Identification of human-induced changes in atmospheric moisture content.

Data from the satellite-based Special Sensor Microwave Imager (SSM/I) show that the total atmospheric moisture content over oceans has increased by 0.41 kg/m(2) per decade since 1988. Results from current climate models indicate that water vapor increases of this magnitude cannot be explained by climate noise alone. In a formal detection and attribution analysis using the pooled results from 22 different climate models, the simulated "fingerprint" pattern of anthropogenically caused changes in water vapor is identifiable with high statistical confidence in the SSM/I data. Experiments in which forcing factors are varied individually suggest that this fingerprint "match" is primarily due to human-caused increases in greenhouse gases and not to solar forcing or recovery from the eruption of Mount Pinatubo. Our findings provide preliminary evidence of an emerging anthropogenic signal in the moisture content of earth's atmosphere.

Forced and unforced ocean temperature changes in Atlantic and Pacific tropical cyclogenesis regions.

Previous research has identified links between changes in sea surface temperature (SST) and hurricane intensity. We use climate models to study the possible causes of SST changes in Atlantic and Pacific tropical cyclogenesis regions. The observed SST increases in these regions range from 0.32 degrees C to 0.67 degrees C over the 20th century. The 22 climate models examined here suggest that century-timescale SST changes of this magnitude cannot be explained solely by unforced variability of the climate system. We employ model simulations of natural internal variability to make probabilistic estimates of the contribution of external forcing to observed SST changes. For the period 1906-2005, we find an 84% chance that external forcing explains at least 67% of observed SST increases in the two tropical cyclogenesis regions. Model "20th-century" simulations, with external forcing by combined anthropogenic and natural factors, are generally capable of replicating observed SST increases. In experiments in which forcing factors are varied individually rather than jointly, human-caused changes in greenhouse gases are the main driver of the 20th-century SST increases in both tropical cyclogenesis regions.

Atmospheric brown clouds: impacts on South Asian climate and hydrological cycle.

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.

Contributions of anthropogenic and natural forcing to recent tropopause height changes.

Observations indicate that the height of the tropopause-the boundary between the stratosphere and troposphere-has increased by several hundred meters since 1979. Comparable increases are evident in climate model experiments. The latter show that human-induced changes in ozone and well-mixed greenhouse gases account for approximately 80% of the simulated rise in tropopause height over 1979-1999. Their primary contributions are through cooling of the stratosphere (caused by ozone) and warming of the troposphere (caused by well-mixed greenhouse gases). A model-predicted fingerprint of tropopause height changes is statistically detectable in two different observational ("reanalysis") data sets. This positive detection result allows us to attribute overall tropopause height changes to a combination of anthropogenic and natural external forcings, with the anthropogenic component predominating.

Influence of satellite data uncertainties on the detection of externally forced climate change.

Two independent analyses of the same satellite-based radiative emissions data yield tropospheric temperature trends that differ by 0.1 degrees C per decade over 1979 to 2001. The troposphere warms appreciably in one satellite data set, while the other data set shows little overall change. These satellite data uncertainties are important in studies seeking to identify human effects on climate. A model-predicted "fingerprint" of combined anthropogenic and natural effects is statistically detectable only in the satellite data set with a warming troposphere. Our findings show that claimed inconsistencies between model predictions and satellite tropospheric temperature data (and between the latter and surface data) may be an artifact of data uncertainties.

South asian summer monsoon variability in a model with doubled atmospheric carbon dioxide concentration.

Doubled atmospheric carbon dioxide concentration in a global coupled ocean-atmosphere climate model produced increased surface temperatures and evaporation and greater mean precipitation in the south Asian summer monsoon region. As a partial consequence, interannual variability of area-averaged monsoon rainfall was enhanced. Consistent with the climate sensitivity results from the model, observations showed a trend of increased interannual variability of Indian monsoon precipitation associated with warmer land and ocean temperatures in the monsoon region.

Interpretation of snow-climate feedback as produced by 17 general circulation models.

Snow feedback is expected to amplify global warming caused by increasing concentrations of atmospheric greenhouse gases. The conventional explanation is that a warmer Earth will have less snow cover, resulting in a darker planet that absorbs more solar radiation. An intercomparison of 17 general circulation models, for which perturbations of sea surface temperature were used as a surrogate climate change, suggests that this explanation is overly simplistic. The results instead indicate that additional amplification or moderation may be caused both by cloud interactions and longwave radiation. One measure of this net effect of snow feedback was found to differ markedly among the 17 climate models, ranging from weak negative feedback in some models to strong positive feedback in others.

Interpretation of cloud-climate feedback as produced by 14 atmospheric general circulation models.

Understanding the cause of differences among general circulation model projections of carbon dioxide-induced climatic change is a necessary step toward improving the models. An intercomparison of 14 atmospheric general circulation models, for which sea surface temperature perturbations were used as a surrogate climate change, showed that there was a roughly threefold variation in global climate sensitivity. Most of this variation is attributable to differences in the models' depictions of cloud-climate feedback, a result that emphasizes the need for improvements in the treatment of clouds in these models if they are ultimately to be used as climatic predictors.