Incorporating model quality information in climate change detection and attribution studies.

In a recent multimodel detection and attribution (D&A) study using the pooled results from 22 different climate models, the simulated "fingerprint" pattern of anthropogenically caused changes in water vapor was identifiable with high statistical confidence in satellite data. Each model received equal weight in the D&A analysis, despite large differences in the skill with which they simulate key aspects of observed climate. Here, we examine whether water vapor D&A results are sensitive to model quality. The "top 10" and "bottom 10" models are selected with three different sets of skill measures and two different ranking approaches. The entire D&A analysis is then repeated with each of these different sets of more or less skillful models. Our performance metrics include the ability to simulate the mean state, the annual cycle, and the variability associated with El Niño. We find that estimates of an anthropogenic water vapor fingerprint are insensitive to current model uncertainties, and are governed by basic physical processes that are well-represented in climate models. Because the fingerprint is both robust to current model uncertainties and dissimilar to the dominant noise patterns, our ability to identify an anthropogenic influence on observed multidecadal changes in water vapor is not affected by "screening" based on model quality.

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.

Amplification of surface temperature trends and variability in the tropical atmosphere.

The month-to-month variability of tropical temperatures is larger in the troposphere than at Earth's surface. This amplification behavior is similar in a range of observations and climate model simulations and is consistent with basic theory. On multidecadal time scales, tropospheric amplification of surface warming is a robust feature of model simulations, but it occurs in only one observational data set. Other observations show weak, or even negative, amplification. These results suggest either that different physical mechanisms control amplification processes on monthly and decadal time scales, and models fail to capture such behavior; or (more plausibly) that residual errors in several observational data sets used here affect their representation of long-term trends.

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.

Seasat scatterometer: results of the gulf of alaska workshop.

The Seasat microwave scatterometer was designed to measure, globally and in nearly all weather, wind speed to an accuracy of +/- 2 meters per second and wind direction to +/- 20 degrees in two swaths 500 kilometers wide on either side of the spacecraft. For two operating modes in rain-free conditions, a limited number of comparisons to high-quality surface truth indicates that these specifications may have been met.

Seasat scanning multichannel microwave radiometer: results of the gulf of alaska workshop.

The scanning multichannel microwave radiometer results for the Gulf of Alaska Seasat Experiment Workshop are quite encouraging, especially in view of the immaturity of the data-processing algorithms. For open ocean, rain-free cells of highest-quality surface truth wind determinations exhibit standard deviations of 3 meters per second about a bias of 1.5 meters per second. The sea-surface temperature shows a standard deviation of approximately 1.5 degrees C about a bias of 3 degrees to 5 degrees C under a variety of changing meteorological conditions.