A  Bayesian posterior predictive framework for weighting ensemble regional climate models

Fan, Yanan; Olson, Roman; Evans, Jason P.

doi:10.5194/gmd-10-2321-2017

Cited by 8 publications

(11 citation statements)

References 37 publications

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“…To our best knowledge, BMA has not been widely used to constrain uncertainties in future projections from global climate models, with the exception of Massoud et al (2019a) which used BMA to constrain the spread of uncertainty in future projections of global atmospheric rivers. There are other studies that used similar methods, such as Olson et al (2016) and Fan et al (2017) which used BMA to make probabilistic projections of temperature in southeast Australia from a suite of regional climate models. Olson et al (2019) used a quasi-Bayesian method to weight climate model projections of summer mean maximum temperature change over Korea.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Model Averaging of Climate Model Projections Constrained by Precipitation Observations over the Contiguous United States

Massoud

Lee

Gibson

et al. 2020

Journal of Hydrometeorology

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This study utilizes Bayesian Model Averaging (BMA) as a framework to constrain the spread of uncertainty in climate projections of precipitation over the contiguous United States (CONUS). We use a subset of historical model simulations and future model projections (RCP8.5) from the Coupled Model Intercomparison Project Phase 5 (CMIP5). We evaluate the representation of five precipitation summary metrics in the historical simulations using observations from the NASA Tropical Rainfall Measuring Mission (TRMM) satellites. The summary metrics include mean, annual and interannual variability, and maximum and minimum extremes of precipitation. The estimated model average produced with BMA is shown to have higher accuracy in simulating mean rainfall than the ensemble mean (RMSE of 0.49 for BMA vs 0.65 for ensemble mean), and a more constrained spread of uncertainty with roughly a third of the total uncertainty than is produced with the multi-model ensemble. The results show that, by the end of the century, the mean daily rainfall is projected to increase for most of the East Coast and the Northwest, may decrease in the Southern US, and with little change expected for the Southwest. For extremes, the wettest year on record is projected to become wetter for the majority of CONUS and the driest year to become drier. We show that BMA offers a framework to more accurately estimate and to constrain the spread of uncertainties of future climate, such as precipitation changes over CONUS.

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Section: Introductionmentioning

confidence: 99%

Bayesian Model Averaging of Climate Model Projections Constrained by Precipitation Observations over the Contiguous United States

Massoud

Lee

Gibson

et al. 2020

Journal of Hydrometeorology

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“…This work assumes stationarity of model weights: if a model is correct during the calibration period, it is also assumed to be correct in the validation period. This is a standard assumption of the BMA method [1,5,20,21,35].…”

Section: Caveatsmentioning

confidence: 99%

“…Previous variability weighting work does not account for such nonlinearities [35]. During the trend weighting we use smoothed output as anomalies with respect to the entire calibration period.…”

Section: Amoc Experimentsmentioning

confidence: 99%

“…The nonlinear trends discussed here may include residual contributions from long-term internal climate variability. However, this can be handled by the trend-weighting part of the method since this part accounts for long-term model error [35].…”

Section: Caveatsmentioning

confidence: 99%

“…Several new studies break important new ground by incorporating variability into the weighting [17,[32][33][34], but they typically assume stationarity of the pdf of the system [17,32,33], or cannot work with complex dynamical models [34]. Some previous work does explicitly weight dynamical models by performance in variability and trends in a statistically-sound way [35].…”

Section: Introductionmentioning

confidence: 99%

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Accounting for skill in trend, variability, and autocorrelation facilitates better multi-model projections: Application to the AMOC and temperature time series

Olson

Fan

et al. 2019

PLoS ONE

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We present a novel quasi-Bayesian method to weight multiple dynamical models by their skill at capturing both potentially non-linear trends and first-order autocorrelated variability of the underlying process, and to make weighted probabilistic projections. We validate the method using a suite of one-at-a-time cross-validation experiments involving Atlantic meridional overturning circulation (AMOC), its temperature-based index, as well as Korean summer mean maximum temperature. In these experiments the method tends to exhibit superior skill over a trend-only Bayesian model averaging weighting method in terms of weight assignment and probabilistic forecasts. Specifically, mean credible interval width, and mean absolute error of the projections tend to improve. We apply the method to a problem of projecting summer mean maximum temperature change over Korea by the end of the 21 st century using a multi-model ensemble. Compared to the trend-only method, the new method appreciably sharpens the probability distribution function (pdf) and increases future most likely, median, and mean warming in Korea. The method is flexible, with a potential to improve forecasts in geosciences and other fields.

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A novel method to test non-exclusive hypotheses applied to Arctic ice projections from dependent models

Olson

Fan

et al. 2019

Nat Commun

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A major conundrum in climate science is how to account for dependence between climate models. This complicates interpretation of probabilistic projections derived from such models. Here we show that this problem can be addressed using a novel method to test multiple non-exclusive hypotheses, and to make predictions under such hypotheses. We apply the method to probabilistically estimate the level of global warming needed for a September ice-free Arctic, using an ensemble of historical and representative concentration pathway 8.5 emissions scenario climate model runs. We show that not accounting for model dependence can lead to biased projections. Incorporating more constraints on models may minimize the impact of neglecting model non-exclusivity. Most likely, September Arctic sea ice will effectively disappear at between approximately 2 and 2.5 K of global warming. Yet, limiting the warming to 1.5 K under the Paris agreement may not be sufficient to prevent the ice-free Arctic.

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A Bayesian posterior predictive framework for weighting ensemble regional climate models

Cited by 8 publications

References 37 publications

Bayesian Model Averaging of Climate Model Projections Constrained by Precipitation Observations over the Contiguous United States

Bayesian Model Averaging of Climate Model Projections Constrained by Precipitation Observations over the Contiguous United States

Accounting for skill in trend, variability, and autocorrelation facilitates better multi-model projections: Application to the AMOC and temperature time series

A novel method to test non-exclusive hypotheses applied to Arctic ice projections from dependent models

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