On the Correspondence between Seasonal Forecast Biases and Long-Term Climate Biases in Sea Surface Temperature

Journal Article · 31 December 2019 · Journal of Climate

DOI: https://doi.org/10.1175/JCLI-D-20-0338.1 · OSTI ID:1706621

Ma, Hsi-Yen ^[1]; Siongco, A. Cheska ^[1]; Klein, Stephen A. ^[1]; Xie, Shaocheng ^[1]; Karspeck, Alicia R. ^[2]; Raeder, Kevin ^[3]; Anderson, Jeffrey L. ^[3]; Lee, Jiwoo ^[1]; Kirtman, Ben P. ^[4]; Merryfield, William J. ^[5]; Murakami, Hiroyuki ^[6]; Tribbia, Joseph J. ^[3]

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1. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
2. Jupiter, Boulder, CO (United States); National Center for Atmospheric Research, Boulder, CO (United States)
3. National Center for Atmospheric Research, Boulder, CO (United States)
4. Univ. of Miami, FL (United States)
5. Environment and Climate Change Canada, Victoria, BC (Canada). Canadian Centre for Climate Modeling and Analysis (CCCma)
6. National Oceanic and Atmospheric Administration (NOAA), Princeton, NJ (United States). Geophysical Fluid Dynamics Lab.; University Corporation for Atmospheric Research, Boulder, CO (United States)

In this paper, the correspondence between mean sea surface temperature (SST) biases in retrospective seasonal forecasts (hindcasts) and long-term climate simulations from five global climate models is examined to diagnose the degree to which systematic SST biases develop on seasonal time scales. The hindcasts are from the North American Multi-Model Ensemble and the climate simulations are from the Coupled Model Intercomparison Project. The analysis suggests that most robust climatological SST biases begin to form within 6 months of a realistically initialized integration, although the growth rate varies with location, time, and model. In regions with large biases, interannual variability and ensemble spread is much smaller than the climatological bias. Additional ensemble hindcasts of the Community Earth System Model with a different initialization method suggest that initial conditions do matter for the initial bias growth, but the overall global bias patterns are similar after 6 months. A hindcast approach is more suitable to study biases over the tropics and sub-tropics than over the extra-tropics because of smaller initial biases and faster bias growth. The rapid emergence of SST biases makes it likely that fast processes with times scales shorter than the seasonal time scales in the atmosphere and upper ocean are responsible for a substantial part of the climatological SST biases. Studying the growth of biases may provide important clues to the causes and ultimately the amelioration of these biases. Further, initialized seasonal hindcasts can profitably be used in the development of high-resolution coupled ocean-atmosphere models.

• Research Organization: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC); Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States); Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Atmospheric Radiation Measurement (ARM) Data Center
• Sponsoring Organization: USDOE National Nuclear Security Administration (NNSA); USDOE Office of Science (SC), Biological and Environmental Research (BER)
• Contributing Organization: Argonne National Laboratory (ANL); Brookhaven National Laboratory (BNL); Oak Ridge National Laboratory (ORNL); Pacific Northwest National Laboratory (PNNL)
• Grant/Contract Number: AC02-05CH11231; AC52-07NA27344
• OSTI ID: 1706621
• Report Number(s): LLNL-JRNL--788840
• Journal Information: Journal of Climate, Journal Name: Journal of Climate Journal Issue: 1 Vol. 34; ISSN 0894-8755
• Publisher: American Meteorological SocietyCopyright Statement
• Country of Publication: United States
• Language: English