How Well Do Coupled Models Simulate Today's Climate?

By T. Reichler, Junsu Kim. 2008

974 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/8c28cbee2aa9d9dba2d797ff66a4ef4419156b5d

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Citations in the corpus

Atmospheric component of the MPI‐M Earth System Model: ECHAM6 (2013, methodology): “[74] During the tuning process, the model development team used a summary of the circulation features, as measured by a simple version of the performance index introduced by Reichler and Kim [2008], as input into their decision making process. The variables contributing to the performance index are ”
Developments in the MPI‐M Earth System Model version 1.2 (MPI‐ESM1.2) and Its Response to Increasing CO2 (2019, methodology): “Traditionally, model mean state biases have been in the focus of model advances, and clearly, large-scale biases in models participating in CMIP have decreased steadily over time (Reichler & Kim, 2008).”
On Constraining Estimates of Climate Sensitivity with Present-Day Observations through Model Weighting (2011, background): “…time, projections would converge to a narrower distribution, but this has not yet proved true: successive generations of climate models have produced improved simulations of the present-day climate (Reichler and Kim 2008) but commensurate distributions of climate sensitivity (Knutti et al. 2008).”
Climate Models: An Assessment of Strengths and Limitations (2008, background): “One important area of research is in the design of metrics to test the ability of models to simulate well-observed climate features (Reichler and Kim 2008; Gleckler, Taylor, and Dou- triaux 2008).”
Exploring consensus in 21st century projections of climatically suitable areas for African vertebrates (2011, background): “In climatology, multi-model ensemble averages have often been shown to improve the outcome of climate simulations (Phillips & Gleckler, 2006; Tebaldi & Knutti, 2007; Gleckler et al. , 2008; Reichler & Kim, 2008; Pierce et al. , 2009; Knutti et al. , 2010; but see Fordham et al. , in press ).”
Improved Climate Simulation by MIROC5: Mean States, Variability, and Climate Sensitivity (2010, methodology): “While we have not employed a quantitative metric to rate the model’s 427 performance (Reichler and Kim 2008), the biases of many atmosphere and ocean 428 variables are evaluated in terms of their global means and spatial patterns at each tuning 429 cycle.”
Bias correction of monthly precipitation and temperature fields from Intergovernmental Panel on Climate Change AR4 models using equidistant quantile matching (2010, background): “Although systematic improvements have been reported in the latest generation of climate models [ Reichler and Kim, 2008 ], a general cold bias is still evident for temperature, and substantial precipitation biases, especially in the tropics, are not uncommon in many models [Randall et al., 2007].”
Climate model genealogy: Generation CMIP5 and how we got there (2013, result): “CMIP5 continues the trend of better agreement with observations [Reichler and Kim, 2008], with the mean distance from observations reduced by approximately 20% from CMIP3 to CMIP5.”
Challenges in Combining Projections from Multiple Climate Models (2010, methodology): “…with observations of presentday climate than any single model, and that the average also consistently scores high in almost all diagnostics (Lambert and Boer 2001; Phillips and Gleckler 2006; Randall et al. 2007; Gleckler et al. 2008; Pincus et al. 2008; Reichler and Kim 2008; Pierce et al. 2009).”
A quantitative performance assessment of cloud regimes in climate models (2009, background): “Recently, there has been increasing interest in developing sets of metrics to provide quantitative information on GCM performance which may then be used in assessing overall confidence in a GCM’s simulation of future climate change (Murphy et al. 2004; Reichler and Kim 2008; Gleckler et al. 2008; ”
A climate model projection weighting scheme accounting for performance and interdependence (2017, background): “On (b), some models are worse than others in how well they represent the observed mean climate and trends [Gleckler et al., 2008; Reichler and Kim, 2008; Knutti et al., 2013].”
EC-Earth V2.2: description and validation of a new seamless earth system prediction model (2012, methodology): “In order to make an objective comparison between climate models, Reichler and Kim (2008) developed a metric for the simulated mean climate.”
Interpreting the cloud cover – aerosol optical depth relationship found in satellite data using a general circulation model (2009, methodology): “For model evaluation, the reader is referred to previous studies (e.g., Tompkins, 2002; Lohmann et al., 2007; Pincus et al., 2008; Reichler and Kim, 2008).”
Analysis on the Dynamics of a Wave-like Teleconnection Pattern along the Summertime Asian Jet Based on a Reanalysis Dataset and Climate Model Simulations (2009, background): “Ceaseless improvements of global climate models, including those contributed to the World Climate Research Programme’s (WCRP) Coupled Model Intercomparison Project phase 3 (CMIP3), have led to their fairly realistic reproduction of the mean state of the present-day climate (Reichler and Kim 2008).”