TY - JOUR
T1 - Magnitudes and Spatial Patterns of Interdecadal Temperature Variability in CMIP6
AU - Parsons, Luke A.
AU - Brennan, M. Kathleen
AU - Wills, Robert C.J.
AU - Proistosescu, Cristian
N1 - Funding Information:
L.A.P. thanks the Washington Research Foundation (WRF) for funding support. M.K.B. was supported by an NSF graduate research fellowship (Grant 1650114). C.P. was supported by a JISAO postdoctoral fellowship and the National Science Foundation (Grant AGS-1752796). R.C.J.W was supported by the National Science Foundation (Grant AGS-1929775). We also thank R. Stouffer, G. Hakim, D. Frierson, R. Wood, and S. Sanchez for valuable input, and P Huybers for the code to calculate multitaper coherence. Instrumental-based surface temperature data provided by the Climate Research Unit, University of East Anglia (https://crudata.uea.ac.uk/cru/data/temperature/) and the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA (https://www.esrl.noaa.gov/psd/). We acknowledge the World Climate Research Program's Working Group on Coupled Modeling, which is responsible for the CMIP, and we thank the climate modeling groups for producing and making available their model output. For CMIP, the U.S. Department of Energy's Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. CMIP data can be found at https://esgf-node.llnl.gov/projects/esgf-llnl/.
PY - 2020/4/16
Y1 - 2020/4/16
N2 - Attribution and prediction of global and regional warming requires a better understanding of the magnitude and spatial characteristics of internal global mean surface air temperature (GMST) variability. We examine interdecadal GMST variability in Coupled Modeling Intercomparison Projects, Phases 3, 5, and 6 (CMIP3, CMIP5, and CMIP6) preindustrial control (piControl), last millennium, and historical simulations and in observational data. We find that several CMIP6 simulations show more GMST interdecadal variability than the previous generations of model simulations. Nonetheless, we find that 100-year trends in CMIP6 piControl simulations never exceed the maximum observed warming trend. Furthermore, interdecadal GMST variability in the unforced piControl simulations is associated with regional variability in the high latitudes and the east Pacific, whereas interdecadal GMST variability in instrumental data and in historical simulations with external forcing is more globally coherent and is associated with variability in tropical deep convective regions.
AB - Attribution and prediction of global and regional warming requires a better understanding of the magnitude and spatial characteristics of internal global mean surface air temperature (GMST) variability. We examine interdecadal GMST variability in Coupled Modeling Intercomparison Projects, Phases 3, 5, and 6 (CMIP3, CMIP5, and CMIP6) preindustrial control (piControl), last millennium, and historical simulations and in observational data. We find that several CMIP6 simulations show more GMST interdecadal variability than the previous generations of model simulations. Nonetheless, we find that 100-year trends in CMIP6 piControl simulations never exceed the maximum observed warming trend. Furthermore, interdecadal GMST variability in the unforced piControl simulations is associated with regional variability in the high latitudes and the east Pacific, whereas interdecadal GMST variability in instrumental data and in historical simulations with external forcing is more globally coherent and is associated with variability in tropical deep convective regions.
KW - CMIP6
KW - climate change
KW - climate dynamics
KW - decadal climate variability
KW - internal and forced variability
KW - model-observation comparison
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U2 - 10.1029/2019GL086588
DO - 10.1029/2019GL086588
M3 - Article
AN - SCOPUS:85083522726
VL - 47
JO - Geophysical Research Letters
JF - Geophysical Research Letters
SN - 0094-8276
IS - 7
M1 - e2019GL086588
ER -