A Strategy for Merging Objective Estimates of Global Daily Precipitation from Gauge Observations, Satellite Estimates, and Numerical Predictions

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 gahge 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 B MEP-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.

Updated Simulation of Tropospheric Ozone and Its Radiative Forcing over the Globe and China Based on a Newly Developed Chemistry–Climate Model

This study evaluates the performance of a newly developed atmospheric chemistry–climate model,BCCAGCM_CUACE2.0(Beijing Climate Center Atmospheric General Circulation Model_China Meteorological Administration Unified Atmospheric Chemistry Environment)model,for determining past(2010)and future(2050)tropospheric ozone(O_(3))levels.The radiative forcing(RF),effective radiative forcing(ERF),and rapid adjustments(RAs,both atmospheric and cloud)due to changes in tropospheric O_(3)are then simulated by using the model.The results show that the model reproduces the tropospheric O_(3)distribution and the seasonal changes in O_(3)surface concentration in 2010 reasonably compared with site observations throughout China.The global annual mean burden of tropospheric O_(3)is simulated to have increased by 14.1 DU in 2010 relative to pre-industrial time,particularly in the Northern Hemisphere.Over the same period,tropospheric O_(3)burden has increased by 21.1 DU in China,with the largest increase occurring over Southeast China.Although the simulated tropospheric O_(3)burden exhibits a declining trend in global mean in the future,it increases over South Asia and Africa,according to the Representative Concentration Pathway(RCP)4.5 and 8.5 scenarios.The global annual mean ERF of tropospheric O_(3)is estimated to be 0.25 W m^(−2)in 1850−2010,and it is 0.50 W m^(−2)over China.The corresponding atmospheric and cloud RAs caused by the increase of tropospheric O_(3)are estimated to be 0.02 and 0.03 W m^(−2),respectively.Under the RCP2.6,RCP4.5,RCP6.0,and RCP8.5 scenarios,the annual mean tropospheric O_(3)ERFs are projected to be 0.29(0.24),0.18(0.32),0.23(0.32),and 0.25(0.01)W m^(−2)over the globe(China),respectively.