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Understanding the joint behavior of temperature and precipitation for climate change impact studies

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Abstract

The multiple downscaled scenario products allow us to assess the uncertainty of the variations of precipitation and temperature in the current and future periods. Probabilistic assessments of both climatic variables help better understand the interdependence of the two and thus, in turn, help in assessing the future with confidence. In the present study, we use ensemble of statistically downscaled precipitation and temperature from various models. The dataset used is multi-model ensemble of 10 global climate models (GCMs) downscaled product from CMIP5 daily dataset using the Bias Correction and Spatial Downscaling (BCSD) technique, generated at Portland State University. The multi-model ensemble of both precipitation and temperature is evaluated for dry and wet periods for 10 sub-basins across Columbia River Basin (CRB). Thereafter, copula is applied to establish the joint distribution of two variables on multi-model ensemble data. The joint distribution is then used to estimate the change in trends of said variables in future, along with estimation of the probabilities of the given change. The joint distribution trends vary, but certainly positive, for dry and wet periods in sub-basins of CRB. Dry season, generally, is indicating a higher positive change in precipitation than temperature (as compared to historical) across sub-basins with wet season inferring otherwise. Probabilities of changes in future, as estimated from the joint distribution, indicate varied degrees and forms during dry season whereas the wet season is rather constant across all the sub-basins.

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Acknowledgments

The authors would like to acknowledge the partial financial support provided by the Institute for Sustainable Solutions at Portland State University. The authors would also like to acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modeling groups for producing and making available their model outputs. For CMIP, the US Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provides coordinating support and leads development of software infrastructure in partnership with the Global Organization for Earth System Science Portals.

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Correspondence to Hamid Moradkhani.

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Fig. S1

Frequency-based histograms of daily precipitation average datasets in dry season for original GCMs and bootstrap-sampled multi-model ensemble over the future period (2070–2099) in 10 sub-basins of CRB. The pink color represents the original GCMs dataset, and the blue color represents the bootstrap-sampled multi-model ensemble dataset. (GIF 49 kb)

High Resolution Image (TIFF 83 kb)

Fig. S2

Frequency-based histograms of daily precipitation average datasets in wet season for original GCMs and bootstrap sampled multi-model ensemble over the future period (2070–2099) in 10 sub-basins of CRB. The pink color represents the original GCMs dataset, and the blue color represents the bootstrap-sampled multi-model ensemble dataset. (GIF 41 kb)

High Resolution Image (TIFF 79 kb)

Fig. S3

Frequency-based histograms of daily temperature average datasets in dry season for original GCMs and bootstrap sampled multi-model ensemble over the future period (2070–2099) in 10 sub-basins of CRB. The pink color represents the original GCMs dataset, and the blue color represents the bootstrap-sampled multi-model ensemble dataset. (GIF 45 kb)

High Resolution Image (TIFF 84 kb)

Fig. S4

Frequency-based histograms of daily temperature average datasets in wet season for original GCMs and bootstrap-sampled multi-model ensemble over the future period (2070–2099) in 10 sub-basins of CRB. The pink color represents the original GCMs dataset, and the blue color represents the bootstrap-sampled multi-model ensemble dataset. (GIF 38 kb)

High Resolution Image (TIFF 77 kb)

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Rana, A., Moradkhani, H. & Qin, Y. Understanding the joint behavior of temperature and precipitation for climate change impact studies. Theor Appl Climatol 129, 321–339 (2017). https://doi.org/10.1007/s00704-016-1774-1

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