During land model development, simulated carbon dynamics are often benchmarked against observational data sets to evaluate model performance. Functional relationship benchmarks are the relationship between a driving variable (e.g., temperature) and a response variable (e.g., ecosystem respiration) and are a promising tool for assessing model performance by evaluating modeled sensitivities to changing environmental conditions. However, observed functional relationships can be influenced by choices made during data collection and throughout the benchmarking process, impacting the inferred skill of land models. To avoid misrepresenting a model's true performance, it is necessary to systematically evaluate best practices when constructing functional relationship benchmarks. We developed a set of guidelines for constructing functional relationship benchmarks, considering the choice of data set, number of daily observations, temporal extent, and temporal resolution across Alaska and Canada over a 20-year period from 2001 to 2020. The temperature sensitivity of ecosystem respiration from observations, evaluated through an apparent Q 10, is highly variable both spatially and as a result of the data processing approach applied in the benchmark formation. When benchmarking 13 models from the Warming Permafrost Model Intercomparison Project (WrPMIP), the range in inferred model skill is substantially impacted by the choices applied in constructing functional relationship benchmarks. The inferred performance of a given model is most sensitive to the number of daily observations and temporal extent, followed by choice of benchmark data set and temporal averaging. Results from this analysis can guide the development of consistent and robust functional relationships for future model evaluation studies.

Climate change has increased forest fire extent in temperate and boreal North America. Here, we quantified the contribution of anthropogenic climate change to human mortality and economic burden from exposure to wildfire particulate matter at the county and state level across the contiguous US (2006 to 2020) by integrating climate projections, climate-wildfire models, wildfire smoke models, and emission and health impact modeling. Climate change contributed to approximately 15,000 wildfire particulate matter deaths over 15 years with interannual variability ranging from 130 (95% confidence interval: 64, 190) to 5100 (95% confidence interval: 2500, 7500) deaths and a cumulative economic burden of $160 billion. Approximately 34% of the additional deaths attributable to climate change occurred in 2020, costing $58 billion. The economic burden was highest in California, Oregon, and Washington. We suggest that absent abrupt changes in climate trajectories, land management, and population, the indirect impacts of climate change on human-health through wildfire smoke will escalate.

Forest gross primary production (GPP) is influenced by the interplay between climate conditions and atmospheric CO _2 levels, which interact in complex ways, generating both compensating and amplifying effects. In this study, eddy covariance flux measurements from 50 forest ecosystems were integrated with simulations from 14 terrestrial biosphere models to investigate how climate conditions and atmospheric CO _2 concentrations regulate forest GPP. This approach bridges site-level observations with biome-scale model estimates to develop a global understanding. Our findings suggest that in boreal and cold temperate regions, temperature primarily constrains the enhancement of the CO _2 fertilization on forest GPP; however, warming and higher atmospheric CO _2 levels are projected to alleviate these limitations. In tropical forests, CO _2 fertilization strongly enhances GPP, but this benefit will be counterbalanced by the adverse impacts of projected climate warming. Consequently, the interplay between climate and atmospheric CO _2 in affecting forest GPP is dynamic and subject to continual change.

RATIONALE Wildfires have increased in frequency and intensity due to climate change and now contribute to nearly half of the annual average of fine particulate matter in the US. While the effects of short-term wildfire-PM2.5 exposure on respiratory diseases are well-described, the impact of climate change on longer duration wildfire-PM2.5 mortality is unknown. Our aim was to determine the contribution of anthropogenic climate change to wildfire smoke PM2.5 mortality on a county-level across the conterminous US between 2006-2020. METHODS We use an attribution model to compare observed wildfire-PM2.5 with a counterfactual climate that excludes the influence of climate change to approximate the fractional climate change contribution to burned area. We then use a machine learning model to determine annual wildfire-PM2.5 concentrations based on the wildfire burned area and meteorological variables. We apply a concentration response function from existing epidemiologic literature to quantify county-level deaths attributable to longer duration wildfire smoke PM2.5 exposure using the environmental Benefits Mapping and Analysis Program—Community Edition tool. Finally, we estimate the economic burden of these additional deaths using the value of a statistical life. RESULTS We estimate 164,000 wildfire smoke PM2.5 attributable deaths between 2006 and 2020, with the largest number of these deaths occurring in 2020 (28,000 wildfire PM2.5 deaths [95% CI 14000, 42000]). Climate change contributed to 14,800 deaths over our 15-year study period and 34% of the climate change attributable deaths occurred in 2020 (5,100 [95% CI 2,500 to 7,500]). Figure 1 shows the annual climate change-attributable wildfire smoke PM2.5 deaths per 100,000 people in conterminous United States between 2006 and 2020. In western US states, which had the greatest number of wildfire smoke PM2.5 deaths, climate change contributed to 26.8-38.3% of the deaths. We estimate the economic burden of wildfire smoke PM2.5 deaths was $114 billion per year, with climate change contributing to $10.6 billion per year. CONCLUSIONS We estimate that climate change is responsible for nearly 10% of wildfire smoke PM2.5 deaths across the United States, with greater contributions in western US states. In a scenario without climate change contributing to wildfire smoke PM2.5, tens of thousands of deaths could be avoided and tens of billions of dollars saved every year.

Land surface models require continuous validation against observations to improve and reduce simulation uncertainty. However, inferred model performance can be heavily influenced by subjective choices made in the selection and application of observational data products. A key area often misrepresented by models is the Arctic–Boreal region, which is a potential tipping point region in Earth’s climate system due to large permafrost carbon stocks that are vulnerable to release with climate warming. We use the International Land Model Benchmarking (ILAMB) framework to evaluate how the model skill of TRENDY-v9 models varies based on the choice of observational-based benchmark and how benchmarks are applied in model evaluation. This analysis uses global datasets integrated into ILAMB and new, regionally-specific observational products from the Arctic–Boreal Vulnerability Experiment. Our results cover the overall time period of 1979–2019 and show that model scores can vary substantially depending on the data product applied, with higher model scores indicating better model performance against observations. The lowest model scores occur when benchmarked against regional, compared to global, datasets. We also evaluate observed and modeled functional relationships between ecosystem respiration and air temperature and between gross primary production and precipitation. Here, we find that the magnitude and shape of the responses are strongly impacted by the choice of observational dataset and the approach used to construct the functional relationship benchmark. These results suggest that model evaluation studies could conclude a false sense of model skill if only using a single benchmark data product or if not applying regional data products when performing a regional model analysis. Collectively, our findings highlight the influence of benchmarking choices on model evaluation and point to the need for benchmarking guidelines when assessing model skill.

Persistent discrepancies between bottom‐up, terrestrial biosphere models (TBMs), and top‐down, atmospheric inversions, have made it difficult to quantify the magnitude of the North American terrestrial carbon sink. Previous studies have compared aggregated continent‐scale estimates of carbon fluxes from TBMs and inversions for all of North America, but this provides limited insights into finer‐scale mismatches that contribute to the overall discrepancies. Here we evaluate agreement between TBM and inversion carbon flux estimates at 1° × 1° resolution to provide more direct insights into where models disagree and what underlying factors drive discrepancies. We find that the additional carbon uptake estimated by inversions, in just 16% of the area of North America, is large enough to account for the discrepancy between TBMs and inversions across the whole continent. The majority of these differences occur in permafrost, peatland, and cropland regions. In these regions, we find a higher likelihood of potential biases in the weaker sink estimates from TBMs, suggesting that the stronger sink implied by inversions is more likely to be realistic. However, the current observational coverage is insufficient for fully assessing the causes of discrepancies or the magnitude of biases in either approach. Encouragingly, improved representation of agricultural processes in a TBM led to better agreement with inversions in croplands. Efforts to accurately model cropland dynamics will help improve agreement between TBMs and inversions. Overall, this work presents a clear path for reconciling the discrepancies between inversion and TBM estimates of the North American carbon sink that have persisted for two decades.

Mapping in-situ eddy covariance measurements of terrestrial land-atmosphere fluxes to the globe is a key method for diagnosing the Earth system from a data-driven perspective. We describe the first global products (called X-BASE) from a newly implemented up-scaling framework, FLUXCOM-X. The X-BASE products comprise of estimates of CO2 net ecosystem exchange (NEE), gross primary productivity (GPP) as well as evapotranspiration (ET) and, for the first time, a novel fully data-driven global transpiration product (ETT), at high spatial (0.05°) and temporal (hourly) resolution. X-BASE estimates the global NEE at -5.75 ± 0.33 Pg C ⋅ yr-1 for the period 2001–2020, showing a much higher consistency with independent atmospheric carbon cycle constraints compared to the previous versions of FLUXCOM. The improvement of global NEE was likely only possible thanks to the international effort to increase the precision and consistency of eddy covariance collection and processing pipelines, as well as to the extension of the measurements to more site-years resulting in a wider coverage of bio-climatic conditions. However, X-BASE global net ecosystem exchange shows a very low inter-annual variability, which is common to state-of-the-art data-driven flux products and remains a scientific challenge. With 125 ± 2.1 Pg C ⋅ yr-1 for the same period, X-BASE GPP is slightly higher than previous FLUXCOM estimates, mostly in temperate and boreal areas. X-BASE evapotranspiration amounts to 74.7x10³ ± 0.9x10³ km3 globally for the years 2001–2020, but exceeds precipitation in many dry areas likely indicating overestimation in these regions. On average 57 % of evapotranspiration are estimated to be transpiration, in good agreement with isotope-based approaches, but higher than estimates from many land surface models. Despite considerable improvements to the previous up-scaling products, many further opportunities for development exist. Pathways of exploration include methodological choices in the selection and processing of eddy-covariance and satellite observations, their ingestion into the framework, and the configuration of machine learning methods. For this, the new FLUXCOM-X framework was specifically designed to have the necessary flexibility to experiment, diagnose, and converge to more accurate global flux estimates.

Mapping in situ eddy covariance measurements of terrestrial land-atmosphere fluxes to the globe is a key method for diagnosing the Earth system from a data-driven perspective. We describe the first global products (called X-BASE) from a newly implemented upscaling framework, FLUXCOM-X, representing an advancement from the previous generation of FLUXCOM products in terms of flexibility and technical capabilities. The X-BASE products are comprised of estimates of CO2 net ecosystem exchange (NEE), gross primary productivity (GPP), evapotranspiration (ET), and for the first time a novel, fully data-driven global transpiration product (ETT), at high spatial (0.05 degrees) and temporal (hourly) resolution. X-BASE estimates the global NEE at -5.75 +/- 0.33 Pg C yr(-1) for the period 2001-2020, showing a much higher consistency with independent atmospheric carbon cycle constraints compared to the previous versions of FLUXCOM. The improvement of global NEE was likely only possible thanks to the international effort to increase the precision and consistency of eddy covariance collection and processing pipelines, as well as to the extension of the measurements to more site years resulting in a wider coverage of bioclimatic conditions. However, X-BASE global net ecosystem exchange shows a very low interannual variability, which is common to state-of-the-art data-driven flux products and remains a scientific challenge. With 125 +/- 2.1 Pg C yr(-1) for the same period, X-BASE GPP is slightly higher than previous FLUXCOM estimates, mostly in temperate and boreal areas. X-BASE evapotranspiration amounts to 74.7x10(3) +/- 0.9x10(3) km(3) globally for the years 2001-2020 but exceeds precipitation in many dry areas, likely indicating overestimation in these regions. On average 57 % of evapotranspiration is estimated to be transpiration, in good agreement with isotope-based approaches, but higher than estimates from many land surface models. Despite considerable improvements to the previous upscaling products, many further opportunities for development exist. Pathways of exploration include methodological choices in the selection and processing of eddy covariance and satellite observations, their ingestion into the framework, and the configuration of machine learning methods. For this, the new FLUXCOM-X framework was specifically designed to have the necessary flexibility to experiment, diagnose, and converge to more accurate global flux estimates.

Global climate change is influencing the seasonal cycle amplitude of atmospheric CO2 (SCA), with the strongest increases at northern high latitudes (NHL; >45° N). In this Review, we explore the changes and underlying mechanisms influencing the NHL SCA, focusing on Arctic and boreal terrestrial ecosystems. Latitudinal gradients in the SCA are largely governed by seasonality in temperature and primary production, and their influence on ecosystem carbon dynamics. In the NHL, the SCA has increased by 50% since the 1960s, mostly due to enhanced seasonality in net carbon dioxide (CO2) exchange in NHL terrestrial ecosystems. Temperature most strongly influences this trend, owing to warming impacts on growing season length and plant productivity; CO2 fertilization effects have a secondary role. Eurasian boreal ecosystems exert the strongest influence on the SCA, and spring and summer are the most influential seasons. Enhanced ecosystem respiration during the non-growing season exhibits most uncertainty in the SCA response to global and landscape drivers. Observed changes in the seasonal amplitude are projected to continue. Key priorities include extending carbon flux and ecosystem observation networks, particularly in tundra ecosystems, and including drivers such as vegetation cover and permafrost in process models to better simulate seasonal dynamics of net CO2 exchange in the NHL. Changes in the seasonal cycle amplitude of atmospheric CO2 (SCA) reflect large-scale changes in the global carbon cycle. This Review summarizes the positive SCA trend in the northern high latitudes, where the signal is strongest, and explores the underlying mechanisms driving the trend and their relative importance.

Urban in-migration, high birth rates and unchecked development are driving the continued growth of African cities. Much of this urbanization occurs in informal settlements, where unplanned growth exacerbates the impacts of climate hazards. Our paper explores the challenges faced by urban planners seeking to address climate change threats in African cities. Focusing on Addis Ababa, we conduct a robust policy analysis of urban government response. Our main finding is that Addis Ababa’s environmental challenges are primarily a result of urban development and inadequate planning and policy enforcement. Environmental challenges are exacerbated by rapidly intensifying climate impacts, but not driven by them. Improved city-level policy responses can potentially mitigate many of Addis Ababa’s current environmental challenges and prepare the city to weather future ones. We highlight four areas of policy weakness: (1) ineffective or absent policies concerning green space; (2) the exclusion of informal settlements from ongoing resilience planning efforts; (3) limited public trust and transparency; and (4) a lack of coordination across the multiple agencies in Addis charged with planning. Holistic, multi-stakeholder planning is inhibited by a lack of collaboration, limited stakeholder participation, and a reluctance to engage in productive dialogue. Resolving all four issues will only occur with sustained increases in social trust, expertise, governance capacity and capital.

Accurate representation of global coastal topography is essential for numerous scientific disciplines, coastal management, and disaster risk assessment. Even with recent improvements to existing global digital elevation models (DEMs), high and persistent errors in these DEMs result in significant uncertainty when analyzing coastal processes. This results in low confidence for current sea level rise inundation risk assessments. We present DiluviumDEM, the first global DEM appropriate (i.e., the root mean square error (RMSE) is half the total water elevation in 2100 under a specific scenario) for mapping sea level rise inundation under the IPCC SSP2–4.5 and SSP5–8.5 scenarios, with an estimated RMSE of 1.13 m for coastal areas with elevations less than 2 m above mean sea level. Out of ten countries used for validation, DiluviumDEM has the lowest RMSE compared to three other DEMs analyzed, the lowest mean absolute error (MAE) for eight, and the mean error (ME) closest to zero for six. By reducing the error of the European Space Agency's Copernicus DEM using a gradient boosted decision tree model, we have created a new global coastal DEM with up to twice the accuracy compared to other global DEMs.

As our planet warms, a critical research question is when and where temperatures will exceed the limits of what the human body can tolerate. Past modeling efforts have investigated the 35°C wet-bulb threshold, proposed as a theoretical upper limit to survivability taking into account physiological and behavioral adaptation. Here, we conduct an extreme value theory analysis of weather station observations and climate model projections to investigate the emergence of an empirically supported heat compensability limit. We show that the hottest parts of the world already experience these heat extremes on a limited basis and that under moderate continued warming parts of every continent, except Antarctica, will see a rapid increase in their extent and frequency. To conclude, we discuss the consequences of the emergence of this noncompensable heat and the need for incorporating different critical thermal limits into heat adaptation planning.

The time required for an ecosystem to recover from severe drought is a key component of ecological resilience. The phenology effects on drought recovery are, however, poorly understood. These effects centre on how phenology variations impact biophysical feedbacks, vegetation growth and, ultimately, recovery itself. Using multiple remotely sensed datasets, we found that more than half of ecosystems in mid- and high-latitudinal Northern Hemisphere failed to recover from extreme droughts within a single growing season. Earlier spring phenology in the drought year slowed drought recovery when extreme droughts occurred in mid-growing season. Delayed spring phenology in the subsequent year slowed drought recovery for all vegetation types (with importance of spring phenology ranging from 46% to 58%). The phenology effects on drought recovery were comparable to or larger than other well-known postdrought climatic factors. These results strongly suggest that the interactions between vegetation phenology and drought must be incorporated into Earth system models to accurately quantify ecosystem resilience.

Forest dynamics in arid and semiarid regions are sensitive to water availability, which is becoming increasingly scarce as global climate changes. The timing and magnitude of precipitation in the semiarid southwestern U.S. (“Southwest”) has changed since the 21 st century began. The region is projected to become hotter and drier as the century proceeds, with implications for carbon storage, pest outbreaks, and wildfire resilience. Our goal was to quantify the importance of summer monsoon precipitation for forested ecosystems across this region. We developed an isotope mixing model in a Bayesian framework to characterize summer (monsoon) precipitation soil water recharge and water use by three foundation tree species ( Populus tremuloides [aspen], Pinus edulis [piñon], and Juniperus osteosperma [Utah juniper]). In 2016, soil depths recharged by monsoon precipitation and tree reliance on monsoon moisture varied across the Southwest with clear differences between species. Monsoon precipitation recharged soil at piñon-juniper (PJ) and aspen sites to depths of at least 60 cm. All trees in the study relied primarily on intermediate to deep (10-60 cm) moisture both before and after the onset of the monsoon. Though trees continued to primarily rely on intermediate to deep moisture after the monsoon, all species increased reliance on shallow soil moisture to varying degrees. Aspens increased reliance on shallow soil moisture by 13% to 20%. Utah junipers and co-dominant ñons increased their reliance on shallow soil moisture by about 6% to 12%. Nonetheless, approximately half of the post-monsoon moisture in sampled piñon (38-58%) and juniper (47-53%) stems could be attributed to the monsoon. The monsoon contributed lower amounts to aspen stem water (24-45%) across the study area with the largest impacts at sites with recent precipitation. Therefore, monsoon precipitation is a key driver of growing season moisture that semiarid forests rely on across the Southwest. This monsoon reliance is of critical importance now more than ever as higher global temperatures lead to an increasingly unpredictable and weaker North American Monsoon.

Abstract Tree resistance to extreme droughts and post‐drought recovery are sensitive to the drought timing. However, how the bioclimatic sensitivity of tree growth may vary with the timing and order of extreme droughts and wetness is still poorly understood. Here, we quantified the bioclimatic sensitivity of tree growth in the period of 1951–2013 under different seasonal extreme drought/wetness regimes over the extra‐tropical Northern Hemisphere, using 1,032 tree ring chronologies from 121 gymnosperm and angiosperm species. We found a negative asymmetry in tree growth under regimes with seasonal extreme droughts. With extreme drought, tree growth in arid and temperate dry regions is more negatively impacted by pre‐growing‐season (PGS) extreme droughts. Clade‐wise, angiosperms are more sensitive to PGS water availability, and gymnosperms to legacy effects of the preceding tree growth conditions in temperate dry and humid regions. Our finding of divergent bioclimatic legacy effects underscores contrasting trends in forest responses to drought across different ecoregions and functional groups in a more extreme climate.

Trees are long-lived organisms that integrate climate conditions across years or decades to produce secondary growth. This integration process is sometimes referred to as 'climatic memory.' While widely perceived, the physiological processes underlying this temporal integration, such as the storage and remobilization of non-structural carbohydrates (NSC), are rarely explicitly studied. This is perhaps most apparent when considering drought legacies (perturbed post-drought growth responses to climate), and the physiological mechanisms underlying these lagged responses to climatic extremes. Yet, drought legacies are likely to become more common if warming climate brings more frequent drought. To quantify the linkages between drought legacies, climate memory and NSC, we measured tree growth (via tree ring widths) and NSC concentrations in three dominant species across the southwestern USA. We analyzed these data with a hierarchical mixed effects model to evaluate the time-scales of influence of past climate (memory) on tree growth. We then evaluated the role of climate memory and the degree to which variation in NSC concentrations were related to forward-predicted growth during the hot 2011-2012 drought and subsequent 4-year recovery period. Populus tremuloides exhibited longer climatic memory compared to either Pinus edulis or Juniperus osteosperma, but following the 2011-2012 drought, P. tremuloides trees with relatively longer memory of temperature conditions showed larger (more negative) drought legacies. Conversely, Pinus edulis trees with longer temperature memory had smaller (less negative) drought legacies. For both species, higher NSC concentrations followed more negative (larger) drought legacies, though the relevant NSC fraction differed between P. tremuloides and P. edulis. Our results suggest that differences in tree NSC are also imprinted upon tree growth responses to climate across long time scales, which also underlie tree resilience to increasingly frequent drought events under climate change.

This repository contains spatial and tabular data central to the analysis reported by Walker et al. (2022) on the global potential for increased storage of carbon on land. All maps provided here are global (excluding Antarctica) in geographic scope at a spatial resolution of ca. 500 meters in the MODIS sinusoidal projection (SR-ORG:6842). Maps are stored individually as single-band GeoTIFFs. Grid cell units for carbon density maps are megagrams of carbon per hectare (MgC/ha). Water has been masked out as NoData (-32768). Associated computer code is available for download from https://github.com/whrc/Global-Potential-Carbon.

Extreme drought occurs on every continent, negatively impacting natural systems and the built environment. Realized and anticipated future warming affects global hydrology, influencing the severity and frequency of both extreme precipitation events and precipitation deficits. Understanding future drought conditions is essential for risk aware water management strategies and to protect food security for a growing human population, while safeguarding natural capital critical to limiting further warming. Here we quantify socioeconomic and ecological exposure to extreme drought. We focus on global, regional, and national scales at increasing levels of climate warming, from today’s 1.0 °C world to 4.0 °C of warming. Drought is quantified using the self-calibrated Palmer drought severity index calculated from globally mosaiced regional climate simulation (REMO2015). Exposure to extreme drought increases monotonically with warming level. For every 0.5 °C warming increase up to 3.0 °C, an additional 619 million people live in areas with 25% likelihood of annual extreme drought, in addition to the 1.7 billion people (25% of 2020 global population) exposed in today’s 1.0 °C world. Spatially, global drying is amplified in the tropics, where drought frequency increases at twice the global rate. Per 0.5 °C increase in warming, extreme drought annual likelihoods increase 1.5 times greater in forested than non-forested areas, jeopardizing climate regulation associated with forested biomes. Cropland exposure to 50% likelihood of annual extreme drought in two of the highest producing countries, China and Brazil, increases 4× and 13× between 1.0 °C and 2.0 °C, spanning a third of national cropland by 3.0 °C. At 1.5 °C (4.0 °C), 16% (39%) of global hydroelectric generating capacity will be exposed to at least a 50% likelihood of annual extreme drought, up from 5% in today’s 1.0 °C world. Given the near-term likelihood of surpassing 1.5 °C, high resolution drought exposure assessments must inform risk aware development and resilience efforts.

Model Intercomparison Projects (MIPs) are fundamental to our understanding of how the land surface responds to changes in climate. However, MIPs are challenging to conduct, requiring the organization of multiple, decentralized modeling teams throughout the world running common protocols. We explored centralizing these models on a single supercomputing system. We ran nine offline terrestrial biosphere models through the Terrestrial Biosphere Model Farm: CABLE, CENTURY, HyLand, ISAM, JULES, LPJ-GUESS, ORCHIDEE, SiB-3, and SiB-CASA. All models were wrapped in a software framework driven with common forcing data, spin-up, and run protocols specified by the Multi-scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP) for years 1901-2100. We ran more than a dozen model experiments. We identify three major benefits and three major challenges. The benefits include: (a) processing multiple models through a MIP is relatively straightforward, (b) MIP protocols are run consistently across models, which may reduce some model output variability, and (c) unique multimodel experiments can provide novel output for analysis. The challenges are: (a) technological demand is large, particularly for data and output storage and transfer; (b) model versions lag those from the core model development teams; and (c) there is still a need for intellectual input from the core model development teams for insight into model results. A merger with the open-source, cloud-based Predictive Ecosystem Analyzer (PEcAn) ecoinformatics system may be a path forward to overcoming these challenges.

Abstract Terrestrial soil organic carbon (SOC) dynamics play an important but uncertain role in the global carbon (C) cycle. Current modeling efforts to quantify SOC dynamics in response to global environmental changes do not accurately represent the size, distribution and flux of C from the soil. Here, we modified the daily Century (DAYCENT) biogeochemical model by tuning decomposition rates of conceptual SOC pools to match measurable C fraction data, followed by historical and future simulations of SOC dynamics. Results showed that simulations using fraction‐constrained DAYCENT (DCfrac) led to better initialization of SOC stocks and distribution compared to default/SOC‐only‐constrained DAYCENT (DCdef) at long‐term research sites. Regional simulation using DCfrac demonstrated higher SOC stocks for both croplands (34.86 vs. 26.17 MgC ha−1) and grasslands (54.05 vs. 40.82 MgC ha−1) compared to DCdef for the contemporary period (2001–2005 average), which better matched observationally constrained data‐driven maps of current SOC distributions. Projection of SOC dynamics in response to land cover change under a high warming climate showed average absolute SOC loss of 8.44 and 10.43 MgC ha−1 for grasslands and croplands, respectively, using DCfrac whereas, SOC losses were 6.55 and 7.85 MgC ha−1 for grasslands and croplands, respectively, using DCdef. The projected SOC loss using DCfrac was 33% and 29% higher for croplands and grasslands compared to DCdef. Our modeling study demonstrates that initializing SOC pools with measurable C fraction data led to more accurate representation of SOC stocks and distribution of SOC into individual carbon pools resulting in the prediction of greater sensitivity to agricultural intensification and warming.