Permafrost stability is significantly influenced by the thermal buffering effects of snow and active-layer peat soils. In the warm season, peat soils act as a barrier to downward heat transfer mainly due to their low thermal conductivity. In the cold season, the snowpack serves as a thermal insulator, retarding the release of heat from the soil to the atmosphere. Currently, many global land models overestimate permafrost soil temperature and active layer thickness (ALT), partially due to inaccurate representations of soil organic matter (SOM) density profiles and snow thermal insulation. In this study, we evaluated the impacts of SOM and snow schemes on ALT simulations at pan-Arctic permafrost sites using the Energy Exascale Earth System Model (E3SM) land model (ELM). We conducted simulations at the Circumpolar Active Layer Monitoring (CALM) sites across the pan-Arctic domain. We improved ELM-simulated site-level ALT using a knowledge-based hierarchical optimization procedure and examined the effects of precipitation-phase partitioning methods (PPMs), snow compaction schemes, and snow thermal conductivity schemes on simulated snow depth, soil temperature, ALT, and CO2 fluxes. Results showed that the optimized ELM significantly improved agreement with observed ALT (e.g. RMSE decreased from 0.83 m to 0.15 m). Our sensitivity analysis revealed that snow-related schemes significantly impact simulated snow thermal insulation levels, soil temperature, and ALT. For example, one of the commonly used snow thermal conductivity schemes (quadratic Sturm or SturmQua) generally produced warmer soil temperatures and larger ALT compared to the other two tested schemes. The SturmQua scheme also amplified the model’s sensitivity to PPMs and predicted deeper ALTs than the other two snow schemes under both current and future climates. The study highlights the importance of accurately representing snow-related processes and peat soils in land models to enhance permafrost dynamics simulations.

Arctic warming has increased vegetation growth and soil respiration during recent decades. The rate of Arctic warming will likely amplify over the 21st century. Previous studies have revealed that the most severe Arctic warming occurred during the cold season (September to May). The cold-season warming has posited significant CO2 emissions to the atmosphere via respiration, possibly offsetting warm-season (June to August) net CO2 uptake. However, prevailing Earth system land models poorly represent cold-season CO2 emissions, making estimates of Arctic tundra annual CO2 budgets highly uncertain. Here, we demonstrate that an improved version of the energy exascale Earth system model (E3SM) land model (ELMv1-ECA) captures the large amount of cold-season CO2 emissions over Alaskan Arctic tundra as reported by two independent, observationally-constrained datasets. We found that the recent seven-decades warming trend of cold-season soil temperature is three times that of the warm-season. The climate sensitivity of warm-season net CO2 uptake, however, is threefold higher than for the cold-season net CO2 loss, mainly due to stronger plant resilience than microbial resilience to hydroclimatic extremes. Consequently, the modeled warm-season net CO2 uptake has a larger positive trend (0.74 ± 0.14 gC m-2 yr-1) than that of cold-season CO2 emissions (0.64 ± 0.11 gC m-2 yr-1) from 1950 to 2017, supported by enhanced plant nutrient uptake and increased light- and water-use efficiency. With continued warming and elevated CO2 concentrations under the representative concentration pathway (RCP) 8.5 scenario, the increasing rate of warm-season net CO2 uptake is more than twice the rate of cold-season emissions (1.33 ± 0.32 gC m-2 yr-1 vs 0.50 ± 0.12 gC m-2 yr-1), making the modeled Alaskan Arctic tundra ecosystem a net CO2 sink by 2100. However, other geomorphological and ecological disturbances (e.g. abrupt permafrost thaw, thermokarst development, landscape-scale hydrological changes, wildfire, and insects) that are not considered here might alter our conclusion.

Field measurements have shown that cold-season methane (CH4) and carbon dioxide (CO2) emissions contribute a substantial portion to the annual net carbon emissions in permafrost regions. However, most earth system land models do not accurately reproduce cold-season CH4 and CO2 emissions, especially over the shoulder (i.e., thawing and freezing) seasons. Here we use the Energy Exascale Earth System Model (E3SM) land model version 1 (ELMv1-ECA) to tackle this challenge and fill the knowledge gap of how cold-season CH4 and CO2 emissions contribute to the annual totals at Alaska Arctic tundra sites. Specifically, we improved the ELMv1-ECA soil water phase-change scheme, environmental controls on microbial activity, and the methane module. Results demonstrate that both soil temperature and the duration of zero-curtain periods (i.e., the fall period when soil temperatures linger around 0°C) simulated by the updated ELMv1-ECA were greatly improved; e.g., the mean absolute error (MAE) in zero-curtain durations at 12 cm depth was reduced by 62 % on average. Furthermore, the MAEs of simulated cold-season carbon emissions at three tundra sites were improved by 72 % and 70 % on average for CH4 and CO2, respectively. Overall, CH4 emitted during the early cold season (September and October), which often includes most of the zero-curtain period in Arctic tundra, accounted for more than 50 % of the total emissions throughout the entire cold season (September to May) in the model, compared with around 49.4 % (43 %-58 %) in observations. From 1950 to 2017, both CO2 emissions during the zero-curtain period and during the entire cold season showed increasing trends, for example, of 0.17 and 0.36 gC m-2 yr-1 at Atqasuk. This study highlights the importance of zero-curtain periods in facilitating cold-season CH4 and CO2 emissions from tundra ecosystems.