Estimating the regional climate responses over river basins to changes in tropical sea surface temperature patterns

Chii Yun Tsai, Chris Forest, Thorsten Wagener

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

We investigate how to identify and assess teleconnection signals between anomalous patterns of sea surface temperature (SST) changes and climate variables related to hydrologic impacts over different river basins. The regional climate sensitivity to tropical SST anomaly patterns is examined through a linear relationship given by the global teleconnection operator (GTO, also generally called a sensitivity matrix or an empirical Green’s function). We assume that the GTO defines a multilinear relation between SST forcing and regional climate response of a target area. The sensitivities are computed based on data from a large ensemble of simulations using the NCAR Community Atmospheric Model version 3.1 (CAM 3.1). The linear approximation is evaluated by comparing the linearly reconstructed response with both the results from the full non-linear atmospheric model and observational data. The results show that the linear approximation can capture regional climate variability that the CAM 3.1 AMIP-style simulations produce at seasonal scales for multiple river basins. The linear method can be used potentially for estimating drought conditions, river flow forecasting, and agricultural water management problems.

Original languageEnglish (US)
Pages (from-to)1965-1982
Number of pages18
JournalClimate Dynamics
Volume45
Issue number7-8
DOIs
StatePublished - Oct 1 2015

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regional climate
sea surface temperature
river basin
teleconnection
Green function
river flow
temperature anomaly
simulation
water management
drought
matrix
climate
atmospheric model
method

All Science Journal Classification (ASJC) codes

  • Atmospheric Science

Cite this

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abstract = "We investigate how to identify and assess teleconnection signals between anomalous patterns of sea surface temperature (SST) changes and climate variables related to hydrologic impacts over different river basins. The regional climate sensitivity to tropical SST anomaly patterns is examined through a linear relationship given by the global teleconnection operator (GTO, also generally called a sensitivity matrix or an empirical Green’s function). We assume that the GTO defines a multilinear relation between SST forcing and regional climate response of a target area. The sensitivities are computed based on data from a large ensemble of simulations using the NCAR Community Atmospheric Model version 3.1 (CAM 3.1). The linear approximation is evaluated by comparing the linearly reconstructed response with both the results from the full non-linear atmospheric model and observational data. The results show that the linear approximation can capture regional climate variability that the CAM 3.1 AMIP-style simulations produce at seasonal scales for multiple river basins. The linear method can be used potentially for estimating drought conditions, river flow forecasting, and agricultural water management problems.",
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Estimating the regional climate responses over river basins to changes in tropical sea surface temperature patterns. / Tsai, Chii Yun; Forest, Chris; Wagener, Thorsten.

In: Climate Dynamics, Vol. 45, No. 7-8, 01.10.2015, p. 1965-1982.

Research output: Contribution to journalArticle

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AB - We investigate how to identify and assess teleconnection signals between anomalous patterns of sea surface temperature (SST) changes and climate variables related to hydrologic impacts over different river basins. The regional climate sensitivity to tropical SST anomaly patterns is examined through a linear relationship given by the global teleconnection operator (GTO, also generally called a sensitivity matrix or an empirical Green’s function). We assume that the GTO defines a multilinear relation between SST forcing and regional climate response of a target area. The sensitivities are computed based on data from a large ensemble of simulations using the NCAR Community Atmospheric Model version 3.1 (CAM 3.1). The linear approximation is evaluated by comparing the linearly reconstructed response with both the results from the full non-linear atmospheric model and observational data. The results show that the linear approximation can capture regional climate variability that the CAM 3.1 AMIP-style simulations produce at seasonal scales for multiple river basins. The linear method can be used potentially for estimating drought conditions, river flow forecasting, and agricultural water management problems.

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