澳大利亚墨累-达令河流域水文气候变化的影响与适应对策

应澳大利亚联邦科学与工业研究组织(Commonwealth scientific and Industrial Research Organisation, CSIRO)邀请,2018年4月清华大学组织科技考察团赴澳大利亚墨累-达令河流域开展为期一周的科学考察。考察团从墨累河出海口逆流而上,通过学习交流、现场考察和访问农场等方式,与澳大利亚同行们进行了深入交流,对澳大利亚墨累-达令河流域的气候变化与水的影响与适应对策研究,尤其是陆面水文-气候、极端水文研究、水资源管理体系的最新动态,及其气候变化应对与减缓、极端水文事件风险管理等进一步了解。此次考察对认识多时空尺度的气候-陆面-水文相互作用机理及其对自然强迫和人类活动(含人为强迫和下垫面人类活动)的响应机制,揭示全球气候系统能量-水循环动态演变规律和极端水文事件变化成因,构建全球增暖背景下应对极端水文事件的风险管理体系,提出中国适应性对策具有重要的借鉴意义。

西北人影作业天气背景条件初步分析

利用高分辨率的气象台站观测资料、再分析资料、卫星遥感资料等,结合高分辨率区域气候模式模拟,对西北地区以及祁连山、天山、六盘山和三江源等试验区的降水场、气流场、水汽场等时空特征开展了研究。结果表明:精细化的降水资料能够刻画出西北试验区复杂地形条件下的降水场时空特征,研发的小时降水融合算法对西北地区高频降水分析是有效的;涝年,由于高原抽吸作用,试验区受低层形成的辐合气流所控制,容易形成降水;旱年,高原地表不存在大范围的辐合区,试验区被干冷的偏北风所控制,不利于成云降水。利用模式资料,分析了西北地区及四个试验区水汽场月、季、年气候特征,分析了水汽来源、大气可降水量、水汽收支情况以及年际变化情况。对MODIS和FY卫星反演的水汽产品进行了校准,并对西北地区水汽含量的气候特征进行了分析。

Performance of the Seasonal Forecasting of the Asian Summer Monsoon by BCC_CSM1.1(m)

This paper provides a comprehensive assessment of Asian summer monsoon prediction skill as a function of lead time and its relationship to sea surface temperature prediction using the seasonal hindcasts of the Beijing Climate Center Climate System Model, BCC_CSM1. l（m）. For the South and Southeast Asian summer monsoon, reasonable skill is found in the model＇s forecasting of certain aspects of monsoon climatology and spatiotemporal variability. Nevertheless, deficiencies such as significant forecast errors over the tropical western North Pacific and the eastern equatorial Indian Ocean are also found. In particular, overestimation of the connections of some dynamical monsoon indices with large-scale circulation and precipitation patterns exists in most ensemble mean forecasts, even for short lead-time forecasts. Variations of SST, measured by the first mode over the tropical Pacific and Indian oceans, as well as the spatiotemporal features over the Nifio3.4 region, are overall well predicted. However, this does not necessarily translate into successful forecasts of the Asian summer monsoon by the model. Diagnostics of the relationships between monsoon and SST show that difficulties in predicting the South Asian monsoon can be mainly attributed to the limited regional response of monsoon in observations but the extensive and exaggerated response in predictions due partially to the application of ensemble average forecasting methods. In contrast, in spite of a similar deficiency, the Southeast Asian monsoon can still be forecasted reasonably, probably because of its closer relationship with large-scale circulation patterns and E1 Nifio-Southern Oscillation.

Relationships between Interannual and Intraseasonal Variations of the Asian–Western Pacific Summer Monsoon Hindcasted by BCC CSM 1.1(m)

Using hindcasts of the Beijing Climate Center Climate System Model, the relationships between interannual variability （IAV） and intraseasonal variability （ISV） of the Asian-western Pacific summer monsoon are diagnosed. Predictions show reasonable skill with respect to some basic characteristics of the ISV and IAV of the western North Pacific summer monsoon （WNPSM） and the Indian summer monsoon （ISM）. However, the links between the seasonally averaged ISV （SAISV） and seasonal mean of ISM are overestimated by the model. This deficiency may be partially attributable to the overestimated frequency of long breaks and underestimated frequency of long active spells of ISV in normal ISM years, although the model is capable of capturing the impact of ISV on the seasonal mean by its shift in the probability of phases. Furthermore, the interannual relationships of seasonal mean, SAISV, and seasonally averaged long-wave variability （SALWV; i.e., the part with periods longer than the intraseasonal scale） of the WNPSM and ISM with SST and low-level circulation are examined. The observed seasonal mean, SAISV, and SALWV show similar correlation patterns with SST and atmospheric circulation, but with different details. However, the model presents these correlation distributions with unrealistically small differences among different scales, and it somewhat overestimates the teleconnection between monsoon and tropical central-eastern Pacific SST for the ISM, but underestimates it for the WNPSM, the latter of which is partially related to the too-rapid decrease in the impact of E1 Nifio-Southern Oscillation with forecast time in the model.

Climate effects of the GlobeLand30 land cover dataset on the Beijing Climate Center climate model simulations

Land cover is one of the most basic input elements of land surface and climate models. Currently, the direct and indirect effects of land cover data on climate and climate change are receiving increasing attentions. In this study, a high resolution(30 m) global land cover dataset(Globe Land30) produced by Chinese scientists was, for the first time, used in the Beijing Climate Center Climate System Model(BCC_CSM) to assess the influences of land cover dataset on land surface and climate simulations. A two-step strategy was designed to use the Globe Land30 data in the model. First, the Globe Land30 data were merged with other satellite remote sensing and climate datasets to regenerate plant functional type(PFT) data fitted for the BCC_CSM. Second, the up-scaling based on an area-weighted approach was used to aggregate the fine-resolution Globe Land30 land cover type and area percentage with the coarser model grid resolutions globally. The Globe Land30-based and the BCC_CSM-based land cover data had generally consistent spatial distribution features, but there were some differences between them. The simulation results of the different land cover type dataset change experiments showed that effects of the new PFT data were larger than those of the new glaciers and water bodies(lakes and wetlands). The maximum value was attained when dataset of all land cover types were changed. The positive bias of precipitation in the mid-high latitude of the northern hemisphere and the negative bias in the Amazon, as well as the negative bias of air temperature in part of the southern hemisphere, were reduced when the Globe Land30-based data were used in the BCC_CSM atmosphere model. The results suggest that the Globe Land30 data are suitable for use in the BCC_CSM component models and can improve the performance of the land and atmosphere simulations.

Implications of diurnal variations in land surface temperature to data assimilation using MODIS LST data

Based on the Beijing Climate Center’s land surface model BCC_AVIM(Beijing Climate Center Atmosphere-Vegetation Interaction Model),the ensemble Kalman filter(EnKF)algorithm has been used to perform an assimilation experiment on the Moderate Resolution Imaging Spectroradiometer(MODIS)land surface temperature(LST)product to study the influence of satellite LST data frequencies on surface temperature data assimilations.The assimilation results have been independently tested and evaluated by Global Land Data Assimilation System(GLDAS)LST products.The results show that the assimilation scheme can effectively reduce the BCC_AVIM model simulation bias and the assimilation results reflect more reasonable spatial and temporal distributions.Diurnal variation information in the observation data has a significant effect on the assimilation results.Assimilating LST data that contain diurnal variation information can further improve the accuracy of the assimilation analysis.Overall,when assimilation is performed using observation data at 6-hour intervals,a relatively good assimilation result can be obtained,indicated by smaller bias(<2.2K)and root-mean-square-error(RMSE)(<3.7K)and correlation coefficients larger than 0.60.Conversely,the assimilation using 24-hour data generally showed larger bias(>2.2K)and RMSE(>4K).Further analysis showed that the sensitivity of assimilation effect to diurnal variations in LST varies with time and space.The assimilation using observations with a time interval of 3 hours has the smallest bias in Oceania and Africa(both<1K);the use of 24-hour interval observation data for assimilation produces the smallest bias(<2.2K)in March,April and July.