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We test several quantitative algorithms as palaeoclimate reconstruction tools for North American and European fossil pollen data, using both classical methods and newer machine-learning approaches based on regression tree ensembles and artificial neural networks. We focus on the reconstruction of secondary climate variables (here, January temperature and annual water balance), as their comparatively small ecological influence compared to the primary variable (July temperature) presents special challenges to palaeo-reconstructions. We test the pollen–climate models using a novel and comprehensive cross-validation approach, running a series ofh-block cross-validations usinghvalues of 100–1500 km. Our study illustrates major benefits of this variableh-block cross-validation scheme, as the effect of spatial autocorrelation is minimized, while the cross-validations with increasinghvalues can reveal instabilities in the calibration model and approximate challenges faced in palaeo-reconstructions with poor modern analogues. We achieve well-performing calibration models for both primary and secondary climate variables, with boosted regression trees providing the overall most robust performance, while the palaeoclimate reconstructions from fossil datasets show major independent features for the primary and secondary variables. Our results suggest that with careful variable selection and consideration of ecological processes, robust reconstruction of both primary and secondary climate variables is possible.

The presence of ground ice in Arctic soils exerts a major effect on permafrost hydrology and ecology, and factors prominently into geomorphic landform development. As most ground ice has accumulated in near-surface permafrost, it is sensitive to variations in atmospheric conditions. Typical and regionally widespread permafrost landforms such as pingos, ice-wedge polygons, and rock glaciers are closely tied to ground ice. However, under ongoing climate change, suitable environmental spaces for preserving landforms associated with ice-rich permafrost may be rapidly disappearing. We deploy a statistical ensemble approach to model, for the first time, the current and potential future environmental conditions of three typical permafrost landforms, pingos, ice-wedge polygons and rock glaciers across the Northern Hemisphere. We show that by midcentury, the landforms are projected to lose more than one-fifth of their suitable environments under a moderate climate scenario (RCP4.5) and on average around one-third under a very high baseline emission scenario (RCP8.5), even when projected new suitable areas for occurrence are considered. By 2061–2080, on average more than 50% of the recent suitable conditions can be lost (RCP8.5). In the case of pingos and ice-wedge polygons, geographical changes are mainly attributed to alterations in thawing-season precipitation and air temperatures. Rock glaciers show air temperature-induced regional changes in suitable conditions strongly constrained by topography and soil properties. The predicted losses could have important implications for Arctic hydrology, geo- and biodiversity, and to the global climate system through changes in biogeochemical cycles governed by the geomorphology of permafrost landscapes. Moreover, our projections provide insights into the circumpolar distribution of various ground ice types and help inventory permafrost landforms in unmapped regions.

Forest microclimates contrast strongly with the climate outside forests. To fully understand and better predict how forests' biodiversity and functions relate to climate and climate change, microclimates need to be integrated into ecological research. Despite the potentially broad impact of microclimates on the response of forest ecosystems to global change, our understanding of how microclimates within and below tree canopies modulate biotic responses to global change at the species, community and ecosystem level is still limited. Here, we review how spatial and temporal variation in forest microclimates result from an interplay of forest features, local water balance, topography and landscape composition. We first stress and exemplify the importance of considering forest microclimates to understand variation in biodiversity and ecosystem functions across forest landscapes. Next, we explain how macroclimate warming (of the free atmosphere) can affect microclimates, and vice versa, via interactions with land‐use changes across different biomes. Finally, we perform a priority ranking of future research avenues at the interface of microclimate ecology and global change biology, with a specific focus on three key themes: (1) disentangling the abiotic and biotic drivers and feedbacks of forest microclimates; (2) global and regional mapping and predictions of forest microclimates; and (3) the impacts of microclimate on forest biodiversity and ecosystem functioning in the face of climate change. The availability of microclimatic data will significantly increase in the coming decades, characterizing climate variability at unprecedented spatial and temporal scales relevant to biological processes in forests. This will revolutionize our understanding of the dynamics, drivers and implications of forest microclimates on biodiversity and ecological functions, and the impacts of global changes. In order to support the sustainable use of forests and to secure their biodiversity and ecosystem services for future generations, microclimates cannot be ignored.