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, 102 (15), 5326-33

Atmospheric Brown Clouds: Impacts on South Asian Climate and Hydrological Cycle

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Atmospheric Brown Clouds: Impacts on South Asian Climate and Hydrological Cycle

V Ramanathan et al. Proc Natl Acad Sci U S A.

Abstract

South Asian emissions of fossil fuel SO(2) and black carbon increased approximately 6-fold since 1930, resulting in large atmospheric concentrations of black carbon and other aerosols. This period also witnessed strong negative trends of surface solar radiation, surface evaporation, and summer monsoon rainfall. These changes over India were accompanied by an increase in atmospheric stability and a decrease in sea surface temperature gradients in the Northern Indian Ocean. We conducted an ensemble of coupled ocean-atmosphere simulations from 1930 to 2000 to understand the role of atmospheric brown clouds in the observed trends. The simulations adopt the aerosol radiative forcing from the Indian Ocean experiment observations and also account for global increases in greenhouse gases and sulfate aerosols. The simulated decreases in surface solar radiation, changes in surface and atmospheric temperatures over land and sea, and decreases in monsoon rainfall are similar to the observed trends. We also show that greenhouse gases and sulfates, by themselves, do not account for the magnitude or even the sign in many instances, of the observed trends. Thus, our simulations suggest that absorbing aerosols in atmospheric brown clouds may have played a major role in the observed regional climate and hydrological cycle changes and have masked as much as 50% of the surface warming due to the global increase in greenhouse gases. The simulations also raise the possibility that, if current trends in emissions continue, the subcontinent may experience a doubling of the drought frequency in the coming decades.

Figures

Fig. 1.
Fig. 1.
Time series of emissions and forcing terms. Published estimates of the emission of SO2 (18) and BC (19) are normalized with their 1950 values. The changes, due to the ABCs, in the net (down minus up) solar fluxes at the surface (surface forcing), at top of the atmosphere (top-of-the-atmosphere forcing), and net solar heating of the atmosphere are taken from climate model (PCM) simulations for the ABC_1998 case. The forcing is for annual mean conditions and is the average for all of South Asia and NIO (0° to 30°N and 60°E to 100°E). The interannual variations in the forcing are due to variations in cloudiness.
Fig. 2.
Fig. 2.
Time series of surface heat budget terms. (A) Simulated (blue) and observed (green) annual mean solar fluxes for India at the surface. The fluxes are for average cloud conditions. The simulations are averaged over 5°N to 25°N and from 70°E to 90°E. The observed values are from 10 surface stations distributed between eastern, western, northern, and southern India. The trend in Global Energy Budget Archive is –0.42 W·m–2 per year (±0.15; 95% confidence level), and the trend in the ABC_1998 run is –0.37 W·m–2 per year (±0.12) (2SD of the trends from the five runs of the ensemble). (B) The simulated annual mean surface heat budget for the Indian Ocean from 10°S to 30°N and from 60°E to 100°E.
Fig. 3.
Fig. 3.
Simulated and observed surface temperature changes (K). (A) The dry season (October to May) temperature changes from observations (green) and from various cases of model simulations. (B) SST trend during 1930–2000 for the Indian Ocean (averaged between 60°E to 100°E) as a function of latitude for the premonsoon season of March to June. The uncertainty of the model trend, as estimated from five different ensemble member runs, is <0.1 K between 10°S and 20°N and <0.2 K outside, whereas the observed trend range at the 95% confidence level is ±0.2 K. For PCM, air temperature at 2 m above the sea surface was used. Observed temperature trends were obtained from the Jones Climatic Research Unit (CRU) data set (archived at the Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, Oak Ridge, TN).
Fig. 4.
Fig. 4.
Vertical profile of the simulated temperature trend by ABC_1998 (red curve) for the 1979–2003 period. The values are averaged over all of India. The blue vertical bar is the observed vertically averaged trend from MSU, and the red bar is the vertically averaged trend simulated by ABC_1998. The red bar was obtained by integrating the simulated vertical profile (red curve) with the MSU weighting function (27). The uncertainty in the simulated trend varies from 0.15 to 0.25 K depending on altitude. The mean uncertainty for the vertically averaged trend (red bar) is ≈0.2 K. The 95% confidence interval for the MSU observed trend is ≈0.2 K.
Fig. 5.
Fig. 5.
Rainfall trends. (A) Time series of observed and simulated summer (June to September) rainfall for India from observations and PCM simulations. The results are the percent deviation of the rainfall from the 1930–1960 average. Observed rainfall data were obtained from ref. . The data are smoothed by an 11-year running mean averaging procedure. (B) Trend for 1930–2000 in monthly mean rainfall for India. The uncertainty of the model trend, as estimated from five realizations, is ≈0.4 mm/day from May to July and <0.2 mm/day in the other months. For the observed trend, the 95% confidence level is ±0.9 mm/day (wet season) down to ±0.2 mm/day (January–March).
Fig. 6.
Fig. 6.
Change in the meridional circulation due to the ABC from 1985 to 2000 for June and July. The fields have been averaged from 60°E to 100°E, essentially covering the entire Indian Ocean and the South Asian region. The changes were obtained by differencing the 1985–2000 averaged streamlines: ABC_1998_GHGs+SO4_1998. The red shade indicates a region with increased sinking motions, and the blue shade indicates regions with increased rising motions.
Fig. 7.
Fig. 7.
Simulated and observed frequency of droughts per decade. Drought is defined to occur when the summer rainfall decrease exceeds 10% of the climatological average, defined here as the average summer rainfall for 1930–1960.

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