Subseasonal variabilities of surface soil moisture in reanalysis datasets and CESM simulations

Using surface soil moisture(SM) from ERA-Interim reanalysis and Climate Forecast System Reanalysis(CFSR) data together with simulated results from CESM, the authors evaluated the subseasonal variability of SM and explored its basic features. Evident subseasonal variability of SM was detected in all seasons and with different datasets. However, the subseasonal variability of SM showed significant regional differences and varied with seasons. It was found that SM has large subseasonal variances in eastern China, North America, South Africa, and Australia in the summer hemisphere. The variances of the low-frequency SM variations given by ERA-Interim and CFSR are different. Overall, CFSR shows stronger variability than ERA-Interim. Through spectral analysis, it was noticed that low-frequency variations of surface SM mainly happen with periods of 10–30 days and 30–50 days. Subseasonal variations with a period of 10–30 days are dominant in eastern China and South Africa. However, subseasonal variations with periods of both 10–30 days and 30–50 days were detected in North America and Australia. Generally, CESM captures the main features of SM subseasonal variation. However, the model overestimates the subseasonal variability in all seasons in most regions, especially in the high latitudes of the Northern Hemisphere.

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.

Intraseasonal oscillation of the rainfall variability over Rwanda and evaluation of its subseasonal forecasting skill

Rwanda is a landlocked country in central-eastern Africa.As a country highly dependent on rain-fed agriculture,Rwanda is vulnerable to rainfall variability.Observational data show that there are two rainy seasons in Rwanda,i.e.,the long rainy season and the short rainy season.This study mainly focuses on the dominant intraseasonal rainfall mode during the long rainy season(February-May),and evaluates the forecast skill for the intraseasonal variability(ISV)over Rwanda and its surrounding regions in a state-of-the-art dynamic model.During the long rainy season,observational results reveal that the dominant intraseasonal rainfall mode in Rwanda exhibits a significant variability on the 10-25-day time scale.One-point-correlation analysis further unveils that the 10-25-day intraseasonal rainfall variability in Rwanda co-varies with that in its adjacent areas,indicating that the overall 10-25-day rainfall variability in Rwanda and its adjacent regions(8°S-3°N,29°-37°E)should be considered collectively when studying the dominant intraseasonal rainfall variability in Rwanda.Composite results show that the development of the 10-25-day rainfall variability is associated with the anomalous westerly wind in Rwanda and its surrounding regions,which may trace back to a pair of westward-propagating equatorial Rossby waves.Based on the observational findings,an ISO_rainfall_index and an ISO_wind_index are proposed for quantitatively evaluating the forecast skill.The ECMWF model has a comparable skill in predicting the wind index and the rainfall index,with both indices showing a skill of 18 days.