The ability of 15 atmospheric GCM models (AGCM) to simulate the tropical intraseasonal oscillation has been studied as part of AMIP. Time series of the daily upper tropospheric velocity potential and zonal wind, averaged over the equatorial belt, were provided from each AGCM simulation. These data were analyzed using a variety of techniques such as time filtering and space-time spectral analysis to identify eastward and westward propagating waves. The results have been compared with an identical assessment of ECMWF analyses for the period 1982-1991. The models display a wide range of skill in simulating the intraseasonal oscillation. Most models show evidence of an eastward propagating anomaly in the velocity potential field, although in some models there is a greater tendency for a standing oscillation, and in one or two the field is rather chaotic with no preferred direction of propagation. Where a model has a clear eastward propagating signal, typical periodicities seem quite reasonable although there is a tendency for the models to simulate shorter periods than in the ECMWF analyses, where it is near 50 days. The results of the space-time spectral analysis have shown that no model has captured the dominance of the intraseasonal oscillation found in the analyses. Several models have peaks at intraseasonal time scales, but nearly all have relatively more power at higher frequencies

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 humancaused 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 '' F ingerprint'' studies, which seek to identify the causes of recent climate change, involve rigorous statistical comparisons of modeled and observed climate change patterns (1). Such work has been influential in shaping the ''discernible human influence'' conclusions of national and international scientific assessments (2-4). Most fingerprint studies have focused on temperature changes at the earth's surface (5, 6), in the free atmosphere (7,8), or in the oceans (9), or have considered variables whose behavior is directly related to changes in atmospheric temperature (10).Despite a growing body of empirical evidence documenting increases in moisture-related variables (11,12), and climate model evidence of a number of robust hydrological responses to global warming (13,14), there have been no formal fingerprint studies involving changes in the total amount of atmospheric water vapor, W. Other aspects of moisture changes have received attention in recent fingerprint work, with identification of an anthropogenic signal in observed records of continental river runoff (15), zonal mean rainfall (16), and surface specific humidity (17).Warming induced by human-caused changes in well mixed greenhouse gases (GHGs) should increase W (11,12). Under the assumption that relative humidity remains approximately constant, for which there is considerable empirical support (13,18,19), the increase in W is estimated to be Ϸ6.0-7.5% per degree Celsius warming of the lower troposphere (13, 18). The observed increase in W over the global ocean, as inferred since late 1987 from microwave radiometry measurements made with the satellite-borne Special Sensor Microwave Imager (SSM/I), is broadly consistent with theory (12,18,20). Observational and Model DataThe SSM/I atmospheric moisture retrievals are based on measurements of microwave emissions from the 22-GHz water vapor absorption line. The distinctive shape of this line provides robust retrievals that are less problematic than other types of satellite measurement. For example, the si...

[1] We examine changes in tropopause height, a variable that has hitherto been neglected in climate change detection and attribution studies. The pressure of the lapse rate tropopause, p LRT , is diagnosed from reanalyses and from integrations performed with coupled and uncoupled climate models. In the National Centers for Environmental Prediction (NCEP) reanalysis, global-mean p LRT decreases by 2.16 hPa/decade over 1979-2000, indicating an increase in the height of the tropopause. The shorter European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis has a global-mean p LRT trend of À1.13 hPa/decade over 1979-1993. Simulated p LRT trends over the past several decades are consistent with reanalysis results. Superimposed on the overall increase in tropopause height in models and reanalyses are pronounced height decreases following the eruptions of El Chichón and Pinatubo. Interpreting these p LRT results requires knowledge of both T(z), the initial atmospheric temperature profile, and ÁT(z), the change in this profile in response to external forcing. T(z) has a strong latitudinal dependence, as does ÁT(z) for forcing by well-mixed greenhouse gases and stratospheric ozone depletion. These dependencies help explain why overall tropopause height increases in reanalyses and observations are amplified toward the poles. The pronounced increases in tropopause height in the climate change integrations considered here indicate that even AGCMs with coarse vertical resolution can resolve relatively small externally forced changes in tropopause height. The simulated decadal-scale changes in p LRT are primarily thermally driven and are an integrated measure of the anthropogenically forced warming of the troposphere and cooling of the stratosphere. Our algorithm for estimating p LRT (based on a thermal definition of tropopause height) is sufficiently sensitive to resolve these large-scale changes in atmospheric thermal structure. Our results indicate that the simulated increase in tropopause height over 1979-1997 is a robust, zero-order response of the climate system to forcing by well-mixed greenhouse gases and stratospheric ozone depletion. At the global-mean level, we find agreement between the simulated decadal-scale p LRT changes and those estimated from reanalyses. While the agreement between simulated p LRT changes and those in NCEP is partly fortuitous (due to excessive stratospheric cooling in NCEP), it is also driven by real pattern similarities. Our work illustrates that changes in tropopause height may be a useful ''fingerprint'' of human effects on climate and are deserving of further attention.

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The present study examines the correspondence between short-and long-term systematic errors in five atmospheric models by comparing the 16 five-day hindcast ensembles from the Transpose Atmospheric Model Intercomparison Project II (Transpose-AMIP II) for July-August 2009 (short term) to the climate simulations from phase 5 of the Coupled Model Intercomparison Project (CMIP5) and AMIP for the JuneAugust mean conditions of the years of 1979-2008 (long term). Because the short-term hindcasts were conducted with identical climate models used in the CMIP5/AMIP simulations, one can diagnose over what time scale systematic errors in these climate simulations develop, thus yielding insights into their origin through a seamless modeling approach.The analysis suggests that most systematic errors of precipitation, clouds, and radiation processes in the long-term climate runs are present by day 5 in ensemble average hindcasts in all models. Errors typically saturate after few days of hindcasts with amplitudes comparable to the climate errors, and the impacts of initial conditions on the simulated ensemble mean errors are relatively small. This robust bias correspondence suggests that these systematic errors across different models likely are initiated by model parameterizations since the atmospheric large-scale states remain close to observations in the first 2-3 days. However, biases associated with model physics can have impacts on the large-scale states by day 5, such as zonal winds, 2-m temperature, and sea level pressure, and the analysis further indicates a good correspondence between shortand long-term biases for these large-scale states. Therefore, improving individual model parameterizations in the hindcast mode could lead to the improvement of most climate models in simulating their climate mean state and potentially their future projections.

The Community Atmospheric Model 2 (CAM2) is run as a short‐term (1–5 days) forecast model initialized with reanalysis data. The intent is to reveal model deficiencies before complex interactions obscure the root error sources. Integrations are carried out for three Atmospheric Radiation Measurement (ARM) Program intensive operational periods (IOPs): June/July 1997, April 1997, and March 2000. The ARM data are used to validate the model in detail for the Southern Great Plains (SGP) site for all the periods and in the tropical west Pacific for the March 2000 period. The model errors establish themselves quickly, and within 3 days the model has evolved into a state distinctly different from the ARM observations. The summer forecasts evince a systematic error in convective rainfall. This error manifests itself in the temperature and moisture profiles after a single diurnal cycle. The same error characteristics are seen in the March 2000 tropical west Pacific forecasts. The model performs well in the spring cases at the SGP. Most of the error is manifested during rainy periods. The ARM cloud radar comparison to the model reveals cloud errors which are consistent with the relative humidity profile errors. The cloud errors are similar to those seen in climatological integrations, but the state variable errors are different. Thus there is the possibility that the some basic parameterization errors are obscured in the climatological integrations. The approach described here will facilitate parameterization experimentation, diagnoses, and validation. One way of reducing cloud feedback uncertainty is to make the physical processes behave in the most realistic manner possible. Paradoxically, perhaps the best way to reduce uncertainty in cloud feedback mechanisms is to evaluate the model processes with realistic forcing before such feedbacks have any significant affect.

We systematically explore the ability of the Community Atmospheric Model version 5 (CAM5) to simulate the Madden-Julian Oscillation (MJO), through an analysis of MJO metrics calculated from a 1100-member perturbed parameter ensemble of 5 year simulations with observed sea surface temperatures. Parameters from the deep convection scheme make the greatest contribution to the variance in MJO simulation quality with a much smaller contribution from parameters in the large-scale cloud, shallow convection, and boundary layer turbulence schemes. Improved MJO variability results from a larger lateral entrainment rate and a reduction in the precipitation efficiency of deep convection that was achieved by a smaller autoconversion of cloud to rainwater and a larger evaporation of convective precipitation. Unfortunately, simulations with an improved MJO also have a significant negative impact on the climatological values of low-level cloud and absorbed shortwave radiation, suggesting that structural in addition to parametric modifications to CAM5's parameterization suite are needed in order to simultaneously well simulate the MJO and mean-state climate.

Estimated global-scale temperature trends at Earth's surface (as recorded by thermometers) and in the lower troposphere (as monitored by satellites) diverge by up to 0.14 degrees C per decade over the period 1979 to 1998. Accounting for differences in the spatial coverage of satellite and surface measurements reduces this differential, but still leaves a statistically significant residual of roughly 0.1 degrees C per decade. Natural internal climate variability alone, as simulated in three state-of-the-art coupled atmosphere-ocean models, cannot completely explain this residual trend difference. A model forced by a combination of anthropogenic factors and volcanic aerosols yields surface-troposphere temperature trend differences closest to those observed.