多区域气候模式集合对中国径流深的模拟评估和未来变化预估

对5组区域气候模式集合模拟的中国径流深进行评估,并且预估了温室气体高排放情景RCP8.5下的未来变化。结果表明：多区域气候模式集合结果能够基本模拟出径流深的观测特征,对年径流深的空间分布特征模拟较好,但量值存在一定的系统偏差,特别是黄河中游、海河和松辽河存在明显的正偏差,且对全国9个流域片中东南、西南和西北诸河的年内分配总体模拟效果相对较差。未来到21世纪末,全国平均年径流深在各个时段都以增加为主,增加幅度多在5%以内。未来变化存在明显的空间差异,大致表现为“北增南减”的分布特征,但不会改变中国水资源南多北少的空间格局;其中,黄河、西南和西北诸河流域片呈显著的增加趋势,淮河、长江和东南诸河流域片呈现显著的减少趋势,海河、松辽和珠江流域的变化趋势不显著。21世纪末期各地的变化多在±30%以内,且多模式预估的正负变化一致性较高。到21世纪末期,各流域片平均的径流深季节分配总体特征没有明显变化,径流深的最大月份基本维持不变,分配比例的数值有±2%以内的变化,且各季节的增减变化存在明显流域间差异。     

A comparison of downscaling techniques in the projection of local climate change and wheat yields

This study aims to evaluate the performance of two mainstream downscaling techniques: statistical and dynamical downscaling and to compare the differences in their projection of future climate change and the resultant impact on wheat crop yields for three locations across New South Wales, Australia. Bureau of Meteorology statistically- and CSIRO dynamically-downscaled climate, derived or driven by the CSIRO Mk 3.5 coupled general circulation model, were firstly evaluated against observed climate data for the period 1980–1999. Future climate projections derived from the two downscaling approaches for the period centred on 2055 were then compared. A stochastic weather generator, LARS-WG, was used in this study to derive monthly climate changes and to construct climate change scenarios. The Agricultural Production System sIMulator-Wheat model was then combined with the constructed climate change scenarios to quantify the impact of climate change on wheat grain yield. Statistical results show that (1) in terms of reproducing the past climate, statistical downscaling performed better over dynamical downscaling in most of the cases including climate variables, their mean, variance and distribution, and study locations, (2) there is significant difference between the two downscaling techniques in projected future climate change except the mean value of rainfall across the three locations for most of the months; and (3) there is significant difference in projected wheat grain yields between the two downscaling techniques at two of the three locations.     

Comparison of regional climate model and statistical downscaling simulations of different winter precipitation change scenarios over Romania

Summary Regional climate model and statistical downscaling procedures are used to generate winter precipitation changes over Romania for the period 2071–2100 (compared to 1961–1990), under the IPCC A2 and B2 emission scenarios. For this purpose, the ICTP regional climate model RegCM is nested within the Hadley Centre global atmospheric model HadAM3H. The statistical downscaling method is based on the use of canonical correlation analysis (CCA) to construct climate change scenarios for winter precipitation over Romania from two predictors, sea level pressure and specific humidity (either used individually or together). A technique to select the most skillful model separately for each station is proposed to optimise the statistical downscaling signal. Climate fields from the A2 and B2 scenario simulations with the HadAM3H and RegCM models are used as input to the statistical downscaling model. First, the capability of the climate models to reproduce the observed link between winter precipitation over Romania and atmospheric circulation at the European scale is analysed, showing that the RegCM is more accurate than HadAM3H in the simulation of Romanian precipitation variability and its connection with large-scale circulations. Both models overestimate winter precipitation in the eastern regions of Romania due to an overestimation of the intensity and frequency of cyclonic systems over Europe. Climate changes derived directly from the RegCM and HadAM3H show an increase of precipitation during the 2071–2100 period compared to 1961–1990, especially over northwest and northeast Romania. Similar climate change patterns are obtained through the statistical downscaling method when the technique of optimum model selected separately for each station is used. This adds confidence to the simulated climate change signal over this region. The uncertainty of results is higher for the eastern and southeastern regions of Romania due to the lower HadAM3H and RegCM performance in simulating winter precipitation variability there as well as the reduced skill of the statistical downscaling model.     

Prediction of climate change in Brunei Darussalam using statistical downscaling model

Theoretical and Applied Climatology - Climate is changing and evidence suggests that the impact of climate change would influence our everyday lives, including agriculture, built environment,...     

Empirical-statistical downscaling and error correction of regional climate models and its impact on the climate change signal

Realizing the error characteristics of regional climate models (RCMs) and the consequent limitations in their direct utilization in climate change impact research, this study analyzes a quantile-based empirical-statistical error correction method (quantile mapping, QM) for RCMs in the context of climate change. In particular the success of QM in mitigating systematic RCM errors, its ability to generate “new extremes” (values outside the calibration range), and its impact on the climate change signal (CCS) are investigated. In a cross-validation framework based on a RCM control simulation over Europe, QM reduces the bias of daily mean, minimum, and maximum temperature, precipitation amount, and derived indices of extremes by about one order of magnitude and strongly improves the shapes of the related frequency distributions. In addition, a simple extrapolation of the error correction function enables QM to reproduce “new extremes” without deterioration and mostly with improvement of the original RCM quality. QM only moderately modifies the CCS of the corrected parameters. The changes are related to trends in the scenarios and magnitude-dependent error characteristics. Additionally, QM has a large impact on CCSs of non-linearly derived indices of extremes, such as threshold indices.     

An estimate of future climate change for western France using a statistical downscaling technique

A statistical downscaling procedure based on an analogue technique is used to determine projections for future climate change in western France. Three ocean and atmosphere coupled models are used as the starting point of the regionalization technique. Models' climatology and day to day variability are found to reproduce the broad main characteristics seen in the reanalyses. The response of the coupled models to a similar CO₂ increase scenario exhibit marked differences for mean sea-level pressure; precipitable water and temperature show arguably less spread. Using the reanalysis fields as predictors, the statistical model parameters are set for daily extreme temperatures and rain occurrences for seventeen stations in western France. The technique shows some amount of skill for all three predictands and across all seasons but failed to give reliable estimates of rainfall amounts. The quality of both local observations and large-scale predictors has an impact on the statistical model skill. The technique is partially able to reproduce the observed climatic trends and inter annual variability, showing the sensitivity of the analogue approach to changed climatic conditions albeit an incomplete explained variance by the statistical technique. The model is applied to the coupled model control simulations and the gain compared with direct model grid-average outputs is shown to be substantial at station level. The method is then applied to altered climate conditions; the impact of large-scale model uncertain responses and model sensitivities are quantified using the three coupled models. The warming in the downscaled projections are reduced compared with their global model counterparts.     

中国气候变化预估数据在河北地区的检验分析

应用观测资料对中国地区预估数据集进行检验分析。结果表明:1)7月降水量,区域模式在张家口南部桑洋河盆地和北部坝上地区的模拟值偏大,而在北京东边兴隆高山区和东南沿海地区的模拟值明显偏小。2)7月最高气温,在北京东边兴隆高山区和东部平原的模拟值明显偏高,而在张家口南部桑洋河盆地和太行山东侧的模拟值偏低。3)1月最低气温,在北京东边兴隆高山区、太行山北段高山区和东部平原地区的模拟值偏高,而在张家口南部桑洋河盆地和太行山东侧的模拟值偏低。4)无论是对降水还是气温,由于全球模式的空间分辨率偏低、很难描述河北地区的地形特征,所以模拟结果很差。由于区域气候模式的分辨率提高、对河北地区地形特征的描述有了改进,所以模拟效果有明显改善;但受分辨率所限,它对局部地形如兴隆高山、桑洋河盆地、太行山北部高山等地形特征的描述不是很好,造成模拟结果在这些地区出现系统性偏差,因此应用这些数据时需加以订正。     

Dynamical downscaling of ERA-40 in complex terrain using the WRF regional climate model

Results from a first-time employment of the WRF regional climate model to climatological simulations in Europe are presented. The ERA-40 reanalysis (resolution 1°) has been downscaled to a horizontal resolution of 30 and 10?km for the period of 1961?C1990. This model setup includes the whole North Atlantic in the 30?km domain and spectral nudging is used to keep the large scales consistent with the driving ERA-40 reanalysis. The model results are compared against an extensive observational network of surface variables in complex terrain in Norway. The comparison shows that the WRF model is able to add significant detail to the representation of precipitation and 2-m temperature of the ERA-40 reanalysis. Especially the geographical distribution, wet day frequency and extreme values of precipitation are highly improved due to the better representation of the orography. Refining the resolution from 30 to 10?km further increases the skill of the model, especially in case of precipitation. Our results indicate that the use of 10-km resolution is advantageous for producing regional future climate projections. Use of a large domain and spectral nudging seems to be useful in reproducing the extreme precipitation events due to the better resolved synoptic scale features over the North Atlantic, and also helps to reduce the large regional temperature biases over Norway. This study presents a high-resolution, high-quality climatological data set useful for reference climate impact studies.     

A cautionary note on automated statistical downscaling methods for climate change

The urge for higher resolution climate change scenarios has been widely recognized, particularly for conducting impact assessment studies. Statistical downscaling methods have shown to be very convenient for this task, mainly because of their lower computational requirements in comparison with nested limited-area regional models or very high resolution Atmosphere–ocean General Circulation Models. Nevertheless, although some of the limitations of statistical downscaling methods are widely known and have been discussed in the literature, in this paper it is argued that the current approach for statistical downscaling does not guard against misspecified statistical models and that the occurrence of spurious results is likely if the assumptions of the underlying probabilistic model are not satisfied. In this case, the physics included in climate change scenarios obtained by general circulation models, could be replaced by spatial patterns and magnitudes produced by statistically inadequate models. Illustrative examples are provided for monthly temperature for a region encompassing Mexico and part of the United States. It is found that the assumptions of the probabilistic models do not hold for about 70 % of the gridpoints, parameter instability and temporal dependence being the most common problems. As our examples reveal, automated statistical downscaling “black-box” models are to be considered as highly prone to produce misleading results. It is shown that the Probabilistic Reduction approach can be incorporated as a complete and internally consistent framework for securing the statistical adequacy of the downscaling models and for guiding the respecification process, in a way that prevents the lack of empirical validity that affects current methods.     

一种基于全球动力模式和SMART原理结合的统计降尺度区域季节气候预测方法

针对江苏夏季旱涝和高温热浪等异常气候的预测难题,以江苏夏季站点降水和气温为预测目标,建立了一种基于全球动力模式BCC_CSM1.1（m）和最优可预测气候模态和异常相对倾向（SMART）原理结合的统计降尺度季节气候预测方法。利用历史观测资料和SVD方法提取决定中国夏季降水异常相对倾向的同期热带地区向外长波辐射（Outgoing Longwave Radiation,OLR）和北半球中高纬500 hPa位势高度场异常相对倾向的最优可预测气候模态,并利用逐步回归法构建其与同期江苏站点降水和气温异常相对倾向同期关系的统计降尺度模型;将动力模式对最优可预测气候模态的预测带入统计降尺度模型,实现对区域降水和气温异常相对倾向的预测;最后通过引入观测的近期背景异常实现对降尺度的降水和气温总距平的预测。通过对1991—2019年江苏夏季降水和气温的回报检验表明,本文建立的统计降尺度模型效果较BCC_CSM1.1（m）动力模式的直接预测效果有显著提高,为区域精细化季节气候预测提供了一种有效的手段。     

全球变暖情景下西北太平洋地区台风活动背景场气候变化的预估

为了分析在全球变暖的情景下西北太平洋地区(WNP)可能发生的气候变化,从而为该地区台风活动的未来变化提供必要的背景信息,文中选取了18个参加CMIP3试验的全球海气耦合模式,考查了在SRES A2试验中21世纪末(2080—2099年)相对于20C3M试验中20世纪末(1980—1999年)对台风活动有重要影响的环境场包括海表面温度(SST)、纬向风的垂直切变(MWS)、海平面气压(SLP)、大气顶向外的长波辐射(OLR)和降水在WNP的变化情况。主要分析了多模式集合的结果以及环境场的变化在模式间的一致性。结果表明:到21世纪末,所有模式在西北太平洋区域SST都是一致的升高趋势,增幅在2℃以上,SST的变化是所有考虑的变量中在模式间一致性最好的,绝大部分区域信噪比都在4以上;降水在整个WNP区域也是一致增强的趋势,在赤道低纬度地区增幅较强,其变化在模式间的一致性较好,大部分区域18个模式中超过12个表现出降水增加的变化,在这些区域信噪比大都在0.6以上;SLP的变化特征主要表现为高低压系统的强度均为减弱的趋势,其中低压的减弱在模式间有着更好的一致性,大部分区域的信噪比都大于0.6;MWS和OLR在空间上没有一致的变化趋势,而且在不同的模式间变化的差异较大,大部分区域的信噪比都在0.3左右。但是在与台风活动有显著负相关的关键区内,大部分模式区域平均的MWS和OLR在SRES A2试验中都变小,多模式集合也是减弱的趋势。从SST、降水、关键区的MWS和OLR的分析来看,在全球变暖的情景下,环境场的变化可能是有利于台风活动的。但是目前关于环境场对于台风活动的影响和相互关系还缺乏足够的认识,环境场的变化对于台风活动会有怎样的具体影响还有待进一步深入分析。     

Elevation gradients of European climate change in the regional climate model COSMO-CLM

A transient climate scenario experiment of the regional climate model COSMO-CLM is analyzed to assess the elevation dependency of 21st century European climate change. A focus is put on near-surface conditions. Model evaluation reveals that COSMO-CLM is able to approximately reproduce the observed altitudinal variation of 2 m temperature and precipitation in most regions and most seasons. The analysis of climate change signals suggests that 21st century climate change might considerably depend on elevation. Over most parts of Europe and in most seasons, near-surface warming significantly increases with elevation. This is consistent with the simulated changes of the free-tropospheric air temperature, but can only be fully explained by taking into account regional-scale processes involving the land surface. In winter and spring, the anomalous high-elevation warming is typically connected to a decrease in the number of snow days and the snow-albedo feedback. Further factors are changes in cloud cover and soil moisture and the proximity of low-elevation regions to the sea. The amplified warming at high elevations becomes apparent during the first half of the 21st century and results in a general decrease of near-surface lapse rates. It does not imply an early detection potential of large-scale temperature changes. For precipitation, only few consistent signals arise. In many regions precipitation changes show a pronounced elevation dependency but the details strongly depend on the season and the region under consideration. There is a tendency towards a larger relative decrease of summer precipitation at low elevations, but there are exceptions to this as well.     

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.     

Dynamical downscaling of regional climate over eastern China using RSM with multiple physics scheme ensembles

Theoretical and Applied Climatology - The parameterization of physical processes is one of the critical elements to properly simulate the regional climate over eastern China. It is essential to...     

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

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

Testing the downscaling ability of a one-way nested regional climate model in regions of complex topography

The downscaling ability of a one-way nested regional climate model (RCM) is evaluated over a region subjected to strong surface forcing: the west of North America. The sensitivity of the results to the horizontal resolution jump and updating frequency of the lateral boundary conditions are also evaluated. In order to accomplish this, a perfect-model approach nicknamed the Big-Brother Experiment (BBE) was followed. The experimental protocol consists of first establishing a virtual-reality reference climate over a fairly large area by using the Canadian RCM with grid spacing of 45 km nested within NCEP analyses. The resolution of the simulated climate is then degraded to resemble that of operational general circulation models (GCM) or observation analyses by removing small scales; the filtered fields are then used to drive the same regional model, but over a smaller sub-area. This set-up permits a comparison between two simulations of the same RCM over a common region. The Big-Brother Experiment has been carried out for four winter months over the west coast of North America. The results show that complex topography and coastline have a strong positive impact on the downscaling ability of the one-way nesting technique. These surface forcings, found to be responsible for a large part of small-scale climate features, act primarily locally and yield good climate reproducibility. Precipitation over the Rocky Mountains region is a field in which such effect is found and for which the nesting technique displays significant downscaling ability. The best downscaling ability is obtained when the ratio of spatial resolution between the nested model and the nesting fields is less than 12, and when the update frequency is more than twice a day. Decreasing the spatial resolution jump from a ratio of 12 to six has more benefits on the climate reproducibility than a reduction of spatial resolution jump from two to one. Also, it is found that an update frequency of four times a day leads to a better downscaling than twice a day when a ratio of spatial resolution of one is used. On the other hand, no improvement was found by using high-temporal resolution when the driving fields were degraded in terms of spatial resolution. Figure legends were missing in original article. Climate Dynamics (2005) 23: 473-493. The complete article is given here. DOI: 10.1007/s00382-004-0438-5     

Testing the downscaling ability of a one-way nested regional climate model in regions of complex topography

The downscaling ability of a one-way nested regional climate model (RCM) is evaluated over a region subjected to strong surface forcing: the west of North America. The sensitivity of the results to the horizontal resolution jump and updating frequency of the lateral boundary conditions are also evaluated. In order to accomplish this, a perfect-model approach nicknamed the Big-Brother Experiment (BBE) was followed. The experimental protocol consists of first establishing a virtual-reality reference climate over a fairly large area by using the Canadian RCM with grid spacing of 45 km nested within NCEP analyses. The resolution of the simulated climate is then degraded to resemble that of operational general circulation models (GCM) or observation analyses by removing small scales; the filtered fields are then used to drive the same regional model, but over a smaller sub-area. This set-up permits a comparison between two simulations of the same RCM over a common region. The Big-Brother Experiment has been carried out for four winter months over the west coast of North America. The results show that complex topography and coastline have a strong positive impact on the downscaling ability of the one-way nesting technique. These surface forcings, found to be responsible for a large part of small-scale climate features, act primarily locally and yield good climate reproducibility. Precipitation over the Rocky Mountains region is a field in which such effect is found and for which the nesting technique displays significant downscaling ability. The best downscaling ability is obtained when the ratio of spatial resolution between the nested model and the nesting fields is less than 12, and when the update frequency is more than twice a day. Decreasing the spatial resolution jump from a ratio of 12 to six has more benefits on the climate reproducibility than a reduction of spatial resolution jump from two to one. Also, it is found that an update frequency of four times a day leads to a better downscaling than twice a day when a ratio of spatial resolution of one is used. On the other hand, no improvement was found by using high-temporal resolution when the driving fields were degraded in terms of spatial resolution.     

基于多区域模式集合的中国西部干旱区极端温度未来预估

利用CORDEX-EA计划11个区域模式模拟结果,集合预估了中国西部干旱区16个极端温度指数未来的变化趋势及空间分布。结果表明:1)区域模式基本上能够再现近30 a西部干旱区极端温度的空间分布。2)多模式集合预估的西部干旱区21世纪中期霜冻日数(FD)和冰封日数(ID)呈现显著的下降趋势,而热夜日数(TR)和夏季日数(SU)则呈现明显的上升趋势。3)未来异常暖昼持续指数(WSDI)和生长期(GSL)呈现增加趋势,异常冷昼持续指数(CSDI)和日较差(DTR)则呈现下降趋势。4)未来气候增温导致冷昼日数(TX90p)、暖夜日数(TN90p)增加,而暖昼日数(TX10p)和冷夜日数(TN10p)减少。5)未来月最高温度极大值(TXx)、月最低温度极大值(TNx)、月最高温度极小值(TXn)和月最低温度极小值(TNn)都呈现增加的趋势。因此,西部干旱区未来发生极端低温事件的概率减小,发生极端高温事件的概率则会增大,但不同的极端温度指数变化的空间分布并不均一,存在明显的区域差异。     

IPCC A2情景下中国区域气候变化的数值模拟

在政府间气候变化委员会(IPCC)排放情景特别报告 (SRES)的A2情景下,利用CSIRO Mark3海气耦合模式模拟现代和未来2个10年的模拟结果,驱动MM5区域气候模式进行中国未来区域气候变化的数值模拟试验,研究了IPCC A2情景下未来中国温度、降水和环流等的变化趋势.结果表明,(1)区域气候模式MM5V3能够再现气候平均环流、降水和温度分布的主要特征,具有较好的区域气候变化模拟能力;(2)IPCC A2情景下,未来中国平均地面气温将有明显的升高,特别是中国的东北、西北和西南地区增幅超过了1 ℃.冬季,地面平均气温的增幅由南至北逐渐增加;夏季,在内蒙和中国西南地区有明显的增温.伴随温度的升高,降水也有明显的变化,年平均降水在中国的东北地区、江淮流域及以南大部分地区都有明显的增强,而中国华北部分地区及西南、西北大部分地区降水将呈减少趋势.不同季节不同地区的降水变化也不同,秋季华北、华南和江淮地区降水都增加,而冬季减少.降水的年内变化也有所增强.