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Title: Detection and Attribution of Regional Climate Change

Abstract

We developed a high resolution global coupled modeling capability to perform breakthrough studies of the regional climate change. The atmospheric component in our simulation uses a 1{sup o} latitude x 1.25{sup o} longitude grid which is the finest resolution ever used for the NCAR coupled climate model CCSM3. Substantial testing and slight retuning was required to get an acceptable control simulation. The major accomplishment is the validation of this new high resolution configuration of CCSM3. There are major improvements in our simulation of the surface wind stress and sea ice thickness distribution in the Arctic. Surface wind stress and ocean circulation in the Antarctic Circumpolar Current are also improved. Our results demonstrate that the FV version of the CCSM coupled model is a state of the art climate model whose simulation capabilities are in the class of those used for IPCC assessments. We have also provided 1000 years of model data to Scripps Institution of Oceanography to estimate the natural variability of stream flow in California. In the future, our global model simulations will provide boundary data to high-resolution mesoscale model that will be used at LLNL. The mesoscale model would dynamically downscale the GCM climate to regional scale onmore » climate time scales.« less

Authors:
;
Publication Date:
Research Org.:
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1036845
Report Number(s):
UCRL-TR-227391
TRN: US201207%%25
DOE Contract Number:
W-7405-ENG-48
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; 58 GEOSCIENCES; CLIMATE MODELS; CLIMATES; CONFIGURATION; DETECTION; DISTRIBUTION; LAWRENCE LIVERMORE NATIONAL LABORATORY; OCEANOGRAPHY; RESOLUTION; SEAS; SIMULATION; TESTING; THICKNESS; VALIDATION

Citation Formats

Bala, G, and Mirin, A. Detection and Attribution of Regional Climate Change. United States: N. p., 2007. Web. doi:10.2172/1036845.
Bala, G, & Mirin, A. Detection and Attribution of Regional Climate Change. United States. doi:10.2172/1036845.
Bala, G, and Mirin, A. Fri . "Detection and Attribution of Regional Climate Change". United States. doi:10.2172/1036845. https://www.osti.gov/servlets/purl/1036845.
@article{osti_1036845,
title = {Detection and Attribution of Regional Climate Change},
author = {Bala, G and Mirin, A},
abstractNote = {We developed a high resolution global coupled modeling capability to perform breakthrough studies of the regional climate change. The atmospheric component in our simulation uses a 1{sup o} latitude x 1.25{sup o} longitude grid which is the finest resolution ever used for the NCAR coupled climate model CCSM3. Substantial testing and slight retuning was required to get an acceptable control simulation. The major accomplishment is the validation of this new high resolution configuration of CCSM3. There are major improvements in our simulation of the surface wind stress and sea ice thickness distribution in the Arctic. Surface wind stress and ocean circulation in the Antarctic Circumpolar Current are also improved. Our results demonstrate that the FV version of the CCSM coupled model is a state of the art climate model whose simulation capabilities are in the class of those used for IPCC assessments. We have also provided 1000 years of model data to Scripps Institution of Oceanography to estimate the natural variability of stream flow in California. In the future, our global model simulations will provide boundary data to high-resolution mesoscale model that will be used at LLNL. The mesoscale model would dynamically downscale the GCM climate to regional scale on climate time scales.},
doi = {10.2172/1036845},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Fri Jan 19 00:00:00 EST 2007},
month = {Fri Jan 19 00:00:00 EST 2007}
}

Technical Report:

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  • Despite being a well-established research field, the detection and attribution of observed climate change to anthropogenic forcing is not yet provided as a climate service. One reason for this is the lack of a methodology for performing tailored detection and attribution assessments on a rapid time scale. Here we develop such an approach, based on the translation of quantitative analysis into the “confidence” language employed in recent Assessment Reports of the Intergovernmental Panel on Climate Change. While its systematic nature necessarily ignores some nuances examined in detailed expert assessments, the approach nevertheless goes beyond most detection and attribution studies inmore » considering contributors to building confidence such as errors in observational data products arising from sparse monitoring networks. When compared against recent expert assessments, the results of this approach closely match those of the existing assessments. Where there are small discrepancies, these variously reflect ambiguities in the details of what is being assessed, reveal nuances or limitations of the expert assessments, or indicate limitations of the accuracy of the sort of systematic approach employed here. Deployment of the method on 116 regional assessments of recent temperature and precipitation changes indicates that existing rules of thumb concerning the detectability of climate change ignore the full range of sources of uncertainty, most particularly the importance of adequate observational monitoring.« less
  • A report from the National Academies of Sciences, Engineering, and Medicine concludes it is now possible to estimate the influence of climate change on some types of extreme events. The science of extreme event attribution has advanced rapidly in recent years, giving new insight to the ways that human-caused climate change can influence the magnitude or frequency of some extreme weather events. This report examines the current state of science of extreme weather attribution, and identifies ways to move the science forward to improve attribution capabilities. Confidence is strongest in attributing types of extreme events that are influenced by climatemore » change through a well-understood physical mechanism, such as, the more frequent heat waves that are closely connected to human-caused global temperature increases, the report finds. Confidence is lower for other types of events, such as hurricanes, whose relationship to climate change is more complex and less understood at present. For any extreme event, the results of attribution studies hinge on how questions about the event's causes are posed, and on the data, modeling approaches, and statistical tools chosen for the analysis.« less
  • It has been hypothesized recently that cooling caused by anthropogenic sulfate aerosols may be obscuring a warming signal associated with changes in greenhouse gas concentrations. Here the authors use results from model experiments in which sulfate and carbon dioxide have been varied individually and in combination in order to determine whether the simulated surface temperature change patterns are increasingly evident in observed records of temperature change. They use centered [R(t)] and uncentered [C(t)] pattern correlation statistics in order to compare observed time-evolving surface temperature change patterns with the model-predicted equilibrium signal patterns. They show that in the case of temperaturemore » signals from the ``CO{sub 2}-only`` and ``sulfate-only`` experiments, the C(t) statistic essentially reduces to a measure of observed global-mean temperature changes, and cannot be used to uniquely attribute observed climate changes to a specific causal mechanism. For the signal from the experiment with combined CO{sub 2}/sulfate aerosol forcing, C(t) provides information on pattern congruence, but trends in C(t) are difficult to interpret without decomposing the statistic into pattern similarity and global-mean change components. They therefore focus on R(t), which is a more useful statistic for discriminating between forcing mechanisms with different pattern signatures but similar rates of global mean change.« less
  • The motivation for this project was to advance the science of climate change and prediction in the Arctic region. Its primary goals were to (i) develop a state-of-the-art Regional Arctic Climate system Model (RACM) including high-resolution atmosphere, land, ocean, sea ice and land hydrology components and (ii) to perform extended numerical experiments using high performance computers to minimize uncertainties and fundamentally improve current predictions of climate change in the northern polar regions. These goals were realized first through evaluation studies of climate system components via one-way coupling experiments. Simulations were then used to examine the effects of advancements in climatemore » component systems on their representation of main physics, time-mean fields and to understand variability signals at scales over many years. As such this research directly addressed some of the major science objectives of the BER Climate Change Research Division (CCRD) regarding the advancement of long-term climate prediction.« less