show
basin-dependent changes in tropical cyclone formation under 2xCO2
conditions. Generally increased frequencies compared to the
control climate were simulated in the western North Pacific,
decreased frequencies in the North Atlantic, and similar frequencies
in the southwest Pacific. These results agree with those of
McDonald (1999) for the high resolution HadAM3 simulation, who also
showed increases in tropical cyclones over the north Indian basin
and a change in the
timing of cyclones in the south-west Pacific.

In the ECHAM3 simulation, Beersma et al. (1997) showed a general small
decrease in north Atlantic cyclones, with regional increases in
the North Sea. A decrease was found in the number of
most intense depressions and an
increases in the number of weak depressions. Again they questioned the
significance of their results due to the small sample size.
[DETAILS ON MAY AND ANDERSON WORK TO FOLLOW IN SECOND DRAFT]

10.4.3 Summary and recommendation
Since the SAR variable and high resolution GCMs have been
used more widely to provide high resolution simulations of climate
change. Clearly the technique is still in its infancy with only a
few modelling studies carried out and for only a limited
number of regions. Also, there is little in depth analysis of the
performance of the models and only preliminary
conclusions may be drawn.

Many aspects of the models' dynamics and large-scale flow are
improved at higher resolution, though this is not uniformly so
geographically or across models. Some models also demonstrate
improvements in their surface climatologies at higher resolution.
However, substantial underlying errors are often still present in
high resolution versions of current AGCMs. Also, the direct use of
high resolution versions of current AGCMs without some allowance
of the dependence of models physical parametrizations on
resolution leads to some deteriorations in the performance of the
models.

Changes in the large scale flow with increased resolution call
into question the consistency between timeslice simulations and the
SSTs and sea-ice forcings used to drive them.
However, regional responses
currently appear more sensitive to the AGCM than the SST
forcing used. This result is partially due to
some of the model responses being dependent on their
control simulations and systematic errors within them. These
factors and the small number of studies carried out imply that little
confidence can be attached to any of the regional predictions
provided by time slice simulations.

The improvements seen with this technique are encouraging,
but more effort should be put in
analysing and possibly improving the performance of current models
at high resolution. This is particularly
important in view of the fact that future AOGCMs will likely
use models approaching the resolution considered here in the next
5-10 years.

---------------------------

10.5 Regional Climate Models

This section is mainly devoted to the use of nested RCMs
to derive regional climate information from coarse
resolution AOGCMs and to developments in regional climate
modelling research since the SAR. The basic methodology
(10.2.3) is inherited from numerical weather prediction
with the pioneering use of RCMs due to Dickinson et al.
(1989) and Giorgi (1990).

10.5.1 Overview of methodological developments, and
improved understanding of weaknesses and strengths.

Since the SAR many fundamental problems in the field of
regional climate modelling have been studied (e.g. Giorgi and
Mearns, 1999) although not all aspects of it have yet
been fully explored.

10.5.1.1 Length of simulation.

A fundamental motivation for the development of RCMs
is to provide high-resolution
information with a physical model over a limited area
for time scales that would make a GCM
simulation of comparable resolution prohibitively
expensive. This goal, along with the relative simplicity
of the nesting technique and the consistency it yields
with AOGCM climate change simulations, explain the
dramatic increase in the number of groups that have
developed RCMs since the SAR. The generation of long term
high-resolution RCM simulations, however, is still
computationally demanding, both in terms of computer time
and data storage. For this reason, to date much RCM work
has focused on problems tractable with relatively short
simulations (months to a few years).

On the other hand, since the SAR it has been increasingly
recognised that multi-year and possibly multi-decadal
simulations should be used for climate change studies.
This is for several reasons (e.g. Machenhauer et al.
1998): to increase the sample size and develop more
meaningful climate statistics; to minimise problems
related to internal model variability and variability of
the climate system; to allow the model to fully reach
internal equilibrium with the land surface conditions.
While for the SAR only a few simulations longer than one
year were available, several groups have now performed
decadal simulations or longer, including one full
transient experiment of 140 years in length (Hennessy et
al. 1998).

10.5.1.2 The role of the lateral boundaries and domain
size

The influence of the lateral boundary forcing on RCM
simulations when using variations of the standard Davies
(1976) relaxation technique was extensively studied, for
example, in the early work of Jones at al. (1995) and
Cress et al. (1995). They showed that systematic errors
in the driving fields from a GCM are generally
transmitted to the nested RCM and numerous authors have
confirmed this conclusion thereafter. Noguer et al.
(1998) elaborated further on the influence of the
external forcing by comparing 10 year RCM simulations
driven by observed and GCM-derived boundary conditions.
The simulation length allowed a decomposition of the
systematic errors into internally generated and
externally driven. Overall, 80-90% of the mean sea level
pressure error was estimated to derive from the external
forcing in all seasons. For mean surface temperature and
precipitation the figures were lower, 40-60% in winter
and 30-50% in spring and autumn. Errors in summer were
mostly generated internally. In contrast, the study also
found that the mesoscale signal generated by the RCM was
relatively insensitive to the source of the boundary
conditions.

With the relative influence of boundary forcing and
internal model physics potentially having a seasonal
dependence, the choice of appropriate domain size for an
experiment is not trivial. Should the latter dominate
then the RCM solution may effectively decouple from the
driving data. WIthin the context of downscaling, a climate
change simulation exhibitng such an inconsistency can lead to
problems in the intepretation of the results (Jones
et al., 1997). Also, the domain size has to be large
enough so that relevant local forcings and effects of
enhanced resolution are not damped or contaminated by the
application of the boundary conditions. Warner et al.
(1997) suggested that if the area of meteorological
interest has length scale L then the lateral boundaries
should be at least a distance L/2 from this area. Seth
and Giorgi (1998) showed that the effect of the location
of the lateral boundaries could be especially important
in studies of model sensitivity to internal parameters.
Finally, a domain should be chosen so that its boundaries
have minimal overlap with mountain ranges, since
differences in resolution between driving and model
fields may lead to inconsistencies and noise generation
(e.g. Hong and Juan 1998).

Contrary to the above experience, when choosing a domain
for simulations of the Indian summer monsoon, Bhaskaran et
al. (1996) showed minimal sensitivity of their results to
the domain size. This was attributed to the main external
forcing for the monsoon originating outside of all the
model domains. The simulations
also deviated little from the driving GCM indicating that
the forcing was transferred to the RCM via the boundary
relaxation technique without modification.

To ensure full consistency between driving and nested
model large scale fields Kida et al. (1991) and Sasaki et
al. (1995) introduced the spectral nesting technique.
Here the relevant components of the large scale driving
fields force the low wave numbers of the RCM simulation
throughout the entire model domain, while the RCM
generates the higher frequencies. Spectral nesting, or
nudging, has been further developed and refined in the
recent works by Locke and Larow (1999), Waldron et al.
(1996), McGregor et al. (1998) and von Storch et al.
(1999). The spectral nudging ensures that the simulated
model state remains close to the driving state at the
large scales and, in this sense, can be viewed as an
indirect assimilation technique. An alternate procedure
that also ensures a close linkage of the RCM to the large-
scale fields consists of frequent (daily or twice daily)
restarts of the RCM based on large-scale initial driving
conditions (Pan et al. 1999a).

10.5.1.3 Surface boundaries.

It is by now well recognised that the surface forcing due
to land, ocean and sea ice greatly affects a regional
climate simulation. For example, Rinke and Dethloff
(1999) found a substantial RCM sensitivity to sea-ice
thickness and SST
over the Arctic. In another study, Maslanik et
al. (1999) illustrated that the sensitivity may derive
from regions not resolved at the GCM grid scale.
Similarly, land surface conditions significantly affected
the RCM simulations of Pan et al. (1999b), Giorgi et al.
(1996), Seth and Giorgi (1998), Christensen (1999),
Pielke et al. (1999), and Chase et al. (1999).

In particular, because most RCM experiments do not start
with equilibrium conditions, initialisation of surface
variables, such as soil moisture and temperature, is
important. It is commonly assumed that a surface soil
layer of a few cm depths reaches equilibrium after a few
days or weeks. For the rooting zone (~ 1 m depth) the
assumption is that soil equilibration is of the order of
a few seasons, while for deeper soils the equilibrium time
can be of years. Christensen
(1999) gives an example where the time scale for soil
temperatures to be in full balance with the atmosphere is
longer than the characteristic diffusive thermal time
scale of the soil layer. This resulted from non-linear
interactions with the atmosphere. Unless the soil
temperature was carefully initialised, it would not reach
equilibrium for 2 to 4 years. Hence, incorrect soil
temperature initialisation could produce a model drift which
can significantly influence a temperature change signal.

10.5.1.4 Model resolution.

To date, regional climate models have been mostly run at
horizontal grid point spacing varying in the range of 20-
120 km, with a few experiments reaching grid point
spacing of less than 10 km. In general, RCMs have shown a
good performance in reproducing the effects of
topographic and surface forcing at the selected
resolution.

In a study of the sensitivity of precipitation
parameterisations to horizontal resolution in their RCM
Giorgi and Marinucci (1996a) showed that the effects of
physical forcings (e.g. topography) could be strongly
modulated by the direct sensitivity of the model physics
formulations to resolution. Laprise et al. (1998) also
found substantially different behaviour of the same
precipitation scheme in their RCM and the driving GCM. In
a study over East Asia, Kato et al. (1999) showed that
though the simulation of intense cyclonic events
generally improved with increased resolution some aspects
of model climatology deteriorated. A similar experience
was reported by Christensen et al. (1998) in a double
nested simulation of present-day climate at 18km
resolution for Scandinavia. They found that the
description of the hydrologic cycle improved with
increasing resolution due to the better topographical
representation but that some biases in the coarser
resolution model where worsened.

Within an RCM domain different sub-regions may require
different resolutions to capture relevant forcings (e.g.
topography). Double (one-way) nesting is one approach to
achieve this objective. Another approach is to use two-
way nested sub-grids, a capability available in many
regional modelling systems but still not applied to
regional climate problems. A third approach is to use
smoothly varying horizontal resolution, or grid
stretching, similar to that used in some global models,
as described in the preliminary study of Qian et al.
(1999). A subject which has received no attention in the
published literature within the context of regional
climate applications is that of the vertical resolution
of RCMs. The number of vertical levels used in RCMs is
generally between 10 and 30 and in many cases it is kept
the same as in the driving GCMs. Increasing horizontal
resolution has been shown to increase the variability and
magnitudes of the vertical velocity (e.g. Jones et al.
1995) which suggests that, at least for stability
reasons, vertical resolution should increase with
horizontal resolution.

10.5.1.5 Model physics

Traditionally, the development of regional climate models
has followed two distinct approaches. In the first, a pre-
existing (and well tested) limited area model system is
suitably modified for climate application (e.g. in the
model physics representations) and is used with driving
conditions obtained either from analyses of observations
or from different GCMs. This is the approach followed,
for example, in the development of the NCAR RegCM (Giorgi
et al. 1993a,b), the regional climate model of Miller and
Kim (1996) and Kim et al. (1998), and the climate version
of the CSU RAMS (Pielke et al. 1992, Copeland et al.
1996) and NCAR/PSU MM5 (e.g. Leung and Ghan 1999a,b). In
the second approach, the full physics of a GCM is
implemented within a regional dynamical framework, and
the regional model thus obtained is mostly run using
driving conditions from the host GCM. This approach is
followed, for example, in the Canadian regional climate
model (Laprise et al., 1988), the CSIRO DARLAM (McGregor
and Walsh 1993), the MPI/DMI HIRHAM
(Christensen et al. 1996) and the UKMO Unified model
(Jones et al. 1995).

These two approaches imply different strategies, which
underlying the use of different physics parameterisations
in the nested and driving models is that each set of
parameterisations is developed and optimised for the
respective model resolutions. The disadvantage of this
strategy is that the interpretation of differences
between nested model and driving GCM results is often
difficult, because these may be caused not only by the
different resolution forcings, but also by the
differences in the physics schemes used. Another
potential disadvantage is that the model physics schemes
might result in such different forcings that spurious
circulation can be produced in the interior of the
domain.

The strategy underlying the use of the same physics
schemes in the nested and driving models is that maximum
compatibility between the models is achieved. The main
disadvantage with this approach is that physics schemes
developed for coarse resolution GCMs may not be adequate
for the high resolutions used in nested regional models.
In addition, a parameterisation scheme (e.g. cumulus
convection) can show a significant sensitivity to
horizontal resolution (e.g. Giorgi and Marinucci 1996a,
Laprise et al. 1998), and thus can present quite
different behaviours in the nested and driving models. In
some cases certain model parameters need to be re-
calibrated for the particular resolution in order to give

Overall, both strategies have shown performance of
similar quality (e.g. IPCC 1996), and depending on the
particular experiment set up and model environment,
either one may be preferable (Giorgi and Mearns 1999). In
the context of climate change, if the physics schemes
have similar behavior at coarse and fine resolutions,
it may be preferable to
use the same physics to provide consistency in the
climate feedbacks associated with perturbations to the

10.5.1.6 Coupling of atmospheric RCM with other
components of the climate system.

Several efforts have gone in the direction of coupling of
atmospheric RCMs to other components of the climate
system, such as ocean/sea ice, chemistry/aerosol, and
land biosphere/hydrology models. An example of a coupled
regional atmosphere/land/ocean/sea ice modelling system
is ARCSyM; the Arctic Region Climate System Model
originally developed by Lynch et al. (1995). ARCSyM has
been used for a variety s studies of
atmosphere/land/ocean interactions for the Arctic region
(Lynch et al. 1997a,b, 1998; Bailey et al. 1997; Maslanik
et al. 1999) and has been recently adapted to the
Antarctic region (Bailey and Lynch 1999a,b).
Weisse et al. (1999) coupled an RCM to a wave
model and an ocean model to assess the role of an actual
simulated sea state on the air-sea exchanges.

Following the work of Hostetler et al. (1993), Small et al.
(1999a,b) coupled atmospheric and lake models for the
Aral Sea Basin, and their coupled model showed a
remarkably good performance in reproducing the seasonal
cycle of lake SST, sea ice extent, and the surface water
and energy budgets of the lake.
Leung et al. (1996) coupled their regional
model (including a parameterisation of sub-grid scale
topography and vegetation) to a basin-hydrology model,
and were able to successfully simulate the hydrologic
budget of basins characterised by complex topography.
Miller and Kim (1996) and Kim et al. (1998) also carried
out coupling between atmosphere and land hydrology
models.

Still, regarding biosphere-atmosphere coupling,
Tsvetsinskaya et al. (1999a,b) coupled a crop model
within the NCAR RegCM and then performed several experiments over
the central Plains of the US to determine the effect of
interactive seasonal plant growth on mesoscale patterns
of temperature and precipitation. They found that the
interactive model runs significantly affected surface
fluxes and resulting tropospheric temperatures.

Finally, RCM-aerosol interactive coupling ws first
attempted by Qian and Giorgi (1999), who
coupled the NCAR RegCM to a radiatively active aerosol
source-transport-removal model,
including both direct and indirect effects. they
described various non-linear interactions between climate
and aerosols.

10.5.1.7 Oceanic RCMs

A large number of regional ocean models have been
developed during the last decades for a wide variety of
applications. However, the specific use of these models to
climate change studies is very limited and only recent.
In particular, Kauker (1998) used an approach similar to
nested RCM modelling to develop a high-resolution ocean
model for the North Sea. He completed multi-decadal
present day and future ocean simulations driven by
atmospheric forcing and lateral ocean forcing from an
AOGCM experiment. While it is still early to evaluate the
use of regional ocean models for climate change studies,
and even though the resolution of some
current global ocean models is already of the order of
several tens of km,
it is clear that the potential for this type of ocean
model application is substantial and work in this direction should
continue in the future.

10.5.2 Validation and simulations of present day climate

Since the SAR, a vast number of RCM simulations have been
conducted. McGregor (1997) presents an exhaustive review
of simulations carried out until mid 1996, and many
others have appeared in the literature since. It is not
our intention to provide an exhaustive review of these
experiments but rather to give an assessment of the
general performance of RCMs in reproducing present day
climate.

An RCM can be validated by comparison with observations
either for specific periods or for a long-term
climatology. In the former case, observed (or "perfect")
boundary conditions to drive the RCM are required,
and are generally
derived from NWP analyses or reanalyses (e.g. ERA, Gibson
et al., 1997; or NCEP re-analysis, REF HERE?).
Due to poor sampling in some areas and to
observational uncertainty these are not error free.
However, over most regions they will give accurate
representation of the large-scale flow and tropospheric
temperature structure. Multiannual RCM
simulations with perfect driving boundary conditions
can also be validated against long term climatologies.
For GCM-driven experiments, in which the boundary
conditions are obtained from GCM climate simulations, the caveats
applied to GCM validation concerning the influence of sample
size and decadal variability apply (see section 10.2, 10.3, and 10.4).
Despite these caveats,
relatively short simulations (several years) can identify
major systematic RCM biases if they yield departures
from observations greater than the observed natural
variability (Christensen et al. 1997, Jones et al. 1999).

Often a serious problem in RCM validation is the lack of
good quality high-resolution observed data. While this data is
available for some regions, over many areas of the globe
observations are extremely sparse or not readily
available. The Arctic and Antarctic regions are obvious
examples but over many populated regions observation data
sets either have low spatial resolution or are not easily
accessible. Other examples of areas where observed data sets are
problematic are regions characterised by complex terrain
with insufficiently dense observing networks. In
addition, only little work has been carried out on how to
use point measurements to validate the grid-box mean
values from a climate model, especially when using sparse
station networks (e.g. Osborn and Hulme, 1997).

A related issue is the type of data used for model
evaluation. Most of the observational data available at
typical RCM resolution (order of 50 km) is for
precipitation and daily minimum and maximum temperature.
While these fields have been shown to be useful for
evaluating model performance, they are also the end
product of a series of complex processes, so that the
evaluation of individual model dynamical and physical
processes is necessarily limited. Additional fields need
to be examined in model evaluation to broaden the
perspective on model performance and to help delineate
sources of model error. Examples are the surface energy
and water fluxes.

Despite these problems, the situation is steadily
improving (New et al. 1999a,b), with various groups
developing high resolution regional observed
climatologies (e.g. Frei et al. 1998, Christensen et al.
1998, VEMAP REFERENCE?). In addition, regional programs
such as the Global Energy and Water Cycle Experiment
(GEWEX) Continental-Scale International Program (GCIP)
have been designed with the purpose of developing sets of
observation data bases at the regional scale for model
validation (GCIP, 1998).

10.5.2.1 Validation using simulations driven by analyses
of observations

Ideally, experiments using analyses of observations to
drive the RCMs should precede any attempt to simulate
climate change. The model behaviour in response to
realistic forcing should be as close as possible to that
of the real atmosphere and analyses of observation-driven
experiments can reveal systematic model biases primarily
due to the internal model dynamics and physics.

A list of RCM simulations driven by analyses for one
month or more and described in the literature is given in
Appendix 10.A. For many of these a common measure of
model skill is the regional bias of seasonally or monthly-
averaged surface air temperature and precipitation, where the
bias is defined as the difference betwee simulated and observed values.
Table 10.5.1 presents regional biases of seasonally-averaged
precipitation and surface air temperature for a sub-set of the experiments
in Appendix 10.A in which a simulation of at least 3-year length was
completed. This table indicates that
current RCMs, when driven by analyses of
observations, can reproduce observed average seasonal
surface air temperature and precipitation over regions of
size 10**5 -- 10**6 km2 with errors mostly in the range
of +/- 0.5-2 K and +/- 5-40% (of observed precipitation),
respectively. [CHECK THESE FIGURES BASED ON THE FINAL TABLE]

In addition, various RCM intercomparison projects have
been carried out to identify different or common model
strengths and weaknesses. Christensen et al. (1997)
compared 7 RCM simulations for summer and winter
conditions over Europe using observed boundary
conditions. The individual simulations used comparable
resolutions (about 50 km) and included a common summer
and winter month, though the domain sizes and length of
simulation varied. A wide range of performance
was reported, with the better models exhibiting a good
simulation of surface
air temperature except over southeastern Europe
during summer. For winter precipitation, because of the
strong forcing imposed by the boundary conditions, biases
were derived mainly from errors due to the internal model
physics and to a systematic tendency to simulate
excessive cyclone activity. In summer, precipitation
biases appeared to result from a partial decoupling of
the RCM flow from the observed driving fields due to
various deficiencies in the model physics.

Tackle et al. (1999) presented results from the Project
to Intercompare Regional Climate Simulations (PIRCS). In
the first experiment, 7 models were compared in a
simulation of the drought of summer 1988 over the
continental U.S. Each model used a similar domain and
resolution (60 km). A major finding was that the model
ability to simulate precipitation episodes would vary
depending on the scale of the relevant dynamical forcing.
Organised synoptic-scale precipitation systems were
generally simulated deterministically in that
precipitation occurred at close to the same time and
location as observed. Episodes of mesoscale and
convective precipitation were represented in a more
stochastic sense, with less degree of agreement with the
observed events and among models both temporally and
spatially. The performance of different models varied for
different aspects of the simulation.

An intercomparison of East Asian summer monsoon
simulations from 3 models was presented by Leung et al.
(1999). The primary result of this work was that cloud
radiative processes in the models represented an
important factor in determining differences between the
model simulations and in determining
model errors. The importance of cloud radiation processes
in RCMs was further studied by Giorgi et al. (1999).

An important development in RCM validation since the SAR
is the extension of analyses from average climate to
interannual variability. Studies in this direction were
carried out by Luethi et al. (1996) for Europe, Giorgi et
al. (1996) and Giorgi and Shields (1999) for the
continental U.S., Sun et al. (1999b) for East Africa,
Small et al. (1999a) for central Asia and Rinke et al.
(1999) for an Arctic region. In all cases, the models
were driven by ECMWF analyses. Overall the models showed
a good performance in simulating interannual anomalies of
precipitation and surface air temperature, both in sign
and magnitude, over sub-regions of the domain varying in
size from a few hundred to about 1000 km (Figure 10.5.1).
These results indicate that, when driven by good quality
large-scale fields, nested regional models can simulate
well interannual surface climate variability at the sub-
continental scale. The model performance can vary from region to
region depending on the physiographic setting and the distance
from the lateral boundaries.

At the intra-seasonal
scale, Fu et al. (1998) studied the evolution of the
monsoon rain belt over East Asia for the period of April
1 to September 30, 1991. They demonstrated that the
timing and positioning of the monsoon rain belt, as
illustrated by a time-latitude cross-section of rainfall,
was reproduced with a high degree of realism (Figure
10.5.2). A similar result was obtained by Emori et al.
(1999) in an RCM study of the Baiu front over East Asia.
Using the NCAR RegCM, Sun et al. (1999a) obtained a good
simulation of intra-seasonal precipitation evolution over
various regions of East Africa during the short rains of
October - December 1988 (Figure 10.5.2).

At even shorter time scales, Dai et al. (1999) examined
the performance of the RCM of Giorgi and Shields (1999)
in simulating the diurnal cycle of precipitation over the
continental U.S. They showed that, despite good
reproduction of climate averages and interannual
variability over the region, the model still had
significant problems in reproducing the observed diurnal
cycles of precipitation, with the model performance
varying substantially from region to region.

10.5.2.2 Simulations of present day climate using GCM
boundary conditions

Since the SAR, evaluation of RCMs driven by GCM
simulations of current climate has gained much attention,
and in fact many groups have performed GCM-driven
experiments even prior to testing the models with
analyses of observations. A list of GCM-driven regional
simulation available in the literature is given in
Appendix 10.B

In general, the performance of RCMs in reproducing
present day climate deteriorates when forced by GCM
fields. Errors introduced by the GCM representation of
large-scale circulation are trasmitted to the RCM as
clearly shown by Noguer et al. (1998). However, since the
SAR, regional biases of seasonal surface air temperature
and precipitation have been reduced (Giorgi and Marinucci
(1996b), Noguer et al. (1998) and Jones et al. (1999) for
Europe, Giorgi et al. (1998) for the continental U.S. and
Hennesy et al. (1998) for Australia). These improvements
are due to both better large-scale boundary condition
fields and improved aspects of internal physics and
dynamics in the RCMs. Table 10.5.2 presents a summary of
these and other representative results.

TABLE 10.5.2 TO BE CONSTRUCTED AND INSERTED HERE

Although the regionally averaged biases in the nested RCM
are not necessarily smaller than those in the driving
GCMs, all the experiments mentioned above, along with
those of Leung et al. (1999a,b), Laprise et al. (1998),
Christensen et al. (1998) and Machenhauer et al. (1998)
clearly show that the spatial patterns produced by the
nested RCMs are in better agreement with observations
(Table 10.5.3). This is essentially due to the better
representation of high-resolution topographical forcings
and improved land/sea contrasts.

[TABLE 10.5.4 2 WITH CORRELATION COEFFICIENTS: to be
constructed]

In most nested RCM experiments, by design, the average
large-scale circulation in the nested and driving models
are similar. However, when studying current climate this
is not necessarily a constraint and Giorgi et al. (1998)
showed that a nested RCMs could improve the large-scale
circulations produced by the global model. This was
especially so in summer when the effects of the internal
model physics are most pronounced and lead to an improved
large-scale precipitation patterns over the central U.S.
This improvement was primarily attributed to a better
representation of the Rocky Mountain chain in the RCM.
Conversely, Machenhauer et al. (1998) showed that for
three different present day climate simulations over
Europe their nested model cyclones became too deep and
traveled too far into the continent. They also
demonstrated that interactions between the large-scale
driving data and high resolution RCM forcings can have negative
effects. During summer, the increased shelter due to the
better-resolved mountains helped to enhance dry
conditions over southeastern Europe.

Some studies of additional climate variables have been
performed. In a detailed study of the hydrologic cycle
over Scandinavia, Christensen et al. (1998) showed that
only at a very-high resolution do the mountain chains in
Norway and Sweden become sufficiently well resolved to
yield a realistic simulation of the annual evolution of the hydrologic
cycle. (Figure 10.5.3). Confirming this result, Leung et
al. (1999a) showed that only through the use of a sub-
grid scale scheme capable of resolving complex
topographical features a realistic simulation could be achieved
of the seasonal evolution of snow formation and melting over the
North-western U.S. (Figure 10.5.4).
Noguer et al. (1998) used surface radiation observations
from GEBA (Ohmura et al., 1989) elucidate the causes of
warming in RCMs relative to their driving models.

Only a few examples are available of analyses of variability
in RCMs driven by GCM fields.
At the intra-seasonal scale, Bhaskaran et al.
(1998) showed that the leading mode of sub-seasonal
variability of the south Asian monsoon, a 30-50 day
oscillation associated with the northward migration of
the circulation and precipitation anomalies, is more
realistically captured by an RCM than in the driving GCM.
Using the same model, Hassell and Jones (1999) showed that
the RCM captured precipitation anomalies in the active
and break phases of the monsoon (5-10 day periods of
anomalous circulation and precipitation) that were absent
from the driving GCM despite similar flow anomalies
(Figure 10.5.5).

At the daily time scales, Jones (1999) investigated the
statistics of heavy precipitation events in RCM
simulations. The RCM produced more realistic statistics
of heavy precipitation events than the driving GCMs,
capturing extreme events completely absent in the GCM.
Much of this is due to the inherent disaggregation of
grid-box mean values resulting from the RCM's higher
horizontal resolution. However, even when aggregated to
the GCM grid scale, the RCM wa closer to observations
(as also demonstrated by Durman et al. 1999).

10.5.3 Climate change simulations

Since the SAR several multi-year RCM simulations of
anthropogenic climate change, either from equilibrium
experiments or for time slices of transient simulations,
have become available. These are given in Appendix 10.C.

An important issue when analysing RCM simulations of
climate change is the significance of the modelled
responses. To date RCM simulations have been aimed at
evaluating the models and processes rather than producing
scenarios and have been relatively short (often only 5
years). At these timescales natural climate variability
may mask all but the largest responses. In an analysis
of RCM responses over Europe in four models, Machenhauer
et al. (1998) concluded that generally only the full area
averaged seasonal mean surface temperature and
precipitation responses were statistically significant.
In only a few cases across all seasons were subdomain
deviations from the mean response significant. Also, in
many of these cases the simulated climate responses
were primarily attributed to a
combination of systematic errors in the flow of the
driving GCMs for present day conditions and to internal RCM
model errors. Jones et al. (1997) estimated that at least
a 30 year sample is required to confidently assess the
mesoscale response in an RCM.

Despite the limitations in simulation length, most RCM
experiments clearly indicate that, while the large-scale
patterns of surface climate change in the nested and
driving models are similar, the mesoscale details of the simulated changes
are quite different. Significantly different patterns of
temperature and rainfall changes were found in the DARLAM
140 year long transient regional climate change
simulation for Australia (Hennesy et al., 1998). This was
most clearly seen in mountainous areas (Figure. 10.5.6).
For example, winter rainfall in southern Victoria
increased in the DARLAM simulation, but decreased in the
driving GCM. Because of improvements in the DARLAM
simulation of current climate relative to the GCM, they
argued that its response was likely to be more plausible.
A high resolution topographical modification of the
regional precipitation change signal in a nested RCM
simulation was also found by Jones et al. (1997) over
Europe and Giorgi et al. (1998) over the continental U.S.
All these studies illustrate the importance of fine
resolution modelling of climate change in topographically
complex regions.

The response in an RCM can also be modified by changes in
regional feedbacks. In a 20 year nested climate change experiment
for the Indian monsoon region, Hassell and Jones (1999)
showed changes in the regional warming patterns. A
maximum of 5 K seen in central northern India in the GCM
simulation was reduced and moved to north-west in the
nested RCM, with a secondary maximum appearing to the
south east (Figure 10.5.7). The shift of the main maximum
was attributed to deficiencies in the GCM control climate
that promoted excessive drying of the soil in northwest
India. The secondary maximum was attributed to a complex
response involving the RCM's better representation of the
flow patterns in southern India resulting from an
improved representation of the Western Ghats. In this
instance, again it was argued that the improved realism of
the RCM's control simulation increases confidence in its
response.

Changes in climate variability in control and doubled CO2
simulations for the US Great Plains are reported by
Mearns (1999) and Mearns et al. (2000). They found
significant decreases in daily temperature variability in
winter and increases in temperature variability in
summer. These changes were very similar to those of the
driving GCM. Changes in variability of precipitation,
however, were quite differentin the nested and
driving models, particularly in summer, with
increases being more pronounced in the regional model. In
a 2CO2 regional climate scenario, Gallardo et al. (1999)
also found that the Iberian Peninsula would be
characterised by a higher seasonal variability than in
the control. They report significant increase for surface
temperatures (greatest in summer) and precipitation in
winter.

Nested RCMs can be used effectively for process studies.
For example Giorgi et al. (1997) analysed the effect of
doubling CO2 on the surface climate change signal over
the European Alps. In their experiments, the simulated
surface air temperature change signal due to CO2
conditions showed a marked elevation dependency, mostly
during the winter and spring seasons, resulting in more
pronounced warming at high elevations than low elevations
(Figure 10.5.8). This was primarily caused by a depletion
of the snow pack in doubled CO2 conditions and was enhanced
by the snow-albedo-feedback mechanism. Interestingly,
this result is consistent with observed temperature
trends for anomalously warm winters over the alpine region
(Giorgi et al. 1997). Changes in precipitation and other
components of the surface energy and water budgets also
showed an elevation signal. These results were confirmed
by the RCM experiments of Leung and Ghan (1999b) and the
GCM experiments of Fyfe and Flato (1999). In general, the
presence of such elevation modulation of the climate
change signal may have important consequences for climate
change impacts on ecosystems and water resources in
regions characterised by complex topographical systems.

Another detailed study of particular climate change
effects was carried out by Knutson et al. (1998), who
analysed tropical hurricane intensities in the Northwest
Pacific with the GFDL hurricane prediction system.
Tropical storm-like features in a coarse mesh GCM control
and climate change experiment were identified and
corresponding 5-day simulations with a regional
hurricane model were completed. Their main result was that the
intensity of the hurricanes increased in a warmer climate
because higher SST and increased environmental convective
available energy (CAPE). Walsh and Ryan (1998) found a
similar intensification of tropical cyclones near
Australia using an RCM. Furthermore, Walsh and Katzfey
(1998) identified a weak poleward shift of the tropical
cyclone activity for double CO2 conditions using the same
set of simulations.

RCMs have also been used to explore the impact of land-use changes
on regional climate by Pielke et al. (1999) and Chase et al. (1999).
They found that the land-use changes due to human activities
can induce climate modifications at the regional and local scale
of magnitude similar to the observed climatic changes during
the last century. The issue of regional climate modification
by land0use change has been little explored within the context of the
global change debate and, because of its potential importance,
is in need of further examination.

A simplified technique of using a RCM for climate change
studies has been pioneered by Schar et al. (1996) and
Frei et al. (1998). They forced a RCM with observed
boundary conditions to simulate present day climate and
developed a surrogate warmer climate by uniformly adding
a temperature perturbation to the driving boundary
conditions. Relative humidity was assumed to remain the
same as present day, resulting in a domain-averaged 15%
increase of the atmospheric moisture content. Their
numerical experiments, carried out for Europe and for the
autumn (i.e. the wettest season over the Alps), indicate
a substantial shift towards more frequent events of heavy
precipitation. The magnitude of the response increases
with the intensity of the event and reaches several tens
of percent for events exceeding 30 mm/day. Jones (1999)
and Durman et al. (1999) found similar results using more
rigorous multi-year GCM-driven simulations of control and
doubled CO2 climate, e.g. 50-80% increases in events over
20 mm/day over parts of the UK. All these studies seem to
indicate an increase in the frequency of high
precipitation events in enhanced GHG climate conditions.

10.5.4 Summary and recommendation

Since the SAR, significant improvements have been achieved in the areas of
development and understanding of the nested regional climate modelling
technique. These include many new RCM systems, multiple nesting, coupling
with different components of the climate system (including aerosol-climate
interactions) and research into the effects of domain size, resolution,
boundary forcing and internal model variability.
Nested RCMs have shown marked improvements in their ability to
reproduce present day average climate, with much of this improvement
due to better quality driving fields provided by GCMs.
It is imperative for the effective use of RCMs in climate change work that
the quality of GCM large scale driving fields continues to improve.

New analyses have shown that RCMs can effectively reproduce interannual
variability when driven by good quality fields. However,
more analysis and improvements are needed of the model performance in
simulating climate variability at short time scales (daily to sub-daily).
Overall, the evidence is strong that regional models consistently
improve the spatial detail of simulated climate compared to GCMs
because of their better representation of sub-GCM grid scale forcings.
This is not necessarily the case for region-averaged climate. The added value
of the better topographic representation is especially relevant for
the simulation of the surface hydrologic budget.

Several RCM studies have been important to understand climate change
processes, such as the elevation signature of the climate change
signal or the effect of climate change on hurricanes. However, a
consistent set of RCM simulations of climate change for different
regions which can be used as likely climate change scenarios for impact
work is still not available. Most RCM climate
change simulations have been individual efforts aimed at specific goals.
The need is there to coordinate RCM simulation efforts so that ensemble
simulations with different models and scenarios for given regions
can be developed to provide useful information for impact assessments.
This will need to be achieved under the auspices of international or large
national programs. Within this context an important issue is to
provide RCM simulations of increasing length so as to minimize
limitations due to sampling problems. Even if the best possible RCM could
be considered to provide a best single scenario available for any region
it would not be advisable to solely use this information in assessing
regional climate change impacts. Different, but equally plausible,
scenarios could be obtained by nesting the RCM in
another set of GCM scenarios.

WORK EXPECTED TO BE INCLUDED IN NEXT DRAFT:

KATO ET AL. RCM RUNS OVER EAST ASIA

---------------------------

10.6 Empirical/statistical and statistical/dynamical methods

10.6.1 Introduction

Formally, the concept of regional climate being conditioned by the
large-scale state may be written as a stochastic and/or deterministic
mapping of a predictor (a set of large-scale variables) on a predictand (a
set of regional climate variables In general, the mapping is unknown and is
modeled dynamically (i.e., through regional climate models) or empirically
from observational (or modeled) data sets. In some cases the predictor and
predictand are the same variables but on different spatial scales (for
example the disaggregation schemes of B�rger, 1997; Wilks, 1999; and
Widmann and Bretherton, 1999), but in most cases they are different. The
mapping commonly employed is, in general, not designed to fully model all
ranges of temporal scales.

When using downscaling for assessing regional climate change, three

- The predictors are variables of relevance and are realistically modeled
by the GCM. Since different variables have different characteristic spatial
scales, some variables are considered more realistically simulated by GCMs
than others. For instance, derived variables (not fundamental to the GCM
physics, but derived from the physics) such as precipitation are usually
not considered as robust information at the regional and grid scale (e.g.,
Osborn and Hulme, 1997; Trigo and Palutikof, 1999). Conversely,
tropospheric quantities like temperature or geopotential height are
intrinsic parameters of the GCM physics and are more skillfully represented
by GCMs. However, there is no consensus in the community about what level
of spatial aggregation (in terms of number of grid cells) is required for
the GCM to be considered skillful. For example Widmann and Bretherton
(1999) find monthly precipitation on spatial scales of three grid lengths
(in their case: 500 km) reliably simulated.

- The transfer function is valid also under altered climatic conditions.
This is an assumption that in principle can not be proven in advance. In
the case of empirical functions, the observational record should cover a
wide range of variations in the past; ideally, all expected future
realizations of the predictors should be contained in the observational
record.

- Critical is the assumption that the predictors employed fully represent
the climate change signal. Too little attention has in the past been paid
to this assumption, but Hewitson (1999) and Charles et. al. (1999) have

A diverse range of downscaling methods has been developed, but in principle
fall into three categories, which are based upon the application of
- weather generators, which are random number generators of realistically
looking sequences conditioned upon the large-scale state (10.6.2.1).
- transfer functions, where a direct quantitative relationship is derived
through, for example, regression (10.6.2.2).
- weather typing schemes based on the more traditional synoptic climatology
concept (including analogs and phase space partitioning) and which relate a
particular atmospheric state to a set of local climate variables (10.6.2.3).

Each of these approaches has relative strength and weaknesses in
representing the range of temporal variance of the local climate
predictand. Consequently, the above approaches are often to some degree
merged in order to compensate for the relative deficiencies in one method.

Most downscaling applications have dealt with temperature and
precipitation. However, a wide array of studies exists in which other
variables have been investigated. Appendix XX provides a non-exhaustive
list of past studies indicating predictands, geographic domain, and
technique category. We are concentrating on references to applications
since to 1995, since studies prior to that date made use of now outdated
global climate change scenarios.

[Appendix XX = Table 10.6.1]

10.6.2 Methodological options

10.6.2.1 Weather generators

Weather generators are statistical models of observed sequences of weather
variables. They can also be regarded as complex random number generators
(Katz and Parlange, 1996), the outputs of which resemble daily weather data
at a particular location (Wilks and Wilby, 1999). There are various types
of daily weather generators, based on the approach to modeling daily
precipitation occurrence: but the types usually fundamentally rely on
stochastic processes. Two of these include the Markov chain approach (e.g.,
Richardson, 1981; Hughes et al., 1993, Lettenmaier, 1995; Hughes et al.,
1999, Bellone et al., 1999) and the spell length approach (Racksko et al.,
1991; Wilks, 1999a). In the spell length approach, which can be viewed as
a natural way of extending the Markov chain approach, the length x of the
spell lengths are simulated based on a probability distribution of the
lengths. In the Markov chain approach precipitation occurrence is simulated
day by day. Wilks (1999a) and Semenov et al. (1998) compare these methods.
An additional approach is the so-called "conceptual model" approach, which
involves chance mechanisms (e.g., clustering) by which storms arise and
which is often used by hydrologists (O'Connell et al. 1999).

Weather generators have been used for generating climate change scenarios
that incorporate changes in climate variability (e.g., Katz, 1996; see
Chapter 13, this volume) and for statistical downscaling, or for both
simultaneously (Semenov and Barrow, 1997, Wilks 1999b). In the context of
statistical downscaling the parameters of the weather generator are
conditioned upon a large-scale state (see Katz and Parlange, 1996; Wilby et
al., 1998; Charles et al., 1999), or relationships can be developed between
large scale parameters sets of the weather generators and local scale
parameters (Wilks, 1999b). Conditioning on large-scale states alleviates to
some degree one of the chronic flaws of many weather generators, which is
the underestimation of interannual variations of the weather variables
(Wilks, 1989), and, to a degree, induces spatial correlation (Hughes and
Guttorp, 1994).

As is the case with other downscaling methods the success of this method is
dependent upon the strength of the relationship between the stochastic
generator parameters and the large-scale circulation index, and the
stability of this relationship over time.

As an illustration, the analysis of Katz and Parlange (1993, 1996) is
discussed in some detail. They conditioned daily precipitation amount for a
location in California on a circulation index, based on sea level pressure
off the coast of California. They modeled the daily time series of
precipitation as a chain dependent process, modeling occurrence as a first
order Markov chain, and a power transform of intensity as normally
distributed. The circulation index was allowed only two states, above and
below normal pressure over a 78-year record. Using the Akaike and Bayesian
Information Criteria they determined that model parameters such as mean
intensity, standard deviation of intensity, and the probability of
precipitation varied significantly with the circulation index state (high
versus low pressure). They found that the conditioned model reproduced the
precipitation variance statistics of the observations better than the
unconditioned model, for example, interannual variance of monthly total
precipitation. They went on to describe the use of their model for climate
change scenario formation, i.e., conditions where the probability of
obtaining a particular circulation index state is shifted. The mean
precipitation changes linearly with the probability of the circulation
state, but the standard deviation of the precipitation amount changes
nonlinearly (Figure 10.6.1). These relationships indicate that the model
allows for changes in the coefficient of variation of monthly total
precipitation, which increases under mean drier conditions and decreases
under mean wetter conditions. This method thus also allows for change in
variability of precipitation along with the mean.

10.6.2.2 Transfer functions

The more common approaches found in the literature are regression-like
techniques or piecewise interpolations using a linear or nonlinear
formulation. The simplest approach is to build multiple regression models
relating free atmosphere grid cell values to surface variables. For
example Sailor and Li (1999) have in this manner modeled local temperature
at a series of US stations. Other regression models use fields of spatially
distributed variables to specify local temperatures in Sweden (e.g.: Chen
et. al., 1999), or principal components of regional geopotential height
fields (e.g.: Hewitson, 1992).

Canonical Correlation Analysis (e.g., von Storch and Zwiers, 1999) has
found wide application. A variant of CCA is redundancy analysis, which is
theoretically attractive as it maximizes the predictands variance; however,
in practical terms it seems similar to CCA (WASA, 1998). Also Singular
Value Decomposition has been used (Huth, 1999).

Most applications have dealt with precipitation; for instance Busuioc and
von Storch (1996) with Rumanian monthly precipitation amounts, or Dehn and
Buma (1999) with a French Alpine site. Kaas et al. (1996) have successfully
specified local pressure tendencies, as a proxy for local storminess, from
large-scale monthly mean air pressure fields.

Oceanic climate and climate impact variables have also been dealt with:
salinity in the German Bight (Heyen and Dippner, 1998); and salinity and
oxygen in the Baltic (Zorita and Laine, 1999); sea level (e.g., Cui at al.,
1996); and a number of ecological variables such as abundances of species
(e.g., Kroencke et al., 1998). In addition statistics of extreme events,
expressed as percentiles within a month or season, have been modeled: storm
surge levels (e.g., von Storch and Reichardt, 1997) and ocean wave heights
(WASA, 1998).

An alternative to linear regression is to use piecewise linear or nonlinear
interpolation; geostatistics offers elegant "kriging" tools to this end
(e.g., Wackernagel, 1995). The potential of this approach has been
demonstrated by Biau et al. (1999), who related local precipitation to
large-scale pressure distributions. Another approach is to use cubic
splines, as was done by Buishand and Klein Tank (1996) for specifying
precipitation in Switzerland. Also Hantel et al. (1998) adopt a nonlinear
design for modelling snow cover duration in Austria with European mean
temperature and altitude.

Another non-linear approach is based on artificial neural networks (ANN;
Hewitson and Crane, 1996), which are generally more powerful than other
techniques, although the interpretation of the dynamical character of the
relationships is less easy. For example, Trigo and Palutikof (1999) map
with an ANN SLP and 500 hPa height values on daily temperature at a station
in Portugal and find significantly improved specification as compared to a
linear ANNs.

Figure 10.6.2 shows two brief examples demonstrating two aspects of
transfer function downscaling. The first involves transfer functions using
predictors based on synoptic pattern (eg: through PCA,CCA techniques). As
with weather typing approaches (see section 10.6.2.3 below), the use of
pattern introduces a vulnerability to questions of stationarity under
future climates. Such pattern dependence thus needs to be coupled with an
analysis of the pattern stability under future climates. For example,
Schubert (1998) utilizes PCA of synoptic fields with subsequent linear
regression, and accompanies this with an analysis of the stationarity of
synoptic pattern. Figure 10.6.2a demonstrates the skill of the transfer
function downscaling in generatings the seasonal characteristics, derived
from the dounscaled daily data.

The second example demonstrates the advantage of transfer functions in
preserving temporal evolution, and the common characteristic of a reduction
of modeled variance. Cavazos and Hewitson (2000) use non-linear neural nets
to derive transfer functions between GCM grid cell predictands (local to
the target downscaling location) and daily precipitation. Figure 10.6.2b
shows a time series of daily observed precipitation along with
precipitation downscaled from the atmospheric predictors, demonstrating the
skill in capturing the time-evolution of events and the reduction in
variance.

Figure 10.6.2.2b: From Cavazos and Hewitson (2000). Salamanca (Spain)
observed daily precipitation (Po, total 102.9mm) and downscaled
precipitation (Ps, total 92.9mm). Skill of 0.67, as measured by r2.

10.6.2.3 Weather typing

This synoptic downscaling approach empirically defines weather classes
related to local and regional climate variations. These weather classes may
be defined synoptically or fitted specifically for downscaling purposes by
constructing indices of airflow (Conway et al., 1996). The frequency
distributions of local or regional climate are then derived by weighting
the local climate states with the relative frequencies of the weather
classes. Climate change is then estimated by determining the change of the
frequency of weather classes.

In many cases, the local and regional climate states are derived from the
observational record. Wanner et al. (1997) used changing global to
continental scale synoptic structures for understanding and reconstructing
Alpine climate variations, while Widmann and Schaer (1997) could not relate
changing Swiss precipitation to changing statistics of weather classes.
Kidson and Watterson (1995) made a similar analysis for New Zealand. Jones
and Davies (1999) apply the technique for studying changing air pollution
mechanisms.

The analog method was introduced into the downscaling context by Zorita et
al (1995). Conceptually similar, but mathematically more demanding are
techniques which partition the large-scale state phase space, for instance
with Classification Tree Analysis, and use a randomized design for picking
regional distributions. This technique was pioneered by Hughes et al
(1993). Lettenmaier (1995) gives a general overview of these techniques.
Both analog and CART approaches return the right level of variance and
correct spatial correlation structures.

In the following, we discuss in some more detail a case of
statistical-dynamical downscaling as suggested first by (Frey-Buness et
al., 1995): Statistical-dynamical downscaling (SDD) is a hybrid approach
with statistical and dynamical elements. In a first step GCM results of a
multi-year climate period are disaggregated into non-overlapping multi-day
episodes of quasi-stationary large-scale flow patterns. Once defined,
similar episodes are grouped in classes of different weather types. Typical
members of these classes, i.e. episodes which in total comprise only a
small fraction of the complete period, are simulated with a regional
climate model (RCM). It is driven at its boundaries by the GCM results of
the respective episodes. Eventually, the RCM results are statistically
evaluated where the frequency of occurrence of the respective classes
determines their statistical weight. An advantage over the SSD technique
over other empirical downscaling techniques is that in this way spatially
distributed local climates are specified. Its feasibility has been
demonstrated by a series of studies on climate and climate change in the
European Alps (see Appendix XX).

As compared with conventional continuos RCM simulations (Section 10.5), the
computational effort of SDD is small and almost independent of the length
of the climate period. That this reduction of computational demands is not
combined with a reduction is accuracy, at least in terms of time-mean
distributions, is demonstrated by a comparison of mean precipitation
distributions as simulated by a continuous RCM simulation and by the SSD
technique. Figure 10.6.3 displays correlation coefficients and mean
absolute differences, conditional upon the degree of disaggregation. When
the computational load is reduced to 20%, the mean absolute error amounts
Thus, in practical applications the intrinsic error of SDD is acceptable if
the overall error is largely determined by the error of the used models
(GCM and RCM).

Figure 10.6.3. Similarity of time mean precipitation distributions obtained
in a continuous RCM simulation and through SSD for different levels of
disaggregation. Top: mean absolute difference [mm/day], bottom: spatial
correlation coefficient. Horizontal axis: computational load of SSD. � is
the number of days simulated in SSD, N the number of days simulated win the
continuous RCM simulation.

10.6.3 Issues in Statistical Downscaling

10.6.3.1 Temporal variance

Transfer function approaches and some of the weather typing approaches
suffer to varying degrees from an under-prediction of temporal climate
variability, since only part of the regional and local temporal variability
of a climate variable is related to large scale climate variations, while
another part is generated regionally. (For the case of regression the
mathematics of this principle are worked out by Katz and Parlange (1996).)
Two approaches for bringing the downscaled climate variables to the right
level of variability are in use: inflation and randomization. In the
inflation approach, originally suggested by Karl et al. (1990), the
variation is increased by the multiplication of a suitable factor; a more
sophisticated approach, named "expanded downscaling", was developed by
B�rger (1996). It is a variant of Canonical Correlation Analysis that
ensures the right level of variability. This approach is utilized by Huth
(1999) and Dehn et al. (1999). In the randomization approach the
unrepresented variability is added as unconditional noise; that is, in the
simplest case, the "missing" variance is added in the form of white noise,
possibly conditioned on synoptic state (Hewitson, 1998). The concept is
worked out in von Storch (2000), and applications are offered by Dehn and
Buma (1999) and Buma and Dehn (1998).

Conversely, weather generators suffer from the inverse of the above, and
have difficulty in representing low frequency variance. However,
conditioning the generator parameters on the large-scale state may
alleviate this to some degree state (see Katz and Parlange, 1996; Wilks,
1999a; Wilby et al., 1998; Charles et al., 1999).

10.6.3.2 Validation

The validation of downscaling techniques is an essential but difficult
requirement. It requires demonstrating the robustness of the downscaling
under future climates, and that the predictors used represent the climate
change signal. Both assumptions are not possible to rigorously test, as no
empirical knowledge is available so far. The analysis of historical
developments as well as simulations with GCMs can provide support for these
assumptions. However, the success of a statistical downscaling technique
for representing present day conditions does not imply legitimacy for
changed climate conditions (Charles et al., 1999).

The classical validation approach is to specify the downscaling technique
from a segment of available observational evidence and then assess the
performance of the empirical model by comparing its predictions with
independent observed values. This approach is particularly valuable when
the observational record is long and documents significant changes in the
course of time. An example is the analysis of absolute pressure tendencies
in the North Atlantic by Kaas et al. (1996), who fitted a regression model
which related spatial air pressure patterns to pressure tendency
statistics. Similarly Wilks (1999) developed a downscaling function on dry
years and found it functioning well in wet years. Hanssen-Bauer and F�rland
(1998) and Hanssen-Bauer (1999) found in their analysis that data series of
50 year length may not be sufficient to derive a valid model.

An alternative approach is to use a series of comparisons between models
and transfer functions, as demonstrated by Busuioc et al (1999), Charles et
al. (1999) and Gonz�lez-Ruoco et al. (199a,b). In the former study, it was
first demonstrated that the GCM incorporated the empirical link; in the
latter, a regional climate model was used. From these findings it was
concluded that the dynamical models would correctly "know" about the
empirical downscaling link; then the climatic change, associated with a
doubling of carbon dioxide, was estimated through the empirical link and
compared with the result of the dynamical model. In both cases, the
dynamical response was found to be consistent with the empirical link,
indicating the validity of the empirical approach and its legitimate
approach in downscaling other global climate change information.

10.6.3.3: Choice of predictors

The list of predictands in the literature is very broad and comprise direct
climate variables (e.g.: precipitation, temperature, salinity, snow pack),
monthly or yearly statistics of climate variables (distributions in wind
speeds, wave heights, water levels, frequency of thunderstorm statistics),
as well as impacted variables (e.g.: frequency of land slides). The
Appendix XX lists a wide range of predictors, predictands, and techniques.
Useful summaries of downscaling techniques and the predictors used are also
presented in Rummukainen (1997), Wilby et al. (1998) and Wilby and Wigley
(1999).

However, outside of passing references in many studies to the effect that a
range of predictors were evaluated, there is little systematic work that
has explicitly evaluated the relevant skill of different atmospheric
predictors (Winkler et al., 1997). The one commonality between most studies
is, not surprisingly, the use of some indicator of the large-scale
circulation.

The choice of the predictor variables is of utmost importance. For example,
Hewitson (1997, 1998) has demonstrated how the downscaled scenario of
future change in precipitation may alter significantly depending on whether
or not humidity is included as a predictor. The implication here is that
while a predictor may or may not appear as the most significant when
developing the downscaling function under present climates, the changes in
that predictor under a future climate may be critical for determining the
climate change. Some estimation procedures, for example stepwise
regression, are not able to recognize this and exclude variables that may
scenarios of change.

A similar issue exists with respect to downscaling temperature. Werner and
von Storch (1993), Hanssen-Bauer (1999) and Mietus (1999) noted that low
frequency changes in local temperature during the 20th century could not be
related to changes in circulation. Schubert (1998) makes a vital point in
noting that changes of local temperature under doubled atmospheric CO2 may
not be driven by circulation changes alone, but may be dominated by changes
in the radiative properties of the atmosphere. This is a particular
vulnerability of any downscaling procedure in light of the propensity to
use circulation predictors alone that do not necessarily reflect the
changed radiative properties of the atmosphere.

One possible solution is to incorporate the large-scale temperature field
from the GCM as a surrogate indicator of the changed radiative properties
of the atmosphere. This approach has been adopted by Dehn and Buma (1999)
in their scenario of future Alpine land slides. Another solution is to use
several large-scale predictors, such as gridded temperature and circulation
fields (e.g., Gyalistras et al., 1998; Huth, 1999).

After the availability of homogeneous re-analyses (Kalnay et al., 1996),
the number of candidate predictor fields has been greatly enhanced (Solman
and Nu�ez, 1999); earlier, the empirical evidence about the co-variability
of regional/local predictands and large-scale predictors was very limited
and made many studies choose either gridded near surface temperature or air
pressure, or both (Gyalistras et al., 1994). These "new" data sets will
allow significant improvements in the design of empirical downscaling
techniques.

Taking advantage of these new data sets Cavazos and Hewitson (2000)
systematically evaluate a broad range of possible predictors for daily
precipitation. They conclude that, generalized across regions, the critical
predictors are some indicator of mid-tropospheric circulation and humidity.
Regions with a significant component of orographic rainfall also benefit
from some indicator of surface flow.

10.6.4 Inter-comparison of downscaling methodologies

An increasing number of studies comparing different downscaling studies
have emerged since SAR. However, there is a paucity of systematic studies
that use common data sets applied to different procedures over the same
geographic region. A number of articles discussing different empirical and
dynamical downscaling approaches (Giorgi and Mearns, 1991; Hewitson &
Crane, 1996; Wilby and Wigley, 1997; Buishand and Brandsma, 1997;
Rummukainen, 1997; Zorita and von Storch, 1997; Gyalistras et al., 1998;
Kidson and Thompson, 1998, Murphy, 1999a,b, von Storch, 1999b, Biau et al.,
1999) do present summaries of the relative merits and shortcomings of
different procedures. These inter-comparisons vary widely with respect to
predictors, predictands and measures of skill. A systematic,
internationally coordinated inter-comparison project would be useful.

The most systematic and comprehensive study so far is that one by Wilby et
al. (1998) and Wilby and Wigley (1997). They compared empirical transfer
functions, weather generators, and circulation classification schemes over
the same geographical region using climate change simulations and
observational data. The study considered a demanding task to downscale
daily precipitation for six locations over North America, spanning arid,
moist tropical, maritime, mid-latitude, and continental climate regimes. A
suite of 14 measures of skill was used, strongly emphasizing daily
statistics. These included such measures as wet spell length, dry spell
length, 95th percentile values, wet-wet day probabilities, and several
measures of standard deviation. Downscaling procedures in the study
included two different weather generators, two variants of an ANN-based
technique, and two stochastic/circulation classification schemes based on
vorticity classes.

The results prove to be illuminating, but require careful evaluation as
they are more indicative of the relative merits and shortcoming of the
different procedures, rather than a recommendation of one procedure over
another. In the validation phase of the study the downscaling results were
compared against the observational data, and indicated that the weather
generator techniques were superior to the stochastic/circulation
classification procedures, which in turn were superior to the ANNs.
However, the superiority of the weather generator when validated against
the observed data is misleading as the weather generators are constrained
to match the original data (Wilby and Wigley, 1997). Similarly, the
improved performance of the circulation classification techniques with
regard to the ANNs is largely a reflection of the measures of skill used
and indicates the tendency of ANNs to over-predict the frequency of trace
rainfall days. In contrast, when the inter-annual attributes of monthly
totals are examined the performance ranking of the techniques is
approximately reversed with the weather generators performing especially
poorly.

The results indicate strength by weather generators to capture the wet-day
occurrence and the amount distributions in the data, but less success at
capturing the inter-annual variability (the low frequency component). The
important question with this procedure is thus how to perturb the weather
generator parameters under future climate conditions. At the other end of
the spectrum the ANN procedures performed well at capturing the low
frequency characteristics of the data, and showed less ability at
representing the range of magnitudes of daily events.The
stochastic/circulation typing schemes, being somewhat a combination of the
principles underlying weather generators and ANNs, appear to be a better
all-round performer.

In application to GCM simulations of future climate, the procedures showed
some consistency with the ANN indicating the largest changes in
precipitation. However, assessing the relative significance of the changes
is non-trivial, and at this level of inter-comparison the results of the
climate change application are perhaps more useful in a diagnostic capacity
of the GCM which appeared to show differences in the strength of the
precipitation-circulation relationship.

What is not evaluated in this study to any great degree is the range of
variance spanned by each technique. Addressing this issue Wilby et al.
(1998) and Conway et al. (1996) apply transfer functions to determine
wet/dry probabilities and then use a stochastic procedure for the magnitude
of precipitation, and in doing so capture some degree of the low frequency
and high frequency variance. Zorita et al (1995) and later Cubasch et al.
(1996) demonstrated that a suitably designed analog technique reproduces
storm interarrival terms well. Similarly, Hewitson (1998) span the range of
variance using an ANN transfer function to predict precipitation magnitude,
and then stochastically model the residual variance as a function of
atmospheric state.

An additional factor not yet fully evaluated in any comparative study is
that of the temporal evolution of daily events. In this respect the manner
in which daily events develop may be critical in some areas of impacts
analysis, for example hydrological modeling. While a downscaling procedure
may correctly represent, for example, the number of rain days, the temporal
sequencing of these may be as important.

A number of analyses have dealt with the relative merits of non-linear and
linear approaches. For example, Conway et al. (1996) and Brandsma and
Buishand (1997) use circulation indicators as predictors and note that the
relationships with precipitation on daily time scales are often non-linear.
Similarly Corte-Real et al. (1995) effectively applied multivariate
adaptive regression splines (MARS) to approximate non-linearity in the
relationships between large-scale circulation and monthly mean
precipitation. However, the application of MARS to large volume daily data
may be more problematic (Corte-Real et. al., 1995). Other non-linear
techniques are kriging and analogs, whose performance were compared by Biau
et al., (1999) and von Storch (1999). Kriging resulted in better
specifications of averaged quantities but too low variance, whereas analogs
returned the right variance but lower correlations. Also analogs can be
usefully constructed only on the basis of a large data set. It appears that
downscaling of the short-term climate variance benefits significantly from
the use of non-linear models. In particular, downscaling of daily
precipitation benefits appreciably from the ability to better capture
convective events.

Most of the comparative studies mentioned above come to the conclusion that
techniques differ in their success of specifying regional climate, and the
relative merits and shortcomings emerge differently in different studies.
This is not surprising, as there is considerable flexibility in setting up
a downscaling procedure, and the suitability of a technique and the
adaptation to the problem at hand varies.

10.6.5 Summary and Recommendations

A broad range of statistical downscaling techniques has been developed in
the past few years. Users of GCM based climate and climate change
information may choose from a large variety of methods conditional upon
their needs. Weather generators provide realistic sequences of events. With
transfer functions statistics, like conditional means or quantiles, of
regional and local climate may consistently be derived from GCM generated
data. Techniques based on weather typing serve both purposes.

Downscaling means post-processing GCM data; it can not account for
insufficiencies in the driving GCM data. As statistical techniques are
combining the existing empirical knowledge, statistical downscaling can
describe only links which have been observed in the past. Thus, it is based
on the assumption that presently found links will prevail under different
climate conditions. It may be, in particular, that under present conditions
some predictors appear less relevant, but become significant in describing
climate change. It is recommended to test statistical downscaling methods
by comparing their estimates with simulations with high-resolution
re-analyses have offered the community many more atmospheric large-scale
variables as possible predictors.

Statistical downscaling requires the availability of long and homogeneous
data series spanning the range of observed variance, while the
computational resources needed are small. Therefore, statistical
downscaling techniques are suitable tools for scientific communities
process-based climate modeling. Often dynamical downscaling methods are
application, so that resorting to the much simpler statistical techniques
may often be advisable. Furthermore, statistical techniques may relate
directly GCM derived data to impact relevant variables, like ecological
variables or ocean wave heights, which are not simulated by contemporary
climate models.

It is concluded that statistical downscaling techniques is many cases a
viable complement to process-based dynamical modeling, and will remain so
in the future.

---------------------------

10.7 Intercomparison of methods

Few formal comparative studies of different regionalization techniques
have been carried out. To date, published work has mostly focused on the
comparison between regional climate model and statistical downscaling
techniques. Early applications of regional climate models for climate
change simulations (Giorgi and Mearns, 1991; Giorgi et al., 1994) compared
the models against observations or against the driving GCMs, but not
against statistical/empirical techniques. Recently three studies have
systematically evaluated a dynamical model against statistical/empirical
techniques.

Kidson and Thompson (1998) compared the RAMS dynamical model and a
statistical regression-based technique. Both approaches were applied to
downscale reanalysis data (ECMWF) over New Zealand to a grid resolution of
50 km. The statistical downscaling used a screening
regression technique to predict local minimum and maximum daily
temperature, and daily precipitation. The regression technique limits
each regression equation to 5 predictors (selected from EOFs of 1000hPa
and 500hPa geopotential height fields, local scalar wind speed and
anomalies of geostrophic wind speed at 500hPa and 1000 hPa, anomalous
1000hPa-500hPa thickness and relative vorticity, and terms of vorticity
advection). The results indicated little difference in skill between the
two techniques, and Kidson and Thompson (1998) suggested that, subject to the
assumption of statistical relationships remaining viable under a future
climate, the computational requirements do not favor the use of the
dynamical model. They also noted, however,
that the dynamical model performed
better with the convective components of precipitation.

More recently Murphy (1998a) evaluated the UK Meteorological Office
Unified Model (UM) regional configuration over Europe against a
statistical downscaling model based on regression. A range of predictors
similar to those used by Kidson and Thompson (1998) (EOFs and regional
values of wind, vorticity, temperature, and additionally in this case,
specific humidity) were used, with the difference that in this case monthly
mean values were downscaled. The results showed similar levels of skill
for the dynamical and statistical methods, in line with the Kidson and
Thompson (1998) study. The statistical method was nominally better for
summertime estimates of temperature, while the dynamical model gave better
estimates of wintertime precipitation. Again the conclusion was made that
the sophistication of the dynamical model shows little advantage over
statistical techniques, at least for present day climates.

Murphy (1998b) continued the comparative study by deriving climate change
scenarios using GCM data from a coupled ocean-atmosphere future climate
simulation (global configuration of the UM). Downscaling of the regional
climates is from the same regional configuration of the UM, and the same
statistical model. Unlike the validation study which compared the
downscaling against observational data, the climate change situation
showed larger differences between the statistical and dynamical
techniques. The study concludes that the differences in the temperature
downscaling do not derive from a breakdown of the statistical
relationships, as might be suspected, but are perhaps related to different
predictor/predictand relationships in the GCM. In contrast, the
downscaled precipitation differences may stem from the exclusion of
specific humidity in the regression equation, as moisture was a weak
predictor of the natural variability. This point would seem to confirm
the humidity issue raised in 10.6.3 (Hewitson, 1997, 1998; Hewitson and
Crane, 1999).

Mearns et al. (1999) compared regional model simulations and
statistical downscaling using the RegCM regional model and
a semi-empirical technique based on stochastic procedures conditioned
on weather types which are
classified from circulation fields (700hPa geopotential
heights). While Mearns et al. suggest that the semi-empirical approach
incorporates more physical meaning into the relationships, this approach
does impose the assumption that the circulation patterns are robust into a
future climate in addition to the normal assumption that the cross-scale
relationships are stationary in time. For both techniques the driving
fields were from the CSIRO AOGCM (Watterson et al., 1995). The variables
of interest were maximum and minimum daily temperature and precipitation
As with the preceding studies, the validation under present
climate conditions indicated similar skill levels for the dynamical and
statistical approaches, with some advantage by the statistical technique.

Also in line with the Murphy (1999) study, larger differences were noted
by Mearns et al. when climate change scenarios were produced. Notably for
temperature, the statistical technique produced an amplified seasonal
cycle compared to both the RegCM and CSIRO data, although similar changes
in daily temperature variances were found in both the RegCM and the
statistical technique (with the statistical approach producing mostly
decreases). The spatial patterns of change showed greater variability
in the RegCM compared to the statistical technique. Mearns et al. suggested
that some of the differences found in the results
were due to the climate change
simulation exceeding the range of data used to develop the statistical
model, while the decreases in variance were likely a true reflections of
changes in the circulation controls.
The precipitation results from Mearns et al. are in contrast to earlier
studies with the RegCM producing few statistically significant changes
(although both increases and decreases were indicated) and almost
half the changes derived from the statistical technique (almost always an
increase) being statistically significant.

Overall, the above comparative studies indicate that for present
climate both techniques have similar skill.
Since statistical models are based on observed relationships between
predictands and predictors, this result may represent a
further validation of the performance of RCMs.
Under future climate conditions more differences are found between
the techniques, and the question arises as to
which is "more correct". While the dynamical model
should clearly provide a better physical basis for change, it is still
unclear whether different regional models generate similar downscaled
changes. With regard to
statistical/empirical techniques it would seem that careful attention must
be given to the choice of predictors, and that methodologies which
internally select predictors based on explanatory power under present
climates may exclude predictors important for determining change under
future climate modes.

---------------------------

10.8 Summary and Conclusions

Today a number of modeling tools are available
to provide climate change information at the regional scale for
impact assessment work. Coupled AOGCMs are the fundamental models used to
simulate the climatic response to anthropogenic forcings and, to date,
results from AOGCM simulations have provided the climate information
for the vast majority of impact studies.
On the other hand, resolution limitations pose severe
constraints on the usefulness of AOGCM information, especially in regions
characterized by complex physiographic settings. Therefore, in the last decade
three classes of regionalization techniques have been developed to enhance the
regional information of coupled AOGCMs:
high resolution and variable resolution time-slice AGCM experiments, regional
climate modeling, and empirical/statistical and statistical/dynamical
approaches. Since the SAR substantial development has been achieved in all
regionalization methods.

It is important to stress that AOGCM information is the starting point
for the application of all regionalization techniques, so that a
foremost requirement in the simulation of regional climate change is that
the AOGCMs simulate well the circulation features that affect regional
climates. In this respect, indications are that the performance
of current AOGCMs is generally improving.

Analysis of different transient simulations with AOGCMs indicates
that average climatic features are generally well simulated at the
large and continental scale. Biases in the simulation of present
day average surface climate variables are highly variable from
region-to-region and among models. When looking at seasonal averages,
regional biases in AOGCM simulations of present day climate are mostly
(but not exclusively)
in the range of +/- 3 K for surface air temperature and -20$\%$ to +50$\%$
of observed value for precipitation (with
several instances of biases still exceeding 3 K for temperature
and 100$\%$ for precipitation).

Regional analysis of AOGCM transient simulations
extending to 2100, for different scenarios of GHG increase and
sulfate aerosol effects, and with a number of modeling systems
(some simulations include ensembles of realizations) indicate that
the average climatic changes for the late decades of the 21st century
(compared to present day climate) vary substantially among models and
among regions. The primary source of uncertainty in the simulated
changes is associated with inter-model range of changes,
with inter-scenario and intra-ensemble range of simulated changes being less
pronounced.

Despite the range of inter-model results some common patterns are emerging
from AOGCM simulations of 21st century climate:

1) All land regions undergo warming in all seasons,
with the warming (and inter-model range of results)
being generally more pronounced over cold climate
regions and seasons. This latter result
is primarily related to the snow/sea ice albedo
feedback mechanism and points to the importance of the description of
cold climate processes in the models.

2) Average precipitation increases over most regions, especially in the
cold season, as a result of an intensified hydrologic cycle. However,
some exceptions occur in which most models concur in simulating decreases
in precipitation. These include broad regions
of Central America, Australia, Southern Africa
and Southern South America in DJF and the Mediterranean region in JJA.

Analysis in simulated interannual variability indicates that the AOGCM
performance in reproducing observed variability varies across
regions and models, but with a prevailing tendency for precipitation
interannual variability (as measured by the standard deviation) to increase
in future climate conditions.

Various AGCMs have been used in time-slice mode and
different variable resolution modeling efforts are under way.
Although the number of available time-slice AGCM climate change experiments
is still small, studies indicate that changing the model resolution
also changes the model response to climatic forcings. In particular,
the climate change response, as measured for example by
the temperature change, is strongly dependent on the bias patterns in the
present day simulations. The importance of using specific changes in forcing
SSTs in time-slice AGCM experiments seemingly plays a secondary role.

Since the SAR, a large number of RCM systems have been developed, with
capability of high resolution multi-decadal simulations in a variety of
regional settings. The analysis of RCM simulations has extended beyond
simple averages to include higher order climate statistics, and has
indicated that RCMs can reproduce well observed interannual variability
given good quality forcing fields. More and improved high resolution
climatologies for RCM validation have been developed since the SAR, but
additional work is still needed in this regard,
especially for remote regions and regions characterized
by complex topography. Compared to AOGCMs, RCMs have been shown to improve
the spatial patterns of surface climate as forced by topography and other
sub-GCM scale processes. However, regionally averaged climate may not be
necessarily improved. The increased resolution of RCMs also allows the
simulation of a broader spectrum of weather events,
in particular as concerning higher
order climate statistics such as daily precipitation intensity distributions.
Analysis of some RCM experiments indicate that this is in the direction
of increased agreement with observations.
The abundance of new regional model studies and the emerging coupling of RCMs
with other components of the climate system illustrates
the flexibility of regional models as tools for regional climate change
research.

A broad range of empirical/statistical and statistical/dynamical
downscaling models are currently available which can be tailored to
the specific needs of the user. These models have improved in particular
since the advent of longer and better quality re-analyses of observations
that can be used to develop the models. Empirical techniques can be easily
implemented and applied to the output of different GCMs and do not
require computationally intensive resources. Therefore they can be
especially useful for groups and countries that do not have access to
large computational resources. Measures of uncertaintt for
statistical downscaling models are application-dependent and preliminary
inter-comparison studies indicate that errors and uncertainties are of the
same order of magnitude across methods and compared to physical models.

Work performed with
all these regionalization techniques indicates that
substantial sub-GCM grid scale structure in the regional climate change
signal occurs in response to regional and local forcings. This is
because of the non-linear nature of the processes that regulate
regional climate. In particular, modeling evidence clearly
indicates that topography and the surface hydrologic cycle strongly affect
the surface climate change signal. We conclude that
the use of AOGCM information for impact studies needs to be taken
cautiously, especially in regions characterized by pronounced sub-GCM grid
scale variability in forcings, and that suitable regionalization
techniques should be used to enhance the AOGCM results over these regions.

A research area which has been little explored to date is
that of regional effects of land-use change.
The exploratory work of Pielke et al. (1999) and Chase et al. (1999)
indicates that land-use change by human activities might produce
local and regional changes in surface climate of similar magnitude
as observed changes for the last decades. However, land use change has
not been included in climate change experiments with AOGCMs and
regionalization techniques. This issue clearly needs to be better

In principle, a simulation of regional climate change should consist of
the following steps: 1) Developemnt of a range of emission and
concentration scenarios;
2) Ensembles of coupled AOGCM simulations for each scenario with different
models; 3) Use of different regionalization techniques, models and
methods to enhance the regional AOGCM information.
Considerations of various type may enter the choice of the regionalization
technique, as different techniques may be most suitable for different
applications and different working environments.

High resolution AGCMs offer the primary
advantage of global coverage and two-way interactions between regional
and global climate. However, due to their computational cost, the
resolution increase that can be expected from these models is limited.
Variable resolution and RCMs yield
a greater increase in resolution, with current RCMs reaching
resolutions as fine as a few tens of km or even less. RCMs can
capture physical processes and feedbacks occurring at the regional scale,
but they do not represent regional-to-global climate feedbacks
and they are affected by the errors of the AOGCM driving fields. Two-way
nesting can capture regional-to-global feedback processes and
some research efforts
in that direction are currently under way. Statistical downscaling techniques
offer the advantages of being computationally inexpensive, of
providing local information which is needed in many impact applications,
and of offering the possibility of being tailored to specific applications.
However these techniques have limitations inherent in their empirical nature.

The joint use of different techniques may
provide the most suitable approach in many instances.
For example, a high resolution AGCM simulation
could represent an important intermediate step between coupled AOGCM
information and RCM or statistical downscaling models.
The convergence of results from different approaches applied to
the same problem can increase the confidence in the results and
differences between approaches can help to understand
the behavior of the models.

Despite recent improvements and developments, regionalization research is still
a maturing process and the related uncertainties are still rather poorly
known. One of the reasons for this is that
most regionalization research activities have been carried out independently
of each other and aimed at specific objectives. Therefore
a coherent picture of regional
climate change via available regionalization techniques cannot yet be drawn.
More coordinated efforts are thus necessary
to evaluate the different methodologies, intercompare methods and models
and apply these methods to climate change research in a comprehensive
strategy.

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