Changing climate in northern regions is causing permafrost to thaw with major implications for the global mercury (Hg) cycle. We estimated Hg in permafrost regions based on in situ measurements of sediment total mercury (STHg), soil organic carbon (SOC), and the Hg to carbon ratio (R_HgC) combined with maps of soil carbon. We measured a median STHg of 43 ± 30 ng Hg g soil⁻¹ and a median R_HgC of 1.6 ± 0.9 μg Hg g C⁻¹, consistent with published results of STHg for tundra soils and 11,000 measurements from 4,926 temperate, nonpermafrost sites in North America and Eurasia. We estimate that the Northern Hemisphere permafrost regions contain 1,656 ± 962 Gg Hg, of which 793 ± 461 Gg Hg is frozen in permafrost. Permafrost soils store nearly twice as much Hg as all other soils, the ocean, and the atmosphere combined, and this Hg is vulnerable to release as permafrost thaws over the next century. Existing estimates greatly underestimate Hg in permafrost soils, indicating a need to reevaluate the role of the Arctic regions in the global Hg cycle.

Climate projections for the 21st century indicate that there could be a pronounced warming and permafrost degradation in the Arctic and sub-Arctic regions. Climate warming is likely to cause permafrost thawing with subsequent effects on surface albedo, hydrology, soil organic matter storage and greenhouse gas emissions. To assess possible changes in the permafrost thermal state and active layer thickness, we implemented the GIPL2-MPI transient numerical model for the entire Alaska permafrost domain. The model input parameters are spatial datasets of mean monthly air temperature and precipitation, prescribed thermal properties of the multilayered soil column, and water content that are specific for each soil class and geographical location. As a climate forcing, we used the composite of five IPCC Global Circulation Models that has been downscaled to 2 by 2 km spatial resolution by Scenarios Network for Alaska Planning (SNAP) group. In this paper, we present the modeling results based on input of a five-model composite with A1B carbon emission scenario. The model has been calibrated according to the annual borehole temperature measurements for the State of Alaska. We also performed more detailed calibration for fifteen shallow borehole stations where high quality data are available on daily basis. To validate the model performance, we compared simulated active layer thicknesses with observed data from Circumpolar Active Layer Monitoring (CALM) stations. The calibrated model was used to address possible ground temperature changes for the 21st century. The model simulation results show widespread permafrost degradation in Alaska could begin between 2040–2099 within the vast area southward from the Brooks Range, except for the high altitude regions of the Alaska Range and Wrangell Mountains.

We present an approach to estimate the feedback from large-scale thawing of permafrost soils using a simplified, data-constrained model that combines three elements: soil carbon (C) maps and profiles to identify the distribution and type of C in permafrost soils; incubation experiments to quantify the rates of C lost after thaw; and models of soil thermal dynamics in response to climate warming. We call the approach the Permafrost Carbon Network Incubation–Panarctic Thermal scaling approach (PInc-PanTher). The approach assumes that C stocks do not decompose at all when frozen, but once thawed follow set decomposition trajectories as a function of soil temperature. The trajectories are determined according to a three-pool decomposition model fitted to incubation data using parameters specific to soil horizon types. We calculate litterfall C inputs required to maintain steady-state C balance for the current climate, and hold those inputs constant. Soil temperatures are taken from the soil thermal modules of ecosystem model simulations forced by a common set of future climate change anomalies under two warming scenarios over the period 2010 to 2100. Under a medium warming scenario (RCP4.5), the approach projects permafrost soil C losses of 12.2–33.4 Pg C; under a high warming scenario (RCP8.5), the approach projects C losses of 27.9–112.6 Pg C. Projected C losses are roughly linearly proportional to global temperature changes across the two scenarios. These results indicate a global sensitivity of frozen soil C to climate change ( γ sensitivity) of −14 to −19 Pg C °C ⁻¹ on a 100 year time scale. For CH ₄ emissions, our approach assumes a fixed saturated area and that increases in CH ₄ emissions are related to increased heterotrophic respiration in anoxic soil, yielding CH ₄ emission increases of 7% and 35% for the RCP4.5 and RCP8.5 scenarios, respectively, which add an additional greenhouse gas forcing of approximately 10–18%. The simplified approach presented here neglects many important processes that may amplify or mitigate C release from permafrost soils, but serves as a data-constrained estimate on the forced, large-scale permafrost C response to warming.

This study investigates and compares soil moisture and hydrology projections of broadly used land models with permafrost processes and highlights the causes and impacts of permafrost zone soil moisture projections. Climate models project warmer temperatures and increases in precipitation (P) which will intensify evapotranspiration (ET) and runoff in land models. However, this study shows that most models project a long-term drying of the surface soil (0–20 cm) for the permafrost region despite increases in the net air–surface water flux (P-ET). Drying is generally explained by infiltration of moisture to deeper soil layers as the active layer deepens or permafrost thaws completely. Although most models agree on drying, the projections vary strongly in magnitude and spatial pattern. Land models tend to agree with decadal runoff trends but underestimate runoff volume when compared to gauge data across the major Arctic river basins, potentially indicating model structural limitations. Coordinated efforts to address the ongoing challenges presented in this study will help reduce uncertainty in our capability to predict the future Arctic hydrological state and associated land–atmosphere biogeochemical processes across spatial and temporal scales.