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Arctic sea-ice loss is emblematic of an amplified Arctic water cycle and has critical feedback implications for global climate. Stable isotopes (δ 18 O, δ 2 H, d-excess ) are valuable tracers for constraining water cycle and climate processes through space and time. Yet, the paucity of well-resolved Arctic isotope data preclude an empirically derived understanding of the hydrologic changes occurring today, in the deep (geologic) past, and in the future. To address this knowledge gap, the Pan-Arctic Precipitation Isotope Network (PAPIN) was established in 2018 to coordinate precipitation sampling at 19 stations across key tundra, subarctic, maritime, and continental climate zones. Here, we present a first assessment of rainfall samples collected in summer 2018 ( n = 281) and combine new isotope and meteorological data with sea ice observations, reanalysis data, and model simulations. Data collectively establish a summer Arctic Meteoric Water Line where δ 2 H = 7.6⋅δ 18 O–1.8 ( r 2 = 0.96, p < 0.01). Mean amount-weighted δ 18 O, δ 2 H, and d-excess values were −12.3, −93.5, and 4.9‰, respectively, with the lowest summer mean δ 18 O value observed in northwest Greenland (−19.9‰) and the highest in Iceland (−7.3‰). Southern Alaska recorded the lowest mean d-excess (−8.2%) and northern Russia the highest (9.9‰). We identify a range of δ 18 O-temperature coefficients from 0.31‰/°C (Alaska) to 0.93‰/°C (Russia). The steepest regression slopes (>0.75‰/°C) were observed at continental sites, while statistically significant temperature relations were generally absent at coastal stations. Model outputs indicate that 68% of the summer precipitating air masses were transported into the Arctic from mid-latitudes and were characterized by relatively high δ 18 O values. Yet 32% of precipitation events, characterized by lower δ 18 O and high d-excess values, derived from northerly air masses transported from the Arctic Ocean and/or its marginal seas, highlighting key emergent oceanic moisture sources as sea ice cover declines. Resolving these processes across broader spatial-temporal scales is an ongoing research priority, and will be key to quantifying the past, present, and future feedbacks of an amplified Arctic water cycle on the global climate system. 

Snow is an important driver of ecosystem processes in cold biomes. Snow accumulation determines ground temperature, light conditions, and moisture availability during winter. It also affects the growing season’s start and end, and plant access to moisture and nutrients. Here, we review the current knowledge of the snow cover’s role for vegetation, plant-animal interactions, permafrost conditions, microbial processes, and biogeochemical cycling. We also compare studies of natural snow gradients with snow experimental manipulation studies to assess time scale difference of these approaches. The number of tundra snow studies has increased considerably in recent years, yet we still lack a comprehensive overview of how altered snow conditions will affect these ecosystems. Specifically, we found a mismatch in the timing of snowmelt when comparing studies of natural snow gradients with snow manipulations. We found that snowmelt timing achieved by snow addition and snow removal manipulations (average 7.9 days advance and 5.5 days delay, respectively) were substantially lower than the temporal variation over natural spatial gradients within a given year (mean range 56 days) or among years (mean range 32 days). Differences between snow study approaches need to be accounted for when projecting snow dynamics and their impact on ecosystems in future climates. 

A tenet of ecology is that temporal variability in ecological structure and processes tends to decrease with increasing spatial scales (from locales to regions) and levels of biological organization (from populations to communities). However, patterns in temporal variability across trophic levels and the mechanisms that produce them remain poorly understood. Here we analyzed the abundance time series of spatially structured communities (i.e., metacommunities) spanning basal resources to top predators from 355 freshwater sites across three continents. Specifically, we used a hierarchical partitioning method to disentangle the propagation of temporal variability in abundance across spatial scales and trophic levels. We then used structural equation modeling to determine if the strength and direction of relationships between temporal variability, synchrony, biodiversity, and environmental and spatial settings depended on trophic level and spatial scale. We found that temporal variability in abundance decreased from producers to tertiary consumers but did so mainly at the local scale. Species population synchrony within sites increased with trophic level, whereas synchrony among communities decreased. At the local scale, temporal variability in precipitation and species diversity were associated with population variability (linear partial coefficient, β = 0.23) and population synchrony (β = −0.39) similarly across trophic levels, respectively. At the regional scale, community synchrony was not related to climatic or spatial predictors, but the strength of relationships between metacommunity variability and community synchrony decreased systematically from top predators (β = 0.73) to secondary consumers (β = 0.54), to primary consumers (β = 0.30) to producers (β = 0). Our results suggest that mobile predators may often stabilize metacommunities by buffering variability that originates at the base of food webs. This finding illustrates that the trophic structure of metacommunities, which integrates variation in organismal body size and its correlates, should be considered when investigating ecological stability in natural systems. More broadly, our work advances the notion that temporal stability is an emergent property of ecosystems that may be threatened in complex ways by biodiversity loss and habitat fragmentation.

 

River ecosystems receive and process vast quantities of terrestrial organic carbon, the fate of which depends strongly on microbial activity. Variation in and controls of processing rates, however, are poorly characterized at the global scale. In response, we used a peer-sourced research network and a highly standardized carbon processing assay to conduct a global-scale field experiment in greater than 1000 river and riparian sites. We found that Earth’s biomes have distinct carbon processing signatures. Slow processing is evident across latitudes, whereas rapid rates are restricted to lower latitudes. Both the mean rate and variability decline with latitude, suggesting temperature constraints toward the poles and greater roles for other environmental drivers (e.g., nutrient loading) toward the equator. These results and data set the stage for unprecedented “next-generation biomonitoring” by establishing baselines to help quantify environmental impacts to the functioning of ecosystems at a global scale.