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Florian Rauser, Peter Gleckler, and Jochem Marotzke

Abstract

We discuss the current code of practice in the climate sciences to routinely create climate model ensembles as ensembles of opportunity from the newest phase of the Coupled Model Intercomparison Project (CMIP). We give a two-step argument to rethink this process. First, the differences between generations of ensembles corresponding to different CMIP phases in key climate quantities are not large enough to warrant an automatic separation into generational ensembles for CMIP3 and CMIP5. Second, we suggest that climate model ensembles cannot continue to be mere ensembles of opportunity but should always be based on a transparent scientific decision process. If ensembles can be constrained by observation, then they should be constructed as target ensembles that are specifically tailored to a physical question. If model ensembles cannot be constrained by observation, then they should be constructed as cross-generational ensembles, including all available model data to enhance structural model diversity and to better sample the underlying uncertainties. To facilitate this, CMIP should guide the necessarily ongoing process of updating experimental protocols for the evaluation and documentation of coupled models. With an emphasis on easy access to model data and facilitating the filtering of climate model data across all CMIP generations and experiments, our community could return to the underlying idea of using model data ensembles to improve uncertainty quantification, evaluation, and cross-institutional exchange.

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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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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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Joao Teixeira, Duane Waliser, Robert Ferraro, Peter Gleckler, Tsengdar Lee, and Gerald Potter

The objective of the Observations for Model Intercomparison Projects (Obs4MIPs) is to provide observational data to the climate science community, which is analogous (in terms of variables, temporal and spatial frequency, and periods) to output from the 5th phase of the World Climate Research Programme's (WCRP) Coupled Model Intercomparison Project (CMIP5) climate model simulations. The essential aspect of the Obs4MIPs methodology is that it strictly follows the CMIP5 protocol document when selecting the observational datasets. Obs4MIPs also provides documentation that describes aspects of the observational data (e.g., data origin, instrument overview, uncertainty estimates) that are of particular relevance to scientists involved in climate model evaluation and analysis. In this paper, we focus on the activities related to the initial set of satellite observations, which are being carried out in close coordination with CMIP5 and directly engage NASA's observational (e.g., mission and instrument) science teams. Having launched Obs4MIPs with these datasets, a broader effort is also briefly discussed, striving to engage other agencies and experts who maintain datasets, including reanalysis, which can be directly used to evaluate climate models. Different strategies for using satellite observations to evaluate climate models are also briefly summarized.

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Robert Ferraro, Duane E. Waliser, Peter Gleckler, Karl E. Taylor, and Veronika Eyring
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Angeline G. Pendergrass, Peter J. Gleckler, L. Ruby Leung, and Christian Jakob
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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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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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Yann Y. Planton, Eric Guilyardi, Andrew T. Wittenberg, Jiwoo Lee, Peter J. Gleckler, Tobias Bayr, Shayne McGregor, Michael J. McPhaden, Scott Power, Romain Roehrig, Jérôme Vialard, and Aurore Voldoire

Abstract

El Niño–Southern Oscillation (ENSO) is the dominant mode of interannual climate variability on the planet, with far-reaching global impacts. It is therefore key to evaluate ENSO simulations in state-of-the-art numerical models used to study past, present, and future climate. Recently, the Pacific Region Panel of the International Climate and Ocean: Variability, Predictability and Change (CLIVAR) Project, as a part of the World Climate Research Programme (WCRP), led a community-wide effort to evaluate the simulation of ENSO variability, teleconnections, and processes in climate models. The new CLIVAR 2020 ENSO metrics package enables model diagnosis, comparison, and evaluation to 1) highlight aspects that need improvement; 2) monitor progress across model generations; 3) help in selecting models that are well suited for particular analyses; 4) reveal links between various model biases, illuminating the impacts of those biases on ENSO and its sensitivity to climate change; and to 5) advance ENSO literacy. By interfacing with existing model evaluation tools, the ENSO metrics package enables rapid analysis of multipetabyte databases of simulations, such as those generated by the Coupled Model Intercomparison Project phases 5 (CMIP5) and 6 (CMIP6). The CMIP6 models are found to significantly outperform those from CMIP5 for 8 out of 24 ENSO-relevant metrics, with most CMIP6 models showing improved tropical Pacific seasonality and ENSO teleconnections. Only one ENSO metric is significantly degraded in CMIP6, namely, the coupling between the ocean surface and subsurface temperature anomalies, while the majority of metrics remain unchanged.

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Clara Orbe, Luke Van Roekel, Ángel F. Adames, Amin Dezfuli, John Fasullo, Peter J. Gleckler, Jiwoo Lee, Wei Li, Larissa Nazarenko, Gavin A. Schmidt, Kenneth R. Sperber, and Ming Zhao

Abstract

We compare the performance of several modes of variability across six U.S. climate modeling groups, with a focus on identifying robust improvements in recent models [including those participating in phase 6 of the Coupled Model Intercomparison Project (CMIP)] compared to previous versions. In particular, we examine the representation of the Madden–Julian oscillation (MJO), El Niño–Southern Oscillation (ENSO), the Pacific decadal oscillation (PDO), the quasi-biennial oscillation (QBO) in the tropical stratosphere, and the dominant modes of extratropical variability, including the southern annular mode (SAM), the northern annular mode (NAM) [and the closely related North Atlantic Oscillation (NAO)], and the Pacific–North American pattern (PNA). Where feasible, we explore the processes driving these improvements through the use of “intermediary” experiments that utilize model versions between CMIP3/5 and CMIP6 as well as targeted sensitivity experiments in which individual modeling parameters are altered. We find clear and systematic improvements in the MJO and QBO and in the teleconnection patterns associated with the PDO and ENSO. Some gains arise from better process representation, while others (e.g., the QBO) from higher resolution that allows for a greater range of interactions. Our results demonstrate that the incremental development processes in multiple climate model groups lead to more realistic simulations over time.

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