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Earth's ecosystems are increasingly threatened by "hot drought," which occurs when hot air temperatures coincide with precipitation deficits, intensifying the hydrological, physiological, and ecological effects of drought by enhancing evaporative losses of soil moisture (SM) and increasing plant stress due to higher vapor pressure deficit (VPD). Drought-induced reductions in gross primary production (GPP) exert a major influence on the terrestrial carbon sink, but the extent to which hotter and atmospherically drier conditions will amplify the effects of precipitation deficits on Earth's carbon cycle remains largely unknown. During summer and autumn 2020, the U.S. Southwest experienced one of the most intense hot droughts on record, with record-low precipitation and record-high air temperature and VPD across the region. Here, we use this natural experiment to evaluate the effects of hot drought on GPP and further decompose those negative GPP anomalies into their constituent meteorological and hydrological drivers. We found a 122 Tg C (>25%) reduction in GPP below the 2015-2019 mean, by far the lowest regional GPP over the Soil Moisture Active Passive satellite record. Roughly half of the estimated GPP loss was attributable to low SM (likely a combination of record-low precipitation and warming-enhanced evaporative depletion), but record-breaking VPD amplified the reduction of GPP, contributing roughly 40% of the GPP anomaly. Both air temperature and VPD are very likely to continue increasing over the next century, likely leading to more frequent and intense hot droughts and substantially enhancing drought-induced GPP reductions.

Forests play a major role in the global carbon cycle. Previous studies on the capacity of forests to sequester atmospheric CO2 have mostly focused on carbon uptake, but the roles of carbon turnover time and its spatiotemporal changes remain poorly understood. Here, we used long-term inventory data (1955 to 2018) from 695 mature forest plots to quantify temporal trends in living vegetation carbon turnover time across tropical, temperate, and cold climate zones, and compared plot data to 8 Earth system models (ESMs). Long-term plots consistently showed decreases in living vegetation carbon turnover time, likely driven by increased tree mortality across all major climate zones. Changes in living vegetation carbon turnover time were negatively correlated with CO2 enrichment in both forest plot data and ESM simulations. However, plot-based correlations between living vegetation carbon turnover time and climate drivers such as precipitation and temperature diverged from those of ESM simulations. Our analyses suggest that forest carbon sinks are likely to be constrained by a decrease in living vegetation carbon turnover time, and accurate projections of forest carbon sink dynamics will require an improved representation of tree mortality processes and their sensitivity to climate in ESMs.

The time it takes for an ecosystem to recover from a disturbance is a key to environmental management. Conventionally, recovery is defined as a return to the pre‐disturbance state, assuming ecosystem stationarity. However, this view does not account for the impact of external forces like climate change, imposing non‐stationarity and trends. Alternatively, the counterfactual approach views recovery as the state the ecosystem would have achieved if the disturbance had not occurred, accounting for external forces. Here, we present a simple method to estimate the counterfactual recovery time. By implementing our method to the greening of the Arctic region, we showed that counterfactual greening recovery is twice as long as conventional recovery over the region. We argue that the well‐documented greening of the region acts as an external force, leading to such a large difference. We advocate for embracing the counterfactual definition of recovery, as it aligns with realistic decision‐making processes. Plain Language Summary: The time required for an ecosystem to recover from a disturbance is a crucial factor in many areas, such as decision‐making. Traditionally, this recovery time has been considered as the period for a disturbed ecosystem to either bounce back to its pre‐disturbance state or reach a new stable state. However, the assumption of stability may not hold true in light of climate change and other external factors. These forces make ecosystems dynamic, potentially with upward or downward trends, rather than reaching a stable state. To account for these external forces, an alternative definition is presented: the time for an ecosystem to reach its potential state, as if the disturbance had not occurred, termed the "counterfactual recovery." In this study, we propose a simple method based on time series analyses to calculate the counterfactual recovery time. We showcase the difference between the two definitions by implementing our approach to the case of greening in the Arctic and boreal regions post‐disturbance. Our method is generalizable to any ecological system, and we envision its contribution to decision‐making processes, such as conservation efforts. Key Points: The conventional definition to recovery as return to pre‐disturbance state ignores the impact of external forces on the recovery processWe proposed a method to estimate the time to recover using counterfactual approach which considers the impact of external forcesImplementing our approach over the greening of the arctic region showed the large difference between the two approaches [ABSTRACT FROM AUTHOR]

Model representation of carbon uptake and storage is essential for accurate projection of the response of the arctic‐boreal zone to a rapidly changing climate. Land model estimates of LAI and aboveground biomass that can have a marked influence on model projections of carbon uptake and storage vary substantially in the arctic and boreal zone, making it challenging to correctly evaluate model estimates of Gross Primary Productivity (GPP). To understand and correct bias of LAI and aboveground biomass in the Community Land Model (CLM), we assimilated the 8‐day Moderate Resolution Imaging Spectroradiometer (MODIS) LAI observation and a machine learning product of annual aboveground biomass into CLM using an Ensemble Adjustment Kalman Filter (EAKF) in an experimental region including Alaska and Western Canada. Assimilating LAI and aboveground biomass reduced these model estimates by 58% and 72%, respectively. The change of aboveground biomass was consistent with independent estimates of canopy top height at both regional and site levels. The International Land Model Benchmarking system assessment showed that data assimilation significantly improved CLM's performance in simulating the carbon and hydrological cycles, as well as in representing the functional relationships between LAI and other variables. To further reduce the remaining bias in GPP after LAI bias correction, we re‐parameterized CLM to account for low temperature suppression of photosynthesis. The LAI bias corrected model that included the new parameterization showed the best agreement with model benchmarks. Combining data assimilation with model parameterization provides a useful framework to assess photosynthetic processes in LSMs. Plain Language Summary: The arctic‐boreal zone is warming rapidly, impacting regional and global carbon cycles. The Community Land Model (CLM) can be used to project future carbon uptake and storage in this region. However, CLM is biased in estimating leave area index (LAI) and aboveground biomass that can significantly affect model projections of carbon uptake and storage. We forced the model estimates of LAI and the aboveground biomass to be consistent with satellite‐derived LAI observations and a high‐quality machine learning product of aboveground biomass in Alaska and Western Canada using data assimilation. The change of aboveground biomass resulted in model estimates of vegetation height consistent with independent estimates at regional and site levels. The assessment using the International Land Model Benchmarking System showed that CLM's performance in simulating carbon and hydrologic cycles was improved. Fixing the model bias in LAI only removed partial bias in carbon uptake, and a new parameterization allowing two key parameters in photosynthesis to vary with leaf temperature was introduced into CLM, to further remove the remaining bias in carbon uptake. Combining data assimilation with this new parameterization yielded more accurate model estimates of carbon uptake. Key Points: Assimilating leaf area index and aboveground biomass observations into CLM reduced model bias in estimating themData assimilation significantly improved CLM's performance in carbon and hydrologic cycles, as well as the functional relationshipsImplementation of a new parameterization of photosynthesis in CLM further reduced model bias in estimating the gross primary productivity [ABSTRACT FROM AUTHOR]

Dryland ecosystems cover 40% of our planet's land surface, support billions of people, and are responding rapidly to climate and land use change. These expansive systems also dominate core aspects of Earth's climate, storing and exchanging vast amounts of water, carbon, and energy with the atmosphere. Despite their indispensable ecosystem services and high vulnerability to change, drylands are one of the least understood ecosystem types, partly due to challenges studying their heterogeneous landscapes and misconceptions that drylands are unproductive "wastelands." Consequently, inadequate understanding of dryland processes has resulted in poor model representation and forecasting capacity, hindering decision making for these at‐risk ecosystems. NASA satellite resources are increasingly available at the higher resolutions needed to enhance understanding of drylands' heterogeneous spatiotemporal dynamics. NASA's Terrestrial Ecology Program solicited proposals for scoping a multi‐year field campaign, of which Adaptation and Response in Drylands (ARID) was one of two scoping studies selected. A primary goal of the scoping study is to gather input from the scientific and data end‐user communities on dryland research gaps and data user needs. Here, we provide an overview of the ARID team's community engagement and how it has guided development of our framework. This includes an ARID kickoff meeting with over 300 participants held in October 2023 at the University of Arizona to gather input from data end‐users and scientists. We also summarize insights gained from hundreds of follow‐up activities, including from a tribal‐engagement focused workshop in New Mexico, conference town halls, intensive roundtables, and international engagements. Plain Language Summary: Drylands are landscapes with limited water availability, which cover 40% of Earth's land surfaces, support billions of humans, and play a substantial role in Earth's weather and climate systems. However, these ecosystems are under threat from droughts and heatwaves. They are also poorly understood because of challenges measuring their highly diverse vegetation types and interspersed vegetation cover and because of incorrect perceptions that they are unimportant "wastelands." These limitations make it challenging to manage their landscapes and quantify how drylands are driving Earth's weather and climate. NASA solicited proposals for a multi‐year field campaign, of which Adaptation and Response in Drylands (ARID) was one of two scoping studies selected. The ARID scoping study aims to design a plan for how NASA satellite, aircraft, and field instruments can be used to better understand dryland ecosystems and their response to change. A primary scoping goal is to engage with scientists and data‐users, especially those who manage land, to understand research and management priorities in drylands. Here, we discuss details of our meeting with over 300 scientists and data‐users in Tucson, AZ in October 2023. We also highlight feedback from our tribal‐focused workshop in New Mexico, conference town halls, and international meetings. Key Points: Adaptation and Response in Drylands (ARID) is a 1‐year scoping study for a multi‐year NASA Terrestrial Ecology dryland field campaignAn ARID workshop was held in Tucson, Arizona in October 2023 with more than 30 data end‐users and 300 scientists in attendanceFurther input from hundreds of researchers and end‐users was obtained through workshops, conference townhalls, and tribal engagement [ABSTRACT FROM AUTHOR]

Biological soil crusts (biocrusts) are widespread soil photosynthetic communities covering about 12% of Earth's land surface, and play crucial roles in terrestrial carbon (C) and nitrogen (N) cycles, yet scalable quantifications of biocrusts and their biogeochemical contributions are notably lacking. While remote sensing has enormous potential to assess, scale, and contextualize biocrusts and their functions, the applicability of hyperspectral data in predicting C‐ and N‐related biocrust traits remains largely unexplored. We address this issue by evaluating the potential of in situ hyperspectral data to predict C and N across a range of biocrust species and different environmental conditions. We found that in situ hyperspectral reflectance measurements can be used to predict biocrust tissue C/N ratios and N concentrations with relatively high accuracy but to a lesser extent for potential biocrust N2 fixation rates. Critical wavelength domains included the visible region of the spectrum from roughly 490–600 nm, which most effectively captured variations in biocrust tissue C, and the shortwave infrared region from 1,150 to 1,350 nm and 1,550–1,650 nm, which most effectively captured biocrust tissue N and N2 fixation potential. Finally, we provide evidence that multi‐ and hyperspectral missions with targeted band placement, such as the proposed 26‐band Landsat Next, could be effective in predicting biocrust traits. This work provides a critical step in understanding how to apply data from new and upcoming satellite missions to the monitoring of biocrusts. Plain Language Summary: Biological soil crusts (biocrusts) are communities of organisms such as cyanobacteria, fungi, and lichens existing on soil surfaces. Biocrusts play an important role in the carbon (C) and nitrogen (N) cycles of terrestrial ecosystems. The C and N status of biocrusts can be examined in various ways. However, the applicability of studying C and N status of biocrusts using spectral reflectance is poorly explored. We combined statistical models and spectral reflectance measured using a spectroradiometer within the wavelength range from 400 to 2,450 nm to predict variations in C and N status of samples from 14 representative biocrust species, and under both wet and dry conditions. We found that variations in biocrust tissue C/N ratios and N concentrations could be accurately predicted. We also found that the wavelength range of 490–600 nm, and 1,150–1,350 nm as well as around 1,600 nm are the most important wavelength domains to study C and N status of biocrusts. Finally, we provide evidence that upcoming satellite missions with proper band placement, such as the proposed 26‐band Landsat Next, could be effective in predicting biocrust traits. Results from this work shed light on applying data from upcoming satellite missions to the monitoring of biocrusts. Key Points: The applicability of hyperspectral data in predicting carbon‐ and nitrogen‐related biocrust functional traits is poorly understoodVisible and shortwave infrared region of the spectrum best predicted variations in carbon‐ and nitrogen‐related biocrust functional traitsMulti‐ and hyperspectral satellite missions with targeted band placement could be effective in predicting biocrust functional traits [ABSTRACT FROM AUTHOR]