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Shuai Hu, Tianjun Zhou, and Bo Wu

Abstract

The year-to-year variations of Tibetan Plateau (TP) summer rainfall have tremendous climate impacts on the adjoining and even global climate, attracting extensive research attention in recent decades to understand the underlying mechanism. In this study, we investigate an open question of how the El Niño-Southern Oscillation (ENSO) influences the TP precipitation. We show that the developing ENSO has significant impacts on the summer rainfall over the southwestern TP (SWTP), which is the second EOF mode of the interannual variability of summer rainfall over the TP. Moisture budget indicates that both the suppressed vertical motion and the deficit of moisture contribute to the reduction of SWTP rainfall during El Niño’s developing summer, with the former contribution four times larger than the latter. Moist static energy analyses indicate that the anomalous advection of climatological moist enthalpy by anomalous zonal wind is responsible for the anomalous descending motions over the SWTP. The El Niño-related southward displacements of the South Asian high and the upper-level cyclonic anomalies over the west of TP stimulated by the suppressed Indian summer monsoon precipitation are two key processes dominating the anomalous zonal moist enthalpy advection over SWTP. Meanwhile, the India-Burma monsoon trough is strengthened during El Niño developing summer, which prevents the water vapor into the SWTP, and thus contributes to the deficit of summer SWTP rainfall. Our results help to understand the complicated ENSO-related air-sea interaction responsible for the variability of TP precipitation and have implications for seasonal prediction of the TP climate.

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Wenmin Qin, Lunche Wang, Ming Zhang, Zigeng Niu, Ming Luo, Aiwen Lin, and Bo Hu

Abstract

Photosynthetically active radiation (PAR) is a key factor for vegetation growth and climate change. Different types of PAR models, including four physically based models and eight artificial intelligence (AI) models, were proposed for predicting daily PAR. Multiyear daily meteorological parameters observed at 29 Chinese Ecosystem Research Network (CERN) stations and 2474 Chinese Meteorological Administration (CMA) stations across China were used for testing, validating, and comparing the above models. The optimized back propagation (BP) neural network based on the mind evolutionary algorithm (MEA-BP) was the model with highest accuracy and strongest robustness. The correlation coefficient R, mean absolute bias error (MAE), and RMSE for MEA-BP were 0.986, 0.302 MJ m−2 day−1 and 0.393 MJ m−2 day−1, respectively. Then, a high-density PAR dataset was constructed for the first time using the MEA-BP model at 2474 CMA stations of China. A quality control process and homogenization test (using RHtestsV4) for the PAR dataset were further conducted. This high-density PAR dataset would benefit many climate and ecological studies.

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Jinjun Tong, Stephen J. Déry, Bo Hu, Yun Chen, Changjun Yang, and Zhiguo Rong

Abstract

Forty-one cloud-free images of Qinghai Lake (QHL) in China and the corresponding digital numbers (DNs) of FengYun-2C (FY-2C) at 0000, 0600, 1200, and 1800 UTC from 1 July to 30 September 2005 are analyzed. The corresponding surface water temperatures of QHL measured by the automated hydrometeorological buoy (AHMB) system and the atmospheric profiles over QHL from the National Centers for Environmental Prediction (NCEP) reanalysis data are inputted into the atmospheric transfer model MODTRAN3.7 to calculate the entrance pupil radiance and brightness temperatures for thermal infrared (TIR) channels of FY-2C. Then, the absolute radiometric calibration coefficients of FY-2C, which are used to calculate the equivalent blackbody (EBB) temperatures T EBB, are calculated by comparing the entrance pupil radiance and brightness temperatures with the corresponding DNs. In addition, the temperatures of onboard blackbody (OBB) T OBB, primary, secondary, refraction, and calibration mirrors on the multichannel scanning radiometer (MSR) of FY-2C are detected remotely. Based on the linear correlation between T EBBT OBB and temperatures of various mirrors, the transform equations from T OBB to T EBB are developed. Finally, the onboard real-time absolute radiometric calibration for TIR channels of geostationary meteorological satellite FY-2C is implemented with an uncertainty of about 1.5 and 2.1 K for TIR 1 and TIR 2 of FY-2C, respectively.

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Gang Hong, Ping Yang, Bo-Cai Gao, Bryan A. Baum, Yong X. Hu, Michael D. King, and Steven Platnick

Abstract

This study surveys the optical and microphysical properties of high (ice) clouds over the Tropics (30°S–30°N) over a 3-yr period from September 2002 through August 2005. The analyses are based on the gridded level-3 cloud products derived from the measurements acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments aboard both the NASA Earth Observing System Terra and Aqua platforms. The present analysis is based on the MODIS collection-4 data products. The cloud products provide daily, weekly, and monthly mean cloud fraction, cloud optical thickness, cloud effective radius, cloud-top temperature, cloud-top pressure, and cloud effective emissivity, which is defined as the product of cloud emittance and cloud fraction. This study is focused on high-level ice clouds. The MODIS-derived high clouds are classified as cirriform and deep convective clouds using the International Satellite Cloud Climatology Project (ISCCP) classification scheme. Cirriform clouds make up more than 80% of the total high clouds, whereas deep convective clouds account for less than 20% of the total high clouds. High clouds are prevalent over the intertropical convergence zone (ITCZ), the South Pacific convergence zone (SPCZ), tropical Africa, the Indian Ocean, tropical America, and South America. Moreover, land–ocean, morning–afternoon, and summer–winter variations of high cloud properties are also observed.

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Bo Dong, John D. Lenters, Qi Hu, Christopher J. Kucharik, Tiejun Wang, Mehmet E. Soylu, and Phillip M. Mykleby

Abstract

Variations in climate have important influences on the hydrologic cycle. Observations over the continental United States in recent decades show substantial changes in hydrologically significant variables, such as decreases in cloud cover and increases in solar radiation (i.e., solar brightening), as well as increases in air temperature, changes in wind speed, and seasonal shifts in precipitation rate and rain/snow ratio. Impacts of these changes on the regional water cycle from 1984 to 2007 are evaluated using a terrestrial ecosystem/land surface hydrologic model (Agro-IBIS). Results show an acceleration of various components of the surface water balance in the Upper Mississippi, Missouri, Ohio, and Great Lakes basins over the 24-yr period, but with significant seasonal and spatial complexity. Evapotranspiration (ET) has increased across most of our study domain and seasons. The largest increase is found in fall, when solar brightening trends are also particularly significant. Changes in runoff are characterized by distinct spatial and seasonal variations, with the impact of precipitation often being muted by changes in ET and soil-water storage rate. In snow-dominated regions, such as the northern Great Lakes basin, spring runoff has declined significantly due to warmer air temperatures and an associated decreasing ratio of snow in total precipitation during the cold season. In the northern Missouri basin, runoff shows large increases in all seasons, primarily due to increases in precipitation. The responses to these changes in the regional hydrologic cycle depend on the underlying land cover type—maize, soybean, and natural vegetation. Comparisons are also made with other hydroclimatic time series to place the decadal-scale variability in a longer-term context.

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Jinyuan Xin, Yuesi Wang, Yuepeng Pan, Dongsheng Ji, Zirui Liu, Tianxue Wen, Yinghong Wang, Xingru Li, Yang Sun, Jie Sun, Pucai Wang, Gehui Wang, Xinming Wang, Zhiyuan Cong, Tao Song, Bo Hu, Lili Wang, Guiqian Tang, Wenkang Gao, Yuhong Guo, Hongyan Miao, Shili Tian, and Lu Wang

Abstract

Based on a network of field stations belonging to the Chinese Academy of Sciences (CAS), the Campaign on Atmospheric Aerosol Research network of China (CARE-China) was recently established as the country’s first monitoring network for the study of the spatiotemporal distribution of aerosol physical characteristics, chemical components, and optical properties, as well as aerosol gaseous precursors. The network comprises 36 stations in total and adopts a unified approach in terms of the instrumentation, experimental standards, and data specifications. This ongoing project is intended to provide an integrated research platform to monitor online PM2.5 concentrations, nine-size aerosol concentrations and chemical component distributions, nine-size secondary organic aerosol (SOA) component distributions, gaseous precursor concentrations (including SO2, NOx, CO, O3, and VOCs), and aerosol optical properties. The data will be used to identify the sources of regional aerosols, the relative contributions from nature and anthropogenic emissions, the formation of secondary aerosols, and the effects of aerosol component distributions on aerosol optical properties. The results will reduce the levels of uncertainty involved in the quantitative assessment of aerosol effects on regional climate and environmental changes and ultimately provide insight into how to mitigate anthropogenic aerosol emissions in China. The present paper provides a detailed description of the instrumentation, methodologies, and experimental procedures used across the network, as well as a case study of observations taken from one station and the distribution of main components of aerosol over China during 2012.

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