cc: tar10@egs.uct.ac.za date: Thu, 23 Sep 1999 01:29:42 -0600 (MDT) from: Linda Mearns subject: section 10.1 and 10.2 to: giorgi@ictp.trieste.it (GIORGI FILIPPO) Dear Filippo and other 10ers. My suggestions for changes to 10.1 and 2 `are below. I will get to the conclusions and what I owe Hans for 10.6 tomorrow. Cheers, Linda Here are my comments on your Sept 17 version of sections 10.1 and 2. Most of my comments are in the body of the text surrounded by brackets [ ]. and stars *** near suggested changes. Most of what I was concerned about in the original 10.1 and 2 has been removed. One over arching idea (which might address one of McAvaney's points) would be to include the three methods in a table with uses, strenghts and weakness listed. I read Jens comments - I more or less think the concept of added value is implicit in the disucssion, but you could make it explicit. One thing that is not made really clear is why the impacts people want the kind of regional detail these techniques provide, but I don't know if you wnat that in here. One other thing I think is missing from the introduction is other reasons for downscaling aside from applications to impacts - isn't the climate analysis and deepening our understanding of climate on a regional scale relevant as a raison d'etre of regional modeling? 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)" ***I'm not sure what McAvaney means by complementary*** 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." [Well, I think this has been done in 10.2 - but again I'm not sure what he means by modes of application.] 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. *** see my suggestion above about a table **** 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? **** [I agree with you and Rene and I make a suggestion for a milder version in the body of the text **** 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) ***[I'm thrilled that you didn't use MSWORD] 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. [However, what is presented in this chapter serves most often as the raw material for formation of climate scenarios. Climate scenario development is discussed in Chapter 13.] *****[the original sentence above may be a little confusing to the readers. you should 1) make it clearer what is meant by scenarios and 2) refer the reader to chapter 13 on climate scenario development.] It is also unclear what you are getting at in the dependent clause, 'since different impact applications etc.' I have tried to improve it. 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. **** I'm not sure the sentence above is necessary. ***** 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" ***change working to operational**** 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. ***Is there any standard rule about using acronyms? Are we supposed to rely on the reader using the glossary? I think it would be better to define acronyms the first time they are used in a chapter**** 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 allow the capture of the effects of sub-GCM grid scale forcings and processes and provision of] 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). ***second half of above sentence is fairly abstruse - I don't think the reader is going to get what this means (I don't) - what is meant by irrelevant runs?r*** 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 ****define what you mean by short time scales - daily?**** 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 ** consider replacing believe with maintain or assert ***** 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 example of little topographic variation is not a good one - too general --- what about areas of highly heterogeneous land types or areas with steep climatic gradients in the absence of topography? You leave the reader with the impression that if there ain't no topgraphy, then there ain't no reason to regionalize**** 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. ***this above section seems out of place - I would delete it. I don;t really see what the point of it is here. Or again refer the reader to chapter 13. ***** 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 ****I assume 10.2 will be replaced by what Richard has contributed?**** 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 **** this is a somewhat confusing explanation, only because of referring only to AOGCMs or time slice experiments, especially given that the references are to nesting in GCMs with mixed layer oceans - or are you trying to make time-slice AGCM sort of refer to this?**** 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) *****above sentence will be lost on most readers - can you explain it a little in simpler terms?**** 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 ***[scientific]*** 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. 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 method*, 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.) ***Here is my suggested change to the above sentence --- [(While some tuning of dynamic model parameters is also performed, such tuning provides a refinement to the model parameterizations, and is not fundamental to them]. ***** 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 requesetd regional or local information is a distinct advantage, it also 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.] **** the above is an ugly long sentence. Try replacing with: [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.] *** 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). ****Is the above reference an example or does it discuss the whole issue? I suspect it is an example and should have an e.g. before it. Also, you might want to throw in a couple of additional examples, for example Daly et al., 1996 - I can get you the ref if you want it).** 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. ----------------- ################################################################ # Filippo Giorgi, Head, # # Physics of Weather and Climate Group # # The Abdus Salam International Centre for Theoretical Physics # # P.O. BOX 586, (Strada Costiera 11 for courier mail) # # 34100 Trieste, ITALY # # Phone: + 39 (040) 2240 425 # # Fax: + 39 (040) 224 163 # # email: giorgi@ictp.trieste.it # ################################################################ -- ****************************************************************************** Dr. Linda O. Mearns Phone: 303 497 8124 Scientist Fax: 303 497 8125 Environmental and Societal Impacts Group e-mail: lindam@ucar.edu NCAR P.O. Box 3000 Boulder, CO 80307