The Seasonal Variations of Aerosols over East Asia as Jointly Inferred from MODIS and OMI

Data on aerosol optical thickness(AOT) and single scattering albedo(SSA) derived from Moderate Resolution Imaging Spectrometer(MODIS) and Ozone Monitoring Instrument(OMI) measurements,respectively,are used jointly to examine the seasonal variations of aerosols over East Asia.The seasonal signals of the total AOT are well defined and nearly similar over the land and over the ocean.These findings indicate a natural cycle of aerosols that originate primarily from natural emissions. In contrast,the small-sized aerosols represented by the fine-mode AOT,which are primarily generated over the land by human activities,do not have evident seasonalscale fluctuations.A persistent maximum of aerosol loadings centered over the Sichuan basin is associated with considerable amounts of fine-mode aerosols throughout the year.Most regions exhibit a general spring maximum. During the summer,however,the aerosol loadings are the most marked over north central China.This occurrence may result from anthropogenic fine particles,such as sulfate and nitrate.Four typical regions were selected to perform a covariation analysis of the monthly gridded AOT and SSA.Over southwestern and southeastern China,if the aerosol loadings are small to moderate they are composed primarily of the highly absorptive aerosols. However,more substantial aerosol loadings probably represent less-absorptive aerosols.The opposite covariation pattern occurring over the coastal-adjacent oceans suggests that the polluted oceanic atmosphere is closely correlated with the windward terrestrial aerosols.North central China is strongly affected by dust aerosols that show moderate absorption.This finding may explain the lower variability in the SSA that accompanies increasing aerosol loadings in this region.

Evaluating the impacts of land use and land cover changes on surface air temperature using the WRF-mosaic approach

Satellite-derived land surface data in 1980 and 2010 were used to represent land use and land cover（LULC） changes caused by the rapid economic development and human activities that have occurred over the past few decades in East Asia and China. The effects of LULC changes on the radiation budget and 2-m surface air temperature（SAT） were explored for the period using the Weather Research and Forecasting（WRF） model. The mosaic approach, which considers the N-most abundant land use types within a model grid cell（here, N = 3） and precisely describes the subgridscale LULC changes, was adopted in the integrations. The impacts of LULC changes based on two 36-year integrations showed that SAT generally decreased, with the sole exception being over eastern China, resulting in decreased SAT in China（-0.062 °C） and East Asian land areas（EAL,-0.061 °C）. The LULC changes induced changes in albedo, which influenced the radiation budget. The radiative forcings at the top of the atmosphere were-0.56 W m-2 across the whole of China, and-0.50 W m-2 over EAL. Meanwhile, the altered roughness length mainly influenced near-surface wind speeds, large-scale and upward moisture fluxes, latent heat fluxes, and cloud fractions at different altitudes. Though the impacts caused by the LULC changes were generally smaller at regional scales, the values at local scales were much stronger.

三江平原稻田地表辐射平衡研究

研究农田中的辐射平衡特征,对揭示农田热状况,特别是温度状况的形成具有重要的意义.本研究使用的地表辐射观测资料是2005-2007通过布置在中国科学院三江平原沼泽湿地生态试验站在稻田内的小气候自动观测系统得来的,通过研究得出：不同天气条件下三江平原水稻田下墊面辐射平衡各分量日变化规律不同。晴天时,辐射平衡各项目变化规则,总辐射、净辐射的变化趋势一致,中午13点左右达到最大值920w·m^2。阴天和降水天时,辐射平衡各分量变化远没有晴天时规则。除向上短波辐射和向上长波辐射外,都有不同程度的减小。阴天时总辐射是晴天时的3/5左右,降水天时总辐射是晴天时的3/10。向下长波辐射晴天、阴天、降水天依次略增。总辐射与净辐射呈线性正相关。

基于OPI方法的模式城市识别偏差对地表辐射收支的影响分析

城市化影响着局地与区域气候变化。区域气候模式是探讨城市与环境问题演变机制与城市化气候效应的重要研究手段,然而模式地表参数化方法处理地表参数时难以全面反映下垫面状况,可能影响模拟结果的合理性。因此以卫星数据源为基础,分析了OPI方法处理下垫面参数导致的偏差,结合其导致的地表辐射收支差异说明不同空间尺度偏差的辐射收支效应,进而通过与CO2排放关联定量分析OPI方法对城市化气候效应的影响。结果发现随着空间尺度的变大,OPI方法导致的城市面积提取误差也在变大,导致城市下垫面转变成了其周边的土地覆盖类型,增强了城市群区域的反照率,降低了城市区域的辐射收支,弱化了城市气候效应。研究发现城市地表单位面积反照率增加0.01相当于减少了2.82kg的CO2排放,京津冀城市群2009年案例分析表明其弱化程度相当于减少了CO2排放2.28×105 t,扩展到中国区域尺度上则相当于减少了1.81×106 t CO2排放。