Change in Extreme Climate Events over China Based on CMIP5

The changes in a selection of extreme climate indices(maximum of daily maximum temperature(TXx),minimum of daily minimum temperature(TNn),annual total precipitation when the daily precipitation exceeds the 95th percentile of wet-day precipitation(very wet days,R95p),and the maximum number of consecutive days with less than 1 mm of precipitation(consecutive dry days,CDD))were projected using multi-model results from phase 5 of the Coupled Model Intercomparison Project in the early,middle,and latter parts of the 21st century under different Representative Concentration Pathway(RCP)emissions scenarios.The results suggest that TXx and TNn will increase in the future and,moreover,the increases of TNn under all RCPs are larger than those of TXx.R95p is projected to increase and CDD to decrease significantly.The changes in TXx,TNn,R95p,and CDD in eight sub-regions of China are different in the three periods of the 21st century,and the ranges of change for the four indices under the higher emissions scenario are projected to be larger than those under the lower emissions scenario.The multi-model simulations show remarkable consistency in their projection of the extreme temperature indices,but poor consistency with respect to the extreme precipitation indices.More substantial inconsistency is found in those regions where high and low temperatures are likely to happen for TXx and TNn,respectively.For extreme precipitation events(R95p),greater uncertainty appears in most of the southern regions,while for drought events(CDD)it appears in the basins of Xinjiang.The uncertainty in the future changes of the extreme climate indices increases with the increasing severity of the emissions scenario.     

Extreme climate events in China: IPCC-AR4 model evaluation and projection

Observations from 550 surface stations in China during 1961–2000 are used to evaluate the skill of seven global coupled climate models in simulating extreme temperature and precipitation indices. It is found that the models have certain abilities to simulate both the spatial distributions of extreme climate indices and their trends in the observed period. The models’ abilities are higher overall for extreme temperature indices than for extreme precipitation indices. The well-simulated temperature indices are frost days (Fd), heat wave duration index (HWDI) and annual extreme temperature range (ETR). The well-simulated precipitation indices are the fraction of annual precipitation total due to events exceeding the 95th percentile (R95T) and simple daily intensity index (SDII). In a general manner, the multi-model ensemble has the best skill. For the projections of the extreme temperature indices, trends over the twenty-first century and changes at the end of the twenty-first century go into the same direction. Both frost days and annual extreme temperature range show decreasing trends, while growing season length, heat wave duration and warm nights show increasing trends. The increases are especially manifested in the Tibetan Plateau and in Southwest China. For extreme precipitation indices, the end of the twenty-first century is expected to have more frequent and more intense extreme precipitation. This is particularly visible in the middle and lower reaches of the Yangtze River, in the Southeast coastal region, in the west part of Northwest China, and in the Tibetan Plateau. In the meanwhile, accompanying the decrease in the maximum number of consecutive dry days in Northeast and Northwest, drought situation will reduce in these regions.     

QM和QDM方法对中国极端气候的高分辨率气候变化模拟的误差订正对比

采用分位数映射（Quantile Mapping, QM）和delta分位数映射（Quantile Delta Mapping, QDM）两种误差订正方法对区域气候模式RegCM4在中国区域内模拟的逐日气温和降水数据进行订正。模式数据是5种不同全球气候模式驱动下的区域模式气候变化模拟结果。计算订正前后的极端气候指数进行对比分析,包括日最高气温极大值（TXx）、日最低气温极小值（TNn）、连续干旱日数（CDD）和最大日降水量（RX1day）。结果表明,5组模拟结果和其集合平均（ensR）都显示气温指数的模拟效果高于降水指数,其中对TXx模拟最好,对CDD的模拟最差;经过订正后,针对不同模式的两种订正结果都能够有效地减小模式与观测的偏差并提高了空间相关系数,且两种方法的订正效果无明显差别。对RCP4.5情景下未来变化的分析中,QM在一定程度上改变了模式模拟的未来变化幅度和空间分布特征,QDM则能够有效地保留所有极端指数的气候变化信号。从全国平均来看,除CDD外,所有指数未来都呈现增加趋势,且QDM订正结果与订正前模式模拟的变化趋势更为接近。建议在气候变化模拟的误差订正中采用QDM方法。     

Projection of future climate change conditions using IPCC simulations, neural networks and Bayesian statistics. Part 1: Temperature mean state and seasonal cycle in South America

Projections for South America of future climate change conditions in mean state and seasonal cycle for temperature during the twenty-first century are discussed. Our analysis includes one simulation of seven Atmospheric-Ocean Global Circulation Models, which participated in the Intergovernmental Panel on Climate Change Project and provided at least one simulation for the twentieth century (20c3m) and one simulation for each of three Special Report on Emissions Scenarios (SRES) A2, A1B, and B1. We developed a statistical method based on neural networks and Bayesian statistics to evaluate the models’ skills in simulating late twentieth century temperature over continental areas. Some criteria [model weight indices (MWIs)] are computed allowing comparing over such large regions how each model captures the temperature large scale structures and contributes to the multi-model combination. As the study demonstrates, the use of neural networks, optimized by Bayesian statistics, leads to two major results. First, the MWIs can be interpreted as optimal weights for a linear combination of the climate models. Second, the comparison between the neural network projection of twenty-first century conditions and a linear combination of such conditions allows the identification of the regions, which will most probably change, according to model biases and model ensemble variance. Model simulations in the southern tip of South America and along the Chilean and Peruvian coasts or in the northern coasts of South America (Venezuela, Guiana) are particularly poor. Overall, our results present an upper bound of potential temperature warming for each scenario. Spatially, in SRES A2, our major findings are that Tropical South America could warm up by about 4°C, while southern South America (SSA) would also undergo a near 2–3°C average warming. Interestingly, this annual mean temperature trend is modulated by the seasonal cycle in a contrasted way according to the regions. In SSA, the amplitude of the seasonal cycle tends to increase, while in northern South America, the amplitude of the seasonal cycle would be reduced leading to much milder winters. We show that all the scenarios have similar patterns and only differ in amplitude. SRES A1B differ from SRES A2 mainly for the late twenty-first century, reaching more or less an 80–90% amplitude compared to SRES A2. SRES B1, however, diverges from the other scenarios as soon as 2025. For the late twenty-first century, SRES B1 displays amplitudes, which are about half those of SRES A2.     

新疆地区未来气候变化的区域气候模式集合预估

本文基于一套在5个全球气候模式结果驱动下,RegCM4区域气候模式对东亚25 km水平分辨率的集合预估,分析了中、高温室气体典型排放路径（RCP4.5和RCP8.5）下,21世纪不同时期新疆地区的未来气候变化。对模式当代气候模拟结果的检验表明,区域模式的模拟集合（ensR）总体上能够很好地再现当代新疆平均气温、降水和极端气温、降水分布特征。ensR预估21世纪未来新疆平均气温和降水将不断升高或增加,RCP8.5下的变化大于RCP4.5。在21世纪末期RCP8.5下,区域年平均气温和降水将分别增加4.9°C和28%（102 mm）,夏季（6～8月）的升温幅度略高于冬季（12～2月）,降水则以冬季增加为主。极端温度以及高温日数同样将不断升高,其中年日最低气温最小值的增幅总体高于年日最高气温最大值,未来新疆地区的极端冷事件将减少,高温、热浪事件将增加。由极端降水指标日最大降水量反应的强降水事件将普遍增加,连续无降水日数总体以减少为主。积雪变化存在一定区域差异,具体表现为除塔里木盆地外的普遍减少。对总径流量和表层土壤湿度的预估分析表明,二者在新疆地区均以增加为主,但水文干旱在北疆会加重。ensR各模拟间无论是在当代模拟还是未来预估中都表现出较好的一致性,但在变化的具体数量及个别情况下符号均存在一定差异。最后,综合考虑ensR对各要素的预估发现,总体而言新疆未来更趋向于“暖湿化”,但这不会改变其干旱、半干旱气候的本质,而且水文干旱频率在一些地区会增加,未来新疆的水资源状况仍不容乐观。     

On regional dynamical downscaling for the assessment and projection of temperature and precipitation extremes across Tasmania, Australia

The ability of an ensemble of six GCMs, downscaled to a 0.1° lat/lon grid using the Conformal Cubic Atmospheric Model over Tasmania, Australia, to simulate observed extreme temperature and precipitation climatologies and statewide trends is assessed for 1961–2009 using a suite of extreme indices. The downscaled simulations have high skill in reproducing extreme temperatures, with the majority of models reproducing the statewide averaged sign and magnitude of recent observed trends of increasing warm days and warm nights and decreasing frost days. The warm spell duration index is however underestimated, while variance is generally overrepresented in the extreme temperature range across most regions. The simulations show a lower level of skill in modelling the amplitude of the extreme precipitation indices such as very wet days, but simulate the observed spatial patterns and variability. In general, simulations of dry extreme precipitation indices are underestimated in dryer areas and wet extremes indices are underestimated in wetter areas. Using two SRES emissions scenarios, the simulations indicate a significant increase in warm nights compared to a slightly more moderate increase in warm days, and an increase in maximum 1- and 5-day precipitation intensities interspersed with longer consecutive dry spells across Tasmania during the twenty-first century.     

RegCM4模式对雄安及周边区域气候变化的集合预估

基于CSIRO-Mk3-6-0、EC-EARTH、HadGEM2-ES和MPI-ESM-MR共4个全球气候模式,分别驱动区域气候模式RegCM4,所进行的RCP4.5（典型浓度路径）中等排放情景下25 km较高水平分辨率东亚区域21世纪气候变化模拟结果,针对雄安新区及周边区域,在对当代（1986~2005）气候进行检验的基础上,进行了该区域未来气候变化的多模拟集合预估,并给出了模拟间的差别。结果表明:RegCM4可以较好地模拟出分析区域当代平均气温和降水的分布及年内月循环变化特征;对与气温相关的极端气候事件指数,日最高气温最高值（TXx）和日最低气温最低值（TNn）,以及和降水相关的指数日最大降水量（RX1day）也有较好的模拟能力。雄安及周边区域未来平均气温、TXx和TNn将不断上升,高温热浪事件在增加的同时,低温事件将减少。未来分析区域平均降水量有所增加;而RX1day的增加更明显,且模拟间的一致性较好,不确定性相对较低,暴雨和洪涝事件的频率和强度均将增大。同时由于气温升高导致的潜在蒸发量相对于降水更大的增加,将使得区域水资源相对不足的现象加重。     

Regional climate model projections for the State of Washington

Global climate models do not have sufficient spatial resolution to represent the atmospheric and land surface processes that determine the unique regional climate of the State of Washington. Regional climate models explicitly simulate the interactions between the large-scale weather patterns simulated by a global model and the local terrain. We have performed two 100-year regional climate simulations using the Weather Research and Forecasting (WRF) model developed at the National Center for Atmospheric Research (NCAR). One simulation is forced by the NCAR Community Climate System Model version 3 (CCSM3) and the second is forced by a simulation of the Max Plank Institute, Hamburg, global model (ECHAM5). The mesoscale simulations produce regional changes in snow cover, cloudiness, and circulation patterns associated with interactions between the large-scale climate change and the regional topography and land-water contrasts. These changes substantially alter the temperature and precipitation trends over the region relative to the global model result or statistical downscaling. To illustrate this effect, we analyze the changes from the current climate (1970–1999) to the mid twenty-first century (2030–2059). Changes in seasonal-mean temperature, precipitation, and snowpack are presented. Several climatological indices of extreme daily weather are also presented: precipitation intensity, fraction of precipitation occurring in extreme daily events, heat wave frequency, growing season length, and frequency of warm nights. Despite somewhat different changes in seasonal precipitation and temperature from the two regional simulations, consistent results for changes in snowpack and extreme precipitation are found in both simulations.     

Projected changes in drought occurrence under future global warming from multi-model,multi-scenario,IPCC AR4 simulations

Recent and potential future increases in global temperatures are likely to be associated with impacts on the hydrologic cycle, including changes to precipitation and increases in extreme events such as droughts. We analyze changes in drought occurrence using soil moisture data for the SRES B1, A1B and A2 future climate scenarios relative to the PICNTRL pre-industrial control and 20C3M twentieth century simulations from eight AOGCMs that participated in the IPCC AR4. Comparison with observation forced land surface model estimates indicates that the models do reasonably well at replicating our best estimates of twentieth century, large scale drought occurrence, although the frequency of long-term (more than 12-month duration) droughts are over-estimated. Under the future projections, the models show decreases in soil moisture globally for all scenarios with a corresponding doubling of the spatial extent of severe soil moisture deficits and frequency of short-term (4–6-month duration) droughts from the mid-twentieth century to the end of the twenty-first. Long-term droughts become three times more common. Regionally, the Mediterranean, west African, central Asian and central American regions show large increases most notably for long-term frequencies as do mid-latitude North American regions but with larger variation between scenarios. In general, changes under the higher emission scenarios, A1B and A2 are the greatest, and despite following a reduced emissions pathway relative to the present day, the B1 scenario shows smaller but still substantial increases in drought, globally and for most regions. Increases in drought are driven primarily by reductions in precipitation with increased evaporation from higher temperatures modulating the changes. In some regions, increases in precipitation are offset by increased evaporation. Although the predicted future changes in drought occurrence are essentially monotonic increasing globally and in many regions, they are generally not statistically different from contemporary climate (as estimated from the 1961–1990 period of the 20C3M simulations) or natural variability (as estimated from the PICNTRL simulations) for multiple decades, in contrast to primary climate variables, such as global mean surface air temperature and precipitation. On the other hand, changes in annual and seasonal means of terrestrial hydrologic variables, such as evaporation and soil moisture, are essentially undetectable within the twenty-first century. Changes in the extremes of climate and their hydrological impacts may therefore be more detectable than changes in their means.     

Projection of future climate change conditions using IPCC simulations, neural networks and Bayesian statistics. Part 2: Precipitation mean state and seasonal cycle in South America

Evaluating the response of climate to greenhouse gas forcing is a major objective of the climate community, and the use of large ensemble of simulations is considered as a significant step toward that goal. The present paper thus discusses a new methodology based on neural network to mix ensemble of climate model simulations. Our analysis consists of one simulation of seven Atmosphere–Ocean Global Climate Models, which participated in the IPCC Project and provided at least one simulation for the twentieth century (20c3m) and one simulation for each of three SRES scenarios: A2, A1B and B1. Our statistical method based on neural networks and Bayesian statistics computes a transfer function between models and observations. Such a transfer function was then used to project future conditions and to derive what we would call the optimal ensemble combination for twenty-first century climate change projections. Our approach is therefore based on one statement and one hypothesis. The statement is that an optimal ensemble projection should be built by giving larger weights to models, which have more skill in representing present climate conditions. The hypothesis is that our method based on neural network is actually weighting the models that way. While the statement is actually an open question, which answer may vary according to the region or climate signal under study, our results demonstrate that the neural network approach indeed allows to weighting models according to their skills. As such, our method is an improvement of existing Bayesian methods developed to mix ensembles of simulations. However, the general low skill of climate models in simulating precipitation mean climatology implies that the final projection maps (whatever the method used to compute them) may significantly change in the future as models improve. Therefore, the projection results for late twenty-first century conditions are presented as possible projections based on the “state-of-the-art” of present climate modeling. First, various criteria were computed making it possible to evaluate the models’ skills in simulating late twentieth century precipitation over continental areas as well as their divergence in projecting climate change conditions. Despite the relatively poor skill of most of the climate models in simulating present-day large scale precipitation patterns, we identified two types of models: the climate models with moderate-to-normal (i.e., close to observations) precipitation amplitudes over the Amazonian basin; and the climate models with a low precipitation in that region and too high a precipitation on the equatorial Pacific coast. Under SRES A2 greenhouse gas forcing, the neural network simulates an increase in precipitation over the La Plata basin coherent with the mean model ensemble projection. Over the Amazonian basin, a decrease in precipitation is projected. However, the models strongly diverge, and the neural network was found to give more weight to models, which better simulate present-day climate conditions. In the southern tip of the continent, the models poorly simulate present-day climate. However, they display a fairly good convergence when simulating climate change response with a weak increase south of 45°S and a decrease in Chile between 30 and 45°S. Other scenarios (A1B and B1) strongly resemble the SRES A2 trends but with weaker amplitudes.     

Climate change scenarios for precipitation extremes in Portugal

Precipitation indices are commonly used as climate change indicators. Considering four Climate Variability and Predictability-recommended indices, this study assesses possible changes in their spatial patterns over Portugal under future climatic conditions. Precipitation data from the regional climate model Consortium for Small-Scale Modelling–Climate version of the Local Model (CCLM) ensemble simulations with ECHAM5/MPI-OM1 boundary conditions are used for this purpose. For recent–past, medians and probability density functions of the CCLM-based indices are validated against station-based and gridded observational dataset from ENSEMBLES-based (gridded daily precipitation data provided by the European Climate Assessment & Dataset project) indices. It is demonstrated that the model is able to realistically reproduce not only precipitation but also the corresponding extreme indices. Climate change projections for 2071–2100 (A1B and B1 SRES scenarios) reveal significant decreases in total precipitation, particularly in autumn over northwestern and southern Portugal, though changes exhibit distinct local and seasonal patterns and are typically stronger for A1B than for B1. The increase in winter precipitation over northeastern Portugal in A1B is the most important exception to the overall drying trend. Contributions of extreme precipitation events to total precipitation are also expected to increase, mainly in winter and spring over northeastern Portugal. Strong projected increases in the dry spell lengths in autumn and spring are also noteworthy, giving evidence for an extension of the dry season from summer to spring and autumn. Although no coupling analysis is undertaken, these changes are qualitatively related to modifications in the large-scale circulation over the Euro-Atlantic area, more specifically to shifts in the position of the Azores High and associated changes in the large-scale pressure gradient over the area.     

Mediterranean climate extremes in synoptic downscaling assessments

The behaviour of precipitation and maximum temperature extremes in the Mediterranean area under climate change conditions is analysed in the present study. In this context, the ability of synoptic downscaling techniques in combination with extreme value statistics for dealing with extremes is investigated. Analyses are based upon a set of long-term station time series in the whole Mediterranean area. At first, a station-specific ensemble approach for model validation was developed which includes (1) the downscaling of daily precipitation and maximum temperature values from the large-scale atmospheric circulation via analogue method and (2) the fitting of extremes by generalized Pareto distribution (GPD). Model uncertainties are quantified as confidence intervals derived from the ensemble distributions of GPD-related return values and described by a new metric called “ratio of overlapping”. Model performance for extreme precipitation is highest in winter, whereas the best models for maximum temperature extremes are set up in autumn. Valid models are applied to a 30-year period at the end of the twenty-first century (2070–2099) by means of ECHAM5/MPI-OM general circulation model data for IPCC SRES B1 scenario. The most distinctive future changes are observed in autumn in terms of a strong reduction of precipitation extremes in Northwest Iberia and the Northern Central Mediterranean area as well as a simultaneous distinct increase of maximum temperature extremes in Southwestern Iberia and the Central and Southeastern Mediterranean regions. These signals are checked for changes in the underlying dynamical processes using extreme-related circulation classifications. The most important finding connected to future changes of precipitation extremes in the Northwestern Mediterranean area is a reduction of southerly displaced deep North Atlantic cyclones in 2070–2099 as associated with a strengthened North Atlantic Oscillation. Thus, the here estimated future changes of extreme precipitation are in line with the discourse about the influence of North Atlantic circulation variability on the changing climate in Europe.     

Projected change in East Asian summer monsoon precipitation under RCP scenario

Future changes in East Asian summer monsoon precipitation climatology, frequency, and intensity are analyzed using historical climate simulations and future climate simulations under the RCP4.5 scenario using the World Climate Research Programme’s (WCRP) Coupled Model Intercomparison Project 5 (CMIP5) multi-model dataset. The model reproducibility is evaluated, and well performance in the present-day climate simulation can be obtained by most of the studied models. However, underestimation is obvious over the East Asian region for precipitation climatology and precipitation intensity, and overestimation is observed for precipitation frequency. The overestimation of precipitation frequency is mainly due to the large positive bias of the light precipitation (precipitation <10 mm/day) days, and the underestimation of precipitation intensity is mainly caused by the negative bias of the intense precipitation (precipitation >10 mm/day) intensity. For the future climate simulations, simple multi-model ensemble (MME) averages using all of the models show increases in precipitation and its intensity over almost all of East Asia, while the precipitation frequency is projected to decrease over eastern China and around Japan and increase in other regions. When the weighted MME is considered, no large difference can be observed compared with the simple MME. For the MME using the six best models that have good performance in simulating the present-day climate, the future climate changes over East Asia are very similar to those predicted using all of the models. Further analysis shows that the frequency and intensity of intense precipitation events are also projected to significantly increase over East Asia. Increases in precipitation frequency and intensity are the main contributors to increases in precipitation, and the contribution of frequency increases (contributed by 40.8 % in the near future and by 58.9 % by the end of the twenty-first century) is much larger than that of intensity increases (contributed by 29.9 % in the near future and by 30.1 % by the end of the twenty-first century). This finding also implies an increased risk of intense precipitation events over the East Asian region under global warming scenario. These results regarding future climate simulations show much greater reliability than those using CMIP3 simulations.     

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.     

Climate change in the northeastern US: regional climate model validation and climate change projections

A high resolution regional climate model (RCM) is used to simulate climate of the recent past and to project future climate change across the northeastern US. Different types of uncertainties in climate simulations are examined by driving the RCM with different boundary data, applying different emissions scenarios, and running an ensemble of simulations with different initial conditions. Empirical orthogonal functions analysis and K-means clustering analysis are applied to divide the northeastern US region into four climatologically different zones based on the surface air temperature (SAT) and precipitation variability. The RCM simulations tend to overestimate SAT, especially over the northern part of the domain in winter and over the western part in summer. Statistically significant increases in seasonal SAT under both higher and lower emissions scenarios over the whole RCM domain suggest the robustness of future warming. Most parts of the northeastern US region will experience increasing winter precipitation and decreasing summer precipitation, though the changes are not statistically significant. The greater magnitude of the projected temperature increase by the end of the twenty-first century under the higher emissions scenario emphasizes the essential role of emissions choices in determining the potential future climate change.     

中国地区IPCC A1B情景下21世纪中期气候变化的数值模拟试验

利用MM5V3区域气候模式单向嵌套ECHAM5全球环流模式的结果,对中国地区实际温室气体浓度下当代气候(1981—2000年)及IPCC A1B情景下21世纪中期气候(2041—2060年)分别进行了水平分辨率为50 km的模拟试验。首先检验全球和区域模式对当代气候的模拟情况,结果表明:区域模式对中国地区地面温度和降水空间分布的模拟能力优于全球模式;与实际观测相比,区域模式模拟的地面温度在中国大部分地区偏低,模拟的降水量偏多,降水位置偏北。IPCCA1B情景下中国地区21世纪中期气候变化的模式结果显示:各季节地面温度在全国范围内都将比当代升高1.2～3.9℃,且升温幅度具有北方大于南方、冬季大于夏季的时空分布特征;降水变化具有一定的区域性和季节性,秋季和冬季降水在全国大部分地区都将增加10%～30%,春季和夏季降水则呈现"北方减少、南方增多"的趋势,变化幅度在-10%～10%之间。21世纪中期地面温度和降水变化还具有一定的年际特征:地面温度在中国地区各子区域均表现为上升趋势,升温速率在0.7～0.9℃/10a之间,温度变率也比当代有所增大;降水在西北地区略呈下降趋势,在其它子区域均为上升,降水变率的变化具有区域性特征。     

A regional climate model downscaling projection of China future climate change

Climate changes over China from the present (1990–1999) to future (2046–2055) under the A1FI (fossil fuel intensive) and A1B (balanced) emission scenarios are projected using the Regional Climate Model version 3 (RegCM3) nests with the National Center for Atmospheric Research (NCAR) Community Climate System Model (CCSM). For the present climate, RegCM3 downscaling corrects several major deficiencies in the driving CCSM, especially the wet and cold biases over the Sichuan Basin. As compared with CCSM, RegCM3 produces systematic higher spatial pattern correlation coefficients with observations for precipitation and surface air temperature except during winter. The projected future precipitation changes differ largely between CCSM and RegCM3, with strong regional and seasonal dependence. The RegCM3 downscaling produces larger regional precipitation trends (both decreases and increases) than the driving CCSM. Contrast to substantial trend differences projected by CCSM, RegCM3 produces similar precipitation spatial patterns under different scenarios except autumn. Surface air temperature is projected to consistently increase by both CCSM and RegCM3, with greater warming under A1FI than A1B. The result demonstrates that different scenarios can induce large uncertainties even with the same RCM-GCM nesting system. Largest temperature increases are projected in the Tibetan Plateau during winter and high-latitude areas in the northern China during summer under both scenarios. This indicates that high elevation and northern regions are more vulnerable to climate change. Notable discrepancies for precipitation and surface air temperature simulated by RegCM3 with the driving conditions of CCSM versus the model for interdisciplinary research on climate under the same A1B scenario further complicated the uncertainty issue. The geographic distributions for precipitation difference among various simulations are very similar between the present and future climate with very high spatial pattern correlation coefficients. The result suggests that the model present climate biases are systematically propagate into the future climate projections. The impacts of the model present biases on projected future trends are, however, highly nonlinear and regional specific, and thus cannot be simply removed by a linear method. A model with more realistic present climate simulations is anticipated to yield future climate projections with higher credibility.     

An assessment of potential change in wildfire activity in the Russian boreal forest zone induced by climate warming during the twenty-first century

The problem of forest fires is very important for Russia. In this paper we consider this problem in the connection with the projection of significant climate change. An approach to determine the magnitude of change in wildfire risk in Russia under the influence of climate warming is discussed. Observations for the European part of Russia and for Siberia have been used in this analysis. A statistical correlation between drought indices calculated by use of monthly sums of temperature and precipitation and the frequency of fire danger was obtained for the forest zone of Russia. The change in fire danger potential was evaluated using temperature and precipitation monthly means at the nodes of a regular spatial grid. Climate change scenarios were obtained from Global Climate Models (GCM) ensemble projections. The maximum increases (about 12–30%) of the number of days with fire danger conditions during the twenty-first century fire season were obtained for the southern forest zone boundary in both the European region of Russia and in Siberia. In the Baikal and Primoriye Regions, fire danger distributions in the twenty-first century are not projected to change significantly.     

Steric sea level rise over the Mediterranean Sea: present climate and scenario simulations

A regional atmosphere–ocean coupled model has been used to estimate sea level rise in the Mediterranean basin under present and future conditions. A present climate simulation has been forced by ERA40 reanalysis covering the period 1958–2001. Moreover a simulation has been forced by the global coupled model ECHAM5-MPIOM under present climate conditions for the period 1951–2000. Two other 50-year simulations have been performed under the SRESA1B scenario for the twenty-first century and differ only in temperature and salinity profiles used to relax the ocean model in the Atlantic buffer zone. The present climate simulation has been verified in terms of temperature, salinity and sea level against observed data, showing good performances both in mean values and variability over the whole Mediterranean Sea and over different sub-basins. The future scenario simulations show that the steric sea level averaged over the entire basin rises of about 2 or 7?cm in 50?years depending on the Atlantic boundary conditions. The difference of about 1?°C and 0.5?psu in the upper layers of the Atlantic sea reflects mainly on the halosteric component that contributes negatively to the sea level rise, when fresher and colder boundary conditions are used in the Atlantic buffer zone, and positively in the other case. The impact of the boundary conditions is not uniform in the basin and is particularly strong in some easternmost regions.     

Future evolution of extreme precipitation in the Mediterranean

Mediterranean basins can be impacted by severe floods caused by extreme rainfall, and there is a growing awareness about the possible increase in these heavy rainfall events due to climate change. In this study, the climate change impacts on extreme daily precipitation in 102 catchments covering the whole Mediterranean basin are investigated using nonstationary extreme value model applied to annual maximum precipitation in an ensemble of high-resolution regional climate model (RCM) simulations from the Euro-CORDEX experiment. Results indicate contrasted trends, with significant increasing trends in Northern catchments and conversely decreasing trends in Southern catchments. For most cases, the time of signal emergence for these trends is before the year 2000. The same spatial pattern is obtained under the two climate scenarios considered (RCP4.5 and RCP8.5) and in most RCM simulations, suggesting a robust climate change signal. The strongest multi-model agreement concerns the positive trends, which can exceed +?20% by the end of the twenty-first century in some simulations, impacting South France, North Italy, and the Balkans. For these areas, society-relevant strong impacts of such Mediterranean extreme precipitation changes could be expected in particular concerning flood-related damages.