Isolating the Evolving Contributions of Anthropogenic Aerosols and Greenhouse Gases: A New CESM1 Large Ensemble Community Resource

Author:

Deser Clara1,Phillips Adam S.1,Simpson Isla R.1,Rosenbloom Nan1,Coleman Dani1,Lehner Flavio1,Pendergrass Angeline G.1,DiNezio Pedro2,Stevenson Samantha3

Affiliation:

1. Climate and Global Dynamics Division, NCAR, Boulder, Colorado

2. The University of Texas at Austin, Austin, Texas

3. University of California at Santa Barbara, Santa Barbara, California

Abstract

AbstractThe evolving roles of anthropogenic aerosols (AER) and greenhouse gases (GHG) in driving large-scale patterns of precipitation and SST trends during 1920–2080 are studied using a new set of “all-but-one-forcing” initial-condition large ensembles (LEs) with the Community Earth System Model version 1 (CESM1), which complement the original “all-forcing” CESM1 LE (ALL). The large number of ensemble members (15–20) in each of the new LEs enables regional impacts of AER and GHG to be isolated from the noise of the model’s internal variability. Our analysis approach, based on running 50-yr trends, accommodates geographical and temporal changes in patterns of forcing and response. AER are shown to be the primary driver of large-scale patterns of externally forced trends in ALL before the late 1970s, and GHG to dominate thereafter. The AER and GHG forced trends are spatially distinct except during the 1970s transition phase when aerosol changes are mainly confined to lower latitudes. The transition phase is also characterized by a relative minimum in the amplitude of forced trend patterns in ALL, due to a combination of reduced AER and partially offsetting effects of AER and GHG. Internal variability greatly limits the detectability of AER- and GHG-forced trend patterns in individual realizations based on pattern correlation metrics, especially during the historical period, highlighting the need for LEs. We estimate that <20% of the spatial variances of observed precipitation and SST trends are attributable to AER and GHG forcing, although model biases in patterns of forced response and signal-to-noise may affect this estimate.

Funder

US Department of Energy

US National Science Foundation

Publisher

American Meteorological Society

Subject

Atmospheric Science

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