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Arctic shrub expansion alters carbon budgets, albedo, and warming rates in high latitudes but remains challenging to predict due to unclear underlying controls. Observational studies and models typically use relationships between observed shrub presence and current environmental suitability (bioclimate and topography) to predict shrub expansion, while omitting shrub demographic processes and non-stationary response to changing climate. Here, we use high-resolution satellite imagery across Alaska and western Canada to show that observed shrub expansion has not been controlled by environmental suitability during 1984–2014, but can only be explained by considering seed dispersal and fire. These findings provide the impetus for better observations of recruitment and for incorporating currently underrepresented processes of seed dispersal and fire in land models to project shrub expansion and climate feedbacks. Integrating these dynamic processes with projected fire extent and climate, we estimate shrubs will expand into 25% of the non-shrub tundra by 2100, in contrast to 39% predicted based on increasing environmental suitability alone. Thus, using environmental suitability alone likely overestimates and misrepresents shrub expansion pattern and its associated carbon sink. Shrub encroachment trends are widespread yet complex. Here the authors demonstrate that not considering dispersal and fire leads to overestimating shrub expansion in Arctic tundra and therefore its role as carbon sink.

Tree mortality is a key driver of forest community composition and carbon dynamics. Strong winds associated with severe convective storms are dominant natural drivers of tree mortality in the Amazon. Why forests vary with respect to their vulnerability to wind events and how the predicted increase in storm events might affect forest ecosystems within the Amazon are not well understood. We found that windthrows are common in the Amazon region extending from northwest (Peru, Colombia, Venezuela, and west Brazil) to central Brazil, with the highest occurrence of windthrows in the northwest Amazon. More frequent winds, produced by more frequent severe convective systems, in combination with well-known processes that limit the anchoring of trees in the soil, help to explain the higher vulnerability of the northwest Amazon forests to winds. Projected increases in the frequency and intensity of convective storms in the Amazon have the potential to increase wind-related tree mortality. A forest demographic model calibrated for the northwestern and the central Amazon showed that northwestern forests are more resilient to increased wind-related tree mortality than forests in the central Amazon. Our study emphasizes the importance of including wind-related tree mortality in model simulations for reliable predictions of the future of tropical forests and their effects on the Earth' system.

Wood harvesting alters land surface properties and energy redistribution, but there is a lack of studies estimating these changes on a global scale. We coupled a vegetation demographic model, the Functionally Assembled Terrestrial Ecosystem Simulator, with the E3SM land model to perform offline model simulation to investigate the land biogeophysical responses, including canopy coverage, leaf area index, albedo, surface roughness length, and energy fluxes, to historical wood harvest on the global scale. In this study, we found 50% less harvested carbon (C) when choosing the area‐based harvest rate as driving data that has not been spatially harmonized, compared to reharmonized mass‐based harvesting. By considering the uncertainty from reconstruction of historical wood harvest time series and the choice of wood harvest approach in the model, continuous wood harvest (1850–2015) results in 5%–10% of canopy coverage loss, contributing 0.5%–1% increase of albedo over disturbed land, which is much stronger than a non‐demographic land surface model. Changes in energy flux from the wood harvest are negligible (<1%), but the responses of land surface properties vary (up to 30%) due to differences in model structure between the single canopy, sun‐shade leaf model and vegetation demographic model. Plain Language Summary We study the impact of historical global wood harvesting on vegetation structure and land surface heating using a land surface model (E3SM land model) coupled with a detailed vegetation demography model (Functionally Assembled Terrestrial Ecosystem Simulator). A more realistic harvest approach based on the amount of wood harvested, rather than the area harvested, is applied to match the total historical harvested wood product. We estimate less vegetation canopy coverage and total leaf amount, and lower effective plant heights historically compared to a land surface model with no representation of individual plant populations. Key Points A vegetation demography model (Functionally Assembled Terrestrial Ecosystem Simulator), coupled to a land surface model simulates historical wood harvest and secondary forest regrowth Accumulated canopy coverage loss (1850–2015) reaches 5%–10%, causing 0.5%–1% global increase of albedo over harvested regions Incorporating vegetation demography strengthens the biogeophysical impact of wood harvest, compared to a single canopy model (E3SM land model)

Forest disturbance and regrowth are key processes in forest dynamics, but detailed information on these processes is difficult to obtain in remote forests such as the Amazon. We used chronosequences of Landsat satellite imagery (Landsat 5 Thematic Mapper and Landsat 7 Enhanced Thematic Mapper Plus) to determine the sensitivity of surface reflectance from all spectral bands to windthrow, clear-cut, and clear-cut and burned (cut + burn) and their successional pathways of forest regrowth in the Central Amazon. We also assessed whether the forest demography model Functionally Assembled Terrestrial Ecosystem Simulator (FATES) implemented in the Energy Exascale Earth System Model (E3SM) Land Model (ELM), ELM-FATES, accurately represents the changes for windthrow and clear-cut. The results show that all spectral bands from the Landsat satellites were sensitive to the disturbances but after 3 to 6 years only the near-infrared (NIR) band had significant changes associated with the successional pathways of forest regrowth for all the disturbances considered. In general, the NIR values decreased immediately after disturbance, increased to maximum values with the establishment of pioneers and early successional tree species, and then decreased slowly and almost linearly to pre-disturbance conditions with the dynamics of forest succession. Statistical methods predict that NIR values will return to pre-disturbance values in about 39, 36, and 56 years for windthrow, clear-cut, and cut + burn disturbances, respectively. The NIR band captured the observed, and different, successional pathways of forest regrowth after windthrow, clear-cut, and cut + burn. Consistent with inferences from the NIR observations, ELM-FATES predicted higher peaks of biomass and stem density after clear-cuts than after windthrows. ELM-FATES also predicted recovery of forest structure and canopy coverage back to pre-disturbance conditions in 38 years after windthrows and 41 years after clear-cut. The similarity of ELM-FATES predictions of regrowth patterns after windthrow and clear-cut to those of the NIR results suggests the NIR band can be used to benchmark forest regrowth in ecosystem models. Our results show the potential of Landsat imagery data for mapping forest regrowth from different types of disturbances, benchmarking, and the improvement of forest regrowth models.

We present a representation of nitrogen and phosphorus cycling in the Functionally Assembled Terrestrial Ecosystem Simulator, a demographic vegetation model within the Energy Exascale Earth System land model. This representation is modular, and designed to allow testing of multiple hypothetical approaches for carbon-nutrient coupling in plants. Novel model hypotheses introduced in this work include, (a) the controls on plant acquisition of aqueous mineralized nutrients in the soil and (b) fairly straight forward methods of allocating nutrients to specific plant organs and their losses through live plant turnover as well as litter fluxes generated through plant mortality. This combines the new with pre-existing hypotheses (such as nitrogen fixation and soil decomposition) into a system that can accommodate plant-soil dynamics for a large number of size- and functional-type-resolved plant cohorts within a time-since-disturbance-resolved ecosystem. Root uptake of nutrients is governed by fine root biomass, and plants vary in their fine root biomass allocation in order to balance carbon and nutrient limitations to growth. We test the sensitivity of the model to a wide range of parameter variations and structural representations, and in the context of observations at Barro Colorado Island, Panama. A key model prediction is that plants in the high-light-availability canopy positions allocate more carbon to fine roots than plants in low-light understory environments, given the widely different carbon versus nutrient constraints of these two niches within a given ecosystem. This model provides a basis for exploring carbon-nutrient coupling with vegetation demography within Earth system models.

Warming climates in the twenty-first century are expected to push tropical ecosystems, which currently reside at the warm and wet edge of bioclimatic life zones, into novel states that have no analog on Earth today (Fig. 1). Shifting precipitation patterns will expose some tropical forest regions to more frequent drought conditions that may reduce carbon (C) assimilation and lead to vegetation dieback, and all regions will experience higher temperatures never encountered by extant taxa. At the same time, direct anthropogenic disturbances, such as the hunting of large seed dispersers, use of fire for land management, deforestation, and agricultural land abandonment, are affecting the capacity of forests to mitigate climate change.

Caribbean tropical forests are subject to hurricane disturbances of great variability. In addition to natural storm incongruity, climate change can alter storm formation, duration, frequency, and intensity. This model-based investigation assessed the impacts of multiple storms of different intensities and occurrence frequencies on the long-term dynamics of subtropical dry forests in Puerto Rico. Using the previously validated individual-based gap model ZELIG-TROP, we developed a new hurricane damage routine and parameterized it with site- and species-specific hurricane effects. A baseline case with the reconstructed historical hurricane regime represented the control condition. Ten treatment cases, reflecting plausible shifts in hurricane regimes, manipulated both hurricane return time (i.e. frequency) and hurricane intensity. The treatment-related change in carbon storage and fluxes were reported as changes in aboveground forest biomass (AGB), net primary productivity (NPP), and in the aboveground carbon partitioning components, or annual carbon accumulation (ACA). Increasing the frequency of hurricanes decreased aboveground biomass by between 5% and 39%, and increased NPP between 32% and 50%. Decadal-scale biomass fluctuations were damped relative to the control. In contrast, increasing hurricane intensity did not create a large shift in the long-term average forest structure, NPP, or ACA from that of historical hurricane regimes, but produced large fluctuations in biomass. Decreasing both the hurricane intensity and frequency by 50% produced the highest values of biomass and NPP. For the control scenario and with increased hurricane intensity, ACA was negative, which indicated that the aboveground forest components acted as a carbon source. However, with an increase in the frequency of storms or decreased storms, the total ACA was positive due to shifts in leaf production, annual litterfall, and coarse woody debris inputs, indicating a carbon sink into the forest over the long-term. The carbon loss from each hurricane event, in all scenarios, always recovered over sufficient time. Our results suggest that subtropical dry forests will remain resilient to hurricane disturbance. However carbon stocks will decrease if future climates increase hurricane frequency by 50% or more.

Emissions‐driven (prognostic CO2) simulations are essential for representing two‐way carbon‐climate feedback in Earth System Models. We present an emissions‐driven land–atmosphere coupled biogeochemistry (BGC) configuration (BGCLNDATM_progCO2) in version 2.1 of the Energy Exascale Earth System Model (E3SMv2.1). This is the first E3SM configuration that performs land‐atmosphere emission‐hindcasts. Here, we document its implementation, evaluate the model's performance against observations and other models, and propose a structured evaluation protocol for such emissions‐driven simulations. We conducted transient historical simulations (1850–2014) with BGCLNDATM_progCO2 and compare them to reference simulations—a land‐atmosphere coupled simulation without BGC and a standalone land simulation with BGC, both using prescribed CO2 concentrations—and to observations. BGCLNDATM_progCO2 overestimates atmospheric CO2 concentrations by 11–23 ppm yet stays within the 40‐ppm spread CMIP6 emission‐driven models and retains physical climate properties comparable to the reference runs. The CO2 biases are partly attributed to underrepresented oceanic CO2 uptake and inadequate representations of some terrestrial processes. In general, introducing prognostic CO2 did not change physical climate metrics at the global scale but had larger regional effects, particularly over land where spatially heterogeneous CO2 and prognostic leaf area index influenced surface energy balance. Finally, we propose a general evaluation protocol including spin‐up assessment, atmospheric CO2 benchmarking, physical climate evaluation, and land biogeochemical analysis to support scientific rigor and facilitate inter‐model comparisons. The new configuration lays the groundwork for future enhancements, including improved terrestrial biogeochemical processes, integrated marine biogeochemistry, and additional human–Earth system interactions. These developments advance E3SM toward fully coupled emissions‐driven simulations, enabling more accurate carbon–climate feedback projections and informing mitigation policy by providing physically consistent carbon‐budget metrics for mitigation scenarios. Plain Language Summary Understanding the impact of carbon dioxide (CO2) emissions on climate is vital for predicting future changes and crafting effective policies. Earth System Models (ESMs) are essential tools for simulating Earth's climate and assessing various influencing factors. In this study, we extended the Energy Exascale Earth System Model (E3SM)'s capabilities so that CO2 levels are calculated directly from human and natural emissions instead of being prescribed as a single global value. This extension allows for a more realistic representation of CO2 exchange between the atmosphere and land. We conducted historical simulations from 1850 to 2014 using this new development and compared results with observations and other models. Our model slightly overestimates atmospheric CO2 levels compared to measurements but is comparable to other models in capturing key climate features. To help other researchers build and test similar “emission‐driven” models, we created a step‐by‐step evaluation framework that checks CO2 behavior, climate variables, and land‐atmosphere interactions. Our work advances E3SM modeling by accurately representing how CO2 emissions affect Earth's systems. This enhancement lays the groundwork for modeling interactions between human‐Earth interactions, thereby enabling future studies that can inform mitigation and adaption. Key Points Implemented emissions‐driven land–atmosphere biogeochemistry in E3SMv2.1 (BGCLNDATM_progCO2), enabling prognostic CO2 simulations Established a structured evaluation protocol ensuring scientific rigor and facilitating inter‐model comparisons of model performance Emissions‐driven BGCLNDATM_progCO2 simulations maintain a physical climate similar to reference runs with prescribed CO2 concentrations

Vegetation distribution and composition are expected to change in northern high latitudes under rapid warming, which regulates ecosystem functions but remains challenging to predict. Vegetation change arises from the interplay of chronic climate trends such as warming and transient demographic processes of recruitment, growth, competition, and mortality. Most predictive models overlooked the role of demographic dynamics controlled by plant traits. Here, we simulate vegetation dynamics at the Kougarok Hillslope site in Alaska under historical and future climates using the E3SM Land Model coupled to the Functionally Assembled Terrestrial Simulator (ELM‐FATES). To evaluate the roles of plant traits, we parameterize the model with 5,265 trait configurations representing diverse physiological and demographic strategies. Results show current modeled biomass, composition, and productivity are most sensitive to traits controlling photosynthetic capacity, carbon allocation, allometry, and phenology. Among all trait configurations, ∼5% reproduce in situ biomass and plant functional type (PFT) composition measured in 2016, that are indistinguishable from these two observed ecosystem states. Notably, these same trait configurations produce diverging biomass, composition, and productivity under future climate, where the uncertainty attributable to traits is twice the change attributable to climate change. The variation of projected productivity arises from emerging PFT composition under novel climate regimes, primarily explained by traits controlling cold‐induced mortality, recruitment, and allometry. Our findings highlight the importance and uncertainty of demographic dynamics and its interaction with climate change in shaping Arctic vegetation change. Improved model predictions will likely benefit from explicit consideration of vegetation demography and better constraints of critical traits. Plain Language Summary Rapid warming in the Arctic is expected to change plant composition and the capability to sequester carbon, which remains challenging to predict accurately. The changing trajectory depends on the strategies of Arctic vegetation coping with climate change via the demographic processes of recruitment, growth, competition, and mortality. Here, we simulate these processes at an Alaskan tundra site under historical and future climates using a dynamic vegetation model, which describes the strategy of each plant type using plant traits. We find that a large set of different strategies can lead to similar plant composition and biomass as seen in the field. However, the same set of strategies produces diverging future trajectories under projected climate, with spreads twice as wide as climate‐induced changes. This uncertainty is rooted in the trade‐offs across traits and is primarily attributed to those controlling how cold tolerant each plant type is, how fast a new individual can be recruited, and how big and tall the canopy can grow at the same stem size. Better quantification of these traits will likely contribute to improved model predictions. Our findings highlight the importance and uncertainty of vegetation demography and its interaction with climate change in shaping Arctic vegetation change. Key Points We use a dynamic vegetation model to simulate vegetation composition and productivity at an Arctic site, constrained by in situ data Diverse trait configurations can reproduce observations but cause projection uncertainty twice the change attributable to climate change Better constraints of critical traits on allometry, recruitment, and mortality will likely improve projected Arctic vegetation change

Current climate change scenarios indicate warmer temperatures and the potential for more extreme droughts in the tropics, such that a mechanistic understanding of the water cycle from individual trees to landscapes is needed to adequately predict future changes in forest structure and function. In this study, we contrasted physiological responses of tropical trees during a normal dry season with the extreme dry season due to the 2015-2016 El Niño-Southern Oscillation (ENSO) event. We quantified high resolution temporal dynamics of sap velocity (V ), stomatal conductance (g ) and leaf water potential (Ψ ) of multiple canopy trees, and their correlations with leaf temperature (T ) and environmental conditions [direct solar radiation, air temperature (T ) and vapor pressure deficit (VPD)]. The experiment leveraged canopy access towers to measure adjacent trees at the ZF2 and Tapajós tropical forest research (near the cities of Manaus and Santarém). The temporal difference between the peak of g (late morning) and the peak of VPD (early afternoon) is one of the major regulators of sap velocity hysteresis patterns. Sap velocity displayed species-specific diurnal hysteresis patterns reflected by changes in T . In the morning, T and sap velocity displayed a sigmoidal relationship. In the afternoon, stomatal conductance declined as T approached a daily peak, allowing Ψ to begin recovery, while sap velocity declined with an exponential relationship with T . In Manaus, hysteresis indices of the variables T -T and Ψ -T were calculated for different species and a significant difference ( < 0.01, α = 0.05) was observed when the 2015 dry season (ENSO period) was compared with the 2017 dry season (\"control scenario\"). In some days during the 2015 ENSO event, T approached 40°C for all studied species and the differences between T and T reached as high at 8°C (average difference: 1.65 ± 1.07°C). Generally, T was higher than T during the middle morning to early afternoon, and lower than T during the early morning, late afternoon and night. Our results support the hypothesis that partial stomatal closure allows for a recovery in Ψ during the afternoon period giving an observed counterclockwise hysteresis pattern between Ψ and T .