Performance metrics for climate models

By P. Gleckler, K. Taylor, Charles M. Doutriaux. 2008

1,257 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/2646c597bb63489e0ed455ca8b42814d81be9b9e

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Citations in the corpus

Evaluating Emergent Constraints on Equilibrium Climate Sensitivity (2018, background): “Unfortunately, models that perform well for some metrics may perform poorly for others (Gleckler et al. 2008), climate predictions from skillful models do not always agree (Waugh and Eyring 2008), and the ability to reproduce current climate does not necessarily imply predictive skill.”
Reduced global warming from CMIP6 projections when weighting models by performance and independence (2020, background): “It has been argued that combining multiple datasets (e.g., by using their full range or their mean) yields more stable results (Gleckler et al., 2008; Brunner et al., 2019).”
Quantifying Progress Across Different CMIP Phases With the ESMValTool (2020, background): “Figure 6 is an extended and updated version of Figure 9.7 of Flato et al. (2013) that is based on Gleckler et al. (2008).”
Bayesian estimation of local signal and noise in multimodel simulations of climate change (2010, methodology): “For historical climate simulations, such simple model averaging (SMA) is often found to replicate observational data better than even the best performing individual simulation [IPCC, 2001; Lambert and Boer, 2001; Phillips and Gleckler, 2006; Gleckler et al., 2008; Pincus et al., 2008; Reichler and K”
Presentation and Evaluation of the IPSL‐CM6A‐LR Climate Model (2020, methodology): “We have used the PCMDI Metrics Package (PMP) to calculate a set of large‐scale performance metrics (Gleckler et al., 2008), also called summary statistics, to summarize the agreement between the climate simulated by the model over the recent period and a set of references (observations and…”
Spatial Variability of Sea Level Rise in Twenty-First Century Projections (2010, background): “1 realistically simulates many features of the climate system such as ocean temperature and salinity (Gleckler et al. 2008).”
Pan evaporation prediction using a hybrid multilayer perceptron-firefly algorithm (MLP-FFA) model: case study in North Iran (2018, background): “Moreover, the normalized RMSE can be resolved into a part due to differences in the overall means and a part due to errors in the pattern of variations (Taylor 2001; IPCC 2007; Gleckler et al. 2008).”
Implications of regional improvement in global climate models for agricultural impact research (2013, methodology): “Several questions arise from the results presented here and elsewhere [19, 32, 39].”
Climate model genealogy: Generation CMIP5 and how we got there (2013, methodology): “[12] We can also use the distance metric of Figure 1 as one example to quantify distance from observations for different CMIP generations (shown in Figure 3), but with the strong caveat that linking model performance metrics to model quality or skill is difficult, subjective, and strongly metric dep”
Exploring consensus in 21st century projections of climatically suitable areas for African vertebrates (2011, background): “Similarities were assessed separately for each variable because the performance of climate models varies for different variables (Lambert & Boer, 2001; Gleckler et al., 2008).”
A quantitative performance assessment of cloud regimes in climate models (2009, methodology): “Of course, GCMs have many more applications than providing a global temperature response, hence we propose that the metrics should be part of a basket of metrics similar to Murphy et al. (2004) or Gleckler et al. (2008) , with CREMpd used in present-day evaluation of GCMs and CREMcc used as part of”
Comparing estimates of climate change impacts from process-based and statistical crop models (2017, background): “This finding reflects a ‘wisdom of the crowds’ often seen in model intercomparisons of other fields such as climate or weather forecasting (Pierce et al 2009, Gleckler et al 2008, Doblas-Reyes et al 2005), which derives from the independence of errors in different individual models.”
Weighting climate model ensembles for mean and variance estimates (2015, methodology): “A complete description of the ensemble generation process, including parameter ranges and the sampling approach, is given in Haughton et al. (2014). The perturbed structure ensemble is intended to emulate an ensemble of single simulations from several climate models, each using comparable initial co”
ENSO representation in climate models: from CMIP3 to CMIP5 (2013, background): “Simulated and observed ENSO characteristics may depend on the mean state in the 277 tropical Pacific Ocean (e.g. Wang and An 2002, Guilyardi 2006, Sun et al 2009).”
Space can substitute for time in predicting climate-change effects on biodiversity (2013, background): “, climate models may be inaccurate (50) and pollen data represent a biased sample of fossil plant taxa (51), but there is no reason to expect that these issues are differential in space vs.”
The University of Washington Shallow Convection and Moist Turbulence Schemes and Their Impact on Climate Simulations with the Community Atmosphere Model (2009, background): “Gleckler et al. (2008) have also experimented with simple metrics of climate model performance.”
Climate model dependence and the replicate Earth paradigm (2013, background): “2005) or sub-selecting (e.g. Perkins et al. 2007; Gleckler et al. 2008) different simulations or models, based on their”