摘要
Trend analysis of atmospheric aerosols enhances confidence in the evaluation of both direct and indirect effects of aerosols on regional climate change. To comprehensively achieve this over East Africa, it’s important to understand aerosols temporal characteristics over well selected sites namely Nairobi (1°S, 36°E), Mbita (0°S, 34°E), Mau Forest (0.0°S - 0.6°S;35.1°E - 35.7°E), Malindi (2°S, 40°E), Mount Kilimanjaro (3°S, 37°E) and Kampala (0°N, 32.1°E). In this context, trend analysis (annual (in Aerosol Optical Depth (AOD) at 550 nm and Ångström Exponent Anomaly (ÅEA) at 470 - 660 nm) and seasonal (AOD)) from Moderate Resolution Imaging Spectroradiometer (MODIS) were performed following the weighted least squares (WLS) fitting method for the period 2000 to 2013. The MODIS AOD annual trends were ground-truthed by AErosol RObotic NETwork (AERONET) data. Tropical Rainfall Measurement Mission (TRMM) was utilized to derive rainfall rates (RR) in order to assess its influence on the observed aerosol temporal characteristics. The derived annual AOD trends utilizing MODIS and AERONET data were consistent with each other. However, monthly AOD and RR were found to be negatively correlated over Nairobi, Mbita, Mau forest complex and Malindi. There was no clear relationship between the two trends over Kampala and Mount Kilimanjaro, which may imply the role of aerosols in cloud modulation and hence RR received. Seasonality is evident between AOD and ÅEA annual trends as these quantities were observed to be modulated by RR. AOD was observed to decrease over East Africa except Nairobi during the study period as a result of RR during the study period. Unlike the other study sites, Nairobi shows positive trends in AOD that may be attributed to increasing populace and fossil fuel, vehicular-industrial emission and biomass and refuse burning during the study period. Negative trends over the rest of the study sites were associated to rain washout. The AOD and ÅEA derived annual trends were found to meet the statistical significance of 95% confidence level over each study site.
Trend analysis of atmospheric aerosols enhances confidence in the evaluation of both direct and indirect effects of aerosols on regional climate change. To comprehensively achieve this over East Africa, it’s important to understand aerosols temporal characteristics over well selected sites namely Nairobi (1°S, 36°E), Mbita (0°S, 34°E), Mau Forest (0.0°S - 0.6°S;35.1°E - 35.7°E), Malindi (2°S, 40°E), Mount Kilimanjaro (3°S, 37°E) and Kampala (0°N, 32.1°E). In this context, trend analysis (annual (in Aerosol Optical Depth (AOD) at 550 nm and Ångström Exponent Anomaly (ÅEA) at 470 - 660 nm) and seasonal (AOD)) from Moderate Resolution Imaging Spectroradiometer (MODIS) were performed following the weighted least squares (WLS) fitting method for the period 2000 to 2013. The MODIS AOD annual trends were ground-truthed by AErosol RObotic NETwork (AERONET) data. Tropical Rainfall Measurement Mission (TRMM) was utilized to derive rainfall rates (RR) in order to assess its influence on the observed aerosol temporal characteristics. The derived annual AOD trends utilizing MODIS and AERONET data were consistent with each other. However, monthly AOD and RR were found to be negatively correlated over Nairobi, Mbita, Mau forest complex and Malindi. There was no clear relationship between the two trends over Kampala and Mount Kilimanjaro, which may imply the role of aerosols in cloud modulation and hence RR received. Seasonality is evident between AOD and ÅEA annual trends as these quantities were observed to be modulated by RR. AOD was observed to decrease over East Africa except Nairobi during the study period as a result of RR during the study period. Unlike the other study sites, Nairobi shows positive trends in AOD that may be attributed to increasing populace and fossil fuel, vehicular-industrial emission and biomass and refuse burning during the study period. Negative trends over the rest of the study sites were associated to rain washout. The AOD and ÅEA derived annual trends were found to meet the statistical significance of 95% confidence level over each study site.
