Climate change in the Tianshan and northern Kunlun Mountains based on GCM simulation ensemble with Bayesian model averaging

Many downscaling techniques have been developed in the past few years for projection of station-scale hydrological variables from large-scale atmospheric variables to assess the hydrological impacts of climate change. To improve the simulation accuracy of downscaling methods, the Bayesian Model Averaging (BMA) method combined with three statistical downscaling methods, which are support vector machine (SVM), BCC/RCG-Weather Generators (BCC/RCG-WG), and Statistics Downscaling Model (SDSM), is proposed in this study, based on the statistical relationship between the larger scale climate predictors and observed precipitation in upper Hanjiang River Basin (HRB). The statistical analysis of three performance criteria (the Nash-Sutcliffe coefficient of efficiency, the coefficient of correlation, and the relative error) shows that the performance of ensemble downscaling method based on BMA for rainfall is better than that of each single statistical downscaling method. Moreover, the performance for the runoff modelled by the SWAT rainfall-runoff model using the downscaled daily rainfall by four methods is also compared, and the ensemble downscaling method has better simulation accuracy. The ensemble downscaling technology based on BMA can provide scientific basis for the study of runoff response to climate change.

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Uncertainty Analysis of Climate Change Impacts on Streamflow Extremes in Zayandeh-Rud River by Bayesian Model Averaging

Journal of Water and Soil Science ◽

10.47176/jwss.23.4.38751 ◽

2019 ◽

Vol 23 (4) ◽

Keyword(s):

Climate Change ◽

Uncertainty Analysis ◽

Bayesian Model ◽

Bayesian Model Averaging ◽

Model Averaging ◽

Climate Change Impacts ◽

Streamflow Extremes

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Probabilistic climate change predictions applying Bayesian model averaging

Philosophical Transactions of The Royal Society A Mathematical Physical and Engineering Sciences ◽

10.1098/rsta.2007.2070 ◽

2007 ◽

Vol 365 (1857) ◽

pp. 2103-2116 ◽

Cited By ~ 50

Author(s):

Seung-Ki Min ◽

Daniel Simonis ◽

Andreas Hense

Keyword(s):

Climate Change ◽

Bayesian Model ◽

Bayes Factor ◽

Bayesian Model Averaging ◽

Model Averaging ◽

Surface Air Temperature ◽

First Century ◽

Intergovernmental Panel ◽

Twenty First Century ◽

Global Mean

This study explores the sensitivity of probabilistic predictions of the twenty-first century surface air temperature (SAT) changes to different multi-model averaging methods using available simulations from the Intergovernmental Panel on Climate Change fourth assessment report. A way of observationally constrained prediction is provided by training multi-model simulations for the second half of the twentieth century with respect to long-term components. The Bayesian model averaging (BMA) produces weighted probability density functions (PDFs) and we compare two methods of estimating weighting factors: Bayes factor and expectation–maximization algorithm. It is shown that Bayesian-weighted PDFs for the global mean SAT changes are characterized by multi-modal structures from the middle of the twenty-first century onward, which are not clearly seen in arithmetic ensemble mean (AEM). This occurs because BMA tends to select a few high-skilled models and down-weight the others. Additionally, Bayesian results exhibit larger means and broader PDFs in the global mean predictions than the unweighted AEM. Multi-modality is more pronounced in the continental analysis using 30-year mean (2070–2099) SATs while there is only a little effect of Bayesian weighting on the 5–95% range. These results indicate that this approach to observationally constrained probabilistic predictions can be highly sensitive to the method of training, particularly for the later half of the twenty-first century, and that a more comprehensive approach combining different regions and/or variables is required.

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Application of Bayesian Model Averaging in the Reconstruction of Past Climate Change Using PMIP3/CMIP5 Multimodel Ensemble Simulations

Journal of Climate ◽

10.1175/jcli-d-14-00752.1 ◽

2015 ◽

Vol 29 (1) ◽

pp. 175-189 ◽

Cited By ~ 10

Author(s):

M. Fang ◽

X. Li

Keyword(s):

Climate Change ◽

Bayesian Model ◽

Climate Models ◽

Bayesian Model Averaging ◽

Model Averaging ◽

Multimodel Ensemble ◽

Model Simulations ◽

Ensemble Simulations ◽

Intercomparison Project ◽

Past Climate Change

Abstract Climate change simulations based on climate models are inevitably uncertain. This uncertainty typically stems from parametric and structural uncertainties in climate models as well as climate forcings. However, combining model simulations with instrumental observations using appropriate statistical methods is an effective approach for describing this uncertainty. In this study, the authors applied Bayesian model averaging (BMA), a statistical postprocessing method, to an ensemble of climate model simulations from the Paleoclimate Modelling Intercomparison Project phase 3 (PMIP3) and phase 5 of the Coupled Model Intercomparison Project (CMIP5). Uncertainties, weights, and variances of individual model simulations were estimated from a training period using the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis dataset. The results presented here demonstrate that the BMA method is successful and attains a positive performance in this study. These results show that the selected proxy-based reconstructions and simulations are consistent with BMA estimates regarding climate variability in the past 1000 years, though differences can be found for some periods. The authors conclude that BMA is an effective tool for describing uncertainties associated with individual model simulations, as it accounts for the diverse capabilities of different models and generates a more credible range of past climate change over a relatively long-term period based on multimodel ensemble simulations and training data.

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Projected Climate Change Impacts on Future Streamflow of the Yarlung Tsangpo-Brahmaputra River

10.5194/hess-2018-251 ◽

2018 ◽

Cited By ~ 1

Author(s):

Ran Xu ◽

Hongchang Hu ◽

Fuqiang Tian ◽

Chao Li ◽

Mohd Yawar Ali Khan

Keyword(s):

Climate Change ◽

Bias Correction ◽

Bayesian Model ◽

Bayesian Model Averaging ◽

Regional Climate ◽

Model Averaging ◽

The Tibetan Plateau ◽

Brahmaputra River ◽

Yarlung Tsangpo ◽

Impacts Of Climate Change

Abstract. The Yarlung Tsangpo-Brahmaputra River (YBR) originating from the Tibetan Plateau (TP), is an important water source for many domestic and agricultural practices in countries including China, India, Bhutan and Bangladesh. To date, only a few studies have investigated the impacts of climate change on water resources in this river basin with dispersed results. In this study, we provide a comprehensive and updated assessment of the impacts of climate change on YBR streamflow by integrating a physically based hydrological model, regional climate integrations from CORDEX (Coordinated Regional Climate Downscaling Experiment), different bias correction methods, and Bayesian model averaging method. We find that (i) bias correction is able to reduce systematic biases in regional climate integrations and thus benefits hydrological simulations over YBR Basin; (ii) Bayesian model averaging, which optimally combines individual hydrological simulations obtained from different bias correction methods, tends to provide hydrological time series superior over individual ones. We show that by the year 2035, the annual mean streamflow is projected to change respectively by 6.8 %, −0.4 %, and −4.1 % under RCP4.5 relative to the historical period (1980–2001) at the Bahadurabad in Bangladesh, the upper Brahmaputra outlet, and Nuxia in China. Under RCP8.5, these percentage changes will substantially increase to 12.9 %, 13.1 %, and 19.9 %. Therefore, the change rate of streamflow shows strong spatial variability along the YBR from downstream to upstream. The increasing rate of streamflow shows an augmented trend from downstream to upstream under RCP8.5 compared to an attenuated pattern under RCP4.5.

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