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Title: Sensitivity of Surface Temperature to Oceanic Forcing via q-Flux Green’s Function Experiments. Part I: Linear Response Function

Abstract

This paper explores the use of linear response function (LRF) to relate the mean sea surface temperature (SST) response to prescribed ocean heat convergence (q-flux) forcings. Two methods for constructing the LRF based on the fluctuation-dissipation theorem (FDT) and Green’s function (GRF) are examined. A 900-year preindustrial simulation from the Community Earth System Model with a slab ocean (CESM-SOM) is used to estimate the LRF using FDT. For GRF, 106 pairs of CESM-SOM simulations with warm and cold q-flux patches are performed. FDT is found to have skill in estimating the SST response to a q-flux forcing when the local SST response is strong, but it fails in inverse estimation of the q-flux forcing for a given SST pattern. In contrast, GRF is shown to be reasonably accurate in estimating both SST response and q-flux forcing. Possible degradation in FDT may be attributed to insufficient data sampling, significant departures of the SST data from Gaussian, and the non-normality of the constructed operator. The accurately estimated GRF-based LRF is used to (i) generate a global surface temperature sensitivity map that shows the q-flux forcing in higher latitudes to be three to four times more effective than in low latitudes in producingmore » global surface warming; (ii) identify the most excitable SST mode (neutral vector) resembling Interdecadal Pacific Oscillation; and (iii) estimate a time-invariant q-flux forcing needed for maintaining the GHG-induced SST warming pattern. The GRF experiments will be used to construct LRF for other variables to further explore climate sensitivities and feedbacks.« less

Authors:
 [1];  [2];  [2];  [2];  [1];  [1]
  1. Physical Oceanography Laboratory/Collaborative Innovation Center of Marine Science and Technology, Ocean University of China, and Qingdao National Laboratory for Marine Science and Technology, Qingdao, China
  2. Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, Washington
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23)
OSTI Identifier:
1433772
Report Number(s):
PNNL-SA-127620
Journal ID: ISSN 0894-8755; KP1703010; KP1703010
DOE Contract Number:  
AC05-76RL01830
Resource Type:
Journal Article
Journal Name:
Journal of Climate
Additional Journal Information:
Journal Volume: 31; Journal Issue: 9; Journal ID: ISSN 0894-8755
Publisher:
American Meteorological Society
Country of Publication:
United States
Language:
English
Subject:
climate feedbacks; ocean dynamical feedbacks; Green's function; polar amplification

Citation Formats

Liu, Fukai, Lu, Jian, Garuba, Oluwayemi, Leung, L. Ruby, Luo, Yiyong, and Wan, Xiuquan. Sensitivity of Surface Temperature to Oceanic Forcing via q-Flux Green’s Function Experiments. Part I: Linear Response Function. United States: N. p., 2018. Web. doi:10.1175/JCLI-D-17-0462.1.
Liu, Fukai, Lu, Jian, Garuba, Oluwayemi, Leung, L. Ruby, Luo, Yiyong, & Wan, Xiuquan. Sensitivity of Surface Temperature to Oceanic Forcing via q-Flux Green’s Function Experiments. Part I: Linear Response Function. United States. doi:10.1175/JCLI-D-17-0462.1.
Liu, Fukai, Lu, Jian, Garuba, Oluwayemi, Leung, L. Ruby, Luo, Yiyong, and Wan, Xiuquan. Tue . "Sensitivity of Surface Temperature to Oceanic Forcing via q-Flux Green’s Function Experiments. Part I: Linear Response Function". United States. doi:10.1175/JCLI-D-17-0462.1.
@article{osti_1433772,
title = {Sensitivity of Surface Temperature to Oceanic Forcing via q-Flux Green’s Function Experiments. Part I: Linear Response Function},
author = {Liu, Fukai and Lu, Jian and Garuba, Oluwayemi and Leung, L. Ruby and Luo, Yiyong and Wan, Xiuquan},
abstractNote = {This paper explores the use of linear response function (LRF) to relate the mean sea surface temperature (SST) response to prescribed ocean heat convergence (q-flux) forcings. Two methods for constructing the LRF based on the fluctuation-dissipation theorem (FDT) and Green’s function (GRF) are examined. A 900-year preindustrial simulation from the Community Earth System Model with a slab ocean (CESM-SOM) is used to estimate the LRF using FDT. For GRF, 106 pairs of CESM-SOM simulations with warm and cold q-flux patches are performed. FDT is found to have skill in estimating the SST response to a q-flux forcing when the local SST response is strong, but it fails in inverse estimation of the q-flux forcing for a given SST pattern. In contrast, GRF is shown to be reasonably accurate in estimating both SST response and q-flux forcing. Possible degradation in FDT may be attributed to insufficient data sampling, significant departures of the SST data from Gaussian, and the non-normality of the constructed operator. The accurately estimated GRF-based LRF is used to (i) generate a global surface temperature sensitivity map that shows the q-flux forcing in higher latitudes to be three to four times more effective than in low latitudes in producing global surface warming; (ii) identify the most excitable SST mode (neutral vector) resembling Interdecadal Pacific Oscillation; and (iii) estimate a time-invariant q-flux forcing needed for maintaining the GHG-induced SST warming pattern. The GRF experiments will be used to construct LRF for other variables to further explore climate sensitivities and feedbacks.},
doi = {10.1175/JCLI-D-17-0462.1},
journal = {Journal of Climate},
issn = {0894-8755},
number = 9,
volume = 31,
place = {United States},
year = {2018},
month = {5}
}