Ubiquity of human-induced changes in climate variability

Abstract

Abstract. While climate change mitigation targets necessarily concern maximum mean state change, 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. Here we present a new 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. 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.

Increased Variability of Biomass Burning Emissions in CMIP6 Amplifies Hydrologic Cycle in the CESM2 Large Ensemble

“…The 80 members were initialized from four different years of the pre‐industrial control simulation (years 1231, 1251, 1281, and 1301). Each initialization year was selected based on the phase of the Atlantic Meridional Overturning Circulation strength (see Rodgers et al., 2021). Twenty members were started from each initialization year by randomly perturbing the temperature field.…”

“…We assess the impact of biomass burning emissions variability on the atmospheric hydrologic cycle using the CESM2‐LE (Rodgers et al., 2021). This large ensemble project used the fully coupled CESM2 configured with the Community Atmosphere Model version 6 (Danabasoglu et al., 2020), Parallel Ocean Program version 2 (Smith et al., 2010), Los Alamos Sea Ice Model version 5.1.2 (Hunke et al., 2015), and Community Land Model version 5 (Lawrence et al., 2019).…”

“…In addition to differences in biomass burning emissions variability, the two ensemble member sets were forced with slight differences in SO 4 and SOAG emissions and CO 2 uptake which were the result of small bug corrections. These differences were determined to be too small to have an effect on climate results (Rodgers et al., 2021). We evaluate the relative impact of the increase in biomass burning variability by comparing the HiVarBB and SmoothBB simulations over the GFED period (1997–2014).…”

“…(2022) and Fasullo et al. (2022), the Community Earth System Model version 2 Large Ensemble Community Project (CESM2‐LE; Rodgers et al., 2021) forced half of its ensemble members with the original CMIP6 biomass burning emissions, and the second half with smoothed biomass burning emissions during the period of increased variability (from 1997 to 2014; Figure 1a, red line). Here, we utilize these two sets of simulations to investigate the impact that this increase in biomass burning emissions variability has on the global atmospheric hydrologic cycle.…”

Global patterns and drivers of C:N:P in marine ecosystems

“…To evaluate the effects of future climatic change on surface community C:P and N:P, we used as input to our GAM derived above with the values of SST, surface nitrate concentration, nutricline depth, and nutrient limitation output from CESM2 Large Ensemble Simulation (CESM2-LENS), which consists of 100 ensemble model simulations which take into the account of the ocean and atmospheric interannual variabilities. The ensemble simulation includes four independent AMOC states and 20 microstates for each AMOC scenarios 63 . At the time of writing this paper, 90 out of 100 model outputs were publicly available, and we extracted environmental variables for each grid cell for each of the 90 model runs and computed ensemble means for the historic period (averaged values for years 2010-2014) and the end of the 21 st century (averaged values for years 2095-2099), the latter considering Shared Socioeconomic Pathway SSP3-7.0 scenario.…”