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ABSTRACT The time scales of the Paris Climate Agreement indicate urgent action is required on climate policies over the next few decades, in order to avoid the worst risks posed [...]

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 Nino. 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. climate modeling | multimodel database | water vapor

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.sup.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. climate change | climate modeling | detection and attribution | water vapor

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-time-scale 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.

The tropopause represents the boundary between the troposphere and stratosphere and is marked by large changes in the thermal, dynamical, and chemical structure of the atmosphere (1-3). Increases in tropopause [...], 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 ~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.

Since 1979, atmospheric microwave emissions have been monitored by the Microwave Sounding Unit (MSU) flown on polar-orbiting satellites (1). Satellite temperature measurements are mass-weighted averages of the microwave emissions from [...], Two independent analyses of the same satellite-based radiative emissions data yield tropospheric temperature trends that differ by 0.1°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.

Historically, the high computational expense of performing lengthy climate simulations has limited the number of possible realizations. Here, we exploit the computational advantage of a recently developed parallel atmospheric general circulation model (AGCM) to determine the number of realizations required to calculate the model's statistics to a specified degree of certainty. Using standard statistical analysis techniques, this minimum ensemble size is found to be highly dependent on which output field is under examination. A strong dependence on location, season and averaging is also revealed. [ABSTRACT FROM AUTHOR]