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.

Simulated and observed variability in ocean temperature and heat content.

Observations show both a pronounced increase in ocean heat content (OHC) over the second half of the 20th century and substantial OHC variability on interannual-to-decadal time scales. Although climate models are able to simulate overall changes in OHC, they are generally thought to underestimate the amplitude of OHC variability. Using simulations of 20th century climate performed with 13 numerical models, we demonstrate that the apparent discrepancy between modeled and observed variability is largely explained by accounting for changes in observational coverage and instrumentation and by including the effects of volcanic eruptions. Our work does not support the recent claim that the 0- to 700-m layer of the global ocean experienced a substantial OHC decrease over the 2003 to 2005 time period. We show that the 2003-2005 cooling is largely an artifact of a systematic change in the observing system, with the deployment of Argo floats reducing a warm bias in the original observing system.

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.

Volcanoes and climate: Krakatoa's signature persists in the ocean.

We have analysed a suite of 12 state-of-the-art climate models and show that ocean warming and sea-level rise in the twentieth century were substantially reduced by the colossal eruption in 1883 of the volcano Krakatoa in the Sunda strait, Indonesia. Volcanically induced cooling of the ocean surface penetrated into deeper layers, where it persisted for decades after the event. This remarkable effect on oceanic thermal structure is longer lasting than has previously been suspected and is sufficient to offset a large fraction of ocean warming and sea-level rise caused by anthropogenic influences.

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.