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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 865 records · Page 48
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Thermokarst rates intensify due to climate change and forest fragmentation in an Alaskan boreal forest lowland

Abstract Lowland boreal forest ecosystems in Alaska are dominated by wetlands comprised of a complex mosaic of fens, collapse‐scar bogs, low shrub/scrub, and forests growing on elevated ice‐rich permafrost soils. Thermokarst has affected the lowlands of the Tanana Flats in central Alaska for centuries, as thawing permafrost collapses forests that transition to wetlands. Located within the discontinuous permafrost zone, this region has significantly warmed over the past half‐century, and much of these carbon‐rich permafrost soils are now within ~0.5 °C of thawing. Increased permafrost thaw in lowland boreal forests in response to warming may have consequences for the climate system. This study evaluates the trajectories and potential drivers of 60 years of forest change in a landscape subjected to permafrost thaw in unburned dominant forest types (paper birch and black spruce) associated with location on elevated permafrost plateau and across multiple time periods (1949, 1978, 1986, 1998, and 2009) using historical and contemporary aerial and satellite images for change detection. We developed (i) a deterministic statistical model to evaluate the potential climatic controls on forest change using gradient boosting and regression tree analysis, and (ii) a 30 × 30 m land cover map of the Tanana Flats to estimate the potential landscape‐level losses of forest area due to thermokarst from 1949 to 2009. Over the 60‐year period, we observed a nonlinear loss of birch forests and a relatively continuous gain of spruce forest associated with thermokarst and forest succession, while gradient boosting/regression tree models identify precipitation and forest fragmentation as the primary factors controlling birch and spruce forest change, respectively. Between 1950 and 2009, landscape‐level analysis estimates a transition of ~15 km² or ~7% of birch forests to wetlands, where the greatest change followed warm periods. This work highlights that the vulnerability and resilience of lowland ice‐rich permafrost ecosystems to climate changes depend on forest type.

Lara, Mark J.↗
DOE OSTI2018

Quantifying climate feedbacks in polar regions

The concept of feedback is key in assessing whether a perturbation to a system is amplified or damped by mechanisms internal to the system. In polar regions, climate dynamics are controlled by both radiative and non-radiative interactions between the atmosphere, ocean, sea ice, ice sheets and land surfaces. Precisely quantifying polar feedbacks is required for a process-oriented evaluation of climate models, a clear understanding of the processes responsible for polar climate changes, and a reduction in uncertainty associated with model projections. This quantification can be performed using a simple and consistent approach that is valid for a wide range of feedbacks, thus offering the opportunity for more systematic feedback analyses and a better understanding of polar climate changes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Biophysical model of eelgrass and water quality in Coos Bay, OR shows greater mitigation potential for ocean acidification than hypoxia

Seagrass beds provide important ecosystem services and are valued, in part, for their potential to mediate stressors such as ocean acidification and hypoxia (OAH) for sensitive species. However, the susceptibility of seagrasses to anthropogenic impacts and recent declines motivate the need to better understand the drivers of seagrass and the water quality consequences that occur with variation in seagrass abundance. To meet this need, we leveraged existing monitoring data (water quality and seagrass), hydrodynamic circulation model, and biogeochemical model framework with seagrass submodel, to produce a biophysical model of Coos Bay estuary, Oregon, U.S. The model includes biogeochemical processes involving water quality, plankton, seagrass, and sediment-water interactions. Ecosystem models like this are useful for evaluating complex estuarine systems because they allow us to extend our understanding of system dynamics beyond existing observations and perform experiments to identify the processes driving observed patterns. We used the biophysical model of Coos Bay to evaluate the dynamics of water quality and native eelgrass (Zostera marina) under three eelgrass abundance scenarios (zero eelgrass, current extent, and maximum observed extent) to elucidate the relationship between eelgrass and OAH. Including eelgrass in the Coos Bay model produced results that more closely resembled water quality observations - dissolved oxygen (DO) and pH were more dynamic in simulations with eelgrass, often having both higher highs and lower lows. While there were some areas of the estuary where DO improved with the addition of eelgrass to the model there was overall a small net increase in harmful DO conditions (based on a salmon physiological threshold). In contrast, ocean acidification conditions, pH and calcium carbonate saturation state for aragonite (Ω), were improved (based on oyster requirements) with the addition of eelgrass - although the magnitude of improvement differed seasonally and spatially. Our new model represents a useful tool - one which accounts for and controls the relevant physical and biogeochemical processes - to evaluate conditions that confer resilience or enhance vulnerability to OAH in an important Pacific Northwest coastal estuary and results can inform the OAH-related dynamics occurring in other eastern boundary current estuaries.

FVCOM-ICM↗
DOE OSTI2016

Biophysical and hydrological effects of future climate change including trends in CO 2 , in the St. Joseph River watershed, Eastern Corn Belt

Future climate change has the potential to significantly impact crop growth, both directly due to CO 2 enhancement and indirectly, through temperature and moisture impacts. Here, this work investigates the biophysical and hydrological effects of future climate change, including trends in CO 2 , in the St. Joseph River watershed, Eastern Corn Belt. In this study, the Soil and Water Assessment Tool (SWAT) was first modified to take dynamic CO 2 concentration as input. A regional crop leaf development curve from Landsat TM imagery was also used to adjust model performance in corn leaf area development for the historical period. A multi-objective calibration strategy was employed to ensure acceptable simulation of streamflow, seasonal crop growth and interannual crop yield simultaneously. The model was then driven by future climate change and CO 2 data from three Atmosphere-Ocean General Circulation Models (AOGCMs), which are GFDL-CM2.1.1, NCAR-PCM1.3, and UKMO-HADCM3.1, under three Special Report on Emissions Scenarios (SRES), including B1, A1 B and A2 to investigate crop and streamflow response in two future periods: the near future (2021–2050) and the far future (2061–2090). The St. Joseph River watershed is expected to experience more winter and spring precipitation, but slightly decreasing summer precipitation. Due to increasing temperature and decreasing summer moisture, more drought stress is predicted. Both annual total aeration and drought stress are projected to be more variable in both future periods. Although future CO 2 enhancement will benefit the crop growth and final yield by improving radiation use efficiency (RUE) and reducing drought stresses, simulated corn yield still decreased by 6% in the near future period, and 16% in the far future period due to the combined effect of both climate change and CO 2 enhancement. Streamflow redistribution is also predicted in the future. Stream discharge is projected to increase for the whole flow range in the near future period. For the far future period, high flows are expected to decrease, while low flows are expected to increase, indicating more hydrologic drought and flood events in the St. Joseph River watershed.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1995

A versatile system for processing geostationary satellite data with run-time visualization capability

To better predict global climate change, scientists are developing climate models that require interdisciplinary and collaborative efforts in their building. We are currently involved in several such projects but will briefly discuss activities in support of two such complementary projects: the Atmospheric Radiation Measurement (ARM) program of the Department of Energy and Sequoia 2000, a joint venture of the University of California, the private sector, and government agencies. Our contribution to the ARM program is to investigate the role of clouds on the top of the atmosphere and on surface radiance fields through the data analysis of surface and satellite observations and complex modeling of the interaction of radiation with clouds. One of our first ARM research activities involves the computation of the broadband shortwave surface irradiance from satellite observations. Geostationary satellite images centered over the first ARM observation site are received hourly over the Internet network and processed in real time to compute hourly and daily composite shortwave irradiance fields. The images and the results are transferred via a high-speed network to the Sequoia 2000 storage facility in Berkeley, where they are archived These satellite-derived results are compared with the surface observations to evaluate the accuracy of the satellite estimate and the spatial representation of the surface observations. In developing the software involved in calculating the surface shortwave irradiance, we have produced an environment whereby we can easily modify and monitor the data processing as required. Through the principles of modular programming, we have developed software that is easily modified as new algorithms for computation are developed or input data availability changes. In addition, the software was designed so that it could be run from an interactive, icon-driven, graphical interface, TCL-TK, developed by Sequoia 2000 participants. In this way, the data flow can be interactively assessed and altered as needed. In this environment, the intermediate data processing 'images' can be viewed, enabling the investigator to easily monitor the various data processing steps as they progress. Additionally, this environment allows the rapid testing of new processing modules and allows their effects to be visually compared with previous results.

Landsfeld, M.↗
NASA NTRS2013

Fast Atmosphere-Ocean Model Runs with Large Changes in CO2

How does climate sensitivity vary with the magnitude of climate forcing? This question was investigated with the use of a modified coupled atmosphere-ocean model, whose stability was improved so that the model would accommodate large radiative forcings yet be fast enough to reach rapid equilibrium. Experiments were performed in which atmospheric CO2 was multiplied by powers of 2, from 1/64 to 256 times the 1950 value. From 8 to 32 times, the 1950 CO2, climate sensitivity for doubling CO2 reaches 8 C due to increases in water vapor absorption and cloud top height and to reductions in low level cloud cover. As CO2 amount increases further, sensitivity drops as cloud cover and planetary albedo stabilize. No water vapor-induced runaway greenhouse caused by increased CO2 was found for the range of CO2 examined. With CO2 at or below 1/8 of the 1950 value, runaway sea ice does occur as the planet cascades to a snowball Earth climate with fully ice covered oceans and global mean surface temperatures near 30 C.

ocean models↗
DOE OSTI2019

Incorporating Climate Nonstationarity and Snowmelt Processes in Intensity–Duration–Frequency Analyses with Case Studies in Mountainous Areas

In this work, downscaled high-resolution climate simulations were used to provide inputs to the physics-based Distributed Hydrology Soil Vegetation Model (DHSVM), which accounts for the combined effects of snowmelt and rainfall processes, to determine spatially distributed available water for runoff (AWR). After quasi-stationary time windows were identified based on model outputs extracted for two different mountainous field sites in Colorado and California, intensity–duration–frequency (IDF) curves for precipitation and AWR were generated and evaluated at each numerical grid to provide guidance on hydrological infrastructure design. Impacts of snowmelt are found to be spatially variable due to spatial heterogeneity associated with topography according to geostatistical analyses. AWR extremes have stronger spatial continuity compared to precipitation. Snowmelt impacts on AWR are more pronounced at the wet California site than at the semiarid Colorado site. The sensitivities of AWR and precipitation IDFs to increasing greenhouse gas emissions are found to be localized and spatially variable. In subregions with significant snowfall, snowmelt can result in an AWR (e.g., 6-h 100-yr events) that is 70% higher than precipitation. For comparison, future greenhouse gas emissions may increase 6-h 100-yr precipitation and AWR by up to 50% and 80%, respectively, toward the end of this century.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Impact of climate change on water availability and its propagation through the Western U.S. power grid

Climate impact assessment for power systems has tended to focus on the effects of water availability on generation without explicitly considering how these impacts propagate through the interconnected grid to affect generation in other locations. We investigate the impact of projected water availability on the operations of the contemporary Western U.S. power grid, focusing specifically on regional interdependencies. Hydrologic simulations derived from three GCMs (CCSM4, INMCM4 and GFDL-CM3) and two radiative scenarios (RCP4.5 and RCP8.5) are used to force a large-scale, distributed water management model (MOSART-WM), which translates water availability into power generation constraints at hydropower plants and water-dependent thermo-electric plants. Power system dynamics are evaluated using the production cost model PLEXOS. We find that the inter-regional connections across the Western U.S. electricity infrastructure are key in managing drastic variations in regional water availability. Net generation in the Northwest and Northern California is substantially sensitive to local water availability. Generation from the Desert Southwest emerges as a critical resource to compensate for variations in water availability in those regions. Climate change impacts on Northwestern water availability therefore drive future changes in other regions’ generation and regional power flows. The large inter-annual hydrological variability of Northern California exacerbates grid stress arising from long-term changes in water availability in the Northwest. While the regional power flow directions seem insensitive to long-term variations in water availability, the analysis highlights the need to consider other compounding regional factors such as changes in Southern California net load or changes in regional fuel prices.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Vegetation biogeography is a main source of uncertainty in modelling the land carbon cycle

The terrestrial biosphere exchanges a large amount of CO 2 with the atmosphere through photosynthesis and respiration, determining the magnitude of land carbon sink and consequently influencing the rate of global warming. The magnitudes of global photosynthesis and respiration, however, vary widely across models (100-200 PgC/year), constituting a key and persistent source of uncertainty in carbon cycle and climate modelling. Here, we argue that the uncertainty in the land carbon cycle modelling is largely attributable to the uncertainty in biogeography – the distribution of plant functional types (PFTs). Using an ensemble of dynamic global vegetation models (DGVMs), we find a strong dependence of total photosynthesis on total area for each PFT. The dependence allows us to reduce the spread of land carbon cycle estimates by ~75% using remote sensing-based PFT maps. We further find that 56 ± 21% of climate-driven changes in global photosynthesis modelled by DGVMs are caused by changes in PFT distribution in the last two decades. Our study identifies vegetation biogeography as a main controlling factor of uncertainty in land carbon cycle modelling and highlights the importance of biogeography-climate interactions in carbon cycle and climate studies.

Zhao, Ruiying [National Univ. of Singapore (Singap↗
DOE OSTI2022

Global stocks and capacity of mineral-associated soil organic carbon

Abstract Soil is the largest terrestrial reservoir of organic carbon and is central for climate change mitigation and carbon-climate feedbacks. Chemical and physical associations of soil carbon with minerals play a critical role in carbon storage, but the amount and global capacity for storage in this form remain unquantified. Here, we produce spatially-resolved global estimates of mineral-associated organic carbon stocks and carbon-storage capacity by analyzing 1144 globally-distributed soil profiles. We show that current stocks total 899 Pg C to a depth of 1 m in non-permafrost mineral soils. Although this constitutes 66% and 70% of soil carbon in surface and deeper layers, respectively, it is only 42% and 21% of the mineralogical capacity. Regions under agricultural management and deeper soil layers show the largest undersaturation of mineral-associated carbon. Critically, the degree of undersaturation indicates sequestration efficiency over years to decades. We show that, across 103 carbon-accrual measurements spanning management interventions globally, soils furthest from their mineralogical capacity are more effective at accruing carbon; sequestration rates average 3-times higher in soils at one tenth of their capacity compared to soils at one half of their capacity. Our findings provide insights into the world’s soils, their capacity to store carbon, and priority regions and actions for soil carbon management.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Nature-based solutions in mountain catchments reduce impact of anthropogenic climate change on drought streamflow

Quantifying how well Nature-based Solutions can offset anthropogenic climate change impacts is important for adaptation planning, but has rarely been done. Here we show that a widely-applied Nature-based Solution in South Africa – invasive alien tree clearing – reduces the impact of anthropogenic climate change on drought streamflow. Using a multi-model joint-attribution of climate and landscape-vegetation states during the 2015–2017 Cape Town “Day Zero” drought, we find that anthropogenic climate change reduced streamflow by 12–29% relative to a counterfactual world with anthropogenic emissions removed. This impact on streamflow was larger than corresponding reductions in rainfall (7–15%) and reference evapotranspiration (1.7–2%). Clearing invasive alien trees could have ameliorated streamflow reductions by 3–16% points for moderate invasions levels. Preventing further invasive alien tree spread avoided potential additional reductions of 10–27% points. Total clearing could not have offset the anthropogenic climate change impact completely. Invasive alien tree clearing is an important form of catchment restoration for managing changing hydroclimatic risk, but will need to be combined with other adaptation options as climate change accelerates.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Projections of leaf area index in earth system models

The area of leaves in the plant canopy, measured as leaf area index (LAI), modulates key land–atmosphere interactions, including the exchange of energy, moisture, carbon dioxide (CO 2 ), and other trace gases and aerosols, and is therefore an essential variable in predicting terrestrial carbon, water, and energy fluxes. Here our goal is to characterize the LAI projections from the latest generation of earth system models (ESMs) for the Representative Concentration Pathway (RCP) 8.5 and RCP4.5 scenarios. On average, the models project increases in LAI in both RCP8.5 and RCP4.5 over most of the globe, but also show decreases in some parts of the tropics. Because of projected increases in variability, there are also more frequent periods of low LAI across broad regions of the tropics. Projections of LAI changes varied greatly among models: some models project very modest changes, while others project large changes, usually increases. Modeled LAI typically increases with modeled warming in the high latitudes, but often decreases with increasing local warming in the tropics. The models with the most skill in simulating current LAI in the tropics relative to satellite observations tend to project smaller increases in LAI in the tropics in the future compared to the average of all the models. Using LAI projections to identify regions that may be vulnerable to climate change presents a slightly different picture than using precipitation projections, suggesting LAI may be an additional useful tool for understanding climate change impacts. Going forward, users of LAI projections from the CMIP5 ESMs evaluated here should be aware that model outputs do not exhibit clear-cut relationships to vegetation carbon and precipitation. Lastly, our findings underscore the need for more attention to LAI projections, in terms of understanding the drivers of projected changes and improvements to model skill.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Detecting Coastal Wetland Degradation by Combining Remote Sensing and Hydrologic Modeling

Sea-level rise and climate change stresses pose increasing threats to coastal wetlands that are vital to wildlife habitats, carbon sequestration, water supply, and other ecosystem services with global significance. However, existing studies are limited in individual sites, and large-scale mapping of coastal wetland degradation patterns over a long period is rare. Our study developed a new framework to detect spatial and temporal patterns of coastal wetland degradation by analyzing fine-scale, long-term remotely sensed Normalized Difference Vegetation Index (NDVI) data. Then, this framework was tested to track the degradation of coastal wetlands at the Alligator River National Wildlife Refuge (ARNWR) in North Carolina, United States, during the period from 1995 to 2019. We identified six types of coastal wetland degradation in the study area. Most of the detected degradation was located within 2 km from the shoreline and occurred in the past five years. Further, we used a state-of-the-art coastal hydrologic model, PIHM-Wetland, to investigate key hydrologic processes/variables that control the coastal wetland degradation. The temporal and spatial distributions of simulated coastal flooding and saltwater intrusion confirmed the location and timing of wetland degradation detected by remote sensing. The combined method also quantified the possible critical thresholds of water tables for wetland degradation. The remote sensing–hydrologic model integrated scheme proposed in this study provides a new tool for detecting and understanding coastal wetland degradation mechanisms. Our study approach can also be extended to other coastal wetland regions to understand how climate change and sea-level rise impact wetland transformations.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2016

Tropical Cyclones in the GISS ModelE2

The authors describe the characteristics of tropical cyclone (TC) activity in the GISS general circulation ModelE2 with a horizontal resolution 1deg x 1deg. Four model simulations are analyzed. In the first, the model is forced with sea surface temperature (SST) from the recent historical climatology. The other three have different idealized climate change simulations, namely (1) a uniform increase of SST by 2 deg., (2) doubling of the CO2 concentration and (3) a combination of the two. These simulations were performed as part of the US Climate Variability and Predictability Program Hurricane Working Group. Diagnostics of standard measures of TC activity are computed from the recent historical climatological SST simulation and compared with the same measures computed from observations. The changes in TC activity in the three idealized climate change simulations, by comparison with that in the historical climatological SST simulation, are also described. Similar to previous results in the literature, the changes in TC frequency in the simulation with a doubling CO2 and an increase in SST are approximately the linear sum of the TC frequency in the other two simulations. However, in contrast with previous results, in these simulations the effects of CO2 and SST on TC frequency oppose each other. Large-scale environmental variables associated with TC activity are then analyzed for the present and future simulations. Model biases in the large-scale fields are identified through a comparison with ERA-Interim reanalysis. Changes in the environmental fields in the future climate simulations are shown and their association with changes in TC activity discussed.

Hurricanes↗
DOE OSTI2023

Super-Resolution for Renewable Energy Resource Data with Climate Change Impacts (Sup3rCC)

The Super-Resolution for Renewable Energy Resource Data with Climate Change Impacts (Sup3rCC) data is a collection of 4km hourly wind, solar, temperature, humidity, and pressure fields for the contiguous United States under various climate change scenarios. Sup3rCC is downscaled Global Climate Model (GCM) data. The downscaling process was performed using a generative machine learning approach called sup3r: Super-Resolution for Renewable Energy Resource Data (linked below as "Sup3r GitHub Repo"). The data includes both historical and future weather years, although the historical years represent the historical climate, not the actual historical weather that we experienced. You cannot use Sup3rCC data to study historical weather events, although other sup3r datasets may be intended for this. The Sup3rCC data is intended to help researchers study the impact of climate change on energy systems with high levels of wind and solar capacity. Please note that all climate change data is only a representation of the possible future climate and contains significant uncertainty. Analysis of multiple climate change scenarios and multiple climate models can help quantify this uncertainty.

Array↗
DOE OSTI2018

Geothermally warmed soils reveal persistent increases in the respiratory costs of soil microbes contributing to substantial C losses

Increasing temperatures can accelerate soil organic matter decomposition and release large amounts of CO 2 to the atmosphere, potentially inducing positive warming feedbacks. Alterations to the temperature sensitivity and physiological functioning of soil microorganisms may play a key role in these carbon (C) losses. Geothermally active areas in Iceland provide stable and continuous soil temperature gradients to test this hypothesis, encompassing the full range of warming scenarios projected by the Intergovernmental Panel on Climate Change for the northern region. We took soils from these geothermal sites 7 years after the onset of warming and incubated them at varying temperatures and substrate availability conditions to detect persistent alterations of microbial physiology to long-term warming. Seven years of continuous warming ranging from 1.8 to 15.9 °C triggered a 8.6–58.0% decrease on the C concentrations in the topsoil (0–10 cm) of these sub-arctic silt-loam Andosols. The sensitivity of microbial respiration to temperature (Q 10 ) was not altered. However, soil microbes showed a persistent increase in their microbial metabolic quotients (microbial respiration per unit of microbial biomass) and a subsequent diminished C retention in biomass. After an initial depletion of labile soil C upon soil warming, increasing energy costs of metabolic maintenance and resource acquisition led to a weaker capacity of C stabilization in the microbial biomass of warmer soils. This mechanism contributes to our understanding of the acclimated response of soil respiration to in situ soil warming at the ecosystem level, despite a lack of acclimation at the physiological level. Furthermore, persistent increases in the respiratory costs of soil microbes in response to warming constitute a fundamental process that should be incorporated into climate change-C cycling models.

58 GEOSCIENCES↗
DOE OSTI2025

Assessing Climate Change-Induced Variability in Generation Potential and Droughts of Renewable Energy Systems in India

Solar photovoltaic (PV) and wind energy systems are crucial for decarbonizing the electricity sector and achieving climate goals. However, these systems are weather-dependent, and ignoring the potential changes in their generation levels due to climate change could compromise achieving climate targets and meeting future electricity demand. This study evaluates the impact of climate change on the generation potential of wind and solar PV systems in India for three future periods, 2030 (2021-2040), 2050 (2041-2060), and 2070 (2061-2080) compared to the baseline year 2000 (1991-2010), under three emission scenarios: SSP245, SSP370, and SSP585. Solar PV generation levels consistently decline (up to 10 %) across all regions and scenarios. Wind energy shows more pronounced variability (-20 % to 30 %). The South and Southeastern regions of India show improvements in wind potential across all scenarios and time periods. This study also investigated the projected changes in the generation droughts of both energy systems. For solar PV, drought days increase across most regions (exceeding 500 days under SSP370 across the 20-year period). In contrast, wind energy sees a reduction in drought days, especially in parts of South and Southeast India (declines exceeding 50 days across different scenarios). For both energy systems, the patterns of generation drought and generation potential are similar, and indicate that Western and Northern India may be less favorable for the future expansion of solar PV and wind energy, respectively. These results highlight the need to account for the potential impacts in future capacity planning.

14 SOLAR ENERGY↗
DOE OSTI2019

Vegetation Functional Properties Determine Uncertainty of Simulated Ecosystem Productivity: A Traceability Analysis in the East Asian Monsoon Region

Global and regional projections of climate change by Earth system models are limited by their uncertain estimates of terrestrial ecosystem productivity. At the middle to low latitudes, the East Asian monsoon region has higher productivity than forests in Europe–Africa and North America, but its estimate by current generation of terrestrial biosphere models (TBMs) has seldom been systematically evaluated. Here, we developed a traceability framework to evaluate the simulated gross primary productivity (GPP) by 15 TBMs in the East Asian monsoon region. The framework links GPP to net primary productivity, biomass, leaf area and back to GPP via incorporating multiple vegetation functional properties of carbon–use efficiency (CUE), vegetation C turnover time ( τ veg ), leaf C fraction (F leaf ), specific leaf area (SLA), and leaf area index (LAI)–level photosynthesis (P LAI ), respectively. We then applied a relative importance algorithm to attribute intermodel variation at each node. The results showed that large intermodel variation in GPP over 1901–2010 were mainly propagated from their different representation of vegetation functional properties. For example, SLA explained 77% of the intermodel difference in leaf area, which contributed 90% to the simulated GPP differences. In addition, the models simulated higher CUE (18.1 ± 21.3%), τ veg (18.2 ± 26.9%), and SLA (27.4±36.5%) than observations, leading to the overestimation of simulated GPP across the East Asian monsoon region. Furthermore, these results suggest the large uncertainty of current TBMs in simulating GPP is largely propagated from their poor representation of the vegetation functional properties and call for a better understanding of the covariations between plant functional properties in terrestrial ecosystems.

54 ENVIRONMENTAL SCIENCES↗