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

Massoud, Elias; Lee, H.; Gibson, Peter B.; Loikith, Paul C.; Waliser, Duane E.

doi:10.1175/jhm-d-19-0258.1

Cited by 42 publications

(52 citation statements)

References 66 publications

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“…For much of the region from 5-20 N, the results are shown to be statistically significant (e.g., Figure 8), however for much of the rest of the region, it is difficult to distinguish changes in future precipitation around MENA, and Figures 8-10 show that that there is little to no projected changes for most of the area. This is not surprising for a variable like precipitation, whose projections of future changes are usually uncertain compared to the multi-model ensemble spread or the natural variability of precipitation for many regions of the globe, especially for arid and semi-arid locations [70,81,82].…”

Section: Statistical Significance Of Expected Changesmentioning

confidence: 99%

Atmospheric Rivers and Precipitation in the Middle East and North Africa (MENA)

Massoud

Guan

et al. 2020

Water

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This study investigates the historical climatology and future projected change of atmospheric rivers (ARs) and precipitation for the Middle East and North Africa (MENA) region. We use a suite of models from the Coupled Model Intercomparison Project Phase 5 (CMIP5, historical and RCP8.5 scenarios) and other observations to estimate AR frequency and mean daily precipitation. Despite its arid-to-semi-arid climate, parts of the MENA region experience strong ARs, which contribute a large fraction of the annual precipitation, such as in the mountainous areas of Turkey and Iran. This study shows that by the end of this century, AR frequency is projected to increase (~20–40%) for the North Africa and Mediterranean areas (including any region with higher latitudes than 35 N). However, for these regions, mean daily precipitation (i.e., regardless of the presence of ARs) is projected to decrease (~15–30%). For the rest of the MENA region, including the Arabian Peninsula and the Horn of Africa, minor changes in AR frequency (±10%) are expected, yet mean precipitation is projected to increase (~50%) for these regions. Overall, the projected sign of change in AR frequency is opposite to the projected sign of change in mean daily precipitation for most areas within the MENA region.

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Section: Statistical Significance Of Expected Changesmentioning

confidence: 99%

Atmospheric Rivers and Precipitation in the Middle East and North Africa (MENA)

Massoud

Guan

et al. 2020

Water

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“…Numerous assessments of GCMs currently exist, e.g., [12][13][14][15][16][17], and downscaling is being investigated in programs such as the Coordinated Regional Downscaling Experiments CORDEX, e.g., [18]. Recently, there has been a transition from using an unweighted, multi-model ensemble mean to more advanced methods that account for the skill and independence of models to inform the weighting strategy [15,16,19,20].…”

Section: Introductionmentioning

confidence: 99%

The Effect of Statistical Downscaling on the Weighting of Multi-Model Ensembles of Precipitation

Wootten

Massoud

Sengupta

et al. 2020

Climate

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Recently, assessments of global climate model (GCM) ensembles have transitioned from using unweighted means to weighted means designed to account for skill and interdependence among models. Although ensemble-weighting schemes are typically derived using a GCM ensemble, statistically downscaled projections are used in climate change assessments. This study applies four ensemble-weighting schemes for model averaging to precipitation projections in the south-central United States. The weighting schemes are applied to (1) a 26-member GCM ensemble and (2) those 26 members downscaled using Localized Canonical Analogs (LOCA). This study is distinct from prior research because it compares the interactions of ensemble-weighting schemes with GCMs and statistical downscaling to produce summarized climate projection products. The analysis indicates that statistical downscaling improves the ensemble accuracy (LOCA average root mean square error is 100 mm less than the CMIP5 average root mean square error) and reduces the uncertainty of the projected ensemble-mean change. Furthermore, averaging the LOCA ensemble using Bayesian Model Averaging reduces the uncertainty beyond any other combination of weighting schemes and ensemble (standard deviation of the mean projected change in the domain is reduced by 40–50 mm). The results also indicate that it is inappropriate to assume that a weighting scheme derived from a GCM ensemble matches the same weights derived using a downscaled ensemble.

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“…We assess models' 'fit for purpose' by selecting performance metrics that are logically 'problem-relevant' (Baumberger et al 2017;Eyring et al 2019;Massoud et al 2020), for the assessment of climate impacts on large-scale surface water balance for hydrological modelling. Accordingly, for the variables temperature (T) and precipitation (P), we define indicators of (i) mean state, including seasonality; (ii) variability, including both the magnitude and the dominant driving process, in this case, simplified to the teleconnection with the El Niño Southern Oscillation (ENSO) that exerts a dominant influence on the region (e.g.…”

Section: W1mentioning

confidence: 99%

“…There are now increasing attempts by climate scientists to understand and to constrain projection uncertainty, as climate models in the available multi-model ensembles are not equally good, nor are they truly independent of each other (Knutti et al 2010;Masson and Knutti 2011;Sanderson et al 2017;Pennell and Reichler 2011;Massoud et al 2019Massoud et al , 2020, despite an acceptance of 'model democracy' in IPCC reports. Approaches have therefore been proposed to discriminate between climate models in terms of their 'trustworthiness' (see Eyring et al 2019 for a review and examples), based on how well the models represent key climate variables (Baumberger et al 2017), i.e.…”

Section: Introductionmentioning

confidence: 99%

Sensitivity of projected climate impacts to climate model weighting: multi-sector analysis in eastern Africa

et al. 2021

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Uncertainty in long-term projections of future climate can be substantial and presents a major challenge to climate change adaptation planning. This is especially so for projections of future precipitation in most tropical regions, at the spatial scale of many adaptation decisions in water-related sectors. Attempts have been made to constrain the uncertainty in climate projections, based on the recognised premise that not all of the climate models openly available perform equally well. However, there is no agreed ‘good practice’ on how to weight climate models. Nor is it clear to what extent model weighting can constrain uncertainty in decision-relevant climate quantities. We address this challenge, for climate projection information relevant to ‘high stakes’ investment decisions across the ‘water-energy-food’ sectors, using two case-study river basins in Tanzania and Malawi. We compare future climate risk profiles of simple decision-relevant indicators for water-related sectors, derived using hydrological and water resources models, which are driven by an ensemble of future climate model projections. In generating these ensembles, we implement a range of climate model weighting approaches, based on context-relevant climate model performance metrics and assessment. Our case-specific results show the various model weighting approaches have limited systematic effect on the spread of risk profiles. Sensitivity to climate model weighting is lower than overall uncertainty and is considerably less than the uncertainty resulting from bias correction methodologies. However, some of the more subtle effects on sectoral risk profiles from the more ‘aggressive’ model weighting approaches could be important to investment decisions depending on the decision context. For application, model weighting is justified in principle, but a credible approach should be very carefully designed and rooted in robust understanding of relevant physical processes to formulate appropriate metrics.

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

Cited by 42 publications

References 66 publications

Atmospheric Rivers and Precipitation in the Middle East and North Africa (MENA)

Atmospheric Rivers and Precipitation in the Middle East and North Africa (MENA)

The Effect of Statistical Downscaling on the Weighting of Multi-Model Ensembles of Precipitation

Sensitivity of projected climate impacts to climate model weighting: multi-sector analysis in eastern Africa

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