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• Archaeological research provides important insights into climate change adaptation. • Norse and Proto-Inuit cultures developed in different learning environments. • Different learning environments produce different responses to environmental change. • Archaeology offers evidence of multi-century information transmission and learning. Using archaeological, historical, and ethnographic analysis of Norse and Inuit toys and miniatures, this paper argues that legacies of childhood learning can create limits to climatic change adaptation and provide lessons from the past relevant today. In Medieval Greenland, Norse children played with objects that would have familiarised them with the expected norms and behaviours of farming, household activities, sailing and conflict, but not with hunting, which was a key omission given the fundamental importance of wild resources to successful climatic adaptation in Greenland after the climate shocks of the mid-13th century. The restricted range of toys combined with an instructional form of learning suggests a high degree of path dependence that limited adaptation to climatic change, and we know the Norse settlement ended with the conjunctures of the 15th century that included climatic change. Inuit children, by contrast, learnt highly adapted behaviours and technologies through objects that taught locally tuned hunting skills. Inuit approaches that prioritised unstructured learning time aided the development of creative skills and problem-solving capabilities, and the Inuit successfully navigated the climatic changes of the Little Ice Age in Greenland. This insight from the past has implications for our approaches to childhood learning in the 21st century and the unfolding climate crisis. Innovative approaches to childhood teaching and learning in the context of climate change adaptation could provide effective solutions, on a timescale commensurate with that of projected climate impacts. [ABSTRACT FROM AUTHOR]

The Preboreal (11.75–10.70 ka BP) is still the least paleogeographically studied time interval in the central part of the East European Plain. High-resolution multi-proxy studies of lacustrine sediments at the Seltso site located in the Desna River floodplain (Dnieper River basin) were conducted. Radiocarbon dating, loss-on-ignition determination, sedimentological and palynological studies and identification of Non-Pollen Palynomorphs in lacustrine sediments allow us to reconstruct changes in vegetation caused by rapid warming at the Younger Dryas–Holocene boundary, short-term climatic fluctuations within the Preboreal and subsequent resumption of warming. Initial Preboreal warming reached its maximum at about 11.5 ka BP when a relatively dry continental climate existed. Between 11.4 and 11.2 ka BP, a short-term cooling corresponding to the Preboreal Oscillation in Greenland occurred, as indicated by a significant reduction of woody vegetation and expansion of open plant communities. In the Late Preboreal, approximately 11.2–10.7 ka BP, warming resumed, which was accompanied by a decrease in the climate continentality. Comparison with high-resolution lithological and palynological data from eight reliably dated sections of the central East European Plain indicates that in northwestern and central Europe, the impact of the Preboreal Oscillation cooling on the vegetation and the lake ecosystems' development was probably somewhat stronger. [ABSTRACT FROM AUTHOR]

Extreme climate events are hotspots in global change. However, research on the changes in future compound events and population exposure is still limited. Leveraging from the data of the sixth phase of the Coupled Model Intercomparison Project (CMIP6), this paper aims to analyze the temporal and spatial changes of global compound temperature and precipitation extreme events in the future. We also predict the risk of population exposure and quantify the contribution of different factors to exposure. The results show that: (1) In the next 80 years, compound hot-dry event (CHDE) will increase at a rate of 0.02, 0.03, and 0.08 days per decade under the three scenarios of SSP1–2.6, SSP2–4.5 and SSP5–8.5, respectively. By comparison, compound hot-humidity event (CHHE) shows a downward trend under the three scenarios, with a downward rate of 0.01, 0.02, and 0.11 days per decade, respectively. (2) Under the SSP1–2.6 and SSP2–4.5 scenarios, CHDE and CHHE have two or more mutation points. Under the SSP5–8.5 scenario, CHDE shows a significant upward and CHHE shows a significant downward trend in the middle and late 21st century. These two indices exhibit periodic changes in all three scenarios (3) South Asia, West Asia, and Northeast Africa have higher CHDE values, while regions with higher CHHE values are located in North Asia and Greenland. (4) Climate change is a major factor affecting population exposure. For CHDE, climate, population, and their synergistic effects contribute about 75%, 20%, and 5% to the exposure, respectively. For CHHE, the contributions of these three factors are 85%, 10%, and 5% respectively. These findings provide scientific guidance for the rational formulation of population policies, the effective avoidance of climate disaster risks and the protection of human health. • Frequency of hot-dry events and hot-humidity events will increase and decrease, respectively, in the future. • There are abrupt points and periodicity in the future time series of compound temperature and precipitation extremes. • Asia is projected to have the greatest exposure and climate change contributes the most to exposure. [ABSTRACT FROM AUTHOR]

Sea ice is crucial in regulating the heat balance between the ocean and atmosphere and quintessential for supporting the prevailing Arctic food web. Due to limited and often local data availability back in time, the sensitivity of sea-ice proxies to long-term climate changes is not well constrained, which renders any comparison with palaeoclimate model simulations difficult. Here we compiled a set of marine sea-ice proxy records with a relatively high temporal resolution of at least 100 years, covering the Common Era (past 2k years) in the Greenland–North Atlantic sector of the Arctic to explore the presence of coherent long-term trends and common low-frequency variability, and we compared those data with transient climate model simulations. We used cluster analysis and empirical orthogonal functions to extract leading modes of sea-ice variability, which efficiently filtered out local variations and improved comparison between proxy records and model simulations. We find that a compilation of multiple proxy-based sea-ice reconstructions accurately reflects general long-term changes in sea-ice history, consistent with simulations from two transient climate models. Although sea-ice proxies have varying mechanistic relationships to sea-ice cover, typically differing in habitat or seasonal representation, the long-term trend recorded by proxy-based reconstructions showed a good agreement with summer minimum sea-ice area from the model simulations. The short-term variability was not as coherent between proxy-based reconstructions and model simulations. The leading mode of simulated sea ice associated with the multidecadal to centennial timescale presented a relatively low explained variance and might be explained by changes in solar radiation and/or inflow of warm Atlantic waters to the Arctic Ocean. Short variations in proxy-based reconstructions, however, are mainly associated with local factors and the ecological nature of the proxies. Therefore, a regional or large-scale view of sea-ice trends necessitates multiple spatially spread sea-ice proxy-based reconstructions, avoiding confusion between long-term regional trends and short-term local variability. Local-scale sea-ice studies, in turn, benefit from reconstructions from well-understood individual research sites. [ABSTRACT FROM AUTHOR]

Given growing concerns about climate tipping points and their risks, it is important to investigate the capability of identifying robust precursor signals for the associated transitions. In general, the variance and short-lag autocorrelations of the fluctuations increase in a stochastically forced system approaching a critical or bifurcation-induced transition, making them theoretically suitable indicators to warn of such transitions. Paleoclimate records provide useful test beds if such a warning of a forthcoming transition could work in practice. The Dansgaard–Oeschger (DO) events are characterized by millennial-scale abrupt climate changes during the glacial period, manifesting most clearly as abrupt temperature shifts in the North Atlantic region. Some previous studies have found such statistical precursor signals for the DO warming transitions. On the other hand, statistical precursor signals for the abrupt DO cooling transitions have not been identified. Analyzing Greenland ice core records, we find robust and statistically significant precursor signals of DO cooling transitions in most of the interstadials longer than roughly 1500 years but not in the shorter interstadials. The origin of the statistical precursor signals is mainly related to so-called rebound events, humps in the temperature observed at the end of interstadial, some decades to centuries prior to the actual transition. We discuss several dynamical mechanisms that give rise to such rebound events and statistical precursor signals. [ABSTRACT FROM AUTHOR]

Many scientific and societal questions that draw on ice sheet modeling necessitate sampling a wide range of potential climatic changes and realizations of internal climate variability. For example, coastal planning literature demonstrates a demand for probabilistic sea level projections with quantified uncertainty. Further, robust attribution of past and future ice sheet change to specific processes or forcings requires a full understanding of the space of possible ice sheet behaviors. The wide sampling required to address such questions is computationally infeasible with sophisticated numerical climate models at the resolution required to accurately force ice sheet models. Stochastic generation of climate forcing of ice sheets offers a complementary alternative. Here, we describe a method to construct a stochastic generator for ice sheet surface mass balance varying in time and space. We demonstrate the method with an application to Greenland Ice Sheet surface mass balance for 1980–2012. We account for spatial correlations among glacier catchments using sparse covariance techniques, and we apply an elevation-dependent downscaling to recover gridded surface mass balance fields suitable for forcing an ice sheet model while including feedback from changing ice sheet surface elevation. The efficiency gained in the stochastic method supports large-ensemble simulations of ice sheet change in a new stochastic ice sheet model. We provide open source Python workflows to support use of our stochastic approach for a broad range of applications. [ABSTRACT FROM AUTHOR]