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

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

Quantifying wildfire drivers and predictability in boreal peatlands using a two-step error-correcting machine learning framework in TeFire v1.0

Wildfires are becoming an increasing challenge to the sustainability of boreal peatland (BP) ecosystems and can alter the stability of boreal carbon storage. However, predicting the occurrence of rare and extreme BP fires proves to be challenging, and gaining a quantitative understanding of the factors, both natural and anthropogenic, inducing BP fires remains elusive. Here, we quantified the predictability of BP fires and their primary controlling factors from 1997 to 2015 using a two-step correcting machine learning (ML) framework that combines multiple ML classifiers, regression models, and an error-correcting technique. We found that (1) the adopted oversampling algorithm effectively addressed the unbalanced data and improved the recall rate by 26.88 %–48.62 % when using multiple datasets, and the error-correcting technique tackled the overestimation of fire sizes during fire seasons; (2) nonparametric models outperformed parametric models in predicting fire occurrences, and the random forest machine learning model performed the best, with the area under the receiver operating characteristic curve ranging from 0.83 to 0.93 across multiple fire datasets; and (3) four sets of factor-control simulations consistently indicated the dominant role of temperature, air dryness, and climate extreme (i.e., frost) for boreal peatland fires, overriding the effects of precipitation, wind speed, and human activities. Our findings demonstrate the efficiency and accuracy of ML techniques in predicting rare and extreme fire events and disentangle the primary factors determining BP fires, which are critical for predicting future fire risks under climate change.</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)

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

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

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

Predicting outcomes in pediatric ulcerative colitis 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 in ulcerative colitis (UC) could improve patient management and reduce complications. We aimed to identify evidence-based predictors for outcomes in pediatric UC, which may be used to optimize treatment algorithms. Methods: Potential outcomes worthy of prediction in UC were determined by surveying 202 experts in pediatric UC. A systematic review of the literature, with selected meta-analysis, was performed to identify studies that investigated predictors for these outcomes. Multiple national and international meetings were held to reach consensus on evidence-based statements. Results: Consensus was reached on 31 statements regarding predictors of colectomy, acute severe colitis (ASC), chronically active pediatric UC, cancer and mortality. At diagnosis, disease extent (6 studies, N = 627; P =.035), Pediatric Ulcerative Colitis Activity Index score (4 studies, n = 318; P &lt;.001), hemoglobin, hematocrit, and albumin may predict colectomy. In addition, family history of UC (2 studies, n = 557; P =.0004), extraintestinal manifestations (4 studies, n = 526; P =.048), and disease extension over time may predict colectomy, whereas primary sclerosing cholangitis (PSC) may be protective. Acute severe colitis may be predicted by disease severity at onset and hypoalbuminemia. Higher Pediatric Ulcerative Colitis Activity Index score and C-reactive protein on days 3 and 5 of hospital admission predict failure of intravenous steroids. Risk factors for malignancy included concomitant diagnosis of primary sclerosing cholangitis, longstanding colitis (&gt;10 years), male sex, and younger age at diagnosis. Conclusions: These evidence-based consensus statements offer predictions to be considered for a personalized medicine approach in treating pediatric UC

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

Evaluating the Community Land Model (CLM4.5) at a coniferous forest site in northwestern United States using flux and carbon-isotope measurements

Droughts in the western United States are expected to intensify with climate change. Thus, an adequate representation of ecosystem response to water stress in land models is critical for predicting carbon dynamics. The goal of this study was to evaluate the performance of the Community Land Model (CLM) version 4.5 against observations at an old-growth coniferous forest site in the Pacific Northwest region of the United States (Wind River AmeriFlux site), characterized by a Mediterranean climate that subjects trees to water stress each summer. CLM was driven by site-observed meteorology and calibrated primarily using parameter values observed at the site or at similar stands in the region. Key model adjustments included parameters controlling specific leaf area and stomatal conductance. Default values of these parameters led to significant underestimation of gross primary production, overestimation of evapotranspiration, and consequently overestimation of photosynthetic 13C discrimination, reflected in reduced 13C : 12C ratios of carbon fluxes and pools. Adjustments in soil hydraulic parameters within CLM were also critical, preventing significant underestimation of soil water content and unrealistic soil moisture stress during summer. After calibration, CLM was able to simulate energy and carbon fluxes, leaf area index, biomass stocks, and carbon isotope ratios of carbon fluxes and pools in reasonable agreement with site observations. Overall, the calibrated CLM was able to simulate the observed response of canopy conductance to atmospheric vapor pressure deficit (VPD) and soil water content, reasonably capturing the impact of water stress on ecosystem functioning. Both simulations and observations indicate that stomatal response from water stress at Wind River was primarily driven by VPD and not soil moisture. The calibration of the Ball–Berry stomatal conductance slope (mbb) at Wind River aligned with findings from recent CLM experiments at sites characterized by the same plant functional type (needleleaf evergreen temperate forest), despite significant differences in stand composition and age and climatology, suggesting that CLM could benefit from a revised mbb value of 6, rather than the default value of 9, for this plant functional type. Conversely, Wind River required a unique calibration of the hydrology submodel to simulate soil moisture, suggesting that the default hydrology has a more limited applicability. This study demonstrates that carbon isotope data can be used to constrain stomatal conductance and intrinsic water use efficiency in CLM, as an alternative to eddy covariance flux measurements. It also demonstrates that carbon isotopes can expose structural weaknesses in the model and provide a key constraint that may guide future model development