Variability of surface climate in simulations of past and future

Abstract

Abstract. It is virtually certain that the mean surface temperature of the Earth will continue to increase under realistic emission scenarios. Yet comparatively little is known about future changes in climate variability. We explore changes in climate variability over the large range of climates simulated by the Coupled Model Intercomparison Project Phases 5 and 6 (CMIP5/6) and the Paleoclimate Modeling Intercomparison Project Phase 3 (PMIP3). This consists of time slices of the Last Glacial Maximum, the Mid Holocene and idealized warming experiments (1 % CO2) and abrupt4×CO2), and encompasses climates with a range of 12 K of global mean temperature change. We examine climate variability from different perspectives: the local interannual change, coherent climate modes and through compositing extremes. The change in the interannual variability of precipitation is strongly dependent upon the local change in the total amount of precipitation. Meanwhile only over tropical land is the change in the interannual temperature variability positively correlated to temperature change, and then weakly. In general, temperature variability is inversely related to mean temperature change – with analysis of power spectra demonstrating that this holds from intra-seasonal to multi-decadal timescales. We systematically investigate changes in the standard deviation of modes of climate variability, such as the North Atlantic Oscillation, with global mean temperature change. While several modes do show consistent relationships (most notably the Atlantic Zonal Mode), no generalisable pattern emerges. By compositing extreme precipitation events across the ensemble, we demonstrate that the atmospheric drivers dominating rainfall variability in Mediterranean climates persist throughout palaeoclimate and future simulations. The robust nature of the response of climate variability, between both cold and warm climates and across multiple timescales, suggests that observations and proxy reconstructions could provide a meaningful constraint on climate variability in future projections.

Weakened impact of the Atlantic Niño on the future equatorial Atlantic and Guinea Coast rainfall

“…The signal in areas where at least 50 % of the models show a significant regression (two-sided Student test at 95 % confidence level) and where at least 80 % of the models agree on the sign of the mean is considered as robust. When we consider the change between two periods, the sign of the change averaged over a subset of models is considered as robust if at least two-thirds of the models agree on the sign of the multimodel mean (Rehfeld et al, 2020), and at least the change of the multimodel mean is significant at 95 % confidence level according to a two-sided Welch t test. Moreover, the percentage of change of the ATL3 SST index standard deviation between two periods is computed as 100 × σ fut −σ his σ his , where σ his is the standard deviation of the JAS ATL3 SST index in the 1985-2014 period, and σ fut the standard deviation of the JAS ATL3 SST index in a future period (the near-term, mid-term and long-term periods).…”

Large-scale emergence of regional changes in year-to-year temperature variability by the end of the 21st century

“…Changes in temperature variability are at least as important as the change in mean temperature because increased variability poses a greater risk to species and human society than global warming 1 , 2 . Despite this relevance, we know little about changes in temperature variability 3 due to the difficulty of reliably quantifying changes in variability based on instrumental records, single climate model simulations or multi-model ensembles from Climate Model Intercomparison Projects (CMIPs). The challenge of disentangling the forced response and changes in internal variability with these traditional tools results in inconclusive estimates of projected change, ranging from no change 4 , slight global-mean decreases 5 – 7 , to regional increases in temperature variability 7 – 9 .…”

Contrasting State‐Dependent Effects of Natural Forcing on Global and Local Climate Variability

“…The question of state‐dependent variability has long motivated studies of past (Ditlevsen et al., 1996; Rehfeld et al., 2018; Shao & Ditlevsen, 2016) and future (Huntingford et al., 2013; Olonscheck et al., 2021; Rehfeld et al., 2020) climate. Our results reveal a decrease in mean local variability with warming (Figure 3).…”

“…The underlying mechanisms remain poorly understood. There is conflicting and incomplete evidence on the spatio‐temporal patterns of change (Brown et al., 2017; Holmes et al., 2016; Huntingford et al., 2013; Pendergrass et al., 2017; Rehfeld et al., 2020). This is a major source of uncertainty for regional climate projections.…”