Taking climate model evaluation to the next level

Eyring, Veronika; Cox, Peter M.; Flato, Gregory M.; Gleckler, P. J.; Abramowitz, Gab; Caldwell, Peter; Collins, William D.; Gier, Bettina K.; Hall, Alex; Hoffman, Forrest M.; Hurtt, George C.; Jahn, Alexandra; Jones, Chris D.; Klein, Stephen A.; Krasting, John P.; Kwiatkowski, Lester; Lorenz, Ruth; Maloney, Eric D.; Meehl, Gerald A.; Pendergrass, Angeline G.; Pincus, Robert; Ruane, Alex C.; Russell, J. L.; Sanderson, Benjamin M.; Santer, Benjamin D.; Sherwood, Steven C.; Simpson, Isla R.; Stouffer, Ronald J.; Williamson, Mark S.

doi:10.1038/s41558-018-0355-y

Cited by 553 publications

(429 citation statements)

References 106 publications

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“…Model averaging is a statistical method used to improve the accuracy of a set of model simulations and is widely used to estimate the conceptual uncertainty of climate model projections. Generally, model averaging can improve the skill of projections and forecasts from ensembles of multi-model prediction systems (Abramowitz & Bishop, 2015;Alexander & Easterbrook, 2015;Collins et al, 2013;Eyring et al, 2019;Massoud et al, 2018).…”

Section: Model Averaging Strategiesmentioning

confidence: 99%

“…Models differ in their skill to simulate observations, and there is an inherent lack of independence between models. There is research now that shows giving equal weights to each model may not be the best strategy (Eyring et al, 2019;Knutti et al, 2017;Sanderson et al, 2015Sanderson et al, , 2017Wenzel et al, 2014). Guan and Waliser (2017) recently showed that there is a range of performance skill compared to reanalysis data across a group of global climate models, and they evaluated various AR performance metrics.…”

Section: Skill Weightingmentioning

confidence: 99%

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Global Climate Model Ensemble Approaches for Future Projections of Atmospheric Rivers

Massoud

Espinoza

Guan³

et al. 2019

Earth's Future

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Atmospheric rivers (ARs) are narrow jets of integrated water vapor transport that are important for the global water cycle and also have large impacts on local weather and regional hydrology. Uniformly weighted multi‐model averages have been used to describe how ARs will change in the future, but this type of estimate does not consider skill or independence of the climate models of interest. Here, we utilize information from various model averaging approaches, such as Bayesian model averaging (BMA), to evaluate 21 global climate models from the Coupled Model Intercomparison Project Phase 5. Model ensemble weighting strategies are based on model independence and AR performance skill relative to ERA‐Interim reanalysis data and result in higher accuracy for the historic period, for example, root mean square error for AR frequency (in % of time steps) of 0.69 for BMA versus 0.94 for the multi‐model ensemble mean. Model weighting strategies also result in lower uncertainties in the future estimates, for example, only 20–25% of the total uncertainties seen in the equal weighting strategy. These model averaging methods show, with high certainty, that globally the frequency of ARs is expected to have average relative increases of ~50% (and ~25% in AR intensity) by the end of the century.

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Section: Model Averaging Strategiesmentioning

confidence: 99%

Section: Skill Weightingmentioning

confidence: 99%

Global Climate Model Ensemble Approaches for Future Projections of Atmospheric Rivers

Massoud

Espinoza

Guan³

et al. 2019

Earth's Future

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“…the representation of clouds) and inclusion of additional Earth system processes (e.g. nutrient limitations on the terrestrial carbon cycle) and components (Eyring et al 2019). Before using CMIP6 projections for policymaking, it is essential to evaluate the performance of CMIP6 historical simulations of climate change, because they serve as an important benchmark for assessing model performance (Eyring et al 2016, Grose Michael et al 2020.…”

Section: Introductionmentioning

confidence: 99%

Variations in start date, end date, frequency and intensity of yearly temperature extremes across China during the period 1961–2017

Han

Miao

Duan

et al. 2020

Environ. Res. Lett.

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LETTERVariations in start date, end date, frequency and intensity of yearly temperature extremes across China during the period Abstract Frequent temperature extremes due to climate change have had serious effects on human society and the natural environment. Using a 0.25°×0.25°gridded Tmax (daily maximum temperature) and Tmin (daily minimum temperature) data set and 12 global climate models simulations from the sixth phase of the Coupled Model Intercomparison Project (CMIP6), we investigated variations in yearly temperature extremes in China during the past five decades with respect to four characteristics, namely, their start date, end date, frequency, and intensity. Results showed that the occurrence of nighttime extremes (the cold nights and warm nights) responded strongly to climate change. For 1961-2017, cold extremes started later (3.25 d/decade) and ended earlier (−4.58 d/decade), with decreased frequency (−6.56 d/decade), especially for cold nights, and weakened intensity (0.14°C/ decade). In the same period, warm extremes started earlier (−3.43 d/decade) and ended later (3.15 d/ decade) with increased frequency (6.79 d/decade), especially for warm nights, and enhanced intensity (0.09°C/decade). The spatial pattern of the variations was complex with anomalous regions. Multimodel ensembles (MMEs) from CMIP6 agreed well with observations regarding the average trends of temperature extremes over China, although detailed changes in spatial pattern were not captured adequately. The hazards of temperature extremes deserve close attention in the future due to the complex changes likely to occur across China for various characteristics of these temperature extremes under conditions of climate change.

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“…Climate modeling has progressed along three different fronts: First, the physical models at the core of ESMs have improved in resolution (spatial detail) and process simulation allowing them to reproduce observed climate behavior ever more closely (Eyring et al, 2019;Flato et al, 2013;Reichler & Kim, 2008). Second, Figure 1.…”

Section: How Do We Document Our Climate Models?mentioning

confidence: 99%

So What Is in an Earth System Model?

Jones

2020

J Adv Model Earth Syst

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Use of numerical models is at the heart of climate science. Models underpin process understanding; our ability to explain observed past changes and subsequently making predictions/projections of the coming decades to help guide climate policy. Roland Séférian and colleagues (2019, https://doi.org/10.1029/2019MS001791) have written an excellent paper documenting CNRM‐CERFACS's latest complex model, “CNRM‐ESM2‐1,” which will be used for CMIP6 simulations and will produce data to be analyzed by the research community for many years to come. Here I explain the importance of Seferian et al. and why it showcases a new degree of comprehensiveness needed for model documentation activity.

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Taking climate model evaluation to the next level

Cited by 553 publications

References 106 publications

Global Climate Model Ensemble Approaches for Future Projections of Atmospheric Rivers

Global Climate Model Ensemble Approaches for Future Projections of Atmospheric Rivers

Variations in start date, end date, frequency and intensity of yearly temperature extremes across China during the period 1961–2017

So What Is in an Earth System Model?

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