Abstract Accurate representation of environmental controllers of soil organic carbon (SOC) stocks in Earth System Model (ESM) land models could reduce uncertainties in future carbon–climate feedback projections. Using empirical relationships between environmental factors and SOC stocks to evaluate land models can help modelers understand prediction biases beyond what can be achieved with the observed SOC stocks alone. In this study, we used 31 observed environmental factors, field SOC observations ( n = 6,213) from the continental United States, and two machine learning approaches (random forest [RF] and generalized additive modeling [GAM]) to (a) select important environmental predictors of SOC stocks, (b) derive empirical relationships between environmental factors and SOC stocks, and (c) use the derived relationships to predict SOC stocks and compare the prediction accuracy of simpler model developed with the machine learning predictions. Out of the 31 environmental factors we investigated, 12 were identified as important predictors of SOC stocks by the RF approach. In contrast, the GAM approach identified six (of those 12) environmental factors as important controllers of SOC stocks: potential evapotranspiration, normalized difference vegetation index, soil drainage condition, precipitation, elevation, and net primary productivity. The GAM approach showed minimal SOC predictive importance of the remaining six environmental factors identified by the RF approach. Our derived empirical relations produced comparable prediction accuracy to the GAM and RF approach using only a subset of environmental factors. The empirical relationships we derived using the GAM approach can serve as important benchmarks to evaluate environmental control representations of SOC stocks in ESMs, which could reduce uncertainty in predicting future carbon–climate feedbacks.
Abstract Wetlands are the largest emitters of biogenic methane (CH 4 ) and represent the highest source of uncertainty in global CH 4 budgets. Here, we aim to improve the realism of wetland representation in the U.S. Department of Energy's Exascale Earth System Model land surface model, ELM, thereby reducing uncertainty of CH 4 flux predictions. We develop an updated version, ELM‐Wet, where we activate a separate landunit for wetlands that handles multiple wetland‐specific eco‐hydrological patch functional types. We introduce more realistic hydrological forcing through prescribing site‐level constraints on surface water elevation, which allows resolving different sustained inundation depth for different patches, and if data exists, prescribing inundation depth. We modified the calculation of aerenchyma transport diffusivity based on observed conductance per leaf area for different vegetation types. We use Bayesian Optimization to parameterize CO 2 and CH 4 fluxes in the developed wet‐landunit. Site‐level simulations of a coastal non‐tidal freshwater wetland in Louisiana were performed with the updated model. Eddy covariance observations of CO 2 and CH 4 fluxes from 2012 to 2013 were used to train the model and data from 2021 were used for validation. Patch‐specific chamber flux observations and observations of CH 4 concentration profiles in the soil porewater from 2021 were used for evaluation of the model performance. Our results show that ELM‐Wet reduces the model's CH 4 emission root mean squared error by up to 33% and is able to represent inter‐daily CO 2 and CH 4 flux variability across the wetland's eco‐hydrological patches, including during periods of extreme dry or wet conditions.
Our current understanding of terrestrial carbon processes is represented in various models used to integrate and scale measurements of CO 2 exchange from remote sensing and other spatiotemporal data. Yet assessments are rarely conducted to determine how well models simulate carbon processes across vegetation types and environmental conditions. Using standardized data from the North American Carbon Program we compare observed and simulated monthly CO 2 exchange from 44 eddy covariance flux towers in North America and 22 terrestrial biosphere models. The analysis period spans ∼220 site‐years, 10 biomes, and includes two large‐scale drought events, providing a natural experiment to evaluate model skill as a function of drought and seasonality. We evaluate models' ability to simulate the seasonal cycle of CO 2 exchange using multiple model skill metrics and analyze links between model characteristics, site history, and model skill. Overall model performance was poor; the difference between observations and simulations was ∼10 times observational uncertainty, with forested ecosystems better predicted than nonforested. Model‐data agreement was highest in summer and in temperate evergreen forests. In contrast, model performance declined in spring and fall, especially in ecosystems with large deciduous components, and in dry periods during the growing season. Models used across multiple biomes and sites, the mean model ensemble, and a model using assimilated parameter values showed high consistency with observations. Models with the highest skill across all biomes all used prescribed canopy phenology, calculated NEE as the difference between GPP and ecosystem respiration, and did not use a daily time step.
The vast amount of organic carbon (OC) stored in soils of the northern circumpolar permafrost region is a potentially vulnerable component of the global carbon cycle. However, estimates of the quantity, decomposability, and combustibility of OC contained in permafrost-region soils remain highly uncertain, thereby limiting our ability to predict the release of greenhouse gases due to permafrost thawing. Substantial differences exist between empirical and modeling estimates of the quantity and distribution of permafrost-region soil OC, which contribute to large uncertainties in predictions of carbon–climate feedbacks under future warming. Here, we identify research challenges that constrain current assessments of the distribution and potential decomposability of soil OC stocks in the northern permafrost region and suggest priorities for future empirical and modeling studies to address these challenges.
Abstract At high latitudes, changes in soil moisture could alter soil temperatures independently of air temperature changes by interacting with the snow thermal rectifier. The authors investigated this mechanism with model experiments in the Community Land Model 4 (CLM4) with prescribed atmospheric forcing and vegetation state. Under equilibrium historical conditions, increasing CO2 concentrations experienced by plants from 285 to 857 ppm caused local increases in soil water-filled pore space of 0.1–0.2 in some regions throughout the globe. In permafrost regions that experienced this moistening, vertical- and annual- mean soil temperatures increased by up to 3°C (0.27°C averaged over all permafrost areas). A similar pattern of moistening and consequent warming occurred in simulations with prescribed June–September (JJAS) rainfall increases of 25% over historical values, a level of increase commensurate with projected future rainfall increases. There was a strong sensitivity of the moistening responses to the baseline hydrological state. Experiments with perturbed physics confirmed that the simulated warming in permafrost soils was caused by increases in the soil latent heat of fusion per unit volume and in the soil thermal conductivity due to the increased moisture. In transient Representative Concentration Pathway 8.5 (RCP8.5) scenario experiments, soil warming due to increased CO2 or JJAS rainfall was smaller in magnitude and spatial extent than in the equilibrium experiments. Active-layer deepening associated with soil moisture changes occurred over less than 8% of the current permafrost area because increased heat of fusion and soil thermal conductivity had compensating effects on active-layer depth. Ongoing modeling challenges make these results tentative.
[1] Ecosystem models are important tools for diagnosing the carbon cycle and projecting its behavior across space and time. Despite the fact that ecosystems respond to drivers at multiple time scales, most assessments of model performance do not discriminate different time scales. Spectral methods, such as wavelet analyses, present an alternative approach that enables the identification of the dominant time scales contributing to model performance in the frequency domain. In this study we used wavelet analyses to synthesize the performance of 21 ecosystem models at 9 eddy covariance towers as part of the North American Carbon Program's site-level intercomparison. This study expands upon previous single-site and single-model analyses to determine what patterns of model error are consistent across a diverse range of models and sites. To assess the significance of model error at different time scales, a novel Monte Carlo approach was developed to incorporate flux observation error. Failing to account for observation error leads to a misidentification of the time scales that dominate model error. These analyses show that model error (1) is largest at the annual and 20–120 day scales, (2) has a clear peak at the diurnal scale, and (3) shows large variability among models in the 2–20 day scales. Errors at the annual scale were consistent across time, diurnal errors were predominantly during the growing season, and intermediate-scale errors were largely event driven. Breaking spectra into discrete temporal bands revealed a significant model-by-band effect but also a nonsignificant model-by-site effect, which together suggest that individual models show consistency in their error patterns. Differences among models were related to model time step, soil hydrology, and the representation of photosynthesis and phenology but not the soil carbon or nitrogen cycles. These factors had the greatest impact on diurnal errors, were less important at annual scales, and had the least impact at intermediate time scales.
Abstract. We conducted high frequency measurements of the δ18O value of atmospheric CO2 from a juniper (Juniperus monosperma) woodland in New Mexico, USA, over a four-year period to investigate climatic and physiological regulation of the δ18O value of ecosystem respiration (δR). Rain pulses reset δR with the dominant water source isotope composition, followed by progressive enrichment of δR. Transpiration (ET) was significantly related to post-pulse δR enrichment because the leaf water δ18O value showed strong enrichment with increasing vapor pressure deficit that occurs following rain. Post-pulse δR enrichment was correlated with both ET and the ratio of ET to soil evaporation (ET/ES). In contrast, the soil water δ18O value was relatively stable and δR enrichment was not correlated with ES. Model simulations captured the large post-pulse δR enrichments only when the offset between xylem and leaf water δ18O value was modeled explicitly and when a gross flux model for CO2 retro-diffusion was included. Drought impacts δR through the balance between evaporative demand, which enriches δR, and low soil moisture availability, which attenuates δR enrichment through reduced ET. The net result, observed throughout all four years of our study, was a negative correlation of post-precipitation δR enrichment with increasing drought.
'/%3e%3cuse xlink:href='%23a' transform='rotate(23.036 .093 25.536)'/%3e%3cuse xlink:href='%23a' transform='rotate(45.87 1.273 16.18)'/%3e%3cuse xlink:href='%23a' transform='rotate(69.945 .996 12.078)'/%3e%3cuse xlink:href='%23a' transform='rotate(20.66 -19.689 31.932)'/%3e%3c/svg%3e)
