ALBERT

Description: Plentiful snowfall is an important resource in northern Xinjiang. However, extreme snowfall events can lead to destructive avalanches, traffic interruptions or even the collapse of buildings. The daily winter precipitation data from 18 stations in northern Xinjiang during 1959/1960–2008/2009 were selected for purpose of analyzing long-term variability of extreme snowfall events. Five extreme snowfall indices, Maximum 1 day snowfall amount (SX1day), Maximum 1-weather process snowfall amount (SX1process), Blizzard days (DSb), Consecutive snow days (DSc) and Blizzard weather processes (PSb), were defined and utilized to quantitatively describe the intensity and frequency of extreme snowfall events. Temporal trends of the five indices were analyzed by Mann–Kendall test and simple linear regression, and their trends were interpolated using universal kriging interpolation. Temporally, we found that most stations have upward trends in the five indices of extreme snowfall events, and over entire northern Xinjiang, they were all increasing at the 0.01 significance level (MK test), with the linear tendency rates of 0.49 mm (10 a)−1 (SX1day), 0.89 mm (10 a)−1 (SX1process), 0.024 days (10 a)−1 (DSb), 0.14 days (10 a)−1 (DSc), and 0.069 times (10 a)−1 (PSb) respectively. Meanwhile, obvious decadal fluctuations besides long-term increasing trends are identified. Trends in the intensity and frequency of extreme snowfall events show a~distinct difference spatially. In general, trends of five indices were found shifting from decreasing to increasing from the northeast to the southwest and from the north to the south of northern Xinjiang. Furthermore, the regions covered by increasing or decreasing extreme snowfall events were identified, implying the hot or cold spots for extreme snowfall events changes. These results may be helpful for northern Xinjiang on the regional and local resource and emergency planning.

Description: Cloud radiative forcing is a very important concept to understand what kind of role the clouds play in climate change with thermal effect or albedo effect. In spite of that much progress has been achieved, the clouds are still poorly described in the climate models. Due to the complex aerosol-cloud-radiation interactions, high surface albedo of snow and ice cover, and without solar radiation in long period of the year, the Arctic strong warming caused by increasing greenhouse gases (as most GCMs suggested) has not been verified by the observations. In this study, we were dedicated to quantify the aerosol effect on the Arctic cloud radiative forcing by Northern Aerosol Regional Climate Model (NARCM). Major aerosol species such as Arctic haze sulphate, black carbon, sea salt, organics and dust have been included during our simulations. By inter-comparisons with the Atmospheric Radiation Measurement (ARM) data, we find surface cloud radiative forcing (SCRF) is −22 W/m2 for shortwave and 36 W/m2 for longwave. Total cloud forcing is 14 W/m2 with minimum of −35 W/m2 in early July. If aerosols are taken into account, the SCRF has been increased during winter while negative SCRF has been enhanced during summer. Our estimate of aerosol forcing is about −6 W/m2 in the Arctic.

Description: Oceanic phytoplankton can affect in-water and atmospheric radiation fields. In this study, we develop case 1 (without noncovarying particles) and case 2 (including noncovarying particles) waters model including Raman scattering in order to examine the chlorophyll impacts on the Total Ozone Mapping Spectrometer (TOMS) Aerosol Index and aerosol single scattering albedo. The waters model is coupled with a radiation transfer model (VLIDORT) for calculating TOMS Aerosol Index and retrieval of aerosol single scattering albedo. The retrieval is constrained by chlorophyll concentration from Sea-viewing Wide Field-of-view Sensor (SeaWiFS) and Moderate Resolution Imaging SpectroRadiometer (MODIS) data, aerosol optical depth from MODIS, and aerosol vertical profiles from a global chemical transport model (GEOS-CHEM). We find the retrieved aerosol single scattering albedo is strongly influenced by chlorophyll concentration, particularly in the regions of subtropical Atlantic Ocean and Indian Ocean. The maximum deviation between the aerosol single scattering albedo retrieved with and withouout considering chlorophyll can reach 10 percent. Thus, it is important to take account of the phytoplankton impacts on atmospheric remote sensing measurements.