Comprehensive land models are subject to significant parametric uncertainty, which can be hard to quantify due to the large number of parameters and high model computational costs. We constructed a large parameter perturbation ensemble (PPE) for the Community Land Model version 5.1 with biogeochemistry configuration (CLM5.1-BGC). We performed more than 2,000 simulations perturbing 211 parameters across six forcing scenarios. This provides an expansive data set, which can be used to identify the most influential parameters on a wide range of output variables globally, by biome, or by plant functional type. We found that parameter effects can exceed scenario effects and that a small number of parameters explains a large fraction of variance across our ensemble. The most important parameters can differ regionally and also based on the forcing scenario. The software infrastructure developed for this experiment has greatly reduced the human and computer time needed for CLM PPEs, which can facilitate routine investigation of parameter sensitivity and uncertainty, as well as automated calibration.

Grass-dominated ecosystems cover wide areas of the land surface yet have received far less attention from the Earth System Model (ESM) community. This limits model projections of ecosystem dynamics in response to global change and coupled vegetation-climate dynamics. We used the Functionally Assembled Terrestrial Ecosystem Simulator (FATES), a dynamic vegetation demography model, to determine ecosystem sensitivity to alternate, observed grass allometries and biophysical traits, and evaluated model performance in capturing California C3 annual grasslands structure and fire regimes. Grass allometry, leaf physiology, plant phenology, and plant mortality all drove the seasonal variation in matter and energy exchange and fire dynamics in California annual grasslands. Allometry influenced grassland structure and function mainly through canopy architecture-mediated space and light competition instead of through carbon partitioning strategy. Regional variation in grassland annual burned area was driven by variation in ecosystem productivity. Our study advances the modeling of grassy ecosystems in ESMs by establishing the importance of grass allometry and plant phenology and mortality in driving C3 annual grassland seasonal dynamics and fire regime. The calibrated annual grass allometry and biophysical traits presented can be applied in future studies to project climate-vegetation-fire feedbacks in annual grass-dominant ecosystems under global change.

Surface ozone (O3) is the primary air pollutant threatening global vegetation. It typically reduces the photosynthetic rate and stomatal conductance, leading to changes in carbon, water, and energy cycles; vegetation structure and composition; and climate. Several parameterization schemes have been developed to integrate the photosynthetic and stomatal responses to O3 exposure in regional and global process-based models to simulate time- and space-varying O3 plant damage and its cascading dynamic influence. However, these schemes are calibrated based on limited observations and often fail to reproduce the response relationships in observations, impeding accurate assessments of the role of O3 plant damage in the Earth system. This study proposes a new parameterization scheme to utilize the extensive observations from O3 fumigation experiments to inform large-scale modeling. It is built on 4210 paired data points of photosynthetic and stomatal responses compiled from the peer-reviewed literature, more than 6 times larger than those employed in earlier schemes. Functions of phytotoxic O3 dose (POD) are found to accurately reproduce the statistically significant linear or nonlinear relationships observed between POD and either relative leaf photosynthetic rate or relative stomatal conductance for needleleaf trees, broadleaf trees, shrubs, grasses, and crops. These eliminate the practice in earlier schemes of setting response functions as constants and applying the response function from one vegetation type to another. It outperforms the old scheme in the Community Land Model (CLM), which skillfully reproduces the observed response for crop photosynthetic rate only. The nonlinear response functions we developed depict decreasing plant sensitivity with increases in POD, enabling models to implicitly capture the variability in plant ozone tolerance and the shift among plant species for both intra- and inter-PFTs (plant functional types) within a vegetation type observed in the real world. Then, the new scheme is incorporated into the Community Earth System Model version 2.2 (CESM2.2), specifically its land component CLM5, to quantify the global impacts of present-day O3 plant damage by comparing the simulations with and without O3 plant damage. Results show that O3 exposure reduces the global leaf photosynthetic rate by 8.5 % and stomatal conductance by 7.4 %, around half the estimates using the old scheme. Furthermore, the new scheme improves global gross primary productivity (GPP) simulations, decreasing RMSE by 11.1 % relative to simulations without O3 plant damage and by 11.7 % compared to the old scheme. These results underscore the importance of including O3 plant damage in large-scale process-based models and the effectiveness of the new scheme in assessing and projecting globally the role of O3 plant damage in the Earth system.

Abstract. Plant community composition influences carbon, water and energy fluxes at regional to global scales. Composition is a dynamic property of ecosystems, arising from complex feedbacks among the environment, disturbance, and plant physiology. Vegetation demographic models (VDMs) allow investigation of the effects of changing climate and disturbance regimes on vegetation composition and fluxes. Such investigation requires that the models can accurately resolve these feedbacks to simulate realistic composition. Vegetation in VDMs is composed of plant functional types (PFTs), which are specified according to plant traits. Defining PFTs is challenging due to large variability in trait observations within and between plant types and a lack of understanding of model sensitivity to these traits. Here we present an approach for developing PFT parameterizations that are connected to the underlying ecological processes determining forest composition in the mixed-conifer forest of the Sierra Nevada Mountains of California, USA. We constrain multiple relative trait values between PFTs, as opposed to randomly sampling within the range of observations. An ensemble of PFT parameterizations are then filtered based on emergent forest properties meeting observation-based ecological criteria under alternate disturbance scenarios. A small ensemble of alternate PFT parameterizations is identified that produces plausible forest composition, and demonstrates variability in response to disturbance frequency and regional environmental variation. Retaining multiple PFT parameterizations allows us to quantify the uncertainty in forest responses due to variability in trait observations. Vegetation composition is a key emergent outcome from VDMs and our methodology provides a foundation for robust PFT parameterization across ecosystems.

As the world's population increases, so will the demand for food and timber resources. Though carbon dioxide (CO2) fertilization and, to a lesser extent, nitrogen (N) deposition are expected to increase future resource production, changes in ozone (O3) and climate have the potential to decrease production due to increased phytotoxic damage, drought, and heat stress. To determine how crop and timber production may change in the future, we use the Community Land Model version 4.5 with prognostic crops to simulate responses of wood biomass and crop yields to CO2, O3, N deposition, and climate under Representative Concentration Pathway 8.5 forcings. Generally, rising CO2 increases wood biomass and crop yields, while projected climate change causes decreases. Small projected changes in O3 and N deposition do not strongly affect yields, though additional research is needed on future O3 and N deposition trends and impacts. By the end of the 21st century, global wood biomass increases by ~16% due to the dominating impact of CO2. The positive effect of CO2 on future crop yields is muted by the negative impacts of climate, with a ~5% net global increase. Future projections suggest that rice and wheat yields typically increase under the combination of future forcings, whereas soy and corn yields are regionally variable. While short‐term resource management strategies can benefit from planting heat‐tolerant species and cultivars, technological advances and intensification, among other management strategies not included here, must be employed to meet the future demand for these resources.

Crop yields are vulnerable to climate change. We assess the global impacts of climate change on agricultural systems under two climate projections (RCP8.5 and RCP4.5) to quantify the difference in impacts if climate change were reduced. We also employ two different socioeconomic pathways (SSP3 and SSP5) to assess the sensitivity of results to the underlying socioeconomic conditions. The integrated-Population-Economy-Technology-Science (iPETS) model, a global integrated assessment model for projecting future energy use, land use and emissions, is used in conjunction with the Community Earth System Model (CESM), and particularly its land surface component, the Community Land Model (CLM), to evaluate climate change impacts on agriculture. iPETS results are produced at the level of nine world regions for the period 2005–2100. We employ climate impacts on crop yield derived from CLM, driven by CESM simulations of the two RCPs. These yield effects are applied within iPETS, imposed on baseline and mitigation scenarios for SSP3 and SSP5 that are consistent with the RCPs. We find that the reduced level of warming in RCP4.5 (relative to RCP8.5) can have either positive or negative effects on the economy since crop yield either increases or decreases with climate change depending on assumptions about CO 2 fertilization. Yields are up to 12 % lower, and crop prices are up to 15 % higher, in RCP4.5 relative to RCP8.5 if CO 2 fertilization is included, whereas yields are up to 22 % higher, and crop prices up to 22 % lower, if it is not. We also find that in the mitigation scenarios (RCP4.5), crop prices are substantially affected by mitigation actions as well as by climate impacts. For the scenarios we evaluated, the development pathway (SSP3 vs SSP5) has a larger impact on outcomes than climate (RCP4.5 vs RCP8.5), by a factor of 3 for crop prices, 11 for total cropland use, and 35 for GDP on global average.

Recent prolonged droughts and catastrophic wildfires in the western United States have raised concerns about the potential for forest mortality to impact forest structure, forest ecosystem services, and the economic vitality of communities in the coming decades. We used the Community Land Model (CLM) to determine forest vulnerability to mortality from drought and fire by the year 2049. We modified CLM to represent 13 major forest types in the western United States and ran simulations at a 4-km grid resolution, driven with climate projections from two general circulation models under one emissions scenario (RCP 8.5). We developed metrics of vulnerability to short-term extreme and prolonged drought based on annual allocation to stem growth and net primary productivity. We calculated fire vulnerability based on changes in simulated future area burned relative to historical area burned. Simulated historical drought vulnerability was medium to high in areas with observations of recent drought-related mortality. Comparisons of observed and simulated historical area burned indicate simulated future fire vulnerability could be underestimated by 3% in the Sierra Nevada and overestimated by 3% in the Rocky Mountains. Projections show that water-limited forests in the Rocky Mountains, Southwest, and Great Basin regions will be the most vulnerable to future drought-related mortality, and vulnerability to future fire will be highest in the Sierra Nevada and portions of the Rocky Mountains. High carbon-density forests in the Pacific coast and western Cascades regions are projected to be the least vulnerable to either drought or fire. Importantly, differences in climate projections lead to only 1% of the domain with conflicting low and high vulnerability to fire and no area with conflicting drought vulnerability. Our drought vulnerability metrics could be incorporated as probabilistic mortality rates in earth system models, enabling more robust estimates of the feedbacks between the land and atmosphere over the 21st century.

Understanding how impacts may differ across alternative levels of future climate change is necessary to inform mitigation and adaptation measures. The Benefits of Reduced Anthropogenic Climate changE (BRACE) project assesses the differences in impacts between two specific climate futures: a higher emissions future with global average temperature increasing about 3.7 °C above pre-industrial levels toward the end of the century and a moderate emissions future with global average warming of about 2.5 °C. BRACE studies in this special issue quantify avoided impacts on physical, managed, and societal systems in terms of extreme events, health, agriculture, and tropical cyclones. Here we describe the conceptual framework and design of BRACE and synthesize its results. Methodologically, the project combines climate modeling, statistical analysis, and impact assessment and draws heavily on large ensembles using the Community Earth System Model. It addresses uncertainty in future societal change by employing two pathways for future socioeconomic development. Results show that the benefits of reduced climate change within this framework vary substantially across types of impacts. In many cases, especially related to extreme heat events, there are substantial benefits to mitigation. The benefits for some heat extremes are statistically significant in some regions as early as the 2020s and are widespread by mid-century. Benefits are more modest for agriculture and exposure to some health risks. Benefits are negative for agriculture when CO2 fertilization is incorporated. For several societal impacts, the effect on outcomes of alternative future societal development pathways is substantially larger than the effect of the two climate scenarios.

We perform CLMcrop simulations of the 20th and 21st centuries to assess potential avoided impacts in (a) crop yield losses and (b) water demand increases if humanity were to choose the representative concentration pathway (RCP) 4.5 instead of 8.5. RCP 8.5 imposes more extreme climatic changes on CLMcrop, while simultaneously exposing the crops to higher CO 2 fertilization than RCP 4.5. As a result CLMcrop simulates global to regional scale changes in yield and water requirements for RCP 8.5 that exceed and sometimes more than double the RCP 4.5 changes relative to today. Under RCP 4.5 then, human societies may confront easier adaptation to changes in crop yields and water requirements. Under both RCPs, CLMcrop projects declining global yields for C3 crops (e.g., wheat, soybean, rice) without CO 2 fertilization and C4 crops (corn, sugarcane) without irrigation. Yield declines of 3 t ha −1 stand out in parts of tropical and subtropical Africa and South America (presently areas of rapid agricultural expansion) and are due to increasing plant respiration and decreasing soil moisture, both due to rising temperatures. Irrigation and CO 2 fertilization mitigate yield losses and in some cases lead to gains, so irrigation may help maintain or increase current yields through the 21st century. However, simulated global irrigation requirements increase: as much as 23 % for C4 crops without CO 2 fertilization under RCP 8.5 and as little as 3 % for C4 crops with CO 2 fertilization under RCP4.5. Nitrogen fertilized crops display greater vulnerability to climate and environmental change than unfertilized crops in our simulations; still relative to unfertilized crops, they deliver significantly higher yields and remain indispensable in supporting a more populous and affluent humanity. These CLMcrop results broadly agree with previously published outcomes for the 21st century. We describe in this article a new version of CLMcrop that represents prognostic crop behavior not only in the mid-latitudes but also the tropics.

This study considers how large-scale application of solar panels will affect climate. Electricity generation leads to regional cooling but this is countered by the power’s use, affecting global circulation patterns with changes in regional rainfall.

Ozone (O-3) is a phytotoxic greenhouse gas that has increased more than threefold at Earth's surface from preindustrial values. In addition to directly increasing radiative forcing as a greenhouse gas, O-3 indirectly impacts climate through altering the plant processes of photosynthesis and transpiration. While global estimates of gross primary productivity (GPP) have incorporated the effects of O-3, few studies have explicitly determined the independent effects of O-3 on transpiration. In this study, the authors include effects of O-3 on photosynthesis and stomatal conductance from a recent literature review to determine the impact on GPP and transpiration and highlight uncertainty in modeling plant responses to O-3. Using the Community Land Model, the authors estimate that present-day O-3 exposure reduces GPP and transpiration globally by 8%-12% and 2%-2.4%, respectively. The largest reductions were in midlatitudes, with GPP decreasing up to 20% in the eastern United States, Europe, and Southeast Asia and transpiration reductions of up to 15% in the same regions. Larger reductions in GPP compared to transpiration decreased water-use efficiency 5%-10% in the eastern United States, Southeast Asia, Europe, and central Africa; increased surface runoff more than 15% in eastern North America; and altered patterns of energy fluxes in the tropics, high latitudes, and eastern North America. Future climate predictions will be improved if plant responses to O-3 are incorporated into models such that stomatal conductance is modified independently of photosynthesis and the effects on transpiration are explicitly considered in surface energy budgets. Improvements will help inform regional decisions for managing changes in hydrology and surface temperatures in response to O-3 pollution.

Considering the combined effects of CO2 fertilization and climate change drivers on plant physiology leads to a modest increase in simulated European forest transpiration in spite of the effects of CO2-induced stomatal closure. The Earth’s carbon and hydrologic cycles are intimately coupled by gas exchange through plant stomata1,2,3. However, uncertainties in the magnitude4,5,6 and consequences7,8 of the physiological responses9,10 of plants to elevated CO2 in natural environments hinders modelling of terrestrial water cycling and carbon storage11. Here we use annually resolved long-term δ13C tree-ring measurements across a European forest network to reconstruct the physiologically driven response of intercellular CO2 (Ci) caused by atmospheric CO2 (Ca) trends. When removing meteorological signals from the δ13C measurements, we find that trees across Europe regulated gas exchange so that for one ppmv atmospheric CO2 increase, Ci increased by ∼0.76 ppmv, most consistent with moderate control towards a constant Ci/Ca ratio. This response corresponds to twentieth-century intrinsic water-use efficiency (iWUE) increases of 14 ± 10 and 22 ± 6% at broadleaf and coniferous sites, respectively. An ensemble of process-based global vegetation models shows similar CO2 effects on iWUE trends. Yet, when operating these models with climate drivers reintroduced, despite decreased stomatal opening, 5% increases in European forest transpiration are calculated over the twentieth century. This counterintuitive result arises from lengthened growing seasons, enhanced evaporative demand in a warming climate, and increased leaf area, which together oppose effects of CO2-induced stomatal closure. Our study questions changes to the hydrological cycle, such as reductions in transpiration and air humidity, hypothesized to result from plant responses to anthropogenic emissions.

This paper presents a regional climate system model RCM–CLM–CN–DV and its validation over Tropical Africa. The model development involves the initial coupling between the ICTP regional climate model RegCM4.3.4 (RCM) and the Community Land Model version 4 (CLM4) including models of carbon–nitrogen dynamics (CN) and vegetation dynamics (DV), and further improvements of the models. Model improvements derive from the new parameterization from CLM4.5 that addresses the well documented overestimation of gross primary production (GPP), a refinement of stress deciduous phenology scheme in CN that addresses a spurious LAI fluctuation for drought-deciduous plants, and the incorporation of a survival rule into the DV model to prevent tropical broadleaf evergreens trees from growing in areas with a prolonged drought season. The impact of the modifications on model results is documented based on numerical experiments using various subcomponents of the model. The performance of the coupled model is then validated against observational data based on three configurations with increasing capacity: RCM–CLM with prescribed leaf area index and fractional coverage of different plant functional types (PFTs); RCM–CLM–CN with prescribed PFTs coverage but prognostic plant phenology; RCM–CLM–CN–DV in which both the plant phenology and PFTs coverage are simulated by the model. Results from these three models are compared against the FLUXNET up-scaled GPP and ET data, LAI and PFT coverages from remote sensing data including MODIS and GIMMS, University of Delaware precipitation and temperature data, and surface radiation data from MVIRI and SRB. Our results indicate that the models perform well in reproducing the physical climate and surface radiative budgets in the domain of interest. However, PFTs coverage is significantly underestimated by the model over arid and semi-arid regions of Tropical Africa, caused by an underestimation of LAI in these regions by the CN model that gets exacerbated through vegetation dynamics in RCM–CLM–CN–DV.