Current status of ENSO prediction skill in coupled ocean–atmosphere models

The overall skill of ENSO prediction in retrospective forecasts made with ten different coupled GCMs is investigated. The coupled GCM datasets of the APCC/CliPAS and DEMETER projects are used for four seasons in the common 22 years from 1980 to 2001. As a baseline, a dynamic-statistical SST forecast and persistence are compared. Our study focuses on the tropical Pacific SST, especially by analyzing the NINO34 index. In coupled models, the accuracy of the simulated variability is related to the accuracy of the simulated mean state. Almost all models have problems in simulating the mean and mean annual cycle of SST, in spite of the positive influence of realistic initial conditions. As a result, the simulation of the interannual SST variability is also far from perfect in most coupled models. With increasing lead time, this discrepancy gets worse. As one measure of forecast skill, the tier-1 multi-model ensemble (MME) forecasts of NINO3.4 SST have an anomaly correlation coefficient of 0.86 at the month 6. This is higher than that of any individual model as well as both forecasts based on persistence and those made with the dynamic-statistical model. The forecast skill of individual models and the MME depends strongly on season, ENSO phase, and ENSO intensity. A stronger El Niño is better predicted. The growth phases of both the warm and cold events are better predicted than the corresponding decaying phases. ENSO-neutral periods are far worse predicted than warm or cold events. The skill of forecasts that start in February or May drops faster than that of forecasts that start in August or November. This behavior, often termed the spring predictability barrier, is in part because predictions starting from February or May contain more events in the decaying phase of ENSO.     

自然湿地甲烷排放模拟研究——模型的灵敏度分析与应用

对甲烷湿地排放模型CH4MODwetland进行灵敏度分析表明,环境驱动因素——温度和地表水深是影响甲烷排放通量的主要因素,地表水深对季节性积水沼泽甲烷排放通量的灵敏度大于常年积水沼泽。模型对植物和土壤输入参数的灵敏度响应依次为Wmax(地上生物量最大值)FR(地下净初级生产力占植物总净初级生产力的比例)fV(植被类型系数)TAmax(植物从生长初期到最大地上生物量所需有效积温)ρ(容量),OM(土壤有积质含量)SAND(土壤砂粒含量)。以经验水位模型驱动CH4MODwetland,模拟三江平原毛果苔草和小叶章沼泽(1950年代～2000年代),以及若尔盖高原木里苔草和乌拉苔草沼泽(1960年代～2000年代)多年甲烷排放通量的变化。结果表明,年代际甲烷排放通量的变化主要受降水量的影响,但气候变暖使得降水量基本相同的年代甲烷排放通量增加:三江平原毛果苔草沼泽和小叶章沼泽(1980年代～2000年代比1950年代～1970年代模拟的甲烷排放通量分别增加了9.5%和8.3%;若尔盖高原乌拉苔草沼泽和木里苔草沼泽(1990年代～2000年代比1960年代～1970年代)分别增加了6.0%和5.5%。该结论能够为评估未来气候变化对中国湿地甲烷排放的影响提供依据。     

风暴尺度天气下利用集合卡尔曼滤波模拟多普勒雷达资料同化试验I.不考虑模式误差的情形

本文在假定模式无偏差的情况下, 利用一次风暴过程的模拟多普勒雷达资料进行一系列风暴天气尺度的集合卡尔曼滤波资料同化试验, 检验集合卡尔曼滤波在风暴天气尺度资料同化方面的效果, 并验证各集合卡尔曼滤波参数对同化效果的影响。试验结果表明, 集合卡尔曼滤波能有效地应用于风暴尺度的资料同化; 40个集合成员以及6 km的局地化尺度能较好地滤除采样误差造成的虚假相关, 同时可以将观测信息传递到无观测的模式格点; 利用背景场加上空间平滑的高斯型随机扰动生成初始成员的方式较未经过平滑的方式有更好的分析效果; 背景场扰动方法能够提高样本的离散度; 只同化反射率的同化试验表明, 反射率的同化效果较明显, 也证明了集合卡尔曼滤波在非常规资料同化中的作用; 增加径向风资料同化的效果优于只进行反射率同化的结果。     

青藏高原积雪日数的气温敏感度分析

根据青藏高原气象台站观测积雪日数和均一化气温数据,对高原1951—2004年积雪日数对气温的敏感度进行了量化分析。研究表明,无论是极值敏感度还是当前气候下的敏感度,空间上都呈现出高原四周积雪较中部对气温的敏感程度高的情况。各台站积雪日数对气温最敏感时的临界气温与海拔有着极好的反相关关系,而极值敏感度与海拔虽然也有一定的反相关,但相关程度远不如前者高。在当前气候状态下,有相当一部分台站的平均气温还未达到临界值,这些台站在秋、冬、春、夏季分别占总台站数的36%、39%、47%和11%。未来气候继续变暖背景下,这部分台站积雪日数对气温的敏感度会进一步加大,即积雪对气温的升高会更加敏感。     

台风麦莎渤海转向的可预报性研究

利用MM5模式研究了2005年第9号台风"麦莎"在渤海向东北转向路径的可预报性.试验用不同的积云参数化方案、不同的预报时效分别从确定性和集合预报角度对"麦莎"在渤海向东北方向的转向过程做了模拟.结果表明,"麦莎"在渤海的转向可预报时效为48小时左右.不同的积云参数化方案对台风麦莎路径的48小时预报结果显示台风均转向东北,预示"麦莎 "基本不会直接影响北京.60和72小时的预报结果显示,Kuo和Betts-Miller积云参数化方案的台风模拟路径与实况比较接近,而Grell和KeinFritch积云参数化方案的台风模拟路径却偏向了实际台风位置的西北,台风有可能直接影响北京.研究表明,对于台风麦莎而言,时效超过两天的转向预报可信度较低,Kuo和Betts-Miller积云参数化方案的预报准确性较高.     

RIEMS2.0不同模拟区域模拟中国北方多年气候平均态比较

Regional Integrated Environment Modeling System Version 2.0 （RIEMS2.0） is now being developed by the Key Laboratory of Regional Climate-Environment for Temperate East Asia, Chinese Academy of Sciences. In order to test the sensitivity of the RIEMS2.0 model domain to simulate long-term climate and its change, and provide a basis for the further development and application of the model, the authors compared results between simulated and observed precipitation and surface-airtemperature using two model domains under different cumulus parameterization schemes. The model was driven by NCEP/NCAR re-analysis data with a simulation duration ranging from 1 January 1979 to 31 December 2007. There were no significant differences found in the spatial distributions of the simulated precipitation and surface-air-temperature, or interannual variations between the two model domains. There were, however, differences observed between the two model domain simulations of local sub-regions. The smaller model domain more accurately simulated precipitation, especially in summer （June, July, and August）, and decreased the bias of surface-airtemperature, especially in winter （December, January, and February）. The weak summer and winter monsoons simulated by the smaller model domain was a result of boundary forcings and may partially account for the improvements of this model.     

Sensitivity of Nonlinearity on the ENSO Cycle in a Simple Air-sea Coupled Model

In this paper,the influence of the El NioSouthern Oscillation (ENSO) cycle on the sensitivity of nonlinear factors in the numerical simulation is investigated by conducting numerical experiments in a simple air-sea coupled model for ENSO prediction.Two sets of experiments are conducted in which zonal nonlinear factors,meridional nonlinear factors,or both are incorporated into the governing equations for the atmosphere or ocean.The results suggest that the ENSO cycle is very sensitive to the nonlinear factor of the governing equation for the atmosphere or ocean.Thus,incorporating nonlinearity into air-sea coupled models is of exclusive importance for improving ENSO simulation.     

长江流域夏季降水的贝叶斯多模式集合概率预测

A Bayesian probabilistic prediction scheme of the Yangtze River Valley (YRV) summer rainfall is proposed to combine forecast information from multi-model ensemble dataset provided by ENSEMBLES project.Due to the low forecast skill of rainfall in dynamic models,the time series of regressed YRV summer rainfall are selected as ensemble members in the new scheme,instead of commonly-used YRV summer rainfall simulated by models.Each time series of regressed YRV summer rainfall is derived from a simple linear regression.The predictor in each simple linear regression is the skillfully simulated circulation or surface temperature factor which is highly linear with the observed YRV summer rainfall in the training set.The high correlation between the ensemble mean of these regressed YRV summer rainfall and observation benefit extracting more sample information from the ensemble system.The results show that the cross-validated skill of the new scheme over the period of 1960 to 2002 is much higher than equally-weighted ensemble,multiple linear regression,and Bayesian ensemble with simulated YRV summer rainfall as ensemble members.In addition,the new scheme is also more skillful than reference forecasts (random forecast at a 0.01 significance level for ensemble mean and climatology forecast for probability density function).     

Forecasting the western Pacific subtropical high index during typhoon activity using a hybrid deep learning model

Seasonal location and intensity changes in the western Pacific subtropical high(WPSH) are important factors dominating the synoptic weather and the distribution and magnitude of precipitation in the rain belt over East Asia. Therefore, this article delves into the forecast of the western Pacific subtropical high index during typhoon activity by adopting a hybrid deep learning model. Firstly, the predictors, which are the inputs of the model, are analysed based on three characteristics: the first...     

The Late Miocene climate response to a modern Sahara desert

The climate cooling and vegetation changes in the Miocene/Pliocene are generally well documented by various proxy data. Some important ecosystem changes occurred at that time. Palaeobotanical evidence suggests that the Sahara desert first appeared in the Pliocene, whereas in the Miocene North Africa was green. In the present study, we investigate the Late Miocene climate response to the appearance of the Sahara desert from a climate modelling sensitivity experiment. We compare a model experiment, which includes a full set of Late Miocene boundary conditions, with another one using the same boundary conditions except that the North African vegetation refers to the present-day situation. Our sensitivity study demonstrates that the introduction of the Sahara desert leads to a cooling and an aridification in Africa. In addition, we observe teleconnection patterns related to the North African desertification at around the Miocene/Pliocene boundary. From our sensitivity experiment, we observe that the Sahara contributes to a cooling in Central Asia and in North America. As compared to hypsodonty data for Central Asia, an increased aridity is underestimated in the Sahara experiment. Finally, we observe that the introduction of the Sahara leads to a cooling in the northern high latitudes. Hence, our sensitivity experiment indicates that the appearance of the Sahara desert is one piece to better understand Late Cenozoic climate cooling being most pronounced in the high latitudes.