Drivers of mean climate change around the Netherlands derived from CMIP5

For the construction of regional climate change scenarios spanning a relevant fraction of the spread in climate model projections, an inventory of major drivers of regional climate change is needed. For the Netherlands, a previous set of regional climate change scenarios was based on the decomposition of local temperature/precipitation changes into components directly linked to the level of global warming, and components related to changes in the regional atmospheric circulation. In this study this decomposition is revisited utilizing the extensive modelling results from the CMIP5 model ensemble in support for the 5th IPCC assessment. Rather than selecting a number of GCMs based on performance metrics or relevant response features, a regression technique was developed to utilize all available model projections. The large number of projections allows a quantification of the separate contributions of emission scenarios, systematic model responses and natural variability to the total likelihood range. Natural variability plays a minor role in modelled differences in the global mean temperature response, but contributes for up to 50 % to the range of mean sea level pressure responses and local precipitation. Using key indicators (“steering variables”) for the temperature and circulation response, the range in local seasonal mean temperature and precipitation responses can be fairly well reproduced.     

Consistency of observed winter precipitation trends in northern Europe with regional climate change projections

Often it is claimed that the recent changes in northern European climate are at least partly anthropogenic even though a human influence has not yet been successfully detected. Hence we investigate whether the recent changes are consistent with regional climate change projections. Therefore, trends in winter (DJF) mean precipitation in northern Europe are compared to human induced changes as predicted by a set of four regional climate model simulations. The patterns of recent trends and predicted changes match reasonably well as indicated by pattern correlation and the similarity is very likely not random. However, the model projections generally underestimate the recent change in winter precipitation. That is, the signal-to-noise ratio of the anthropogenic precipitation change is either rather low or the presently used simulations are significantly flawed in their ability to project changes into the future. European trends contain large signals related to the North Atlantic Oscillation (NAO), of which a major unknown part may be unrelated to the anthropogenic signal. Therefore, we also examine the consistency of recent and projected changes after subtracting the NAO signal in both the observations and in the projections. It turns out that even after the removal of the NAO signal, the pattern of trends in the observations is similar to those projected by the models. At the same time, the magnitude of the trends is considerably reduced and closer to the magnitude of the change in the projections.     

CMIP5全球气候模式对上海极端气温和降水的情景预估

基于国际耦合模式比较计划第五阶段(Coupled Model Intercomparison Project Phase 5,以下简称CMIP5)28个模式的数值模拟结果和1981~2010年华东和上海气温和降水观测数据,评估了该28个气候模式对华东和上海气温和降水的模拟能力,并预估了RCP4.5(Representative Concentration Pathway 4.5)情景下上海2021~2030年极端气温和降水气候的变化趋势和不确定性。结果表明:与观测值相比,模式对华东和上海年平均气温的模拟大多均值偏高、方差偏低;对年总降水量的模拟大多均值偏高,但方差以华东偏高、上海偏低为主;26个模式的气温变化趋势和12个模式的降水变化趋势与观测值相同。选出8个模式的预估结果表明:与2001~2010年相比,2021~2030年上海冬天极端低温的出现日数(冷夜日数)呈减少趋势,不确定性最小;夏天暖夜日数呈增加的趋势,不确定性较小;其他极端气温事件的变化趋势则存在较大的不确定性,冷夜指标的不确定性最大。强降水发生日数和强降水的强度都呈现增加的趋势,且不确定性较小。     

Does the model regional bias affect the projected regional climate change? An analysis of global model projections

An analysis is presented of the dependence of the regional temperature and precipitation change signal on systematic regional biases in global climate change projections. The CMIP3 multi-model ensemble is analyzed over 26 land regions and for the A1B greenhouse gas emission scenario. For temperature, the model regional bias has a negligible effect on the projected regional change. For precipitation, a significant correlation between change and bias is found in about 30% of the seasonal/regional cases analyzed, covering a wide range of different climate regimes. For these cases, a performance-based selection of models in producing climate change scenarios can affect the resulting change estimate, and it is noted that a minimum of four to five models is needed to obtain robust precipitation change estimates. In a number of cases, models with largely different precipitation biases can still produce changes of consistent sign. Overall, it is assessed that in the present generation of models the regional bias does not appear to be a dominant factor in determining the simulated regional change in the majority of cases.     

Assessing the robustness of projected precipitation changes over central Africa on the basis of a multitude of global and regional climate projections

It is well accepted within the scientific community that a large ensemble of different projections is required to achieve robust climate change information for a specific region. For this purpose we have compiled a state-of-the-art multi-model multi-scenario ensemble of global and regional precipitation projections. This ensemble combines several global projections from the CMIP3 and CMIP5 databases, along with some recently downscaled regional CORDEX-Africa projections. Altogether daily precipitation data from 77 different climate change projections is analysed; separated into 31 projections for a high and 46 for a low emission scenario. We find a robust indication that, independent of the underlying emission scenario, annual total precipitation amounts over the central African region are not likely to change severely in the future. However some robust changes in precipitation characteristics, like the intensification of heavy rainfall events as well as an increase in the number of dry spells during the rainy season are projected for the future. Further analysis shows that over some regions the results of the climate change assessment clearly depend on the size of the analyzed ensemble. This indicates the need of a “large-enough” ensemble of independent climate projections to allow for a reliable climate change assessment.     

Future Precipitation Extremes in China under Climate Change and Their Physical Quantification Based on a Regional Climate Model and CMIP5 Model Simulations

The atmospheric water holding capacity will increase with temperature according to Clausius-Clapeyron scaling and affects precipitation.The rates of change in future precipitation extremes are quantified with changes in surface air temperature.Precipitation extremes in China are determined for the 21st century in six simulations using a regional climate model,RegCM4,and 17 global climate models that participated in CMIP5.First,we assess the performance of the CMIP5 models and RCM runs in their simulation of extreme precipitation for the current period(RF:1982-2001).The CMIP5 models and RCM results can capture the spatial variations of precipitation extremes,as well as those based on observations:OBS and XPP.Precipitation extremes over four subregions in China are predicted to increase in the mid-future(MF:2039-58)and far-future(FF:2079-98)relative to those for the RF period based on both the CMIP5 ensemble mean and RCM ensemble mean.The secular trends in the extremes of the CMIP5 models are predicted to increase from 2008 to 2058,and the RCM results show higher interannual variability relative to that of the CMIP5 models.Then,we quantify the increasing rates of change in precipitation extremes in the MF and FF periods in the subregions of China with the changes in surface air temperature.Finally,based on the water vapor equation,changes in precipitation extremes in China for the MF and FF periods are found to correlate positively with changes in the atmospheric vertical wind multiplied by changes in surface specific humidity(significant at the p<0.1 level).     

Probabilistic projections of regional temperature and precipitation extending from observed time series

Probabilistic projections of change in regional temperature and precipitation previously derived allow for the range of sensitivities to global warming simulated by CMIP3 models. However, the changes were relative to an idealized base climate for 1980–1999, disregarding observed trends, such as those in rainfall in some Australian regions. Here we propose a method that represents projections for both forced change and decadal means as time series that extend from the observed series, illustrated using data for central Victoria. The main idea is to estimate the time-evolving underlying (or forced) past climate then convert this to a series of absolute values, by using the mean of the full observational record. We again use the pattern scaling assumption, and combine the CMIP3 sensitivities used for future change with a global warming series beginning at 1900. Like the confidence interval of regression theory, the analysis gives an estimate of the range of the underlying climate at each decade. This range can be augmented to allow for natural variability. A Bayesian theory can be applied to combine the model-based sensitivity with that estimated from observations. The time series are modified and the persistence of current observed anomalies considered, ultimately merging the probabilistic projections with the observed record. For some other cases, such as rainfall in southwest and north Australia and temperature in the state of Iowa, the two sensitivity estimates appear less compatible, and possible additional forcings are considered. Examples of the potential use of such time series are presented.     

IPCC第六次评估报告中的《图集》

决策者和公众正在越来越多地关注气候变化带来的影响,而这需要更加丰富的、区域尺度上的当前和未来气候状况的精细信息。《图集》与IPCC第六次评估报告（AR6）第一工作组（WGI）报告中其他章节相协调,评估区域气候变化的观测、归因、预估的基本信息,并建立了在线交互图集系统。《图集》包含图集章节和交互图集两部分：图集章节基于新的区域划分,评估了各区域的气候变化,重点关注地表温度和降水的观测趋势、归因以及预估的未来变化。交互图集是AR6 WGI报告的一个新组成部分,基于观测、全球（CMIP5和CMIP6）和区域（CORDEX）模式数据,以互动地图的形式提供观测和预估时间段的气候变化和归因的综合信息。     

The role of the simulation setup in a long-term high-resolution climate change projection for the southern African region

High-resolution regional climate change simulations have proven to offer an added value compared to available global climate model simulations. However, over many regions of the globe, long-term high-resolution climate change projections are rather sparse. We present a transient high-resolution climate change projection with the regional climate model with the regional climate model REMO over the southern African region, following the SRES A1B emission scenario. The simulation was conducted at 18?km grid spacing for the period from 1960 to 2100, making it to the longest available climate change projection at such a high resolution for the region. In the first part of the study, we focus on the impact of the model setup on the simulated rainfall over the southern African region. In the standard setup, we used the output of the global climate model ECHAM5/MPIOM directly to force REMO. This setup led to a very strong wet bias over the region. Changing it to the double-nesting setup significantly reduced this bias, but a substantial wet bias still persists. The remaining bias could partly be attributed to a warm bias in the SST forcing over the southern Atlantic Ocean. Thus, we applied an SST correction based on the anomaly approach to the data, which led to a further improvement of the rainfall simulation. As the SST bias in the southern Atlantic is a common feature of all global climate models assessed by the IPCC, we recommend the chosen model setup, including the SST correction, as general procedure for dynamical downscaling studies over the southern African region. In the second part, we present the projected spatial and temporal changes of temperature and precipitation, including several rainfall characteristics, over the southern African region. Herby we compare the projections of the high-resolution REMO simulation to those of the forcing regional and global models. We generally find that for temperature the magnitude of the projected changes of the regional model only slightly differs from the GCM projection; however, the spatial patterns are much better resolved in the RCM projections. For precipitation, REMO shows a more intense drying toward the end of the twenty-first century than it is simulated by the global model. This can have a major influence when investigating the impacts of future climate change on a regional or even local scale. In combination with the improved spatial patterns, the application of high-resolution climate change information could therefore improve the results of such applications.     

Extended regional climate model projections for Europe until the mid-twentyfirst century: combining ENSEMBLES and CMIP3

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.     

Assessment of CMIP3 climate models and projected changes of precipitation and temperature in the Yangtze River Basin, China

The projected changes of precipitation and temperature in the Yangtze River Basin in the 20th Century from 20 models of the CMIP3 (phase 3 of the Coupled Model Inter-comparison Project) dataset are analyzed based on the observed precipitation and temperature data of 147 meteorological stations in the Yangtze River Basin. The results show that all models tend to underestimate the annual mean temperature over the Yangtze River Basin, and to overestimate the annual mean precipitation. The temporal changes of simulated annual mean precipitation and temperature are broadly comparable with the observations, but with large variability among the results of the models. Most of the models can reproduce maximum precipitation during the monsoon season, while all models tend to underestimate the mean temperature of each month over the Yangtze River Basin. The Taylor diagram shows that the differences between modeled and observed temperature are relatively smaller as compared to differences in precipitation. For a detailed investigation of regional characteristics of climate change in the Yangtze River Basin during 2011–2050, the multi-model ensembles produced by an upgraded REA method are carried out for more reliable projections. The projected precipitation and temperature show large spatial variability in the Yangtze River Basin. Mean precipitation will increase under the A1B and B1 scenarios and decrease under the A2 scenario, with linear trends ranging from ?21 to 28.5?mm/decade. Increasing mean temperature can be found in all scenarios with linear trends ranging from 0.15 to 0.48°C/decade. Grids in the head region of the Jingshajiang catchment show distinct increasing trends for all scenarios. Some physical processes associated with precipitation are not well represented in the models.     

Climate change simulated by full and mixed-layer ocean versions of CSIRO Mk3.5 and Mk3.0: The Asia-Pacific region

The patterns of climate change in the Asia-Pacific region simulated by versions of the CSIRO Mk3.5 and Mk3.0 climate models are examined and compared with those from 23 CMIP3 models. Using fields standardized by global warming, it is seen that both CSIRO coupled models simulate larger surface warming in the tropical western Pacific Ocean, and smaller warming in the eastern Indian Ocean, than the CMIP3 average, and also model versions with a mixed-layer ocean. Corresponding differences in the changes in the pressure, winds, rainfall and other quantities were simulated. Introducing the coupled Mk3.5’s sea surface temperature field for the present climate, which has a warm bias, as the base climate for the MLO version had only a minor effect on the MLO version’s pattern of climate change. A Pacific-Indian Dipole index quantifying the amplitude of the warming pattern explains much of the variation in rainfall change simulated by the CMIP3 models over Australia and the Indonesian and Melanesian regions. It relates more strongly to Australian average rainfall than several other indices representing southern hemispheric circulation changes. The decline in Australian rainfall produced by the full ocean coupling is largest in summer, but occurs in each season, and extends across the continent. Further assessment of the importance of the dipole change pattern in new simulations is warranted. Analyses aimed at reducing the uncertainty in its potential amplitude could help narrow the range of projections for change in the Australasian region.     

Climate change projections of the North American Regional Climate Change Assessment Program (NARCCAP)

We investigate major results of the NARCCAP multiple regional climate model (RCM) experiments driven by multiple global climate models (GCMs) regarding climate change for seasonal temperature and precipitation over North America. We focus on two major questions: How do the RCM simulated climate changes differ from those of the parent GCMs and thus affect our perception of climate change over North America, and how important are the relative contributions of RCMs and GCMs to the uncertainty (variance explained) for different seasons and variables? The RCMs tend to produce stronger climate changes for precipitation: larger increases in the northern part of the domain in winter and greater decreases across a swath of the central part in summer, compared to the four GCMs driving the regional models as well as to the full set of CMIP3 GCM results. We pose some possible process-level mechanisms for the difference in intensity of change, particularly for summer. Detailed process-level studies will be necessary to establish mechanisms and credibility of these results. The GCMs explain more variance for winter temperature and the RCMs for summer temperature. The same is true for precipitation patterns. Thus, we recommend that future RCM-GCM experiments over this region include a balanced number of GCMs and RCMs.     

Projections of Annual Mean Air Temperature and Precipitation over the Globe and in China During the 21st Century bythe BCC Climate System Model BCC_CSM1.0

Evaluating the projection capability of climate models is an important task in climate model development and climate change studies. The projection capability of the Beijing Climate Center (BCC) Climate System Model BCC_CSM1.0 is analyzed in this study. We focus on evaluating the projected annual mean air temperature and precipitation during the 21st century under three emission scenarios (Special Report on Emission Scenarios (SRES) B1, A1B, and A2) of the BCC_CSM1.0 model, along with comparisons with 22 CMIP3 (Coupled Model Intercomparison Project Phase 3) climate models. Air temperature averaged both globally and within China is projected to increase continuously throughout the 21st century, while precipitation increases intermittently under each of the three emission scenarios, with some specific temporal and spatial characteristics. The changes in globally-averaged and China-averaged air temperature and precipitation simulated by the BCC_CSM1.0 model are within the range of CMIP3 model results. On average, the changes of precipitation and temperature are more pronounced over China than over the globe, which is also in agreement with the CMIP3 models. The projection capability of the BCC_CSM1.0 model is comparable to that of other climate system models. Furthermore, the results reveal that the climate change response to greenhouse gas emissions is stronger over China than in the global mean, which implies that China may be particularly sensitive to climate change in the 21st century.     

Why ensemble mean projection of south Asian monsoon rainfall by CMIP5 models is not reliable?

Future projections of the Indian summer monsoon rainfall (ISMR) and its large-scale thermodynamic driver are studied by using CMIP5 model outputs. While all models project an increasing precipitation in the future warming scenario, most of them project a weakening large-scale thermodynamic driver arising from a weakening of the upper tropospheric temperature (UTT) gradient over south Asian summer monsoon region. The weakening of the UTT gradient under global warming scenarios is related to the increase in sea surface temperature (SST) over the equatorial Indian Ocean (EIO) leading to a stronger increase of UTT over the EIO region relative to the northern Indian region, a hypothesis supported by a series of Atmospheric General Circulation Model (AGCM) experiments forced by projected SSTs. To diagnose the inconsistency between the model projections of precipitation and the large-scale thermodynamic driver, we have examined the rate of total precipitation explained by convective and stratiform precipitations in observations and in CMIP5 models. It is found that most models produce too much (little) convective (stratiform) precipitation compared to observations. In addition, we also find stronger precipitable water—precipitation relationship in most CMIP5 models as compared to observations. Hence, the atmospheric moisture content produced by the model immediately gets converted to precipitation even though the large-scale thermodynamics in models weaken. Therefore, under global warming scenarios, due to increased temperature and resultant increased atmospheric moisture supply, these models tend to produce unrealistic local convective precipitation often not in tune with other large-scale variables. Our results questions the reliability of the ISMR projections in CMIP5 models and highlight the need to improve the convective parameterization schemes in coupled models for the reliable projections of the ISMR.     

Evaluating historical simulations of CMIP5 GCMs for key climatic variables in Zhejiang Province,China

Assessing the regional impact of climate change on agriculture, hydrology, and forests is vital for sustainable management. Trustworthy projections of climate change are needed to support these assessments. In this paper, 18 global climate models (GCMs) from the fifth phase of the Coupled Model Intercomparison Project (CMIP5) are evaluated for their ability to simulate regional climate change in Zhejiang Province, Southeast China. Simple graphical approaches and three indices are used to evaluate the performance of six key climatic variables during simulations from 1971 to 2000. These variables include maximum and minimum air temperature, precipitation, wind speed, solar radiation, and relative humidity. These variables are of great importance to researchers and decision makers in climate change impact studies and developing adaptation strategies. This study found that most GCMs failed to reproduce the observed spatial patterns, due to insufficient resolution. However, the seasonal variations of the six variables are simulated well by most GCMs. Maximum and minimum air temperatures are simulated well on monthly, seasonal, and yearly scales. Solar radiation is reasonably simulated on monthly, seasonal, and yearly scales. Compared to air temperature and solar radiation, it was found that precipitation, wind speed, and relative humidity can only be simulated well at seasonal and yearly scales. Wind speed was the variable with the poorest simulation results across all GCMs.     

An Overview of BCC Climate System Model Development and Application for Climate Change Studies

This paper reviews recent progress in the development of the Beijing Climate Center Climate System Model(BCC-CSM) and its four component models(atmosphere,land surface,ocean,and sea ice).Two recent versions are described:BCC-CSM1.1 with coarse resolution(approximately 2.8125°×2.8125°) and BCC-CSM1.1(m) with moderate resolution(approximately 1.125°×1.125°).Both versions are fully coupled climate-carbon cycle models that simulate the global terrestrial and oceanic carbon cycles and include dynamic vegetation.Both models well simulate the concentration and temporal evolution of atmospheric CO_2 during the 20th century with anthropogenic CO2 emissions prescribed.Simulations using these two versions of the BCC-CSM model have been contributed to the Coupled Model Intercomparison Project phase five(CMIP5) in support of the Intergovernmental Panel on Climate Change(IPCC) Fifth Assessment Report(AR5).These simulations are available for use by both national and international communities for investigating global climate change and for future climate projections.Simulations of the 20th century climate using BCC-CSMl.l and BCC-CSMl.l(m) are presented and validated,with particular focus on the spatial pattern and seasonal evolution of precipitation and surface air temperature on global and continental scales.Simulations of climate during the last millennium and projections of climate change during the next century are also presented and discussed.Both BCC-CSMl.l and BCC-CSMl.l(m) perform well when compared with other CMIP5 models.Preliminary analyses indicate that the higher resolution in BCC-CSM1.1(m) improves the simulation of mean climate relative to BCC-CSMl.l,particularly on regional scales.     

Present-day and future precipitation in the Baltic Sea region as simulated in a suite of regional climate models

Here we investigate simulated changes in the precipitation climate over the Baltic Sea and surrounding land areas for the period 2071–2100 as compared to 1961–1990. We analyze precipitation in 10 regional climate models taking part in the European PRUDENCE project. Forced by the same global driving climate model, the mean of the regional climate model simulations captures the observed climatological precipitation over the Baltic Sea runoff land area to within 15% in each month, while single regional models have errors up to 25%. In the future climate, the precipitation is projected to increase in the Baltic Sea area, especially during winter. During summer increased precipitation in the north is contrasted with a decrease in the south of this region. Over the Baltic Sea itself the future change in the seasonal cycle of precipitation is markedly different in the regional climate model simulations. We show that the sea surface temperatures have a profound impact on the simulated hydrological cycle over the Baltic Sea. The driving global climate model used in the common experiment projects a very strong regional increase in summertime sea surface temperature, leading to a significant increase in precipitation. In addition to the common experiment some regional models have been forced by either a different set of Baltic Sea surface temperatures, lateral boundary conditions from another global climate model, a different emission scenario, or different initial conditions. We make use of the large number of experiments in the PRUDENCE project, providing an ensemble consisting of more than 25 realizations of climate change, to illustrate sources of uncertainties in climate change projections.     

An analysis of global model projections over Italy, with particular attention to the Italian Greater Alpine Region (GAR)

This study surveys the most recent projections of future climate change provided by 20 Atmospheric-Ocean General Circulation Models (AOGCMs) participating in the Coupled Model Intercomparison Project 3 (CMIP3) with focus on the Italian region and in particular on the Italian Greater Alpine Region (GAR). We analyze historical and future simulations of monthly-mean surface air temperature (T) and total precipitation (P). We first compare simulated T and P from the AOGCMs with observations over Italy for the period 1951–2000, using bias indices as a metric for estimating the performance of each model. Using these bias indices and different ensemble averaging methods, we construct ensemble mean projections of future climate change over these regions under three different IPCC emission scenarios (A2, A1B, and B1). We find that the emissions pathway chosen has a greater impact on future simulated climate than the criteria used to obtain the ensemble means. Across all averaging methods and emission scenarios, the models project annual mean increase in T of 2–4°C over the period 1990–2100, with more pronounced increases in summer and warming of similar magnitude at high and low elevations areas (according to a threshold of 400 m). The models project decreases in annual-mean P over this same time period both over the Italian and GAR regions. This decrease is more pronounced over Italy, since a small increase in precipitation over the GAR is projected in the winter season.