Redox processes, aqueous and solid-phase chemistry, and pH dynamics are key drivers of subsurface biogeochemical cycling in terrestrial and wetland ecosystems but are typically not included in terrestrial carbon cycle models. These omissions may introduce errors when simulating systems where redox interactions and pH fluctuations are important, such as wetlands where saturation of soils can produce anoxic conditions and coastal systems where sulfate inputs from seawater can influence biogeochemistry. Integrating cycling of redox-sensitive elements could therefore allow models to better represent key elements of carbon cycling and greenhouse gas production. We describe a model framework that couples the Energy Exascale Earth System Model (E3SM) Land Model (ELM) with PFLOTRAN biogeochemistry, allowing geochemical processes and redox interactions to be integrated with land surface model simulations. We implemented a reaction network including aerobic decomposition, fermentation, sulfate reduction, sulfide oxidation, and methanogenesis as well as pH dynamics along with iron oxide and iron sulfide mineral precipitation and dissolution. We simulated biogeochemical cycling in tidal wetlands subject to either saltwater or freshwater inputs driven by tidal hydrological dynamics. In simulations with saltwater tidal inputs, sulfate reduction led to accumulation of sulfide, higher dissolved inorganic carbon concentrations, lower dissolved organic carbon concentrations, and lower methane emissions than simulations with freshwater tidal inputs. Model simulations compared well with measured porewater concentrations and surface gas emissions from coastal wetlands in the Northeastern United States. These results demonstrate how simulating geochemical reaction networks can improve land surface model simulations of subsurface biogeochemistry and carbon cycling.

The fate of organic carbon (C) in permafrost soils is important to the climate system due to the large global stocks of permafrost C. Thawing permafrost can be subject to dynamic hydrology, making redox processes an important factor controlling soil organic matter (SOM) decomposition rates and greenhouse gas production. In iron (Fe)-rich permafrost soils, Fe(III) can serve as a terminal electron acceptor, suppressing methane (CH4) production and increasing carbon dioxide (CO2) production. Current large-scale models of Arctic C cycling do not include Fe cycling or pH interactions. Here, we coupled Fe redox reactions and C cycling in a geochemical reaction model to simulate the interactions of SOM decomposition, Fe(III) reduction, pH dynamics, and greenhouse gas production in permafrost soils subject to dynamic hydrology. We evaluated the model using measured CO2 and CH4 fluxes as well as changes in pH, Fe(II), and dissolved organic C concentrations from oxic and anoxic incubations of permafrost soils from polygonal permafrost sites in northern Alaska, United States. In simulations of higher frequency oxic-anoxic cycles, rapid oxidation of Fe(II) to Fe(III) during oxic periods and gradual Fe(III) reduction during anoxic periods reduced cumulative CH4 fluxes and increased cumulative CO2 fluxes. Lower pH suppressed CH4 fluxes through its direct impact on methanogenesis and by increasing Fe(III) bioavailability. Our results suggest that models that do not include Fe-redox reactions and its pH dependence could overestimate CH4 production and underestimate CO2 emissions and SOM decomposition rates in Fe-rich, frequently waterlogged Arctic soils.

Accurate simulations of high latitude ecosystems are critical for confident Earth system model (ESM) projections of carbon cycle feedbacks to global climate change. Land surface model components of ESMs, including the E3SM Land Model (ELM), simulate vegetation growth and ecosystem responses to changing climate and atmospheric CO2 concentrations by grouping heterogeneous vegetation into like sets of plant functional types (PFTs). Such models often represent high-latitude vegetation using only two PFTs (shrub and grass), thereby missing the diversity of vegetation growth forms and functional traits in the Arctic. Here, we use field observations of biomass and leaf traits across a gradient of plant communities on the Seward Peninsula in northwest Alaska to replace the original ELM configuration for the first time with nine arctic-specific PFTs. The PFTs that are new to the model include: 1) nonvascular mosses and lichens, 2) deciduous and evergreen shrubs of various height classes, including an alder PFT, 3) graminoids, and 4) forbs. Improvements relative to the original model configuration included greater belowground biomass allocation, persistent fine roots and rhizomes of nonwoody plants, and better representation of variability in total plant biomass across sites with varying plant communities and depth to bedrock. Simulations through 2100 using the RCP8.5 climate scenario showed alder-dominated plant communities gaining more biomass and lichen-dominated communities gaining less biomass compared to default PFTs. Our results highlight how representing the diversity of arctic vegetation and confronting models with measurements from varied plant communities improves the representation of arctic vegetation in terrestrial ecosystem models.

We synthesized 1323 combinations of phospholipid fatty acid-derived fungal biomass C (FBC), bacterial biomass C (BBC), and fungi:bacteria (F:B) ratio in topsoil, spanning 11 major biomes. We found that the FBC, BBC, and F:B ratio display clear biogeographic patterns along latitude and environmental gradients including mean annual temperature, mean annual precipitation, net primary productivity, root C density, soil temperature, soil moisture, and edaphic properties. At the biome level, the highest FBC and BBC densities are observed in tundra, at 3684 (95% confidence interval: 1678~8084) mg kg-1 and 428 (237~774) mg kg-1, respectively. The lowest FBC and BBC densities were found in deserts, at 16.92 (14.4~19.89) mg kg-1 and 6.83 (6.1~7.65) mg kg-1, respectively. While the F:B ratio ranges from 1.8 (1.6~2.1) in savanna to 8.6 (6.7~11.0) in tundra. Combining an empirical model of F:B ratio with the global dataset of soil microbial biomass C, we then produced global maps for FBC and BBC in 0-30 cm topsoil. Global stock of C was estimated to be 12.6 (6.6~16.4) Pg C in FBC and 4.3 (0.5~10.3) Pg C in BBC in topsoil. This work creates a benchmark for explicit use of microbial data in modelling biosphere-atmosphere feedbacks in a changing environment.