Downscaled simulations of the ECHAM5, CCSM3 and HadCM3 global models for the eastern Mediterranean–Black Sea region: evaluation of the reference period

The outputs of three GCMs, ECHAM5, CCSM3 and HadCM3, are downscaled for the eastern Mediterranean–Black Sea region for the period 1961–1990 using a regional climate model, RegCM3, to assess the capability of these models in simulating the climatology of the region. In addition, the NCEP/NCAR Reanalysis data are also downscaled for the same period to display the performance of the regional climate model for the same region, which constitutes a relatively complex terrain and rich variety of climates. The gridded observational dataset of CRU is primarily used in the evaluation of the models, however, a regional dataset, which is based on a relatively dense gauging network, is also used to see how it affects the performance measures of the models. The reanalysis simulation indicates that RegCM3 is able to simulate the precipitation and surface temperature as well as the upper level fields reasonably well. However, it tends to overestimate the precipitation over the mountainous areas. All three GCM models are found to be highly skilled in simulating the winter precipitation and temperature in the region. The two models, ECHAM5 and HadCM3, are also good at simulating the summer precipitation and temperature, but the CCSM3 simulation generates dryer and warmer conditions than the observations for the whole region, which are most likely a result of the dryness in the upper levels of the original outputs. The use of the regional observational dataset does not necessarily improve the pattern correlations, but it yields better match between the modeled and observed precipitation in terms of variability and root-mean-square difference. It could be said that the outputs of these GCMs can be used in the climate change downscaling and impact assessment studies for the region, given that their strengths and weaknesses that are displayed in the present study are considered.     

Potential climate change impact on wind energy resources in northern Europe: analyses using a regional climate model

There is considerable interest in the potential impact of climate change on the feasibility and predictability of renewable energy sources including wind energy. This paper presents dynamically downscaled near-surface wind fields and examines the impact of climate change on near-surface flow and hence wind energy density across northern Europe. It is shown that: Simulated wind fields from the Rossby Centre coupled Regional Climate Model (RCM) (RCAO) with boundary conditions derived from ECHAM4/OPYC3 AOGCM and the HadAM3H atmosphere-only GCM exhibit reasonable and realistic features as documented in reanalysis data products during the control period (1961–1990). The near-surface wind speeds calculated for a climate change projection period of 2071–2100 are higher than during the control run for two IPCC emission scenarios (A2, B2) for simulations conducted using boundary conditions from ECHAM4/OPYC3. The RCAO simulations conducted using boundary conditions from ECHAM4/OPYC3 indicate evidence for a small increase in the annual wind energy resource over northern Europe between the control run and climate change projection period and for more substantial increases in energy density during the winter season. However, the differences between the RCAO simulations for the climate projection period and the control run are of similar magnitude to differences between the RCAO fields in the control period and the NCEP/NCAR reanalysis data. Additionally, the simulations show a high degree of sensitivity to the boundary conditions, and simulations conducted using boundary conditions from HadAM3H exhibit evidence of slight declines or no change in wind speed and energy density between 1961–1990 and 2071–2100. Hence, the uncertainty of the projected wind changes is relatively high.     

中国气温的降尺度研究

Monthly mean temperatures at 562 stations in China are estimated using a statistical downscaling technique. The technique used is multiple linear regressions （MLRs） of principal components （PCs）. A stepwise screening procedure is used for selecting the skilful PCs as predictors used in the regression equation. The predictors include temperature at 850 hPa （7）, the combination of sea-level pressure and temperature at 850 hPa （P＋T） and the combination of geo-potential height and temperature at 850 hPa （H＋T）. The downscaling procedure is tested with the three predictors over three predictor domains. The optimum statistical model is obtained for each station and month by finding the predictor and predictor domain corresponding to the highest correlation. Finally, the optimum statistical downscaling models are applied to the Hadley Centre Coupled Model, version 3 （HadCM3） outputs under the Special Report on Emission Scenarios （SRES） A2 and B2 scenarios to construct local future temperature change scenarios for each station and month, The results show that （1） statistical downscaling produces less warming than the HadCM3 output itself; （2） the downscaled annual cycles of temperature differ from the HadCM3 output, but are similar to the observation; （3） the downscaled temperature scenarios show more warming in the north than in the south; （4） the downscaled temperature scenarios vary with emission scenarios, and the A2 scenario produces more warming than the B2, especially in the north of China.     

中国21世纪气候变化的情景模拟分析

利用HadCM2和ECHAM4气候模式比较分析了温室气体排放综合效果相当于CO2浓度逐年递增1％情景下中国区域21世纪地面气温和降水量的变化趋势。结果表明：在温室气体渐进递增情景下，至21世纪末期，相对于1961-1990年的气候基准值，全国地面平均气温增幅可达5—6℃。与地面气温的变化相比，降水量的波动幅度较大，但全国范围内降水量变化的总趋势也是增加的。中国区域地面气温和降水量变化的地理分布显示：降水量的增加主要集中在南方区域，HadCM2预测21世纪末期降水增加可达0．9mm／d，ECHAM4预测可达0．6mm／d；ECHAM4模式模拟的气温增幅比HadCM2高，尤其是在冬季及中国北方和青藏高原地区，而HadCM2模式模拟的降水量的增加较大，但两个气候模式模拟的地面气温和降水量变化的总趋势大体一致。     

Uncertainty analysis of statistically downscaled temperature and precipitation regimes in Northern Canada

Summary Uncertainty analysis is used to make a quantitative evaluation of the reliability of statistically downscaled climate data representing local climate conditions in the northern coastlines of Canada. In this region, most global climate models (GCMs) have inherent weaknesses to adequately simulate the climate regime due to difficulty in resolving strong land/sea discontinuities or heterogeneous land cover. The performance of the multiple regression-based statistical downscaling model in reproducing the observed daily minimum/maximum temperature, and precipitation for a reference period (1961–1990) is evaluated using climate predictors derived from NCEP reanalysis data and those simulated by two coupled GCMs (the Canadian CGCM2 and the British HadCM3). The Wilcoxon Signed Rank test and bootstrap confidence-interval estimation techniques are used to perform uncertainty analysis on the downscaled meteorological variables. The results show that the NCEP-driven downscaling results mostly reproduced the mean and variability of the observed climate very well. Temperatures are satisfactorily downscaled from HadCM3 predictors while some of the temperatures downscaled from CGCM2 predictors are statistically significantly different from the observed. The uncertainty in precipitation downscaled with CGCM2 predictors is comparable to the ones downscaled from HadCM3. In general, all downscaling results reveal that the regression-based statistical downscaling method driven by accurate GCM predictors is able to reproduce the climate regime over these highly heterogeneous coastline areas of northern Canada. The study also shows the applicability of uncertainty analysis techniques in evaluating the reliability of the downscaled data for climate scenarios development. Authors’ addresses: Dr. Yonas B. Dibike, NSERC Research Fellow, OURANOS Consortium, 550 Sherbrooke Street West, 19^th Floor, Montreal (QC) H3A 1B9, Canada; Philippe Gachon, Adaptation and Impact Research Division (AIRD), Atmospheric Science and Technology Directorate, Environment Canada at Ouranos, Montreal (QC), Canada; André St-Hilaire and Taha B. M. J. Ouarda, Institut National de la Recherche Scientifique Centre Eau, Terre & Environnement (INRS-ETE), University of Québec, 490 Rue de La Couronne, Québec (QC) G1K 9A9, Canada; Van T.-V. Nguyen, Department of Civil Engineering and Applied Mechanics, McGill University, 817 Sherbrooke Street West, Montreal (QC) H3A 2K6, Canada.     

Evaluation of the twenty-first century RCM simulations driven by multiple GCMs over the Eastern Mediterranean–Black Sea region

In this study, human-induced climate change over the Eastern Mediterranean–Black Sea region has been analyzed for the twenty-first century by performing regional climate model simulations forced with large-scale fields from three different global circulation models (GCMs). Climate projections have been produced with Special Report on Emissions Scenarios A2, A1FI and B1 scenarios, which provide greater diversity in climate information for future period. The gradual increases for temperature are widely apparent during the twenty-first century for each scenario simulation, but ECHAM5-driven simulation generally has a weaker signal for all seasons compared to CCSM3 simulations except for the Fertile Crescent. The contrast in future temperature change between the winter and summer seasons is very strong for CCSM3-A2-driven and HadCM3-A2-driven simulations over Carpathians and Balkans, 4–5 °C. In addition, winter runoff over mountainous region of Turkey, which feeds many river systems including the Euphrates and Tigris, increases in second half of the century since the snowmelt process accelerates where the elevation is higher than 1,500 m. Moreover, analysis of daily temperature outputs reveals that the gradual decrease in daily minimum temperature variability for January during the twenty-first century is apparent over Carpathians and Balkans. Analysis of daily precipitation extremes shows that positive trend is clear during the last two decades of the twenty-first century over Carpathians for both CCSM3-driven and ECHAM5-driven simulations. Multiple-GCM driven regional climate simulations contribute to the quantification of the range of climate change over a region by performing detailed comparisons between the simulations.     

统计降尺度法对华北地区未来区域气温变化情景的预估

迄今为止,大部分海气耦合气候模式（AOGCM）的空间分辨率还较低,很难对区域尺度的气候变化情景做合理的预测。降尺度法已广泛用于弥补AOGCM在这方面的不足。作者采用统计降尺度方法对1月和7月华北地区49个气象观测站的未来月平均温度变化情景进行预估。采用的统计降尺度方法是主分量分析与逐步回归分析相结合的多元线性回归模型。首先,采用1961～2000年的 NCEP再分析资料和49个台站的观测资料建立月平均温度的统计降尺度模型,然后把建立的统计降尺度模型应用于HadCM3 SRES A2 和 B2 两种排放情景, 从而生成各个台站1950～2099年1月份和7月份温度变化情景。结果表明:在当前气候条件下,无论1月还是7月,统计降尺度方法模拟的温度与观测的温度有很好的一致性,而且在大多数台站,统计降尺度模拟气温与观测值相比略微偏低。对于未来气候情景的预估方面,无论1月还是7月,也无论是HadCM3 SRES A2 还是B2排放情景驱动统计模型,结果表明大多数的站点都存在温度的明显上升趋势,同时7月的上升趋势与1月相比偏低。     

Long-Term Trend of Temperature Derived by Statistical Downscaling Based on EOF Analysis

This study analyzes the ability of statistical downscaling models in simulating the long-term trend of temperature and associated causes at 48 stations in northern China in January and July 1961–2006. The statistical downscaling models are established through multiple stepwise regressions of predictor principal components (PCs). The predictors in this study include temperature at 850 hPa (T850), and the combination of geopotential height and temperature at 850 hPa (H850+T850). For the combined predictors, Empirical Orthogonal Function (EOF) analysis of the two combined fields is conducted. The modeling results from HadCM3 and ECHAM5 under 20C3M and SERS A1B scenarios are applied to the statistical downscaling models to construct local present and future climate change scenarios for each station, during which the projected EOF analysis and the common EOF analysis are utilized to derive EOFs and PCs from the two general circulation models (GCMs). The results show that (1) the trend of temperature in July is associated with the first EOF pattern of the two combined fields, not with the EOF pattern of the regional warming; (2) although HadCM3 and ECHAM5 have simulated a false long-term trend of temperature, the statistical downscaling method is able to well reproduce a correct long-term trend of temperature in northern China due to the successful simulation of the trend of main PCs of the GCM predictors; (3) when the two-field combination and the projected EOF analysis are used, temperature change scenarios have a similar seasonal variation to the observed one; and (4) compared with the results of the common EOF analysis, those of the projected EOF analysis have been much more strongly determined by the observed large-scale atmospheric circulation patterns.     

Refinement of dynamically downscaled precipitation and temperature scenarios

A method for adjusting dynamically downscaled precipitation and temperature scenarios representing specific sites is presented. The method reproduces mean monthly values and standard deviations based on daily observations. The trend obtained in the regional climate model both for temperature and precipitation is maintained, and the frequency of modelled and observed rainy days shows better agreement. Thus, the method is appropriate for tailoring dynamically downscaled temperature and precipitation values for climate change impact studies. One precipitation and temperature scenario dynamically downscaled with HIRHAM from the Atmospheric-Ocean General Circulation Model at the Max-Planck Institute in Hamburg, ECHAM4/OPYC4 GSDIO with emission scenario IS92a, is chosen to illustrate the adjustment method.     

The Climate Change Effect on the Water Regime. The Case Study: the Karun Catchment,Iran

One of the most important effects of climate change is changes in the water regime and the frequency of flood occurrence. The Karun catchment is one of the most important Iran catchments, but it has never been studied specifically. This study considers the effect of climate change on the annual and the maximum runoff of the Karun catchment in the Shalu bridge area. First, temperature and monthly precipitation of the HadCM3 model were downscaled based on three scenarios, AlB, A2, and B1, ustng the LARS-WG model. Then data were spatially downscaled based on the change factor model, and the SRM model was used to simulate runoff. The results show that the climate change affects the water regime of this catchment.     

An assessment of future dryness over Korea based on the ECHAM5-RegCM3 model chain under A1B emission scenario

We analyze the future dryness over Korea based on the projected temperature and precipitation. For fine-scale climate information, the ECHAM5/MPI-OM A1B simulation has been dynamically downscaled using the RegCM3 double-nested system. A 130-year long-term climatology (1971?C2100) from the mother domain (East Asia: 60 km) and nested domain (South Korea: 20 km) is discussed. Based on the intercomparison with CMIP3 participant models, the ECHAM5/MPI-OM provides climatic change information over the East Asia that is not markedly different from other projections. However, the reduction of summer precipitation over Korea is rather different with ensemble mean of CMIP3 participant models. The downscaled results generally follow the behavior of ECHAM5/MPIOM, but substantial fine-scale details are found in the spatial pattern and the change signals become more enhanced at the local scale. In the future projection, significant warming is found regardless of the season and region while the change in precipitation shows a mixed feature with both increasing and decreasing patterns. The increase of temperature enhances the evapotranspiration, and hence the actual water stress becomes more pronounced in the warmer climate. This is related to the negative trends of the self-calibrating Palmer Drought Severity Index (PDSI) to measure the drought condition in Korea. Although PDSI is overall associated with the precipitation variation, its long-term trend tends to be modulated by the temperature trend. It is confirmed that the detrended temperature is shown to mask the decreasing tendency of the PDSI. The result indicates that without an increase in precipitation appropriate for atmospheric moisture demand, future dryness is a more likely condition under global warming.     

Uncertainties in the regional climate models simulations of South-Asian summer monsoon and climate change

The uncertainties in the regional climate models (RCMs) are evaluated by analyzing the driving global data of ERA40 reanalysis and ECHAM5 general circulation models, and the downscaled data of two RCMs (RegCM4 and PRECIS) over South-Asia for the present day simulation (1971–2000) of South-Asian summer monsoon. The differences between the observational datasets over South-Asia are also analyzed. The spatial and the quantitative analysis over the selected climatic regions of South-Asia for the mean climate and the inter-annual variability of temperature, precipitation and circulation show that the RCMs have systematic biases which are independent from different driving datasets and seems to come from the physics parameterization of the RCMs. The spatial gradients and topographically-induced structure of climate are generally captured and simulated values are within a few degrees of the observed values. The biases in the RCMs are not consistent with the biases in the driving fields and the models show similar spatial patterns after downscaling different global datasets. The annual cycle of temperature and rainfall is well simulated by the RCMs, however the RCMs are not able to capture the inter-annual variability. ECHAM5 is also downscaled for the future (2071–2100) climate under A1B emission scenario. The climate change signal is consistent between ECHAM5 and RCMs. There is warming over all the regions of South-Asia associated with increasing greenhouse gas concentrations and the increase in summer mean surface air temperature by the end of the century ranges from 2.5 to 5 °C, with maximum warming over north western parts of the domain and 30 % increase in rainfall over north eastern India, Bangladesh and Myanmar.     

Using Statistical Downscaling to Quantify the GCM-Related Uncertainty in Regional Climate Change Scenarios： A Case Study of Swedish Precipitation

There are a number of sources of uncertainty in regional climate change scenarios. When statistical downscaling is used to obtain regional climate change scenarios, the uncertainty may originate from the uncertainties in the global climate models used, the skill of the statistical model, and the forcing scenarios applied to the global climate model. The uncertainty associated with global climate models can be evaluated by examining the differences in the predictors and in the downscaled climate change scenarios based on a set of different global climate models. When standardized global climate model simulations such as the second phase of the Coupled Model Intercomparison Project （CMIP2） are used, the difference in the downscaled variables mainly reflects differences in the climate models and the natural variability in the simulated climates. It is proposed that the spread of the estimates can be taken as a measure of the uncertainty associated with global climate models. The proposed method is applied to the estimation of global-climate-model-related uncertainty in regional precipitation change scenarios in Sweden. Results from statistical downscaling based on 17 global climate models show that there is an overall increase in annual precipitation all over Sweden although a considerable spread of the changes in the precipitation exists. The general increase can be attributed to the increased large-scale precipitation and the enhanced westerly wind. The estimated uncertainty is nearly independent of region. However, there is a seasonal dependence. The estimates for winter show the highest level of confidence, while the estimates for summer show the least.     

IPCC-AR4 climate simulations for the Southwestern US: the importance of future ENSO projections

Future climate trends for the Southwestern US, based on the climate models included in the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report, project a more arid climate in the region during the 21st century. However, future climate variability associated with El Niño Southern Oscillation (ENSO)—an important driver for winter climate variability in the region—have not been addressed. In this work we evaluate future winter ENSO projections derived from two selected IPCC models, and their effect on Southwestern US climate. We first evaluate the ability of the IPCC coupled models to represent the climate of the Southwest, selecting the two models that best capture seasonal precipitation and temperature over the region and realistically represent ENSO variability (Max Planck Institute’s ECHAM5 and the UK Met Office HadCM3). Our work shows that the projected future aridity of the region will be dramatically amplified during La Niña conditions, as anomalies over a drier mean state, and will be characterized by higher temperatures (~0.5°C) and lower precipitation (~3 mm/mnt) than the projected trends. These results have important implications for water managers in the Southwest who must prepare for more intense winter aridity associated with future ENSO conditions.     

Modelling the response of glaciers to a doubling in atmospheric CO2: a case study of Storglaciären, northern Sweden

To predict the evolution of glaciers in an enhanced greenhouse climate, results from a global climate model, a glacier melt/accumulation model, and a glacier flow model were combined. The method was applied to Storglaciären, a small well-studied glacier in northern Sweden. The difference between the present climate and a 2 × CO₂ climate around the year 2050 was extracted from a model experiment with the ECHAM4-T106 high resolution climate model for time slices at present and in 2050, using prescribed boundary conditions of sea surface temperature and sea-ice distribution, which are derived from a lower resolution transient run of the ECHAM4-T42/OPIC-coupled atmosphere ocean model between present and 2050. The local climatic conditions on the glacier for 2050 were obtained by adding the modelled local climate changes to the observed local present-day climate. The combination of the comprehensive models presented offers a tool to test and calibrate simplified models which are applicable to a much larger sample of glaciers. For the region of Storglaciären, the GCM projected temperature is found to increase most strongly during the winter months, but also shows a warming during the transition from spring to summer, and again between summer and fall, thus extending the melt season by three to four weeks. Precipitation, on the other hand, decreases by approximately 5% during May to September while there is a stronger increase of approximately 14% for the rest of the year. The consequent increase in winter accumulation on Storglaciären is more than compensated by the increase in ablation during the melt season. The glacier flow model predicts a 300 m retreat of the glacier terminus by the middle of the next century, and a loss of 30% of the present ice mass.     

The impacts of climate change on river flood risk at the global scale

This paper presents an assessment of the implications of climate change for global river flood risk. It is based on the estimation of flood frequency relationships at a grid resolution of 0.5?×?0.5°, using a global hydrological model with climate scenarios derived from 21 climate models, together with projections of future population. Four indicators of the flood hazard are calculated; change in the magnitude and return period of flood peaks, flood-prone population and cropland exposed to substantial change in flood frequency, and a generalised measure of regional flood risk based on combining frequency curves with generic flood damage functions. Under one climate model, emissions and socioeconomic scenario (HadCM3 and SRES A1b), in 2050 the current 100-year flood would occur at least twice as frequently across 40 % of the globe, approximately 450 million flood-prone people and 430 thousand km² of flood-prone cropland would be exposed to a doubling of flood frequency, and global flood risk would increase by approximately 187 % over the risk in 2050 in the absence of climate change. There is strong regional variability (most adverse impacts would be in Asia), and considerable variability between climate models. In 2050, the range in increased exposure across 21 climate models under SRES A1b is 31–450 million people and 59 to 430 thousand km² of cropland, and the change in risk varies between ?9 and +376 %. The paper presents impacts by region, and also presents relationships between change in global mean surface temperature and impacts on the global flood hazard. There are a number of caveats with the analysis; it is based on one global hydrological model only, the climate scenarios are constructed using pattern-scaling, and the precise impacts are sensitive to some of the assumptions in the definition and application.     

The transition from the present-day climate to a modern Snowball Earth

We use the coupled atmosphere–ocean general circulation model ECHAM5/MPI-OM to investigate the transition from the present-day climate to a modern Snowball Earth, defined as the Earth in modern geography with complete sea-ice cover. Starting from the present-day climate and applying an abrupt decrease of total solar irradiance (TSI) we find that the critical TSI marking the Snowball Earth bifurcation point is between 91 and 94% of the present-day TSI. The Snowball Earth bifurcation point as well as the transition times are well reproduced by a zero-dimensional energy balance model of the mean ocean potential temperature. During the transition, the asymmetric distribution of continents between the Northern and Southern Hemisphere causes heat transports toward the more water-covered Southern Hemisphere. This is accompanied by an intensification of the southern Hadley cell and the wind-driven subtropical ocean cells by a factor of 4. If we set back TSI to 100% shortly before the transition to a modern Snowball Earth is completed, a narrow band of open equatorial water is sufficient for rapid melting. This implies that for 100% TSI the point of unstoppable glaciation separating partial from complete sea-ice cover is much closer to complete sea-ice cover than in classical energy balance models. Stable states can have no greater than 56.6% sea-ice cover implying that ECHAM5/MPI-OM does not exhibit stable states with near-complete sea-ice cover but open equatorial waters.     

Climate Change Scenarios for the Hudson Bay Region: An Intermodel Comparison

General circulation models (GCMs) are unanimous in projecting warmer temperatures in an enhanced CO₂ atmosphere, with amplification of this warming in higher latitudes. The Hudson Bay region, which is located in the Arctic and subarctic regions of Canada, should therefore be strongly influenced by global warming. In this study, we compare the response of Hudson Bay to a transient warming scenario provided by six-coupled atmosphere-ocean models. Our analysis focuses on surface temperature, precipitation, sea-ice coverage, and permafrost distribution. The results show that warming is expected to peak in winter over the ocean, because of a northward retreat of the sea-ice cover. Also, a secondary warming peak is observed in summer over land in the Canadian and Australian-coupled GCMs, which is associated with both a reduction in soil moisture conditions and changes in permafrost distribution. In addition, a relationship is identified between the retreat of the sea-ice cover and an enhancement of precipitation over both land and oceanic surfaces. The response of the sea-ice cover and permafrost layer to global warming varies considerably among models and thus large differences are observed in the projected regional increase in temperature and precipitation. In view of the important feedbacks that a retreat of the sea-ice cover and the distribution of permafrost are likely to play in the doubled and tripled CO₂ climates of Hudson Bay, a good representation of these two parameters is necessary to provide realistic climate change scenarios. The use of higher resolution regional climate model is recommended to develop scenarios of climate change for the Hudson Bay region.     

Validation of general circulation climate models and projectionsof temperature and rainfall changes in Cameroon and someof its neighbouring areas

Summary  Four coupled atmosphere-ocean general circulation models were examined for the ability of their control runs to simulate present climate given present forcings. The area of study is mainly Cameroon and some of its surrounding areas (0–25° E, 5° S-30° N). These models are from the UK Meteorological Office Hadley Centre (HadCM2), the German Max-Planck-Institut für Meteorologie (ECHAM4), the Canadian Centre for Climate Modelling and Analysis (CGCM1) and the Australian Commonwealth Science and Industrial Research Organisation (CSIRO-Mk2). The ability of the models to reproduce the observed spatial and temporal patterns was studied. ECHAM4 and HadCM2 were found to reproduce the spatial pattern well, with a correlation of more than 90%. They also simulated the main annual features of both temperature and rainfall. The CSIRO-Mk2 model was slightly less successful and the CGCM1 had the worst results for the area, especially as concern rainfall. In view of these results, ECHAM4 and HADCM2 were used to evaluate projected changes in rainfall and temperature resulting from increased concentration of greenhouse gases in the atmosphere for the 30 year period 2040 to 2070. Received February 15, 1999/Revised March 10, 2000     

Grid-based performance evaluation of GCM-RCM combinations for rainfall reproduction

Prior to hydrological assessment of climate change at catchment scale, an applied methodology is necessary to evaluate the performance of climate models available for a given catchment. This study presents a grid-based performance evaluation approach as well as an intercomparison framework to evaluate the uncertainty of climate models for rainfall reproduction. For this purpose, we used outputs of two general circulation models (GCMs), namely ECHAM5 and CCSM3, downscaled by a regional climate model (RCM), namely RegCM3, over ten small to mid-size catchments in Rize Province, Turkey. To this end, five rainfall-borne climatic statistics are computed from the outputs of ECHAM5-RegCM3 and CCSM3-RegCM3 combinations in order to compare with those of observations in the province for the reference period 1961–1990. Performance of each combination is tested by means of scatter diagram, bias, mean absolute bias, root mean squared error, and model performance index (MPI) measures. Our results indicated that ECHAM5-RegCM3 overestimates the total monthly rainfall observations whereas CCSM3-RegCM3 tends to underestimate. In terms of maximum monthly and annual maximum rainfall reproduction, ECHAM5-RegCM3 shows higher performance than CCSM3-RegCM3, particularly in the coastland areas. In contrast, CCSM3-RegCM3 outperforms ECHAM5-RegCM3 in reproducing the number of rainy days, especially in the inland areas. The results also revealed that if a GCM-RCM combination performs well for a portion (statistic) of a catchment, it is not necessarily appropriate for the other portions (statistics). Moreover, the MPI measure demonstrated the superiority of ECHAM5-RegCM3 to CCSM3-RegCM3 up to 33 % excelling for annual rainfall reproduction in Rize Province.