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.

Accurately simulating gross primary productivity (GPP) in terrestrial ecosystem models is critical because errors in simulated GPP propagate through the model to introduce additional errors in simulated biomass and other fluxes. We evaluated simulated, daily average GPP from 26 models against estimated GPP at 39 eddy covariance flux tower sites across the United States and Canada. None of the models in this study match estimated GPP within observed uncertainty. On average, models overestimate GPP in winter, spring, and fall, and underestimate GPP in summer. Models overpredicted GPP under dry conditions and for temperatures below 0°C. Improvements in simulated soil moisture and ecosystem response to drought or humidity stress will improve simulated GPP under dry conditions. Adding a low‐temperature response to shut down GPP for temperatures below 0°C will reduce the positive bias in winter, spring, and fall and improve simulated phenology. The negative bias in summer and poor overall performance resulted from mismatches between simulated and observed light use efficiency (LUE). Improving simulated GPP requires better leaf‐to‐canopy scaling and better values of model parameters that control the maximum potential GPP, such asε_max (LUE), V_cmax (unstressed Rubisco catalytic capacity) or J_max (the maximum electron transport rate).

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.

Biogeochemical models must include a broad variety of biological and physical processes to test our understanding of the terrestrial carbon cycle and to predict ecosystem biomass and carbon fluxes. We combine the photosynthesis and biophysical calculations in the Simple Biosphere model, Version 2.5 (SiB2.5) with the biogeochemistry from the Carnegie‐Ames‐Stanford Approach (CASA) model to create SiBCASA, a hybrid capable of estimating terrestrial carbon fluxes and biomass from diurnal to decadal timescales. We add dynamic allocation of Gross Primary Productivity to the growth and maintenance of leaves, roots, and wood and explicit calculation of autotrophic respiration. We prescribe leaf biomass using Leaf Area Index (LAI) derived from remotely sensed Normalized Difference Vegetation Index. Simulated carbon fluxes and biomass are consistent with observations at selected eddy covariance flux towers in the AmeriFlux network. Major sources of error include the steady state assumption for initial pool sizes, the input weather data, and biases in the LAI.