[U-02] Projection and detection of global environmental change

[U02-P06] Introducing the CESM2-Large Ensemble: Exploring the ubiquity of human-induced changes in climate variability

*Keith Bradley Rodgers¹, Sun-Seon Lee^1,2, Nan Rosenbloom³, Axel Timmermann^1,2, Gokhan Danabasoglu³, Clara Deser^1,2, Jim Edwards^1,2, Ji-Eun Kim^1,2, Isla Simpson³, Karl Stein^1,2, Malte F Stuecker⁴, Ryohei Yamaguchi^1,2, Tamas Bodai^1,2, Eui-Seok Chung⁵, Lei Huang^1,2, Who Kim³, Jean-Francois Lamarque³, Danica Lombardozzi³, William Wieder^3,6, Stephen G Yeager³ (1.Center for Climate Physics, INstitute for Basic Science, Busan, South Korea, 2.Pusan National University, Busan, South Korea, 3.National Center for Atmospheric Research, Boulder, USA, 4.Department of Oceanography and International Pacific Research Center, University of Hawaii at Manoa, Honlulu, USA, 5.Korea Polar Research Institue, Incheon, Korea, 6.Institute of Arctic and Alpine Research, University of Colorado, Boulder, USA)

Keywords:Climate change, Climate Variability, Large Ensemble

Here we present a 100-member large ensemble of climate change projections conducted with the Community Earth System Model version 2 to examine the sensitivity of internal climate fluctuations to greenhouse warming. While climate change mitigation targets necessarily concern maximum mean state changes, understanding impacts and developing adaptation strategies will be largely contingent on how climate variability responds to increasing anthropogenic perturbations. Thus far Earth system modeling efforts have primarily focused on projected mean state changes and the sensitivity of specific modes of climate variability, such as the El Niño-Southern Oscillation. However, our knowledge of forced changes in the overall spectrum of climate variability and higher order statistics is relatively limited. Our unprecedented simulations reveal that changes in variability, considered broadly in terms of probability, distribution, amplitude, frequency, phasing, and patterns, are ubiquitous and span a wide range of physical and ecosystem variables across many spatial and temporal scales. Greenhouse warming will in particular alter variance spectra of Earth system variables that are characterized by non-Gaussian probability distributions, such as rainfall, primary production, or fire occurrence. Our modeling results have important implications for climate adaptation efforts, resource management, seasonal predictions, and for assessing potential stressors for terrestrial and marine ecosystems.