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At least 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Biogeophysical Effects of Land-Use and Land-Cover Change Not Detectable in Warmest Month

Land-use and land-cover changes (hereafter simply “land use”) alter climates biogeophysically by affecting surface fluxes of energy and water. Yet, near-surface temperature responses to land use across observational versus model-based studies and spatial-temporal scales can be inconsistent. Here we assess the prevalence of the historical land use signal of daily maximum temperatures averaged over the warmest month of the year (t LU ) using regularized optimal fingerprinting for detection and attribution. We use observations from the Climatic Research Unit and Berkeley Earth alongside historical simulations with and without land use from phase 6 of the Coupled Model Intercomparison Project to reconstruct an experiment representing the effects of land use on climate. To assess the signal of land use at spatially resolved continental and global scales, we aggregate all input data across reference regions and continents, respectively. At both scales, land use does not comprise a significantly detectable set of forcings for two of four Earth system models and their multimodel mean. Furthermore, using a principal component analysis, we find that t LU is mostly composed of the nonlocal effects of land use rather than its local effects. These findings show that, at scales relevant for climate attribution, uncertainties in Earth system model representations of land use are too high relative to the effects of internal variability to confidently assess land use.

54 ENVIRONMENTAL SCIENCES↗
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 OSTI2023

Using Convolutional Neural Network to Emulate Seasonal Tropical Cyclone Activity

Abstract It has been widely recognized that tropical cyclone (TC) genesis requires favorable large‐scale environmental conditions. Based on these linkages, numerous efforts have been made to establish an empirical relationship between seasonal TC activities and large‐scale environmental favorability in a quantitative way, which lead to conceptual functions such as the TC genesis index. However, due to the limited amount of reliable TC observations and complexity of the climate system, a simple analytic function may not be an accurate portrait of the empirical relationship between TCs and their ambiences. In this research, we use convolution neural networks (CNNs) to disentangle this complex relationship. To circumvent the limited amount of seasonal TC observation records, we implement transfer‐learning technique to train ensemble of CNNs first on suites of high‐resolution climate model simulations with realistic seasonal TC activities and large‐scale environmental conditions, and then on a state‐of‐the‐art reanalysis from 1950 to 2019. The trained CNNs can well reproduce the historical TC records and yields significant seasonal prediction skills when the large‐scale environmental inputs are provided by operational climate forecasts. Furthermore, by inputting the ensemble CNNs with 20th century reanalysis products and Phase 6 of the Coupled Model Intercomparison Project (CMIP6) simulations, we investigated TC variability and its changes in the past and future climates. Specifically, our ensemble CNNs project a decreasing trend of global mean TC activity in the future warming scenario, which is consistent with our future projections using high‐resolution climate model.

Meteorology & Atmospheric Sciences↗
DOE OSTI2023

Historical evaluation and future projections of compound heatwave and drought extremes over the conterminous United States in CMIP6 *

Abstract Independently, both droughts and heatwaves can induce severe impacts on human and natural systems. However, when these two climate extremes occur concurrently in a given region, their compound impacts are often more pronounced. With the improvement in both the spatiotemporal resolution and representation of complex climate processes in the global climate models (GCMs), they are increasingly used to study future changes in these extremes and associated regional impacts. However, GCM selection for such impact assessments is generally based on historical performance and/or future mean changes, without considering individual or compound extremes. In contrast, this study evaluates historical performance and projected changes in heatwaves, droughts, and compound heatwave-droughts using an ensemble of GCMs from the latest Phase 6 of Coupled Models Intercomparison Project at a regional scale across the conterminous United States. Additionally, we explore the inter-model differences in the projected changes that are associated with various characteristics of extremes and the choice of drought indices. Our analysis reveals considerable variation among the GCMs, as well as substantial differences in the projected changes based on the choice of drought indices and region of interest. For example, the projected increases in both the frequency and intensity of drought and associated compound extreme days, based on the standardized precipitation evapotranspiration index far exceed those derived from the standard precipitation index. Further, the largest changes in the frequency of compound extremes are projected over the Southwest, South Central, and parts of the Southeast while the smallest changes are projected over the Northeast. Overall, this study provides important insights for the interpretation and selection of GCMs for future assessment studies that are crucial for the development of regional adaptation strategies in the face of climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Selection of representative general circulation models under climatic uncertainty for Western North America

Abstract Climate change research uses an ensemble of general circulation model runs (GCMs-runs) to predict future climate under uncertainties. To reduce computational costs, this study selects representative GCM-runs (RGCM-runs) for Western North America (WNA) based on their performance in replicating historical climate conditions from 1981 to 2005 and projecting future changes from 1981–2010 to 2071–2100. This evaluation is conducted under two representative concentration pathways (RCPs) scenarios, RCP4.5 and RCP8.5, from the Coupled Model Intercomparison Project 5. By using an envelope-based selection technique and a multi-objective distance-based approach, we identify four RGCM-runs per RCP representing diverse climatic conditions, including wet-warm, wet-cold, dry-warm, and dry-cold. Compared to the full-set, these selected runs show a decreased mean absolute error (MAE) between the reference and RGCM-runs concerning the monthly average mean air temperature (T̄) and precipitation (P̄). For RCP4.5, T̄ MAE is 0.45 (vs. 0.58 in the full-set) and P̄ MAE is 0.31 (vs. 0.42). For RCP8.5, T̄ MAE is 0.51 (vs. 0.75) and P̄ MAE is 0.25 (vs. 0.36). The lower MAE values in the RGCM-run set indicate closer alignment between predicted and reference values, making the RGCM-run suitable for climate impact assessments in the region.

Water Resources↗
DOE OSTI2022

Semi-Annual Report for Modular Integrated Gas High Temperature Reactor Development during Performance Period April 2022 - September 2022

Horizontal Compact High Temperature Gas Reactor (HC-HTGR) is being designed by a multidisciplinary team of nuclear, mechanical, and structural engineers under the support of a DOE-NE Advanced Reactor Demonstration Program’s Advanced Reactor Concepts-20 (ARC-20) award. The objective of this ARC-20 project is to deliver a conceptual design for the proposed HC-HTGR in 3 years and support its commercialization as a safe, low-cost HTGR. Argonne National Laboratory (Argonne) is responsible for the design and analysis of the reactor cavity cooling system (RCCS) as a safety system for passive decay heat removal of the reactor concept. Additionally, Argonne is providing analysis of the primary coolant system to ensure temperatures within the core remain below safety margins during steady-state and potential accident scenarios. This second semi-annual report summarized the progress made at Argonne on the two tasks during the second half of FY22. As a part of the RCCS design task, a scoping calculation in estimating HVAC capability for the HC-HTGR reactor building was performed. A water panel modeling study was first performed with the test case, which confirms the capability of the RELAP5-3D modeling approach to explore various design options of the HC-HTGR RCCS under consideration. Then, a reference RELAP5-3D model for the unit geometry of the preliminary design of the HC-HTGR RCCS was developed. A preliminary performance analysis was conducted to evaluate the performances of a single-phase natural circulation and panel conduction in various operating conditions. For the primary coolant system analysis task, preliminary thermal hydraulic analysis of the HC-HTGR core design was performed with a high resolution 1D fluid-3D solid coupled model using the System Analysis Module (SAM) to assess the assembly coolant channel and bypass flow mass flow rate distribution. Some preliminary work on the development of a full core reduced order model was discussed following the assembly level model to predict the core wide coolant flow distribution and to model certain operational and accidental transients.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2023

Quantifying uncertainties due to irreducible three-body forces in deuteron-nucleus reactions

Deuteron-induced nuclear reactions are an essential tool for probing the structure of nuclei as well as astrophysical information such as (n, γ) cross sections. The deuteron-nucleus system is typically described within a Faddeev three-body model consisting of a neutron (n), a proton (p), and the target nucleus (A) interacting through pairwise phenomenological potentials. While Faddeev techniques enable the exact description of the three-body dynamics, their predictive power is limited in part by the omission of irreducible neutron-proton-nucleus three-body force (n–p–A 3BF). Here, our goal is to quantify systematic uncertainties stemming from the reduction of deuteron-nucleus (d + A) dynamics to a picture of three pointlike nuclear clusters interacting via pairwise nucleon-nucleus forces, using as testing grounds d + α scattering and the 6 Li ground state. We particularly focus on quantifying uncertainties arising from the full antisymmetrization of the (A + 2)-body system with the target nucleus fixed in its ground state. We adopt the ab initio no-core shell model coupled with the resonating group method (NCSM/RGM) to compute microscopic n–α and p–α interactions, and use them in a three-body description of the d + α system by means of momentum-space Faddeev-type equations. Simultaneously, we also carry out ab initio calculations of d + α scattering and 6 Li ground state by means of six-body NCSM/RGM calculations to serve as a benchmark for the three-body model predictions given by the Faddeev calculations. By comparing the Faddeev and NCSM/RGM results, we show that the irreducible n–p–α 3BF has a non-negligible effect on bound state and scattering observables alike. Specifically, the Faddeev approach yields a 6 Li ground state that is approximately 600 keV shallower than the one obtained with the NCSM/RGM. Additionally, the Faddeev calculations for d + α scattering yield a 3 + resonance that is located approximately 400 keV higher in energy compared to the NCSM/RGM result. The shape of the d + α angular distributions computed using the two approaches also differ, owing to the discrepancy in the predictions of the 3 + resonance energy. The Faddeev three-body model predictions for d + α scattering and 6 Li using microscopic n–α and p–α potentials differ from those computed microscopically with the NCSM/RGM. These discrepancies are due to the n–p–α 3BF, which arises from two-nucleon exchange terms in the microscopic d–α interaction and are not accounted for in the three-body model Faddeev calculations. This study lays the foundation for future parametrizations of the 3BF due to Pauli exclusion principle effects in improved three-body calculations of deuteron-induced reactions.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Steady-state radiochemical transport model of the molten salt reactor experiment

A radiochemical transport model of the Molten Salt Reactor Experiment (MSRE) was formulated based on the material derivative featuring system level 1-D flow providing spatial dependence accounting for multiple phenomena including radioactive decay, chemical removal, and fuel depletion across custom volume regions. Further, the steady-state version, which is based on a system of ODE's, can be solved efficiently and is useful for sensitivity studies utilizing Monte Carlo sampling. Accounting for multiple phenomena in a fully coupled model is necessary to understand the complexities of transport and distribution of radionuclides throughout the primary loop of a flowing fuel reactor.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
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 OSTI2023

Extrapolation of the Rainflow-Counted Load Ranges for Fatigue Assessment of the Wind Turbine's Blades

Wind turbine design standards recommend the use of statistical modeling coupled with extrapolation of the short-term load data to long-term periods for fatigue reliability assessment. However, statistical error and computational expense can limit the accuracy of such approaches. In the case of wind turbine blades, the errors are more significant because of the high material fatigue exponent that makes the damage estimations more sensitive to variations. In addition, due to different excitation sources, the flapwise load range histogram is not unimodal, and thus its statistical modeling is complex. In the present work, we provide three methods for statistical modeling of the flapwise bending moment ranges including a novel approach based on frequency-based separation of the modes. The first two methods are simplified approaches for modeling the most crucial load ranges using unimodal distributions and the third method involves multimodal distribution fitting. The research is based on 3600 10-minute aeroelastic simulations of DTU 10MW case study wind turbine from which a benchmark damage equivalent load (DEL) is calculated. The DEL calculated by each of the three proposed methods is compared to this reference. The results show that the conventional approach based on using 6 seeds as well as using mixture models fitted on the limited data lead to under-conservative results with errors up to 23%. On the other hand, the simplified unimodal approaches provided in this work can provide conservative estimations of the fatigue damage with mean values 5% and 12% higher than the benchmark. However, the variability of the DEL estimates is higher when using unimodal extrapolation of the load ranges, and the data can be conservative by 17.5%. The proposed unimodal fits suggested for modeling and extrapolation of the blade's load ranges provide less errors relatively and most importantly conservative DEL estimations while maintaining computational efficiency.

blade fatigue↗
DOE OSTI2020

Quasi-stationary Planetary Scale Waves in Modern Climate Models

We evaluate climate model simulations of geopotential height at the largest space-time scales, considering the first Fourier components (in longitude) of long-term climatological means. Quasi-stationary planetary scale waves emerge from this analysis. Variations of these waves are important components of climate change, for example associated with regional drought. Systematic examination of their long-term climatology provides context for understanding their evolution in time. We compare four reanalysis datasets with “historical runs” from the latest version of the Coupled Model Intercomparison Project (CMIP) and find general agreement, but we also identify model errors outside the range of both observational uncertainty and the uncertainty implied by interannual variability. Errors in wave phase have possible implications for climate model projections of regional climate and of shifting weather patterns. One model in the set we examine, originally created nearly 20 years before the others, exhibits noticeably greater errors.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Isolating the effect of biomass burning aerosol emissions on 20th century hydroclimate in South America and Southeast Asia

Abstract Biomass burning is a significant source of aerosol emissions in some regions and has a considerable impact on regional climate. Earth system model simulations indicate that increased biomass burning aerosol emissions contributed to statistically significant decreases in tropical precipitation over the 20th century. In this study, we use the Community Earth System Model version 1 Large Ensemble (CESM1-LENS) experiment to evaluate the mechanisms by which biomass burning aerosol contributed to decreased tropical precipitation, with a focus on South America and Southeast Asia. We analyze the all-but-one forcing simulations in which biomass burning aerosol emissions are held constant while other forcings (e.g., greenhouse gas concentrations) vary throughout the 20th century. This allows us to isolate the influence of biomass burning aerosol on processes that contribute to decreasing precipitation, including cloud microphysics, the radiative effects of absorbing aerosol particles, and alterations in regional circulation. We also show that the 20th century reduction in precipitation identified in the CESM1-LENS historical and biomass burning experiments is consistent across Coupled Model Intercomparison Project Phase 5 models with interactive aerosol schemes and the CESM2 single-forcing experiment. Our results demonstrate that higher concentrations of biomass burning aerosol increases the quantity of cloud condensation nuclei and cloud droplets, limiting cloud droplet size and precipitation formation. Additionally, absorbing aerosols (e.g., black carbon) contribute to a warmer cloud layer, which promotes cloud evaporation, increases atmospheric stability, and alters regional circulation patterns. Corresponding convectively coupled circulation responses, particularly over the tropical Andes, contribute to further reducing the flow of moisture and moisture convergence over tropical land. These results elucidate the processes that affect the water cycle in regions prone to biomass burning and inform our understanding of how future changes in aerosol emissions may impact tropical precipitation over the 21st century.

Environmental Sciences & Ecology↗
DOE OSTI2025

Evaluation of CMIP6 Streamflow in the Arctic

Earth system models (ESMs) serve as the primary basis for projecting future streamflow changes, but they are biased in terms of the their ability to reproduce historical observations of streamflow. Rigorous downscaling and bias-correction procedures, which are time-consuming and introduce uncertainties, are commonly used to address these biases in streamflow or runoff. However, the applicable limits of streamflow projections remain largely untested through direct comparisons with observations. In this work, we compare historical streamflow time series observations with a suite of Coupled Model Intercomparison Project phase 6 (CMIP6) ESMs to assess the space and time scales at which each ESM represents historic streamflow observations. We focus our analysis on streamflow across the Arctic given the higher degree of warming experienced over this region, underscoring its importance for understanding the future changes. A series of metrics—volume, seasonality, extreme capturing streamflow variability, drought, and flooding is tested to assess ESM simulation skill for total events and overall distributions. Our results indicate that although improvements are necessary in ESM runoff and streamflow projections, the use of temporal averaging and targeting the best-fit model for comparison to observations when a suite of ESMs is considered can provide robust streamflow projections. Moreover, this work provides a basis for contextualizing the limits of ESM applicability for studies aiming to assess streamflow in the future.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Heat transfer in directly-irradiated high-temperature solid–gas flows laden with polydisperse particles

Heat transfer in directly-irradiated high-temperature solid–gas flows laden with polydisperse particles is investigated using a novel transient three-dimensional computational fluid dynamics model. The model couples particle–gas hydrodynamics of solid–gas flows laden with polydisperse particles, radiative heat transfer in non-grey absorbing, emitting and anisotropically-scattering multi-component participating media, conduction heat transfer in the gas phase, and interfacial convection heat transfer. The multiphase particle-in-cell method is used to predict high-fidelity solid–gas flow characteristics, such as the local discrete particle size distribution, with increased computational efficiency by combining the advantages of both Eulerian and Lagrangian methods. The multi-component radiative transfer model is implemented using an advanced collision-based Monte Carlo ray-tracing method. The number of the prescribed discrete particle components is found to be the key parameter affecting the computational accuracy and efficiency, which primarily depends on the size distribution of the particles. For the model particle–gas flow featuring free-falling Gamma-distributed ceramic particles exposed to concentrated solar irradiation, the particle volume fraction, radiative, fluid flow and thermal characteristics appear to converge with the increasing number of the discrete particle components. Five particle components are sufficient to obtain physically meaningful results. A further increase in the number of the particle components only slightly increases the accuracy of the numerical predictions at the expense of a rapidly increasing computational time. For five particle components, the particle vertical velocity at the receiver exit for particles with the diameter of 43.4 μm is 57% of that for the particles with the diameter of 202.8 μm. The temperatures of these two particle components increase from the initial ambient values by factors of 2 and 1.2, respectively, during the simulation time. In conclusion, the model developed allows for increased fidelity of particle–gas flow simulations with significant radiative effects.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Evaluating Northern Hemisphere Growing Season Net Carbon Flux in Climate Models Using Aircraft Observations

Understanding terrestrial ecosystems and their response to anthropogenic climate change requires quantification of land-atmosphere carbon exchange. However, top-down and bottom-up estimates of large-scale land-atmosphere fluxes, including the northern extratropical growing season net flux (GSNF), show significant discrepancies. We developed a data-driven metric for the GSNF using atmospheric carbon dioxide concentration observations collected during the High-Performance Instrumented Airborne Platform for Environmental Research Pole-to-Pole Observations and Atmospheric Tomography Mission flight campaigns. This aircraft-derived metric is bias-corrected using three independent atmospheric inversion systems. We estimate the northern extratropical GSNF to be 5.7 ± 0.3 Pg C and use it to evaluate net biosphere productivity from the Coupled Model Intercomparison Project phase 5 and 6 (CMIP5 and CMIP6) models. While the model-to-model spread in the GSNF has decreased in the CMIP6 models relative to that of the CMIP5 models, there is still disagreement on the magnitude and timing of seasonal carbon uptake with most models underestimating the GSNF and overestimating the length of the growing season relative to the observations. We also use an emergent constraint approach to estimate annual northern extratropical gross primary productivity to be 56 ± 17 Pg C, heterotrophic respiration to be 25 ± 13 Pg C, and net primary productivity to be 28 ± 12 Pg C. The flux inferred from these aircraft observations provides an additional constraint on large-scale gross fluxes in prognostic Earth system models that may ultimately improve our ability to accurately predict carbon-climate feedbacks.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Atmospheric teleconnection associated with the Atlantic multidecadal variability in summer: assessment of the CESM1 model

Observational analysis shows that the Atlantic multidecadal variability (AMV) is associated with climate variability in the Northern Hemisphere through a zonal atmospheric teleconnection extending from the North Atlantic Ocean and propagating eastward around the Northern Hemisphere. Here, we studied the fidelity of model simulations in reproducing the observed summer AMV and the associated impacts on the mid-latitude climate by analysing simulations using the National Centre for Atmospheric Research Community Earth System Model Version 1 (CESM1), including CESM1 North Atlantic idealized and pacemaker simulations, CESM1 large ensemble twentieth century uninitialized simulations and large ensemble initialized CESM1 decadal predictions. To further compare the fidelity of CESM1, we also analysed large ensemble simulations from three other models. Our results suggest that the uninitialized large ensemble simulations from all models can produce an AMV time evolution and its regional climate impacts similar to the observations to certain degree. By initializing the observed oceanic condition in decadal prediction simulations, the simulated AMV and its regional impacts are closer to the observed ones than those in uninitialized ensemble simulations. In addition, the pacemaker simulations that nudged the time-evolving observed North Atlantic sea surface temperature anomalies produce spatiotemporal characteristics of the AMV and AMV climate impacts closer to the observed ones than the uninitialized simulations. We conclude that although coupled models can produce AMV and its regional impacts similar to observed, proper initialization and bias correction of the sea surface temperature spatial and temporal structure can improve this capability.

Initialization↗
DOE OSTI2020

Research note: LED lighting – A global enterprise

There is considerable focus on technology manufacturing locations and the value in funding innovation when upstream products are not manufactured within a domestic economy. Light-emitting diodes (LEDs) are such an upstream product. Here, we evaluate the value added along the supply chain of luminaires, a product that utilizes LEDs, using bottom-up cost modelling. Coupling the results with analysis of shipping cost and time, this analysis examines how advances in LEDs can incentivize LED luminaire adoption leading to the potential for significant domestic manufacturing effects even if the upstream products are produced in other economies.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Improving and testing machine learning methods for benchmarking soil carbon dynamics representation of land surface models

Representation of soil organic carbon (SOC) dynamics in Earth system models (ESMs) is a key source of uncertainty in predicting carbon climate feedbacks. The magnitude of this uncertainty can be reduced by accurate representation of environmental controllers of SOC stocks in ESMs. In this study, we used data of environmental factors, field SOC observations, ESM projections and machine learning approaches to identify dominant environmental controllers of SOC stocks and derive functional relationships between environmental factors and SOC stocks. Our derived functional relationships predicted SOC stocks with similar accuracy as the machine learning approach. We used the derived relationships to benchmark the coupled model intercomparison project phase six ESM representation of SOC stocks. We found divergent environmental control representation in ESMs in comparison to field observations. Representation of SOC in ESMs can be improved by including additional environmental factors and representing their functional relationships with SOC consistent with observations.

54 ENVIRONMENTAL SCIENCES↗