Patterns of changing surface climate variability from the Last Glacial Maximum to present in transient model simulations

Abstract. As of 2023, global mean temperature has risen by about 1.45 ± 0.12 °C with respect to the 1850–1900 pre-industrial baseline according to the World Meteorological Organization. This rise constitutes the first period of substantial global warming since the Last Deglaciation, when global temperatures rose over several millennia by about 4.0–7.0 °C according to proxy reconstructions. Similar levels of warming could be reached in the coming centuries considering current and possible future emissions. Such warming causes widespread changes in the climate system of which the mean state provides only an incomplete picture. Indeed, climate’s variability and the distributions of climate variables change with warming, impacting for example ecosystems and the frequency and intensity of extremes. However, climate variability during transition periods like the Last Deglaciation remains largely unexplored. Therefore, we investigate changes of climate variability on annual to millennial timescales in fifteen transient climate model simulations of the Last Deglaciation. This ensemble consists of models of varying complexity, from an energy balance model to Earth System Models and includes sensitivity experiments, which differ only in terms of their underlying ice sheet reconstruction, meltwater protocol, or consideration of volcanic forcing. While the ensemble simulates an increase of global mean temperature of 3.0–6.6 °C between the Last Glacial Maximum and Holocene, we examine whether common patterns of variability emerge in the ensemble. To this end, we compare the variability of surface climate during the Last Glacial Maximum, Deglaciation and Holocene by estimating and analyzing the distributions and power spectra of surface temperature and precipitation. For analyzing the distribution shapes, we turn to the higher order moments of variance, skewness and kurtosis. These show that the distributions cannot be assumed to be normal, a precondition for commonly used statistical methods. During the LGM and Holocene, they further reveal significant differences as most simulations feature larger variance during the LGM than Holocene, in-line with results from reconstructions. As a transition period, the Deglaciation stands out as a time of high variance of surface temperature and precipitation, especially on decadal and longer timescales. In general, this dependency on the mean state increases with model complexity, although there is a large spread between models of similar complexity. Some of that spread can be explained by differences in ice sheet, meltwater and volcanic forcings, revealing the impact of simulation protocols on simulated variability. The forcings affect variability not only on their characteristic timescales, rather, we find that they impact variability on all timescales from annual to millennial. The different forcing protocols further have a stronger imprint on the distributions of temperature than precipitation. A reanalysis of the LGM exhibits similar global mean variability to most of the ensemble, but spatial patterns vary. However, whether current paleoclimate data assimilation approaches reconstruct accurate levels of variability is unclear. As such, uncertainty around the models’ abilities to capture climate variability likewise remains, affecting simulations of all time periods, past, present and future. Decreasing this uncertainty warrants a systematic model-data comparisons of simulated variability during periods of warming.