Magnitudes and Spatial Patterns of Interdecadal Temperature Variability in CMIP6
L. A. Parsons,M. Kathleen Brennan,Robert C. J. Wills,Cristian Proistosescu,Cristian Proistosescu +4 more
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TLDR
In this paper, the authors examined 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.Abstract:
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. Plain Language Summary Ongoing and future global and regional warming will progress as a combination of internal climate variability and forced climate change. Understanding the magnitude and spatial patterns associated with internal climate variability is an important aspect of being able to predict when, where, and how climate change will be felt around the globe. Here, we show that the latest climate model simulations, which will be used in the Intergovernmental Panel on Climate Change (IPCC) Assessment Report 6 (AR6), simulate a large range in magnitudes of internal global mean temperature variability. Although there are large unforced global temperature trends in some models, we find that even the most variable models never generate unforced global temperature trends equal to the recently observed global warming trends forced by greenhouse gas emissions. We examine the regions associated with internal climate variability and forced climate change in climate model simulations and find that only forced simulations show a pattern of warming consistent with instrumental data.read more
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Climate model projections from the Scenario Model Intercomparison Project (ScenarioMIP) of CMIP6
Claudia Tebaldi,Kevin Debeire,Veronika Eyring,Veronika Eyring,Erich M. Fischer,John C. Fyfe,Pierre Friedlingstein,Pierre Friedlingstein,Reto Knutti,Jason Lowe,Jason Lowe,Brian C. O'Neill,Brian C. O'Neill,Benjamin Sanderson,Detlef P. van Vuuren,Keywan Riahi,Malte Meinshausen,Zebedee Nicholls,Katarzyna B. Tokarska,George C. Hurtt,Elmar Kriegler,Jean-Francois Lamarque,Gerald A. Meehl,Richard H. Moss,Susanne E. Bauer,Olivier Boucher,Victor Brovkin,Victor Brovkin,Young-Hwa Byun,Martin Dix,Silvio Gualdi,Huan Guo,Jasmin G. John,Slava Kharin,Young Ho Kim,Young Ho Kim,Tsuyoshi Koshiro,Libin Ma,Dirk Jan Leo Oliviè,Swapna Panickal,Fangli Qiao,Xinyao Rong,Nan Rosenbloom,Martin Schupfner,Roland Séférian,Alistair Sellar,Tido Semmler,Xiaoying Shi,Zhenya Song,Christian Steger,Ronald J. Stouffer,Neil C. Swart,Kaoru Tachiiri,Qi Tang,Hiroaki Tatebe,Aurore Voldoire,Evgeny Volodin,Klaus Wyser,Xiaoge Xin,Shuting Yang,Yongqiang Yu,Tilo Ziehn +61 more
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