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A novel tree stem‐water model was developed to capture the dynamics of stem‐water storage and its contribution to daily transpiration. The module was incorporated into the Community Land Model (CLM), where it was used to test model sensitivity to stem‐water content for an evergreen rainforest site in Amazonia, that is, the BR‐Sa3 eddy covariance site. With the inclusion of the stem‐water storage, CLM produced greater dry‐season latent heat flux that was closer to observations, facilitated by easier canopy access to a nearby stem‐water source, rather than solely dependent on soil water. The simulated stem‐water content also showed seasonal variations in magnitude, along with the seasonal variations in sap flow rate. Stored stem‐water of a single mature tree was estimated to contribute 20–80 kg/day of water to transpiration during the wet season and 90–110 kg/day during the dry season, thereby partially replacing soil water and maintaining plant transpiration during the dry season. Diurnally, stem‐water content declined as water was extracted for transpiration in the morning and then was refilled from soil water beginning in the afternoon and through the night. The dynamic discharge and recharge of stem storage was also shown to be regulated by multiple environmental drivers. Our study indicates that the inclusion of stem capacitance in CLM significantly improves model simulations of dry‐season water and heat fluxes, in terms of both magnitude and timing. [ABSTRACT FROM AUTHOR]

Satellite‐derived sun‐induced chlorophyll fluorescence (SIF) has been increasingly used for estimating gross primary production (GPP). However, the relationship between SIF and GPP has not been well defined, impeding the translation of satellite observed SIF to GPP. Previous studies have generally assumed a linear relationship between SIF and GPP at daily and longer time scales, but support for this assumption is lacking. Here, we used the GPP/SIF ratio to investigate seasonal variations in the relationship between SIF and GPP over the Northern Hemisphere (NH). Based on multiple SIF products and MODIS and FLUXCOM GPP data, we found strong seasonal hump‐shaped patterns for the GPP/SIF ratio over northern latitudes, with higher values in the summer than in the spring or autumn. This hump‐shaped GPP/SIF seasonal variation was confirmed by examining different SIF products and was evident for most vegetation types except evergreen broadleaf forests. The seasonal amplitude of the GPP/SIF ratio decreased from the boreal/arctic region to drylands and the tropics. For most of the NH, the lowest GPP/SIF values occurred in October or September, while the maximum GPP/SIF values were evident in June and July. The most pronounced seasonal amplitude of GPP/SIF occurred in intermediate temperature and precipitation ranges. GPP/SIF was positively related to temperature in the early and late parts of the growing season, but not during the peak growing months. These shifting relationships between temperature and GPP/SIF across different months appeared to play a key role in the seasonal dynamics of GPP/SIF. Several mechanisms may explain the patterns we observed, and future research encompassing a broad range of climate and vegetation settings is needed to improve our understanding of the spatial and temporal relationships between SIF and GPP. Nonetheless, the strong seasonal variation in GPP/SIF we identified highlights the importance of incorporating this behavior into SIF‐based GPP estimations. [ABSTRACT FROM AUTHOR]

Models are an important tool to predict Earth system dynamics. An accurate prediction of future states of ecosystems depends on not only model structures but also parameterizations. Model parameters can be constrained by data assimilation. However, applications of data assimilation to ecology are restricted by highly technical requirements such as model-dependent coding. To alleviate this technical burden, we developed a model-independent data assimilation (MIDA) module. MIDA works in three steps including data preparation, execution of data assimilation, and visualization. The first step prepares prior ranges of parameter values, a defined number of iterations, and directory paths to access files of observations and models. The execution step calibrates parameter values to best fit the observations and estimates the parameter posterior distributions. The final step automatically visualizes the calibration performance and posterior distributions. MIDA is model independent, and modelers can use MIDA for an accurate and efficient data assimilation in a simple and interactive way without modification of their original models. We applied MIDA to four types of ecological models: the data assimilation linked ecosystem carbon (DALEC) model, a surrogate-based energy exascale earth system model: the land component (ELM), nine phenological models and a stand-alone biome ecological strategy simulator (BiomeE). The applications indicate that MIDA can effectively solve data assimilation problems for different ecological models. Additionally, the easy implementation and model-independent feature of MIDA breaks the technical barrier of applications of data–model fusion in ecology. MIDA facilitates the assimilation of various observations into models for uncertainty reduction in ecological modeling and forecasting. [ABSTRACT FROM AUTHOR]

A letter to the editor is presented in response to the article "Angiopoietin-1 and Angiopoietin-2 As Clinically Informative Prognostic Biomarkers of Morbidity and Mortality in Severe Sepsis," by D. R. Ricciuto and colleagues in the 2011 issue of the periodical.

We present a representation of nitrogen and phosphorus cycling in the Functionally Assembled Terrestrial Ecosystem Simulator, a demographic vegetation model within the Energy Exascale Earth System land model. This representation is modular, and designed to allow testing of multiple hypothetical approaches for carbon‐nutrient coupling in plants. Novel model hypotheses introduced in this work include, (a) the controls on plant acquisition of aqueous mineralized nutrients in the soil and (b) fairly straight forward methods of allocating nutrients to specific plant organs and their losses through live plant turnover as well as litter fluxes generated through plant mortality. This combines the new with pre‐existing hypotheses (such as nitrogen fixation and soil decomposition) into a system that can accommodate plant‐soil dynamics for a large number of size‐ and functional‐type‐resolved plant cohorts within a time‐since‐disturbance‐resolved ecosystem. Root uptake of nutrients is governed by fine root biomass, and plants vary in their fine root biomass allocation in order to balance carbon and nutrient limitations to growth. We test the sensitivity of the model to a wide range of parameter variations and structural representations, and in the context of observations at Barro Colorado Island, Panama. A key model prediction is that plants in the high‐light‐availability canopy positions allocate more carbon to fine roots than plants in low‐light understory environments, given the widely different carbon versus nutrient constraints of these two niches within a given ecosystem. This model provides a basis for exploring carbon‐nutrient coupling with vegetation demography within Earth system models. Plain Language Summary: This work introduces a new set of nutrient cycling hypotheses incorporated into a terrestrial biosphere model. This includes the cycling of carbon, nitrogen and phosphorus, and focuses mainly on plant acquisition, allocation, and turnover. An analysis shows the model offers reasonable responses to perturbations in parameter constants and variability in climate forcing, considering its design balance between process complexity and parameterization burden. Key Points: The nutrient enabled ELM‐FATES model represents expected pattern responses to nutrient availability and parameter perturbationsThe model has been designed to introduce a reasonably small parameterization burdenThese hypothesis can capture some but not all elements of carbon‐nutrient dynamics and can be further inter‐compared with other hypotheses [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Large across-model spread in simulating land carbon (C) dynamics has been ubiquitously demonstrated in model intercomparison projects (MIPs), and became a major impediment in advancing climate change prediction. Thus, it is imperative to identify underlying sources of the spread. Here, we used a novel matrix approach to analytically pin down the sources of across-model spread in transient peatland C dynamics in response to a factorial combination of two atmospheric CO2 levels and five temperature levels. We developed a matrix-based MIP by converting the C cycle module of eight land models (i.e., TEM, CENTURY4, DALEC2, TECO, FBDC, CASA, CLM4.5 and ORCHIDEE) into eight matrix models. While the model average of ecosystem C storage was comparable to the measurement, the simulation differed largely among models, mainly due to inter-model difference in baseline C residence time. Models generally overestimated net ecosystem production (NEP), with a large spread that was mainly attributed to inter-model difference in environmental scalar. Based on the sources of spreads identified, we sequentially standardized model parameters to shrink simulated ecosystem C storage and NEP to almost none. Models generally captured the observed negative response of NEP to warming, but differed largely in the magnitude of response, due to differences in baseline C residence time and temperature sensitivity of decomposition. While there was a lack of response of NEP to elevated CO2 (eCO2) concentrations in the measurements, simulated NEP responded positively to eCO2 concentrations in most models, due to the positive responses of simulated net primary production. Our study used one case study in Minnesota peatland to demonstrate that the sources of across-model spreads in simulating transient C dynamics can be precisely traced to model structures and parameters, regardless of their complexity, given the protocol that all the matrix models were driven by the same gross primary production and environmental variables. [ABSTRACT FROM AUTHOR]

Terrestrial vegetation is a crucial component of Earth's biosphere, regulating global carbon and water cycles and contributing to human welfare. Despite an overall greening trend, terrestrial vegetation exhibits a significant inter-annual variability. The mechanisms driving this variability, particularly those related to climatic and anthropogenic factors, remain poorly understood, which hampers our ability to project the long-term sustainability of ecosystem services. Here, by leveraging diverse remote sensing measurements, we pinpointed 2020 as a historic landmark, registering as the greenest year in modern satellite records from 2001 to 2020. Using ensemble machine learning and Earth system models, we found this exceptional greening primarily stemmed from consistent growth in boreal and temperate vegetation, attributed to rising CO 2 levels, climate warming, and reforestation efforts, alongside a transient tropical green-up linked to the enhanced rainfall. Contrary to expectations, the COVID-19 pandemic lockdowns had a limited impact on this global greening anomaly. Our findings highlight the resilience and dynamic nature of global vegetation in response to diverse climatic and anthropogenic influences, offering valuable insights for optimizing ecosystem management and informing climate mitigation strategies. • Earth recorded its highest level of vegetation greenness in 2020 since early 2000s. • This greening largely linked to continuous growth in boreal and temperate vegetation. • Complemented by a transient tropical vegetation boost due to increased rainfall. • Marginal greening effects from COVID-19 lockdowns at the global scale. • Potential regulation of global carbon dynamics in the pandemic year of 2020. [ABSTRACT FROM AUTHOR]

Wildfires are becoming an increasing challenge to the sustainability of boreal peatland (BP) ecosystems and can alter the stability of boreal carbon storage. However, a quantitative understanding of natural and anthropogenic influences on the changes in BP fires remains elusive. Here, we quantified the predictability of BP fires and their primary controlling factors from 1997 to 2016 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) non-parametric models outperformed parametric models in predicting fire occurrences, and the machine learning model of Random Forest performed the best with the area under the Receiver Operating Characteristic curve ranging from 0.83 to 0.93 across multiple fire data sets, 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 BP fire prediction and disentangle the primary factors determining BP fires, which are critical for predicting future fire risks under climate change. [ABSTRACT FROM AUTHOR]

Perennial bioenergy crops are increasingly important for the production of ethanol and other renewable fuels, and as part of an agricultural system that alters the climate through its impact on biogeophysical and biogeochemical properties of the terrestrial ecosystem. Few Earth System Models (ESMs) represent such crops, however. In this study, we expand the Energy Exascale Earth System Land Model to include perennial bioenergy crops with a high potential for mitigating climate change. We focus on high‐productivity miscanthus and switchgrass, estimating various parameters associated with their different growth stages and performing a global sensitivity analysis to identify and optimize these parameters. The sensitivity analysis identifies five parameters associated with phenology, carbon/nitrogen allocation, stomatal conductance, and maintenance respiration as the most sensitive parameters for carbon and energy fluxes. We calibrated and validated the model against observations and found that the model closely captures the observed seasonality and the magnitude of carbon fluxes. The validated model represents the latent heat flux fairly well, but sensible heat flux for miscanthus is not well captured. Finally, we validated the model against observed leaf area index (LAI) and harvest amount and found modeled LAI captured observed seasonality, although the model underestimates LAI and harvest amount. This work provides a foundation for future ESM analyses of the interactions between perennial bioenergy crops and carbon, water, and energy dynamics in the larger Earth system, and sets the stage for studying the impact of future biofuel expansion on climate and terrestrial systems. Plain Language Summary: Perennial bioenergy crops are not well represented in global land models, despite projected increase in their production. Our study expands Energy Exascale Earth System Land Model to include perennial bioenergy crops and calibrates the model for miscanthus and switchgrass. The calibrated model captures the seasonality and magnitude of carbon and energy fluxes. This study provides the foundation for future research examining the impact of perennial bioenergy crop expansion. Key Points: The study expands the Energy Exascale Earth System Land Model to include perennial bioenergy crops miscanthus and switchgrassCalibrated model captures the observed seasonality and the magnitude of carbon and energy fluxesThis study provides the foundation for future research examining the impact of perennial bioenergy crop expansion [ABSTRACT FROM AUTHOR]

Inland waters serve as important hydrological connections between the terrestrial landscape and oceans but are often overlooked in global carbon (C) budgets and Earth System Models. Terrestrially derived C entering inland waters from the watershed can be transported to oceans but over 83% is either buried in sediments or emitted to the atmosphere before reaching oceans. Anthropogenic pressures such as climate and landscape changes are altering the magnitude of these C fluxes in inland waters. Here, we synthesize the most recent estimates of C fluxes and the differential contributions across inland waterbody types (rivers, streams, lakes, reservoirs, and ponds), including recent measurements that incorporate improved sampling methods, small waterbodies, and dried areas. Across all inland waters, we report a global C emission estimate of 4.40 Pg C/year (95% confidence interval: 3.95–4.85 Pg C/year), representing a 13% increase from the most recent estimate. We also review the mechanisms by which the most globally widespread anthropogenically driven climate and landscape changes influence inland water C fluxes. The majority of these drivers are expected to influence terrestrial C inputs to inland waters due to alterations in terrestrial C quality and quantity, hydrological pathways, and biogeochemical processing. We recommend four research priorities for the future study of anthropogenic alterations to inland water C fluxes: (1) before‐and‐after measurements of C fluxes associated with climate change events and landscape changes, (2) better quantification of C input from land, (3) improved assessment of spatial coverage and contributions of small inland waterbodies to C fluxes, and (4) integration of dried and drawdown areas to global C flux estimates. Improved measurements of inland water C fluxes and quantification of uncertainty in these estimates will be vital to understanding both terrestrial C losses and the "moving target" of inland water C emissions in response to rapid and complex anthropogenic pressures. [ABSTRACT FROM AUTHOR]

• The ELM-SPRUCE was applied to understand the CH 4 processes under warming and elevated CO 2. • Warming and elevated CO 2 enhanced CH 4 emission and altered hydrology in peatland. • Warming-induced hydrological feedback mitigated the warming's effects on CH 4 emission. Peatland carbon cycling is critical for the land–atmosphere exchange of greenhouse gases, particularly under changing environments. Warming and elevated atmospheric carbon dioxide (eCO 2) concentrations directly enhance peatland methane (CH 4) emission, and indirectly affect CH 4 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 CH 4 emission in a temperate peatland under a warming gradient and eCO 2 treatments. We found that the water table level was a critical regulator of hydrological feedbacks that affect peatland CH 4 dynamics; the simulated water table levels dropped as warming intensified but slightly increased under eCO 2. Evaporation and vegetation transpiration determined the water table level in peatland ecosystems. Although warming significantly stimulated CH 4 emission, the hydrological feedbacks leading to a reduced water table mitigated the stimulating effects of warming on CH 4 emission. The hydrological feedback for eCO 2 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 CH 4 flux under warming and eCO 2. The ELM-SPRUCE model showed relatively small parameter-induced uncertainties on hydrological variables and their impacts on CH 4 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 CH 4 cycling suggest that the indirect effect of warming on hydrological feedbacks is fundamental for accurately projecting peatland CH 4 flux under climate warming. [ABSTRACT FROM AUTHOR]

• The phenology schemes in ELM of the E3SM were improved using PhenoCam observations. • Earlier spring onset and stronger temperature responses of phenology were produced in new ELM. • Higher carbon and water fluxes and stronger flux responses to warming were produced in new ELM. • Selected phenology parameters are critical to uncertainty in key carbon and water cycle variables. Phenological transitions determine the timing of changes in land surface properties and the seasonality of exchanges of biosphere-atmosphere energy, water, and carbon. Accurate mechanistic modeling of phenological processes is therefore critical to understand and correctly predict terrestrial ecosystem feedbacks with changing atmospheric and climate conditions. However, the phenological components in the land model of the US Department of Energy's (DOE) Energy Exascale Earth System Model (ELM of E3SM) were previously unable to accurately capture the observed phenological responses to environmental conditions in a well-studied boreal peatland forest. In this research, we introduced new seasonal-deciduous phenology schemes into version 1.0 of ELM and evaluated their performance against the PhenoCam observations at the Spruce and Peatland Responses Under Changing Environments (SPRUCE) experiment in northern Minnesota from 2015 to 2018. We found that phenology simulated by the revised ELM (i.e., earlier spring onsets and stronger warming responses of spring onsets and autumn senescence) was closer to observations than simulations from the original algorithms for both the deciduous conifer (Larix laricina) and mixed shrub layers. Moreover, the revised ELM generally produced higher carbon and water fluxes (e.g., photosynthesis and evapotranspiration) during the growing season and stronger flux responses to warming than the default ELM. A parameter sensitivity analysis further indicated the significant contribution of phenology parameters to uncertainty in key carbon and water cycle variables, underscoring the importance of precise phenology parameterization. This phenological modeling effort demonstrates the potential to enhance the E3SM representation of land-climate interactions at broader spatiotemporal scales, especially under anticipated elevated CO 2 and warming conditions. [ABSTRACT FROM AUTHOR]

Background: Reliable reports on hand hygiene performance throughout the COVID-19 pandemic are lacking as most hospitals continue to rely on direct observation to measure this quality indicator. Using group electronic hand hygiene monitoring, we sought to assess the impact of COVID-19 on adherence to hand hygiene. Methods: Across 12 Ontario hospitals (5 university and 7 community teaching hospitals), a group electronic hand hygiene monitoring system was installed before the pandemic to provide continuous measurement of hand hygiene adherence across 978 ward and 367 critical care beds. We performed an interrupted time-series study of institutional hand hygiene adherence in association with a COVID-19 inpatient census and the Ontario daily count of COVID-19 cases during a baseline period (Nov. 1, 2019, to Feb. 29, 2020), the pre-peak period of the first wave of the pandemic (Mar. 1 to Apr. 24, 2020), and the post-peak period of the first wave (Apr. 25 to July 5, 2020). We used a Poisson regression model to assess the association between the hospital COVID-19 census and institutional hand hygiene adherence while adjusting for the correlation within inpatient units. Results: At baseline, the rate of hand hygiene adherence was 46.0% (6 325 401 of 13 750 968 opportunities) and this improved beginning in March 2020 to a daily peak of 79.3% (66 640 of 84 026 opportunities) on Mar. 30, 2020. Each patient admitted with COVID-19 was associated with improved hand hygiene adherence (incidence rate ratio [IRR] 1.0621, 95% confidence interval [CI] 1.0619–1.0623). Increasing Ontario daily case count was similarly associated with improved hand hygiene (IRR 1.0026, 95% CI 1.0021–1.0032). After peak COVID-19 community and inpatient numbers, hand hygiene adherence declined and returned to baseline. Interpretation: The first wave of the COVID-19 pandemic was associated with significant improvement in hand hygiene adherence, measured using a group electronic monitoring system. Future research should seek to determine whether strategies that focus on health care worker perception of personal risk can achieve sustainable improvements in hand hygiene performance. [ABSTRACT FROM AUTHOR]

Aims: Slow decomposition and isolation from groundwater mean that ombrotrophic peatlands store a large amount of soil carbon (C) but have low availability of nitrogen (N) and phosphorus (P). To better understand the role these limiting nutrients play in determining the C balance of peatland ecosystems, we compile comprehensive N and P budgets for a forested bog in northern Minnesota, USA. Methods: N and P within plants, soils, and water are quantified based on field measurements. The resulting empirical dataset are then compared to modern-day, site-level simulations from the peatland land surface version of the Energy Exascale Earth System Model (ELM-SPRUCE). Results: Our results reveal N is accumulating in the ecosystem at 0.2 ± 0.1 g N m−2 year−1 but annual P inputs to this ecosystem are balanced by losses. Biomass stoichiometry indicates that plant functional types differ in N versus P limitation, with trees exhibiting a stronger N limitation than ericaceous shrubs or Sphagnum moss. High biomass and productivity of Sphagnum results in the moss layer storing and cycling a large proportion of plant N and P. Comparing our empirically-derived nutrient budgets to ELM-SPRUCE shows the model captures N cycling within dominant plant functional types well. Conclusions: The nutrient budgets and stoichiometry presented serve as a baseline for quantifying the nutrient cycling response of peatland ecosystems to both observed and simulated climate change. Our analysis improves our understanding of N and P dynamics within nutrient-limited peatlands and represents a crucial step toward improving C-cycle projections into the twenty-first century. [ABSTRACT FROM AUTHOR]

• Coastal systems have not been included in most earth system models (ESMs). • A terrestrial ESM was altered to mimic a coastal saltmarsh. • Vegetation and hydrology were altered to represent marsh plants and tidal flow. • The new model had some success in mimicking elevated CO 2 and temperature responses. • We identified parameters for targeted data collection to improve simulations. The Energy Exascale Earth System Model (E3SM) simulates fully coupled processes and interactions among water, energy, carbon and nutrient cycles. E3SM connects vegetation and soil dynamics through nutrient uptake, plant production, litterfall and decomposition as a function of abiotic parameters (e.g. temperature and moisture). However, E3SM is designed to characterize terrestrial ecosystems and connects land and open ocean systems using a single streamflow transport term, ignoring the complex dynamics of energy, water, carbon, and nutrients in coastal systems. The goals of our project were to: (1) Parameterize a point version of E3SM to capture coastal wetland habitats and (2) Determine marsh community responses to increased temperature and elevated CO 2. We adapted a version of the E3SM land model, previously configured to represent forested bog hydrology to a coastal ecosystem using datasets from field experiments conducted at the Smithsonian Environmental Research Center's Global Change Research Wetland (GCReW). Tidal forcing in a marsh environment was simulated using a two-column system in which the columns are connected by lateral hydrologic flows. One column simulates interactions between vegetation and soil while a second column simulates variation in water level (both tidal and sea level rise). The updated model captures many aspects of the field experiments, showing that plant community responses to environmental change are non-linear, non-additive and different between plant types. Elevated CO 2 treatments increased C 3 plant biomass more than C 4 (33% vs 17%). Temperature exacerbated CO 2 responses in C 3 plants (0 °C: 26%, 5.1 °C: 56%). We were more successful at characterizing C 3 than C 4 responses and simulating above rather than belowground biomass production. Next steps will include updates to key physiological parameters such as root:shoot carbon allocation and the addition of mechanistic feedbacks between vegetation and biogeochemical processes. [Display omitted] [ABSTRACT FROM AUTHOR]

Mechanistic photosynthesis models are at the heart of terrestrial biosphere models (TBMs) simulating the daily, monthly, annual and decadal rhythms of carbon assimilation (A). These models are founded on robust mathematical hypotheses that describe how A responds to changes in light and atmospheric CO2 concentration. Two predominant photosynthesis models are in common usage: Farquhar (FvCB) and Collatz (CBGB). However, a detailed quantitative comparison of these two models has never been undertaken. In this study, we unify the FvCB and CBGB models to a common parameter set and use novel multi‐hypothesis methods (that account for both hypothesis and parameter variability) for process‐level sensitivity analysis. These models represent three key biological processes: carboxylation, electron transport, triose phosphate use (TPU) and an additional model process: limiting‐rate selection. Each of the four processes comprises 1–3 alternative hypotheses giving 12 possible individual models with a total of 14 parameters. To broaden inference, TBM simulations were run and novel, high‐resolution photosynthesis measurements were made. We show that parameters associated with carboxylation are the most influential parameters but also reveal the surprising and marked dominance of the limiting‐rate selection process (accounting for 57% of the variation in A vs. 22% for carboxylation). The limiting‐rate selection assumption proposed by CBGB smooths the transition between limiting rates and always reduces A below the minimum of all potentially limiting rates, by up to 25%, effectively imposing a fourth limitation on A. Evaluation of the CBGB smoothing function in three TBMs demonstrated a reduction in global A by 4%–10%, equivalent to 50%–160% of current annual fossil fuel emissions. This analysis reveals a surprising and previously unquantified influence of a process that has been integral to many TBMs for decades, highlighting the value of multi‐hypothesis methods. [ABSTRACT FROM AUTHOR]

Vegetation plays an important role in regulating global carbon cycles and is a key component of the Earth system models (ESMs) that aim to project Earth's future climate. In the last decade, the vegetation component within ESMs has witnessed great progress from simple "big-leaf" approaches to demographically structured approaches, which have a better representation of plant size, canopy structure, and disturbances. These demographically structured vegetation models typically have a large number of input parameters, and sensitivity analysis is needed to quantify the impact of each parameter on the model outputs for a better understanding of model behavior. In this study, we conducted a comprehensive sensitivity analysis to diagnose the Community Land Model coupled to the Functionally Assembled Terrestrial Simulator, or CLM4.5(FATES). Specifically, we quantified the first- and second-order sensitivities of the model parameters to outputs that represent simulated growth and mortality as well as carbon fluxes and stocks for a tropical site with an extent of 1×1 ∘. While the photosynthetic capacity parameter (Vc,max25) is found to be important for simulated carbon stocks and fluxes, we also show the importance of carbon storage and allometry parameters, which determine survival and growth strategies within the model. The parameter sensitivity changes with different sizes of trees and climate conditions. The results of this study highlight the importance of understanding the dynamics of the next generation of demographically enabled vegetation models within ESMs to improve model parameterization and structure for better model fidelity. [ABSTRACT FROM AUTHOR]

The Community Land Model (CLM) is an advanced process-based land surface model that simulates carbon, nitrogen, water vapor and energy exchanges between terrestrial ecosystems and the atmosphere at various spatial and temporal scales. We use observed carbon and water fluxes from five representative Chinese Terrestrial Ecosystem Flux Research Network (ChinaFLUX) eddy covariance tower sites to systematically evaluate the new version CLM4.5 and old version CLM4.0, and to generate insights that may inform future model developments. CLM4.5 underestimates the annual carbon sink at three forest sites and one alpine grassland site but overestimates the carbon sink of a semi-arid grassland site. The annual carbon sink underestimation for the deciduous-dominated forest site results from underestimated daytime carbon sequestration during summer and overestimated nighttime carbon emission during spring and autumn. Compared to CLM4.0, the bias of annual gross primary production (GPP) is reduced by 24% and 28% in CLM4.5 at two subtropical forest sites. However, CLM4.5 still presents a large positive bias in annual GPP. The improvement in net ecosystem exchange (NEE) is limited, although soil respiration bias decreases by 16%–43% at three forest sites. CLM4.5 simulates lower soil water content in the dry season than CLM4.0 at two grassland sites. Drier soils produce a significant drop in the leaf area index and in GPP and an increase in respiration for CLM4.5. The new fire parameterization approach in CLM4.5 causes excessive burning at the Changbaishan forest site, resulting in an unexpected underestimation of NEE, vegetation carbon, and soil organic carbon by 46%, 95%, and 87%, respectively. Overall, our study reveals significant improvements achieved by CLM4.5 compared to CLM4.0, and suggests further developments on the parameterization of seasonal GPP and respiration, which will require a more effective representation of seasonal water conditions and the partitioning of net radiation between sensible and heat fluxes. [ABSTRACT FROM AUTHOR]

• We compared vegetation phenology from a SIF product (CSIF) and from MODIS NDVI. • We found similar spatial patterns in CSIF and NDVI derived phenology. • NDVI data indicated an earlier start and later end of the growing season than CSIF. • Models based on leaf greenness may overestimate photosynthesis active period. Vegetation phenology is highly sensitive to climate change, although the data and methods used to estimate key phenological states can influence this sensitivity. Because of its direct relation to leaf photosynthetic carbon uptake, remotely sensed solar-induced chlorophyll fluorescence (SIF) can provide new insight assessing changes in vegetation phenology. Here, we investigated the potential of using a SIF time series product named contiguous SIF (CSIF) to estimate spring, summer, and autumn phenology in the extratropical Northern Hemisphere (>30°N) and compared the results with those based on Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) for the period 2001–2018. Overall, we found similar spatial patterns in phenological states. However, specific dates of key phenological events differed when using CSIF vs. MODIS NDVI data. NDVI data indicated that the growing season started earlier (by 10.1 days on average) and ended later (11.5 days on average) relative to CSIF data. This implies that actual periods for photosynthetic activity are shorter (by 21.6 days on average) than those estimated from vegetation indices more directly related to changes in canopy structure. These large differences between results from NDVI and that from CSIF suggest that vegetation indices such as NDVI seem to overestimate the period for active photosynthesis over the extratropical Northern Hemisphere. Furthermore, while phenology of the early growing season is dominated by temperature for both NDVI and CSIF data, phenology of the late growing season is mainly controlled by temperature for NDVI but by precipitation for CSIF. Our findings were further confirmed by other SIF (GOME-2 SIF) and vegetation index (MODIS EVI) datasets. Phenology modes in Earth system modelling are often parameterized using leaf unfolding and senescence from either station or satellite observations. Our results imply that canopy structure-based parameterization schemes may have overestimated photosynthesis active period, and thus productivity responses. We conclude that SIF data offers a novel and unique approach for assessing phenological change - one that is more directly tied to the carbon cycle and how it is being influenced by climate change. [ABSTRACT FROM AUTHOR]

Blastomycosis dermatitidis is a fungal pathogen endemic to North America. Infection from inhalation of its conidia has diffuse clinical manifestations. These can include genitourinary system infections which may manifest as ulcerative, draining skin lesions. Tissue culture is essential as anerobic bacteria and fungi grow more reliably through this medium, than with tissue swabs. A more accurate and efficient diagnosis of blastomycosis, as evidenced in this case, would have prevented prolonged treatment for presumed bacterial epididymo-orchitis and would have eliminated concern regarding possible disseminated malignancy., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

Competing Interests: Intérêts concurrents :: Julianne Kus préside le comité scientifique de mycologie de l’Institute for Quality Management in Healthcare. Aucun autre intérêt concurrent n’a été déclaré.

Competing Interests: Competing interests:: Julianne Kus is chair of the mycology scientific committee with the Institute for Quality Management in Healthcare. None declared.

With continuing global warming and urbanization, it is increasingly important to understand the resilience of urban vegetation to extreme high temperatures, but few studies have examined urban vegetation at large scale or both concurrent and delayed responses. In this study, we performed an urban-rural comparison using the Enhanced Vegetation Index and months that exceed the historical 90th percentile in mean temperature (referred to as "hot months") across 85 major cities in the contiguous United States. We found that hot months initially enhanced vegetation greenness but could cause a decline afterwards, especially for persistent (≥4 months) and intense (≥+2 °C) episodes in summer. The urban responses were more positive than rural in the western United States or in winter, but more negative during spring-autumn in the eastern United States. The east-west difference can be attributed to the higher optimal growth temperatures and lower water stress levels of the western urban vegetation than the rural. The urban responses also had smaller magnitudes than the rural responses, especially in deciduous forest biomes, and least in evergreen forest biomes. Within each biome, analysis at 1 km pixel level showed that impervious fraction and vegetation cover, local urban heat island intensity, and water stress were the key drivers of urban-rural differences. These findings advance our understanding of how prolonged exposure to warm extremes, particularly within urban environments, affects vegetation greenness and vitality. Urban planners and ecosystem managers should prioritize the long and intense events and the key drivers in fostering urban vegetation resilience to heat waves., (© The Author(s) 2024. Published by Oxford University Press on behalf of National Academy of Sciences.)

Current knowledge of the spatiotemporal patterns of changes in soil moisture-based terrestrial aridity has considerable uncertainty. Using Standardized Soil Moisture Index (SSI) calculated from multi-source merged data sets, we find widespread drying in the global midlatitudes, and wetting in the northern subtropics and in spring between 45°N-65°N, during 1971-2016. Formal detection and attribution analysis shows that human forcings, especially greenhouse gases, contribute significantly to the changes in 0-10 cm SSI during August-November, and 0-100 cm during September-April. We further develop and apply an emergent constraint method on the future SSI's signal-to-noise (S/N) ratios and trends under the Shared Socioeconomic Pathway 5-8.5. The results show continued significant presence of human forcings and more rapid drying in 0-10 cm than 0-100 cm. Our findings highlight the predominant human contributions to spatiotemporally heterogenous terrestrial aridification, providing a basis for drought and flood risk management., (© 2022. UT-Battelle, LLC.)

Plants use only a fraction of their photosynthetically derived carbon for biomass production (BP). The biomass production efficiency (BPE), defined as the ratio of BP to photosynthesis, and its variation across and within vegetation types is poorly understood, which hinders our capacity to accurately estimate carbon turnover times and carbon sinks. Here, we present a new global estimation of BPE obtained by combining field measurements from 113 sites with 14 carbon cycle models. Our best estimate of global BPE is 0.41 ± 0.05, excluding cropland. The largest BPE is found in boreal forests (0.48 ± 0.06) and the lowest in tropical forests (0.40 ± 0.04). Carbon cycle models overestimate BPE, although models with carbon-nitrogen interactions tend to be more realistic. Using observation-based estimates of global photosynthesis, we quantify the global BP of non-cropland ecosystems of 41 ± 6 Pg C/year. This flux is less than net primary production as it does not contain carbon allocated to symbionts, used for exudates or volatile carbon compound emissions to the atmosphere. Our study reveals a positive bias of 24 ± 11% in the model-estimated BP (10 of 14 models). When correcting models for this bias while leaving modeled carbon turnover times unchanged, we found that the global ecosystem carbon storage change during the last century is decreased by 67% (or 58 Pg C)., (© 2019 John Wiley & Sons Ltd.)

Simplified representations of processes influencing forest biomass in Earth system models (ESMs) contribute to large uncertainty in projections. We evaluate forest biomass from eight ESMs outputs archived in the Coupled Model Intercomparison Project Phase 5 (CMIP5) using the biomass data synthesized from radar remote sensing and ground-based observations across northern extratropical latitudes. ESMs exhibit large biases in the forest distribution, forest fraction, and mass of carbon pools that contribute to uncertainty in forest total biomass (biases range from -20 Pg C to 135 Pg C). Forest total biomass is primarily positively correlated with precipitation variations, with surface temperature becoming equally important at higher latitudes, in both simulations and observations. Relatively small differences in forest biomass between the pre-industrial period and the contemporary period indicate uncertainties in forest biomass were introduced in the pre-industrial model equilibration (spin-up), suggesting parametric or structural model differences are a larger source of uncertainty than differences in transient responses. Our findings emphasize the importance of improved (1) models of carbon allocation to biomass compartments, (2) distribution of vegetation types in models, and (3) reproduction of pre-industrial vegetation conditions, in order to reduce the uncertainty in forest biomass simulated by ESMs.

Background: Staphylococcus aureus bacteremia (SAB) is an important infection. Methicillin-resistant S aureus (MRSA) screening is performed on hospitalized patients for infection control purposes., Objective: To assess the usefulness of past MRSA screening for guiding empirical antibiotic therapy for SAB., Methods: A retrospective cohort study examined consecutive patients with confirmed SAB and previous MRSA screening swab from six academic and community hospitals between 2007 and 2010. Diagnostic test properties were calculated for MRSA screening swab for predicting methicillin resistance of SAB., Results: A total of 799 patients underwent MRSA screening swabs before SAB. Of the 799 patients, 95 (12%) had a positive and 704 (88%) had a negative previous MRSA screening swab. There were 150 (19%) patients with MRSA bacteremia. Overall, previous MRSA screening swabs had a positive likelihood ratio of 33 (95% CI 18 to 60) and a negative likelihood ratio of 0.45 (95% CI 0.37 to 0.54). Diagnostic accuracy differed depending on mode of acquisition (ie, community-acquired, nosocomial or health care-associated infection) (P<0.0001) and hospital (P=0.0002). At best, for health care-associated infection, prior MRSA screening swab had a positive likelihood ratio of 16 (95% CI 9 to 28) and a negative likelihood ratio of 0.27 (95% CI 0.17 to 0.41)., Conclusions: A negative prior MRSA screening swab cannot reliably rule out MRSA bacteremia and should not be used to guide empirical antibiotic therapy for SAB. A positive prior MRSA screening swab greatly increases likelihood of MRSA, necessitating MRSA coverage in empirical antibiotic therapy for SAB.