ALBERT

Description: Given the large amount of climate model output generated from the series of simulations from phase 5 of the Coupled Model Intercomparison Project (CMIP5), a standard set of performance metrics would facilitate model intercomparison and tracking performance improvements. However, no framework exists for the evaluation of performance metrics. The proposed framework systematically integrates observations into metric assessment to quantitatively evaluate metrics. An optimal metric is defined in this framework as one that measures a behavior that is strongly linked to model quality in representing mean-state present-day climate. The goal of the framework is to objectively and quantitatively evaluate the ability of a performance metric to represent overall model quality. The framework is demonstrated, and the design principles are discussed using a novel set of performance metrics, which assess the simulation of top-of-atmosphere (TOA) and surface radiative flux variance and probability distributions within 34 CMIP5 models against Clouds and the Earth’s Radiant Energy System (CERES) observations and GISS Surface Temperature Analysis (GISTEMP). Of the 44 tested metrics, the optimal metrics are found to be those that evaluate global-mean TOA radiation flux variance.

Description: The twentieth-century climatology and twenty-first-century trend in precipitation P, evaporation E, and P − E for selected semiarid U.S. Southwest and Mediterranean regions are compared between ensembles from phases 3 and 5 of the Coupled Model Intercomparison Project (CMIP3 and CMIP5). The twentieth-century simulations are validated with precipitation from observation and evaporation from reanalysis. It is found that the Special Report on Emissions Scenarios (SRES) A1B simulations in CMIP3 and the simulations with representative concentration pathways (RCPs) 4.5 and 8.5 in CMIP5 produce qualitatively similar seasonal cycles of the twenty-first-century trend in P − E for both semiarid regions. For the southwestern United States, it is characterized by a strong drying trend in spring, a weak moistening trend in summer, a weak drying trend in winter, and an overall drying trend for the annual mean. For the Mediterranean region, a drying trend is simulated for all seasons with an October maximum and July minimum. The consistency between CMIP3 and CMIP5 scenarios indicates that the simulated trend is robust; however, while the trend in P − E is negative in spring for the southwestern United States for all CMIP ensembles, CMIP3 predicts a strongly negative trend in P and minor negative trend in E whereas both CMIP5 scenarios predict a nearly zero trend in P and positive trend in E. For the twentieth-century simulations, the P, E, and P − E of the two model ensembles are statistically indistinguishable for most seasons. This “stagnation” of the simulated climatology from CMIP3 to CMIP5 implies that the hydroclimatic variable biases have not decreased in the newer generation of models. Notably, over the southwestern United States the CMIP3 models produce too much precipitation in the cold season. This bias remains almost unchanged in CMIP5.

Description: The observed slow-down in the global-mean surface temperature (GST) warming from 1998 to 2012 has been called a “warming hiatus.” Certain climate models, operating under experiments which simulate warming by increasing radiative forcing, have been shown to reproduce periods which resemble the observed hiatus. The present study provides a comprehensive analysis of 38 CMIP5 climate models to provide further evidence that models produce warming hiatus periods during warming experiments. GST rates are simulated in each model for the 21st century using two experiments: a moderate warming scenario (RCP4.5) and high-end scenario (RCP8.5). Warming hiatus periods are identified in model simulations by detecting (1) ≥15-year periods lacking a statistically meaningful trend and (2) rapid changes in the GST rate which resemble the observed 1998–2012 hiatus. Under the RCP4.5 experiment, all tested models produce warming hiatus periods. However, once radiative forcing exceeds 5 W/m2—about 2°C GST increase—as simulated in the RCP8.5 experiment after 2050, nearly all models produce only positive warming trends. All models show evidence of rapid changes in the GST rate resembling the observed hiatus, showing that the climate variations associated with warming hiatus periods are still evident in the models, even under accelerated warming conditions.

Description: Recent changes in the climate system have led to growing concern, especially in communities which are highly vulnerable to resource shortages and weather extremes. There is an urgent need for better climate information to develop solutions and strategies for adapting to a changing climate. Climate models provide excellent tools for studying the current state of climate and making future projections. However, these models are subject to biases created by structural uncertainties. Performance metrics-or the systematic determination of model biases-succinctly quantify aspects of climate model behavior. Efforts to standardize climate model experiments and collect simulation data-such as the Coupled Model Intercomparison Project (CMIP)-provide the means to directly compare and assess model performance. Performance metrics have been used to show that some models reproduce present-day climate better than others. Simulation data from multiple models are often used to add value to projections by creating a consensus projection from the model ensemble, in which each model is given an equal weight. It has been shown that the ensemble mean generally outperforms any single model. It is possible to use unequal weights to produce ensemble means, in which models are weighted based on performance (called "intelligent" ensembles). Can performance metrics be used to improve climate projections? Previous work introduced a framework for comparing the utility of model performance metrics, showing that the best metrics are related to the variance of top-of-atmosphere outgoing longwave radiation. These metrics improve present-day climate simulations of Earth's energy budget using the "intelligent" ensemble method. The current project identifies several approaches for testing whether performance metrics can be applied to future simulations to create "intelligent" ensemble-mean climate projections. It is shown that certain performance metrics test key climate processes in the models, and that these metrics can be used to evaluate model quality in both current and future climate states. This information will be used to produce new consensus projections and provide communities with improved climate projections for urgent decision-making.

Description: Earth's climate is changing and will continue to change into the foreseeable future. Expected changes in the climatological distribution of precipitation, surface temperature, and surface solar radiation will significantly impact agriculture. Adaptation strategies are, therefore, required to reduce the agricultural impacts of climate change. Climate change projections of precipitation, surface temperature, and surface solar radiation distributions are necessary input for adaption planning studies. These projections are conventionally constructed from an ensemble of climate model simulations (e.g., the Coupled Model Intercomparison Project 5 (CMIP5)) as an equal weighted average, one model one vote. Each climate model, however, represents the array of climate-relevant physical processes with varying degrees of fidelity influencing the projection of individual climate variables differently. Presented here is a new approach, termed the "Intelligent Ensemble, that constructs climate variable projections by weighting each model according to its ability to represent key physical processes, e.g., precipitation probability distribution. This approach provides added value over the equal weighted average method. Physical process metrics applied in the "Intelligent Ensemble" method are created using a combination of NASA and NOAA satellite and surface-based cloud, radiation, temperature, and precipitation data sets. The "Intelligent Ensemble" method is applied to the RCP4.5 and RCP8.5 anthropogenic climate forcing simulations within the CMIP5 archive to develop a set of climate change scenarios for precipitation, temperature, and surface solar radiation in each USDA Farm Resource Region for use in climate change adaptation studies.

Description: Recent changes in the climate system have led to growing concern, especially in communities which are highly vulnerable to resource shortages and weather extremes. There is an urgent need for better climate information to develop solutions and strategies for adapting to a changing climate. Climate models provide excellent tools for studying the current state of climate and making future projections. However, these models are subject to biases created by structural uncertainties. Performance metrics-or the systematic determination of model biases-succinctly quantify aspects of climate model behavior. Efforts to standardize climate model experiments and collect simulation data-such as the Coupled Model Intercomparison Project (CMIP)-provide the means to directly compare and assess model performance. Performance metrics have been used to show that some models reproduce present-day climate better than others. Simulation data from multiple models are often used to add value to projections by creating a consensus projection from the model ensemble, in which each model is given an equal weight. It has been shown that the ensemble mean generally outperforms any single model. It is possible to use unequal weights to produce ensemble means, in which models are weighted based on performance (called "intelligent" ensembles). Can performance metrics be used to improve climate projections? Previous work introduced a framework for comparing the utility of model performance metrics, showing that the best metrics are related to the variance of top-of-atmosphere outgoing longwave radiation. These metrics improve present-day climate simulations of Earth's energy budget using the "intelligent" ensemble method. The current project identifies several approaches for testing whether performance metrics can be applied to future simulations to create "intelligent" ensemble-mean climate projections. It is shown that certain performance metrics test key climate processes in the models, and that these metrics can be used to evaluate model quality in both current and future climate states. This information will be used to produce new consensus projections and provide communities with improved climate projections for urgent decision-making.