Reducing Model Structural Uncertainty in Climate Model Projections—A Rank-Based Model Combination Approach

Bhowmik, Rajarshi Das; Sharma, Ashish; Sankarasubramanian, A.

doi:10.1175/jcli-d-17-0225.1

Cited by 22 publications

(11 citation statements)

References 49 publications

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“…GCMs that are capable of reproducing relevant weather patterns for the impact sector of interest. In addition, the development of the most likely probabilistic climate projection by weighting the performance of various GCMs in long-term hindcast simulations may be a promising solution(Das Bhowmik, Sharma, & Sankarasubramanian, 2017). The crop model parameters were perturbed based on the calibrated set of crop model parameters and their possible ranges.…”

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confidence: 99%

Contribution of crop model structure, parameters and climate projections to uncertainty in climate change impact assessments

Tao

Rötter

Palosuo

et al. 2018

Global Change Biology

175

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Climate change impact assessments are plagued with uncertainties from many sources, such as climate projections or the inadequacies in structure and parameters of the impact model. Previous studies tried to account for the uncertainty from one or two of these. Here, we developed a triple-ensemble probabilistic assessment using seven crop models, multiple sets of model parameters and eight contrasting climate projections together to comprehensively account for uncertainties from these three important sources. We demonstrated the approach in assessing climate change impact on barley growth and yield at Jokioinen, Finland in the Boreal climatic zone and Lleida, Spain in the Mediterranean climatic zone, for the 2050s. We further quantified and compared the contribution of crop model structure, crop model parameters and climate projections to the total variance of ensemble output using Analysis of Variance (ANOVA). Based on the triple-ensemble probabilistic assessment, the median of simulated yield change was -4% and +16%, and the probability of decreasing yield was 63% and 31% in the 2050s, at Jokioinen and Lleida, respectively, relative to 1981-2010. The contribution of crop model structure to the total variance of ensemble output was larger than that from downscaled climate projections and model parameters. The relative contribution of crop model parameters and downscaled climate projections to the total variance of ensemble output varied greatly among the seven crop models and between the two sites. The contribution of downscaled climate projections was on average larger than that of crop model parameters. This information on the uncertainty from different sources can be quite useful for model users to decide where to put the most effort when preparing or choosing models or parameters for impact analyses. We concluded that the triple-ensemble probabilistic approach that accounts for the uncertainties from multiple important sources provide more comprehensive information for quantifying uncertainties in climate change impact assessments as compared to the conventional approaches that are deterministic or only account for the uncertainties from one or two of the uncertainty sources.

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confidence: 99%

Contribution of crop model structure, parameters and climate projections to uncertainty in climate change impact assessments

Tao

Rötter

Palosuo

et al. 2018

Global Change Biology

175

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“…Note that, GCM 20th century run and future projections under different RCPs exhibit a temporal mismatch with the observed records and with each other. The model uncertainty in projected streamflow can be reduced by adopting a performance-based multimodel combination approach or simply by applying equal weight to ensemble members [36]. Table 1).…”

Section: Estimation Of Future Changesmentioning

confidence: 99%

Streamflow Simulation Using Bayesian Regression with Multivariate Linear Spline to Estimate Future Changes

Bhowmik

Seo

Sahoo³

2018

Water

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Statistical models for hydrologic simulation are a common choice among researchers particularly when catchment information is limited. In this study, we adopt a new statistical approach, namely Bayesian regression with multivariate linear spline (BMLS) for long-term simulation of streamflow on a Hydroclimate Data Network (HCDN) site in the United States. The study aims to: (i) evaluate the performance of the BMLS model; (ii) compare the performance of climate model outputs as predictors in hydrologic simulation; and (iii) estimate the changes in streamflow caused by anthropogenic climate change which is defined as the projected change in precipitation and temperature under different greenhouse gas emission scenarios. Performance of the BMLS model is compared with climatology for the validation period. Results suggest that the BMLS model forced with observed monthly precipitation and average temperature exhibits information that is not presented in the climatology of the validation period. Later, we consider Coupled Model Intercomparison Project Phase 5 (CMIP5) historical and hindcast runs to simulate streamflow at the HCDN site. The study found that sea-surface temperature-initialized decadal hindcast runs are performing no better than 20th century historical runs regarding hydrologic simulation. Finally, the changes in mean and variability in streamflow at the HCDN site are estimated by forcing the model with CMIP5 future projections for the period 2000-2049.

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“…Although, multiple climate models was suggested over a single model, Knutti (2008) reported that major simulation errors might arise in multiple GCMs either from model parameterization or from the inadequate understanding of physical processes. Therefore, multiple GCMs or multiple members within a GCM ensemble show significant differences in their monthly outputs (Johnson et al ., 2011; Das Bhowmik et al ., 2017). Future projections of GCMs exhibit different magnitudes of change in the global mean surface air temperature, as compared to the 20th century records (Stocker et al ., 2013).…”

Section: Introductionmentioning

confidence: 99%

“…Most optimal model combination techniques have focused primarily on weather and climate forecasts, mostly ignoring the prospect of performance‐based model combination in reducing uncertainty in climate change projections. A recent study showed multi‐model combination based on asynchronous measurement can perform better than equal weighting approach in estimating future projections of precipitation (Das Bhowmik et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

“…In the current study, our interest in climate change projections is mostly on the changes in climate attributes with respect to the base period, hence we focus on addressing the spatial correlation in the changes in climate attributes through multi‐model combination. To reduce the model uncertainty and model bias under near‐term climate change, we propose a performance‐based optimal multi‐model combination framework that has shown significant improvements in seasonal forecasting and extreme climate projections; therefore, it needs to be evaluated in the context of near‐term climate change (Das Bhowmik et al ., 2017). Towards this, we analyse the distribution of change in the mean seasonal temperature to identify the impact of anthropogenic climate change under different RCPs.…”

Section: Introductionmentioning

confidence: 99%

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A performance‐based multi‐model combination approach to reduce uncertainty in seasonal temperature change projections

Bhowmik

Sankarasubramanian

2020

Intl Journal of Climatology

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Future changes in climate variable exhibit prominent impact on flood magnitudes, crop yields, and freshwater withdrawal. Researchers typically ignore the large degree of uncertainty translated from climate projections to the estimated climate change magnitudes while applying pre-processing approaches on climate change projections. General Circulation Models (GCM) exhibit substantial uncertainty in projecting future changes in the seasonal temperature, which is evaluated by estimating the shift in either the mean or variance. Bias between the observed changes (1950-1999) and GCM simulated changes vary across models, climate regions, seasons, and under emission scenarios. The simplest approach to reduce model structural uncertainty, equal weighting of GCMs, does not consider superiority of one or multiple GCMs compared to the rest. The current study adopts a performance-based model combination approach that has shown efficiency in streamflow and weather forecasting, and GCM precipitation simulation. The optimal model combination approach has been modified to combine multi-model climate change information, while yielding the spatial correlation in climate change information within a geographic region. The optimal model combination approach, along with a simple bias-correction, is applied on 10 GCMs over nine climate regions across the coterminous United States (CONUS). We found that the optimal combination exhibits lower RMSE values as compared to the equal combination. Correlations between the model combined projections under optimal combination and the observed changes are strong and positive (>0.5). Future (2000-49) model combined projections exhibit an increase in the mean seasonal temperature by 2 C for winter and by 1 C for summer over almost all climate regions.

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Reducing Model Structural Uncertainty in Climate Model Projections—A Rank-Based Model Combination Approach

Cited by 22 publications

References 49 publications

Contribution of crop model structure, parameters and climate projections to uncertainty in climate change impact assessments

Contribution of crop model structure, parameters and climate projections to uncertainty in climate change impact assessments

Streamflow Simulation Using Bayesian Regression with Multivariate Linear Spline to Estimate Future Changes

A performance‐based multi‐model combination approach to reduce uncertainty in seasonal temperature change projections

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