Title: How May the Choice of Downscaling Techniques and Meteorological Reference Observations Affect Future Hydroclimate Projections?

Abstract

Abstract We present an intercomparison of a suite of high‐resolution downscaled climate projections based on a six‐member General Circulation Model (GCM) ensemble from Coupled Models Intercomparison Project (CMIP6). The CMIP6 GCMs have been downscaled using dynamical and statistical downscaling techniques based on two meteorological reference observations over the conterminous United States. We use the regional climate model, RegCM4, for dynamical downscaling, double bias correction constructed analogs method for statistical downscaling, and Daymet and Livneh datasets as the reference observations for statistical training and bias‐correction. We evaluate the performances of downscaled data in both historical and future periods under the SSP585 scenario. While dynamical downscaling improves the simulation of some performance evaluation indices, it adds an extra bias in others, highlighting the need for statistical correction before its use in impact assessments. Downscaled datasets after bias‐correction compare exceptionally well with observations. However, the choice of downscaling techniques and the underlying reference observations influence the hydroclimate characteristics of downscaled data. For instance, the statistical downscaling generally preserves the GCMs climate change signal but overestimates the frequency of hot extremes. Similarly, simulated future changes are sensitive to the choice of reference observations, particularly for precipitation extremes that exhibit a higher projected increase inmore » the ensembles trained and/or corrected by Daymet than Livneh. Overall, these results demonstrate that multiple factors, including downscaling techniques and reference observations, can substantially influence the outcome of downscaled climate projections and stress the need for a comprehensive understanding of such method‐based uncertainties.« less

Authors:

Rastogi, Deeksha  ^[1];

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• Search DOE PAGES for ORCID "0000-0002-0462-4027"

Kao, Shih‐Chieh  ^[2];

• Search DOE PAGES for author "Kao, Shih‐Chieh"
• Search DOE PAGES for ORCID "0000-0002-3207-5328"

Ashfaq, Moetasim  ^[1]

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• Search DOE PAGES for ORCID "0000-0003-4106-3027"

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+ Show Author Affiliations

1. Oak Ridge National Laboratory Computational Sciences and Engineering Division Oak Ridge TN USA
2. Oak Ridge National Laboratory Environmental Sciences Division Oak Ridge TN USA

Publication Date:

Thu Aug 11 00:00:00 EDT 2022

Research Org.:

Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Org.:

USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Water Power Technologies Office

OSTI Identifier:

1880879

Alternate Identifier(s):

OSTI ID: 1883923; OSTI ID: 1996021

Grant/Contract Number:  

AC05-00OR22725

Resource Type:

Published Article

Journal Name:

Earth's Future

Additional Journal Information:

Journal Name: Earth's Future Journal Volume: 10 Journal Issue: 8; Journal ID: ISSN 2328-4277

Publisher:

American Geophysical Union (AGU)

Country of Publication:

United States

Language:

English

Subject:

54 ENVIRONMENTAL SCIENCES