A Bayesian approach to climate model evaluation and multi-model averaging with an application to global mean surface temperatures from IPCC AR4 coupled climate models

2006 ◽  
Vol 33 (8) ◽  
Author(s):  
Seung-Ki Min ◽  
Andreas Hense
Keyword(s):  
Bayesian Approach ◽  
Model Evaluation ◽  
Climate Models ◽  
Climate Model ◽  
Model Averaging ◽  
Surface Temperatures ◽  
Coupled Climate Models ◽  
Climate Model Evaluation ◽  
Ipcc Ar4 ◽  
Global Mean

scholarly journals Climate Model Evaluation in the Presence of Observational Uncertainty: Precipitation Indices over the Contiguous United States

2019 ◽  
Vol 20 (7) ◽  
pp. 1339-1357 ◽  
Author(s):  
Peter B. Gibson ◽  
Duane E. Waliser ◽  
Huikyo Lee ◽  
Baijun Tian ◽  
Elias Massoud
Keyword(s):  
United States ◽  
Model Evaluation ◽  
Climate Models ◽  
Climate Model ◽  
Dynamical Downscaling ◽  
Daily Precipitation ◽  
Regional Climate ◽  
Climate Model Evaluation ◽  
Observational Uncertainty ◽  
Precipitation Indices

Abstract Climate model evaluation is complicated by the presence of observational uncertainty. In this study we analyze daily precipitation indices and compare multiple gridded observational and reanalysis products with regional climate models (RCMs) from the North American component of the Coordinated Regional Climate Downscaling Experiment (NA-CORDEX) multimodel ensemble. In the context of model evaluation, observational product differences across the contiguous United States (CONUS) are also deemed nontrivial for some indices, especially for annual counts of consecutive wet days and for heavy precipitation indices. Multidimensional scaling (MDS) is used to directly include this observational spread into the model evaluation procedure, enabling visualization and interpretation of model differences relative to a “cloud” of observational uncertainty. Applying MDS to the evaluation of NA-CORDEX RCMs reveals situations of added value from dynamical downscaling, situations of degraded performance from dynamical downscaling, and the sensitivity of model performance to model resolution. On precipitation days, higher-resolution RCMs typically simulate higher mean and extreme precipitation rates than their lower-resolution pairs, sometimes improving model fidelity with observations. These results document the model spread and biases in daily precipitation extremes across the full NA-CORDEX model ensemble. The often-large divergence between in situ observations, satellite data, and reanalysis, shown here for CONUS, is especially relevant for data-sparse regions of the globe where satellite and reanalysis products are extensively relied upon. This highlights the need to carefully consider multiple observational products when evaluating climate models.

scholarly journals Regional Climate Model Evaluation System powered by Apache Open Climate Workbench v1.3.0: an enabling tool for facilitating regional climate studies

2018 ◽  
Author(s):  
Huikyo Lee ◽  
Alexander Goodman ◽  
Lewis McGibbney ◽  
Duane Waliser ◽  
Jinwon Kim ◽  
...  
Keyword(s):  
Regional Climate Model ◽  
Model Evaluation ◽  
Evaluation System ◽  
Climate Models ◽  
Performance Metrics ◽  
Climate Model ◽  
Regional Climate ◽  
The United States ◽  
Climate Data ◽  
Climate Model Evaluation

Abstract. The Regional Climate Model Evaluation System (RCMES) is an enabling tool of the National Aeronautics and Space Administration to support the United States National Climate Assessment. As a comprehensive system for evaluating climate models on regional and continental scales using observational datasets from a variety of sources, RCMES is designed to yield information on the performance of climate models and guide their improvement. Here we present a user-oriented document describing the latest version of RCMES, its development process and future plans for improvements. The main objective of RCMES is to facilitate the climate model evaluation process at regional scales. RCMES provides a framework for performing systematic evaluations of climate simulations, such as those from the Coordinated Regional Climate Downscaling Experiment (CORDEX), using in-situ observations as well as satellite and reanalysis data products. The main components of RCMES are: 1) a database of observations widely used for climate model evaluation, 2) various data loaders to import climate models and observations in different formats, 3) a versatile processor to subset and regrid the loaded datasets, 4) performance metrics designed to assess and quantify model skill, 5) plotting routines to visualize the performance metrics, 6) a toolkit for statistically downscaling climate model simulations, and 7) two installation packages to maximize convenience of users without Python skills. RCMES website is maintained up to date with brief explanation of these components. Although there are other open-source software (OSS) toolkits that facilitate analysis and evaluation of climate models, there is a need for climate scientists to participate in the development and customization of OSS to study regional climate change. To establish infrastructure and to ensure software sustainability, development of RCMES is an open, publicly accessible process enabled by leveraging the Apache Software Foundation's OSS library, Apache Open Climate Workbench (OCW). The OCW software that powers RCMES includes a Python OSS library for common climate model evaluation tasks as well as a set of user-friendly interfaces for quickly configuring a model evaluation task. OCW also allows users to build their own climate data analysis tools, such as the statistical downscaling toolkit provided as a part of RCMES.

scholarly journals A More Powerful Reality Test for Climate Models

Eos ◽  
2016 ◽  
Vol 97 ◽  
Author(s):  
Peter Gleckler ◽  
Charles Doutriaux ◽  
Paul Durack ◽  
Karl Taylor ◽  
Yuying Zhang ◽  
...  
Keyword(s):  
Community Engagement ◽  
Model Evaluation ◽  
Climate Models ◽  
Climate Model ◽  
Model Development ◽  
Climate Model Evaluation

A new climate model evaluation package will deliver objective comparisons between models and observations for research and model development and provide a framework for community engagement.

scholarly journals Regional Climate Model Evaluation System powered by Apache Open Climate Workbench v1.3.0: an enabling tool for facilitating regional climate studies

2018 ◽  
Vol 11 (11) ◽  
pp. 4435-4449 ◽  
Author(s):  
Huikyo Lee ◽  
Alexander Goodman ◽  
Lewis McGibbney ◽  
Duane E. Waliser ◽  
Jinwon Kim ◽  
...  
Keyword(s):  
Regional Climate Model ◽  
Model Evaluation ◽  
Evaluation System ◽  
Climate Models ◽  
Performance Metrics ◽  
Climate Model ◽  
Regional Climate ◽  
The United States ◽  
Climate Data ◽  
Climate Model Evaluation

Abstract. The Regional Climate Model Evaluation System (RCMES) is an enabling tool of the National Aeronautics and Space Administration to support the United States National Climate Assessment. As a comprehensive system for evaluating climate models on regional and continental scales using observational datasets from a variety of sources, RCMES is designed to yield information on the performance of climate models and guide their improvement. Here, we present a user-oriented document describing the latest version of RCMES, its development process, and future plans for improvements. The main objective of RCMES is to facilitate the climate model evaluation process at regional scales. RCMES provides a framework for performing systematic evaluations of climate simulations, such as those from the Coordinated Regional Climate Downscaling Experiment (CORDEX), using in situ observations, as well as satellite and reanalysis data products. The main components of RCMES are (1) a database of observations widely used for climate model evaluation, (2) various data loaders to import climate models and observations on local file systems and Earth System Grid Federation (ESGF) nodes, (3) a versatile processor to subset and regrid the loaded datasets, (4) performance metrics designed to assess and quantify model skill, (5) plotting routines to visualize the performance metrics, (6) a toolkit for statistically downscaling climate model simulations, and (7) two installation packages to maximize convenience of users without Python skills. RCMES website is maintained up to date with a brief explanation of these components. Although there are other open-source software (OSS) toolkits that facilitate analysis and evaluation of climate models, there is a need for climate scientists to participate in the development and customization of OSS to study regional climate change. To establish infrastructure and to ensure software sustainability, development of RCMES is an open, publicly accessible process enabled by leveraging the Apache Software Foundation's OSS library, Apache Open Climate Workbench (OCW). The OCW software that powers RCMES includes a Python OSS library for common climate model evaluation tasks as well as a set of user-friendly interfaces for quickly configuring a model evaluation task. OCW also allows users to build their own climate data analysis tools, such as the statistical downscaling toolkit provided as a part of RCMES.

scholarly journals The ARM Data-Oriented Metrics and Diagnostics Package for Climate Models: A New Tool for Evaluating Climate Models with Field Data

2020 ◽  
Vol 101 (10) ◽  
pp. E1619-E1627
Author(s):  
C. Zhang ◽  
S. Xie ◽  
C. Tao ◽  
S. Tang ◽  
T. Emmenegger ◽  
...  
Keyword(s):  
Model Evaluation ◽  
Land Surface ◽  
Climate Models ◽  
Climate Model ◽  
Climate Modeling ◽  
Model Development ◽  
Coupled Model ◽  
Department Of Energy ◽  
Climate Model Evaluation ◽  
User Facility

AbstractThe U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) program User Facility produces ground-based long-term continuous unique measurements for atmospheric state, precipitation, turbulent fluxes, radiation, aerosol, cloud, and the land surface, which are collected at multiple sites. These comprehensive datasets have been widely used to calibrate climate models and are proven to be invaluable for climate model development and improvement. This article introduces an evaluation package to facilitate the use of ground-based ARM measurements in climate model evaluation. The ARM data-oriented metrics and diagnostics package (ARM-DIAGS) includes both ARM observational datasets and a Python-based analysis toolkit for computation and visualization. The observational datasets are compiled from multiple ARM data products and specifically tailored for use in climate model evaluation. In addition, ARM-DIAGS also includes simulation data from models participating the Coupled Model Intercomparison Project (CMIP), which will allow climate-modeling groups to compare a new, candidate version of their model to existing CMIP models. The analysis toolkit is designed to make the metrics and diagnostics quickly available to the model developers.

Evaluating temperature extremes in CMIP6 simulations using statistically proper evaluation methods

2021 ◽  
Author(s):  
Thordis Thorarinsdottir ◽  
Jana Sillmann ◽  
Marion Haugen ◽  
Nadine Gissibl ◽  
Marit Sandstad
Keyword(s):  
Air Temperature ◽  
Model Evaluation ◽  
Climate Models ◽  
Climate Model ◽  
Mean Squared Error ◽  
Surface Air Temperature ◽  
Cmip5 Models ◽  
Model Output ◽  
Intergovernmental Panel ◽  
Near Surface

<p>Reliable projections of extremes in near-surface air temperature (SAT) by climate models become more and more important as global warming is leading to significant increases in the hottest days and decreases in coldest nights around the world with considerable impacts on various sectors, such as agriculture, health and tourism.</p><p>Climate model evaluation has traditionally been performed by comparing summary statistics that are derived from simulated model output and corresponding observed quantities using, for instance, the root mean squared error (RMSE) or mean bias as also used in the model evaluation chapter of the fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR5). Both RMSE and mean bias compare averages over time and/or space, ignoring the variability, or the uncertainty, in the underlying values. Particularly when interested in the evaluation of climate extremes, climate models should be evaluated by comparing the probability distribution of model output to the corresponding distribution of observed data.</p><p>To address this shortcoming, we use the integrated quadratic distance (IQD) to compare distributions of simulated indices to the corresponding distributions from a data product. The IQD is the proper divergence associated with the proper continuous ranked probability score (CRPS) as it fulfills essential decision-theoretic properties for ranking competing models and testing equality in performance, while also assessing the full distribution.</p><p>The IQD is applied to evaluate CMIP5 and CMIP6 simulations of monthly maximum (TXx) and minimum near-surface air temperature (TNn) over the data-dense regions Europe and North America against both observational and reanalysis datasets. There is not a notable difference between the model generations CMIP5 and CMIP6 when the model simulations are compared against the observational dataset HadEX2. However, the CMIP6 models show a better agreement with the reanalysis ERA5 than CMIP5 models, with a few exceptions. Overall, the climate models show higher skill when compared against ERA5 than when compared against HadEX2. While the model rankings vary with region, season and index, the model evaluation is robust against changes in the grid resolution considered in the analysis.</p>

scholarly journals Atmospheric circulation and hydroclimate impacts of alternative warming scenarios for the Eocene

2017 ◽  
Vol 13 (8) ◽  
pp. 1037-1048 ◽  
Author(s):  
Henrik Carlson ◽  
Rodrigo Caballero
Keyword(s):  
Surface Temperature ◽  
Radiative Forcing ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Warm Season ◽  
Cloud Albedo ◽  
Cloud Properties ◽  
Atmospheric Co2 Concentrations ◽  
Global Mean

Abstract. Recent work in modelling the warm climates of the early Eocene shows that it is possible to obtain a reasonable global match between model surface temperature and proxy reconstructions, but only by using extremely high atmospheric CO2 concentrations or more modest CO2 levels complemented by a reduction in global cloud albedo. Understanding the mix of radiative forcing that gave rise to Eocene warmth has important implications for constraining Earth's climate sensitivity, but progress in this direction is hampered by the lack of direct proxy constraints on cloud properties. Here, we explore the potential for distinguishing among different radiative forcing scenarios via their impact on regional climate changes. We do this by comparing climate model simulations of two end-member scenarios: one in which the climate is warmed entirely by CO2 (which we refer to as the greenhouse gas (GHG) scenario) and another in which it is warmed entirely by reduced cloud albedo (which we refer to as the low CO2–thin clouds or LCTC scenario) . The two simulations have an almost identical global-mean surface temperature and equator-to-pole temperature difference, but the LCTC scenario has  ∼  11 % greater global-mean precipitation than the GHG scenario. The LCTC scenario also has cooler midlatitude continents and warmer oceans than the GHG scenario and a tropical climate which is significantly more El Niño-like. Extremely high warm-season temperatures in the subtropics are mitigated in the LCTC scenario, while cool-season temperatures are lower at all latitudes. These changes appear large enough to motivate further, more detailed study using other climate models and a more realistic set of modelling assumptions.

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