skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Accelerating development of a predictive science of climate.

Abstract

Climate change and studies of its implications are front page news. Could the heat waves of July 2006 in Europe and the US be caused by global warming? Are increased incidences of strong tropical storms and hurricanes like Katrina to be expected? Will coastal cities be flooded due to sea level rise? The National Climatic Data Center (NCDC) which archives all weather data for the nation reports that global surface temperatures have increased at a rate near 0.6 C over the last century but that the trend is three times larger since 1976 [Easterling, 2006]. Will this rate continue or will climate change be even more abrupt? Stepping back from the flurry of questions, scientists must take a systematic approach and develop a predictive framework. With responsibility for advising on energy and technology strategies, the Department of Energy Office of Biological and Environmental Research has chosen to bolster the science of climate in order to get the story straight on the factors that cause climate change and the role of carbon loading from fossil fuel use.

Authors:
 [1];  [2]
  1. ORNL
  2. Los Alamos National Laboratory (LANL)
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Center for Computational Sciences
Sponsoring Org.:
USDOE Office of Science (SC)
OSTI Identifier:
946092
DOE Contract Number:
DE-AC05-00OR22725
Resource Type:
Journal Article
Resource Relation:
Journal Name: SciDAC Review; Journal Issue: 3
Country of Publication:
United States
Language:
English
Subject:
29 ENERGY PLANNING, POLICY AND ECONOMY; CARBON; CLIMATES; FOSSIL FUELS; GREENHOUSE EFFECT; HURRICANES; SEA LEVEL; STORMS; WEATHER; CLIMATIC CHANGE; climate modeling

Citation Formats

Drake, John B, and Jones, Phil. Accelerating development of a predictive science of climate.. United States: N. p., 2007. Web.
Drake, John B, & Jones, Phil. Accelerating development of a predictive science of climate.. United States.
Drake, John B, and Jones, Phil. Mon . "Accelerating development of a predictive science of climate.". United States. doi:.
@article{osti_946092,
title = {Accelerating development of a predictive science of climate.},
author = {Drake, John B and Jones, Phil},
abstractNote = {Climate change and studies of its implications are front page news. Could the heat waves of July 2006 in Europe and the US be caused by global warming? Are increased incidences of strong tropical storms and hurricanes like Katrina to be expected? Will coastal cities be flooded due to sea level rise? The National Climatic Data Center (NCDC) which archives all weather data for the nation reports that global surface temperatures have increased at a rate near 0.6 C over the last century but that the trend is three times larger since 1976 [Easterling, 2006]. Will this rate continue or will climate change be even more abrupt? Stepping back from the flurry of questions, scientists must take a systematic approach and develop a predictive framework. With responsibility for advising on energy and technology strategies, the Department of Energy Office of Biological and Environmental Research has chosen to bolster the science of climate in order to get the story straight on the factors that cause climate change and the role of carbon loading from fossil fuel use.},
doi = {},
journal = {SciDAC Review},
number = 3,
volume = ,
place = {United States},
year = {Mon Jan 01 00:00:00 EST 2007},
month = {Mon Jan 01 00:00:00 EST 2007}
}
  • The Community Climate System Model results from a multi-agency collaboration designed to construct cutting-edge climate science simulation models for a broad research community. Predictive climate simulations are currently being prepared for the petascale computers of the near future. Modeling capabilities are continuously being improved in order to provide better answers to critical questions about Earth's climate. Climate change and its implications are front page news in today's world. Could global warming be responsible for the July 2006 heat waves in Europe and the United States? Should more resources be devoted to preparing for an increase in the frequency of strongmore » tropical storms and hurricanes like Katrina? Will coastal cities be flooded due to a rise in sea level? The National Climatic Data Center (NCDC), which archives all weather data for the nation, reports that global surface temperatures have increased over the last century, and that the rate of increase is three times greater since 1976. Will temperatures continue to climb at this rate, will they decline again, or will the rate of increase become even steeper? To address such a flurry of questions, scientists must adopt a systematic approach and develop a predictive framework. With responsibility for advising on energy and technology strategies, the DOE is dedicated to advancing climate research in order to elucidate the causes of climate change, including the role of carbon loading from fossil fuel use. Thus, climate science--which by nature involves advanced computing technology and methods--has been the focus of a number of DOE's SciDAC research projects. Dr. John Drake (ORNL) and Dr. Philip Jones (LANL) served as principal investigators on the SciDAC project, 'Collaborative Design and Development of the Community Climate System Model for Terascale Computers.' The Community Climate System Model (CCSM) is a fully-coupled global system that provides state-of-the-art computer simulations of the Earth's past, present, and future climate states. The collaborative SciDAC team--including over a dozen researchers at institutions around the country--developed, validated, documented, and optimized the performance of CCSM using the latest software engineering approaches, computational technology, and scientific knowledge. Many of the factors that must be accounted for in a comprehensive model of the climate system are illustrated in figure 1.« less
  • The analysis of climate data has relied heavily on hypothesis-driven statistical methods, while projections of future climate are based primarily on physics-based computational models. However, in recent years a wealth of new datasets has become available. Therefore, we take a more data-centric approach and propose a unified framework for studying climate, with an aim towards characterizing observed phenomena as well as discovering new knowledge in the climate domain. Specifically, we posit that complex networks are well-suited for both descriptive analysis and predictive modeling tasks. We show that the structural properties of climate networks have useful interpretation within the domain. Further,more » we extract clusters from these networks and demonstrate their predictive power as climate indices. Our experimental results establish that the network clusters are statistically significantly better predictors than clusters derived using a more traditional clustering approach. Using complex networks as data representation thus enables the unique opportunity for descriptive and predictive modeling to inform each other.« less
  • In this study, the sources and strengths of statistical short-term climate predictability for local surface climate (temperature and precipitation) and 700-mb geopotential height in the Northern Hemisphere are explored at all times of the year at lead times of up to one year. Canonical correlation analysis is the linear statistical methodology employed. Predictor and predictand averaging periods of 1 and 3 months are used, with four consecutive predictor periods, followed by a lead time and then a single predictand period. Predictor fields are quasi-global sea surface temperature (SST), Northern Hemisphere 700-mb height, and prior values of the predictand field itself.more » Cross-validation is used to obtain, to first order, uninflated skill estimates. Results reveal mainly modest statistical predictive skill except for certain fields, locations, and times of the year when predictability is far above chance expectation and good enough to be beneficial to appropriate users. The time of year when skills are generally highest is January through April. Global SST is the most skill-producing predictor field, perhaps because (1) the lower boundary condition is a more consistent influence on climate on timescales of 1 to 3 months than the atmosphere's internal dynamics, or (2) SST is the only field in this study that provides tropical information directly. Prediction is generally more skillful on the 3-month than 1-month timescale. The skill of the forecasts is often insensitive to the forecast lead time; that is, inserting 3, or sometimes 6 or more, months between the predictor and predictand periods causes little skill decrease from that of 1 month or less.« less
  • We explore the possibility accelerating the convergence to statistical equilibrium of seasonal climate models whose ocean is a simple mixed layer. The procedure developed involves reducing the thermal inertia of the ocean and increasing the frequency of the solar forcing cycle during an acceleration period. Determining the appropriate duration and ending time for the acceleration is important, especially for avoiding problems posed by the annual temperature cycle. We present a method for calculating the ending time that requires no knowledge beforehand about details of the model's behavior other than an estimate of the temperature cycle's lag behind the solar forcingmore » cycle. Uncertainties about the coupling between a model's atmosphere and ocean limit the degree of allowable acceleration. Model noise can also limit successful application if high accuracy is demanded. Using a numerical climate model, we obtain savings of over 50% in the amount of time necessary to reach convergence. Our success occurs in part because the accelerated integration's time average temperature field is nearly the same as that of the nonaccelerated integration. The procedure will be less effective in a model whose time average temperature is instead a strong function of the degree of acceleration. The procedure developed is simple to apply and can allow more extensive use of these models to study climate change.« less
  • Approximate methods for electronic structure, implemented in sophisticated computer codes and married to ever-more powerful computing platforms, have become invaluable in chemistry and materials science. The maturing and consolidation of quantum chemistry codes since the 1980s, based upon explicitly correlated electronic wave functions, has made them a staple of modern molecular chemistry. Here, the impact of first principles electronic structure in physics and materials science had lagged owing to the extra formal and computational demands of bulk calculations.