Satellite remotely sensed (RS) soil moisture (SM) is commonly assimilated to leverage land surface modeling. However, the improvement in deep soil simulation remains challenging as limited by the relative shallow penetration depth (< 5 cm). With advancements in satellite RS technologies like P-band sensors, retrieving deeper SM has become increasingly feasible. Here, we demonstrate the potentially added value of enriched deep soil moisture information in land surface data assimilation (DA) by using ground-based SM observations from a dense monitoring network in the central Tibetan Plateau. Specifically, a cost function-based multi-layer SM DA framework is developed to optimize soil texture profiles and organic matter content. A series of DA experiments were conducted to explore the optimal assimilation and optimization depths. The results suggest that assimilation of top 20 cm SM is adequate to reasonably optimize key soil parameters for all soil layers, and thereby improves SM estimates to the depth of 40 cm. This improvement can further propagate into soil temperature profiles and surface flux (e.g., evapotranspiration) estimates. Besides, the performance of deep SM DA is found insensitive to assimilation frequency varying up to 5 days, highlighting the promise and feasibility of regional land DA with 0-20 cm SM products, which are readily accessible from future satellite missions.

Current snow depth datasets demonstrate large discrepancies in the spatial pattern in Eurasia, and the lagging updates of datasets do not meet the operational requirements of the meteorological service department. This study developed a dynamic retrieval method for daily snow depth over Eurasia based on cross-sensor calibrated microwave brightness temperatures to enhance retrieval accuracy and meet the requirements of operational work. These brightness temperatures were detected by microwave radiometer imager carried on the FengYun 3 (FY-3) satellite and the special sensor microwave imager/sounder carried on the USA Defense Meteorological Satellite Program series satellites, which use the fewest sensors to provide the longest data and consequently introduce minimal errors during inter-sensor calibration. Firstly, inter-sensor calibration was conducted amongst brightness temperatures collected by the three sensors. A spatiotemporal dynamic relationship between snow depth and microwave brightness temperature gradient was then established, overcoming the large uncertainties induced by varying snow characteristics. This relationship can be utilised in FY-3 satellite data for operational service to obtain real-time snow depth. The generated daily snow depth dataset from 1988 to 2021 presents similar spatial patterns of snow depth to those observed in situ. Against in situ snow depth, the overall bias and root mean square error are −2.04 and 6.49 cm, respectively, facilitating considerable improvements in accuracy compared with the Advanced Microwave Scanning Radiometer 2 snow depth product, which adopts the static algorithm. Further analysis shows an overall decreasing trend from 1988 to 2021 for annual and monthly mean snow depths, demonstrating a noticeable reduction since around 2000. The reduction in monthly mean snow depth started earlier in shallow snow months than in deep snow months.

Based on the Beijing Climate Center's land surface model BCC_AVIM2.0, an ensemble Kalman filter (EnKF) algorithm is developed to assimilate the land surface temperature (LST) product of the first satellite of Fengyun-4 series meteorological satellites of China to study the influence of LST data with different time frequencies on the surface temperature data assimilations. The MODIS daytime and nighttime LST products derived from Terra and Aqua satellites are used as independent validation data to test the assimilation results. The results show that diurnal variation information in the FY-4A LST data has significant effect on the assimilation results. When the time frequencies of the assimilated FY-4A LST data are sufficient, the assimilation scheme can effectively reduce the errors and the assimilation results reflect more reasonable spatial and temporal distributions. The assimilation experiments with a 3 h time frequency show less bias as well as RMSEs and higher temporal correlations than that of the model simulations at both daytime and nighttime periods. As the temporal frequency of assimilated LST observations decreases, the assimilation effects gradually deteriorate. When diurnal variation information is not considered at all in the assimilation, the assimilation with 24 h time frequency showed the largest errors and smallest time correlations in all experiments. The results demonstrate the potential of assimilating high-frequency FY-4A LST data to improve the performance of the BCC_AVIM2.0 land surface model. Furthermore, this study indicates that the diurnal variation information is a necessary factor needed to be considered when assimilating the FY-4A LST.

The Beijing Climate Center of the China Meteorological Administration (BCC/CMA) has developed a gauge-satellite-model merged gridded daily precipitation dataset with complete global coverage, called BCC Merged Estimation of Precipitation (BMEP). Using the unified rain gauge dataset from the CPC (CPC-U) as the independent benchmark, BMEP and the four most widely used global daily precipitation products, including the Global Precipitation Climatology Project one-degree daily (GPCP-1DD), the NCEP Climate Forecast System Reanalysis (CFSR), the Interim ECMWF Re-analysis (ERA-interim), and the 55 year Japanese Reanalysis Project (JRA-55), are evaluated over the global land area from January 2003 to December 2016. The results show that all gridded datasets capture the overall spatiotemporal variation of global daily precipitation. All gridded datasets can basically capture the overall spatiotemporal variation of global daily precipitation. However, CFSR data tend to overestimate precipitation intensity and exhibit a spurious positive trend after 2010, attributed to the transition from CFSR to NCEP’s Climate Forecast System Version 2 (CFSv2). On the other hand, JRA-55 and ERA-interim data demonstrate higher skill in characterizing spatial and temporal variations, bias, correlation, and RMSE. GPCP-1DD data perform well in terms of bias but show limitations in detecting the interannual variability and RMSE of daily precipitation. Among these evaluated products, BMEP data exhibit the best agreement with CPC-U data in terms of the spatiotemporal variation, pattern, magnitude of variability, and occurrence of rainfall events across different thresholds. These findings indicate that BMEP gridded precipitation data effectively capture the actual characteristics of daily precipitation over global land areas.

To determine whether the capability of the CMIP6 version of Beijing Climate Center (BCC) models (BCC-ESM1 and BCC-CSM2-MR) in simulating China summer surface air temperature (SAT) has improved, we presented a multidimensional evaluation of the summer SAT in China including the trends, modes, and influencing factors. Critical comparisons are also made with the results of CMIP5 (BCC-CSM1.1 and BCC-CSM1.1m). In general, the CMIP6, especially BCC-CSM2-MR, has smaller deviations in the trends, the means, the mutations, the maximum centers, the variances, and the spatial patterns of the dominant modes from observatio relative to those of CMIP5. However, the BCC CMIP6 models still underestimate the SAT variation in the Qinghai‒Tibetan Plateau and the northeastern regions of China, and the performance is unsatisfactory with respect to the physical drivers of the dominant modes. Importantly, all the BCC models can capture the spatio-temporal characteristics of the first mode well and can, in general, characterize the spatial pattern of the second mode, but none of the models perform well in the principal component of the second mode (PC2) due to the low performance with respect to the interannual variation of PC2. Furthermore, the factors influencing the leading two modes are evaluated. The two CMIP6 can simulate better the Northern Hemisphere subtropical high northern boundary affecting the first mode. Another factor, the Asia polar vortex area, can only be simulated better by two low-resolution models (BCC-CSM1.1 and BCC-ESM1). For the second mode, all four models simulate the influence of Asian zonal circulation well, but poorly simulate that of the southern Indian Ocean dipole due to a large deviation in the Indian Ocean surface temperature.

A warm Arctic‐cold continent (WACC) pattern has been one of the main characteristics of the Northern Hemisphere (NH) climate variability in winter during the last two decades. However, the factors contributing this pattern remain unclear. We compared the two leading modes of surface temperature in the NH winter on an interdecadal timescale and explored the role of high‐latitude concurrent blockings (HCBs) and the Arctic oscillation (AO) in these two modes. Results show that the first mode resembling the WACC pattern is more related to enhanced HCBs over the Ural Mountains and the North Pacific than the AO. The HCBs induce a warmer Arctic by simultaneously transporting large amounts of moisture and heat. The HCBs excite a tropospheric anticyclonic anomaly near the Arctic Circle, which, in turn, triggers anomalies in the propagation of planetary waves and the meridional circulation, with a subsequent redistribution of momentum and heat. This is accompanied by a weak polar night jet and a poleward shift in the subtropical westerly jet, resulting in mild cold mid‐latitude continents. The second mode represents the AO pattern, which has a different mechanism from WACC. The high‐pressure anomaly over the North Pacific has played an increasingly important part in the WACC mode in recent decades and the WACC pattern with HCBs is more easily reproduced in simulations with human activity. HCBs therefore require further consideration under the current and future conditions of global warming.

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MODlS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.

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.

The extreme monthly mean air and land surface temperatures simulated by the Beijing Climate Center (BCC) climate model from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) are evaluated and compared with those from the version for the fifth phase Coupled Model Intercomparison Project (CMIP5) at global mean, zonal mean, and regional mean scales. Comparisons with the European Centre for Medium-Range Weather Forecasts (ECMWF) Interim Re-Analysis (ERA-Interim) and the Global Land Data Assimilation System (GLDAS) datasets indicate that both the CMIP5 and CMIP6 versions of the BCC model can simulate the warm and cold extremes of monthly air and land surface temperatures reasonably well for the period of 1979–2005. Compared with reanalyses, the CMIP6 version has better consistencies in the magnitudes, narrower spreads of zonal extremes, and smaller root mean square errors (RMSEs) than the CMIP5 version due to an update with a new cloud fraction scheme and a snow-albedo scheme. When the annual climatological cycle is removed, the CMIP6 version performs better than the CMIP5 version on the interannual variability of both warm and cold extremes. Changes in the air and land surface temperature extremes for the CMIP6 version are more similar to the reanalyses. We found that the warming rates of both warm and cold extremes at regional scales are significantly higher than the global averaged warming rate of surface temperatures, especially at the high latitudes of the Northern Hemisphere, which is consistent with the previous studies on monthly mean air temperature. Further, both the BCC models and reanalyses show that the cold extremes warm faster than the warm extremes, and the land surface temperature extremes warm more quickly than the air temperature extremes during this period.

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.

In this study, we compared the following four different gridded monthly precipitation products: the National Centers for Environmental Prediction version 2 (NCEP-2) reanalysis data, the satellite-based Climate Prediction Center Morphing technique (CMORPH) data, the merged satellite-gauge Global Precipitation Climatology Project (GPCP) data, and the merged satellite-gauge-model data from the Beijing Climate Center Merged Estimation of Precipitation (BMEP). We evaluated the performances of these products using monthly precipitation observations spanning the period of January 2003 to December 2013 from a dense, national, rain gauge network in China. Our assessment involved several statistical techniques, including spatial pattern, temporal variation, bias, root-mean-square error (RMSE), and correlation coefficient (CC) analysis. The results show that NCEP-2, GPCP, and BMEP generally overestimate monthly precipitation at the national scale and CMORPH underestimates it. However, all of the datasets successfully characterized the northwest to southeast increase in the monthly precipitation over China. Because they include precipitation gauge information from the Global Telecommunication System (GTS) network, GPCP and BMEP have much smaller biases, lower RMSEs, and higher CCs than NCEP-2 and CMORPH. When the seasonal and regional variations are considered, NCEP-2 has a larger error over southern China during the summer. CMORPH poorly reproduces the magnitude of the precipitation over southeastern China and the temporal correlation over western and northwestern China during all seasons. BMEP has a lower RMSE and higher CC than GPCP over eastern and southern China, where the station network is dense. In contrast, BMEP has a lower CC than GPCP over western and northwestern China, where the gauge network is relatively sparse.

To highlight the compatibility of climate model simulation and proxy reconstruction at different timescales, a timescale separation merging method combining proxy records and climate model simulations is presented. Annual mean surface temperature anomalies for the last millennium (851–2005 AD) at various scales over the land of the Northern Hemisphere were reconstructed with 2° × 2° spatial resolution, using an optimal interpolation (OI) algorithm. All target series were decomposed using an ensemble empirical mode decomposition method followed by power spectral analysis. Four typical components were obtained at inter-annual, decadal, multidecadal, and centennial timescales. A total of 323 temperature-sensitive proxy chronologies were incorporated after screening for each component. By scaling the proxy components using variance matching and applying a localized OI algorithm to all four components point by point, we obtained merged surface temperatures. Independent validation indicates that the most significant improvement was for components at the inter-annual scale, but this became less evident with increasing timescales. In mid-latitude land areas, 10–30% of grids were significantly corrected at the inter-annual scale. By assimilating the proxy records, the merged results reduced the gap in response to volcanic forcing between a pure reconstruction and simulation. Difficulty remained in verifying the centennial information and quantifying corresponding uncertainties, so additional effort should be devoted to this aspect in future research.

介绍了我国新一代静止气象卫星风云四号(FY-4)卫星应用及其发展.给出了FY-4卫星装载的先进静止轨道辐射成像仪、静止轨道干涉式红外探测仪、闪电成像仪和空间环境监测仪4种主要观测仪器,以及卫星的观测能力和提供的定量化产品,并与我国现有的业务卫星风云二号(FY-2)卫星和国际同期在轨静止气象卫星性能进行了比较.FY-4光学卫星系列与美国GOES-R、日本Himawari-8/9卫星和欧洲MTG卫星性能相似,属于与国际同期发展的先进静止气象卫星.给出了FY-4A星获得的图像和数据.列出了FY-4A星的基本定量产品,给出了使用的仪器、数据特性、物理意义,以及应用领域、方法和范例.描述FY-4卫星提供的定量化产品及其在数值天气预报、气候、生态环境、专业气象服务、人工影响天气、空间天气监测预警等领域的应用,并介绍了FY-4A星在轨测试期间的部分应用.对FY-4后续业务卫星发展进行了展望.

This paper describes a strategy for merging daily precipitation information from gauge observations, satellite estimates (SEs), and numerical predictions at the global scale. The strategy is designed to remove systemic bias and random error from each individual daily precipitation source to produce a better gridded global daily precipitation product through three steps. First, a cumulative distribution function matching procedure is performed to remove systemic bias over gauge-located land areas. Then, the overall biases in SEs and model predictions (MPs) over ocean areas are corrected using a rescaled strategy based on monthly precipitation. Third, an optimal interpolation (OI)–based merging scheme (referred as the HL-OI scheme) is used to combine unbiased gauge observations, SEs, and MPs to reduce random error from each source and to produce a gauge—satellite–model merged daily precipitation analysis, called BMEP-d (Beijing Climate Center Merged Estimation of Precipitation with daily resolution), with complete global coverage. The BMEP-d data from a four-year period (2011–14) demonstrate the ability of the merging strategy to provide global daily precipitation of substantially improved quality. Benefiting from the advantages of the HL-OI scheme for quantitative error estimates, the better source data can obtain more weights during the merging processes. The BMEP-d data exhibit higher consistency with satellite and gauge source data at middle and low latitudes, and with model source data at high latitudes. Overall, independent validations against GPCP-1DD (GPCP one-degree daily) show that the consistencies between BMEP-d and GPCP-1DD are higher than those of each source dataset in terms of spatial pattern, temporal variability, probability distribution, and statistical precipitation events.

Large-scale climate history of the past millennium reconstructed solely from tree-ring data is prone to underestimate the amplitude of low-frequency variability. In this paper, we aimed at solving this problem by utilizing a novel method termed "MDVM", which was a combination of the ensemble empirical mode decomposition (EEMD) and variance matching techniques. We compiled a set of 211 tree-ring records from the extratropical Northern Hemisphere (30-90°N) in an effort to develop a new reconstruction of the annual mean temperature by the MDVM method. Among these dataset, a number of 126 records were screened out to reconstruct temperature variability longer than decadal scale for the period 850-2000 AD. The MDVM reconstruction depicted significant low-frequency variability in the past millennium with evident Medieval Warm Period (MWP) over the interval 950-1150 AD and pronounced Little Ice Age (LIA) cumulating in 1450-1850 AD. In the context of 1150-year reconstruction, the accelerating warming in 20th century was likely unprecedented, and the coldest decades appeared in the 1640s, 1600s and 1580s, whereas the warmest decades occurred in the 1990s, 1940s and 1930s. Additionally, the MDVM reconstruction covaried broadly with changes in natural radiative forcing, and especially showed distinct footprints of multiple volcanic eruptions in the last millennium. Comparisons of our results with previous reconstructions and model simulations showed the efficiency of the MDVM method on capturing low-frequency variability, particularly much colder signals of the LIA relative to the reference period. Our results demonstrated that the MDVM method has advantages in studying large-scale and low-frequency climate signals using pure tree-ring data.

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 (GlobeLand30) 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 GlobeLand30 data in the model. First, the GlobeLand30 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 GlobeLand30 land cover type and area percentage with the coarser model grid resolutions globally. The GlobeLand30-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 GlobeLand30-based data were used in the BCC_CSM atmosphere model. The results suggest that the GlobeLand30 data are suitable for use in the BCC_CSM component models and can improve the performance of the land and atmosphere simulations.

By conducting several sets of hindcast experiments using the Beijing Climate Center Climate System Model, which participates in the Sub-seasonal to Seasonal (S2S) Prediction Project, we systematically evaluate the model’s capability in forecasting MJO and its main deficiencies. In the original S2S hindcast set, MJO forecast skill is about 16 days. Such a skill shows significant seasonal-to-interannual variations. It is found that the model-dependent MJO forecast skill is more correlated with the Indian Ocean Dipole (IOD) than with the El Niño–Southern Oscillation. The highest skill is achieved in autumn when the IOD attains its maturity. Extended skill is found when the IOD is in its positive phase. MJO forecast skill’s close association with the IOD is partially due to the quickly strengthening relationship between MJO amplitude and IOD intensity as lead time increases to about 15 days, beyond which a rapid weakening of the relationship is shown. This relationship transition may cause the forecast skill to decrease quickly with lead time, and is related to the unrealistic amplitude and phase evolutions of predicted MJO over or near the equatorial Indian Ocean during anomalous IOD phases, suggesting a possible influence of exaggerated IOD variability in the model. The results imply that the upper limit of intraseasonal predictability is modulated by large-scale external forcing background state in the tropical Indian Ocean. Two additional sets of hindcast experiments with improved atmosphere and ocean initial conditions (referred to as S2S_IEXP1 and S2S_IEXP2, respectively) are carried out, and the results show that the overall MJO forecast skill is increased to 21–22 days. It is found that the optimization of initial sea surface temperature condition largely accounts for the increase of the overall MJO forecast skill, even though the improved initial atmosphere conditions also play a role. For the DYNAMO/CINDY field campaign period, the forecast skill increases to 27 days in S2S_IEXP2. Nevertheless, even with improved initialization, it is still difficult for the model to predict MJO propagation across the western hemisphere–western Indian Ocean area and across the eastern Indian Ocean–Maritime Continent area. Especially, MJO prediction is apparently limited by various interrelated deficiencies (e.g., overestimated IOD, shorter-than-observed MJO life cycle, Maritime Continent prediction barrier), due possibly to the model bias in the background moisture field over the eastern Indian Ocean and Maritime Continent. Thus, more efforts are needed to correct the deficiency in model physics in this region, in order to overcome the well-known Maritime Continent predictability barrier.