https://aurora.auburn.edu/handle/11200/49743 Sun, 05 Apr 2026 20:00:36 GMT 2026-04-05T20:00:36Z https://aurora.auburn.edu/handle/11200/50030 Toward more realistic projections of soil carbon dynamics by Earth system models Soil carbon (C) is a critical component of Earth system models (ESMs), and its diverse representations are a major source of the large spread across models in the terrestrial C sink from the third to fifth assessment reports of the Intergovernmental Panel on Climate Change (IPCC). Improving soil C projections is of a high priority for Earth system modeling in the future IPCC and other assessments. To achieve this goal, we suggest that (1) model structures should reflect real-world processes, (2) parameters should be calibrated to match model outputs with observations, and (3) external forcing variables should accurately prescribe the environmental conditions that soils experience. First, most soil C cycle models simulate C input from litter production and C release through decomposition. The latter process has traditionally been represented by first-order decay functions, regulated primarily by temperature, moisture, litter quality, and soil texture. While this formulation well captures macroscopic soil organic C (SOC) dynamics, better understanding is needed of their underlying mechanisms as related to microbial processes, depth-dependent environmental controls, and other processes that strongly affect soil C dynamics. Second, incomplete use of observations in model parameterization is a major cause of bias in soil C projections from ESMs. Optimal parameter calibration with both pool-and flux-based data sets through data assimilation is among the highest priorities for near-term research to reduce biases among ESMs. Third, external variables are represented inconsistently among ESMs, leading to differences in modeled soil C dynamics. We recommend the implementation of traceability analyses to identify how external variables and model parameterizations influence SOC dynamics in different ESMs. Overall, projections of the terrestrial C sink can be substantially improved when reliable data sets are available to select the most representative model structure, constrain parameters, and prescribe forcing fields. https://aurora.auburn.edu/handle/11200/50030 https://aurora.auburn.edu/handle/11200/49816 Global Pyrogenic Carbon Production During Recent Decades Has Created the Potential for a Large, Long-Term Sink of Atmospheric CO2 Fires play an important role in the terrestrial biosphere carbon cycle, not only through direct carbon release but also contributing to a potential long-term storage as pyrogenic carbon (PyC). PyC is formed through fires, and, because it may resist further biological and chemical degradation, is more stable in soil and sediment than original biomass. At the global scale, contributions of fires to both atmospheric CO2 emissions and PyC accumulation are potentially large but difficult to estimate. Our analysis was based on existing simulation results from two different modeling approaches (Global Fire Emissions Database version 4 [GFED4s] and Terrestrial Ecosystem Model version 6 [TEM6]) that used global area burned data to provide recent, retrospective estimates of CO2 emissions from vegetation combustion, together with published, biome- and continental-scale conversion ratios that relate CO2 emissions to PyC production (PyC/CO2) during combustion. The estimates of global CO2 emissions from fires differed substantially between the two models' results. GFED4s estimated 2,041TgC/year during the 2000-2016 time period, whereas the TEM6 estimate was considerably lower at 643TgC/year from 2000 to 2010. Global PyC production estimates from fires were 153.418.7 and 49.54.9TgC/year based on the emission estimates from GFED4s and TEM6, respectively. Our results suggest that African tropical savanna fires produced the largest amount of CO2 emissions and PyC among global biomes, the most significant interannual variations in CO2 emissions and PyC production were found in tropical forests, and the magnitude of PyC produced by fires each year represented a potentially significant long-term sink of atmospheric CO2. https://aurora.auburn.edu/handle/11200/49816 https://aurora.auburn.edu/handle/11200/49784 Vegetation Functional Properties Determine Uncertainty of Simulated Ecosystem Productivity: A Traceability Analysis in the East Asian Monsoon Region Global and regional projections of climate change by Earth system models are limited by their uncertain estimates of terrestrial ecosystem productivity. At the middle to low latitudes, the East Asian monsoon region has higher productivity than forests in Europe-Africa and North America, but its estimate by current generation of terrestrial biosphere models (TBMs) has seldom been systematically evaluated. Here, we developed a traceability framework to evaluate the simulated gross primary productivity (GPP) by 15 TBMs in the East Asian monsoon region. The framework links GPP to net primary productivity, biomass, leaf area and back to GPP via incorporating multiple vegetation functional properties of carbon-use efficiency (CUE), vegetation C turnover time (tau(veg)), leaf C fraction (F-leaf), specific leaf area (SLA), and leaf area index (LAI)-level photosynthesis (P-LAI), respectively. We then applied a relative importance algorithm to attribute intermodel variation at each node. The results showed that large intermodel variation in GPP over 1901-2010 were mainly propagated from their different representation of vegetation functional properties. For example, SLA explained 77% of the intermodel difference in leaf area, which contributed 90% to the simulated GPP differences. In addition, the models simulated higher CUE (18.1 21.3%), tau(veg) (18.2 26.9%), and SLA (27.436.5%) than observations, leading to the overestimation of simulated GPP across the East Asian monsoon region. These results suggest the large uncertainty of current TBMs in simulating GPP is largely propagated from their poor representation of the vegetation functional properties and call for a better understanding of the covariations between plant functional properties in terrestrial ecosystems. https://aurora.auburn.edu/handle/11200/49784 https://aurora.auburn.edu/handle/11200/49782 Century-long increasing trend and variability of dissolved organic carbon export from the Mississippi River basin driven by natural and anthropogenic forcing There has been considerable debate as to how natural forcing and anthropogenic activities alter the timing and magnitude of the delivery of dissolved organic carbon (DOC) to the coastal ocean, which has ramifications for the ocean carbon budget, land-ocean interactions, and coastal life. Here we present an analysis of DOC export from the Mississippi River to the Gulf of Mexico during 1901-2010 as influenced by changes in climate, land use and management practices, atmospheric CO2, and nitrogen deposition, through the integration of observational data with a coupled hydrologic/biogeochemical land model. Model simulations show that DOC export in the 2000s increased more than 40% since the 1900s. For the recent three decades (1981-2010), however, our simulated DOC export did not show a significant increasing trend, which is consistent with observations by U.S. Geological Survey. Our factorial analyses suggest that land use and land cover change, including land management practices (LMPs: i.e., fertilization, irrigation, tillage, etc.), were the dominant contributors to the century-scale trend of rising total riverine DOC export, followed by changes in atmospheric CO2, nitrogen deposition, and climate. Decadal and interannual variations of DOC export were largely attributed to year-to-year climatic variability and extreme flooding events, which have been exacerbated by human activity. LMPs show incremental contributions to DOC increase since the 1960s, indicating the importance of sustainable agricultural practices in coping with future environmental changes such as extreme flooding events. Compared to the observational-based estimate, the modeled DOC export was 20% higher, while DOC concentrations were slightly lower. Further refinements in model structure and input data sets should enable reductions in uncertainties in our prediction of century-long trends in DOC. https://aurora.auburn.edu/handle/11200/49782