Efficient surrogate modeling methods for large-scale Earth system models based on machine-learning techniques

Improving predictive understanding of Earth system variability and change requires data–model integration. Efficient data–model integration for complex models requires surrogate modeling to reduce model evaluation time. However, building a surrogate of a large-scale Earth system model (ESM) with many output variables is computationally intensive because it involves a large number of expensive ESM simulations. In this effort, we propose an efficient surrogate method capable of using a few ESM runs to build an accurate and fast-to-evaluate surrogate system of model outputs over large spatial and temporal domains. We first use singular value decomposition to reduce the output dimensions and then use Bayesian optimization techniques to generate an accurate neural network surrogate model based on limited ESM simulation samples. Our machine-learning-based surrogate methods can build and evaluate a large surrogate system of many variables quickly. Thus, whenever the quantities of interest change, such as a different objective function, a new site, and a longer simulation time, we can simply extract the information of interest from the surrogate system without rebuilding new surrogates, which significantly reduces computational efforts. We apply the proposed method to a regional ecosystem model to approximate the relationship between eight model parameters and 42&thinsp;660 carbon flux outputs. Results indicate that using only 20 model simulations, we can build an accurate surrogate system of the 42&thinsp;660 variables, wherein the consistency between the surrogate prediction and actual model simulation is 0.93 and the mean squared error is 0.02. This highly accurate and fast-to-evaluate surrogate system will greatly enhance the computational efficiency of data–model integration to improve predictions and advance our understanding of the Earth system.</p

Mechanistic Modeling of Microtopographic Impacts on CO2 and CH4 Fluxes in an Alaskan Tundra Ecosystem Using the CLM-Microbe Model

Spatial heterogeneities in soil hydrology have been confirmed as a key control on CO2 and CH4 fluxes in the Arctic tundra ecosystem. In this study, we applied a mechanistic ecosystem model, CLM-Microbe, to examine the microtopographic impacts on CO2 and CH4 fluxes across seven landscape types in Utqiaġvik, Alaska: trough, low-centered polygon (LCP) center, LCP transition, LCP rim, high-centered polygon (HCP) center, HCP transition, and HCP rim. We first validated the CLM-Microbe model against static-chamber measured CO2 and CH4 fluxes in 2013 for three landscape types: trough, LCP center, and LCP rim. Model application showed that low-elevation and thus wetter landscape types (i.e., trough, transitions, and LCP center) had larger CH4 emissions rates with greater seasonal variations than high-elevation and drier landscape types (rims and HCP center). Sensitivity analysis indicated that substrate availability for methanogenesis (acetate, CO2&nbsp;+&nbsp;H2) is the most important factor determining CH4 emission, and vegetation physiological properties largely affect the net ecosystem carbon exchange and ecosystem respiration in Arctic tundra ecosystems. Modeled CH4 emissions for different microtopographic features were upscaled to the eddy covariance (EC) domain with an area-weighted approach before validation against EC-measured CH4 fluxes. The model underestimated the EC-measured CH4 flux by 20% and 25% at daily and hourly time steps, suggesting the importance of the time step in reporting CH4 flux. The strong microtopographic impacts on CO2 and CH4 fluxes call for a model-data integration framework for better understanding and predicting carbon flux in the highly heterogeneous Arctic landscape

Seeing the canopy for the branches: Improved within canopy scaling of leaf nitrogen

Abstract Transitioning across biological scales is a central challenge in land surface models. Processes that operate at the scale of individual leaves must be scaled to canopies, and this is done using dedicated submodels. Here, we focus on a submodel that prescribes how light and nitrogen are distributed through plant canopies. We found a mathematical inconsistency in a submodel implemented in the Community and Energy Land Models (CLM and ELM), which incorporates twigs, branches, stems, and dead leaves in nitrogen scaling from leaf to canopy. The inconsistency leads to unrealistic (physically impossible) values of the nitrogen scaling coefficient. The mathematical inconsistency is a general mistake, that is, would occur in any model adopting this particular submodel. We resolve the inconsistency by allowing distinct profiles of stems and branches versus living leaves. We implemented the updated scheme in the ELM and find that the correction reduces global mean gross primary production (GPP) by 3.9 Pg C (3%). Further, when stems and branches are removed from the canopy in the updated model (akin to models that ignore shading from stems), global GPP increases by 4.1 Pg C (3.2%), because of reduced shading. Hence, models that entirely ignore stem shading also introduce errors in the global spatial distribution of GPP estimates, with a strong signal in the tropics, increasing GPP there by over 200 g C m−2 yr−1. Appropriately incorporating stems and other nonphotosynthesizing material into the light and nitrogen scaling routines of global land models, will improve their biological realism and accuracy

The role of phosphorus dynamics in tropical forests – a modeling study using CLM-CNP

Tropical forests play a significant role in the global carbon cycle and global climate. However, tropical carbon cycling and the feedbacks from tropical ecosystems to the climate system remain critical uncertainties in the current generation of carbon–climate models. One of the major uncertainties comes from the lack of representation of phosphorus (P), currently believed to be the most limiting nutrient in tropical regions. Here we introduce P dynamics and C–N–P interactions into the CLM4-CN (Community Land Model version 4 with prognostic Carbon and Nitrogen) model and investigate the role of P cycling in controlling the productivity of tropical ecosystems. The newly developed CLM-CNP model includes all major biological and geochemical processes controlling P availability in soils and the interactions between C, N, and P cycles. Model simulations at sites along a Hawaiian soil chronosequence indicate that the introduction of P limitation greatly improved the model performance at the P-limited site. The model is also able to capture the shift in nutrient limitation along this chronosequence (from N limited to P limited), as shown in the comparison of model-simulated plant responses to fertilization with the observed data. Model simulations at Amazonian forest sites show that CLM-CNP is capable of capturing the overall trend in NPP (net primary production) along the P availability gradient. This comparison also suggests a significant interaction between nutrient limitation and land use history. Model experiments under elevated atmospheric CO2 ([CO2]) conditions suggest that tropical forest responses to increasing [CO2] will interact strongly with changes in the P cycle. We highlight the importance of two feedback pathways (biochemical mineralization and desorption of secondary mineral P) that can significantly affect P availability and determine the extent of P limitation in tropical forests under elevated [CO2]. Field experiments with elevated CO2 are therefore needed to help quantify these important feedbacks. CO2 doubling model experiments show that tropical forest response to elevated [CO2] can only be predicted if the interactions between C cycle and nutrient dynamics are well understood and represented in models. Predictive modeling of C–nutrient interactions will have important implications for the prediction of future carbon uptake and storage in tropical ecosystems and global climate change

Global evaluation of terrestrial biogeochemistry in the Energy Exascale Earth System Model (E3SM) and the role of the phosphorus cycle in the historical terrestrial carbon balance

The importance of carbon (C)–nutrient interactions to the prediction of future C uptake has long been recognized. The Energy Exascale Earth System Model (E3SM) land model (ELM) version 1 is one of the few land surface models that include both N and P cycling and limitation (ELMv1-CNP). Here we provide a global-scale evaluation of ELMv1-CNP using the International Land Model Benchmarking (ILAMB) system. We show that ELMv1-CNP produces realistic estimates of present-day carbon pools and fluxes. Compared to simulations with optimal P availability, simulations with ELMv1-CNP produce better performance, particularly for simulated biomass, leaf area index (LAI), and global net C balance. We also show ELMv1-CNP-simulated N and P cycling is in good agreement with data-driven estimates. We compared the ELMv1-CNP-simulated response to CO2 enrichment with meta-analysis of observations from similar manipulation experiments. We show that ELMv1-CNP is able to capture the field-observed responses for photosynthesis, growth, and LAI. We investigated the role of P limitation in the historical balance and show that global C sources and sinks are significantly affected by P limitation, as the historical CO2 fertilization effect was reduced by 20 % and C emission due to land use and land cover change was 11 % lower when P limitation was considered. Our simulations suggest that the introduction of P cycle dynamics and C–N–P coupling will likely have substantial consequences for projections of future C uptake.</p

Hydrological Feedbacks on Peatland CH4 Emission Under Warming and Elevated CO2: A Modeling Study

Peatland carbon cycling is critical for the land–atmosphere exchange of greenhouse gases, particularly under changing environments. Warming and elevated atmospheric carbon dioxide (eCO2) concentrations directly enhance peatland methane (CH4) emission, and indirectly affect CH4 processes by altering hydrological conditions. An ecosystem model ELM-SPRUCE, the land model of the E3SM model, was used to understand the hydrological feedback mechanisms on CH4 emission in a temperate peatland under a warming gradient and eCO2 treatments. We found that the water table level was a critical regulator of hydrological feedbacks that affect peatland CH4 dynamics; the simulated water table levels dropped as warming intensified but slightly increased under eCO2. Evaporation and vegetation transpiration determined the water table level in peatland ecosystems. Although warming significantly stimulated CH4 emission, the hydrological feedbacks leading to a reduced water table mitigated the stimulating effects of warming on CH4 emission. The hydrological feedback for eCO2 effects was weak. The comparison between modeled results with data from a field experiment and a global synthesis of observations supports the model simulation of hydrological feedbacks in projecting CH4 flux under warming and eCO2. The ELM-SPRUCE model showed relatively small parameter-induced uncertainties on hydrological variables and their impacts on CH4 fluxes. A sensitivity analysis confirmed a strong hydrological feedback in the first three years and the feedback diminished after four years of warming. Hydrology-moderated warming impacts on CH4 cycling suggest that the indirect effect of warming on hydrological feedbacks is fundamental for accurately projecting peatland CH4 flux under climate warming

Evaluating the E3SM land model version 0 (ELMv0) at a temperate forest site using flux and soil water measurements

Accurate simulations of soil respiration and carbon dioxide (CO2) fluxes are critical to project global biogeochemical cycles and the magnitude of carbon–climate feedbacks in Earth system models (ESMs). Currently, soil respiration is not represented well in ESMs, and few studies have attempted to address this deficiency. In this study, we evaluated the simulation of soil respiration in the Energy Exascale Earth System Model (E3SM) land model version 0 (ELMv0) using long-term observations from the Missouri Ozark AmeriFlux (MOFLUX) forest site in the central US. Simulations using the default model parameters underestimated soil water potential (SWP) during peak growing seasons and overestimated SWP during non-growing seasons and consequently underestimated annual soil respiration and gross primary production (GPP). A site-specific soil water retention curve greatly improved model simulations of SWP, GPP, and soil respiration. However, the model continued to underestimate the seasonal and interannual variabilities and the impact of the extreme drought in 2012. Potential reasons may include inadequate representations of vegetation mortality, the soil moisture function, and the dynamics of microbial organisms and soil macroinvertebrates. Our results indicate that the simulations of mean annual GPP and soil respiration can be significantly improved by better model representations of the soil water retention curve.</p

Source-Sink Manipulation and Its Impacts on Canola Seed Filling Period

Canola yield production is driven by the balance between source (leaves) and sink (pods and seeds) activity during the reproductive period of a crop. However, previous literature has not reported the impact of source-sink limitations under different nitrogen (N) supplies, and its effect on seed filling. Therefore, the objectives of this study were to 1) explore the impact of source-sink manipulations during the seed filling period and its main parameters: duration and rate; and 2) understand the interactions between N supply and source-sink manipulations to explain variations in seed weight. With these objectives, a field experiment was conducted during 2019–2020 and 2020–2021 (Kansas, U.S.). One winter canola hybrid was tested under two N fertilization levels (0 and 134 lb/a), and three source-sink modifications (control; reduced sink, 50% pod removal at pod setting; and reduced source, 100% defoliation at pod setting). The reduced sink treatment resulted in a larger seed weight relative to the control. The duration of seed filling was longer for the control relative to the rest of the treatments. Even though no significant differences were found with different N fertilization, the highest seed weight values were obtained with the high N level (134 lb/a)

An Integrative Model for Soil Biogeochemistry and Methane Processes: I. Model Structure and Sensitivity Analysis

Environmental changes are anticipated to generate substantial impacts on carbon cycling in peatlands, affecting terrestrial-climate feedbacks. Understanding how peatland methane (CH4) fluxes respond to these changing environments is critical for predicting the magnitude of feedbacks from peatlands to global climate change. To improve predictions of CH4 fluxes in response to changes such as elevated atmospheric CO2 concentrations and warming, it is essential for Earth system models to include increased realism to simulate CH4 processes in a more mechanistic way. To address this need, we incorporated a new microbial-functional group-based CH4 module into the Energy Exascale Earth System land model (ELM) and tested it with multiple observational data sets at an ombrotrophic peatland bog in northern Minnesota. The model is able to simulate observed land surface CH4 fluxes and fundamental mechanisms contributing to these throughout the soil profile. The model reproduced the observed vertical distributions of dissolved organic carbon and acetate concentrations. The seasonality of acetoclastic and hydrogenotrophic methanogenesis—two key processes for CH4 production—and CH4 concentration along the soil profile were accurately simulated. Meanwhile, the model estimated that plant-mediated transport, diffusion, and ebullition contributed to ∼23.5%, 15.0%, and 61.5% of CH4 transport, respectively. A parameter sensitivity analysis showed that CH4 substrate and CH4 production were the most critical mechanisms regulating temporal patterns of surface CH4 fluxes both under ambient conditions and warming treatments. This knowledge will be used to improve Earth system model predictions of these high-carbon ecosystems from plot to regional scales

Predicting outcomes in pediatric Crohn's disease for management optimization: systematic review and consensus statements from the pediatric inflammatory bowel disease–ahead program

Background &amp; Aims: A better understanding of prognostic factors within the heterogeneous spectrum of pediatric Crohn's disease (CD) should improve patient management and reduce complications. We aimed to identify evidence-based predictors of outcomes with the goal of optimizing individual patient management. Methods: A survey of 202 experts in pediatric CD identified and prioritized adverse outcomes to be avoided. A systematic review of the literature with meta-analysis, when possible, was performed to identify clinical studies that investigated predictors of these outcomes. Multiple national and international face-to-face meetings were held to draft consensus statements based on the published evidence. Results: Consensus was reached on 27 statements regarding prognostic factors for surgery, complications, chronically active pediatric CD, and hospitalization. Prognostic factors for surgery included CD diagnosis during adolescence, growth impairment, NOD2/CARD15 polymorphisms, disease behavior, and positive anti-Saccharomyces cerevisiae antibody status. Isolated colonic disease was associated with fewer surgeries. Older age at presentation, small bowel disease, serology (anti-Saccharomyces cerevisiae antibody, antiflagellin, and OmpC), NOD2/CARD15 polymorphisms, perianal disease, and ethnicity were risk factors for penetrating (B3) and/or stenotic disease (B2). Male sex, young age at onset, small bowel disease, more active disease, and diagnostic delay may be associated with growth impairment. Malnutrition and higher disease activity were associated with reduced bone density. Conclusions: These evidence-based consensus statements offer insight into predictors of poor outcomes in pediatric CD and are valuable when developing treatment algorithms and planning future studies. Targeted longitudinal studies are needed to further characterize prognostic factors in pediatric CD and to evaluate the impact of treatment algorithms tailored to individual patient risk