The influence of dependence in characterizing multi-variable uncertainty for climate change impact assessments

Sajjad Eghdamirad; Fiona Johnson; Ashish Sharma; Joong Hoon Kim

doi:10.1080/02626667.2019.1602777

The influence of dependence in characterizing multi-variable uncertainty for climate change impact assessments

Sajjad Eghdamirad, Fiona Johnson, Ashish Sharma, Joong Hoon Kim

Research output: Contribution to journal › Article › peer-review

5 Citations (Scopus)

Abstract

Few approaches exist that explicitly use the uncertainty associated with the spread of climate model simulations in assessing climate change impacts. An approach that does so is second-order approximation (SOA). This incorporates quantification of uncertainty to ascertain its impact on the derived response using a Taylor series expansion of the model. This study uses SOA in a statistical downscaling model of monthly streamflow, with a focus on the influence of dependence in the uncertainty of multiple atmospheric variables. Uncertainty is quantified using the square root error variance concept with a new extension that allows the inter-dependence terms among the atmospheric variable uncertainty to be specified. Applying the model to selected point locations in Australia, it is noted that the downscaling results differ considerably from downscaling that ignores uncertainty. However, when the effects of dependence in uncertainty are incorporated, the results differ according to the regional variations in dependence structure.

Original language	English
Pages (from-to)	731-738
Number of pages	8
Journal	Hydrological Sciences Journal
Volume	64
Issue number	6
DOIs	https://doi.org/10.1080/02626667.2019.1602777
Publication status	Published - 2019 Apr 26

Bibliographical note

Funding Information:
This work was supported by a grant from National Research Foundation (NRF) of Korea, funded by the Korean government (MSIP) (No. 2016R1A2A1A05005306). We acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for the CMIP, and we would like to thank the climate modelling groups for producing and making available their model output. For the CMIP, the US Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provided coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. NCEP re-analysis data were provided by the National Oceanic and Atmospheric Administration, Office of Oceanic and Atmospheric Research, Earth System Research Laboratory, Physical Sciences Division Boulder, Colorado, USA (http://www.esrl.noaa.gov/psd/data/gridded). Hydrological data were provided by the Australian Bureau of Meteorology (http://www.bom.gov.au/water/hrs/feature.shtml).

Funding Information:
This work was supported by a grant from National Research Foundation (NRF) of Korea, funded by the Korean government (MSIP) (No. 2016R1A2A1A05005306).

Publisher Copyright:
© 2019, © 2019 IAHS.

Keywords

Taylor series
climate variable uncertainty dependence
statistical downscaling
uncertainty

ASJC Scopus subject areas

Water Science and Technology

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1080/02626667.2019.1602777

Cite this

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abstract = "Few approaches exist that explicitly use the uncertainty associated with the spread of climate model simulations in assessing climate change impacts. An approach that does so is second-order approximation (SOA). This incorporates quantification of uncertainty to ascertain its impact on the derived response using a Taylor series expansion of the model. This study uses SOA in a statistical downscaling model of monthly streamflow, with a focus on the influence of dependence in the uncertainty of multiple atmospheric variables. Uncertainty is quantified using the square root error variance concept with a new extension that allows the inter-dependence terms among the atmospheric variable uncertainty to be specified. Applying the model to selected point locations in Australia, it is noted that the downscaling results differ considerably from downscaling that ignores uncertainty. However, when the effects of dependence in uncertainty are incorporated, the results differ according to the regional variations in dependence structure.",

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The influence of dependence in characterizing multi-variable uncertainty for climate change impact assessments

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

UN SDGs

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