date: Fri, 17 Sep 1999 16:38:54 +0200 (MET DST)
from: GIORGI FILIPPO <giorgi@ictp.trieste.it>
subject: sections 10.1, 10.2 revised, need for feedback on 2 points
to: tar 10 site <tar10@egs.uct.ac.za>

Dear Chapter 10ers

I am adding at the end of this message the revised sections 10.1 (Intro)
and 10.2. I have done the following (at least I think)

1) For 10.1 I did a bit of rewriting and added a sentence saying we do not
do scenarios
2) For 10.2 I took Hans's last version, did some editing to it, included
material from 10.3.1 and 10.4.1. SO Peter and Richard you should take
those sub-sections out of your sections and double check that you like the
way I incorporated them.
3) I tried to address all the reviewer's comments for these two sections.

In this regard I am having difficulties with two specific comments and I
need feedback on this:

1) McAvaney says:

"More emphasis should be put on the complementary relationship between the
three approaches (section 10.2)"

and

"It would be better to have this section (10.2) concentrating on the three
general techniques and the differences in their modes of application
highlighted."

I am not sure to what extent we do or do not do this and to what extent we
should do this in 10.2 as opposed to other sections (10.7 or 10.8). PLEASE 
THINK ABOUT THIS AND GIVE SOME FEEDBACK.

2) Laprise says:

"(In some way, a similar assumption is implicit in dynamical models, as
some model parameters are ``tuned" to reproduce present day climate
characteristics.)" (This is our statetment)

  "The counter-argument to this is the fact that models have to model a
wide range of climates, including diurnal and seasonal variations,
climates under different latitudes, weather regimes and climatic eras. The
fact that models sucessfully model these variations and also reproduce
some of the interannual variability, constitute SOME proof that the
parameters might be more realistic than simply tuned." (his comment)

I happen to agree with him and I actually think our statement is too
strong. It almost seems that we are suggesting physical models should be
thought of as empirical ones, which, despite the tuning, I think is an
overstatement. This statement essentially came from Hans, but I would like
to come up with a "milder" version of it. ANY SUGGESTION? 


In summary, PLEASE READ THESE REVISIONS AND GIVE FEEDBACK ON EVERYTHING
AND ESPECIALLY ON THE TWO POINTS ABOVE. 

I also remind you that I need revisions of your sections (all included) by
the end of next week (sept. 24).

Below find the revisions (sorry for NOT USING MSWORD)  

Have a good weekend,   Filippo


HERE ARE THE REVISED SECTIONS


-----------------------------
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. However,
due to limited computational resources, the horizontal atmospheric
resolution of present day AOGCMs is still of the order of 300-500 
km. At this resolution, the climatic effects of local and regional forcings
and circulations as well as the fine scale structure of climate variables
needed for impact assessment studies are not explicitly captured. 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.
We refer to these as "regionalization" techniques and classify them 
into three categories:
1) high resolution or 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 provide guidelines for the
use of different methods. It is not the purpose of this chapter
to provide actual scenarios of regional climate change for use 
in impact work, since different impact applications generally require
relevant climatic input of widely different nature. 

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. 

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 intercompared, 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-GCM 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) effects of important sub-GCM grid scale
forcings, processes and circulations. 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 a "working" definition 
based on the range of "regional scales" 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 ENSO and 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 interactions occur
at a range of temporal 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 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 
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, as these aloow to capture the effects of 
sub-GCM grid scale forcings and processes and to provide 
high-resolution climate information. The basic principles behind 
the three categories 
of 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 information provided by AOGCMs 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. Uncertainty due to inter-model (or inter-run) differences can 
thus be allowed for (e.g.  Hulme and Brown 1998), and selectivity can be 
employed to exclude those model runs considered less relevant 
(e.g. Whetton et al, 1996a). Also, data can be obtained for a 
large range of variables down to very short 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 broadscale 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
responsible for rainshadow effects at the broader scale.
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 believe 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). 

Advantages and disadvantages of using AOGCM information in impact
studies can weigh-up differently
depending on the region and variables of interest.
For example, where subgridscale variation is weak (e.g.
mean sea level pressure in most regions, or mean temperature and
precipitation in regions of little topographical variation) the
practical advantages of using direct AOGCM data may predominate.
A common procedure adopted in impact
work has been to utilize the differences between future and present day
climate simulations by coupled AOGCMs as perturbations of
observed climatology. The underlying assumption is that even if the present
day climate of a region is not well reproduced by an AOGCM, 
better confidence can be
placed in the simulation of the climate perturbation, i.e. that some of the
systematic biases in the model may cancel out when perturbations are taken.  

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

One method that has been employed to provide high-resolution climate
information, more specifically for application at the regional scale, is
the use of high resolution and variable resolution AGCMs in the so-called
"time-slice" mode (Bengtsson et. Al., 1995; Cubasch et al., 1995).
"Time-slices" are time intervals in a transient climate evolution which are
in principle long enough to yield statistics representative of a model's
climatology. In practice, most often considerations of availability of
computational resources have entered into the selection of the length of
time-slices. To date, experiments have used time slices of 5 to 30 years.

Once a time slice is selected (say the time periods from 1960 to 1990, or
from 2070 to 2100), time-dependent fields of SST and sea ice
distribution are extracted from the transient AOGCM run
and are used as lower boundary conditions for corresponding simulations 
with high resolution or variable resolution AGCMs. 
Time-dependent GHG and aerosol 
concentrations (or aerosol forcing) in the AGCM experiments are the same 
as in the coupled AOGCM corresponding time slice. Initial atmospheric
and land surface conditions for the AGCM experiments are also interpolated 
from the AOGCM fields.

Because only the atmospheric 
component is run, and because the time slice is of limited length, the 
AGCM can be integrated at relatively high horizontal resolutions. 
Recent time-slice AGCM simulations have reached
horizontal resolutions corresponding to a grid point spacing of about 120 km. 
In the variable resolution AGCMs, the horizontal resolution gradually
increases over a region of interest and decreases towards the antipodes of
this region, or is uniform outside of the region. Maximum grid point spacing  
over the region of interest in recent variable resolution experiments
is of the order of 50 km.  

Different methods can be used to design the SST, sea ice, GHG
and aerosol forcing values of the
time-slice experiments. The most direct method 
is to take these values from the corresponding periods in the AOGCM simulation.
Alternatively, for the control (present day climate) simulation the forcing 
values could be derived from observations 
or from an AOGCM control simulation, while
for the anomaly experiment (future climate), perturbations of the control
forcing values could be derived from an AOGCM experiment.
  
The strategy behind the use of time-slice AGCM simulations is that, given
the SST, sea-ice, GHG and aerosol forcing, relatively high resolution 
information can be obtained globally or regionally, 
with full two-way atmospheric
interactions between regional and global climates, 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, 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 validity of the first of these assumptions represents the main theoretical
weakness of the time-slice AGCM approach. This is related to the issue
of degree of model convergence with resolution increase. 
As resolution increases it is assumed
that model simulations of the resolved large-scale variables would
asymptote to a common state. 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, in which case increasing the resolution will lead
to the inconsistency problems just described.

A practical weakness of high resolution AGCMs is that they generally
use the same formulation as at coarse resolution, with the tuning used
in the latter not necessarily appropriate for the finer scales. 
Many years of experience have gone into developing these
formulations to give accurate simulations of current climate at
coarse resolution, but this process is in its early stages for the higher
resolution models. It is thus currently the case that increasing the
resolution both enhances and degrades different aspects of the
simulations.

Use of high resolution and variable resolution global models is
computationally very demanding, which poses limits to the length of the
simulations and increase in resolution. On the other hand, use of global
AGCMs has the important advantages of capturing two-way interactions
between global and regional climates
and of providing global information for each run.
In fact it has been suggested that high resolution AGCMs could be used to
obtain forcing fields for regional model experiments or statistical
downscaling, thus effectively providing an intermediate step between coarse
coupled AOGCMs and regional and empirical models. 

Finally, two issues need to be carefully examined when using global
variable resolution models. First, 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 system. Second,
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.


10.2.3 Regional climate models

What is commonly referred to as nested regional climate modeling technique
consists of using output from coupled AOGCM (or time-slice AGCM) 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 transient AOGCM 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 low wave number
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 regional model simulations 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
interactions and circulations 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 regional climate feedback mechanisms.
A number of widely used limited area modeling systems have been
adapted to, or developed for, climate application. 

On the other hand, the fundamental limitations of this technique are by now
well known: lack of two-way interactions between global and regional
climate; 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. 
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 AOGCM 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 
RCM experiments. 

Of particular interest is the direction taken by recent RCM modeling efforts 
towards the 
coupling of atmospheric models with other regional 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 and 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 meso scale
models to develop the statistical models. 
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), and Murphy (1999a,b). 
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).

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. (In some way, a similar assumption is implicit in
dynamical models, as some model parameters are "tuned" to reproduce present
day climate characteristics.) Another caveat is that these  empirically
based techniques cannot account for possible systematic changes 
in regional forcing conditions or feedback processes.

While the possibility of "ad-hoc" tailoring the statistical model 
to the requested regional or local information is a
distinct advantage, it also has the drawback that a systematic assessment of 
the uncertainty of this type of techniques, as well as a comparison with other
techniques, is difficult and may need to be carried out on a case-by-case
basis. In section 10.6 a number of
examples are presented along with a discussion of the associated inherent
uncertainties.
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. 

AOGCMs are designed to generate robust 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.
The added value introduced by the use of regionalization methods clearly
depends on the problem and region of interest.

The clearest added value of regionalization techniques 
is their ability to provide climate information at sub-GCM spatial grid scale.
This is especially important for regions and variables influenced by 
forcing characterized by fine spatial variability, such as 
complex topography and land surface conditions. Hence, as an example,
the spatial patterns of precipitation and temperature over complex terrain 
is generally improved with increasing resolution. 
In this regard an aspect which should be considered for a specific
demand of regional
information is whether this can be obtained by simple 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).

A further added value
of increased spatial resolution is the capability of better describing
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 such as 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 are often related in atmospheric phenomena,
the added value of regionalization techniques can extend to high frequency 
temporal scales, such as daily or diurnal. This is despite the fact that AOGCMs 
do provide high resolution temporal information. Therefore, regionalization
models can be expected 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. 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 point out 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, 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.

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 provide valuable insights into the capability of models to
simulate climatic changes and useful methodologies for evaluating 
the long term prediction performance of climate models.


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# Filippo Giorgi, Head,                                        # 
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