大样本初始化十年际预测试验（CESM-DPLE）对东亚夏季气候预测的评估

基于气温和降水观测资料以及美国国家环境预报中心/国家大气研究中心（NCEP/NCAR）大气再分析资料,系统评估了大样本初始化十年际预测试验（CESM-DPLE）对1959—2016年东亚夏季气候预测的能力。结果表明,CESM-DPLE能较好地模拟东亚夏季气候以及相关主要大气环流系统的基本态特征,在年际尺度上对东亚气温有很高的预测技巧但对降水几乎没有预测能力。CESM-DPLE再现了北大西洋多年代际振荡（AMO）经由激发遥相关波列所引起的中高纬度大气环流、东亚夏季风和气候的异常。20世纪90年代末之后,北大西洋多年代际振荡由冷位相转为暖位相,遥相关波列位相调整,东亚受异常低压控制,东亚夏季风偏强,夏季气温偏高、降水偏多。总体上,尽管还存在着不足,但CESM-DPLE对东亚夏季温度年际变化以及与20世纪90年代末北大西洋多年代际振荡位相转变相联的东亚夏季气候年代际变化具备一定的预测能力,是目前研究和预测东亚气候变化的一套较好试验数据。      

Bayesian multi-model projection of climate: bias assumptions and interannual variability

Current climate change projections are based on comprehensive multi-model ensembles of global and regional climate simulations. Application of this information to impact studies requires a combined probabilistic estimate taking into account the different models and their performance under current climatic conditions. Here we present a Bayesian statistical model for the distribution of seasonal mean surface temperatures for control and scenario periods. The model combines observational data for the control period with the output of regional climate models (RCMs) driven by different global climate models (GCMs). The proposed Bayesian methodology addresses seasonal mean temperatures and considers both changes in mean temperature and interannual variability. In addition, unlike previous studies, our methodology explicitly considers model biases that are allowed to be time-dependent (i.e. change between control and scenario period). More specifically, the model considers additive and multiplicative model biases for each RCM and introduces two plausible assumptions (“constant bias” and “constant relationship”) about extrapolating the biases from the control to the scenario period. The resulting identifiability problem is resolved by using informative priors for the bias changes. A sensitivity analysis illustrates the role of the informative prior. As an example, we present results for Alpine winter and summer temperatures for control (1961–1990) and scenario periods (2071–2100) under the SRES A2 greenhouse gas scenario. For winter, both bias assumptions yield a comparable mean warming of 3.5–3.6°C. For summer, the two different assumptions have a strong influence on the probabilistic prediction of mean warming, which amounts to 5.4°C and 3.4°C for the “constant bias” and “constant relation” assumptions, respectively. Analysis shows that the underlying reason for this large uncertainty is due to the overestimation of summer interannual variability in all models considered. Our results show the necessity to consider potential bias changes when projecting climate under an emission scenario. Further work is needed to determine how bias information can be exploited for this task.     

东亚夏季风强度的多尺度统计预测模型

东亚夏季风强度的变化与中国雨带和旱涝分布密切相关。为了做好东亚夏季风强度的短期气候预测,采用小波分析、Lanczos滤波器、交叉检验等方法,研究了东亚夏季风强度的多尺度变化特征,在年际与年代际尺度上分别寻找了它在前冬海温场、200 hPa纬向风场上的前兆信号,并利用最优子集回归建立了东亚夏季风强度的多尺度统计物理预测模型。结果表明:东亚夏季风强度存在准4年、准13年和准43年的周期振荡。年际尺度上,前冬赤道东太平洋(10°N~10°S,160°W~80°W)海温与东亚夏季风强度有最强的显著负相关,且它与东亚夏季风强度在200 hPa纬向风场上的前兆信号有较强的负相关;年代际尺度上,南半球60°S与35°S附近200 hPa纬向风之差与东亚夏季风强度有最强的显著正相关,且它与东亚夏季风强度在热带印度洋、低纬度东南太平洋、低纬度南大西洋的海温及亚洲副热带200 hPa纬向风等前兆信号有强的正相关。通过探讨这两个前兆因子对东亚夏季风强度的预测意义,揭示了他们影响东亚夏季风强度年际和年代际变化的可能物理过程。所建立的东亚夏季风强度多尺度最优子集回归预测模型,不仅对东亚夏季风强度的年际变化具有较好的预测能力,而且对异常极值年份也具有一定的预测能力。     

Climate Variability-Observations, Reconstructions, and Model Simulations for the Atlantic-European and Alpine Region from 1500-2100 AD

A detailed analysis is undertaken of the Atlantic-European climate using data from 500-year-long proxy-based climate reconstructions, a long climate simulation with perpetual 1990 forcing, as well as two global and one regional climate change scenarios. The observed and simulated interannual variability and teleconnectivity are compared and interpreted in order to improve the understanding of natural climate variability on interannual to decadal time scales for the late Holocene. The focus is set on the Atlantic-European and Alpine regions during the winter and summer seasons, using temperature, precipitation, and 500 hPa geopotential height fields. The climate reconstruction shows pronounced interdecadal variations that appear to “lock” the atmospheric circulation in quasi-steady long-term patterns over multi-decadal periods controlling at least part of the temperature and precipitation variability. Different circulation patterns are persistent over several decades for the period 1500 to 1900. The 500-year-long simulation with perpetual 1990 forcing shows some substantial differences, with a more unsteady teleconnectivity behaviour. Two global scenario simulations indicate a transition towards more stable teleconnectivity for the next 100 years. Time series of reconstructed and simulated temperature and precipitation over the Alpine region show comparatively small changes in interannual variability within the time frame considered, with the exception of the summer season, where a substantial increase in interannual variability is simulated by regional climate models.     

夏季亚洲对流层中上层温度年际变率的预测水平评估及其在我国东部降水预测中的应用

夏季亚洲对流层温度异常与中国东部夏季降水紧密相关并可能作为降水的有效预报因子。基于欧盟ENSEMBLES计划的季节预测试验耦合模式每年5月1日开始的回报试验，分析了其对1960～2005年夏季亚洲对流层中上层温度（以200～500 hPa厚度替代，简称对流层温度）年际变率的预测结果，发现模式集合平均对夏季亚洲对流层温度年际变率具有较高的预报技巧，可以合理回报其前两个EOF（Empirical Orthogonal Function）主导模态（EOF1、EOF2），只是未能回报出EOF2高纬度的温度异常，模式集合平均预测的第一模态主成分（PC1）和第二模态主成分（PC2）与再分析资料的时间相关系数分别达到0.63和0.77。再分析资料中前两个EOF模态分别由ENSO（El Ni?o–Southern Oscillation）发展年印度夏季降水异常所激发的丝绸之路遥相关波列和ENSO衰减年西北太平洋夏季降水异常对应的太平洋—日本遥相关波列导致。ENSEMBLES计划可以合理预测出相应的海温异常及遥相关波列，进而合理预测出前两个EOF模态。对流层温度PC1和PC2分别表征了欧亚大陆与周围海洋之间的纬向和经向热力对比异常，模式对由PC1的预报技巧远高于前人定义的纬向热力对比的东亚夏季风指数，对前人定义的经向热力对比指数的预测技巧与PC2相当。将PC1和前人定义的经向热力对比指数作为预报因子，建立了中国夏季降水的动力—统计降尺度预测模型，交叉检验的结果表明该预报模型显著提高了东北和长江流域上游夏季降水的预报技巧。本文提出的亚洲对流层温度年际变率的EOF1及PC1，既能较好表征纬向热力对比与中国东部夏季降水显著相关，又能被模式合理预测，可以作为我国中高纬度地区，特别是东北地区降水的重要预测因子之一。     

西北偏湿背景下青海省夏季降水的统计降尺度方法研究

本文研制建立了一个预测青海省夏季降水的动力—统计相结合的组合降尺度预测方法(Hybrid Statistical Downscaling Prediction,HSDP),该方法综合利用了 气候模式 Climate Forecast System 2.0版本(CFSv2)实时预测的高可预报性环流信息及前期观测的与青海夏...     

用 IAP／LASG GOALS模式模拟CO2增加引起的东亚地区气候变化

Two simulations, one for the control run and another for the perturbation run, with a global coupled ocean-atmosphere-land system model (IAP / LASG GOALS version 4) have been carried out to study the global warming, with much detailed emphasis on East Asia. Results indicate that there is no climate drift in the control run and at the time of CO2 doubling the global temperature increases about 1.65℃. The GOALS model is able to simulate the observed spatial distribution and annual cycles of temperature and precipitation for East Asia quite well. But, in general, the model underestimates temperature and overestimates rainfall amount for regional annual average. For the climate change in East Asia, the temperature and precipitation in East Asia increase 2. l℃ and 5% respectively, and the maximum warming occurs at middle-latitude continent and the maximum precipitation increase occurs around 25°N with reduced precipitation in the tropical western Pacific.     

1961—2010年东北地区夏季降水时空变化的研究

东北地区地处中高纬欧亚大陆东岸,处在东亚季风区的边缘,其降水主要集中在夏季,并且降水量具有显著的地域差异和年际变化。研究东北夏季降水特点,可为降水预报、气候预测和农业生产提供可靠依据。本文利用东北地区27个测站1961—2010年夏季逐月降水资料,通过E-OF和小波分析的方法研究东北地区夏季降水的时空变化规律,结果表明：东北地区夏季降水量主要表现为三种空间分布型：总体一致型、南-北反位相型以及东南-西北反位相型;东北地区夏季降水量存在明显的年际变化,降水变化周期为8—11a;东北地区夏季各月降水量的差异,主要是由地形原因和纬线跨度差异造成的。