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1.
In order to better understand the effect associated with global climate change on Iran’s climate condition, it is important to quantify possible shifts in different climatic types in the future. To this end, monthly mean minimum and maximum temperature, and precipitation from 181 synoptic meteorological stations (average 1970–2005) have been collected from the meteorological organization of Iran. In this paper, to study spatial changes of Iran’s climatic zones affected by climate changes, Extended De Martonne’s classification (originally formulated by De Martonne and extended by Khalili (1992)) was used. Climate change scenarios were simulated in two future climates (average conditions during the 2050s and the 2080s) under each of the SRES A1B and A2, for the CSIRO-MK3, HadCM3, and CGCM3 climate models. Coarse outputs of GCMs were downscaled by delta method. We produced all maps for three time periods (one for the current and two for the future) according to Extended De Martonne’s classification. Finally, for each climatic zone, changes between the current and the future were compared. As the main result, simulated changes indicate shifts to warmer and drier zones. For example, in the current, extra arid-cold (A1.1m2) climate is covering the largest area of the country (21.4 %), whereas in both A1B and A2 scenarios in the 2050s and the 2080s, extra arid-moderate (A1.1m3) and extra arid-warm (A1.1m4) will be the climate and will occupy the largest area of the country, about 21 and 38 %, respectively. This analysis suggests that the global climate change will have a profound effect on the future distribution of severe aridity in Iran.  相似文献   

2.
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  相似文献   

3.
We analyze ensembles (four realizations) of historical and future climate transient experiments carried out with the coupled atmosphere-ocean general circulation model (AOGCM) of the Hadley Centre for Climate Prediction and Research, version HADCM2, with four scenarios of greenhouse gas (GHG) and sulfate forcing. The analysis focuses on the regional scale, and in particular on 21 regions covering all land areas in the World (except Antarctica). We examine seasonally averaged surface air temperature and precipitation for the historical period of 1961–1990 and the future climate period of 2046–2075. Compared to previous AOGCM simulations, the HADCM2 model shows a good performance in reproducing observed regional averages of summer and winter temperature and precipitation. The model, however, does not reproduce well observed interannual variability. We find that the uncertainty in regional climate change predictions associated with the spread of different realizations in an ensemble (i.e. the uncertainty related to the internal model variability) is relatively low for all scenarios and regions. In particular, this uncertainty is lower than the uncertainty due to inter-scenario variability and (by comparison with previous regional analyses of AOGCMs) with inter-model variability. The climate biases and sensitivities found for different realizations of the same ensemble were similar to the corresponding ensemble averages and the averages associated with individual realizations of the same ensemble did not differ from each other at the 5% confidence level in the vast majority of cases. These results indicate that a relatively small number of realizations (3 or 4) is sufficient to characterize an AOGCM transient climate change prediction at the regional scale. Received: 12 January 1998 / Accepted: 7 July 1999  相似文献   

4.
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, 19th 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.  相似文献   

5.
Measured air temperature and precipitation data from three high mountainous Bulgarian stations were used along with data from 18 global climate models (GCMs). Air temperature and precipitation outputs of preindustrial control experiment were compared with actually observed values. GCM with the best overall performance is BCCR BCM 2.0 for air temperatures (period 1941?C2009) and CGCM 3.1/T47 for precipitation (period 1947?C2009). Statistical methods were used in this research??nonparametric Spearman correlation, Mann?CWhitney test, multiple linear regression, etc. Projections were made for the following future decades: 2015?C2024, 2045?C2054 and 2075?C2084. The best months, described by multiple linear regression (MLR) model of air temperatures, are November, January, March, and May. The worst described are summer months. There is not any pattern in the relationship between constructed MLR models and measured precipitation. Models that perform the best in different months at the three investigated stations are MIUB ECHO-G, GISS AOM, CGCM 3.1/T63, and CNRM CM3 for air temperatures and GFDL CM 2.1, GISS AOM, and MIUB ECHO-G for precipitation. The fit between statistical models' outputs and values observed at stations is different, better in cold part of the year. There will be mixed future changes of air temperatures at all the three high mountainous stations. An increase of temperatures is expected in April, November, and December. A decrease will happen in February, July, and October. Mean annual temperatures are expected to rise by 0.1?°C (Botev) to 0.2?°C (Musala and Cherni vrah) in the decade 2075?C2084, but mean annual temperatures at the end of the period with measurements (2009) has already exceeded by far projected values. Trends in precipitation are mixed both in spatial and in temporal directions. Observed decrease of precipitation, especially in the warm half of the year, is not described well in MLR models. The same is valid for annual amounts, which are projected to be higher than those measured in the end of instrumental period (2009). This is opposite to observed trends in recent decades, especially at stations Cherni vrah and Botev, where a significant decrease of precipitation amounts has happened.  相似文献   

6.
This work presents an analysis of simulated temperature and precipitation variability and trends throughout the twentieth century over 22 land regions of sub-continental scale in the HADCM3 and HADCM2 (two realizations) coupled models. Regional temperature biases in the HADCM3 and HADCM2 are mostly in the range of -5 K to +3 K for the seasonal averages and -3 K to +2 K for the annual average. Seasonal precipitation biases are mostly in the range of -50% to 75% of present day precipitation, with a tendency in both models to overpredict cold season precipitation. Except for cold season temperature in mid- and high-latitude Northern Hemisphere regions, the average climatology of the HADCM2 and HADCM3 is of comparable quality despite the lack of an ocean flux adjustment in the HADCM3. Both models show warming trends of magnitude in line with observations, although the observed inter-regional patterns of warming trend are not well reproduced. Measures of temperature and precipitation interannual to interdecadal variability in the models are in general agreement with observations except for Northern Hemisphere summer temperature variability, which is overestimated. The models somewhat underestimate the inter-decadal variations in interannual variability measures observed during the century and overestimate the range of anomalies. Both models tend to overpredict the occurrences of short persistences (1-3 years) and underpredict the occurrence and maximum length of long persistences (greater than three years), which is an indication of a deficiency in the simulation of long-lived anomaly regimes. Compared to observations, the models produce a higher magnitude of temporal anomaly correlation across regions and correlation between temperature and precipitation anomalies for a given region. This suggests that local processes that may be effective in decoupling the observed regional anomalies are not captured well. Overall, the variability measures in the HADCM2 and HADCM3 are of similar quality, indicating that the use of a flux correction in the HADCM2 does not strongly affect the regional variability characteristics of the model.  相似文献   

7.
Climate change may affect ocean and ice conditions in coastal oceans and thus have significant impacts on coastal infrastructure, marine navigation, and marine ecosystems. In this study a three-dimensional ice–ocean model is developed to examine likely changes of ocean and ice conditions over the Newfoundland and Labrador Shelves in response to climate change. The model is configured with a horizontal grid of approximately 7?km and a vertical grid of 46 levels and is run from 1979 to 2069. The projection period is 2011 to 2069 under a median emission scenario A1B used by the Intergovernmental Panel on Climate Change. For the projection period, the surface atmospheric forcing fields used are from the Canadian Regional Climate Model over the North Atlantic. The open boundary conditions come from the Canadian Global Climate Model, Version 3 (CGCM3), adjusted for the 1981–2010 mean of the Simple Ocean Data Assimilation model output. The simulated fields over the 1981–2010 period have patterns consistent with observations. Over the Newfoundland and Labrador Shelves during the projection period, the model shows general trends of warming, freshening, and decreasing ice. From 2011 to 2069, the model projects that under A1B sea surface temperature will increase by 1.4°C; bottom temperature will increase by 1.6°C; sea surface salinity will decrease by 0.7; bottom salinity will decrease by 0.3; and sea-ice extent will decrease by 70%. The sea level will rise by 0.11?m at the St. John's tide-gauge station because of oceanographic change, and the freshwater transport of the Labrador Current will double as a result of freshening. The regional ice–ocean model reproduces more realistic present climate conditions and projects considerably different future climate conditions than CGCM3.  相似文献   

8.
基于统计降尺度模型的江淮流域极端气候的模拟与预估   总被引：4，自引：0，他引：4  
利用江淮流域29个代表站点1961--2000年逐日最高温度、最低温度和逐日降水资料,以及NCEP逐日大尺度环流场资料,引入基于多元线性回归与随机天气发生器相结合的统计降尺度模型SDSM（statistical downscalingmodel）,通过对每个站点建模,确立SDSM参数,并将该模型应用于SRESA2排放情景下HadCM3和cGcM3模式,得到了江淮流域各代表台站21世纪的逐日最高、最低温度和降水序列以及热浪、霜冻、强降水等极端气候指数。结果表明,当前气候下,统计降尺度方法模拟的极端温度指数与观测值有很好的一致性,能有效纠正耦合模式的“冷偏差”,如SDSM对江淮平均的冬季最高、最低温度的模拟偏差较CGCM3模式分别减少3℃和4．5℃。对于极端降水则能显著纠正耦合模式模拟的降水强度偏低的问题,如CGCM3对江淮流域夏季降水强度的模拟偏差为-60．6％,但降尺度后SDSM—CGCM3的偏差仅为-6％,说明降尺度模型SDSM的确有“增加值”的作用。21世纪末期在未来SRESA2情景下,对于极端温度,无论Had．CM3还是CGCM3模式驱动统计模型,江淮流域所有代表台站,各个季节的最高、最低温度都显著增加,且以夏季最为显著,增幅在2—4℃;与之相应霜冻天数将大幅减少,热浪天数大幅增多,各站点冬季霜冻天数减少幅度为5—25d,夏季热浪天数增加幅度为4～14d;对于极端降水指数,在两个不同耦合模式HadCM3和CGCM3驱动下的变化尤其是变化幅度的一致性比温度差,但大部分站点各个季节极端强降水事件将增多,强度增强,SDSM—HadCM3和SDSM-CGCM3预估的夏季极端降水贡献率将分别增加26％和27％。  相似文献   

9.
Climate sensitivity estimated from ensemble simulations of glacial climate   总被引：1，自引：0，他引：1  
The concentration of greenhouse gases (GHGs) in the atmosphere continues to rise, hence estimating the climate system’s sensitivity to changes in GHG concentration is of vital importance. Uncertainty in climate sensitivity is a main source of uncertainty in projections of future climate change. Here we present a new approach for constraining this key uncertainty by combining ensemble simulations of the last glacial maximum (LGM) with paleo-data. For this purpose we used a climate model of intermediate complexity to perform a large set of equilibrium runs for (1) pre-industrial boundary conditions, (2) doubled CO2 concentrations, and (3) a complete set of glacial forcings (including dust and vegetation changes). Using proxy-data from the LGM at low and high latitudes we constrain the set of realistic model versions and thus climate sensitivity. We show that irrespective of uncertainties in model parameters and feedback strengths, in our model a close link exists between the simulated warming due to a doubling of CO2, and the cooling obtained for the LGM. Our results agree with recent studies that annual mean data-constraints from present day climate prove to not rule out climate sensitivities above the widely assumed sensitivity range of 1.5–4.5°C (Houghton et al. 2001). Based on our inferred close relationship between past and future temperature evolution, our study suggests that paleo-climatic data can help to reduce uncertainty in future climate projections. Our inferred uncertainty range for climate sensitivity, constrained by paleo-data, is 1.2–4.3°C and thus almost identical to the IPCC estimate. When additionally accounting for potential structural uncertainties inferred from other models the upper limit increases by about 1°C.  相似文献   

10.
A regional sea-ice?Cocean model was used to investigate the response of sea ice and oceanic heat storage in the Hudson Bay system to a climate-warming scenario. Projections of air temperature (for the years 2041?C2070; effective CO2 concentration of 707?C950?ppmv) obtained from the Canadian Regional Climate Model (CRCM 4.2.3), driven by the third-generation coupled global climate model (CGCM 3) for lateral atmospheric and land and ocean surface boundaries, were used to drive a single sensitivity experiment with the delta-change approach. The projected change in air temperature varies from 0.8°C (summer) to 10°C (winter), with a mean warming of 3.9°C. The hydrologic forcing in the warmer climate scenario was identical to the one used for the present climate simulation. Under this warmer climate scenario, the sea-ice season is reduced by 7?C9?weeks. The highest change in summer sea-surface temperature, up to 5°C, is found in southeastern Hudson Bay, along the Nunavik coast and in James Bay. In central Hudson Bay, sea-surface temperature increases by over 3°C. Analysis of the heat content stored in the water column revealed an accumulation of additional heat, exceeding 3?MJ?m?3, trapped along the eastern shore of James and Hudson bays during winter. Despite the stratification due to meltwater and river runoff during summer, the shallow coastal regions demonstrate a higher capacity of heat storage. The maximum volume of dense water produced at the end of winter was halved under the climate-warming perturbation. The maximum volume of sea ice is reduced by 31% (592?km3) while the difference in the maximum cover is only 2.6% (32,350?km2). Overall, the depletion of sea-ice thickness in Hudson Bay follows a southeast?Cnorthwest gradient. Sea-ice thickness in Hudson Strait and Ungava Bay is 50% thinner than in present climate conditions during wintertime. The model indicates that the greatest changes in both sea-ice climate and heat content would occur in southeastern Hudson Bay, James Bay, and Hudson Strait.  相似文献   

11.
利用近30年 (1961~1990年) 观测的温度 (包括平均、最高、最低)、降水、日较差、水汽的季和年平均资料, 对IPCC提供的5个全球海气耦合模式 (ECHAM4, HADCM2, GFDL, CGCM1, CSIRO) 在同样时段只考虑CO2等温室气体的影响和既有CO2等温室气体又有气溶胶的影响两种情形对东亚地区气候变化进行了检测。分析表明, 考虑温室气体与硫化物气溶胶作用, 冬季最低温度模拟效果较只考虑温室气体与观测更接近。结果还表明, 在两种情形下, 这些模式对东亚和中国地区的气候都有一定的模拟能力, 但同时各个模式的模拟场也都有各自的系统误差; 气溶胶的作用使东亚地区气温下降; 从相关系数计算表明, 模拟最好的变量是温度, 其次是水汽和降水, 最差的是日较差; 在空间的分布上, 各变量冬季的模拟效果最好; 考虑总体情况, ECHAM4与HADCM2两个模式对东亚和中国地区的气候模拟效果在5个模式中是最好的。  相似文献   

12.
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.  相似文献   

13.
Using a continuous multi-decadal simulations over the period 1981–2010, subseasonal to seasonal simulations of the Climate Forecast System version 2 (CFSv2) over Iran against the Climatic Research Unit (CRU) dataset are evaluated. CFSv2 shows cold biases over northern hillsides of the Alborz Mountains with the Mediterranean climate and warm biases over northern regions of the Persian Gulf and the Oman Sea with a dry climate. Magnitude of the model bias for 2-m temperature over different regions of Iran varies by season, with the least bias in temperate seasons of spring and autumn, and the largest bias in summer. The model bias decreases as temporal averaging period increases from seasonal to annual. The forecast generally produces dry and wet biases over dry and wet regions of Iran, respectively. In general, 2-m temperature over Iran is better captured than precipitation, but the prediction skill of precipitation is generally high over western Iran. Averaged over Iran, observations indicated that 2-m temperature has been gradually increasing during the studied period, with a rate of approximately 0.5 °C per decade, and the upward trend is well simulated by CFSv2. Averaged over Iran, both observations and simulation results indicated that precipitation has been decreasing in spring, with averaged decreasing trends of 0.8 mm (observed) and 1.7 mm (simulated) per season each year during the period 1981–2010. Observations indicated that the maximum increasing trend of 2-m temperature has occurred over western Iran (nearly 0.7 °C per decade), while the maximum decreasing trend of annual precipitation has occurred over western and parts of southern Iran (nearly 45 to 50 mm per decade).  相似文献   

14.
This study provides some guidance on the choice of predictor variables from both reanalysis products and the third version of the Canadian Coupled Global Climate Model (CGCM3) outputs for regression-based statistical downscaling models (SDMs) for climate change application in southern Québec (Canada). Twenty CGCM3 grid points and four surface observation sites in the study area were employed. Twenty-five deseasonalized predictors and four deseasonalized predictands (daily maximum and minimum temperatures, precipitation occurrence and wet day precipitation amount) were used to investigate correlation coefficients among predictors and to evaluate their predictive ability when used in a multiple linear regression (MLR) downscaling model. The basic statistical characteristics of vorticity at 1,000-, 850- and 500-hPa levels, U-component of velocity at 1,000-hPa level, temperature at 2?m (T 2) and wind direction at 1,000- and 500-hPa level of CGCM3 showed a larger difference with those of the NCEP reanalysis data. Therefore, those seven variables require high caution to be included as predictors in statistical downscaling models. Specific humidity at 1,000-, 850- and 500-hPa levels, geopotential height at 850- and 500-hPa levels and T 2 were the most sensitive predictors for future climate conditions (i.e. A1B and A2 emission scenarios). Specific humidity and geopotential height at different levels and T 2 were important explainable predictors for the daily temperatures. Mean sea level pressure, specific humidity, U and V components and divergence showed potential as predictors for daily precipitation. Spatial explained variance of MLRs between predictors of every different CGCM3 grid points and the four predictands showed large values at the CGCM3 grid points located near the observation sites, whereas relatively small values were shown at the CGCM3 grid points located more than 400?km from the sites. The explained variance of the downscaled predictands by predictors of three or four CGCM3 grid points located near the observation site produced 2–5% larger R-squares than those by predictors of the nearest grid point. The results illustrated that the use of predictors from more than one AOGCM grid points located near the observation site can increase the skill of the MLR downscaling models.  相似文献   

15.
Abstract

The impacts of climate change on surface air temperature (SAT) and winds in the Gulf of St. Lawrence (GSL) are investigated by performing simulations from 1970 to 2099 with the Canadian Regional Climate Model (CRCM), driven by a five-member ensemble. Three members are from Canadian Global Climate Model (CGCM3) simulations following scenario A1B from the Intergovernmental Panel on Climate Change (IPCC); one member is from the Community Climate System Model, version 3 (CCSM3) simulation, also following the A1B scenario; and one member is from the CCSM4 (version 4) simulation following the Representative Concentration Pathway (RCP8.5) scenario. Compared with North America Regional Reanalysis (NARR) data, it is shown that CRCM can reproduce the observed SAT spatial patterns; for example, both CRCM simulations and NARR data show a warm SAT tongue along the eastern Gulf; CRCM simulations also capture the dominant northwesterly winds in January and the southwesterly winds in July. In terms of future climate scenarios, the spatial patterns of SAT show plausible seasonal variations. In January, the warming is 3°–3.5°C in the northern Gulf and 2.5°–3°C near Cabot Strait during 2040–2069, whereas the warming is more uniform during 2070–2099, with SAT increases of 4°–5°C. In summer, the warming gradually decreases from the western side of the GSL to the eastern side because of the different heat capacities between land and water. Moreover, the January winds increase by 0.2–0.4?m?s?1 during 2040–2069, related to weakening stability in the atmospheric planetary boundary layer. However, during 2070–2099, the winds decrease by 0.2–0.4?m?s?1 over the western Gulf, reflecting the northeastward shift in northwest Atlantic storm tracks. In July, enhanced baroclinicity along the east coast of North America dominates the wind changes, with increases of 0.2–0.4?m?s?1. On average, the variance for the SAT changes is about 10% of the SAT increase, and the variance for projected wind changes is the same magnitude as the projected changes, suggesting uncertainty in the latter.  相似文献   

16.
Trend analysis of temperature parameters in Iran   总被引：1，自引：1，他引：0  
In this study, long-term annual and monthly trends in mean maximum, mean minimum and mean temperature are investigated at 35 synoptic stations in Iran. The statistical significance of trends is assessed by the Mann–Kendall test. Most stations, especially those in western and eastern parts of country, had significant positive trends in monthly temperature time series in summer season. However, the maximum number of stations with the positive trend were observed in April (30 stations), and then in August (29 stations) while the negative trends were seen in February (16 stations) and March (15 stations). On annual scale, most stations in western and southern parts of Iran had significant positive trend. Overall, about 71%, 66% and about 40% of stations had statistically significant trends in mean annual temperature, mean annual minimum temperature and in mean annual maximum temperature, respectively. These results, however, indicate that the climate in Iran is growing warmer, especially in summer.  相似文献   

17.
Multiyear (1983?C2006) hindcast simulation of summer monsoon over South Asia has been carried out using the regional climate model of the Beijing Climate Centre (BCC_RegCM1.0). The regional climate model (hereafter BCC RCM) is nested into the global climate model of the Beijing Climate Centre BCC_CGCM1.0 (here after CGCM). The regional climate model is initialized on 01 May and integrated up to the end of the September for 24?years. Compared to the driving CGCM the BCC RCM reproduces reasonably well the intensity and magnitude of the large-scale features associated with the South Asia summer monsoon such as the upper level anticyclone at 200?hPa, the mid-tropospheric warming over the Tibetan plateau, the surface heat low and the 850?hPa moisture transport from ocean to the land. Both models, i.e., BCC RCM and the driving CGCM overestimates (underestimates) the 850?hPa southwesterly flow over the northern (southern) Arabian Sea. Moreover, both models overestimate the seasonal mean precipitation over much of the South Asia region compared to the observations. However, the precipitation biases are significantly reduced in the BCC RCM simulations. Furthermore, both models simulate reasonably the interannual variability of the summer monsoon over India. The precipitation index simulated by BCC RCM shows significant correlation (0.62) with the observed one. The BCC RCM simulates reasonably well the spatial and temporal variation of the precipitation and surface air temperature compared to the driving CGCM. Further, the temperature biases are significantly reduced (1?C4°C) in the BCC RCM simulations. The simulated vertical structure of the atmosphere show biases above the four sub-regions, however, these biases are significantly reduced in the BCC RCM simulations compared to the driving CGCM. Compared to the driving CGCM, the evolution processes of the onset of summer monsoon, e.g., the meridional temperature gradient and the vertical wind shear are well simulated by the BCC RCM. The 24-year simulations also show that with a little exception the BCC RCM is capable to reproduce the monsoon active and break phases and the intraseasonal precipitation variation over the Indian subcontinent.  相似文献   

18.
德国马普研究所海气耦合摸式ECHAM4/OPYC3对东亚地区2 m温度年循环的模拟尽管有一些偏差,但还是相当成功的.其模拟的东亚夏季风偏弱,而冬季风偏强,此偏差可能与2 m温度以及西太平洋副热带高压模拟偏差有关.该模式模拟的东亚季风区夏季降水量偏弱,这与上述夏季风环流的模拟结果是一致的.该模式较好地抓住了华北地区经向环流和降水量的年循环特征.利用最新的温室气体和SO2排放方案,即政府间气候变化委员会(IPCC)排放方案特别报告(SRES)的A2和B2方案,通过该模式111年的积分结果讨论了东亚季风气候在21世纪后30年中的变化,其主要结果为:全球变暖导致夏季海陆温差增大和冬季海陆温差减弱,进而使东亚季风环流在夏季加强,冬季减弱.长江流域和华北地区的夏季降水量显著增强,而后者的增强更为显著,使得东亚季风区的夏季多雨区向北延伸;东亚季风区9月份的降水量在两个方案中都显著增加,说明在全球变暖条件下东亚季风区的多雨季节将延迟一个月.  相似文献   

19.
Climate changes may have great impacts on the fragile agro-ecosystems of the Loess Plateau of China, which is one of the most severely eroded regions in the world. We assessed the site-specific impacts of climate change during 2010?C2039 on hydrology, soil loss and crop yields in Changwu tableland region in the Loess Plateau of China. Projections of four climate models (CCSR/NIES, CGCM2, CSIRO-Mk2 and HadCM3) under three emission scenarios (A2, B2 and GGa) were used. A simple spatiotemporal statistical method was used to downscale GCMs monthly grid outputs to station daily weather series. The WEPP (Water and Erosion Prediction Project) model was employed to simulate the responses of agro-ecosystems. Compared with the present climate, GCMs projected a ?2.6 to 17.4% change for precipitation, 0.6 to 2.6°C and 0.6 to 1.7°C rises for maximum and minimum temperature, respectively. Under conventional tillage, WEPP predicted a change of 10 to 130% for runoff, ?5 to 195% for soil loss, ?17 to 25% for wheat yield, ?2 to 39% for maize yield, ?14 to 18% for plant transpiration, ?8 to 13% for soil evaporation, and ?6 to 9% for soil water reserve at two slopes during 2010?C2039. However, compared with conventional tillage under the present climate, conservation tillage would change runoff by ?34 to 71%, and decrease soil loss by 26 to 77% during 2010?C2039, with other output variables being affected slightly. Overall, climate change would have significant impacts on agro-ecosystems, and adoption of conservation tillage has great potential to reduce the adverse effects of future climate changes on runoff and soil loss in this region.  相似文献   

20.
This study aims at sharpening the existing knowledge of expected seasonal mean climate change and its uncertainty over Europe for the two key climate variables air temperature and precipitation amount until the mid-twentyfirst century. For this purpose, we assess and compensate the global climate model (GCM) sampling bias of the ENSEMBLES regional climate model (RCM) projections by combining them with the full set of the CMIP3 GCM ensemble. We first apply a cross-validation in order to assess the skill of different statistical data reconstruction methods in reproducing ensemble mean and standard deviation. We then select the most appropriate reconstruction method in order to fill the missing values of the ENSEMBLES simulation matrix and further extend the matrix by all available CMIP3 GCM simulations forced by the A1B emission scenario. Cross-validation identifies a randomized scaling approach as superior in reconstructing the ensemble spread. Errors in ensemble mean and standard deviation are mostly less than 0.1 K and 1.0 % for air temperature and precipitation amount, respectively. Reconstruction of the missing values reveals that expected seasonal mean climate change of the ENSEMBLES RCM projections is not significantly biased and that the associated uncertainty is not underestimated due to sampling of only a few driving GCMs. In contrast, the spread of the extended simulation matrix is partly significantly lower, sharpening our knowledge about future climate change over Europe by reducing uncertainty in some regions. Furthermore, this study gives substantial weight to recent climate change impact studies based on the ENSEMBLES projections, since it confirms the robustness of the climate forcing of these studies concerning GCM sampling.  相似文献   

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