• J. Hansen
    NASA Goddard Institute for Space Studies New York New York USA
  • M. Sato
    Columbia University Earth Institute New York New York USA
  • R. Ruedy
    SGT Incorporated New York New York USA
  • L. Nazarenko
    Columbia University Earth Institute New York New York USA
  • A. Lacis
    NASA Goddard Institute for Space Studies New York New York USA
  • G. A. Schmidt
    NASA Goddard Institute for Space Studies New York New York USA
  • G. Russell
    NASA Goddard Institute for Space Studies New York New York USA
  • I. Aleinov
    Columbia University Earth Institute New York New York USA
  • M. Bauer
    Columbia University Earth Institute New York New York USA
  • S. Bauer
    Columbia University Earth Institute New York New York USA
  • N. Bell
    Columbia University Earth Institute New York New York USA
  • B. Cairns
    Department of Applied Physics and Applied Mathematics Columbia University New York New York USA
  • V. Canuto
    NASA Goddard Institute for Space Studies New York New York USA
  • M. Chandler
    Columbia University Earth Institute New York New York USA
  • Y. Cheng
    SGT Incorporated New York New York USA
  • A. Del Genio
    NASA Goddard Institute for Space Studies New York New York USA
  • G. Faluvegi
    Columbia University Earth Institute New York New York USA
  • E. Fleming
    NASA Goddard Space Flight Center Greenbelt Maryland USA
  • A. Friend
    Laboratoire des Sciences du Climat et de l'Environnement Orme des Merisiers Gif‐sur‐Yvette France
  • T. Hall
    NASA Goddard Institute for Space Studies New York New York USA
  • C. Jackman
    NASA Goddard Space Flight Center Greenbelt Maryland USA
  • M. Kelley
    Laboratoire des Sciences du Climat et de l'Environnement Orme des Merisiers Gif‐sur‐Yvette France
  • N. Kiang
    NASA Goddard Institute for Space Studies New York New York USA
  • D. Koch
    Columbia University Earth Institute New York New York USA
  • J. Lean
    Naval Research Laboratory Washington, D. C. USA
  • J. Lerner
    Columbia University Earth Institute New York New York USA
  • K. Lo
    SGT Incorporated New York New York USA
  • S. Menon
    Lawrence Berkeley National Laboratory Berkeley California USA
  • R. Miller
    NASA Goddard Institute for Space Studies New York New York USA
  • P. Minnis
    NASA Langley Research Center Hampton Virginia USA
  • T. Novakov
    Lawrence Berkeley National Laboratory Berkeley California USA
  • V. Oinas
    SGT Incorporated New York New York USA
  • Ja. Perlwitz
    Department of Applied Physics and Applied Mathematics Columbia University New York New York USA
  • Ju. Perlwitz
    Columbia University Earth Institute New York New York USA
  • D. Rind
    NASA Goddard Institute for Space Studies New York New York USA
  • A. Romanou
    NASA Goddard Institute for Space Studies New York New York USA
  • D. Shindell
    NASA Goddard Institute for Space Studies New York New York USA
  • P. Stone
    Center for Meteorology Massachusetts Institute of Technology Cambridge Massachusetts USA
  • S. Sun
    NASA Goddard Institute for Space Studies New York New York USA
  • N. Tausnev
    SGT Incorporated New York New York USA
  • D. Thresher
    Department of Earth and Environmental Sciences Columbia University New York New York USA
  • B. Wielicki
    NASA Langley Research Center Hampton Virginia USA
  • T. Wong
    NASA Langley Research Center Hampton Virginia USA
  • M. Yao
    SGT Incorporated New York New York USA
  • S. Zhang
    Columbia University Earth Institute New York New York USA

書誌事項

公開日
2005-09-27
権利情報
  • http://onlinelibrary.wiley.com/termsAndConditions#vor
DOI
  • 10.1029/2005jd005776
公開者
American Geophysical Union (AGU)

この論文をさがす

説明

<jats:p>We use a global climate model to compare the effectiveness of many climate forcing agents for producing climate change. We find a substantial range in the “efficacy” of different forcings, where the efficacy is the global temperature response per unit forcing relative to the response to CO<jats:sub>2</jats:sub> forcing. Anthropogenic CH<jats:sub>4</jats:sub> has efficacy ∼110%, which increases to ∼145% when its indirect effects on stratospheric H<jats:sub>2</jats:sub>O and tropospheric O<jats:sub>3</jats:sub> are included, yielding an effective climate forcing of ∼0.8 W/m<jats:sup>2</jats:sup> for the period 1750–2000 and making CH<jats:sub>4</jats:sub> the largest anthropogenic climate forcing other than CO<jats:sub>2</jats:sub>. Black carbon (BC) aerosols from biomass burning have a calculated efficacy ∼58%, while fossil fuel BC has an efficacy ∼78%. Accounting for forcing efficacies and for indirect effects via snow albedo and cloud changes, we find that fossil fuel soot, defined as BC + OC (organic carbon), has a net positive forcing while biomass burning BC + OC has a negative forcing. We show that replacement of the traditional instantaneous and adjusted forcings, Fi and Fa, with an easily computed alternative, Fs, yields a better predictor of climate change, i.e., its efficacies are closer to unity. Fs is inferred from flux and temperature changes in a fixed‐ocean model run. There is remarkable congruence in the spatial distribution of climate change, normalized to the same forcing Fs, for most climate forcing agents, suggesting that the global forcing has more relevance to regional climate change than may have been anticipated. Increasing greenhouse gases intensify the Hadley circulation in our model, increasing rainfall in the Intertropical Convergence Zone (ITCZ), Eastern United States, and East Asia, while intensifying dry conditions in the subtropics including the Southwest United States, the Mediterranean region, the Middle East, and an expanding Sahel. These features survive in model simulations that use all estimated forcings for the period 1880–2000. Responses to localized forcings, such as land use change and heavy regional concentrations of BC aerosols, include more specific regional characteristics. We suggest that anthropogenic tropospheric O<jats:sub>3</jats:sub> and the BC snow albedo effect contribute substantially to rapid warming and sea ice loss in the Arctic. As a complement to a priori forcings, such as Fi, Fa, and Fs, we tabulate the a posteriori effective forcing, Fe, which is the product of the forcing and its efficacy. Fe requires calculation of the climate response and introduces greater model dependence, but once it is calculated for a given amount of a forcing agent it provides a good prediction of the response to other forcing amounts.</jats:p>

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