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Peter J. Gleckler and Bryan C. Weare

Abstract

A methodology to define uncertainties associated with ocean surface heat flux calculations has been developed and applied to a global climatology that utilizes a summary of the Comprehensive Ocean–Atmosphere Data Set surface observations. Systematic and random uncertainties in the net oceanic heat flux and each of its four components at individual grid points and for zonal averages have been estimated for each calendar month and for the annual mean. The most important uncertainties of the 2° × 2° grid cell values of each of the heat fluxes are described. Annual mean net shortwave flux random uncertainties associated with errors in estimating cloud cover in the Tropics yield total uncertainties that are greater than 25 W m−2. In the northern latitudes, where the large number of observations substantially reduces the influence of these random errors, the systematic uncertainties in the utilized parameterization are largely responsible for total uncertainties in the shortwave fluxes, which usually remain greater than 10 W m−2. Systematic uncertainties dominate in the zonal means because spatial averaging has led to a further reduction of the random errors. The situation for the annual mean latent heat flux is somewhat different in that even for gridpoint values, the contributions of the systematic uncertainties tend to be larger than those of the random uncertainties at most latitudes. Latent heat flux uncertainties are greater than 20 W m−2 nearly everywhere south of 40°N and in excess of 30 W m−2 over broad areas of the subtropics, even those with large numbers of observations. Resulting zonal mean latent heat uncertainties are largest (∼30 W m−2) in the middle latitudes and subtropics and smallest (∼10–25 W m−2) near the equator and over the northernmost regions. Preliminary comparison of zonal average fluxes suggests that most atmospheric general circulation models produce excessively large ocean surface fluxes of net solar heating and evaporative cooling when forced with realistic sea surface temperatures. It is expected that the method introduced here will be refined to produce increasingly reliable estimates of uncertainties in surface flux atlases derived from ship observations.

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Shuaiqi Tang, Peter Gleckler, Shaocheng Xie, Jiwoo Lee, Min-Seop Ahn, Curt Covey, and Chengzhu Zhang

Abstract

The diurnal and semidiurnal cycle of precipitation simulated from CMIP6 models during 1996–2005 are evaluated globally between 60°S and 60°N as well as at 10 selected locations representing three categories of diurnal cycle of precipitation: 1) afternoon precipitation over land, 2) early morning precipitation over ocean, and 3) nocturnal precipitation over land. Three satellite-based and two ground-based rainfall products are used to evaluate the climate models. Globally, the ensemble mean of CMIP6 models shows a diurnal phase of 3 to 4 h earlier over land and 1 to 2 h earlier over ocean when compared with the latest satellite products. These biases are in line with what were found in previous versions of climate models but reduced compared to the CMIP5 ensemble mean. Analysis at the selected locations complemented with in situ measurements further reinforces these results. Several CMIP6 models have shown a significant improvement in the diurnal cycle of precipitation compared to their CMIP5 counterparts, notably in delaying afternoon precipitation over land. This can be attributed to the use of more sophisticated convective parameterizations. Most models are still unable to capture the nocturnal peak associated with elevated convection and propagating mesoscale convective systems, with a few exceptions that allow convection to be initiated above the boundary layer to capture nocturnal elevated convection. We also quantify an encouraging consistency between the satellite- and ground-based precipitation measurements despite differing spatiotemporal resolutions and sampling periods, which provides confidence in using them to evaluate the diurnal and semidiurnal cycle of precipitation in climate models.

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Detelina P. Ivanova, Peter J. Gleckler, Karl E. Taylor, Paul J. Durack, and Kate D. Marvel

Abstract

Reproducing characteristics of observed sea ice extent remains an important climate modeling challenge. This study describes several approaches to improve how model biases in total sea ice distribution are quantified, and applies them to historically forced simulations contributed to phase 5 of the Coupled Model Intercomparison Project (CMIP5). The quantity of hemispheric total sea ice area, or some measure of its equatorward extent, is often used to evaluate model performance. A new approach is introduced that investigates additional details about the structure of model errors, with an aim to reduce the potential impact of compensating errors when gauging differences between simulated and observed sea ice. Using multiple observational datasets, several new methods are applied to evaluate the climatological spatial distribution and the annual cycle of sea ice cover in 41 CMIP5 models. It is shown that in some models, error compensation can be substantial, for example resulting from too much sea ice in one region and too little in another. Error compensation tends to be larger in models that agree more closely with the observed total sea ice area, which may result from model tuning. The results herein suggest that consideration of only the total hemispheric sea ice area or extent can be misleading when quantitatively comparing how well models agree with observations. Further work is needed to fully develop robust methods to holistically evaluate the ability of models to capture the finescale structure of sea ice characteristics; however, the “sector scale” metric used here aids in reducing the impact of compensating errors in hemispheric integrals.

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Jiwoo Lee, Kenneth R. Sperber, Peter J. Gleckler, Karl E. Taylor, and Céline J. W. Bonfils

Abstract

We evaluate extratropical modes of variability in the three most recent phases of the Coupled Model Intercomparison Project (CMIP3, CMIP5, and CMIP6) to gauge improvement of climate models over time. A suite of high-level metrics is employed to objectively evaluate how well climate models simulate the observed northern annular mode (NAM), North Atlantic Oscillation (NAO), Pacific–North America pattern (PNA), southern annular mode (SAM), Pacific decadal oscillation (PDO), North Pacific Oscillation (NPO), and North Pacific Gyre Oscillation (NPGO). We apply a common basis function (CBF) approach that projects model anomalies onto observed empirical orthogonal functions (EOFs), together with the traditional EOF approach, to CMIP Historical and AMIP models. We find simulated spatial patterns of those modes have been significantly improved in the newer models, although the skill improvement is sensitive to the mode and season considered. We identify some potential contributions to the pattern improvement of certain modes (e.g., the Southern Hemisphere jet and high-top vertical coordinate); however, the performance changes are likely attributed to gradual improvement of the base climate and multiple relevant processes. Less performance improvement is evident in the mode amplitude of these modes and systematic overestimation of the mode amplitude in spring remains in the newer climate models. We find that the postdominant season amplitude errors in atmospheric modes are not limited to coupled runs but are often already evident in AMIP simulations. This suggests that rectifying the egregious postdominant season amplitude errors found in many models can be addressed in an atmospheric-only framework, making it more tractable to address in the model development process.

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David W. Pierce, Tim P. Barnett, Krishna M. AchutaRao, Peter J. Gleckler, Jonathan M. Gregory, and Warren M. Washington

Abstract

Observations show the oceans have warmed over the past 40 yr, with appreciable regional variation and more warming at the surface than at depth. Comparing the observations with results from two coupled ocean–atmosphere climate models [the Parallel Climate Model version 1 (PCM) and the Hadley Centre Coupled Climate Model version 3 (HadCM3)] that include anthropogenic forcing shows remarkable agreement between the observed and model-estimated warming. In this comparison the models were sampled at the same locations as gridded yearly observed data. In the top 100 m of the water column the warming is well separated from natural variability, including both variability arising from internal instabilities of the coupled ocean–atmosphere climate system and that arising from volcanism and solar fluctuations. Between 125 and 200 m the agreement is not significant, but then increases again below this level, and remains significant down to 600 m. Analysis of PCM’s heat budget indicates that the warming is driven by an increase in net surface heat flux that reaches 0.7 W m−2 by the 1990s; the downward longwave flux increases by 3.7 W m−2, which is not fully compensated by an increase in the upward longwave flux of 2.2 W m−2. Latent and net solar heat fluxes each decrease by about 0.6 W m−2. The changes in the individual longwave components are distinguishable from the preindustrial mean by the 1920s, but due to cancellation of components, changes in the net surface heat flux do not become well separated from zero until the 1960s. Changes in advection can also play an important role in local ocean warming due to anthropogenic forcing, depending on the location. The observed sampling of ocean temperature is highly variable in space and time, but sufficient to detect the anthropogenic warming signal in all basins, at least in the surface layers, by the 1980s.

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Curt Covey, Peter J. Gleckler, Charles Doutriaux, Dean N. Williams, Aiguo Dai, John Fasullo, Kevin Trenberth, and Alexis Berg

Abstract

Metrics are proposed—that is, a few summary statistics that condense large amounts of data from observations or model simulations—encapsulating the diurnal cycle of precipitation. Vector area averaging of Fourier amplitude and phase produces useful information in a reasonably small number of harmonic dial plots, a procedure familiar from atmospheric tide research. The metrics cover most of the globe but down-weight high-latitude wintertime ocean areas where baroclinic waves are most prominent. This enables intercomparison of a large number of climate models with observations and with each other. The diurnal cycle of precipitation has features not encountered in typical climate model intercomparisons, notably the absence of meaningful “average model” results that can be displayed in a single two-dimensional map. Displaying one map per model guides development of the metrics proposed here by making it clear that land and ocean areas must be averaged separately, but interpreting maps from all models becomes problematic as the size of a multimodel ensemble increases.

Global diurnal metrics provide quick comparisons with observations and among models, using the most recent version of the Coupled Model Intercomparison Project (CMIP). This includes, for the first time in CMIP, spatial resolutions comparable to global satellite observations. Consistent with earlier studies of resolution versus parameterization of the diurnal cycle, the longstanding tendency of models to produce rainfall too early in the day persists in the high-resolution simulations, as expected if the error is due to subgrid-scale physics.

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W. Lawrence Gates, James S. Boyle, Curt Covey, Clyde G. Dease, Charles M. Doutriaux, Robert S. Drach, Michael Fiorino, Peter J. Gleckler, Justin J. Hnilo, Susan M. Marlais, Thomas J. Phillips, Gerald L. Potter, Benjamin D. Santer, Kenneth R. Sperber, Karl E. Taylor, and Dean N. Williams

The Atmospheric Model Intercomparison Project (AMIP), initiated in 1989 under the auspices of the World Climate Research Programme, undertook the systematic validation, diagnosis, and intercomparison of the performance of atmospheric general circulation models. For this purpose all models were required to simulate the evolution of the climate during the decade 1979–88, subject to the observed monthly average temperature and sea ice and a common prescribed atmospheric CO2 concentration and solar constant. By 1995, 31 modeling groups, representing virtually the entire international atmospheric modeling community, had contributed the required standard output of the monthly means of selected statistics. These data have been analyzed by the participating modeling groups, by the Program for Climate Model Diagnosis and Intercomparison, and by the more than two dozen AMIP diagnostic subprojects that have been established to examine specific aspects of the models' performance. Here the analysis and validation of the AMIP results as a whole are summarized in order to document the overall performance of atmospheric general circulation–climate models as of the early 1990s. The infrastructure and plans for continuation of the AMIP project are also reported on.

Although there are apparent model outliers in each simulated variable examined, validation of the AMIP models' ensemble mean shows that the average large-scale seasonal distributions of pressure, temperature, and circulation are reasonably close to what are believed to be the best observational estimates available. The large-scale structure of the ensemble mean precipitation and ocean surface heat flux also resemble the observed estimates but show particularly large intermodel differences in low latitudes. The total cloudiness, on the other hand, is rather poorly simulated, especially in the Southern Hemisphere. The models' simulation of the seasonal cycle (as represented by the amplitude and phase of the first annual harmonic of sea level pressure) closely resembles the observed variation in almost all regions. The ensemble's simulation of the interannual variability of sea level pressure in the tropical Pacific is reasonably close to that observed (except for its underestimate of the amplitude of major El Niños), while the interannual variability is less well simulated in midlatitudes. When analyzed in terms of the variability of the evolution of their combined space–time patterns in comparison to observations, the AMIP models are seen to exhibit a wide range of accuracy, with no single model performing best in all respects considered.

Analysis of the subset of the original AMIP models for which revised versions have subsequently been used to revisit the experiment shows a substantial reduction of the models' systematic errors in simulating cloudiness but only a slight reduction of the mean seasonal errors of most other variables. In order to understand better the nature of these errors and to accelerate the rate of model improvement, an expanded and continuing project (AMIP II) is being undertaken in which analysis and intercomparison will address a wider range of variables and processes, using an improved diagnostic and experimental infrastructure.

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