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Title: Is Climate Change Predictable? Really?

Abstract

This project is the first application of a completely different approach to climate modeling, in which new prognostic equations are used to directly compute the evolution of two-point correlations. This project addresses three questions that are critical for the credibility of the science base for climate prediction: (1) What is the variability spectrum at equilibrium? (2) What is the rate of relaxation when subjected to external perturbations? (3) Can variations due to natural processes be distinguished from those due to transient external forces? The technical approach starts with the evolution equation for the probability distribution function and arrives at a prognostic equation for ensemble-mean two-point correlations, bypassing the detailed weather calculation. This work will expand our basic understanding of the theoretical limits of climate prediction and stimulate new experiments to perform with conventional climate models. It will furnish statistical estimates that are inaccessible with conventional climate simulations and likely will raise important new questions about the very nature of climate change and about how (and whether) climate change can be predicted. Solid progress on such issues is vital to the credibility of the science base for climate change research and will provide policymakers evaluating tradeoffs among energy technology options andmore » their attendant environmental and economic consequences.« less

Authors:
;
Publication Date:
Research Org.:
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
928157
Report Number(s):
UCRL-TR-217203
TRN: US200815%%508
DOE Contract Number:  
W-7405-ENG-48
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
29 ENERGY PLANNING, POLICY AND ECONOMY; CLIMATE MODELS; CLIMATES; DISTRIBUTION FUNCTIONS; ECONOMICS; FORECASTING; PROBABILITY; RELAXATION; SIMULATION; TRANSIENTS; WEATHER

Citation Formats

Dannevik, W P, and Rotman, D A. Is Climate Change Predictable? Really?. United States: N. p., 2005. Web. doi:10.2172/928157.
Dannevik, W P, & Rotman, D A. Is Climate Change Predictable? Really?. United States. doi:10.2172/928157.
Dannevik, W P, and Rotman, D A. Mon . "Is Climate Change Predictable? Really?". United States. doi:10.2172/928157. https://www.osti.gov/servlets/purl/928157.
@article{osti_928157,
title = {Is Climate Change Predictable? Really?},
author = {Dannevik, W P and Rotman, D A},
abstractNote = {This project is the first application of a completely different approach to climate modeling, in which new prognostic equations are used to directly compute the evolution of two-point correlations. This project addresses three questions that are critical for the credibility of the science base for climate prediction: (1) What is the variability spectrum at equilibrium? (2) What is the rate of relaxation when subjected to external perturbations? (3) Can variations due to natural processes be distinguished from those due to transient external forces? The technical approach starts with the evolution equation for the probability distribution function and arrives at a prognostic equation for ensemble-mean two-point correlations, bypassing the detailed weather calculation. This work will expand our basic understanding of the theoretical limits of climate prediction and stimulate new experiments to perform with conventional climate models. It will furnish statistical estimates that are inaccessible with conventional climate simulations and likely will raise important new questions about the very nature of climate change and about how (and whether) climate change can be predicted. Solid progress on such issues is vital to the credibility of the science base for climate change research and will provide policymakers evaluating tradeoffs among energy technology options and their attendant environmental and economic consequences.},
doi = {10.2172/928157},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Mon Nov 14 00:00:00 EST 2005},
month = {Mon Nov 14 00:00:00 EST 2005}
}

Technical Report:

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