Convergence in simulating global soil organic carbon by structurally different models after data assimilation.

Current biogeochemical models produce carbon-climate feedback projections with large uncertainties, often attributed to their structural differences when simulating soil organic carbon (SOC) dynamics worldwide. However, choices of model parameter values that quantify the strength and represent properties of different soil carbon cycle processes could also contribute to model simulation uncertainties. Here, we demonstrate the critical role of using common observational data in reducing model uncertainty in estimates of global SOC storage. Two structurally different models featuring distinctive carbon pools, decomposition kinetics, and carbon transfer pathways simulate opposite global SOC distributions with their customary parameter values yet converge to similar results after being informed by the same global SOC database using a data assimilation approach. The converged spatial SOC simulations result from similar simulations in key model components such as carbon transfer efficiency, baseline decomposition rate, and environmental effects on carbon fluxes by these two models after data assimilation. Moreover, data assimilation results suggest equally effective simulations of SOC using models following either first-order or Michaelis-Menten kinetics at the global scale. Nevertheless, a wider range of data with high-quality control and assurance are needed to further constrain SOC dynamics simulations and reduce unconstrained parameters. New sets of data, such as microbial genomics-function relationships, may also suggest novel structures to account for in future model development. Overall, our results highlight the importance of observational data in informing model development and constraining model predictions.

Increased drought and extreme events over continental United States under high emissions scenario.

The frequency, severity, and extent of climate extremes in future will have an impact on human well-being, ecosystems, and the effectiveness of emissions mitigation and carbon sequestration strategies. The specific objectives of this study were to downscale climate data for US weather stations and analyze future trends in meteorological drought and temperature extremes over continental United States (CONUS). We used data from 4161 weather stations across the CONUS to downscale future precipitation projections from three Earth System Models (ESMs) participating in the Coupled Model Intercomparison Project Phase Six (CMIP6), specifically for the high emission scenario SSP5 8.5. Comparing historic observations with climate model projections revealed a significant bias in total annual precipitation days and total precipitation amounts. The average number of annual precipitation days across CONUS was projected to be 205 ± 26, 184 ± 33, and 181 ± 25 days in the BCC, CanESM, and UKESM models, respectively, compared to 91 ± 24 days in the observed data. Analyzing the duration of drought periods in different ecoregions of CONUS showed an increase in the number of drought months in the future (2023-2052) compared to the historical period (1989-2018). The analysis of precipitation and temperature changes in various ecoregions of CONUS revealed an increased frequency of droughts in the future, along with longer durations of warm spells. Eastern temperate forests and the Great Plains, which encompass the majority of CONUS agricultural lands, are projected to experience higher drought counts in the future. Drought projections show an increasing trend in future drought occurrences due to rising temperatures and changes in precipitation patterns. Our high-resolution climate projections can inform policy makers about the hotspots and their anticipated future trajectories.

Impact of bioenergy feedstock carbon farming on sustainable aviation fuel viability in the United States.

Biomass-derived sustainable aviation fuel holds significant potential for decarbonizing the aviation sector. Its long-term viability depends on crop choice, longevity of soil organic carbon (SOC) sequestration, and the biomass-to-biojet fuel conversion efficiency. We explored the impact of fuel price and SOC value on viable biojet fuel production scale by integrating an agroecosystem model with a field-to-biojet fuel production process model for 1,4-dimethylcyclooctane (DMCO), a representative high-performance biojet fuel molecule, from Miscanthus, sorghum, and switchgrass. Assigning monetary value to SOC sequestration results in substantially different outcomes than an increased fuel selling price. If SOC accumulation is valued at $185/ton CO2, planting Miscanthus for conversion to DMCO would be economically cost-competitive across 66% of croplands across the continental United States (US) by 2050 if conventional jet fuel remains at $0.74/L (in 2020 US dollars). Cutting the SOC sequestration value in half reduces the viable area to 54% of cropland, and eliminating any payment for SOC shrinks the viable area to 16%. If future biojet fuel prices increase to $1.24/L-Jet A-equivalent, 48 to 58% of the total cultivated land in the United States could support a more diverse set of feedstocks including Miscanthus, sorghum, or switchgrass. Among these options, only 8-14% of the area would be suitable exclusively for Miscanthus cultivation. These findings highlight the intersection of natural solutions for carbon removal and the use of deep-rooted feedstocks for biofuels and biomanufacturing. The results underscore the need to establish clear and consistent values for SOC sequestration to enable the future bioeconomy.

Microbial carbon use efficiency promotes global soil carbon storage.

Soils store more carbon than other terrestrial ecosystems1,2. How soil organic carbon (SOC) forms and persists remains uncertain1,3, which makes it challenging to understand how it will respond to climatic change3,4. It has been suggested that soil microorganisms play an important role in SOC formation, preservation and loss5-7. Although microorganisms affect the accumulation and loss of soil organic matter through many pathways4,6,8-11, microbial carbon use efficiency (CUE) is an integrative metric that can capture the balance of these processes12,13. Although CUE has the potential to act as a predictor of variation in SOC storage, the role of CUE in SOC persistence remains unresolved7,14,15. Here we examine the relationship between CUE and the preservation of SOC, and interactions with climate, vegetation and edaphic properties, using a combination of global-scale datasets, a microbial-process explicit model, data assimilation, deep learning and meta-analysis. We find that CUE is at least four times as important as other evaluated factors, such as carbon input, decomposition or vertical transport, in determining SOC storage and its spatial variation across the globe. In addition, CUE shows a positive correlation with SOC content. Our findings point to microbial CUE as a major determinant of global SOC storage. Understanding the microbial processes underlying CUE and their environmental dependence may help the prediction of SOC feedback to a changing climate.

Global soil profiles indicate depth-dependent soil carbon losses under a warmer climate.

Soil organic carbon (SOC) changes under future climate warming are difficult to quantify in situ. Here we apply an innovative approach combining space-for-time substitution with meta-analysis to SOC measurements in 113,013 soil profiles across the globe to estimate the effect of future climate warming on steady-state SOC stocks. We find that SOC stock will reduce by 6.0 ± 1.6% (mean±95% confidence interval), 4.8 ± 2.3% and 1.3 ± 4.0% at 0-0.3, 0.3-1 and 1-2 m soil depths, respectively, under 1 °C air warming, with additional 4.2%, 2.2% and 1.4% losses per every additional 1 °C warming, respectively. The largest proportional SOC losses occur in boreal forests. Existing SOC level is the predominant determinant of the spatial variability of SOC changes with higher percentage losses in SOC-rich soils. Our work demonstrates that warming induces more proportional SOC losses in topsoil than in subsoil, particularly from high-latitudinal SOC-rich systems.

Regional environmental controllers influence continental scale soil carbon stocks and future carbon dynamics.

Understanding the influence of environmental factors on soil organic carbon (SOC) is critical for quantifying and reducing the uncertainty in carbon climate feedback projections under changing environmental conditions. We explored the effect of climatic variables, land cover types, topographic attributes, soil types and bedrock geology on SOC stocks of top 1 m depth across conterminous United States (US) ecoregions. Using 4559 soil profile observations and high-resolution data of environmental factors, we identified dominant environmental controllers of SOC stocks in 21 US ecoregions using geographically weighted regression. We used projected climatic data of SSP126 and SSP585 scenarios from GFDL-ESM 4 Earth System Model of Coupled Model Intercomparison Project phase 6 to predict SOC stock changes across continental US between 2030 and 2100. Both baseline and predicted changes in SOC stocks were compared with SOC stocks represented in GFDL-ESM4 projections. Among 56 environmental predictors, we found 12 as dominant controllers across all ecoregions. The adjusted geospatial model with the 12 environmental controllers showed an R2 of 0.48 in testing dataset. Higher precipitation and lower temperatures were associated with higher levels of SOC stocks in majority of ecoregions. Changes in land cover types (vegetation properties) was important in drier ecosystem as North American deserts, whereas soil types and topography were more important in American prairies. Wetlands of the Everglades was highly sensitive to projected temperature changes. The SOC stocks did not change under SSP126 until 2100, however SOC stocks decreased up to 21% under SSP585. Our results, based on environmental controllers of SOC stocks, help to predict impacts of changing environmental conditions on SOC stocks more reliably and may reduce uncertainties found in both, geospatial and Earth System Models. In addition, the description of different environmental controllers for US ecoregions can help to describe the scope and importance of global and local models.

Performance-Based Payments for Soil Carbon Sequestration Can Enable a Low-Carbon Bioeconomy.

Incentivizing bioenergy crop production in locations with marginal soils, where low-input perennial crops can provide net carbon sequestration and economic benefits, will be crucial to building a successful bioeconomy. We developed an integrated assessment framework to compare switchgrass cultivation with corn-soybean rotations on the basis of production costs, revenues, and soil organic carbon (SOC) sequestration at a 100 m spatial resolution. We calculated profits (or losses) when marginal lands are converted from a corn-soy rotation to switchgrass across a range of farm gate biomass prices and payments for SOC sequestration in the State of Illinois, United States. The annual net SOC sequestration and switchgrass yields are estimated to range from 0.1 to 0.4 Mg ha-1 and 7.3 to 15.5 Mg dry matter ha-1, respectively, across the state. Without payments for SOC sequestration, only a small fraction of marginal corn-soybean land would achieve a 20% profit margin if converted to switchgrass, but $40-80 Mg-1 CO2e compensation could increase the economically viable area by 140-414%. With the compensation, switchgrass cultivation for 10 years on 1.6 million ha of marginal land in Illinois will produce biomass worth $1.6-2.9 billion (0.95-1.8 million Mg dry biomass) and mitigate 5-22 million Mg CO2e.

Spatial heterogeneity and environmental predictors of permafrost region soil organic carbon stocks.

Large stocks of soil organic carbon (SOC) have accumulated in the Northern Hemisphere permafrost region, but their current amounts and future fate remain uncertain. By analyzing dataset combining >2700 soil profiles with environmental variables in a geospatial framework, we generated spatially explicit estimates of permafrost-region SOC stocks, quantified spatial heterogeneity, and identified key environmental predictors. We estimated that Pg C are stored in the top 3 m of permafrost region soils. The greatest uncertainties occurred in circumpolar toe-slope positions and in flat areas of the Tibetan region. We found that soil wetness index and elevation are the dominant topographic controllers and surface air temperature (circumpolar region) and precipitation (Tibetan region) are significant climatic controllers of SOC stocks. Our results provide first high-resolution geospatial assessment of permafrost region SOC stocks and their relationships with environmental factors, which are crucial for modeling the response of permafrost affected soils to changing climate.

Ensemble Machine Learning Approach Improves Predicted Spatial Variation of Surface Soil Organic Carbon Stocks in Data-Limited Northern Circumpolar Region.

Various approaches of differing mathematical complexities are being applied for spatial prediction of soil properties. Regression kriging is a widely used hybrid approach of spatial variation that combines correlation between soil properties and environmental factors with spatial autocorrelation between soil observations. In this study, we compared four machine learning approaches (gradient boosting machine, multinarrative adaptive regression spline, random forest, and support vector machine) with regression kriging to predict the spatial variation of surface (0-30 cm) soil organic carbon (SOC) stocks at 250-m spatial resolution across the northern circumpolar permafrost region. We combined 2,374 soil profile observations (calibration datasets) with georeferenced datasets of environmental factors (climate, topography, land cover, bedrock geology, and soil types) to predict the spatial variation of surface SOC stocks. We evaluated the prediction accuracy at randomly selected sites (validation datasets) across the study area. We found that different techniques inferred different numbers of environmental factors and their relative importance for prediction of SOC stocks. Regression kriging produced lower prediction errors in comparison to multinarrative adaptive regression spline and support vector machine, and comparable prediction accuracy to gradient boosting machine and random forest. However, the ensemble median prediction of SOC stocks obtained from all four machine learning techniques showed highest prediction accuracy. Although the use of different approaches in spatial prediction of soil properties will depend on the availability of soil and environmental datasets and computational resources, we conclude that the ensemble median prediction obtained from multiple machine learning approaches provides greater spatial details and produces the highest prediction accuracy. Thus an ensemble prediction approach can be a better choice than any single prediction technique for predicting the spatial variation of SOC stocks.

Soil carbon inventory to quantify the impact of land use change to mitigate greenhouse gas emissions and ecosystem services.

Currently the land use and land use change (LULUC) emits 1.3 ±â€¯0.5 Pg carbon (C) year-1, equivalent to 8% of the global annual emissions. The objectives of this study were to quantify (1) the impact of LULUC on greenhouse gas (GHG) emissions in a subtropical region and (2) the role of conservation agriculture to mitigate GHG emissions promoting ecosystem services. We developed a detailed IPCC Tier 2 GHG inventory for the Campos Gerais region of southern Brazil that has large cropland area under long-term conservation agriculture with high crop yields. The inventory accounted for historical and current emissions from fossil fuel combustion, LULUC and other minor sources. We used Century model to simulate the adoption of conservation best management practices, to all croplands in the region from 2017 to 2117. Our results showed historical (1930-2017) GHG emissions of 412 Tg C, in which LULUC contributes 91% (376 ±â€¯130 Tg C), the uncertainties ranged between 13 and 36%. Between 1930 and 1985 LULUC was a major source of GHG emission, however from 1985 to 2015 fossil fuel combustion became the primary source of GHG emission. Forestry sequestered 52 ±â€¯24 Tg C in 0.6 Mha in a period of 47 years (1.8 Tg C Mha-1 year-1) and no-till sequestered 30.4 ±â€¯24 Tg C in 2 Mha in a period of 32 years (0.5 Tg C Mha-1 year-1) being the principal GHG mitigating activities in the study area. The model predictions showed that best management practices have the potential to mitigate 13 years of regional emissions (330 Tg C in 100 years) or 105 years of agriculture, forestry and livestock emissions (40 Tg C in 100 years) making the agriculture sector a net carbon (C) sink and promoting ecosystem services.

Reforestation can sequester two petagrams of carbon in US topsoils in a century.

Soils are Earth's largest terrestrial carbon (C) pool, and their responsiveness to land use and management make them appealing targets for strategies to enhance C sequestration. Numerous studies have identified practices that increase soil C, but their inferences are often based on limited data extrapolated over large areas. Here, we combine 15,000 observations from two national-level databases with remote sensing information to address the impacts of reforestation on the sequestration of C in topsoils (uppermost mineral soil horizons). We quantify C stocks in cultivated, reforesting, and natural forest topsoils; rates of C accumulation in reforesting topsoils; and their contribution to the US forest C sink. Our results indicate that reforestation increases topsoil C storage, and that reforesting lands, currently occupying >500,000 km2 in the United States, will sequester a cumulative 1.3-2.1 Pg C within a century (13-21 Tg C·y-1). Annually, these C gains constitute 10% of the US forest sector C sink and offset 1% of all US greenhouse gas emissions.

Characterizing permafrost active layer dynamics and sensitivity to landscape spatial heterogeneity in Alaska.

An important feature of the Arctic is large spatial heterogeneity in active layer conditions, which is generally poorly represented by global models and can lead to large uncertainties in predicting regional ecosystem responses and climate feedbacks. In this study, we developed a spatially integrated modelling and analysis framework combining field observations, local scale (~ 50 m resolution) active layer thickness (ALT) and soil moisture maps derived from airborne low frequency (L+P-band) radar measurements, and global satellite environmental observations to investigate the ALT sensitivity to recent climate trends and landscape heterogeneity in Alaska. Modelled ALT results show good correspondence with in situ measurements in higher permafrost probability (PP ≥ 70%) areas (n = 33, R = 0.60, mean bias = 1.58 cm, RMSE = 20.32 cm), but with larger uncertainty in sporadic and discontinuous permafrost areas. The model results also reveal widespread ALT deepening since 2001, with smaller ALT increases in northern Alaska (mean trend = 0.32 ± 1.18 cm yr-1) and much larger increases (> 3 cm yr-1) across interior and southern Alaska. The positive ALT trend coincides with regional warming and a longer snow-free season (R = 0.60 ± 0.32). A spatially integrated analysis of the radar retrievals and model sensitivity simulations demonstrated that uncertainty in the spatial and vertical distribution of soil organic carbon (SOC) was the largest factor affecting modeled ALT accuracy, while soil moisture played a secondary role. Potential improvements in characterizing SOC heterogeneity, including better spatial sampling of soil conditions and advances in remote sensing of SOC and soil moisture, will enable more accurate predictions of active layer conditions and refinement of the modelling framework across a larger domain.