http://www.ipcc.ch/ipccreports/sres/regional/312.htm
http://www.ipcc-data.org
http://www.ipcc.ch/pdf/assessment-report/ar4/wg1/ar4-wg1-chapter8.pdf
http://www.ipcc.ch/pdf/assessment-report/ar4/wg1/ar4-wg1-chapter11.pdf
http://www.ipcc-data.org/guidelines/dgm_no1_v1_10-2003.pdf
http://www.ipcc-data.org/guidelines/dgm_no2_v1_09_2004.pdf

IPCC Climate Models: regional scenarios
A large number of climate change experiments using General Circulation Models (GCMs) have been completed in recent years, both equilibrium and transient experiments, and both experiments forced with changes in greenhouse gas concentrations alone and those forced with greenhouse gas and sulphate aerosol changes. The IPCC Task Group on Scenarios for Climate Impact Assessment (TGCIA) defined a set of criteria to identify GCMexperiments whose results could be used for further evaluation and policy making   (http://www.ipcc-data.org/sres/gcm_data.html).

Based on IPCC TCGIA criteria, models should:

• be full 3D coupled ocean-atmospheric GCMs,
• be documented in the peer reviewed literature,
• have performed a multi-century control run (for stability reasons) and
• have participated in CMIP2 (Second Coupled Model Intercomparison Project).

In addition, the models preferably should:

• have performed a 2 x CO2 mixed layer run,
• have participated in AMIP (Atmospheric Model Intercomparison Project)
• have a resolution of at least T40, R30 or 3� latitude x 3� longitude
• consider explicit greenhouse gases (e.g. CO2, CH4, etc.)

The results of experiments at several modeling centers are currently available in IPCC DDC (Data Distribution Centre): http://www.ipcc-data.org. Overview of AR4 (Fourth Assessment Report�) GCM data and available SRES Scenario runs can be found at: http://www.mad.zmaw.de/IPCC_DDC/html/SRES_AR4/index.html � Some critics have been raised about IPCC climate model predictions. Here are some of the problems discussed:

• treatment of specific physical processes, such as water vapor, clouds, ocean currents etc. in climate models, especially representation of clouds and water vapor in clouds
• determining/quantifying feedback effects
• margin of error used in the IPCC report, presumably much smaller (a 60 percent confidence level), than traditionally used by scientists, who generally report results at the 95 or even 99 percent confidence level
• cumulative errors from different model assumptions
• underestimated challenges of reducing and stabilizing greenhouse-gas emissions, initial estimates of greenhouse-gas are maybe too high

The IPCC AR4 addresses some of these issues in their report on Climate Models and Their Evaluation:
http://www.ipcc.ch/pdf/assessment-report/ar4/wg1/ar4-wg1-chapter8.pdf

Mediterranean Climate Predictions
IPCC AR4 gives some of the regional climate change projections based on information from four potential sources:

1. AOGCM simulations;
2. downscaling of AOGCM-simulated data using techniques to enhance regional detail;
3. physical understanding of the processes governing regional responses;
4. recent historical climate change.

The majhor expectations include:

• Annual mean temperatures in Europe are likely to increase more than the global mean.
• Seasonally, the largest warming is likely to be in northern Europe in winter and in the Mediterranean area in summer.
• Maximum summer temperatures are likely to increase more than the average in southern and central Europe.
• Annual precipitation is very likely to increase in most of northern Europe and decrease in most of the Mediterranean area.
• The annual number of precipitation days is very likely to decrease in the Mediterranean area.
• Risk of summer drought is likely to increase in central Europe and in the Mediterranean area.
• The duration of the snow season is very likely to shorten, and snow depth is likely to decrease in most of Europe.

Figure 1: Temperature anomalies with respect to 1901 to 1950 for Mediterranean region for 1906 to 2005 (black line) and as simulated (red envelope) by MMD models incorporating known forcings; and as projected for 2001 to 2100 by MMD models for the A1B scenario (orange envelope). The bars at the end of the orange envelope represent the range of projected changes for 2091 to 2100 for the B1 scenario (blue), the A1B scenario (orange) and the A2 scenario (red).

Regional Climate Models capture the geographical variation of temperature and precipitation in Europe better than global models but tend to simulate conditions that are too dry and warm in southeastern Europe in summer, both when driven by analyzed boundary conditions (Hagemann et al., 2004) and when driven by GCM data (e.g., Jacob et al., 2007). Most but not all RCMs also overestimate the interannual variability of summer temperatures in southern and central Europe (Jacob et al., 2007; Lenderink et al., 2007; Vidale et al., 2007). The excessive temperature variability coincides with excessive interannual variability in either shortwave radiation or evaporation, or both (Lenderink et al., 2007). A need for improvement in the modeling of soil, boundary layer and cloud processes is implied. One of the key model parameters may be the depth of the hydrological soil reservoir, which appears to be too small in many RCMs (van den Hurk et al., 2005).

The ability of RCMs to simulate climate extremes in Europe has been addressed in several studies. In the Prediction of Regional scenarios and Uncertainties for Defining European Climate change risks and Effects (PRUDENCE) simulations, the biases in the tails of the temperature distribution varied substantially between models but were generally larger than the biases in average temperatures (Kjellström et al., 2007). Inspection of the individual models showed similarity between the biases in daily and interannual variability, suggesting that similar mechanisms may be affecting both.

Read more about Regional Climate Projections in IPCC AR4:
http://www.ipcc.ch/pdf/assessment-report/ar4/wg1/ar4-wg1-chapter11.pdf

IPCC Guidelines for Regional Downscaling
Dynamical downscaling is a method for obtaining high-resolution climate or climate change information from relatively coarse-resolution global climate models (GCMs) which do not capture the effects of local and regional forcing in areas of complex surface physiography. GCMs are run at spatial resolution of 150-300 km by 150-300 km and are unable to resolve important sub-grid scale features such as clouds and topography. Many impacts models require information at scales of 50 km or less, so several methods are developed to estimate the smaller-scale information. Dynamical downscaling uses a limited-area, high-resolution model (a regional climate model, or RCM) driven by boundary conditions from a GCM to derive smaller-scale information.

From: Special report on the Regional Impacts of Climate Change: Simulations using Statistical Downscaling and Regional Climate Modeling Systems
(http://www.ipcc.ch/ipccreports/sres/regional/312.htm)

Since IPCC (1992), significant progress has been achieved in the development and testing of statistical downscaling and regional modeling techniques for the generation of high-resolution regional climate information from coarse-resolution GCM simulations.

The (one-way) nested modeling technique has been increasingly applied to climate change studies in the last few years. This technique consists of using output from GCM simulations to provide initial and driving lateral meteorological boundary conditions for high-resolution Regional Climate Model (RCM) simulations, with no feedback from the RCM to the driving GCM. Hence, a regional increase in resolution can be attained through the use of nested RCMs to account for sub-GCM grid-scale forcings. The most relevant advance in nested regional climate modeling activities was the production of continuous RCM multi-year climate simulations. Previous regional climate change scenarios were mostly produced using samples of month-long simulations (IPCC 1996, WG I). The primary improvement represented by continuous long-term simulations consists of equilibration of model climate with surface hydrology and simulation of the full seasonal cycle for use in impact models. In addition, the capability of producing long-term runs facilitates the coupling of RCMs to other regional process models, such as lake models, dynamical sea ice models, and possibly regional ocean (or coastal) and ecosystem models.

Continuous month- or season-long to multi-year experiments for present-day conditions with RCMs driven either by analyses of observations or by GCMs were generated for regions in North America, Asia, Europe, Australia, and Africa. Equilibrium regional climate change scenarios due to doubled CO2 concentration were produced for the continental U.S., Tasmania, Eastern Asia, and Europe. None of these experiments included the effects of atmospheric aerosols.

In the experiments mentioned above, the model horizontal grid point spacing varied in the range of 15 to 125 km and the length of runs from 1 month to 10 years. The main results of the validation and present-day climate experiments with RCMs can be summarized in the following points:

• When driven by analyses of observations, RCMs simulated realistic structure and evolution of synoptic events. Averaged over regions on the order of 104-106 km2 in size, temperature biases were mostly in the range of a few tenths of °C to a few °C, and precipitation biases were mostly in the range of 10-40% of observed values. The biases generally increased as the size of the region decreased.
• The RCM performance was critically affected by the quality of the driving large-scale fields, and tended to deteriorate when the models were driven by GCM output, mostly because of the poorer quality of the driving large-scale data compared to the analysis data (e.g., position and intensity of storm tracks).
• Compared to the driving GCMs, RCMs generally produced more realistic regional detail of surface climate as forced by topography, large lake systems, or narrow land masses. However, the validation experiments also showed that RCMs can both improve and degrade aspects of regional climate compared to the driving GCM runs, especially when regionally averaged.
• Overall, the models performed better at mid-latitudes than in tropical regions.
• The RCM performance improved as the resolution of the driving GCM increased, mostly because the GCM simulation of large-scale circulation patterns improved with increasing resolution.
• Seasonal as well as diurnal temperature ranges were simulated reasonably well.
• An important problem in the validation of RCMs has been the lack of adequately dense observational data, since RCMs can capture fine structure of climate patterns. This problem is especially relevant in mountainous areas, where only a relatively small number of high-elevation stations are often available. When applied to the production of climate change scenarios, nested model experiments showed the following (IPCC 1996, WG I):
• For temperature, the differences between RCM- and GCM-simulated region-averaged change scenarios were in the range of 0.1 to 1.4�C. For precipitation, the differences between RCM and GCM scenarios were more pronounced than for temperature, in some instances by one order of magnitude or even in sign. These differences between RCM- and GCM-produced regional scenarios are due to the combined contributions of the different resolution of surface forcing (e.g., topography, lakes, coastlines) and atmospheric circulations, and in some instances the different behavior of model parameterizations designed for the fine- and coarse-resolution models. In summer, differences between RCM and GCM results were generally more marked than in winter due to the greater importance of local processes.
• While the simulated temperature changes obtained with nested models were generally larger than the corresponding biases, the precipitation changes were generally of the same order of, or smaller than, the precipitation biases.
• Finally, of relevance for the simulation of regional climate change is the development of a variable-resolution global model technique, whereby the model resolution gradually increases over the region of interest.

The main theoretical limitations of dynamical downscaling technique are the effects of systematic errors in the driving large scale fields provided by global models (which is common to all downscaling methodologies using AOGCM output) and the lack of two-way interactions between regional and global climate. 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, and the 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 with the boundary forcing, which adds another factor of uncertainty in regional climate change simulations (Ji and Vernekar, 1997; Giorgi and Bi 2000, Christensen et al., 2001).

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. Typical regional biases of seasonal surface temperature and precipitation are usually within the range of 2 deg. C and 50 to 60% of observations, respectively (e.g. Jones et al., 1995, Giorgi and Marinucci, 1996, and Jones et al., 1999 for Europe; Giorgi et al., 1998, Pan et al., 2001, Leung et al., 2004 for the continental U.S.; 8 McGregor et al., 1998 for southeast Asia; and Hudson and Jones, 2002a for southern Africa). While the regional biases of the RCM are not necessarily lower than those of the driving GCM, the spatial patterns of climate produced by the RCMs are usually in better agreement with observations compared to those of the GCMs. There is also evidence that RCMs reproduce precipitation extremes well at scales not accessible to GCMs (e.g. Frei et al., 2003, Huntingford et al., 2002, Christensen and Christensen, 2003) and better than GCMs on their gridscale (Durman et al., 2001).

Table 1: The role of some types of climate scenarios and an evaluation of their advantages and disadvantages. Note that in some applications a combination of methods may be used (e.g. regional modelling and a weather generator). (Modified from Mearns et al., 2001).

Statistical downscaling
From special report on the Regional Impacts of Climate Change: Simulations using Statistical Downscaling and Regional Climate Modeling Systems (http://www.ipcc.ch/ipccreports/sres/regional/312.htm)

Statistical downscaling is a two-step process basically consisting of i) development of statistical relationships between local climate variables (e.g., surface air temperature and precipitation) and large-scale predictors, and ii) application of such relationships to the output of GCM experiments to simulate local climate characteristics. A range of statistical downscaling models have been developed (IPCC 1996, WG I), mostly for U.S., European, and Japanese locations where better data for model calibration are available. The main progress achieved in the last few years has been the extension of many downscaling models from monthly and seasonal to daily time scales, which allows the production of data more suitable for a broader set of impact assessment models (e.g., agriculture or hydrologic models).

When optimally calibrated, statistical downscaling models have been quite successful in reproducing different statistics of local surface climatology (IPCC 1996, WG I). Limited applications of statistical downscaling models to the generation of climate change scenarios has occurred showing that in complex physiographic settings local temperature and precipitation change scenarios generated using downscaling methods were significantly different from, and had a finer spatial scale structure than, those directly interpolated from the driving GCMs (IPCC 1996, WG I). Read more about Guidelines for Use of Climate Scenarios Developed from Statistical Downscaling Methods: http://www.ipcc-data.org/guidelines/dgm_no2_v1_09_2004.pdf

References Hay, L.E. and M.P. Clark. 2003. Use of statistically and dynamically downscaled atmospheric model output for hydrologic simulations in three mountainous basins in the western United States. Journal of Hydrology 282:56-75. Leung, L.R., L.O. Mearns, F. Giorgi, and R.L. Wilby. 2003. Workshop on regional climate research: Needs and opportunities. Bull. Amer. Met. Soc. 84:89-95. Giorgi, F., B. Hewitson, J. Christensen, M. Hulme, H. Von Storch, P. Whetton, R. Jones, L. Mearns, and C. Fu. 2001. Regional climate information � Evaluation and projections. In Climate Change 2001. The Scientific Basis, Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change, J.T. Houghton, Y. Ding, D.J. Griggs, M. Noguer, P.J. van der Linden, X. Dai, K. Maskell, and C.A. Johnson (eds.). Cambridge University Press, Cambridge, UK, pp. 583-638. Randall, D.A., R.A. Wood, S. Bony, R. Colman, T. Fichefet, J. Fyfe, V. Kattsov, A. Pitman, J. Shukla, J. Srinivasan, R.J. Stouffer, A. Sumi and K.E. Taylor, 2007: Cilmate Models and Their Evaluation. In: Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M.Tignor and H.L. Miller (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. Christensen, J.H., B. Hewitson, A. Busuioc, A. Chen, X. Gao, I. Held, R. Jones, R.K. Kolli, W.-T. Kwon, R. Laprise, V. Maga�a Rueda, L. Mearns, C.G. Men�ndez, J. R�is�nen, A. Rinke, A. Sarr and P. Whetton, 2007: Regional Climate Projections. In: Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M. Tignor and H.L. Miller (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.