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Central African evergreen broadleaved forests around the equator exhibit a double annual cycle for canopy phenology and carbon uptake seasonality. The underlying drivers of this behavior are poorly understood and the double seasonality is not captured by land surface models (LSM). In this study, we developed a new leaf phenology module into the ORCHIDEE LSM (hereafter ORCHIDEE‐AFP), which utilizes short‐wave incoming radiation (SWd) as the main driver of leaf shedding and partial rejuvenation of the canopy, to simulate the double seasonality of central African forests. The ORCHIDEE‐AFP model has been evaluated by using field data from two forest sites and satellite observations of the enhanced vegetation index (EVI), which is a proxy of young leaf area index (LAIYoung) with leafage less than 6 months, as well as six products of GPP or GPP proxies. Results demonstrate that ORCHIDEE‐AFP successfully reproduces observed leaf turnover (R = 0.45) and young leaf abundance (R = 0.74), and greatly improve the representation of the bimodal leaf phenology. The proportion of grid cells with a significant positive correlation between the seasonality of modeled LAIYoung and observed EVI increased from 0.2% in the standard model to 27% in the new model. For photosynthesis, the proportions of grid cells with significant positive correlations between modeled and observed seasonality range from 26% to 65% across the six GPP evaluation products. The improved performance of the ORCHIDEE‐AFP model in simulating leaf phenology and photosynthesis of central African forests will allow a more accurate assessment of the impacts of climate change in tropical forests. Plain Language Summary: Evergreen broadleaved forests in central Africa near the equator have a unique behavior where their leaf growth and ability to take in carbon peak twice a year. However, the reason underlying this behavior is not well understood, and the current process‐oriented terrestrial biosphere models can not represent this double peak. In this study, we integrated a new module, which uses sunlight as the main factor for when leaves fall and new ones grow in the forest, into a popular process‐oriented terrestrial biosphere model called ORCHIDEE, to simulate this unique behavior in central African forests (hereafter ORCHIDEE‐AFP). We tested our model using real‐world data from the forests acquired at the site level and satellite images. The results show that our new model can successfully simulate when leaves change and how much carbon the forests take in. The new model demonstrates better performance than the standard model. Our improved model will be useful for predicting the future of these forests more accurately under climate change. Key Points: Solar radiation is an adequate climate factor to drive the bimodal leaf phenology of central tropical African rainforestsWe developed a new phenology scheme with solar radiation‐triggered leaf shedding and flushing for the ORCHIDEE land surface model (LSM)The ORCHIDEE LSM with the new phenology scheme captures central tropical African rainforests' bimodal leaf phenology and photosynthesis [ABSTRACT FROM AUTHOR]

Involving the effect of atmospheric CO2 fertilization is effective for improving the accuracy of estimating gross primary production (GPP) using light use efficiency (LUE) models. However, the widely used LUE model, the remote sensing-driven Carnegie–Ames–Stanford Approach (CASA) model, scarcely considers the effects of atmospheric CO2 fertilization, which causes GPP estimation uncertainties. Therefore, this study proposed an improved method for estimating GPP by integrating the atmospheric CO2 concentration into the CASA model and generated a long time series GPP dataset with high precision for the Qinghai–Tibet Plateau. The CASA model was improved by considering the impact of atmospheric CO2 on vegetation productivity and discerning variations in CO2 gradients within the canopy and leaves. A 500 m monthly GPP dataset for the Qinghai–Tibet Plateau from 2003 to 2020 was generated. The results showed that the improved GPP estimation model achieved better performances on estimating GPP (R2 = 0.68, RMSE = 406 g C/m2/year) than the original model (R2 = 0.67, RMSE = 499.32 g C/m2/year) and MODIS GPP products (R2 = 0.49, RMSE = 522.56 g C/m2/year). The GPP on the Qinghai–Tibet Plateau increased significantly with the increase in atmospheric CO2 concentration and the gradual accumulation of dry matter. The improved method can also be used for other regions and the generated GPP dataset is valuable for further understanding the ecosystem carbon cycles on the Qinghai–Tibet Plateau. [ABSTRACT FROM AUTHOR]

Climate is one of the key factors driving changes in vegetation, and the response of the vegetation to climate often occurs with a time delay. However, research on the cumulative lagged response of the vegetation to meteorological factors in large-scale regions is limited. Therefore, this study first evaluated the performance of the Gross Primary Productivity (GPP) products provided by Moderate Resolution Imaging Spectroradiometer (MODIS) and Penman–Monteith–Leuning (PML) over the past 20 years in China and then determined the lagged relationships between the GPP and major meteorological factors in different regions and land-use types in China based on a partial correlation analysis. The results indicate that (1) GPP_PML outperforms GPP_MODIS products in the regional context of China; (2) China's regional GPP has shown a fluctuating upward trend over the past 20 years, with a stepwise increase in the multi-year average from the northwest inland to the southeast coastal regions, and a higher contribution from the southern regions than the northern ones; (3) unlike the recent upward trend in regional temperatures, both precipitation and radiation have decreased, with these two factors showing completely opposite multi-year trends in most regions; and (4) the proportion of regions with lagged effects of the GPP on meteorological factors is higher than those with cumulative effects in China. Among these, GPP exhibits a higher proportion of a 3-month lagged response to precipitation, which is particularly pronounced at altitudes between 500 and 2500 m and above 5500 m. the proportion of the areas with no lag cumulative response to temperature and radiation with GPP in China is the highest due to the influence of more barren land and grassland in the northwest interior. Simultaneously, grassland and barren land have a higher proportion of the non-lagged cumulative responses to temperature and precipitation. This study contributes to our understanding of vegetation dynamics in the context of global climate change and provides a theoretical foundation for regional ecological conservation and high-quality coordinated development. [ABSTRACT FROM AUTHOR]

The emergence of Green Public Procurement created an innovative means of procuring goods and labour in line with global environment standards and regulations. Green Public Procurement has become a major mechanism for addressing global issues confronting the environment. This article employs a doctrinal research method, reviewing relevant existing literatures and legislations to consider the prospects of Green Public Procurement having a positive effect in Nigeria, Zambia, South Africa, China and Brazil. To achieve a holistic implementation process, this article advocates for concerted efforts to conduct a sustainability assessment, the establishment of clear sustainability goals, along with training and retraining of procurement staff engaged in the protection of the environment through Green Public Procurement. [ABSTRACT FROM AUTHOR]

Modeling leaf photosynthesis is essential for quantifying the carbon, water, and energy fluxes of the terrestrial biosphere. However, due to the lack of simultaneous measurements of leaf light absorption and gas exchange, canopy radiative transfer (RT) and photosynthesis modeling often rely on simplified assumptions about light absorption and electron transport. These assumptions ignore variations in leaf biophysical traits and environmental conditions. In this study, we utilized a next‐generation land surface model (LSM)—CliMA Land, which incorporates hyperspectral canopy RT and provides a more accurate representation of trait variations. We evaluated the potential bias in electron transport estimates introduced by the broadband RT schemes used in traditional LSMs. Additionally, we explored the impact of different leaf electron transport parameterization schemes on global‐scale photosynthesis and fluorescence modeling. We showed that (a) traditional LSMs that disregard the impacts of leaf temperature and leaf traits on electron transport tend to overestimate electron transport rates. (b) Photosynthesis and fluorescence within a grid can exhibit biases exceeding 20%, with these biases demonstrating contrasting seasonality. (c) Global estimates of integrated photosynthesis and fluorescence differ by 8.1% and 8.8%, respectively. These results underscore the importance of adopting more sophisticated and accurate modeling schemes, such as hyperspectral canopy RT, in future LSMs and Earth system modeling to enhance the reliability of modeling outcomes. Plain Language Summary: The way sunlight interacts within the forest canopy is often simulated using just two broad channels: one for light that helps plants grow (photosynthetically active radiation) and one for near‐infrared light. Unfortunately, these simulations don't take into account key things about leaves, like their color (determined by chlorophyll). These simplifications mean that the models ignore differences in how different leaves respond to light. For instance, green light is more common in the lower canopy, but the models treat it the same as red and blue light. The problem is that plants can use red and blue light more effectively for photosynthesis. So, while these simplified models are faster, they can lead to big mistakes when estimating how much light leaves can absorb and how much they can photosynthesize. To address this issue, we used a more detailed model that considers many different wavelengths of light. We looked at how much the simplified models might mess up estimates of photosynthesis and fluorescence. Our findings show that these errors can be larger than 20% for specific locations. To help make the simplified models more accurate, we've provided data and formulas that consider differences in leaf traits and light conditions throughout the canopy. Key Points: Hyperspectral canopy radiative transfer model is used to assess the biases in electron transport, photosynthesis, and fluorescenceVegetation gross primary productivity and solar‐induced fluorescence may be substantially biased in broadband radiative transfer modelsApproaches are provided for broadband radiative transfer models [ABSTRACT FROM AUTHOR]

Wildfire modifies the short- and long-term exchange of carbon between terrestrial ecosystems and the atmosphere, with impacts on ecosystem services such as carbon uptake. Dry western US forests historically experienced low-intensity, frequent fires, with patches across the landscape occupying different points in the fire-recovery trajectory. Contemporary perturbations, such as recent severe fires in California, could shift the historic stand-age distribution and impact the legacy of carbon uptake on the landscape. Here, we combine flux measurements of gross primary production (GPP) and chronosequence analysis using satellite remote sensing to investigate how the last century of fires in California impacted the dynamics of ecosystem carbon uptake on the fire-affected landscape. A GPP recovery trajectory curve of more than five thousand fires in forest ecosystems since 1919 indicated that fire reduced GPP by [Formula: see text] g C m[Formula: see text] y[Formula: see text]([Formula: see text]) in the first year after fire, with average recovery to prefire conditions after [Formula: see text] y. The largest fires in forested ecosystems reduced GPP by [Formula: see text] g C m[Formula: see text] y[Formula: see text] (n = 401) and took more than two decades to recover. Recent increases in fire severity and recovery time have led to nearly [Formula: see text] MMT CO[Formula: see text] (3-y rolling mean) in cumulative forgone carbon uptake due to the legacy of fires on the landscape, complicating the challenge of maintaining California's natural and working lands as a net carbon sink. Understanding these changes is paramount to weighing the costs and benefits associated with fuels management and ecosystem management for climate change mitigation.

Abstract Central African evergreen broadleaved forests around the equator exhibit a double annual cycle for canopy phenology and carbon uptake seasonality. The underlying drivers of this behavior are poorly understood and the double seasonality is not captured by land surface models (LSM). In this study, we developed a new leaf phenology module into the ORCHIDEE LSM (hereafter ORCHIDEE‐AFP), which utilizes short‐wave incoming radiation (SWd) as the main driver of leaf shedding and partial rejuvenation of the canopy, to simulate the double seasonality of central African forests. The ORCHIDEE‐AFP model has been evaluated by using field data from two forest sites and satellite observations of the enhanced vegetation index (EVI), which is a proxy of young leaf area index (LAIYoung) with leafage less than 6 months, as well as six products of GPP or GPP proxies. Results demonstrate that ORCHIDEE‐AFP successfully reproduces observed leaf turnover (R = 0.45) and young leaf abundance (R = 0.74), and greatly improve the representation of the bimodal leaf phenology. The proportion of grid cells with a significant positive correlation between the seasonality of modeled LAIYoung and observed EVI increased from 0.2% in the standard model to 27% in the new model. For photosynthesis, the proportions of grid cells with significant positive correlations between modeled and observed seasonality range from 26% to 65% across the six GPP evaluation products. The improved performance of the ORCHIDEE‐AFP model in simulating leaf phenology and photosynthesis of central African forests will allow a more accurate assessment of the impacts of climate change in tropical forests.

Recent progress in satellite observations has provided unprecedented opportunities to monitor vegetation activity at global scale. However, a major challenge in fully utilizing remotely sensed data to constrain land surface models (LSMs) lies in inconsistencies between simulated and observed quantities. For example, gross primary productivity (GPP) and transpiration (T) that traditional LSMs simulate are not directly measurable from space, although they can be inferred from spaceborne observations using assumptions that are inconsistent with those LSMs. In comparison, canopy reflectance and fluorescence spectra that satellites can detect are not modeled by traditional LSMs. To bridge these quantities, we presented an overview of the next generation land model developed within the Climate Modeling Alliance (CliMA), and simulated global GPP, T, and hyperspectral canopy radiative transfer (RT; 400–2,500 nm for reflectance, 640–850 nm for fluorescence) at hourly time step and 1° spatial resolution using CliMA Land. CliMA Land predicts vegetation indices and outgoing radiances, including solar-induced chlorophyll fluorescence (SIF), normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and near infrared reflectance of vegetation (NIRv) for any given sun-sensor geometry. The spatial patterns of modeled GPP, T, SIF, NDVI, EVI, and NIRv correlate significantly with existing data-driven products (mean R2 = 0.777 for 9 products). CliMA Land would be also useful in high temporal resolution simulations, for example, providing insights into when GPP, SIF, and NIRv diverge.

Federica Rega, Federica Trovato, Giulio Bortone, Giovanni Pellacani, Antonio Giovanni Richetta, Annunziata Dattola Dermatology Unit, Department of Clinical Internal, Anesthesiological and Cardiovascular Science, University of La Sapienza, Rome, ItalyCorrespondence: Giulio Bortone, Dermatology Clinic, Department of Clinical Internal, Anesthesiological and Cardiovascular Sciences, Sapienza University of Rome, Rome, Italy, Email giuliobortone93@gmail.comAbstract: Generalized pustular psoriasis (GPP) is a rare, chronic, and severe skin disorder characterized by the eruption of non-infectious pustules on an erythematous background often associated with systemic symptoms. It may appear in association with plaque psoriasis or occur in previously healthy individuals. It differs from psoriasis vulgaris in clinical presentation, immunopathogenesis, histology, and therapeutic strategies. Overexpression of interleukin 36 (IL-36) or a loss-of-function mutation of IL-36 receptor antagonist (IL-36RA) are thought to play a pivotal role in the pathogenesis of this disease. There are currently no globally approved guidelines for the treatment of GPP, and the therapies used so far, with variable results, have given unsatisfactory results. Spesolimab, a selective humanized antibody against the IL-36 receptor that blocks its activation, is the first biologic drug approved in Europe in December 2022 for the treatment of GPP flares. It represents a promising therapy, demonstrating efficacy in reducing disease severity and improving patient outcomes. In our review, we have analyzed the latest advancements and findings regarding the efficacy and safety of spesolimab in the context of GPP management.Keywords: spesolimab, anti-IL36R, GPP, pustular psoriasis

Abstract Dynamics of vegetation in arid areas have drawn worldwide attention. The expansion of irrigated cropland (ICE) in arid regions contributes to increased food security and impacts on the extent and development of regional vegetation. However, the quantitative attribution of vegetation growth variation from ICE and biogeochemical factors (e.g., atmospheric CO2 concentration, climatic factors) is still lacking. Here, we assessed key drivers of vegetation growth in the inland arid region of Northwest China (IANC) from 1982 to 2018, including ICE, increased nitrogen rates, elevated atmospheric CO2 concentration (eCO2) and climate drivers, using normalized difference vegetation index (NDVI) and ecosystem gross primary productivity (GPP) as measures. These variables were quantified through trend decomposition, machine learning algorithms, and a satellite-based model. The results show that vegetation growth was increased in IANC mainly due to eCO2 and ICE. After 1995, as the regional climatic aridity intensified, the CO2 fertilization effect on vegetation growth decreased, as the atmospheric CO2 concentration continued to increase. Meanwhile, irrigated cropland area increased sharply, and ICE-driven GPP variation exceeded that driven by eCO2 in the whole region, while the ICE-driven NDVI variation exceeded that due to eCO2 when the ICE reached 6.38%. The ICE effect on regional vegetation growth rather than the CO2 fertilization effect has mitigated the slowdown of the rate of vegetation growth caused by climate changes. Although the ICE is conducive to food security and continuous greening of arid areas, further reclamation will exacerbate water scarcity. Our results provide research base for identifying the scale of sustainable agricultural development.

ABSTRACTWith the increasing frequency of extreme events, daily gross primary productivity (GPP) is necessary to be accurately assessed to determine when and how much it is affected. Fluctuating environmental conditions contribute to diverse diurnal photosynthetic patterns influenced by changing atmospheric factors, including solar radiation, CO2 concentrations, and leaf temperature. This complexity underscores the challenge of accurately estimating daily GPP. We quantitatively assessed three temporal upscaling approaches – cosine of the solar zenith angle, extraterrestrial solar irradiance, and photosynthetic active radiation(PAR) – under varying sky conditions. Additionally, we introduced a novel correction method for universal temporal upscaling ratios. Instantaneous GPP values from the FLUXNET2015 dataset, acquired around the MODIS overpassing time, were utilized. The upscaled daily GPP exhibited minimal deviation from the half-hourly GPPs collected between 11 am and 1 pm, with increased discrepancies for GPPs later in the afternoon. The PAR-based approach demonstrated superior accuracy in the afternoon, effectively capturing incoming radiation changes due to clouds. Instant environmental variables-GPP relationships were weak under clear skies but exhibited moderate-to-high positive correlations under cloudy conditions. Analyzing the impact of the diffuse ratio of incoming photosynthetic active radiation on instantaneous GPP revealed limited enhancement at short time scales. On a daily scale, all three temporal upscaling approaches, under different ranges of clearness index, consistently underestimated daily GPP. We proposed a novel correction method, normalizing the difference between maximum and minimum air temperature in a day, notably reduced errors by approximately 10.69 and 21.07 gC m−2 d−1 for cosine of the solar zenith angle and extraterrestrial solar irradiance-based temporal upscaling approaches. We recommend adopting the corrected temporal upscaling factor globally due to its simplicity and improved accuracy.

The low-lying islands of the Indian Sundarbans Delta (ISD) contain a unique ecosystem with rich biodiversity, which are at the forefront of the impacts of climate change and natural disasters, such as cyclones. Therefore, in this study, we have analyzed the impact of Cyclone Yaas at the local level in the ISD. We utilized various indices derived from MODIS satellite images to analyze the local-level impacts. The results of our study revealed widespread impacts from high storm surges of 9–16 feet. In general, the daytime land surface temperatures (LSTs) were higher before compared to during the storm, due to lower insolation associated with cloudy skies. However, higher values were observed during the storm for nighttime LSTs and the vegetation indices. More specifically, at the local level, the differences were more pronounced in the vegetated and low-lying coastal areas of the islands. The results of the image analyses were also corroborated with field observations in some of the islands, which showed saltwater encroachment in agricultural lands, collapsed embankments built for protection against storm surge, and food insecurity. The results of our study highlighted the vulnerability of these islands to extreme weather events, and long-lasting impacts on the local communities. [ABSTRACT FROM AUTHOR]

Dynamics of vegetation in arid areas have drawn worldwide attention. The expansion of irrigated cropland (ICE) in arid regions contributes to increased food security and impacts on the extent and development of regional vegetation. However, the quantitative attribution of vegetation growth variation from ICE and biogeochemical factors (e.g., atmospheric CO2 concentration, climatic factors) is still lacking. Here, we assessed key drivers of vegetation growth in the inland arid region of Northwest China (IANC) from 1982 to 2018, including ICE, increased nitrogen rates, elevated atmospheric CO2 concentration (eCO2) and climate drivers, using normalized difference vegetation index (NDVI) and ecosystem gross primary productivity (GPP) as measures. These variables were quantified through trend decomposition, machine learning algorithms, and a satellite-based model. The results show that vegetation growth was increased in IANC mainly due to eCO2 and ICE. After 1995, as the regional climatic aridity intensified, the CO2 fertilization effect on vegetation growth decreased, as the atmospheric CO2 concentration continued to increase. Meanwhile, irrigated cropland area increased sharply, and ICE-driven GPP variation exceeded that driven by eCO2 in the whole region, while the ICE-driven NDVI variation exceeded that due to eCO2 when the ICE reached 6.38%. The ICE effect on regional vegetation growth rather than the CO2 fertilization effect has mitigated the slowdown of the rate of vegetation growth caused by climate changes. Although the ICE is conducive to food security and continuous greening of arid areas, further reclamation will exacerbate water scarcity. Our results provide research base for identifying the scale of sustainable agricultural development. Highlights: Vegetation growth was enhanced in Northwest China mainly due to the CO2 fertilization effect and irrigation expansion during 1982–2018. The effect of irrigation on vegetation growth approached or even outweighed that of elevated atmospheric CO2 concentration after 1995. The effect of irrigation expansion mitigates the slowdown of vegetation growth trend, instead of the CO2 fertilization effect. [ABSTRACT FROM AUTHOR]

In the context of global warming, the frequent occurrence of drought has become one of the main reasons affecting the loss of gross primary productivity (GPP) of terrestrial ecosystems. Under the influence of human activities, the vegetation greening trend of the Loess Plateau increased significantly. Therefore, it is of great significance to study the response of GPP to drought in the Loess Plateau under the greening trend. Here, we comprehensively assessed the ability of vegetation indices (VIs) and solar-induced chlorophyll fluorescence (SIF) to capture GPP changes at different seasonal scales and during drought. Specifically, we utilized three vegetation indices: normalized difference vegetation index (NDVI), near-infrared reflectance of vegetation (NIRV), and kernel NDVI index (kNDVI), and determined the drought period of the Loess Plateau in 2001 based on the standardized precipitation evapotranspiration index (SPEI) and the standardized soil moisture index (SSMI). Moreover, the anomalies of VIs and SIF during the drought period and the relationship with GPP anomalies were compared. The results showed that both SIF and VIs were able to capture changes during the drought period as well as in normal years. Overall, SIF captured drought changes better due to water and heat stress as well as GPP changes compared to VIs. Across different time scales, SIF showed the strongest relationship with GPP (meanR2 = 0.85), followed by NIRV (meanR2 = 0.84), NDVI (meanR2 = 0.76), and kNDVI (meanR2 = 0.74), suggesting that SIF is more sensitive to physiological changes in vegetation. Notably, kNDVI performed best in sparse vegetation (meanR2 = 0.85). In capture during drought, NIRV and kNDVI performed better in less productive land classes; SIF showed superior capture as land use class productivity increased. In addition, GPP anomalies correlated better with kNDVI anomalies (meanR2 = 0.50) than with other index anomalies. In the future, efforts to integrate the respective strengths of SIF, NIRV, and kNDVI will improve our understanding of GPP changes. [ABSTRACT FROM AUTHOR]

Maize gross primary productivity (GPP) contributes the most to the global cropland GPP, making it crucial to accurately estimate maize GPP for the global carbon cycle. Previous research validated machine learning (ML) methods using remote sensing and meteorological data to estimate plant GPP, yet they disregard vegetation physiological dynamics driven by phenology. Leaf nitrogen content per unit leaf area (i.e., specific leaf nitrogen (SLN)) greatly affects photosynthesis. Its maximum allowable value correlates with a phenological factor conceptualized as normalized maize phenology (NMP). This study aims to validate SLN and NMP for maize GPP estimation using four ML methods (random forest (RF), support vector machine (SVM), convolutional neutral network (CNN), and extreme learning machine (ELM)). Inputs consist of vegetation index (NDVI), air temperature, solar radiation (SSR), NMP, and SLN. Data from four American maize flux sites (NE1, NE2, and NE3 sites in Nebraska and RO1 site in Minnesota) were gathered. Using data from three NE sites to validate the effect of SLN and MMP shows that the accuracy of four ML methods notably increased after adding SLN and MMP. Among these methods, RF and SVM achieved the best performance of Nash–Sutcliffe efficiency coefficient (NSE) = 0.9703 and 0.9706, root mean square error (RMSE) = 1.5596 and 1.5509 gC·m−2·d−1, and coefficient of variance (CV) = 0.1508 and 0.1470, respectively. When evaluating the best ML models from three NE sites at the RO1 site, only RF and CNN could effectively incorporate the impact of SLN and NMP. But, in terms of unbiased estimation results, the four ML models were comprehensively enhanced by adding SLN and NMP. Due to their fixed relationship, introducing SLN or NMP alone might be more effective than introducing both simultaneously, considering the data redundancy for methods like CNN and ELM. This study supports the integration of phenology and leaf-level photosynthetic factors in plant GPP estimation via ML methods and provides a reference for similar research. [ABSTRACT FROM AUTHOR]

The current communication presents the application of a consolidated model combination strategy to analyze the medium-term carbon fluxes in two Mediterranean pine wood ecosystems. This strategy is based on the use of a NDVI-driven parametric model, Modified C-Fix, and of a biogeochemical model, BIOME-BGC, the outputs of which are combined taking into account the actual development phase of each ecosystem. The two pine ecosystems examined correspond to an old-growth forest and to a secondary succession after clearcuts, which differently respond to the same climatic condition during a ten-year period (2013–2022). Increasing dryness, in fact, exerts a fundamental role in controlling the gross primary and net ecosystem production of the mature stand, while the effect of forest regeneration is prevalent for the uprising of the same variables in the other stand. In particular, the simulated net carbon exchange fluctuates around 200 g C m−2 year−1 in the first stand and rises to over 600 g C m−2 year−1 in the second stand; correspondingly, the accumulation of new biomass is nearly undetectable in the former case while becomes notable in the latter. The study, therefore, supports the potential of the applied strategy for predicting the forest carbon balances consequent on diversified natural and human-induced factors. [ABSTRACT FROM AUTHOR]

The strong correlation between gross primary production (GPP) and sun-induced chlorophyll fluorescence (SIF) has been reported in many studies and is the basis of the SIF-based GPP estimation. However, GPP and SIF are not fully synchronous under various environmental conditions, which may destroy a stable GPP–SIF relationship. Therefore, exploring the difference between responses of GPP and SIF to the environment is essential to correctly understand the GPP–SIF relationship. As the common driver of GPP and SIF, the incident radiation could cause GPP and SIF to have similar responses to the environment, which may obscure the discrepancies in the responses of GPP and SIF to the other environmental variables, and further result in the ambiguity of the GPP–SIF relationship and uncertainties in the application of SIF. Therefore, we tried to exclude the dominant role of radiation in the responses of GPP and SIF to the environment based on the binning method, in which continuous tower-based SIF, satellite SIF, and eddy covariance GPP data from two growing seasons were used to investigate the differences in the responses of GPP and SIF to radiation, air temperature (Ta), and evaporation fraction (EF). We found that the following: (1) At both the site and satellite scales, there were divergences in the light response speeds between GPP and SIF which were affected by Ta and EF. (2) SIF and its light response curves were insensitive to EF and Ta compared to GPP, and the consistency in GPP and SIF light responses was gradually improved with the improvement of Ta and EF. (3) The dynamic slope values of the GPP–SIF relationship were mostly caused by the different sensitivities of GPP and SIF to EF and Ta. Our results highlighted that GPP and SIF were not highly consistent, having differences in environmental responses that further confused the GPP–SIF relationship, leading to complex SIF application. [ABSTRACT FROM AUTHOR]

Abstract Modeling leaf photosynthesis is essential for quantifying the carbon, water, and energy fluxes of the terrestrial biosphere. However, due to the lack of simultaneous measurements of leaf light absorption and gas exchange, canopy radiative transfer (RT) and photosynthesis modeling often rely on simplified assumptions about light absorption and electron transport. These assumptions ignore variations in leaf biophysical traits and environmental conditions. In this study, we utilized a next‐generation land surface model (LSM)—CliMA Land, which incorporates hyperspectral canopy RT and provides a more accurate representation of trait variations. We evaluated the potential bias in electron transport estimates introduced by the broadband RT schemes used in traditional LSMs. Additionally, we explored the impact of different leaf electron transport parameterization schemes on global‐scale photosynthesis and fluorescence modeling. We showed that (a) traditional LSMs that disregard the impacts of leaf temperature and leaf traits on electron transport tend to overestimate electron transport rates. (b) Photosynthesis and fluorescence within a grid can exhibit biases exceeding 20%, with these biases demonstrating contrasting seasonality. (c) Global estimates of integrated photosynthesis and fluorescence differ by 8.1% and 8.8%, respectively. These results underscore the importance of adopting more sophisticated and accurate modeling schemes, such as hyperspectral canopy RT, in future LSMs and Earth system modeling to enhance the reliability of modeling outcomes.

ABSTRACTThe terrestrial vegetation GPP of Qinghai Province is an important variable that characterizes the carbon cycling pattern. However, there is still a lack of a high-resolution GPP dataset for Qinghai Province. To address this issue, we processed all Landsat images of Qinghai from 1987 to 2021 using the GEE, and we combined multi-source auxiliary data to estimate GPP using the revised EC-LUE model. We compared our GPP dataset with flux observations to verify its accuracy. The results showed that our GPP dataset had a high correlation with the flux tower observations, with correlation coefficients of 0.984 at CF-AM site and 0.976 at CN-Ha2 site, respectively, and each site had an RMSE of [Formula: see text] and 12.986 [Formula: see text], respectively. There are different deviations between our GPP dataset and the mainstream GPP datasets in various vegetation types, with the average correlation coefficient ranging from 0.431 to 0.943. By comparing with the flux observations and the related analysis, we demonstrated that our GPP dataset features better accuracy, higher spatial resolution, and more temporal coverage than mainstream GPP datasets. This study offers the first long-term high-resolution GPP dataset for Qinghai Province, and we believe that this dataset has important implications for ecological management and climate research.

Jason E Hawkes,1,* Dale V Reisner,2 Tina Bhutani3,* 1Department of Dermatology, University of California at Davis, Sacramento, CA, USA; 2Patient Author, Yankton, SD, USA; 3Psoriasis and Skin Treatment Center, Department of Dermatology, University of California at San Francisco, San Francisco, CA, USA*These authors contributed equally to this workCorrespondence: Tina Bhutani, Email tina.bhutani@ucsf.eduAbstract: Generalized pustular psoriasis (GPP) is a rare, chronic, and debilitating disease characterized by flares of widespread erythema, desquamation, and pustule formation. GPP flares can be accompanied by systemic symptoms including fever, fatigue, malaise, and skin pain; severe cases may be fatal if untreated. Although GPP may occur concurrently with plaque psoriasis, they represent two distinct inflammatory conditions. Patients with GPP experience a substantial burden of disease, and the impact of GPP on an individual’s mental health and quality-of-life (QoL) goes far beyond skin pain and discomfort. The rarity of GPP may result in a misdiagnosis, as the sudden onset of skin pustules may be mistaken for a primary infection. Misdiagnosis with a subsequent delay in treatment has tremendous negative consequences for the affected patient. In September 2022, spesolimab became the first FDA-approved medication in the US for the treatment of GPP flares in adults. Spesolimab has since been approved by regulatory agencies in numerous countries, including Japan, Mainland China, and the EU. Prior to spesolimab, the clinical management of GPP relied on the off-label use of systemic or biologic therapies approved for plaque psoriasis or other inflammatory conditions. There is a need for increased education among healthcare providers regarding the clinical diagnosis, risk stratification, and therapeutic management of this rare disease, including the other novel GPP-specific therapies in development. In this podcast, two dermatologists and a patient who has plaque psoriasis and GPP discuss the clinical presentation, symptoms, disease burden, QoL impacts, diagnostic challenges, and therapeutic strategies for the management of GPP.Keywords: generalized pustular psoriasis, GPP, pustules, quality-of-life, IL-36, spesolimab, plaque psoriasis, inflammatory skin disease

Key message: Annual stem CO2 efflux increases with stem wood production rates and are inhibited by daily moisture stress. Abstract: Tropical forests cycle a large amount of CO2 between the land and atmosphere, with a substantial portion of the return flux due tree respiratory processes. However, in situ estimates of woody tissue respiratory fluxes and carbon use efficiencies (CUEW) and their dependencies on physiological processes including stem wood production (Pw) and transpiration in tropical forests remain scarce. Here, we synthesize monthly Pw and daytime stem CO2 efflux (ES) measurements over 1 year from 80 trees with variable biomass accumulation rates in the central Amazon. On average, carbon flux to woody tissues, expressed in the same stem area normalized units as ES, averaged 0.90 ± 1.2 µmol m−2 s−1 for Pw, and 0.55 ± 0.33 µmol m−2 s−1 for daytime ES. A positive linear correlation was found between stem growth rates and stem CO2 efflux, with respiratory carbon loss equivalent to 15 ± 3% of stem carbon accrual. CUEW of stems was non-linearly correlated with growth and was as high as 77–87% for a fast-growing tree. Diurnal measurements of stem CO2 efflux for three individuals showed a daytime reduction of ES by 15–50% during periods of high sap flow and transpiration. The results demonstrate that high daytime ES fluxes are associated with high CUEW during fast tree growth, reaching higher values than previously observed in the Amazon Basin (e.g., maximum CUEW up to 77–87%, versus 30–56%). The observations are consistent with the emerging view that diurnal dynamics of stem water status influences growth processes and associated respiratory metabolism.

Generalized pustular psoriasis (GPP) is a rare, chronic, inflammatory skin disorder characterized by recurrent flares associated with skin erythema, desquamation, and widespread superficial sterile pustules, which may be severe (“lakes of pus”). Systemic symptoms are often present, including malaise, fever, and skin pain. In GPP, innate immune responses are driven by abnormal activation of the interleukin (IL)-36-chemokine-neutrophil axis and excessive neutrophil infiltration. This review highlights the IL-36 pathway in the context of the IL-1 superfamily and describes how unopposed IL-36 signaling can lead to the development of GPP. Targeted inhibition of the IL-36 receptor (IL-36R) is an attractive therapeutic strategy in the treatment of GPP, including flare prevention and sustained disease control. Spesolimab is a first-in-class, humanized, monoclonal antibody that binds specifically to the IL-36R and antagonizes IL36 signaling. Spesolimab was approved by the US Food and Drug Administration in September 2022 to treat GPP flares in adults and was subsequently approved for GPP flare treatment in other countries across the world. Anti-IL-36R therapy, such as spesolimab, can mitigate flares and address flare prevention in GPP, presumably through rebalancing IL-36 signaling and modulating the proinflammatory response of the downstream effectors. [ABSTRACT FROM AUTHOR]

Invasive species often transform environmental conditions, exclude native species and alter ecosystem functioning, including key ecosystem processes underpinning nutrient and energy cycles. However, such impacts have been most documented during periods of invasive species dominance; their influences on functioning at lower relative abundances and after long‐term establishment are less well‐known.We investigated the effects of Elodea canadensis, a macrophyte native to North America with a long invasion history in many regions of the world, on the biomass accrual and metabolism of littoral zone biofilms growing on organic and inorganic substrates.We deployed nutrient diffusing substrates (NDS) in 18 replicate transects distributed across six lakes, comprising three invaded by E. canadensis and three uninvaded reference lakes. NDS were amended with nitrogen (N), phosphorus (P) or N + P together, or were deployed as unamended controls. E. canadensis relative abundance varied widely in the invaded transects, ranging from 13% to 93% of all macrophyte cover.On control substrates, algal biomass, quantified as Chlorophyll‐a, and gross primary production (GPP) were 42% and 78% greater in the invaded compared to uninvaded lakes, respectively. Respiration rates, attributable to responses of both autotrophs and heterotrophs, were 45% greater on control substrates in invaded lakes. By contrast, N‐limitation of both biofilm GPP and respiration was 25% and 35% greater in uninvaded compared with invaded lakes.There was no evidence for differences in nutrients, light availability or grazing pressure between invaded and uninvaded transects. Rather, the observed differences in metabolism suggest that the presence of E. canadensis increases availability of N at local scales, reducing N‐limitation of biofilms and resulting in elevated rates of biofilm productivity.Our results demonstrate that invasive elodeids might have significant impacts on biofilms and processes associated with the cycling of nutrients, even when long‐established and present at lower relative abundances. [ABSTRACT FROM AUTHOR]

Solar‐induced chlorophyll fluorescence (SIF) shows enormous promise as a proxy for photosynthesis and as a tool for modeling variability in gross primary productivity and net biosphere exchange (NBE). In this study, we explore the skill of SIF and other vegetation indicators in predicting variability in global atmospheric CO2 observations, and thus global variability in NBE. We do so using a 4‐year record of CO2 observations from NASA's Orbiting Carbon Observatory 2 satellite and using a geostatistical inverse model. We find that existing SIF products closely correlate with space‐time variability in atmospheric CO2 observations, particularly in the extratropics. In the extratropics, all SIF products exhibit greater skill in explaining variability in atmospheric CO2 observations compared to an ensemble of process‐based CO2 flux models and other vegetation indicators. With that said, other vegetation indicators, when multiplied by photosynthetically active radiation, yield similar results as SIF and may therefore be an effective structural SIF proxy at regional to global spatial scales. Furthermore, we find that using SIF as a predictor variable in the geostatistical inverse model shifts the seasonal cycle of estimated NBE and yields an earlier end to the growing season relative to other vegetation indicators. These results highlight how SIF can help constrain global‐scale variability in NBE. Plain Language Summary: When plants undergo photosynthesis, the chlorophyll in the plant emits small amounts of radiation, known as solar‐induced fluorescence (SIF). SIF can therefore serve as a predictor of photosynthesis in plants, a key component of net biosphere carbon exchange (NBE). Several satellite missions now provide global SIF observations, including NASA's Orbiting Carbon Observatory 2 (OCO‐2) satellite, which also collects observations of atmospheric CO2. In this study, we explore the relationships between existing, global SIF products and NBE, as estimated using CO2 observations from OCO‐2. Overall, we find that SIF is a more skilled predictor of NBE across most regions of the globe compared to other proxies of photosynthesis, including measures of plant greenness, and compared to state‐of‐the‐art, process‐based models of the global carbon cycle. In addition, SIF indicates an earlier seasonal decline in CO2 uptake during the Northern Hemisphere fall compared to NBE estimated without SIF. Overall, our results highlight several advantages and challenges of using SIF products to help predict global patterns in photosynthesis and NBE. Key Points: Solar‐induced fluorescence (SIF) products adeptly explain global variability in atmospheric CO2 observations, and thus in net biosphere exchange (NBE)Inverse estimates of NBE that are informed by SIF exhibit a different seasonality in the extratropics compared to other vegetation productsOther vegetation products, when multiplied by photosynthetically active radiation, may also be an effective structural SIF proxy [ABSTRACT FROM AUTHOR]

The Yellow River Basin (YRB) as a source of the oldest world cultures, flows through vast arid and semi‐arid regions of China. Its fragile vegetation is suffering the obvious effect from regional climate change, and easily turns from a critical state into qualitatively different modes of change, which implies significant impacts on humans and ecosystems. Our results show that in the period of 1982–2015, the normalized difference vegetation index (NDVI) appeared significantly discontinuous changes by stagnant warming and strengthening wetting, especially in the central YRB (104.5°–111° E, 35.5°–38° N). The NDVI in this region shifted from slowly increasing (5.42 × 10−4 a−1) to rapidly increasing (5.13 × 10−3 a−1) around 2003, called the turning point (TP). Such shift change is mainly a result of increased moderate rain, with frequency trend changed from 0.083 times·a−1 to 0.324 times·a−1. Meanwhile, these changes on vegetation led to a reversal of the gross primary productivity (GPP) from decreasing to increasing. Such results indicated the vulnerable ecosystem of the central YRB has played a positive contribution to the carbon balance, and more sustainable management of vegetation is required for the ecological development and engaging adaptive strategy. [ABSTRACT FROM AUTHOR]

Five countries in the Lancang–Mekong region, including Myanmar, Laos, Thailand, Cambodia, and Vietnam, are facing the threat of deforestation, despite having a high level of forest coverage. Quantitatively assessing the forest ecosystem status and its variations based on remote sensing products for vegetation parameters is a crucial prerequisite for the ongoing phase of our future project. In this study, we analyzed forest health in the year 2020 using four vegetation indicators: forest coverage index (FCI), leaf area index (LAI), fraction of green vegetation cover (FVC), and gross primary productivity (GPP). Additionally, we introduced an ecosystem quality index (EQI) to assess the quality of forest health. To understand the long-term trends in the vegetation indicators and EQI, we also performed a linear regression analysis from 2010 to 2020. The results revealed that Laos ranked as the top-performing country for forest ecosystem status in the Lancang–Mekong region in 2020. However, the long-term trend analysis results showed that Cambodia experienced the most significant decline across all indicators, while Vietnam and Thailand demonstrated varying degrees of improvement. This study provides a quality assessment of forest health and its variations in the Lancang–Mekong region, which is crucial for implementing effective conservation strategies. [ABSTRACT FROM AUTHOR]

Misregulation of histone lysine methylation is associated with several human cancers and with human developmental disorders. DOT1L is an evolutionarily conserved gene encoding a lysine methyltransferase (KMT) that methylates histone 3 lysine-79 (H3K79) and was not previously associated with a Mendelian disease in OMIM. We have identified nine unrelated individuals with seven different de novo heterozygous missense variants in DOT1L through the Undiagnosed Disease Network (UDN), the SickKids Complex Care genomics project, and GeneMatcher. All probands had some degree of global developmental delay/intellectual disability, and most had one or more major congenital anomalies. To assess the pathogenicity of the DOT1L variants, functional studies were performed in Drosophila and human cells. The fruit fly DOT1L ortholog, grappa , is expressed in most cells including neurons in the central nervous system. The identified DOT1L variants behave as gain-of-function alleles in flies and lead to increased H3K79 methylation levels in flies and human cells. Our results show that human DOT1L and fly grappa are required for proper development and that de novo heterozygous variants in DOT1L are associated with a Mendelian disease. This study identified individuals with de novo DOT1L variants associated with developmental delay and congenital anomalies. DOT1L encodes a histone methyltransferase and the variants led to increased methylation in flies and human cells. Hence, DOT1L now joins the list of histone lysine methyltransferases associated with Mendelian developmental disorders. [ABSTRACT FROM AUTHOR]

Drought poses a significant environmental risk and can deeply affect the growth of grasslands. However, there is still uncertainty regarding the precise impact of varying levels of drought on grassland growth. To address this gap, we utilized several key indicators, including the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Global Orbiting Carbon Observatory-2-based Solar-induced Chlorophyll Fluorescence (GOSIF), and Gross Primary Productivity (GPP), in conjunction with drought indices (the Standardized Precipitation Evapotranspiration Index (SPEI) and soil moisture (SM). Our study aimed to comprehensively assess the consistency of spatiotemporal patterns in grassland vegetation and its responsiveness to different drought levels in the Inner Mongolia region from 2002 to 2020. The results indicated that NDVI, EVI, GOSIF, and GPP in grassland vegetation across Inner Mongolia exhibited significant increasing trends from 2002 to 2020. Specifically, NDVI, EVI, GOSIF, and GPP all displayed consistent spatial patterns, with 25.83%, 21.18%, 22.65%, and 48.13% of the grassland area showing significant increases, respectively. Drought events, as described through SPEI and SM, from June 2007 to September 2007 and June 2017 to July 2017 were selected to evaluate the response of grassland vegetation to drought. The drought events of 2007 and 2017 resulted in reductions in NDVI, EVI, GOSIF, and GPP relative to the multi-year average (2002–2020). GOSIF exhibited a more intense response to drought, suggesting that GOSIF may reflect the inhibition of water stress on grassland photosynthesis better than NDVI and EVI for the drought in 2007 and 2017. The reductions in NDVI, EVI, GOSIF, and GPP in grassland increased significantly across different drought levels, with the sharpest reductions observed during extreme drought. Under the severe and extreme drought events, the most substantial reductions in NDVI, EVI, GOSIF, and GPP were observed in the temperate steppe (TS). Moreover, the effects of different drought severity levels within the same grassland type varied, with the most significant reductions in NDVI, EVI, GOSIF, and GPP observed during extreme drought. Our results provide new perspectives for developing and implementing effective strategies to address grassland carbon cycling management and climate change in Inner Mongolia. [ABSTRACT FROM AUTHOR]

Terrestrial ecosystems play a central role in the global carbon cycle and climate mitigation due to their offering of a large carbon sink. More than one‐fifth of the geographical area of India, one of the largest nations on the Earth, is forested, which is highly diverse in vegetation and climate types, offers huge potential for carbon sequestration, but remains vulnerable to climate change. Hence, it is imperative to know the future changes in the terrestrial carbon budget over this region. Gross primary productivity (GPP) represents the carbon uptake by terrestrial ecosystems. The multimodel ensembles of GPP simulated by the sixth phase of the Coupled Model Intercomparison Project (CMIP6) provide a useful means in this regard. In this work, we study the strength and variability of GPP over India in the near‐past and future using these simulations. In future, all the models show an increasing trend in GPP, however, with widely varying trends. The preferred month of carbon uptake differs among the models. A comparison with a satellite biophysical record shows the models underestimated the GPP during the near‐past over India. The carbon uptake in the Eastern Himalaya dominates the Western Himalaya and central Indian regions. Specifically, till 2100, the growth rate of GPP varies from 4.9 to 16.69 gC m−2 y−2, from 2.47 to 18.91 gC m−2 y−2, and from 0.32 to 21.95 gC m−2 y−2 over these three regions, respectively. Plain Language Summary: Climate change is an imminent problem driven by greenhouse gases, with adverse impacts on the Earth system. Carbon dioxide is a major greenhouse gas. Plants offer a natural strategy to mitigate climate change by their photosynthetic carbon uptake. For climate change impact assessment, projections of climate models are widely used, developed from our understanding of the Earth system, as measured by the observations. India is a large and populous country that remains highly vulnerable to climate change; it also plays a key role in global climate change and its mitigation. However, to devise a suitable mitigation strategy the future carbon sequestration by the natural ecosystems in this region needs to be quantified. The World Climate Research Program developed the Coupled Model Intercomparison Project, a multi‐model ensemble study providing future projections of climate variables under different climate change scenarios. In this work, we use these ensembles to study the future photosynthetic carbon uptake over India and its three key forested regions. We find the photosynthetic carbon uptake to increase manifold over India, with significant inter‐model uncertainties. We also find the Eastern Himalaya to have stronger biospheric carbon removal from the atmosphere than the Western Himalaya and Central Indian regions. Key Points: Photosynthetic carbon uptake increases over India in future with the annual growth rate varying from 3 to 15 gC m−2 y−2The photosynthetic carbon uptake is larger in Eastern Himalaya compared to Western Himalaya and Central IndiaThere is a large discord on the strength, trend and variability of Gross primary productivity over India among CMIP6 models and Moderate Resolution Imaging Spectroradiometer [ABSTRACT FROM AUTHOR]

The accurate estimation of gross primary productivity (GPP) plays an important role in accurately projecting the terrestrial carbon cycle and climate change. Satellite-driven near-infrared reflectance (NIRv) can be used to estimate GPP based on their nearly linear relationship. Notably, previous studies have reported that the relationship between NIRv and GPP seems to be biome-specific (or land cover) at the ecosystem scale due to both biotic and abiotic effects. Hence, the NIRv-based estimation of GPP may be influenced by land cover changes (LCC) and the discrepancies in multisource products (DMP). However, these issues have not been well understood until now. Therefore, this study took the Yellow River basin (YRB) as the study area. This area has experienced remarkable land cover changes in recent decades. We used Moderate-Resolution Imaging Spectroradiometer (MODIS) and European Space Agency (ESA) Climate Change Initiative (CCI) land cover products (termed MCD12C1 and ESACCI, respectively) during 2001–2018 to explore the impact of land cover on NIRv-estimated GPP. Paired comparisons between the static and dynamic schemes of land cover using the two products were carried out to investigate the influences of LCC and DMP on GPP estimation by NIRv. Our results showed that the dominant land cover types in the YRB were grassland, followed by cropland and forest. Meanwhile, the main transfer was characterized by the conversion from other land cover types (e.g., barren) to grassland in the northwest of the YRB and from grassland and shrubland to cropland in the southeast of the YRB during the study period. Moreover, the temporal and spatial pattern of GPP was highly consistent with that of NIRv, and the average increase in GPP was 2.14 gCm−2yr−1 across the YRB. Nevertheless, it is shown that both LCC and DMP had significant influences on the estimation of GPP by NIRv. That is, the areas with obvious differences in NIRv-based GPP closely correspond to the areas where land cover types dramatically changed. The achievements of this study indicate that considering the land cover change and discrepancies in multisource products would help to improve the accuracy of NIRv-based estimated GPP. [ABSTRACT FROM AUTHOR]

Understanding the global ecosystem carbon cycle requires a quantitative estimation of terrestrial gross primary production (GPP). Although most existing GPP estimates can accurately reflect the spatial distribution, interannual trends and even seasonal cycles of GPP under average conditions to some extent, the GPP estimates of different models under extreme climate conditions are still unsatisfactory. In this study, based on the random forest algorithm, we integrated the multimodel GPP simulation results published by the Multiscale Synthesis and Terrestrial Model Intercomparison Project, the FLUXNET flux-site-observed GPP, the standardized precipitation index (SPI) and the standardized temperature index (STI) to generate a set of global GPP time-series data products from 2001 to 2010. The new GPP product was named DFRF-GPP, referring to the GPP generated by data fusion based on random forest. The quality of DFRF-GPP was evaluated by comparison with current commonly used GPP datasets and GPP observations at FLUXNET flux sites. The results showed that DFRF-GPP is in great agreement with other GPP products and can reproduce the spatial and temporal patterns and interannual trends of global GPP, with optimal performance in regions such as those north of 30° in the Northern Hemisphere where there are more FLUXNET flux sites. DFRF-GPP has higher accuracy and excellent performance on drought and high-temperature samples, with higher sensitivity to drought and high temperature. This study not only provides a set of time-series products of global gross primary production but also provides a convenient and practical solution for enhancing the applicability of GPP estimation products in large-scale extreme climate studies.

SignificanceThe magnitude of the CO2 fertilization effect on terrestrial photosynthesis is uncertain because it is not directly observed and is subject to confounding effects of climatic variability. We apply three well-established eco-evolutionary optimality theories of gas exchange and photosynthesis, constraining the main processes of CO2 fertilization using measurable variables. Using this framework, we provide robust observationally inferred evidence that a strong CO2 fertilization effect is detectable in globally distributed eddy covariance networks. Applying our method to upscale photosynthesis globally, we find that the magnitude of the CO2 fertilization effect is comparable to its in situ counterpart but highlight the potential for substantial underestimation of this effect in tropical forests for many reflectance-based satellite photosynthesis products.

La degradación de los ecosistemas se ha incrementado en las últimas décadas y se prevé que el cambio climático incremente su riesgo, sobre todo en los ecosistemas áridos y semiáridos. El objetivo de este trabajo es la caracterización del estado de los ecosistemas terrestres del territorio español peninsular y las Islas Baleares a través del análisis temporal de la variable RUE (Rain Use Efficiency) durante el periodo 2004-2018. Las imágenes de RUE anual se han calculado como el cociente entre la producción primaria bruta (GPP) anual y la precipitación acumulada (PPT) anual a una resolución espacial de 1 km, y posteriormente se han normalizado los valores. La GPP anual se deriva de la GPP diaria, obtenida utilizando una optimización del modelo de Monteith y la PPT a partir de las imágenes de precipitación diaria, que proceden de aplicar un kriging a los datos de las estaciones de la red de AEMet. El análisis temporal de la RUE se ha realizado calculando su pendiente con el test de Mann-Kendall y el método de Sen-Theil. La RUE se ha analizado a tres niveles de estudio: a nivel regional, por tipos de vegetación y a nivel de píxel. Los resultados han mostrado una tendencia negativa de la RUE normalizada (entre -0.05 y -0.25 año-1) para la mayoría del territorio, para las 9 clases de vegetación (siendo las clases forestales las que han presentado las pendientes más acusadas) y en 5 de los 8 ecosistemas analizados a nivel de píxel. Un declive en la RUE indica cierta degradación en la cubierta vegetal. Del análisis de los resultados se ha extraído que la variabilidad interanual de la RUE se encuentra mediada en gran parte por la precipitación (correlación negativa). Se ha observado que la GPP ha experimentado en los últimos años un incremento progresivo conocido como greening.

Involving the effect of atmospheric CO2 fertilization is effective for improving the accuracy of estimating gross primary production (GPP) using light use efficiency (LUE) models. However, the widely used LUE model, the remote sensing-driven Carnegie–Ames–Stanford Approach (CASA) model, scarcely considers the effects of atmospheric CO2 fertilization, which causes GPP estimation uncertainties. Therefore, this study proposed an improved method for estimating GPP by integrating the atmospheric CO2 concentration into the CASA model and generated a long time series GPP dataset with high precision for the Qinghai–Tibet Plateau. The CASA model was improved by considering the impact of atmospheric CO2 on vegetation productivity and discerning variations in CO2 gradients within the canopy and leaves. A 500 m monthly GPP dataset for the Qinghai–Tibet Plateau from 2003 to 2020 was generated. The results showed that the improved GPP estimation model achieved better performances on estimating GPP (R2 = 0.68, RMSE = 406 g C/m2/year) than the original model (R2 = 0.67, RMSE = 499.32 g C/m2/year) and MODIS GPP products (R2 = 0.49, RMSE = 522.56 g C/m2/year). The GPP on the Qinghai–Tibet Plateau increased significantly with the increase in atmospheric CO2 concentration and the gradual accumulation of dry matter. The improved method can also be used for other regions and the generated GPP dataset is valuable for further understanding the ecosystem carbon cycles on the Qinghai–Tibet Plateau.

Climate is one of the key factors driving changes in vegetation, and the response of the vegetation to climate often occurs with a time delay. However, research on the cumulative lagged response of the vegetation to meteorological factors in large-scale regions is limited. Therefore, this study first evaluated the performance of the Gross Primary Productivity (GPP) products provided by Moderate Resolution Imaging Spectroradiometer (MODIS) and Penman–Monteith–Leuning (PML) over the past 20 years in China and then determined the lagged relationships between the GPP and major meteorological factors in different regions and land-use types in China based on a partial correlation analysis. The results indicate that (1) GPP_PML outperforms GPP_MODIS products in the regional context of China; (2) China’s regional GPP has shown a fluctuating upward trend over the past 20 years, with a stepwise increase in the multi-year average from the northwest inland to the southeast coastal regions, and a higher contribution from the southern regions than the northern ones; (3) unlike the recent upward trend in regional temperatures, both precipitation and radiation have decreased, with these two factors showing completely opposite multi-year trends in most regions; and (4) the proportion of regions with lagged effects of the GPP on meteorological factors is higher than those with cumulative effects in China. Among these, GPP exhibits a higher proportion of a 3-month lagged response to precipitation, which is particularly pronounced at altitudes between 500 and 2500 m and above 5500 m. the proportion of the areas with no lag cumulative response to temperature and radiation with GPP in China is the highest due to the influence of more barren land and grassland in the northwest interior. Simultaneously, grassland and barren land have a higher proportion of the non-lagged cumulative responses to temperature and precipitation. This study contributes to our understanding of vegetation dynamics in the context of global climate change and provides a theoretical foundation for regional ecological conservation and high-quality coordinated development.

In the context of adopting the 2030 Agenda for Sustainable Development, green public procurement has gained much importance for achieving the targets set by Sustainable Development Goal (SDG) 12. Although green public procurement (GPP) is a voluntary instrument, the strategic EU framework set by the European Green Deal indicates that, at least for specific products with considerable impact on climate change, the contracting authorities shall have to use green criteria in tenders. This research aims to explain the factors influencing the adoption of GPP rules, their implementation, and the actual use percentage in Romania employing questionnaires answered by Romanian contracting authorities, interviews with public procurement officers and documentary analysis of the tender books published between 2018 and 2022 for three products regulated by the Romanian GPP Guide. The results of this research indicate that more than half of the respondents have never used the GPP criteria set by the Romanian government or the ones elaborated by the European Commission. The thorough verification of the technical specifications for the products mentioned in the Romanian GPP Guide shows that roughly only 20% of the tenders published between 2018-2022 are green. The main finding is that the principal factor that explains the small percentage of GPP in Romania is the absence of mandatory requirements and targets for GPP. Although the public procurement clerks are slightly aware of GPP’s importance, the lack of training, knowledge, skills and, furthermost, the absence of a strategic GPP approach and leadership support make achieving the neutrality targets using this environmental policy tool a slow and laborious process.

ABSTRACTIn this study, Eco-hydrometeorological Remote Sensing-based Penman-Monteith algorithm (Eh-RSPM) was developed by implementing the gross primary productivity into the revised Remote Sensing based Penman-Monteith algorithm (RS-PM). Evaluation of Eh-RSPM was conducted through comparison with in-situ measurements as well as model-based products (e.g. MODerate resolution Imaging Spectroradiometer (MODIS) 16 global ET products (MOD16 ET) and Surface Energy Balance System (SEBS)) during two years (2004 and 2012) in Northeast Asia. Comparison of ET from Eh-RSPM algorithm with five flux tower measurement agreed well with the flux tower datasets at the entire validation sites. Especially, Eh-RSPM showed advantages in improving the accuracy of ET at stations with relatively short canopy height (e.g. QHB and KBU site) as well as the forest site (e.g. SMK). Focusing on the forest site, Eh-RSPM exhibited slightly better statistical performance compared to MOD16. Specifically, the temporal mean bias and RMSD showed a slight improvement, decreasing from −15.40 W m−2 to −12.58 W m−2 and from 28.41 W m−2 to 25.26 W m−2, respectively. This is a key finding of this study, demonstrating the applicability of the improved ET algorithm to regions with significant forest cover. Similarly, spatial distribution of Eh-RSPM showed similar patterns with MOD16 and SEBS. Eh-RSPM strongly showed advantages over the land cover types with relatively shorter canopy height (e.g. grassland and alpine meadow) as well as the heterogeneous forest showed significant improvement in Eh-RSPM through considering the actual physiological behavior variation and influence of photosynthesis into ET calculation.

Generalized pustular psoriasis (GPP) is a rare, chronic, inflammatory skin disorder characterized by recurrent flares associated with skin erythema, desquamation, and widespread superficial sterile pustules, which may be severe (“lakes of pus”). Systemic symptoms are often present, including malaise, fever, and skin pain. In GPP, innate immune responses are driven by abnormal activation of the interleukin (IL)-36-chemokine-neutrophil axis and excessive neutrophil infiltration. This review highlights the IL-36 pathway in the context of the IL-1 superfamily and describes how unopposed IL-36 signaling can lead to the development of GPP. Targeted inhibition of the IL-36 receptor (IL-36R) is an attractive therapeutic strategy in the treatment of GPP, including flare prevention and sustained disease control. Spesolimab is a first-in-class, humanized, monoclonal antibody that binds specifically to the IL-36R and antagonizes IL-36 signaling. Spesolimab was approved by the US Food and Drug Administration in September 2022 to treat GPP flares in adults and was subsequently approved for GPP flare treatment in other countries across the world. Anti-IL-36R therapy, such as spesolimab, can mitigate flares and address flare prevention in GPP, presumably through rebalancing IL-36 signaling and modulating the pro-inflammatory response of the downstream effectors.

Climate change leads to increasing temperature and more extreme hot and drought events. Ecosystem capability to cope with climate warming depends on vegetation's adjusting pace with temperature change. How environmental stresses impair such a vegetation pace has not been carefully investigated. Here we show that dryness substantially dampens vegetation pace in warm regions to adjust the optimal temperature of gross primary production (GPP) (ToptGPP) in response to change in temperature over space and time. ToptGPP spatially converges to an increase of 1.01°C (95% CI: 0.97, 1.05) per 1°C increase in the yearly maximum temperature (Tmax) across humid or cold sites worldwide (37oS–79oN) but only 0.59°C (95% CI: 0.46, 0.74) per 1°C increase in Tmax across dry and warm sites. ToptGPP temporally changes by 0.81°C (95% CI: 0.75, 0.87) per 1°C interannual variation in Tmax at humid or cold sites and 0.42°C (95% CI: 0.17, 0.66) at dry and warm sites. Regardless of the water limitation, the maximum GPP (GPPmax) similarly increases by 0.23 g C m−2 day−1 per 1°C increase in ToptGPP in either humid or dry areas. Our results indicate that the future climate warming likely stimulates vegetation productivity more substantially in humid than water‐limited regions. [ABSTRACT FROM AUTHOR]

Generalized pustular psoriasis (GPP) is a rare subset of psoriasis involving episodes of sterile pustules accompanied by inflammation and, often, systemic involvement. The inflammatory nature of GPP has potential for severe multisystem complications including high‐output cardiac failure, infections, digestive system issues, and disfiguring or lethal acute flare episodes. The disease tends to have higher prevalence in females and Asians. The IL‐1/IL‐36 inflammatory pathway is a critical facet of GPP's pathology. Genetic mutations that are associated with GPP include modifications of Interleukin 36 Receptor Antagonist (IL36RN), Caspase Recruitment Domain Family Member 14 (CARD14), Adaptor Related Protein Complex 1 Subunit Sigma 3 (AP1S3), Myeloperoxidase (MPO) and Serpin Peptidase Inhibitor Clade A Member 3 (SERPINA3) genes. Treatment guidelines for GPP are not well‐entrenched. Currently, only one GPP‐specific treatment, the interleukin‐36 receptor antagonist (IL‐36Ra) spesolimab, has been approved for use in the United States. Additional anti‐IL‐36 pathway therapies are currently being developed. Other treatment options include other biologic therapies such as IL‐17 inhibitors, IL‐23 inhibitors and TNFα inhibitors. Non‐biologic therapeutic options include retinoids, cyclosporine and methotrexate. Treatment options differ throughout the world; most countries utilize retinoids, cyclosporine and methotrexate as first‐line non‐biologic options. China and United Kingdom have no GPP‐specific biologic therapies approved for use, while several biologic therapies are approved for use in Japan. This review aims to serve as an update on the current global management of GPP while also including relevant aspects of disease pathogenesis, diagnosis, clinical presentation, histopathology, aetiology and epidemiology. [ABSTRACT FROM AUTHOR]

IntroductionForest and grassland are the two main carbon-collecting terrestrial ecosystems, and detecting their solar-induced chlorophyll fluorescence (SIF) enables evaluation of their photosynthetic intensity and carbon-collecting capacity. Since SIF that is retrieved directly from satellite observations suffers from low spatial resolution, discontinuity, or low temporal resolution, some vegetation indexes (VIs) and meteorological factors are used as predictors to reconstruct SIF products. Yet, unlike VIs, certain meteorological factors feature a relatively low space resolution and their observations are not always accessible. This study aimed to explore the potential of reconstructing SIF from fewer predictors whose high-resolution observations are easily accessible.MethodsA total of six forest and grassland regions across low, mid, and high latitudes were selected, and the commonly used predictors-normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and land surface temperature (LST)—were compared for their correlation with SIF. Results show that the combination of EVI and LST is more strongly correlated with SIF, but each contributed differently to SIF at differing growth stages of forest and grassland. Accordingly, we proposed the idea of a combined sampling approach that considers both location and phenological phase, to explore the extent to which time and space coverage samples' span could enlarge the disparity of EVI data in particular regions at specific growth stages. To do that, three kinds of sample combination methods were proposed: monthly regression at a global scale, seasonal regression at a regional scale, and monthly regression at a regional scale. Following this, Sentinel-3 EVI and MODIS LST data were used to reconstruct 500 m SIF in the six regions by implementing the proposed methodology.Results and discussionThese results showed that the R2 values were ≥0.90 between the reconstructed SIF and MODIS GPP (gross primary productivity), 0.70 with GOME-2 SIF and 0.77 with GOSIF, thus proving the proposed methodology could produce reliable results for reconstruction of 500 m SIF. This proposed approach, which bypasses dependence of traditional SIF reconstruction model on numerous predictors not easy to obtain, can serve as a better option for more efficient and accurate high-resolution SIF reconstructions in the future.