TY - JOUR
T1 - Estimating the regional climate responses over river basins to changes in tropical sea surface temperature patterns
AU - Tsai, Chii Yun
AU - Forest, Chris E.
AU - Wagener, Thorsten
PY - 2014/12/30
Y1 - 2014/12/30
N2 - 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.
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.
KW - Empirical Green’s functions
KW - Global atmospheric teleconnections
KW - Hydrologic climate response
KW - Regional climate sensitivity
KW - Regional climate variability
KW - Sea surface temperature (SST) variability
UR - http://www.scopus.com/inward/record.url?scp=84920167138&partnerID=8YFLogxK
U2 - 10.1007/s00382-014-2449-1
DO - 10.1007/s00382-014-2449-1
M3 - Article (Academic Journal)
JO - Climate Dynamics
JF - Climate Dynamics
SN - 0930-7575
ER -