This study examines the Arctic surface air temperature response to regional aerosol emissions reductions using three fully coupled chemistry-climate models: National Center for Atmospheric Research-Community Earth System Model version 1, Geophysical Fluid Dynamics Laboratory-Coupled Climate Model version 3 (GFDL-CM3) and Goddard Institute for Space Studies-ModelE version 2. Each of these models was used to perform a series of aerosol perturbation experiments, in which emissions of different aerosol types (sulfate, black carbon (BC), and organic carbon) in different northern mid-latitude source regions, and of biomass burning aerosol over South America and Africa, were substantially reduced or eliminated. We find that the Arctic warms in nearly every experiment, the only exceptions being the U.S. and Europe BC experiments in GFDL-CM3 in which there is a weak and insignificant cooling. The Arctic warming is generally larger than the global mean warming (i.e. Arctic amplification occurs), particularly during non-summer months. The models agree that changes in the poleward atmospheric moisture transport are the most important factor explaining the spread in Arctic warming across experiments: the largest warming tends to coincide with the largest increases in moisture transport into the Arctic. In contrast, there is an inconsistent relationship (correlation) across experiments between the local radiative forcing over the Arctic and the simulated Arctic warming, with this relationship being positive in one model (GFDL-CM3) and negative in the other two. Our results thus highlight the prominent role of poleward energy transport in driving Arctic warming and amplification, and suggest that the relative importance of poleward energy transport and local forcing/feedbacks is likely to be model dependent.

Understanding the carbon-water coupling over permafrost regions is essential to projecting global ecosystem carbon sequestration and water dynamics. Ecosystem water use efficiency (EWUE), defined as the ratio of gross primary productivity (GPP) and evapotranspiration (ET), reflects plant acclimation strategies with varying ecosystem functioning against environmental stress. Yet EWUE change and its potential drivers across the northern permafrost regions remain poorly quantified, hampering our understanding of permafrost carbon-climatefeedback. Here, we compared and analyzed the difference using satellite observations and process based models to estimate the spatio-temporal variations of EWUE in 1982-2018 over northern permafrost regions. Using flux measurements as truth data, satellite-derived EWUE was more reliable than model-based EWUE. Satellite-derived EWUE showed biome-dependent spatial patterns, with a steady temporal trend (0.01 g C mm-1 decade-1, P > 0.05) for spatially averaged EWUE over northern permafrost regions. Carbon dioxide (CO2) concentration and nitrogen deposition positively affected interannual variations of EWUE, while vapor pressure deficit and other climatic factors (i.e., temperature, precipitation, and radiation) negatively controlled EWUE. Compared to satellite-derived EWUE, we found that EWUEs derived from an ensemble of process-based carbon cycle models are overestimated in seven out of ten models, with an increasing trend of 0.11 g C mm-1 decade 1 (P < 0.001) for spatially averaged EWUE of the ensemble mean. The relationships between climatic factors and EWUE are partially misinterpreted in model estimates, especially with overstated CO2 sensitivity and the opposite temperature effect. The fluctuating sensitivities to climate over time and the diminishing effect of CO2 fertilization on gross primary productivity (GPP) may partially explain the discrepancy observed between satellite-derived and model-based estimates of EWUE. Thus, this study calls for caution concerning model-based EWUE and aids in understanding permafrost-climate feedbacks and projections of carbon and water cycles.

Plant-associated microbiomes are structured by environmental conditions and plant associates, both of which are being altered by climate change. The future structure of plant microbiomes will depend on the, largely unknown, relative importance of each. This uncertainty is particularly relevant for arctic peatlands, which are undergoing large shifts in plant communities and soil microbiomes as permafrost thaws, and are potentially appreciable sources of climate change feedbacks due to their soil carbon (C) storage. We characterized phyllosphere and rhizosphere microbiomes of six plant species, and bulk peat, across a permafrost thaw progression (from intact permafrost, to partially- and fully-thawed stages) via 16S rRNA gene amplicon sequencing. We tested the hypothesis that the relative influence of biotic versus environmental filtering (the role of plant species versus thaw-defined habitat) in structuring microbial communities would differ among phyllosphere, rhizosphere, and bulk peat. Using both abundance- and phylogenetic-based approaches, we found that phyllosphere microbial composition was more strongly explained by plant associate, with little influence of habitat, whereas in the rhizosphere, plant and habitat had similar influence. Network-based community analyses showed that keystone taxa exhibited similar patterns with stronger responses to drivers. However, plant associates appeared to have a larger influence on organisms belonging to families associated with methane-cycling than the bulk community. Putative methanogens were more strongly influenced by plant than habitat in the rhizosphere, and in the phyllosphere putative methanotrophs were more strongly influenced by plant than was the community at large. We conclude that biotic effects can be stronger than environmental filtering, but their relative importance varies among microbial groups. For most microbes in this system, biotic filtering was stronger aboveground than belowground. However, for putative methane-cyclers, plant associations have a stronger influence on community composition than environment despite major hydrological changes with thaw. This suggests that plant successional dynamics may be as important as hydrological changes in determining microbial relevance to C-cycling climate feedbacks. By partitioning the degree that plant versus environmental filtering drives microbiome composition and function we can improve our ability to predict the consequences of warming for C-cycling in other arctic areas undergoing similar permafrost thaw transitions.