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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 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Unexpected Changes of Aerosol Burdens With Decreased Convection in the Context of Scale-Aware Convection Schemes

Atmospheric aerosols in global climate models (GCMs) can be removed by both large-scale and convective precipitation. As the horizontal resolution of GCMs increases, it is expected that large-scale precipitation increases and convective precipitation decreases if scale-aware convection schemes are well developed. Here, to explore its impact on aerosol burdens, this study develops a novel method and applies it to the National Center for Atmospheric Research Community Atmosphere Model version 5.3 to mimic the behavior of scale-aware convective schemes. Surprisingly, it is found that aerosol burden increases globally as convective precipitation decreases and large-scale precipitation increases, which can be attributed to the reduced light rain. In the light-rain intensity category, the decreased frequency of convective precipitation is not offset by the increased frequency of large-scale precipitation. Therefore, for aerosol simulations in high-resolution GCMs using scale-aware convection schemes, subgrid convection and large-scale condensation should be coordinated to assure light rain stays the same.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Ensemble‐Based Spatially Distributed CLM5 Hydrological Parameter Estimation for the Continental United States

Abstract One of the major challenges in large‐domain hydrological modeling efforts lies in the estimation of spatially distributed hydrological parameters while simultaneously accounting for their associated uncertainties. Addressing this challenge is particularly difficult in ungauged locations. With growing societal demands for large‐scale streamflow projections to inform water resource management and long‐term planning, evaluating and constraining hydrological parameter uncertainty is increasingly vital. This study introduces a hybrid regionalization approach to enhance hydrological predictions of the Community Land Model version 5 (CLM5) across the Continental United States (CONUS), with a total of 50,629 1/8° grid cells. This hybrid method combines the strengths of two existing techniques: parameter regionalization and streamflow signature regionalization. It identifies ensemble behavioral parameters for each 1/8° grid cell across the CONUS domain, tailored to three distinct streamflow signatures focused on low flows, high flows, and annual water balance. Evaluating this hybrid method for 464 CAMELS (Catchment Attributes and Meteorology for Large‐sample Studies) basins demonstrates a significant improvement in CLM5 hydrological predictions, even in challenging arid regions. In CONUS applications, the derived spatially distributed parameter sets capture both spatial continuity and variation of parameters, highlighting their heterogeneous nature within specific regions. Overall, this hybrid regionalization approach offers a promising solution to the complex task of improving hydrological modeling over large domains for important hydrological applications.

CLM5↗
DOE OSTI2020

Expanding the design space of stratospheric aerosol geoengineering to include precipitation-based objectives and explore trade-offs

Previous climate modeling studies demonstrate the ability of feedback-regulated, stratospheric aerosol geoengineering with injection at multiple independent latitudes to meet multiple simultaneous temperature-based objectives in the presence of anthropogenic climate change. However, the impacts of climate change are not limited to rising temperatures but also include changes in precipitation, loss of sea ice, and many more; knowing how a given geoengineering strategy will affect each of these climate metrics is vital to understanding the limits and trade-offs of geoengineering. Here, we first introduce a new method of visualizing the design space in which desired climate outcomes are represented by 2-D surfaces on a 3-D graph. Surface orientations represent how different injection choices influence that objective, and intersecting surfaces represent objectives which can be met simultaneously. Using this representation as a guide, we present simulations of two new strategies for feedback-regulated aerosol injection, using the Community Earth System Model with the Whole Atmosphere Community Climate Model – CESM1(WACCM). The first simultaneously manages global mean temperature, tropical precipitation centroid, and Arctic sea ice extent, while the second manages global mean precipitation, tropical precipitation centroid, and Arctic sea ice extent. Both simulations control the tropical precipitation centroid to within 5 % of the goal, and the latter controls global mean precipitation to within 1% of the goal. Additionally, the first simulation overcompensates sea ice, while the second undercompensates sea ice; all of these results are consistent with the expectations of our design space model. In addition to showing that precipitation-based climate metrics can be managed using feedback alongside other goals, our simulations validate the utility of our design space visualization in predicting our climate model behavior under a given geoengineering strategy, and together they help illustrate the fundamental limits and trade-offs of stratospheric aerosol geoengineering.

58 GEOSCIENCES↗
DOE OSTI2026

CMIP7 data request: land and land ice priorities and opportunities

The Land and Land Ice Theme in the Coupled Model Intercomparison Project Phase 7 (CMIP7) represents the current understanding of physical processes in land surface ecosystems, hydrology, cryosphere, and their physical interactions with other Earth system components. Simulations from Earth system models (ESMs) could provide crucial information for assessing planetary safety, such as critical tipping elements, and be used to inform climate risks for improving climate impact assessments and policy decisions. This paper presents a collaborative effort to identify scientific opportunities in the Land and Land Ice Theme of the CMIP7 Data Request. The proposed opportunities build upon advances in ESMs, including new freshwater system and land ice processes being included in CMIP7, as well as the scientific community's demand for high-frequency and sub-grid-scale land surface outputs. In total, 25 variable groups that contain 716 variables have been identified to be potentially available to the broad scientific audience for performing analysis in land–atmosphere coupling, hydrological processes and freshwater systems, glacier and ice sheet mass balance and their influence on the sea levels, land use, and plant phenology. Key reflections from this data request effort include advocacy for closer engagement between the user community and modeling groups, reduction in the technical barriers to tracking existing parameters and defining new variables, and more streamlined variable management. These will be essential to enhance the usability and reliability of CMIP7 outputs for climate and Earth system research and applications to a broad audience that relies on the CMIP7 endeavor.

Li, Yue [Univ. of California, Los Angeles, CA (Uni↗
DOE OSTI2024

Data Imbalance, Uncertainty Quantification, and Transfer Learning in Data‐Driven Parameterizations: Lessons From the Emulation of Gravity Wave Momentum Transport in WACCM

Abstract Neural networks (NNs) are increasingly used for data‐driven subgrid‐scale parameterizations in weather and climate models. While NNs are powerful tools for learning complex non‐linear relationships from data, there are several challenges in using them for parameterizations. Three of these challenges are (a) data imbalance related to learning rare, often large‐amplitude, samples; (b) uncertainty quantification (UQ) of the predictions to provide an accuracy indicator; and (c) generalization to other climates, for example, those with different radiative forcings. Here, we examine the performance of methods for addressing these challenges using NN‐based emulators of the Whole Atmosphere Community Climate Model (WACCM) physics‐based gravity wave (GW) parameterizations as a test case. WACCM has complex, state‐of‐the‐art parameterizations for orography‐, convection‐, and front‐driven GWs. Convection‐ and orography‐driven GWs have significant data imbalance due to the absence of convection or orography in most grid points. We address data imbalance using resampling and/or weighted loss functions, enabling the successful emulation of parameterizations for all three sources. We demonstrate that three UQ methods (Bayesian NNs, variational auto‐encoders, and dropouts) provide ensemble spreads that correspond to accuracy during testing, offering criteria for identifying when an NN gives inaccurate predictions. Finally, we show that the accuracy of these NNs decreases for a warmer climate (4 × CO 2 ). However, their performance is significantly improved by applying transfer learning, for example, re‐training only one layer using ∼1% new data from the warmer climate. The findings of this study offer insights for developing reliable and generalizable data‐driven parameterizations for various processes, including (but not limited to) GWs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Impacts of Suppressing Excessive Light Rain on Aerosol Radiative Effects and Health Risks

Global climate models (GCMs) have been used widely to study radiative forcing and health risks of aerosols. A recent study using two GCMs found that light rain plays a dominant role in controlling aerosol loading. However, “too much light rain and too little heavy rain” is a longstanding bias in GCMs. It is unclear how much light rain affects aerosol-cloud-radiation interactions and health risks from air pollution. Here, in this work, we show that, with the correction of the rainfall intensity spectrum in the National Center for Atmospheric Research Community Atmosphere Model version 5.3 by introducing a stochastic deep convection scheme, the reduced frequency of light rain (1–20 mm d -1 ) results in changes of aerosol direct radiative effects (DRE) of up to -0.5 ± 0.03 W/m 2 and aerosol cloud radiative effects (CRE) of up to -0.9 ± 0.03 W/m 2 . The total (CRE + DRE) radiative effects of light rain-mediated aerosol changes exceed the present-day anthropogenic forcing of aerosols relative to preindustrial levels from the Coupled Model Intercomparison Project (CMIP5&6) models. However, the correction of the rainfall intensity spectrum has little effect on anthropogenic aerosol forcing (defined as the radiative perturbation due to changes in aerosol concentrations between the industrial era and preindustrial levels). Due to increased exposure to fine particulates (PM 2.5 ), the estimated global total premature mortality is much higher than previously estimated, by 300,000 ± 60,000 deaths per year, and is more severe in populous regions such as India and China. The findings in this study highlight the need to understand uncertainties in radiative effects and health risks of aerosols due to simulation biases of precipitation in GCMs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Optimal band structure for thermoelectrics with realistic scattering and bands

Abstract Understanding how to optimize electronic band structures for thermoelectrics is a topic of long-standing interest in the community. Prior models have been limited to simplified bands and/or scattering models. In this study, we apply more rigorous scattering treatments to more realistic model band structures—upward-parabolic bands that inflect to an inverted-parabolic behavior—including cases of multiple bands. In contrast to common descriptors (e.g., quality factor and complexity factor), the degree to which multiple pockets improve thermoelectric performance is bounded by interband scattering and the relative shapes of the bands. We establish that extremely anisotropic “flat-and-dispersive” bands, although best-performing in theory, may not represent a promising design strategy in practice. Critically, we determine optimum bandwidth, dependent on temperature and lattice thermal conductivity, from perfect transport cutoffs that can in theory significantly boost z T beyond the values attainable through intrinsic band structures alone. Our analysis should be widely useful as the thermoelectric research community eyes z T > 3.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Interpreting and Stabilizing Machine-Learning Parametrizations of Convection

Neural networks are a promising technique for parameterizing subgrid-scale physics (e.g., moist atmospheric convection) in coarse-resolution climate models, but their lack of interpretability and reliability prevents widespread adoption. For instance, it is not fully understood why neural network parameterizations often cause dramatic instability when coupled to atmospheric fluid dynamics. This paper introduces tools for interpreting their behavior that are customized to the parameterization task. First, we assess the nonlinear sensitivity of a neural network to lower-tropospheric stability and the midtropospheric moisture, two widely studied controls of moist convection. Second, we couple the linearized response functions of these neural networks to simplified gravity wave dynamics, and analytically diagnose the corresponding phase speeds, growth rates, wavelengths, and spatial structures. To demonstrate their versatility, these techniques are tested on two sets of neural networks, one trained with a super-parameterized version of the Community Atmosphere Model (SPCAM) and the second with a near-global cloud-resolving model (GCRM). Additionally, even though the SPCAM simulation has a warmer climate than the cloud-resolving model, both neural networks predict stronger heating/drying in moist and unstable environments, which is consistent with observations. Moreover, the spectral analysis can predict that instability occurs when GCMs are coupled to networks that support gravity waves that are unstable and have phase speeds larger than 5 m s -1 . In contrast, standing unstable modes do not cause catastrophic instability. Using these tools, differences between the SPCAM-trained versus GCRM-trained neural networks are analyzed, and strategies to incrementally improve both of their coupled online performance unveiled.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Cohort-based learning for microbiome research community standards

Microbiome data standards are key to enabling data reuse, yet awareness and community adoption continue to be significant barriers to their broad implementation. The National Microbiome Data Collaborative launched an Ambassador Program based around a community learning model to broaden foundational knowledge and technical skills regarding microbiome metadata standards and best practices in data stewardship.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Ecosystems and Networks Integrated with Genes and Molecular Assemblies (ENIGMA). Final report

The goal of the Stahl lab is to use a combination of isotopic, genomic, and activity-based analyses in the field, of selected field isolates, and defined synthetic communities to identify sources and sinks of different nitrogen species in the field, focusing on the FRC at the ORNL. The most general research objective is to characterize variants in the pathway for denitrification among field isolates and synthetic microbial assemblies as a foundation for predicting the biotic and abiotic controls of nitrogen and carbon transformations in the field. Specifically, we have focused on characterizing environmental controls of nitrous oxide emissions, an important greenhouse gas and ozone depleting molecule. Studies have mapped the flux of nitrous oxide and controlling genetic elements within the FRC subsurface, indicating the importance of different enzyme variants in nitrous oxide production and consumption in controlling surface emissions. We have developed methods to quantify and catalog the isotopic signatures imparted on nitrous oxide by different nitrous oxide reductase variants in isolated organisms and model synthetic communities. We have developed methods to quantify and catalog the nitrous oxide affinity of field relevant isolates and their biomass yield when respiring nitrous oxide on different carbon substrates. We continue to expand the collection of field-relevant microbial isolates within ENIGMA by focused enrichment and isolation from field material. These objectives have been facilitated by the development of new technologies, including: 1) the development of a multiplexed automated optical density measurement system for quantifying growth kinetics of microorganisms dependent on gaseous substrates in monoculture or gas exchange in synthetic communities and 2) the development of a microcalorimetric analysis format to quantify the impact of environmental stress on microbial maintenance energy. Since field relevant processes are mediated by assemblies of organisms interacting with each other and their local environment, we have also collaborated with other ENIGMA investigators on the assembly and characterization of synthetic communities. These studies have evaluated the role of organisms lacking a complete pathway for denitrification in the control of field processes, measuring process rates of assemblies of organisms that individually lack the full denitrification pathway but mediate the complete denitrification when growing together. These efforts have so far resulted in the publication of 18 peer reviewed papers ranging from ecological and physiological drivers of pathway optimization to field-scale process characterization.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Flexible dynamic boundary microgrid operation considering network and load unbalances

Flexible microgrids with dynamic boundaries have recently been introduced in the literature. With the ability to reconfigure the topology of the microgrids dynamically through remotely controlled switches, flexible microgrids with dynamic boundaries can further improve the resiliency and energy efficiency of microgrids with distributed energy resources (DERs). This paper focuses on the optimal operation considering one of the predominant characteristics of microgrids and distribution systems – unbalanced networks and loads. In existing literature, balanced modeling of microgrids is more common due to its attractive simplicity. The three-phase power unbalance has not been considered as a constraint on the generation units in a microgrid. Further, negative sequence constraints have also been neglected. In this article, we propose a set of constraints that is specifically related to the capabilities of inverter interfaced resources to supply unbalanced current/power when the microgrid is islanded from the main distribution grid. We incorporate the new set of constraints into two optimization formulations leveraging two convex relaxations of the three-phase power flow equations: mixed-integer linear programming (MILP) and mixed-integer semidefinite programming (MISDP) that optimize the dispatch of controllable switches and DERs in the microgrid. The algorithms are then extended to networked microgrids with grid-forming sources. We test the algorithms on a realistic community microgrid model in Puerto Rico as well as standardized IEEE distribution test feeders. The testing results demonstrate the performance of the proposed algorithms. The MILP is fast and scalable, and the MISDP enforces the negative sequence voltage constraints.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Center for Integrated Simulation of Fusion Relevant RF Actuators

This project was part of the “Center for Integrated Simulation of Fusion Relevant RF Actuators” SciDAC-4 project, led by Dr. Paul Bonoli (MIT). Rather than use an acronym (CISFRRFA), the project will be referred to in this document as the “RF-SciDAC4”. The larger SciDAC-4 project goals were to: 1. Develop an integrated simulation of the antenna + sheath + scrape-off-layer + core plasma system which fully utilizes leadership class computing resources to move towards a quantitative predictive capability for the response to RF power. 2. Work closely with the SciDAC-4 Whole Device Modeling (WDM) community to make both our new code development efforts, as well as the established hierarchy of RF tools, available within their environment, and to utilize WDM technologies to implement the couplings below. 3. Validate this predictive capability on appropriately diagnosed experiments including dedicated RF test stands, linear devices, and existing tokamaks. 4. Use these tools to inform design of robust, impurity-mitigating RF heating and current drive sources for future fusion devices.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

A Computational Workflow of Elucidating Viral Impact on Mediating Microbial Response to In-situ Experimental Warming: Bridging microbial modeling to carbon and mineral modeling

Viruses are abundant in soils and shape microbial communities in ways that can potentially influence ecosystem processes, yet their contributions to carbon cycling and mineral transformations remain poorly understood. Here we present a multi-phase framework that links virus-host interactions to soil biogeochemistry by combining ecological simulations, genome- and community-scale metabolic modeling, and statistical and machine-learning analyses. We first calibrated microbial abundance profiles under explicit infection scenarios to capture how viral pressure alters community structure, then explored alternative interaction strategies, including kill-the-winner, piggyback-the-winner, and mixed lytic-lysogenic modes, through forward simulations. These ecological shifts were translated into metabolic consequences using exchange fluxes summarized into biologically meaningful categories, while integrated statistical and machine-learning screens elevated subtle but consistent signals. Application of this framework revealed that viral infections shift the balance between organic and inorganic fluxes, redirecting metabolism from diffuse organic transformations toward inorganic pools such as protons and CO 2 , directly linking viral regulation to respiration and soil carbon balance. The roll-up analysis also isolated perturbations in critical mineral ions, including magnesium, manganese, zinc, and copper, which serve as essential enzymatic cofactors. In piggyback-the-winner scenarios, uptake of these ions was strongly suppressed. Contrasting viral strategies produced distinct community structures and metabolic outcomes, from broad suppression under kill-the-winner dynamics to dramatic redistributions under high-lytic and high-gain lysogenic regimes that collapsed vulnerable microbial populations while promoting opportunists. Together, these results provide a tractable path to trace viral perturbations from host abundance shifts to metabolic flux adjustments and ecosystem-scale processes, offering a practical way to include viruses in earth system models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

A New Theoretical Framework for Parameterizing Nonequilibrium Fractionation During Evaporation From the Ocean

The evaporation model for water isotopes proposed by Craig and Gordon (1965, https://books.google.co.in/books?id=6wIKAQAAIAAJ) is used in most isotope-enabled atmospheric models for the parameterization of nonequilibrium fractionation during evaporation from the ocean. In this model, one of the most uncertain parameters is the nonequilibrium fractionation factor 𝛼 𝑘 . Many isotope models use the formulation of Merlivat and Jouzel (1979, https://doi.org/10.1029/jc084ic08p05029), which parameterizes 𝛼 𝑘 as a function of wind speed and distinguishes between a smooth and a rough regime to account for the effect of ocean waves. The resulting discontinuity in 𝛼 𝑘 between smooth and rough regimes has been disputed by several empirical studies. Here, we present a new approach to parameterizing 𝛼𝑘 by explicitly accounting for the influence of wave drag on the momentum flux near the surface. Following Cifuentes-Lorenzen et al. (2018, https://doi.org/10.1007/s10546-018-0376-0), we add a third wave-induced component to the total momentum flux, in addition to the viscous and turbulent components, and extend the definition of the eddy viscosity to account for the momentum flux due to waves and turbulent dissipation near the surface. The new scheme predicts a slight decrease of 𝛼 𝑘 with wind speed, similar to the smooth-regime parameterization of Merlivat and Jouzel (1979, https://doi.org/10.1029/jc084ic08p05029). This new parameterization is incorporated into the isotope-enabled Community Atmosphere Model, where it improves the correlation of simulated and measured vapor deuterium excess relative to the default version and a version with constant 𝛼 𝑘 , suggesting that it may be used as a valid representation of fractionation during evaporation from the ocean in future isotope models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Late Holocene glacier and climate fluctuations in the Mackenzie and Selwyn mountain ranges, northwestern Canada

Over the last century, northwestern Canada experienced some of the highest rates of tropospheric warming globally, which caused glaciers in the region to rapidly retreat. Our study seeks to extend the record of glacier fluctuations and assess climate drivers prior to the instrumental record in the Mackenzie and Selwyn mountains of northwestern Canada. We collected 27 10 Be surface exposure ages across nine cirque and valley glacier moraines to constrain the timing of their emplacement. Cirque and valley glaciers in this region reached their greatest Holocene extents in the latter half of the Little Ice Age (1600–1850 CE). Four erratic boulders, 10–250 m distal from late Holocene moraines, yielded 10 Be exposure ages of 10.9–11.6 ka, demonstrating that by ca. 11 ka, alpine glaciers were no more extensive than during the last several hundred years. Estimated temperature change obtained through reconstruction of equilibrium line altitudes shows that since ca. 1850 CE, mean annual temperatures have risen 0.2–2.3 °C. We use our glacier chronology and the Open Global Glacier Model (OGGM) to estimate that from 1000 CE, glaciers in this region reached a maximum total volume of 34–38 km 3 between 1765 and 1855 CE and had lost nearly half their ice volume by 2019 CE. OGGM was unable to produce modeled glacier lengths that match the timing or magnitude of the maximum glacier extent indicated by the 10 Be chronology. However, when applied to the entire Mackenzie and Selwyn mountain region, past millennium OGGM simulations using the Max Planck Institute Earth System Model (MPI-ESM) and the Community Climate System Model 4 (CCSM4) yield late Holocene glacier volume change temporally consistent with our moraine and remote sensing record, while the Meteorological Research Institute Earth System Model 2 (MRI-ESM2) and the Model for Interdisciplinary Research on Climate (MIROC) fail to produce modeled glacier change consistent with our glacier chronology. Finally, OGGM forced by future climate projections under varying greenhouse gas emission scenarios predicts 85 % to over 97 % glacier volume loss by the end of the 21st century. The loss of glaciers from this region will have profound impacts on local ecosystems and communities that rely on meltwater from glacierized catchments.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Integration of evidence across human and model organism studies: A meeting report

The National Institute on Drug Abuse and Joint Institute for Biological Sciences at the Oak Ridge National Laboratory hosted a meeting attended by a diverse group of scientists with expertise in substance use disorders (SUDs), computational biology, and FAIR (Findability, Accessibility, Interoperability, and Reusability) data sharing. The meeting's objective was to discuss and evaluate better strategies to integrate genetic, epigenetic, and 'omics data across human and model organisms to achieve deeper mechanistic insight into SUDs. Specific topics were to (a) evaluate the current state of substance use genetics and genomics research and fundamental gaps, (b) identify opportunities and challenges of integration and sharing across species and data types, (c) identify current tools and resources for integration of genetic, epigenetic, and phenotypic data, (d) discuss steps and impediment related to data integration, and (e) outline future steps to support more effective collaboration—particularly between animal model research communities and human genetics and clinical research teams. This review summarizes key facets of this catalytic discussion with a focus on new opportunities and gaps in resources and knowledge on SUDs.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Global impacts of vegetation clumping on regulating land surface heat fluxes

The clumping index (CI) quantifies the non-random distribution of vegetation across space, which regulates the canopy radiative transfer processes and land surface carbon, water, and energy cycles. However, its impact on global surface energy budget, particularly sensible heat fluxes and surface temperature, is not well understood. Additionally, while there have been studies showing significant seasonal variations in CI, the impacts of these variations on surface energy fluxes remain unclear. In this study, we incorporated satellite-derived spatially and temporally explicit CI data into the Community Land Model version 5 (CLM5) to evaluate the effects of CI on global land energy fluxes. Our results showed that including CI increased the global mean sensible heat flux dissipated from ground by 3.9 W m -2 (~18%), while decreasing the global mean vegetation sensible heat flux by 4.9 W m -2 (~65%), resulting in a total sensible heat decrease of 1.0 W m -2 (~3%). In contrast, CI increased the global mean latent heat flux by 0.8 W m -2 (~2%), primarily due to increased evapotranspiration (up to 11 W m -2 ) in tropical regions. We also found considerable impacts of seasonal variations in CI, particularly on sensible heat fluxes from ground and vegetation in evergreen needleleaf forests and deciduous needleleaf forests. Using constant CI rather than considering seasonal variations resulted in significant overestimation and underestimation of the sensible heat fluxes from vegetation and ground, respectively, in boreal summer. In conclusion, these changes in surface energy fluxes caused by CI and its seasonal variations led to up to 1.7 and 0.5 K differences in simulated mean ground temperature. These findings highlight the importance of including CI and considering its seasonal variations in modeling land surface energy fluxes.

54 ENVIRONMENTAL SCIENCES↗