The ability to estimate terrestrial water storage(TWS)is essential for monitoring hydrological extremes(e.g.,droughts and floods)and predicting future changes in the hydrological cycle.However,inadequacies in model ph...The ability to estimate terrestrial water storage(TWS)is essential for monitoring hydrological extremes(e.g.,droughts and floods)and predicting future changes in the hydrological cycle.However,inadequacies in model physics and parameters,as well as uncertainties in meteorological forcing data,commonly limit the ability of land surface models(LSMs)to accurately simulate TWS.In this study,the authors show how simulations of TWS anomalies(TWSAs)from multiple meteorological forcings and multiple LSMs can be combined in a Bayesian model averaging(BMA)ensemble approach to improve monitoring and predictions.Simulations using three forcing datasets and two LSMs were conducted over China's Mainland for the period 1979–2008.All the simulations showed good temporal correlations with satellite observations from the Gravity Recovery and Climate Experiment during 2004–08.The correlation coefficient ranged between 0.5 and 0.8 in the humid regions(e.g.,the Yangtze river basin,Huaihe basin,and Zhujiang basin),but was much lower in the arid regions(e.g.,the Heihe basin and Tarim river basin).The BMA ensemble approach performed better than all individual member simulations.It captured the spatial distribution and temporal variations of TWSAs over China's Mainland and the eight major river basins very well;plus,it showed the highest R value(>0.5)over most basins and the lowest root-mean-square error value(<40 mm)in all basins of China.The good performance of the BMA ensemble approach shows that it is a promising way to reproduce long-term,high-resolution spatial and temporal TWSA data.展开更多
Recent satellite data analysis has provided improved data sets relevant to the surface energy budget in the Arctic Ocean. In this paper, surface radiation properties in the Arctic Ocean obtained from the Surface Radia...Recent satellite data analysis has provided improved data sets relevant to the surface energy budget in the Arctic Ocean. In this paper, surface radiation properties in the Arctic Ocean obtained from the Surface Radiation Budget（SRB3.0） and the International Satellite Cloud Climatology Project（ISCCP-FD） during 1984– 2007 are analyzed and compared. Our analysis suggests that these datasets show encouraging agreement in basin-wide averaged seasonal cycle and spatial distribution of surface albedo; net surface shortwave and all-wave radiative fluxes; and shortwave, longwave, and all-wave cloud radiative forcings. However, a systematic large discrepancy is detected for the net surface longwave radiative flux between the two data sets at a magnitude of ~ 23 W m–2, which is primarily attributed to significant differences in surface temperature, particularly from April to June. Moreover, the largest difference in surface shortwave and all-wave cloud radiative forcings between the two data sets is apparent in early June at a magnitude of 30 W m–2.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.41405083 and 91437220)the Natural Science Foundation of Hunan Province,China(Grant No.2015JJ3098)+1 种基金the Key Research Program of Frontier Sciences,CAS(QYZDY-SSW-DQC012)the Fund Project for The Education Department of Hunan Province(Grant No.16A234)
文摘The ability to estimate terrestrial water storage(TWS)is essential for monitoring hydrological extremes(e.g.,droughts and floods)and predicting future changes in the hydrological cycle.However,inadequacies in model physics and parameters,as well as uncertainties in meteorological forcing data,commonly limit the ability of land surface models(LSMs)to accurately simulate TWS.In this study,the authors show how simulations of TWS anomalies(TWSAs)from multiple meteorological forcings and multiple LSMs can be combined in a Bayesian model averaging(BMA)ensemble approach to improve monitoring and predictions.Simulations using three forcing datasets and two LSMs were conducted over China's Mainland for the period 1979–2008.All the simulations showed good temporal correlations with satellite observations from the Gravity Recovery and Climate Experiment during 2004–08.The correlation coefficient ranged between 0.5 and 0.8 in the humid regions(e.g.,the Yangtze river basin,Huaihe basin,and Zhujiang basin),but was much lower in the arid regions(e.g.,the Heihe basin and Tarim river basin).The BMA ensemble approach performed better than all individual member simulations.It captured the spatial distribution and temporal variations of TWSAs over China's Mainland and the eight major river basins very well;plus,it showed the highest R value(>0.5)over most basins and the lowest root-mean-square error value(<40 mm)in all basins of China.The good performance of the BMA ensemble approach shows that it is a promising way to reproduce long-term,high-resolution spatial and temporal TWSA data.
基金supported by the National Basic Research Program of China(2011CB30970)the National Natural Science Foundation of China(41176169 and 40930848)
文摘Recent satellite data analysis has provided improved data sets relevant to the surface energy budget in the Arctic Ocean. In this paper, surface radiation properties in the Arctic Ocean obtained from the Surface Radiation Budget（SRB3.0） and the International Satellite Cloud Climatology Project（ISCCP-FD） during 1984– 2007 are analyzed and compared. Our analysis suggests that these datasets show encouraging agreement in basin-wide averaged seasonal cycle and spatial distribution of surface albedo; net surface shortwave and all-wave radiative fluxes; and shortwave, longwave, and all-wave cloud radiative forcings. However, a systematic large discrepancy is detected for the net surface longwave radiative flux between the two data sets at a magnitude of ~ 23 W m–2, which is primarily attributed to significant differences in surface temperature, particularly from April to June. Moreover, the largest difference in surface shortwave and all-wave cloud radiative forcings between the two data sets is apparent in early June at a magnitude of 30 W m–2.
