Search Results (17)

The mold Aspergillus fumigatus employs two high-affinity uptake systems, reductive iron assimilation (RIA) and siderophore-mediated iron acquisition (SIA), for the acquisition of the essential trace element iron. SIA has previously been shown to be crucial for virulence in mammalian hosts. Here, we show that a lack of AcuK or AcuM, transcription factors required for the activation of gluconeogenesis, decreases the production of both extra- and intracellular siderophores in A. fumigatus. The lack of AcuM or AcuK did not affect the expression of genes involved in RIA and SIA, suggesting that these regulators do not directly regulate iron homeostasis genes, but indirectly affect siderophore production through their influence on metabolism. Consistent with this, acetate supplementation reversed the intracellular siderophore production defect of ΔacuM and ΔacuK. Moreover, ΔacuM and ΔacuK displayed a similar growth defect under iron limitation and iron sufficiency, which suggests they have a general role in carbon metabolism apart from gluconeogenesis. In agreement with a potential role of the glyoxylate cycle in adaptation to iron starvation, transcript levels of the malate synthase-encoding acuE were found to be upregulated by iron limitation that is partially dependent on AcuK and AcuM. Together, these data demonstrate the influence of iron availability on carbon metabolism. Full article

Global warming has become indisputable in recent years; however, the mechanisms by which water vapor, radiation, and greenhouse gases such as carbon dioxide contribute to driving global warming remain unclear, and it is becoming increasingly important to clarify their respective effects on temperature warming. In this study, we used the Global Land Data Assimilation System (GLDAS) datasets and National Oceanic and the Atmospheric Administration (NOAA) Global Monitoring Laboratory (GML) data to investigate the spatiotemporal variation characteristics of global warming and its driving mechanisms. The effects of water vapor, radiation (net longwave radiation), and CO₂ on temperature rise are quantified from the perspective of the coupled land–atmosphere system, and water vapor is characterized in terms of the vapor pressure deficit (difference between saturated and actual water vapor pressures) to explicitly characterize its impact on the global water–heat cycle. The results show the following: (1) Under significant global warming, the vapor pressure deficit (VPD) exhibits an increasing trend, which is attributed to the rate of increase in actual water vapor being relatively slower than saturated water vapor. (2) Compared with the significant positive contribution of water vapor to global warming, CO₂ is not, as generally expected, the most critical greenhouse gas causing global warming. (3) Water vapor and net longwave radiation (NLR) have significant mutual feedbacks on global warming. (4) A remarkable complementary mechanism of global warming that involves water vapor and NLR was identified, whereby the increased saturated water vapor induced by the rising temperature dominates the decrease in NLR. The results from this study have important theoretical value by enabling a more complete understanding of the contribution of VPD to global climate change and shedding light on the large-scale water vapor–climate change mutual feedback mechanism through research. Full article

Due to the substantial gross exchange fluxes with the atmosphere, the terrestrial carbon cycle plays a significant role in the global carbon budget. Drought commonly affects terrestrial carbon absorption negatively. Terrestrial biosphere models exhibit significant uncertainties in capturing the carbon flux response to drought, which have an impact on estimates of the global carbon budget. Through plant physiological processes, soil moisture tightly regulates the carbon cycle in the environment. Therefore, accurate observations of soil moisture may enhance the modeling of carbon fluxes in a model–data fusion framework. We employ the Carbon Cycle Data Assimilation System (CCDAS) to assimilate 36-year satellite-derived surface soil moisture observations in combination with flask samples of atmospheric CO₂ concentrations. We find that, compared to the default model, the performance of optimized net ecosystem productivity (NEP) and gross primary productivity (GPP) has increased with the RMSEs reduced by 1.62 gC/m²/month and 10.84 gC/m²/month, which indicates the added value of the ESA-CCI soil moisture observations as a constraint on the terrestrial carbon cycle. Additionally, the combination of soil moisture and CO₂ concentration in this study improves the representation of inter-annual variability of terrestrial carbon fluxes as well as the atmospheric CO₂ growth rate. We thereby investigate the ability of the optimized GPP in responding to drought by comparing continentally aggregated GPP with the drought index. The assimilation of surface soil moisture has been shown to efficiently capture the influences of the sub-annual (≤9 months drought durations) and large-scale (e.g., regional to continental scales) droughts on GPP. This study highlights the significant potential of satellite soil moisture for constraining inter-annual models of the terrestrial biosphere’s carbon cycle and for illustrating how GPP responds to drought at a continental scale. Full article

Microalgae have a growing recognition of generating biomass and capturing carbon in the form of CO₂. The genus Chlorella has especially attracted scientists’ attention due to its versatility in algal mass cultivation systems and its potential in mitigating CO₂. However, some aspects of how these green microorganisms respond to increasing concentrations of CO₂ remain unclear. In this work, we analyzed Chlorella sorokiniana and Chlorella vulgaris cells under low and high CO₂ levels. We monitored different processes related to carbon flux from photosynthetic capacity to carbon sinks. Our data indicate that high concentration of CO₂ favors growth and photosynthetic capacity of the two Chlorella strains. Different metabolites related to the tricarboxylic acid cycle and ATP levels also increased under high CO₂ concentrations in Chlorella sorokiniana, reaching up to two-fold compared to low CO₂ conditions. The signaling molecules, inositol polyphosphates, that regulate photosynthetic capacity in green microalgae were also affected by the CO₂ levels, showing a deep profile modification of the inositol polyphosphates that over-accumulated by up to 50% in high CO₂ versus low CO₂ conditions. InsP₄ and InsP₆ increased 3- and 0.8-fold, respectively, in Chlorella sorokiniana after being subjected to 5% CO₂ condition. These data indicate that the availability of CO₂ could control carbon flux from photosynthesis to carbon storage and impact cell signaling integration and energy levels in these green cells. The presented results support the importance of further investigating the connections between carbon assimilation and cell signaling by polyphosphate inositols in microalgae to optimize their biotechnological applications. Full article

Herbaspirillum seropedicae is an endophytic bacterium that can fix nitrogen and synthesize phytohormones, which can lead to a plant growth-promoting effect when used as a microbial inoculant. Studies focused on mechanisms of action are crucial for a better understanding of the bacteria-plant interaction and optimization of plant growth-promoting response. This work aims to understand the underlined mechanisms responsible for the early stimulatory growth effects of H. seropedicae inoculation in maize. To perform these studies, we combined transcriptomic and proteomic approaches with physiological analysis. The results obtained eight days after inoculation (d.a.i) showed increased root biomass (233 and 253%) and shoot biomass (249 and 264%), respectively, for the fresh and dry mass of maize-inoculated seedlings and increased green content and development. Omics data analysis, before a positive biostimulation phenotype (5 d.a.i.) revealed that inoculation increases N-uptake and N-assimilation machinery through differentially expressed nitrate transporters and amino acid pathways, as well carbon/nitrogen metabolism integration by the tricarboxylic acid cycle and the polyamine pathway. Additionally, phytohormone levels of root and shoot tissues increased in bacterium-inoculated-maize plants, leading to feedback regulation by the ubiquitin-proteasome system. The early biostimulatory effect of H. seropedicae partially results from hormonal modulation coupled with efficient nutrient uptake-assimilation and a boost in primary anabolic metabolism of carbon–nitrogen integrative pathways. Full article

Evapotranspiration ($ET$) connects the land to the atmosphere, linking water, energy, and carbon cycles. $ET$ is an essential climate variable with a fundamental importance, and accurate assessments of the spatiotemporal trends and variability in $ET$ are needed from regional to continental scales. This study compared eight global actual $ET$ datasets ($E{T}_{gl}$) and the average actual $ET$ ensemble ($E{T}_{ens}$) based on remote sensing, climate reanalysis, land-surface, and biophysical models to $ET$ computed from basin-scale water balance ($E{T}_{wb}$) in South America on monthly time scale. The 50 small-to-large basins covered major rivers and different biomes and climate types. We also examined the magnitude, seasonality, and interannual variability of $ET$, comparing $E{T}_{gl}$ and $E{T}_{ens}$ with $E{T}_{wb}$. Global $ET$ datasets were evaluated between 2003 and 2014 from the following datasets: Breathing Earth System Simulator (BESS), ECMWF Reanalysis 5 (ERA5), Global Land Data Assimilation System (GLDAS), Global Land Evaporation Amsterdam Model (GLEAM), MOD16, Penman–Monteith–Leuning (PML), Operational Simplified Surface Energy Balance (SSEBop) and Terra Climate. By using $E{T}_{wb}$ as a basis for comparison, correlation coefficients ranged from 0.45 (SSEBop) to 0.60 ($E{T}_{ens}$), and RMSE ranged from 35.6 ($E{T}_{ens}$) to 40.5 mm·month⁻¹ (MOD16). Overall, $E{T}_{gl}$ estimates ranged from 0 to 150 mm·month⁻¹ in most basins in South America, while $E{T}_{wb}$ estimates showed maximum rates up to 250 mm·month⁻¹. $E{T}_{gl}$ varied by hydroclimatic regions: (i) basins located in humid climates with low seasonality in precipitation, including the Amazon, Uruguay, and South Atlantic basins, yielded weak correlation coefficients between monthly $E{T}_{gl}$ and $E{T}_{wb}$, and (ii) tropical and semiarid basins (areas where precipitation demonstrates a strong seasonality, as in the São Francisco, Northeast Atlantic, Paraná/Paraguay, and Tocantins basins) yielded moderate-to-strong correlation coefficients. An assessment of the interannual variability demonstrated a disagreement between $E{T}_{gl}$ and $E{T}_{wb}$ in the humid tropics (in the Amazon), with $E{T}_{gl}$ showing a wide range of interannual variability. However, in tropical, subtropical, and semiarid climates, including the Tocantins, São Francisco, Paraná, Paraguay, Uruguay, and Atlantic basins (Northeast, East, and South), we found a stronger agreement between $E{T}_{gl}$ and $E{T}_{wb}$ for interannual variability. Assessing $ET$ datasets enables the understanding of land–atmosphere exchanges in South America, to improvement of $ET$ estimation and monitoring for water management. Full article

Hydrological processes are a key component of land surface models and link to the energy budget and carbon cycle. This study assessed the global hydrological processes of the Atmosphere–Vegetation Interaction Model (AVIM) using multiple datasets, including the Global Land Data Assimilation System (GLDAS), the University of New Hampshire and Global Runoff Data Centre (UNH-GRDC), the European Space Agency (ESA) Climate Change Initiative (CCI), the Global Land Evaporation Amsterdam Model (GLEAM), and the Modern-Era Retrospective Analysis for Research and Applications Version 2 (MERRA-2) datasets. The comparisons showed that the AVIM gives a reasonable spatial pattern for surface soil moisture and surface runoff, but a less satisfactory spatial pattern for evapotranspiration. The AVIM clearly underestimates surface runoff worldwide and overestimates the surface soil moisture in the high latitudes of the Northern Hemisphere, while yielding moderately higher evapotranspiration in arid areas and lower evapotranspiration in low-latitude areas near the equator. The annual cycle of evapotranspiration in the AVIM shows good agreement with the GLEAM dataset, whereas the surface soil moisture in the AVIM has a poor annual cycle relative to the CCI dataset. The AVIM simulates a late start time for snowmelt, which leads to a two-month delay in the peak surface runoff. These results clearly point out the directions required for improvements in the AVIM, which will support future investigations of water–carbon–atmosphere interactions. In addition, the evapotranspiration in the MERRA-2 dataset had an overall good performance comparable with that of the GLEAM dataset, but its surface soil moisture did not perform well when validated against the CCI dataset. Full article

The global increase in food demand in the context of climate change requires a clear understanding of cropland function and of its impact on biogeochemical cycles. However, although gas exchange between croplands and the atmosphere is measurable in the field, it is difficult to quantify at the plot scale over relatively large areas because of the heterogeneous character of landscapes and differences in crop management. However, assessing accurate carbon and water budgets over croplands is essential to promote sustainable agronomic practices and reduce the water demand and the climatic impacts of croplands while maintaining sufficient yields. From this perspective, we developed a crop model, SAFYE-CO₂, that assimilates high spatial- and temporal-resolution (HSTR) remote sensing products to estimate daily crop biomass, water and CO₂ fluxes, annual yields, and carbon budgets at the parcel level over large areas. This modeling approach was evaluated for sunflower against two in situ datasets. First, the model’s output was compared to data acquired during two cropping seasons at the Auradé integrated carbon observation system (ICOS) instrumented site in southwestern France. The model accurately simulated the daily net CO₂ flux (root mean square error (RMSE) = 0.97 gC·m⁻²·d⁻¹ and determination coefficient (R²) = 0.83) and water flux (RMSE = 0.68 mm·d⁻¹ and R² = 0.79). The model’s performance was then evaluated against biomass and yield data collected from 80 plots located in southwestern France. The model was able to satisfactorily estimate biomass dynamics and yield (RMSE = 66 and 54 g·m⁻², respectively). To investigate the potential application of the proposed approach at a large scale, given that soil properties are important factors affecting the model, a sensitivity analysis of two existing soil products (GlobalSoilMap and SoilGrids) was carried out. Our results show that these products are not sufficiently accurate for inclusion as inputs to the model, which requires more accurate information on soil water retention capacity to assess water fluxes. Additionally, we argue that no water stress should be considered in the crop growth computation since this stress is already present because of remote sensing information in the proposed approach. This study should be considered a first step to fulfill the existing gap in quantifying carbon budgets at the plot scale over large areas and to accurately estimate the effects of management practices, such as the use of cover crops or specific crop rotations on cropland C and water budgets. Full article

This work evaluates the sensitivity of CO₂ air–sea gas exchange in a coastal site to four different model system configurations of the 1D coupled hydrodynamic–ecosystem model GOTM–ERSEM, towards identifying critical dynamics of relevance when specifically addressing quantification of air–sea CO₂ exchange. The European Sea Regional Ecosystem Model (ERSEM) is a biomass and functional group-based biogeochemical model that includes a comprehensive carbonate system and explicitly simulates the production of dissolved organic carbon, dissolved inorganic carbon and organic matter. The model was implemented at the coastal station L4 (4 nm south of Plymouth, 50°15.00’N, 4°13.02’W, depth of 51 m). The model performance was evaluated using more than 1500 hydrological and biochemical observations routinely collected at L4 through the Western Coastal Observatory activities of 2008–2009. In addition to a reference simulation (A), we ran three distinct experiments to investigate the sensitivity of the carbonate system and modeled air–sea fluxes to (B) the sea-surface temperature (SST) diurnal cycle and thus also the near-surface vertical gradients, (C) biological suppression of gas exchange and (D) data assimilation using satellite Earth observation data. The reference simulation captures well the physical environment (simulated SST has a correlation with observations equal to 0.94 with a p > 0.95). Overall, the model captures the seasonal signal in most biogeochemical variables including the air–sea flux of CO₂ and primary production and can capture some of the intra-seasonal variability and short-lived blooms. The model correctly reproduces the seasonality of nutrients (correlation > 0.80 for silicate, nitrate and phosphate), surface chlorophyll-a (correlation > 0.43) and total biomass (correlation > 0.7) in a two year run for 2008–2009. The model simulates well the concentration of DIC, pH and in-water partial pressure of CO₂ (pCO₂) with correlations between 0.4–0.5. The model result suggest that L4 is a weak net source of CO₂ (0.3–1.8 molCm⁻² year⁻¹). The results of the three sensitivity experiments indicate that both resolving the temperature profile near the surface and assimilation of surface chlorophyll-a significantly impact the skill of simulating the biogeochemistry at L4 and all of the carbonate chemistry related variables. These results indicate that our forecasting ability of CO₂ air–sea flux in shelf seas environments and their impact in climate modeling should consider both model refinements as means of reducing uncertainties and errors in any future climate projections. Full article

Alfalfa is the most extensively cultivated forage legume worldwide, and salinity constitutes the main environmental scourge limiting its growth and productivity. To unravel the potential molecular mechanism involved in salt tolerance in alfalfa, we accomplished a combined analysis of parallel reaction monitoring-based proteomic technique and targeted metabolism. Based on proteomic analysis, salt stress induced 226 differentially abundant proteins (DAPs). Among them, 118 DAPs related to the antioxidant system, including glutathione metabolism and oxidation-reduction pathways, were significantly up-regulated. Data are available via ProteomeXchange with identifier PXD017166. Overall, 107 determined metabolites revealed that the tricarboxylic acid (TCA) cycle, especially the malate to oxaloacetate conversion step, was strongly stimulated by salt stress. This leads to an up-regulation by about 5 times the ratio of NADPH/NADP⁺, as well as about 3 to 5 times in the antioxidant enzymes activities, including those of catalase and peroxidase and proline contents. However, the expression levels of DAPs related to the Calvin–Benson–Bassham (CBB) cycle and photorespiration pathway were dramatically inhibited following salt treatment. Consistently, metabolic analysis showed that the metabolite amounts related to carbon assimilation and photorespiration decreased by about 40% after exposure to 200 mM NaCl for 14 d, leading ultimately to a reduction in net photosynthesis by around 30%. Our findings highlighted also the importance of the supplied extra reducing power, thanks to the TCA cycle, in the well-functioning of glutathione to remove and scavenge the reactive oxygen species (ROS) and mitigate subsequently the oxidative deleterious effect of salt on carbon metabolism including the CBB cycle. Full article

The opening and closing of plant stomata regulates the global water, carbon and energy cycles. Biophysical feedbacks on climate are highly dependent on transpiration, which is mediated by vegetation phenology and plant responses to stress conditions. Here, we explore the potential of satellite observations of solar-induced chlorophyll fluorescence (SIF)—normalized by photosynthetically-active radiation (PAR)—to diagnose the ratio of transpiration to potential evaporation (‘transpiration efficiency’, τ). This potential is validated at 25 eddy-covariance sites from seven biomes worldwide. The skill of the state-of-the-art land surface models (LSMs) from the eartH2Observe project to estimate τ is also contrasted against eddy-covariance data. Despite its relatively coarse (0.5°) resolution, SIF/PAR estimates, based on data from the Global Ozone Monitoring Experiment 2 (GOME-2) and the Clouds and Earth’s Radiant Energy System (CERES), correlate to the in situ τ significantly (average inter-site correlation of 0.59), with higher correlations during growing seasons (0.64) compared to decaying periods (0.53). In addition, the skill to diagnose the variability of in situ τ demonstrated by all LSMs is on average lower, indicating the potential of SIF data to constrain the formulations of transpiration in global models via, e.g., data assimilation. Overall, SIF/PAR estimates successfully capture the effect of phenological changes and environmental stress on natural ecosystem transpiration, adequately reflecting the timing of this variability without complex parameterizations. Full article

The carbon cycle of the terrestrial biosphere plays a vital role in controlling the global carbon balance and, consequently, climate change. Reliably modeled CO₂ fluxes between the terrestrial biosphere and the atmosphere are necessary in projections of policy strategies aiming at constraining carbon emissions and of future climate change. In this study, SMOS (Soil Moisture and Ocean Salinity) L3 soil moisture and JRC-TIP FAPAR (Joint Research Centre—Two-stream Inversion Package Fraction of Absorbed Photosynthetically Active Radiation) data with respective original resolutions at 10 sites were used to constrain the process-based terrestrial biosphere model, BETHY (Biosphere, Energy Transfer and Hydrology), using the carbon cycle data assimilation system (CCDAS). We find that simultaneous assimilation of these two datasets jointly at all 10 sites yields a set of model parameters that achieve the best model performance in terms of independent observations of carbon fluxes as well as soil moisture. Assimilation in a single-site mode or using only a single dataset tends to over-adjust related parameters and deteriorates the model performance of a number of processes. The optimized parameter set derived from multi-site assimilation with soil moisture and FAPAR also improves, when applied at global scale simulations, the model-data fit against atmospheric CO₂. This study demonstrates the potential of satellite-derived soil moisture and FAPAR when assimilated simultaneously in a model of the terrestrial carbon cycle to constrain terrestrial carbon fluxes. It furthermore shows that assimilation of soil moisture data helps to identity structural problems in the underlying model, i.e., missing management processes at sites covered by crops and grasslands. Full article

As an essential variable in linking water, carbon, and energy cycles, evapotranspiration (ET) is difficult to measure. Remote sensing, reanalysis, and land surface model-based ET products offer comprehensive alternatives at different spatio-temporal intervals, but their performance varies. In this study, we selected four popular ET global products: The Global Land Evaporation Amsterdam Model version 3.0a (GLEAM3.0a), the Modern Era Retrospective-Analysis for Research and Applications-Land (MERRA-Land) project, the Global Land Data Assimilation System version 2.0 with the Noah model (GLDAS2.0-Noah) and the EartH2Observe ensemble (EartH2Observe-En). Then, we comprehensively evaluated the performance of these products over China using a stratification method, six validation criteria, and high-quality eddy covariance (EC) measurements at 12 sites. The aim of this research was to provide important quantitative information to improve and apply the ET models and to inform choices about the appropriate ET product for specific applications. Results showed that, within one stratification, the performance of each ET product based on a certain criterion differed among classifications of this stratification. Furthermore, the optimal ET (OET) among these products was identified by comparing the magnitudes of each criterion. Results suggested that, given a criterion (a stratification classification), the OETs varied among stratification classifications (the selected six criteria). In short, no product consistently performed best, according to the selected validation criterion. Thus, multi-source ET datasets should be employed in future studies to enhance confidence in ET-related conclusions. Full article

Accurate quantification of the terrestrial evapotranspiration ( $ET$ ) components of plant transpiration (T), soil evaporation (E) and evaporation of the intercepted water (I) is necessary for improving our understanding of the links between the carbon and water cycles. Recent studies have noted that, among the modeled estimates, large disagreements exist in the relative contributions of T, E and I to the total $ET$ . As these models are often used in data assimilation environments for incorporating and extending $ET$ relevant remote sensing measurements, understanding the sources of inter-model differences in $ET$ components is also necessary for improving the utilization of such remote sensing measurements. This study quantifies the contributions of two key factors explaining inter-model disagreements to the uncertainty in total $ET$ : (1) contribution of the local partitioning and (2) regional distribution of $ET$ . The analysis is conducted by using outputs from a suite of land surface models in the North American Land Data Assimilation System (NLDAS) configuration. For most of these models, transpiration is the dominant component of the $ET$ partition. The results indicate that the uncertainty in local partitioning dominates the inter-model spread in modeled soil evaporation E. The inter-model differences in T are dominated by the uncertainty in the distribution of $ET$ over the Eastern U.S. and the local partitioning uncertainty in the Western U.S. The results also indicate that uncertainty in the T estimates is the primary driver of total $ET$ errors. Over the majority of the U.S., the contribution of the two factors of uncertainty to the overall uncertainty is non-trivial. Full article

The Farquhar—von Caemmerer—Berry (FvCB) biochemical model of photosynthesis, commonly used to estimate CO₂ assimilation at various spatial scales from leaf to global, has been used to assess the impacts of climate change on crop and ecosystem productivities. However, it is widely known that the parameters in the FvCB model are difficult to accurately estimate. The objective of this study was to assess the methods of Sharkey et al. and Gu et al., which are often used to estimate the parameters of the FvCB model. We generated A_n/C_i datasets with different data accuracies, numbers of data points, and data point distributions. The results showed that neither method accurately estimated the parameters; however, Gu et al.’s approach provided slightly better estimates. Using Gu et al.’s approach and datasets with measurement errors and the same accuracy as a typical open gas exchange system (i.e., Li-6400), the majority of the estimated parameters—V_cmax (maximal Rubisco carboxylation rate), K_co (effective Michaelis-Menten coefficient for CO₂), g_m (internal (mesophyll) conductance to CO₂ transport) and Γ* (chloroplastic CO₂ photocompensation point)—were underestimated, while the majority of R_d (day respiration) and α (the non-returned fraction of the glycolate carbon recycled in the photorespiratory cycle) were overestimated. The distributions of T_p (the rate of triose phosphate export from the chloroplast) were evenly dispersed around the 1:1 line using both approaches. This study revealed that a high accuracy of leaf gas exchange measurements and sufficient data points are required to correctly estimate the parameters for the biochemical model. The accurate estimation of these parameters can contribute to the enhancement of food security under climate change through accurate predictions of crop and ecosystem productivities. A further study is recommended to address the question of how the measurement accuracies can be improved. Full article