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.
A series of quality control(QC) procedures were performed on a gauge-based global daily precipitation dataset from the Global Telecommunication System(GTS) for the period 1980-2009.A new global daily precipitation(NGDP) dataset was constructed by applying those QC procedures to eliminate erroneous records.The NGDP dataset was evaluated using the NOAA Climate Prediction Center Merged Analysis of Precipitation(CMAP) and the Global Precipitation Climatology Project(GPCP) precipitation datasets.The results showed that the frequency distribution and spatial distribution pattern of NGDP had a nice match with those from the CMAP and GPCP datasets.The global mean correlation coefficients with the CMAP and GPCP data increased from 0.24 for original GTS precipitation data to about 0.70 for NGDP data.Correspondingly,the root mean square errors(RMSE) decreased from 12 mm per day to 1 mm per day.The interannual variabilities of NGDP monthly precipitation are consistent with the CMAP and GPCP datasets in Asia.Meanwhile,the seasonal variabilities for most land areas on the Earth of NGDP dataset are also consistent with the CMAP and GPCP precipitation products.
NIE Su-PingLUO YongLI Wei-PingWU Tong-WenSHI Xue-LiWANG Zai-Zhi
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.
Using observational data spanning the period from February to December 2009 and recorded at the Suli station in Qinghai Province,the land-surface model CLM3.0 was employed to simulate the freezing and melting of soil.The results indicate that the simulated soil temperature is higher than the observed soil temperature and the ultimate thawing date is earlier than the observed date during the melting period.During the freezing period,the simulated soil temperature is lower than the observed soil temperature and the ultimate freezing of the deep soil is earlier than that observed.Overall,the simulation of freezing is better than that of melting,and the simulation of a shallow layer is better than that of a deeper layer.In the original CLM3.0,it is assumed that frozen soil begins to melt when the soil temperature exceeds 0C,which is inconsistent with observations.The critical freeze-thaw temperature was calculated according to thermodynamics equations and the freeze-thaw condition was modified.In this work,the melting rate for frozen soil was reduced using the modified scheme,and the simulated soil temperature was lowered. Meanwhile,the refreezing of soil during the melting season was well simulated and more closely matched observations.Additionally,it was found that the rates of melting and freezing differ,with the former being slower than the latter,but refreezing during the melting season is rather quick.
XIA Kun 1,2,3,LUO Yong 2,4,5 & LI WeiPing 2 1 Institute of Atmospheric Physics,Chinese Academy of Sciences,Beijing 100029,China
