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Earth system models have been continously improved over the past decades, but systematic errors compared with observations and uncertainties in climate projections remain. This is due mainly to the imperfect representation of subgrid-scale or unknown processes. Here we propose a next-generation Earth system modelling approach with artificial intelligence that calls for accelerated models, machine-learning integration, systematic use of Earth observations and modernized infrastructures. The synergistic approach will allow faster and more accurate policy-relevant climate information delivery. We argue a multiscale approach is needed, making use of kilometre-scale climate models and improved coarser-resolution hybrid Earth system models that include essential Earth system processes and feedbacks yet are still fast enough to deliver large ensembles for better quantification of internal variability and extremes. Together, these can form a step change in the accuracy and utility of climate projections, meeting urgent mitigation and adaptation needs of society and ecosystems in a rapidly changing world.

Climate modelling and analysis are facing new demands to enhance projections and climate information. Here we argue that now is the time to push the frontiers of machine learning beyond state-of-the-art approaches, not only by developing machine-learning-based Earth system models with greater fidelity, but also by providing new capabilities through emulators for extreme event projections with large ensembles, enhanced detection and attribution methods for extreme events, and advanced climate model analysis and benchmarking. Utilizing this potential requires key machine learning challenges to be addressed, in particular generalization, uncertainty quantification, explainable artificial intelligence and causality. This interdisciplinary effort requires bringing together machine learning and climate scientists, while also leveraging the private sector, to accelerate progress towards actionable climate science.

Plants acclimate to temperature by adjusting their photosynthetic capacity over weeks to months. However, most evidence for photosynthetic acclimation derives from leaf-scale experiments. Here we address the scarcity of evidence for canopy-scale photosynthetic acclimation by examining the correlation between maximum photosynthetic rates (Amax,2,000) and growth temperature (Tair¯) across a range of concurrent temperatures and canopy foliage quantity, using data from >200 eddy covariance sites. We detect widespread thermal acclimation of canopy-scale photosynthesis, demonstrated by enhanced Amax,2,000under higher Tair¯, across flux sites with adequate water availability. A 14-day period is identified as the most relevant timescale for acclimation across all sites, with a range of 12–25 days for different plant functional types. The mean apparent thermal acclimation rate across all ecosystems is 0.41 (−0.38–1.04 for 5th–95th percentile range) µmol m−2s−1°C−1, with croplands showing the largest acclimation rates and grasslands the lowest. Incorporating an optimality-based prediction of leaf photosynthetic capacities into a biochemical photosynthesis model is shown to improve the representation of thermal acclimation. Our results underscore the critical need for enhanced understanding and modelling of canopy-scale photosynthetic capacity to accurately predict plant responses to warmer growing seasons.

The terrestrial biosphere absorbs about 30% of the CO2emitted by human activities each year. The persistence of such a carbon sink, however, depends on vegetation responses to future changes in water availability. While evidence suggests an increasing frequency and intensity of meteorological droughts, the potential systematic impact on vegetation productivity and its associated carbon absorption is unquantified. Using data from CMIP6 models and multiple meteorological drought indices and comparing the historical baseline (1851–2000) to the end of the century (2076–2100), we show that the global mean drought-associated reductions in vegetation productivity are projected to increase by 2.3 times under a sustainable development scenario (shared socioeconomic pathway [SSP] 1–2.6) and 3.5 times under fossil-fueled development (SSP5–8.5). These drought-induced productivity losses are greater than the predicted increase in productivity from greater CO2“fertilization,” particularly for cropland. Our results indicate that the exacerbation of meteorological droughts under future warming will risk global food security and may transform the terrestrial ecosystem from a carbon sink into a carbon source.

Clouds play a key role in regulating climate change but are difficult to simulate within Earth system models (ESMs). Improving the representation of clouds is one of the key tasks toward more robust climate change projections. This study introduces a new machine-learning-based framework relying on satellite observations to improve understanding of the representation of clouds and their relevant processes in climate models. The proposed method is capable of assigning distributions of established cloud types to coarse data. It facilitates a more objective evaluation of clouds in ESMs and improves the consistency of cloud process analysis. The method is built on satellite data from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument labeled by deep neural networks with cloud types defined by the World Meteorological Organization (WMO), using cloud-type labels from CloudSat as ground truth. The method is applicable to datasets with information about physical cloud variables comparable to MODIS satellite data and at sufficiently high temporal resolution. We apply the method to alternative satellite data from the Cloud_cci project (ESA Climate Change Initiative), coarse-grained to typical resolutions of climate models. The resulting cloud-type distributions are physically consistent and the horizontal resolutions typical of ESMs are sufficient to apply our method. We recommend outputting crucial variables required by our method for future ESM data evaluation. This will enable the use of labeled satellite data for a more systematic evaluation of clouds in climate models.

The time required for an ecosystem to recover from severe drought is a key component of ecological resilience. The phenology effects on drought recovery are, however, poorly understood. These effects centre on how phenology variations impact biophysical feedbacks, vegetation growth and, ultimately, recovery itself. Using multiple remotely sensed datasets, we found that more than half of ecosystems in mid- and high-latitudinal Northern Hemisphere failed to recover from extreme droughts within a single growing season. Earlier spring phenology in the drought year slowed drought recovery when extreme droughts occurred in mid-growing season. Delayed spring phenology in the subsequent year slowed drought recovery for all vegetation types (with importance of spring phenology ranging from 46% to 58%). The phenology effects on drought recovery were comparable to or larger than other well-known postdrought climatic factors. These results strongly suggest that the interactions between vegetation phenology and drought must be incorporated into Earth system models to accurately quantify ecosystem resilience.

The parameterization of suspended sediments in vegetated flows presents a significant challenge, yet it is crucial across various environmental and geophysical disciplines. This study focuses on modeling suspended sediment concentrations (SSC) in vegetated flows with a canopy density of avH∈ [0.3, 1.0] by examining turbulent dispersive flux. While conventional studies disregard dispersive momentum flux for avH> 0.1, our findings reveal significant dispersive sediment flux for large particles with a diameter‐to‐Kolmogorov length ratio when dp/η> 0.1. Traditional Rouse alike approaches therefore must be revised to account for this effect. We introduce a hybrid methodology that combines physical modeling with machine learning to parameterize dispersive flux, guided by constraints from diffusive and settling fluxes, characterized using recent covariance and turbulent settling methods, respectively. The model predictions align well with reported SSC data, demonstrating the versatility of the model in parameterizing sediment‐vegetation interactions in turbulent flows. This research focuses on the movement of suspended sediments in rivers, specifically in areas with vegetation. Traditional models cannot depict this process, prompting the development of a new methodology that merges conventional scientific techniques with advanced machine learning. The proposed approach has demonstrated superior performance, particularly in transitional areas between vegetated and non‐vegetated zones. The significance of this study lies in its contribution to modeling an often‐overlooked spatial term (called ”turbulent dispersive flux”) by showing that its magnitude is much greater than conventionally supposed. This study also advances the current methodology of including physics‐based artificial intelligence to model sediment dynamics in vegetated river systems. The proposed approach is versatile and can be included in the large‐scale transport models. Rouse budget without dispersive flux cannot describe coarse sediment concentration in vegetated flowsA machine‐assisted physical closure for turbulent dispersive flux from spatial average is proposedPredicted sediment concentration results agree with reported experimental data in the literature Rouse budget without dispersive flux cannot describe coarse sediment concentration in vegetated flows A machine‐assisted physical closure for turbulent dispersive flux from spatial average is proposed Predicted sediment concentration results agree with reported experimental data in the literature

Day-to-day changes in CO2emissions from human activities, in particular fossil-fuel combustion and cement production, reflect a complex balance of influences from seasonality, working days, weather and, most recently, the COVID-19 pandemic. Here, we provide a daily CO2emissions dataset for the whole year of 2020, calculated from inventory and near-real-time activity data. We find a global reduction of 6.3% (2,232 MtCO2) in CO2emissions compared with 2019. The drop in daily emissions during the first part of the year resulted from reduced global economic activity due to the pandemic lockdowns, including a large decrease in emissions from the transportation sector. However, daily CO2emissions gradually recovered towards 2019 levels from late April with the partial reopening of economic activity. Subsequent waves of lockdowns in late 2020 continued to cause smaller CO2reductions, primarily in western countries. The extraordinary fall in emissions during 2020 is similar in magnitude to the sustained annual emissions reductions necessary to limit global warming at 1.5 °C. This underscores the magnitude and speed at which the energy transition needs to advance.

Surface-water availability, defined as precipitation minus evapotranspiration, can be affected by changes in vegetation. These impacts can be local, due to the modification of evapotranspiration and precipitation, or non-local, due to changes in atmospheric moisture transport. However, the teleconnections of vegetation changes on water availability in downwind regions remain poorly constrained by observations. By linking measurements of local precipitation to a new hydrologically weighted leaf area index that accounts for both local and upwind vegetation contributions, we demonstrate that vegetation changes have increased global water availability at a rate of 0.26 mm yr−2for the 2001–2018 period. Critically, this increase has attenuated about 15% of the recently observed decline in global water availability. The water availability increase is due to a greater rise in precipitation relative to evapotranspiration for over 53% of the global land surface. We also quantify the potential hydrological impacts of regional vegetation increases at any given location across global land areas. We find that enhanced vegetation is beneficial to both local and downwind water availability for ~45% of the land surface, whereas it is adverse elsewhere, primarily in water-limited or high-elevation regions. Our results highlight the potential strong effects of deliberate vegetation changes, such as afforestation programmes, on water resources beyond local and regional scales.

Year-to-year changes in carbon uptake by terrestrial ecosystems have an essential role in determining atmospheric carbon dioxide concentrations1. It remains uncertain to what extent temperature and water availability can explain these variations at the global scale2–5. Here we use factorial climate model simulations6and show that variability in soil moisture drives 90 per cent of the inter-annual variability in global land carbon uptake, mainly through its impact on photosynthesis. We find that most of this ecosystem response occurs indirectly as soil moisture–atmosphere feedback amplifies temperature and humidity anomalies and enhances the direct effects of soil water stress. The strength of this feedback mechanism explains why coupled climate models indicate that soil moisture has a dominant role4, which is not readily apparent from land surface model simulations and observational analyses2,5. These findings highlight the need to account for feedback between soil and atmospheric dryness when estimating the response of the carbon cycle to climatic change globally5,7, as well as when conducting field-scale investigations of the response of the ecosystem to droughts8,9. Our results show that most of the global variability in modelled land carbon uptake is driven by temperature and vapour pressure deficit effects that are controlled by soil moisture.

Global CO2emissions in 2021 were only 1% less than the record levels of 2019, driven by increases in power- and industry-related emissions from China and India and a return of the carbon intensity of electricity to pre-pandemic levels. Is this resumed growth in fossil energy, or a final fleeting surge before a long decline?

Global warming alters surface water availability (precipitation minus evapotranspiration, P–E) and hence freshwater resources. However, the influence of land–atmosphere feedbacks on future P–E changes and the underlying mechanisms remain unclear. Here we demonstrate that soil moisture (SM) strongly impacts future P–E changes, especially in drylands, by regulating evapotranspiration and atmospheric moisture inflow. Using modelling and empirical approaches, we find a consistent negative SM feedback on P–E, which may offset ~60% of the decline in dryland P–E otherwise expected in the absence of SM feedbacks. The negative feedback is not caused by atmospheric thermodynamic responses to declining SM; rather, reduced SM, in addition to limiting evapotranspiration, regulates atmospheric circulation and vertical ascent to enhance moisture transport into drylands. This SM effect is a large source of uncertainty in projected dryland P–E changes, underscoring the need to better constrain future SM changes and improve the representation of SM–atmosphere processes in models.

Atmospheric warming threatens to accelerate the retreat of the Antarctic Ice Sheet by increasing surface melting and facilitating ‘hydrofracturing’1–7, where meltwater flows into and enlarges fractures, potentially triggering ice-shelf collapse3–5,8–10. The collapse of ice shelves that buttress11–13the ice sheet accelerates ice flow and sea-level rise14–16. However, we do not know if and how much of the buttressing regions of Antarctica’s ice shelves are vulnerable to hydrofracture if inundated with water. Here we provide two lines of evidence suggesting that many buttressing regions are vulnerable. First, we trained a deep convolutional neural network (DCNN) to map the surface expressions of fractures in satellite imagery across all Antarctic ice shelves. Second, we developed a stability diagram of fractures based on linear elastic fracture mechanics to predict where basal and dry surface fractures form under current stress conditions. We find close agreement between the theoretical prediction and the DCNN-mapped fractures, despite limitations associated with detecting fractures in satellite imagery. Finally, we used linear elastic fracture mechanics theory to predict where surface fractures would become unstable if filled with water. Many regions regularly inundated with meltwater today are resilient to hydrofracture—stresses are low enough that all water-filled fractures are stable. Conversely, 60 ± 10 per cent of ice shelves (by area) both buttress upstream ice and are vulnerable to hydrofracture if inundated with water. The DCNN map confirms the presence of fractures in these buttressing regions. Increased surface melting17could trigger hydrofracturing if it leads to water inundating the widespread vulnerable regions we identify. These regions are where atmospheric warming may have the largest impact on ice-sheet mass balance.

Global warming is projected to shift the phenology and increase the productivity of northern ecosystems1–6. Both changes will further feed back to climate through biophysical and biogeochemical processes and are critical for future prediction7,8. However, it remains unclear whether warming and the extended growing season, especially in autumn, would lead to increased net ecosystem carbon uptake9,10. Here we analyse satellite observations, field measurements and model simulations and show a prevalent radiation limitation on carbon uptake in northern ecosystems, especially in autumn. By comparing the start and end of the growing season estimated from vegetation indices and from solar-induced chlorophyll fluorescence (a proxy for gross primary production11,12(GPP)), we find a greater change in greenness-based start and end of season than that from GPP, mostly caused by the radiation limitation on photosynthesis. This radiation limitation explains the contrasting responses of autumn net carbon exchanges to warming, using both eddy covariance measurements and model simulations from Coupled Model Intercomparison Project Phase 5. Regions with weak radiation limitation benefit more from warming and enhanced vegetation greenness in autumn, where GPP increases can outweigh the warming-induced respiration carbon losses. With continued warming, radiation limitation will increase and exert a strong upper bound on northern ecosystems to act as carbon sinks.

Climate models are essential to understand and project climate change, yet long‐standing biases and uncertainties in their projections remain. This is largely associated with the representation of subgrid‐scale processes, particularly clouds and convection. Deep learning can learn these subgrid‐scale processes from computationally expensive storm‐resolving models while retaining many features at a fraction of computational cost. Yet, climate simulations with embedded neural network parameterizations are still challenging and highly depend on the deep learning solution. This is likely associated with spurious non‐physical correlations learned by the neural networks due to the complexity of the physical dynamical system. Here, we show that the combination of causality with deep learning helps removing spurious correlations and optimizing the neural network algorithm. To resolve this, we apply a causal discovery method to unveil causal drivers in the set of input predictors of atmospheric subgrid‐scale processes of a superparameterized climate model in which deep convection is explicitly resolved. The resulting causally‐informed neural networks are coupled to the climate model, hence, replacing the superparameterization and radiation scheme. We show that the climate simulations with causally‐informed neural network parameterizations retain many convection‐related properties and accurately generate the climate of the original high‐resolution climate model, while retaining similar generalization capabilities to unseen climates compared to the non‐causal approach. The combination of causal discovery and deep learning is a new and promising approach that leads to stable and more trustworthy climate simulations and paves the way toward more physically‐based causal deep learning approaches also in other scientific disciplines. Climate models have biases compared to observations that have been present for a long time because certain processes, like convection, are only approximated using simplified methods. Neural networks can better represent these processes, but often learn incorrect connections leading to unreliable results and climate model crashes. To solve this, we used a new method that informs neural networks with causal drivers, therefore, respecting the underlying physical processes. By doing so, we developed more reliable and trustworthy neural networks, allowing us to accurately represent the climate of the original high‐resolution simulation on which these neural networks were trained. Causal discovery unveils causal drivers of subgrid‐scale processes across climatesThe causally‐informed hybrid model runs stably and generates a climate close to the original high‐resolution simulationSpurious correlations are evident in the non‐causal parameterization, leading to underestimate feature importance of physical drivers Causal discovery unveils causal drivers of subgrid‐scale processes across climates The causally‐informed hybrid model runs stably and generates a climate close to the original high‐resolution simulation Spurious correlations are evident in the non‐causal parameterization, leading to underestimate feature importance of physical drivers

Estimating ecosystem evapotranspiration (ET) is important to understanding the global water cycle and to study land‐atmosphere interactions. We developed a physics constrained machine learning (ML) model (hybrid model) to estimate latent heat flux (LE), which conserves the surface energy budget. By comparing model predictions with observations at 82 eddy covariance tower sites, our hybrid model shows similar performance to the pure ML model in terms of mean metrics (e.g., mean absolute percent errors) but, importantly, the hybrid model conserves the surface energy balance, while the pure ML model does not. A second key result is that the hybrid model extrapolates much better than the pure ML model, emphasizing the benefits of combining physics with ML for increased generalizations. The hybrid model allows inferring the structural dependence of ET and surface resistance (rs), and we find that vegetation height and soil moisture are the main regulators of ET and rs. A physics constrained machine learning model is developed using the FLUXNET2015 Tier 1 data set. This new approach is able to effectively retrieve latent heat flux while constraining energy conservation in the surface energy budget. This hybrid model has better performance in extrapolation than a pure machine learning model. A physics‐constrained machine learning model of evapotranspiration (hybrid model) is developed and trained using the FLUXNET 2015 data setThe evapotranspiration retrieved by the hybrid model is as accurate as pure machine learning model and also conserves surface energy balanceThe hybrid model better reproduces extremes and thus better extrapolates compared to the pure machine learning approach

Convective organization has a large impact on precipitation and feeds back on larger‐scale circulations in the tropics. The degree of this convective organization changes with modes of climate variability like the El Niño–Southern Oscillation (ENSO), but because organization is not represented in current climate models, a quantitative assessment of these shifts has not been possible. Here, we construct multidecade satellite climatologies of occurrence of tropical convective organization and its properties and assess changes with ENSO phase. The occurrence of organized deep convection becomes more concentrated, increasing threefold in the eastern and central Pacific during El Niño and decreasing twofold outside of these regions. Both horizontal extent of the cold cloud shield and convective depth increase in regions of positive sea surface temperature anomaly (SSTa); however, the regions of greatest convective deepening are those of large‐scale ascent, rather than those of warmest SSTa. Extent decreases with SSTa at a rate of about 20 km/K, while the SSTa dependence of depth is only about 0.2 K/K. We introduce two values to describe convective changes with ENSO more succinctly: (1) an information entropy metric to quantify the clustering of convective system occurrences and (2) a growth metric to quantify deepening relative to spreading over the system lifetime. Finally, with collocated precipitation data, we see that rainfall attributable to convective organization jumps up to 5% with warming. Rain intensity and amount increase for a given system size during El Niño, but a given rain amount may actually fall with higher intensity during La Niña. Tropical storms at an intermediate scale between individual thunderstorms and cyclones or hurricanes are not represented by climate models. As the satellite data record now spans several decades, we can use these observations to understand how large storms change during El Niño periods. Here, we find that warmer waters in the central and eastern Pacific during El Niño cause a dramatic increase in storm occurrence. Storms are more likely to occur near to other storms and to become larger in both the vertical and horizontal. Changes to large‐scale winds are more influential on these structural factors than locally warmer ocean water. The hydrological impact of these storms is also important because they bring the majority of precipitation in some regions. We see that a storm of a certain extent brings larger volume and intensity of rain during El Niño. But a certain rain volume may actually fall with greater intensity during La Niño. Convective organization becomes more frequent throughout the tropics, and especially over warmer ocean waters, during El NiñoThe vertical depth, horizontal extent, and attributable precipitation of tropical organized convection increase during El NiñoWe present new metrics that quantify the relative importance of convective system deepening to spreading and their degree of organization

Extreme temperatures are responsible for damages to society and ecosystems. There is evidence that severe episodes of extreme heat have been occurring more frequently and more severely in recent periods. Driven primarily by oceanic and atmospheric effects as well as land‐climate feedbacks, those extreme events are expected to increase with climate change. Vegetation, which regulates the energy, water, and carbon cycles, is a key player of land‐atmosphere interactions that has been proven to be determinant in recent extreme events. Using an ensemble of Earth System Models simulations, we show that physiological effects globally increase the annual daily maximum temperature (Txx) with rising [CO2], accounting globally for around 13% of the full Txx trend. Due to physiological effects, Txx can reinforce (e.g., central Europe) or reduce (e.g., central North America) the mean temperature increase. Heat waves cause damages to society and natural environment. We observed a trend of increasing frequency and intensity of extreme temperature episodes in recent years. Climate change on one side and the complex feedbacks of the climate with the land on the other side are the main drivers of this trend. Vegetation is an important factor of the feedback as it connects the land surface and the atmosphere, regulating the exchanges of water and heat. Using an ensemble of Earth System Models simulations, we show that the effects of vegetation globally increase extreme temperatures with rising [CO2], accounting globally for around 13% of the full trend. This effect can, however, regionally be larger (e.g., central Europe) or smaller (e.g., central North America) than the mean temperature increase. Physiological effects increase the annual daily maximum temperature (Txx) with rising [CO2], accounting globally for around 15% of its trendThe Txx increasing trends can be reinforced by vegetation physiological feedbacks, especially in regions like Europe or the AmazonIn several regions, such as North America, vegetation physiological feedbacks can oppositely mitigate the radiatively driven increase in Txx

Although the terrestrial biosphere absorbs about 25 per cent of anthropogenic carbon dioxide (CO2) emissions, the rate of land carbon uptake remains highly uncertain, leading to uncertainties in climate projections1,2. Understanding the factors that limit or drive land carbon storage is therefore important for improving climate predictions. One potential limiting factor for land carbon uptake is soil moisture, which can reduce gross primary production through ecosystem water stress3,4, cause vegetation mortality5and further exacerbate climate extremes due to land–atmosphere feedbacks6. Previous work has explored the impact of soil-moisture availability on past carbon-flux variability3,7,8. However, the influence of soil-moisture variability and trends on the long-term carbon sink and the mechanisms responsible for associated carbon losses remain uncertain. Here we use the data output from four Earth system models9from a series of experiments to analyse the responses of terrestrial net biome productivity to soil-moisture changes, and find that soil-moisture variability and trends induce large CO2fluxes (about two to three gigatons of carbon per year; comparable with the land carbon sink itself1) throughout the twenty-first century. Subseasonal and interannual soil-moisture variability generate CO2as a result of the nonlinear response of photosynthesis and net ecosystem exchange to soil-water availability and of the increased temperature and vapour pressure deficit caused by land–atmosphere interactions. Soil-moisture variability reduces the present land carbon sink, and its increase and drying trends in several regions are expected to reduce it further. Our results emphasize that the capacity of continents to act as a future carbon sink critically depends on the nonlinear response of carbon fluxes to soil moisture and on land–atmosphere interactions. This suggests that the increasing trend in carbon uptake rate may not be sustained past the middle of the century and could result in accelerated atmospheric CO2growth.

Cloud shading limits surface radiation, thus reducing vegetation water stress and, presumably, flammability. Since the early 1970s, cloud observations from airfields in coastal Southern California (CSCA) indicate reductions of ~25–50% in warm‐season frequency of daytime stratus clouds at many sites, including fire‐prone wildland‐urban interface zones. We use 10 years of meteorological, surface radiation, and cloud observations to statistically model the effects of clouds on warm‐season surface energy fluxes in CSCA. Forcing our model with cloud observations, we estimate that reduced warm‐season cloud shading since the 1970s significantly enhanced daytime solar radiation and evaporative demand throughout much of CSCA, particularly in greater Los Angeles and northern San Diego. Correlation with burned area and live fuel moisture implicates stratus cloud shading as an important driver of warm‐season wildfire activity in CSCA. Large reductions in cloud shading have likely enhanced warm‐season wildfire potential in many CSCA areas when and where fuels are not limiting. In much of coastal Southern California, the frequency of summer clouds has declined rapidly in recent decades due to warming from urbanization and greenhouse gases. These reductions have significantly reduced cloud shading and increased evaporative demand, particularly in greater Los Angeles and northern San Diego, such that a relatively cloudy summer today is similar to a relatively clear summer in the 1970s. Clouds appear to be important regulators of summer wildfire activity in this region, as the shade they provide slows loss of moisture from vegetation. On the vegetated mountainsides that ring coastal Southern California's large cities, increases in summer sunlight and evaporative demand have likely enhanced summer wildfire potential over the past several decades. This effect is expected to continue due to continued urban expansion and positive feedbacks, where warming due to cloud loss promotes further warming and cloud loss. Warm‐season daytime cloud frequency significantly declined in much of coastal Southern California over the past half centuryBased on a statistical model, observed reductions in coastal cloud frequency significantly increased net radiation and evaporative demandCorrelation analysis suggests that summer cloud frequency significantly affects fuel moisture and burned area in coastal Southern California

Climate change is altering the dynamics, structure and function of the Amazon, a biome deeply connected to the Earth’s carbon cycle. Climate factors that control the spatial and temporal variations in forest photosynthesis have been well studied, but the influence of forest height and age on this controlling effect has rarely been considered. Here, we present remote sensing observations of solar-induced fluorescence (a proxy for photosynthesis), precipitation, vapour-pressure deficit and canopy height, together with estimates of forest age and aboveground biomass. We show that photosynthesis in tall Amazonian forests, that is, forests above 30 m, is three times less sensitive to precipitation variability than in shorter (less than 20 m) forests. Taller Amazonian forests are also found to be older, have more biomass and deeper rooting systems1, which enable them to access deeper soil moisture and make them more resilient to drought. We suggest that forest height and age are an important control of photosynthesis in response to interannual precipitation fluctuations. Although older and taller trees show less sensitivity to precipitation variations, they are more susceptible to fluctuations in vapour-pressure deficit. Our findings illuminate the response of Amazonian forests to water stress, droughts and climate change. Tall trees are less sensitive to variation in precipitation than short trees, according to analyses of photosynthetic sensitivity to drought in tall and short Amazon forests. The results demonstrate higher resilience of tall trees to drought.

Light‐use efficiency (LUE), which quantifies the plants' efficiency in utilizing solar radiation for photosynthetic carbon fixation, is an important factor for gross primary production estimation. Here we use satellite‐based solar‐induced chlorophyll fluorescence as a proxy for photosynthetically active radiation absorbed by chlorophyll (APARchl) and derive an estimation of the fraction of APARchl(fPARchl) from four remotely sensed vegetation indicators. By comparing maximum LUE estimated at different scales from 127 eddy flux sites, we found that the maximum daily LUE based on PAR absorption by canopy chlorophyll ( εmaxchl), unlike other expressions of LUE, tends to converge across biome types. The photosynthetic seasonality in tropical forests can also be tracked by the change of fPARchl, suggesting the corresponding εmaxchlto have less seasonal variation. This spatio‐temporal convergence of LUE derived from fPARchlcan be used to build simple but robust gross primary production models and to better constrain process‐based models. Plants absorb light to fix carbon dioxide; the efficiency of this process is termed as light‐use efficiency and can be calculated based on different light absorption definitions. Among the light being absorbed by plants, only a fraction is captured by chlorophyll and can be further used for photosynthesis. In this study, we used satellite data and derived an estimation of the fraction of light that is absorbed by chlorophyll. We found that different plants have a similar efficiency using chlorophyll‐absorbed light to fix carbon dioxide; this efficiency is also found to be stable throughout the season in tropical forest. The results of this study can be used to improve models' capability to estimate the total carbon fixed by plants at global scale. EVI and MTCI can be better proxies of fraction of photosynthetically active radiation absorbed by canopy chlorophyllMaximum daily light‐use efficiency for chlorophyll‐absorbed photosynthetically active radiation exhibits less variation across biome typesMaximum daily light‐use efficiency for chlorophyll‐absorbed photosynthetically active radiation exhibits less variation across season

The concept of isohydry/anisohydry describes the degree to which plants regulate their water status, operating from isohydric with strict regulation to anisohydric with less regulation. Though some species level measures of isohydry/anisohydry exist at a few locations, ecosystem‐scale information is still largely unavailable. In this study, we use diurnal observations from active (Ku‐Band backscatter from QuikSCAT) and passive (X‐band vegetation optical depth (VOD) from Advanced Microwave Scanning Radiometer on EOS Aqua) microwave satellite data to estimate global ecosystem isohydry/anisohydry. Here diurnal observations from both satellites approximate predawn and midday plant canopy water contents, which are used to estimate isohydry/anisohydry. The two independent estimates from radar backscatter and VOD show reasonable agreement at low and middle latitudes but diverge at high latitudes. Grasslands, croplands, wetlands, and open shrublands are more anisohydric, whereas evergreen broadleaf and deciduous broadleaf forests are more isohydric. The direct validation with upscaled in situ species isohydry/anisohydry estimates indicates that the VOD‐based estimates have much better agreement than the backscatter‐based estimates. The indirect validation with prior knowledge suggests that both estimates are generally consistent in that vegetation water status of anisohydric ecosystems more closely tracks environmental fluctuations of water availability and demand than their isohydric counterparts. However, uncertainties still exist in the isohydry/anisohydry estimate, primarily arising from the remote sensing data and, to a lesser extent, from the methodology. The comprehensive assessment in this study can help us better understand the robustness, limitation, and uncertainties of the satellite‐derived isohydry/anisohydry estimates. The ecosystem isohydry/anisohydry has the potential to reveal new insights into spatiotemporal ecosystem response to droughts. Isohydry/anisohydry estimates from backscatter and VOD data show reasonable agreement at low and middle latitudes but diverge at high latitudesGrasslands, croplands, and open shrublands are more anisohydric, while evergreen broadleaf and deciduous broadleaf forests are more isohydricVOD‐based isohydry/anisohydry estimates show better agreement with upscaled in situ measurements than backscatter‐based estimates

Elevated atmospheric CO2concentrations are expected to enhance photosynthesis and reduce stomatal conductance, thus increasing plant water use efficiency. A recent study based on eddy covariance flux observations from Northern Hemisphere forests showed a large increase in inherent water use efficiency (IWUE). Here we used an updated version of the same data set and robust uncertainty quantification to revisit these contemporary IWUE trends. We tested the hypothesis that the observed IWUE increase could be attributed to interannual trends in plant functional traits, potentially triggered by environmental change. We found that IWUE increased by ~1.3% yr−1, which is less than previously reported but still larger than theoretical expectations. Numerical simulations with the Tethys‐Chloris ecosystem model using temporally static plant functional traits cannot explain this increase. Simulations with plant functional trait plasticity, i.e., temporal changes in model parameters such as specific leaf area and maximum Rubisco capacity, match the observed trends in IWUE. Our results show that trends in plant functional traits, equal to 1.0% yr−1, can explain the observed IWUE trends. Thus, at decadal or longer time scales, trait plasticity could potentially influence forest water, carbon, and energy fluxes with profound implications for both the monitoring of temporal changes in plant functional traits and their representation in Earth system models. Ecosystem inherent water use efficiency (IWUE) increased on average by 1.3% yr−1over 20 Northern Hemisphere forests between 1992 and 2014A mechanistic model can explain this larger‐than‐expected increase in IWUE only when trends in plant physiological traits are enabledLong‐term plant physiological measurements could capture the rate of plant trait plasticity and acclimation to environmental change

The terrestrial biosphere and atmosphere interact through a series of feedback loops. Variability in terrestrial vegetation growth and phenology can modulate fluxes of water and energy to the atmosphere, and thus affect the climatic conditions that in turn regulate vegetation dynamics. Here we analyse satellite observations of solar-induced fluorescence, precipitation, and radiation using a multivariate statistical technique. We find that biosphere–atmosphere feedbacks are globally widespread and regionally strong: they explain up to 30% of precipitation and surface radiation variance in regions where feedbacks occur. Substantial biosphere–precipitation feedbacks are often found in regions that are transitional between energy and water limitation, such as semi-arid or monsoonal regions. Substantial biosphere–radiation feedbacks are often present in several moderately wet regions and in the Mediterranean, where precipitation and radiation increase vegetation growth. Enhancement of latent and sensible heat transfer from vegetation accompanies this growth, which increases boundary layer height and convection, affecting cloudiness, and consequently incident surface radiation. Enhanced evapotranspiration can increase moist convection, leading to increased precipitation. Earth system models underestimate these precipitation and radiation feedbacks mainly because they underestimate the biosphere response to radiation and water availability. We conclude that biosphere–atmosphere feedbacks cluster in specific climatic regions that help determine the net CO2balance of the biosphere.

Large-eddy simulations of shallow convection are used to evaluate the role of convective mixing and evaporative cooling in the vertical transport of mass, heat, and moisture in nonprecipitating shallow convection. Evaporative cooling is found to increase mass flux and the magnitude of heat and moisture fluxes, comparing twin large-eddy simulations with either suppressed or active evaporative cooling. Nonetheless, subsiding shells transport mass downward even when evaporative cooling is suppressed, emphasizing that evaporative cooling is not the primary cause of existence of subsiding shells and accompanied buoyancy reversal. Instead, vertical convective mixing is found to be the primary reason of buoyancy reversal. Evaporative cooling yet accelerates downdrafts (updrafts) in the shell (cloudy) regions as well as increases the cloud cover in the lower cloud layer. The cloudy regions are more humid, and the liquid water potential temperature is lower compared to the evaporative-cooling-suppressed experiment. The primary effect of evaporative cooling is thus to increase the updraft core anomalies, thus enhancing vertical turbulent fluxes. Cumulus subsiding shells exist even when evaporative cooling is suppressedVertical convective mixing and condensation induce buoyancy reversal in shallow convectionEvaporative cooling increases the mass flux and amplitude of the vertical moisture and heat fluxes

Taylors' frozen turbulence hypothesis suggests that all turbulent eddies are advected by the mean streamwise velocity, without changes in their properties. This hypothesis has been widely invoked to compute Reynolds averaging using temporal turbulence data measured at a single point in space. However, in the atmospheric surface layer, the exact relationship between convection velocity and wave number khas not been fully revealed since previous observations were limited by either their spatial resolution or by the sampling length. Using Distributed Temperature Sensing (DTS), acquiring turbulent temperature fluctuations at high temporal and spatial frequencies, we computed convection velocities across wave numbers using a phase spectrum method. We found that convection velocity decreases as k-1/3at the higher wave numbers of the inertial subrange instead of being independent of wave number as suggested by Taylor's hypothesis. We further corroborated this result using large eddy simulations. Applying Taylor's hypothesis thus systematically underestimates turbulent spectrum in the inertial subrange. A correction is proposed for point-based eddy-covariance measurements, which can improve surface energy budget closure and estimates of CO2fluxes. Taylor's hypothesis does not apply in the atmospheric surface layerInstead of being constant, the convective velocity exhibits a k-1/3dependence at high wave numbers in the inertial subrangeA correction is proposed for eddy-covariance measurements

The effects of precipitation (Pr) and evapotranspiration (ET) on surface soil moisture (SSM) play an essential role in the land‐atmosphere system. Here we evaluate multimodel differences of these effects within the Coupled Model Intercomparison Project Phase 5 (CMIP5) compared to Soil Moisture Active Passive (SMAP) products and ECMWF Reanalysis v5 (ERA5) as references in a frequency domain. The variability of SSM, Pr, and ET within three frequency bands (1/7 ∼ 1/30 days−1, 1/30 ∼ 1/90 days−1, and 1/90 ∼ 1/365 days−1) after normalization is quantified using Fourier transform. We analyze the impact of ET and Pr on SSM variability based on a transfer function assuming that these variables form a linear time‐invariant (LTI) system. For the total effects of ET and Pr on SSM variability, the CMIP5 estimations are smaller than the reference data in the two higher frequency bands and are larger than the reference data in the lowest frequency band. Besides, the effects on SSM by Pr and ET are found to be different across the three frequency bands. In each frequency band, the variability of the factor that dominates SSM (i.e., Pr or ET) from CMIP5 is smaller than that from the references. This study identifies the spatiotemporal distribution of differences between CMIP5 models and references (SMAP and ERA5) in simulating ET and Pr effects on SSM within three frequency bands. This study provides insightful information on how soil moisture variability is affected by varying precipitation and evapotranspiration at different time scales within Earth System Models. Climate is influenced by the interactions between the land surface and atmosphere boundary, and soil moisture is a key component of these physical processes. Precipitation and evapotranspiration, as two major variables involved in these interactions, have been largely regarded as essential processes affecting soil moisture dynamics. However, Earth System Models have large uncertainties in simulating these effects. This study compares the average performance of 14 Earth System Models in capturing the effects of precipitation and evapotranspiration on surface soil moisture variability. We find that (a) soil moisture is mainly affected by precipitation at weekly to seasonal time scales and by evapotranspiration at seasonal to annual time scales; (b) compared to two largely used reference data, the total effects of precipitation and evapotranspiration on soil moisture is smaller at weekly to seasonal time scales and are larger at seasonal to annual time scale; and (c) spatially, models tend to simulate less variability of precipitation or evapotranspiration as a major control on surface soil moisture. The effects of precipitation and evapotranspiration on soil moisture variability can be analyzed in a frequency domainPrecipitation dominates weekly to seasonal variability and evapotranspiration dominates seasonal to annual variability of soil moistureEarth System Models shall be improved in simulating soil moisture temporal variabilities The effects of precipitation and evapotranspiration on soil moisture variability can be analyzed in a frequency domain Precipitation dominates weekly to seasonal variability and evapotranspiration dominates seasonal to annual variability of soil moisture Earth System Models shall be improved in simulating soil moisture temporal variabilities

Previous studies seldom consider humidity when examining heat‐related extremes, and none have explored the effects of humidity on concurrent extremes of high heat stress and low river streamflow. Here, we present the first global picture of projected changes in compound lethal heat stress (Th)‐drought hazards (CHD) across 11,637 catchments. Our observational datasets show that atmospheric conditions (e.g., energy and vapor flux) play an important role in constraining the heat extremes, and that Th(32% ± 11%) yields a higher coincidence rate of global CHD than wet‐bulb temperature (28% ± 11%), driven by lower relative humidity (RH) and thus air dryness in Thextremes. Our large model ensemble projects a 10‐fold intensification of bivariate CHD risks by 2071–2100, mainly driven by increases in heat extremes. Future declines in RH, wind, snow, and precipitation in many regions are likely to exacerbate such water and weather‐related hazards (e.g., drought and CHD). Water and weather‐related extremes such as droughts and heatwaves typically occur simultaneously, posing larger risks to humans and ecosystem than any individual hazard. Extreme high lethal heat stress, which is governed by both temperature and humidity, inhibits the evaporative cooling function of the human body. However, changes in lethal heat stress under past and future climate are poorly understood at the global scale, and no study has yet explored their influences on the risk of compound heat stress‐drought events. Here, we provide the first global picture of the concurrent high heat stress–low streamflow extremes and their projected changes. The fraction of lethal heat extremes accompanied by drought hazards is projected to rise markedly throughout the 21st century. The global magnitude and occurrence of compound hazards is projected to intensify by 4–10 times by 2071–2100, mainly due to the increasing severity of heat extremes. Our study reveals an increasing global risk of compounding hazards, highlighting the need to better prepare adaptation and mitigation solutions in the Anthropocene. We present the first global assessment of compound extremes of high lethal heat stress and low river streamflowLethal heat stress yields a higher coincidence rate of compound hazards than wet‐bulb temperatureFuture lethal heat stress‐drought events are mainly exacerbated by changes in heat extremes We present the first global assessment of compound extremes of high lethal heat stress and low river streamflow Lethal heat stress yields a higher coincidence rate of compound hazards than wet‐bulb temperature Future lethal heat stress‐drought events are mainly exacerbated by changes in heat extremes

There are significant biases and uncertainties in the simulated soil moisture with land surface models. Here we evaluate multimodel differences in Coupled Model Intercomparison Project Phase 5 (CMIP5) compared to Soil Moisture Active Passive (SMAP) products on different time scales. The variability of surface soil moisture (SSM) within three frequency bands (7–30 days, 30–90 days, and 90–365 days) after normalization is quantified using Fourier transform for the evaluation. Compared to the SMAP observations, the simulated SSM variability within CMIP5 is underestimated in the two higher frequency bands (by 72% and 56%, respectively) and overestimated in the lowest frequency band (by 113%). In addition, these differences concentrate in regions with larger SSM. Finally, these multimodel differences are found to be significantly correlated with mean climate conditions rather than soil texture. This study identifies the spatiotemporal distribution of the model deficiencies within CMIP5 and finds they are systematic in the long‐term simulation on a global scale. Soil moisture has been largely regarded as a key variable in Earth system and plays an important role in climate prediction. However, land surface models have large uncertainties in simulating soil moisture. This study identifies that (a) land surface models underestimate soil moisture variability on weekly to seasonal time scales and overestimate it on seasonal to annual time scales compared to a remote sensing observation, (b) both the underestimation and overestimation are concentrated in the wetter regions, and (c) the differences between these models and the observation are more closely related to vegetation condition and surface temperature than soil sand and clay content. Using satellite observed data, this study reveals the deficiencies of land surface models in simulating temporal variability of soil moisture, which will help improve the soil moisture predictability of these models. Land surface models tend to underestimate weekly to seasonal variability and overestimate seasonal to annual variability of soil moistureSimulated surface soil moisture variability is more closely related to climate conditions than soil textureModel‐based soil moisture data may need to be improved to capture soil moisture variability on various frequency bands Land surface models tend to underestimate weekly to seasonal variability and overestimate seasonal to annual variability of soil moisture Simulated surface soil moisture variability is more closely related to climate conditions than soil texture Model‐based soil moisture data may need to be improved to capture soil moisture variability on various frequency bands

Documenting the large scale variability of tropical forest structure and function is needed for improved understanding of the carbon and water cycles. The seasonal and diurnal cycles of passive and active microwave satellite observations are jointly analyzed for the first time, using the Global Precipitation Mission (GPM). Collocated backscattering coefficients from the GPM Dual‐frequency Precipitation Radar (DPR) and emissivities derived from the GPM Microwave Imager (GMI) are studied for 4 yr over the Tropics, at the available multiple frequencies, angles, and polarizations. Our analysis confirms the diurnal patterns already observed with radars, with a maximum backscatter in the early morning attributed to the water xylem refill at night. At the same time, early in the morning, we evidence a minimum emissivity over most of tropical forests: the backscatter and emissivity diurnal cycles tend to be in phase opposition, as well as their seasonal cycles. However, during the dry season in the south eastern Amazonian forest where precipitation can be low for several months, the emissivity diurnal cycle changes, implying a different behavior of the forest in this area, related to the state of the canopy. The tropical forest plays an important role in the global water and carbon cycles, and it can be analyzed with satellite observations. The seasonal and diurnal cycles of passive and active microwave satellite observations are studied here simultaneously for the first time, thanks to the radar and radiometer on board the Global Precipitation Mission. Four years of observations are collected and analyzed over the Tropics. Our analysis shows that the passive and active observations vary both seasonally and diurnally in tropical forests, with similar amplitude within a day than over the year. The radar and radiometer signals tend to vary in phase opposition, seasonally and diurnally, over most of the tropical forests. However, during the dry season in the south eastern Amazonian forest where precipitation can be low for several months in a row, changes in the seasonal and diurnal cycles of the radiometer observations are evidenced, implying a different behavior of the forest in this area, related to the state of the canopy. Strong diurnal cycles over tropical forests, for both microwave backscatter and emissivity, with amplitude similar to seasonal variationsSeasonal and diurnal cycles of backscattering and emissivity temporally anti‐correlated over most parts of the tropical forestsOver Amazonia, seasonal cycle of backscatter and emissivity in phase in dry regions and diurnal cycle also in phase there during dry season Strong diurnal cycles over tropical forests, for both microwave backscatter and emissivity, with amplitude similar to seasonal variations Seasonal and diurnal cycles of backscattering and emissivity temporally anti‐correlated over most parts of the tropical forests Over Amazonia, seasonal cycle of backscatter and emissivity in phase in dry regions and diurnal cycle also in phase there during dry season

Precise and high-resolution carbon dioxide (CO2) emission data is of great importance in achieving carbon neutrality around the world. Here we present for the first time the near-real-time Global Gridded Daily CO2Emissions Dataset (GRACED) from fossil fuel and cement production with a global spatial resolution of 0.1° by 0.1° and a temporal resolution of 1 day. Gridded fossil emissions are computed for different sectors based on the daily national CO2emissions from near-real-time dataset (Carbon Monitor), the spatial patterns of point source emission dataset Global Energy Infrastructure Emissions Database (GID), Emission Database for Global Atmospheric Research (EDGAR), and spatiotemporal patters of satellite nitrogen dioxide (NO2) retrievals. Our study on the global CO2emissions responds to the growing and urgent need for high-quality, fine-grained, near-real-time CO2emissions estimates to support global emissions monitoring across various spatial scales. We show the spatial patterns of emission changes for power, industry, residential consumption, ground transportation, domestic and international aviation, and international shipping sectors from January 1, 2019, to December 31, 2020. This gives thorough insights into the relative contributions from each sector. Furthermore, it provides the most up-to-date and fine-grained overview of where and when fossil CO2emissions have decreased and rebounded in response to emergencies (e.g., coronavirus disease 2019 [COVID-19]) and other disturbances of human activities of any previously published dataset. As the world recovers from the pandemic and decarbonizes its energy systems, regular updates of this dataset will enable policymakers to more closely monitor the effectiveness of climate and energy policies and quickly adapt.

Relationships among productivity, leaf phenology, and seasonal variation in moisture and light availability are poorly understood for evergreen broadleaved tropical/subtropical forests, which contribute 25% of terrestrial productivity. On the one hand, as moisture availability declines, trees shed leaves to reduce transpiration and the risk of hydraulic failure. On the other hand, increases in light availability promote the replacement of senescent leaves to increase productivity. Here, we provide a comprehensive framework that relates the seasonality of climate, leaf abscission, and leaf productivity across the evergreen broadleaved tropical/subtropical forest biome. The seasonal correlation between rainfall and light availability varies from strongly negative to strongly positive across the tropics and maps onto the seasonal correlation between litterfall mass and productivity for 68 forests. Where rainfall and light covary positively, litterfall and productivity also covary positively and are always greater in the wetter sunnier season. Where rainfall and light covary negatively, litterfall and productivity are always greater in the drier and sunnier season if moisture supplies remain adequate; otherwise productivity is smaller in the drier sunnier season. This framework will improve the representation of tropical/subtropical forests in Earth system models and suggests how phenology and productivity will change as climate change alters the seasonality of cloud cover and rainfall across tropical/subtropical forests.

Currently available evapotranspiration (ET) products have not provided us with an accurate estimation for global irrigated land area. Thermal energy balance (EB) model has the potential to solve this problem. The accurate estimation of aerodynamic resistances is a major complication in most remote sensing ET models. An EB model using a column canopy‐air turbulent heat diffusion method was developed to more realistically depict dynamic changes in aerodynamic resistance. In order to estimate global ET and land surface fluxes for all weather conditions, Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua and Terra land surface temperature fields were combined and a nearest‐evaporative‐fraction gap‐filling method was merged into the EB model. A global ET product covering the period 2003–2017 was produced using the EB model. By combining thermal and optional information from MODIS satellites and surface meteorological forcing data from ERA‐Interim reanalysis data, the EB model provides a 5 × 5 km resolution estimate of daily ET without spatio‐temporal gaps at the global scale. Assessment of the daily EB ET at 238 flux sites showed a mean bias of 0.05 mm/day and an RMSE of 1.56 mm/day. Analysis of global precipitation minus ET demonstrated that EB ET has a relatively higher potential for agriculture water resource management than currently available global ET products, such as Landflux, GLEAM, MOD16, GLDAS, and ERA‐Interim ET. In addition, the EB model developed in this study can be applied to both polar and geostationary satellite thermal sensors. A new global evapotranspiration (ET) product was derived from satellite data using an energy balance (EB) model. The product has a daily scale and at a high spatial resolution (5 km for a global scale) for 2003–2017, which is particularly useful for agricultural studies. The model makes use of satellite remotely sensed land surface temperature (LST) and roughness length to calculate instantaneous sensible and latent heat fluxes. These instantaneous fluxes were further used to estimate daily ET. To provide a spatially and temporally continuous map of daily ET globally, two gap‐filling methods were employed to correct for the gaps in LST and evaporative fraction products under cloudy sky conditions. The daily ET product has been successfully evaluated with ground based flux tower data. The global ET dataset has a relatively higher potential for agriculture water resource management than currently available global ET products. In addition, the EB model developed in this study can be applied to other polar and geostationary satellites. An energy balance (EB) model was developed to take into account more accurate dynamic changes of aerodynamic resistance than other evapotranspiration (ET) modelsThe thermal EB model can estimate daily ET for all weather conditionsThe EB ET data has a higher potential for application in agriculture water resource management than most of the global ET products An energy balance (EB) model was developed to take into account more accurate dynamic changes of aerodynamic resistance than other evapotranspiration (ET) models The thermal EB model can estimate daily ET for all weather conditions The EB ET data has a higher potential for application in agriculture water resource management than most of the global ET products

Plants employ a range of strategies to modulate the impact of water stress, including changes to rooting depth and hydraulic conductance (e.g., xylem conductance). However, it is still poorly understood how these strategies vary in relation to climate and land cover types and how they could coordinate globally. Based on daily microwave vegetation optical depth (VOD) from AMSR‐E and AMSR2 over 2002–2011, we estimate two proxies for stress regulation: (a) an effective plant rooting depth (Zr∗)and (b) the effective plant hydraulic conductance (Kplant∗)to delineate two strategies: deep rooting and strong physiological regulation. We find that plants with deeper Zr∗(e.g., evergreen/deciduous broadleaf forest) are mostly distributed in warm or wet regions, and maintain a relatively steady nighttime VOD because of access to deeper water. Taller plants exhibit greater drop in daytime VOD due to their greater physiological vulnerability. While physiological regulation appears to be the dominant water stress regulation strategy at Northern high latitudes where open shrubland and (woody) savannas are distributed, this physiological regulation is coupled with deep rooting in forest and (woody) savanna areas in the tropics, Eastern US, and Southeastern China. Meanwhile, grasslands in the Western US, Central Asia, Northeastern China, and Mongolia Plateau may be the regions most susceptible to water stress impact because neither water stress mitigation strategy is present. This new framework paves the road for a better understanding of plant water stress strategies at the global scale, and for enhancing large‐scale drought prediction and drought impact assessment in Earth system models by improving plant water stress response. Plants with deeper roots maintain relatively steady nighttime vegetation optical depth (VOD) and taller plants drop daytime VOD morePhysiological regulation is the dominant strategy in Northern high latitudes, but it couples with deep rooting in the tropics, Eastern USGrassland areas in the Western US, Central Asia, Northeastern China, and Mongolia Plateau lack strategies for water stress Plants with deeper roots maintain relatively steady nighttime vegetation optical depth (VOD) and taller plants drop daytime VOD more Physiological regulation is the dominant strategy in Northern high latitudes, but it couples with deep rooting in the tropics, Eastern US Grassland areas in the Western US, Central Asia, Northeastern China, and Mongolia Plateau lack strategies for water stress

Amazonian evergreen forests show distinct canopy phenology and photosynthetic seasonality but the climatic triggers are not well understood. This imposes a challenge for modeling leaf phenology and photosynthesis seasonality in land surface models (LSMs) across Amazonian evergreen forest biome. On continental scale, we tested two climatic triggers suggested by site observations, vapor pressure deficit (VPD), and short‐wave incoming radiation (SW) for defining leaf shedding and incorporated VPD‐ and SW‐triggered new canopy phenology modules in the ORCHIDEE LSM (hereafter VPD‐AP and SW‐AP versions). Our results show that both VPD and SW are plausible precursors of large scale litterfall seasonality across the basin by comparing against in situdata from 14 sites. Specially, both VPD‐AP and SW‐AP correctly capture the increases in litterfall during the early dry season, followed by a flush of new leaves with increasing photosynthetic rates during the later dry season. The VPD‐AP version performs better than the SW‐AP version in capturing a dry‐season increase of photosynthesis across the wet Amazonia areas where mean annual precipitation exceeds 2,000 mm yr−1, consistent with previous satellite data analysis. Both VPD‐AP and SW‐AP model versions perform well in northern, central and southern Amazon regions where the SW seasonality is unimodal, but miss the seasonality of satellite GPP proxies in the eastern region off the coast of Guyana shield where SW seasonality is bimodal. Our findings imply that atmospheric dryness and sunlight availability likely explain the seasonality of leaf shedding and leaf flush processes, respectively, and consequently control canopy photosynthesis in Amazonian evergreen forests. The new canopy phenology module captures the large‐scale spatial and seasonal patterns of Amazonian forest photosynthesisVapor pressure deficits and sunlight explain seasonality of Amazonian forest phenology and photosynthesisThis study for the first time integrates site‐level process knowledge to match diagnostic evidence at large scale across Amazonian forests The new canopy phenology module captures the large‐scale spatial and seasonal patterns of Amazonian forest photosynthesis Vapor pressure deficits and sunlight explain seasonality of Amazonian forest phenology and photosynthesis This study for the first time integrates site‐level process knowledge to match diagnostic evidence at large scale across Amazonian forests

Water deficit in the atmosphere and soil are two key interactive factors that constrain transpiration and vegetation productivity. It is not clear which of these two factors is more important for the water and carbon flux response to drought stress in ecosystems. In this study, field data and numerical modeling were used to isolate their impact on evapotranspiration (ET) and gross primary productivity (GPP) at a tropical forest site in Barro Colorado Island (BCI), Panama, focusing on their response to the drought induced by the El Niño event of 2015–2016. Numerical simulations were performed using a plant hydrodynamic scheme (HYDRO) and a heuristic approach that ignores stomatal sensitivity to leaf water potential in the Energy Exascale Earth System Model (E3SM) Land Model (ELM). The sensitivity of canopy conductance (Gs) to vapor pressure deficit (VPD) obtained from eddy‐covariance fluxes and measured sap flux shows that, at both ecosystem and plant scale, soil water stress is more important in limiting Gsthan VPD at BCI during the El Niño event. The model simulations confirmed the importance of water stress limitation on Gs, but overestimated the VPD impact on Gscompared to that estimated from the observations. We also found that the predicted soil moisture is less sensitive to the diversity of plant hydraulic traits than ET and GPP. During the dry season at BCI, seasonal ET, especially soil evaporation at VPD > 0.42 kPa, simulated using HYDRO and ELM, were too strong and will require alternative parameterizations. Plants close stomata to regulate water loss through transpiration when stressed by the dry soil or high atmospheric water demand (vapor pressure deficit, VPD) or both. Tropical forests have experienced periodic droughts in the past. Recent drought‐related plant mortality has been attributed to increasing VPD associated with climate change. To evaluate whether water stress from dry soil or VPD has more limiting effect on plant transpiration, we analyzed the results from statistical models fitted to two types of field observation data and a land surface model which can simulate water movement in the soil and the water transport within the plant at a tropical forest site in Panama. We found dry soil is more important in limiting plant water loss at the site during the drought of the El Niño event of 2015–2016 and identified future model improvement need. Canopy conductance (Gs) sensitivity to vapor pressure deficit (VPD) was analyzed from observations using multiple methodsEstimated Gsindicates stronger limitation by soil water stress than VPD at Barro Colorado Island during the 2015 El Niño eventWith a plant hydrodynamics scheme in Energy Exascale Earth System Model (E3SM) Land Model, variation of plant hydraulic traits has greater effects on Gslimitation than on soil moisture Canopy conductance (Gs) sensitivity to vapor pressure deficit (VPD) was analyzed from observations using multiple methods Estimated Gsindicates stronger limitation by soil water stress than VPD at Barro Colorado Island during the 2015 El Niño event With a plant hydrodynamics scheme in Energy Exascale Earth System Model (E3SM) Land Model, variation of plant hydraulic traits has greater effects on Gslimitation than on soil moisture

Turbulence parameterizations for convective boundary layer in coarse‐scale atmospheric models usually consider a combination of the eddy‐diffusive transport and a non‐local transport, typically in the form of a mass flux term, such as the widely adopted eddy‐diffusivity mass‐flux (EDMF) approach. These two types of turbulent transport are generally considered to be independent of each other. Using results from large‐eddy simulations, here, we show that a Taylor series expansion of the updraft and downdraft mass‐flux transport can be used to approximate the eddy‐diffusivity transport in the atmospheric surface layer and the lower part of the mixed layer, connecting both eddy‐diffusivity and mass‐flux transport theories in convective conditions, which also quantifies departure from the Monin‐Obukhov similarity (MOS) in the surface layer. This study provides a theoretical support for a unified EDMF parameterization applied to both the surface layer and mixed layer and highlights important correction required for surface models relying on MOS. The atmospheric boundary layer is a key layer connecting the atmosphere and the surface. This boundary layer during daytime can be split into the lower surface layer where large gradients in momentum and scalars occur, overlaid by the mixed layer. In the surface layer, the concept of eddy‐diffusivity transport is typically assumed and used, but it does not apply in the mixed layer, where non‐local transport is essential to maintain transport under vanishing vertical gradients. These two types of transport are generally considered to be independent of each other and are usually represented in coarse‐scale atmospheric models with a combination of the eddy‐diffusive transport and a non‐local transport, typically in the form of a mass flux term, such as the widely adopted eddy‐diffusivity mass‐flux (EDMF) approach. Here, we show that a Taylor series expansion of the mass‐flux transport can be used to approximate the eddy‐diffusivity transport in the surface layer and lower part of the mixed layer, connecting both eddy‐diffusivity and mass‐flux transport theories in convective conditions. This study provides a theoretical support for a unified EDMF parameterization applied to both the surface layer and mixed layer and highlights the need to account for non‐local transport for Monin‐Obukhov similarity‐based surface‐layer models. The linearized mass‐flux transport contributes to eddy‐diffusivity in the surface and lower mixed layersThis contribution quantifies departure from the Monin‐Obukhov similarity scaling under increasing convective conditionsIt implies that a unified eddy‐diffusivity mass‐flux parameterization can be applied to both the surface and mixed layers The linearized mass‐flux transport contributes to eddy‐diffusivity in the surface and lower mixed layers This contribution quantifies departure from the Monin‐Obukhov similarity scaling under increasing convective conditions It implies that a unified eddy‐diffusivity mass‐flux parameterization can be applied to both the surface and mixed layers

Global satellite precipitation estimation at high spatiotemporal resolutions is crucial for hydrological and meteorological applications but is still a challenging task. One major challenge is that the microwave data are discontinuous in space and time. We present a novel approach to merge incomplete passive microwave (PMW) precipitation estimates using the conditional information provided by complete infrared (IR) precipitation estimates based on the generative adversarial network (GAN), and name the algorithm PrecipGAN. PrecipGAN decomposes the precipitation system into content and evolution subspaces to propagate PMW estimates to regions outside the orbit coverage of PMW sensors. PrecipGAN can skillfully simulate the spatiotemporal changes of precipitation events, and produce precipitation estimates with overall better statistical performance than the baseline product Integrated MultisatellitE Retrievals for GPM (IMERG) Uncalibrated over the Continental US. PrecipGAN provides an alternative of accurate and computationally efficient algorithm that can be implemented globally to produce satellite‐based precipitation estimates. Producing accurate precipitation estimates globally using satellite data at high spatial and temporal resolutions remains a major challenge for the remote sensing community. Uncertain precipitation products limit our capacity in various applications, such as hydrology, meteorology, or climatology. One difficult part in satellite precipitation estimation lies in combining infrared with passive microwave data effectively, as infrared data suffers from low detection accuracy but is superior in terms of spatiotemporal coverage while passive microwave data suffers from low spatiotemporal coverage but does excellent jobs at detecting precipitation accurately. Here, we present a novel deep learning model based on generative adversarial network to merge infrared and passive microwave data with a perspective based on the physical process of precipitation. The performance of the new algorithm is verified through comprehensive comparison with a state‐of‐the‐art precipitation product. The results show that this new algorithm has the potential to be a powerful alternative for generating global precipitation product merging infrared and microwave data. A deep learning model called PrecipGAN is developed to merge passive microwave with infrared signals for seamless precipitation estimationPrecipGAN is designed based on the physical process of precipitation by decomposing it into content and evolution subspacesPrecipGAN can produce precipitation estimates with overall better statistical performance compared to an operational satellite product A deep learning model called PrecipGAN is developed to merge passive microwave with infrared signals for seamless precipitation estimation PrecipGAN is designed based on the physical process of precipitation by decomposing it into content and evolution subspaces PrecipGAN can produce precipitation estimates with overall better statistical performance compared to an operational satellite product

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

A constellation of satellites is now in orbit providing information about terrestrial carbon and water storage and fluxes. These combined observations show that the tropical biosphere has changed significantly in the last 2 decades from the combined effects of climate variability and land use. Large areas of forest have been cleared in both wet and dry forests, increasing the source of carbon to the atmosphere. Concomitantly, tropical fire emissions have declined, at least until 2016, from changes in land‐use practices and rainfall, increasing the net carbon sink. Measurements of carbon stocks and fluxes from disturbance and recovery and of vegetation photosynthesis show significant regional variability of net biosphere exchange and gross primary productivity across the tropics and are tied to seasonal and interannual changes in water fluxes and storage. Comparison of satellite based estimates of evapotranspiration, photosynthesis, and the deuterium content of water vapor with patterns of total water storage and rainfall demonstrate the presence of vegetation‐atmosphere interactions and feedback mechanisms across tropical forests. However, these observations of stocks, fluxes and inferred interactions between them do not point unambiguously to either positive or negative feedbacks in carbon and water exchanges. These ambiguities highlight the need for assimilation of these new measurements with Earth System models for a consistent assessment of process interactions, along with focused field campaigns that integrate ground, aircraft and satellite measurements, to quantify the controlling carbon and water processes and their feedback mechanisms. Changes to the carbon sequestered in tropical forests and soils, as a result of human activities and changes in rainfall, temperature, and CO2concentrations, have a substantial impact on Earth's climate. This review summarizes recent results highlighting how the constellation of satellites now in orbit are providing new understanding of the tropical carbon cycle and how it interacts with climate variability through the water cycle, and how satellite data can be used to improve our process description of the Earth System. Almost two decades are available of satellite measurements of multiple carbon and water tracers for quantifying tropical carbon and water states, fluxes, and interactionsSubstantial progress has been made in quantifying the role of water variability on carbon variability in the wet and dry tropicsSatellite data provide evidence of the role of vegetation on the water cycleNew measurements are needed of key states and fluxes controlling C/W interactions (e.g. soil moisture, evapotranspiration, soil carbon and respiration) to reduce uncertainty in the tropical carbon sink and its evolutionModels that can integrate this wealth of data are needed to infer the hidden processes controlling carbon and water Almost two decades are available of satellite measurements of multiple carbon and water tracers for quantifying tropical carbon and water states, fluxes, and interactions Substantial progress has been made in quantifying the role of water variability on carbon variability in the wet and dry tropics Satellite data provide evidence of the role of vegetation on the water cycle New measurements are needed of key states and fluxes controlling C/W interactions (e.g. soil moisture, evapotranspiration, soil carbon and respiration) to reduce uncertainty in the tropical carbon sink and its evolution Models that can integrate this wealth of data are needed to infer the hidden processes controlling carbon and water

Atmospheric moisture holding capacity increases with temperature by about 7% per °C according to the Clausius‐Clapeyron relationship. Thermodynamically then, precipitation intensity should exponentially intensify and thus worsen flood conditions as the climate warms. However, regional and global analyses often report a nonmonotonic (hook) scaling of precipitation and runoff, in which extremes strengthen with rising temperature up to a maximum or peak point (Tpp) and decline thereafter. The underlying cause of this hook structure is not yet well‐understood, and whether it may shift and/or regulate storm runoff extremes under anthropogenic climate warming remains unknown. Here, we examine temperature scaling of precipitation and storm runoff extremes under different climate conditions using observations and large ensemble hydroclimatic simulations over mainland China. In situ observations suggest a spatially homogeneous, negative response of relative humidity to warming climates over 34.6% of the land area, and the remaining hook‐dominated regions usually show a colder Tppthan that of precipitation or storm runoff extremes. The precipitation and streamflow series over mainland China's catchments throughout the 21st century are projected by a model cascade chain under a high‐end emission scenario (RCP 8.5), which involves 31 CMIP5 climate models, 11 CMIP6 climate members, a daily bias correction method, and four lumped conceptual hydrological models. The CMIP5 ensemble projects that the hook structures shift toward warmer temperature bins, resulting in 10%–30% increases in storm runoff extremes over mainland China, while the CMIP6 ensemble projects more severe flood conditions in future warming climates. China is threatened by river floods and has frequently suffered from significant ecosystem damage and economic loss under a changing environment. Storm runoff extremes, an important indicator of flooding regime, have received increasing attention, especially regarding their response to a warming climate. Previous studies have observed a nonmonotonic relationship (called a hook structure) between storm runoff extremes and atmospheric temperatures, in which the extremes strengthen with warming up to a peak point and decline thereafter in a hotter environment. Here, we investigate the ability of such behavior to constrain the intensification of future storm runoff extremes, as well as the underlying mechanisms. We find that the hook structures are not fixed under climate warming but peak at warmer temperatures and higher storm runoff rates. As a result, future flooding risks in mainland China will not be constrained by the hook structures but progressively increase by roughly 10%–30%. The temperature scaling of precipitation, storm runoff, and relative humidity are characterized over mainland China's catchmentsPrecipitation and storm runoff usually exhibit a hook structure, with a warmer peak point temperature than that of relative humidityThe hook structures are not fixed under climate change but shift toward warmer temperatures and higher extreme rainfall and runoff rates The temperature scaling of precipitation, storm runoff, and relative humidity are characterized over mainland China's catchments Precipitation and storm runoff usually exhibit a hook structure, with a warmer peak point temperature than that of relative humidity The hook structures are not fixed under climate change but shift toward warmer temperatures and higher extreme rainfall and runoff rates

The terrestrial biosphere is currently slowing down global warming by absorbing about 30% of human emissions of carbon dioxide (CO2). The largest flux of the terrestrial carbon uptake is gross primary production (GPP) defined as the production of carbohydrates by photosynthesis. Elevated atmospheric CO2concentration is expected to increase GPP (“CO2fertilization effect”). However, Earth system models (ESMs) exhibit a large range in simulated GPP projections. In this study, we combine an existing emergent constraint on CO2fertilization with a machine learning approach to constrain the spatial variations of multimodel GPP projections. In a first step, we use observed changes in the CO2seasonal cycle at Cape Kumukahi to constrain the global mean GPP at the end of the 21st century (2091–2100) in Representative Concentration Pathway 8.5 simulations with ESMs participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5) to 171 ± 12 Gt C yr−1, compared to the unconstrained model range of 156–247 Gt C yr−1. In a second step, we use a machine learning model to constrain gridded future absolute GPP and gridded fractional GPP change in two independent approaches. For this, observational data are fed into the machine learning algorithm that has been trained on CMIP5 data to learn relationships between present‐day physically relevant diagnostics and the target variable. In a leave‐one‐model‐out cross‐validation approach, the machine learning model shows superior performance to the CMIP5 ensemble mean. Our approach predicts an increased GPP change in northern high latitudes compared to regions closer to the equator. About a quarter of human emissions of carbon dioxide (CO2) is absorbed by vegetation and soil on the Earth's surface and hence does not contribute to global warming caused by CO2in the atmosphere. Thus, in order to better define the remaining carbon budgets left to meet particular warming targets like the 1.5°C of the Paris Agreement, it is important to accurately quantify the carbon uptake by plants in the future. Currently, this is modeled by Earth system models yet with great uncertainties. In this work, we present an alternative machine learning approach to reduce spatial uncertainties of vegetation carbon uptake in future climate projections using observations of today's conditions. An emergent constraint on CO2seasonal cycle amplitude changes reduces uncertainties in global mean gross primary production projectionsA machine learning model with multiple predictors can further constrain the spatial distribution of gross primary productionHigh‐latitude ecosystems show higher gross primary production increase over the 21st century compared to regions closer to the equator

Mesoscale convection generates the majority of extreme precipitation in tropical regions. Changes to these precipitation intensities, P, with long‐term modes of climate variability have been hard to assess because they are not well represented in current climate models. Here we stratify a satellite climatology of convective systems by El Niño phase and cloud top temperature. We find that gains (losses) in high precipitation intensity ( P˙>10 mm hr−1) are largest for the deepest (least deep) systems during El Niño relative to La Niña. The surface temperature and wind changes that define El Niño manifest as surface flux changes but are not sufficient to explain these P˙trends. We explore also the dynamical component of precipitation generation with a vertical momentum budget. Midtropospheric drying in the vicinity of the deepest systems boosts instability and ascent rates during El Niño, while the strengthened large‐scale ascent minimizes the drag force on their updrafts. In the tropics, the majority of high‐intensity precipitation comes from the organization of multiple thunderstorms into a convective system. These systems are not yet well represented in global models, and studies of how their precipitation changes with long‐term modes of climate variability like the El Niño–Southern Oscillation have been limited. Using long‐term satellite data, we find here that the most intense precipitation becomes two times more probable for the deepest systems and 20% less probable for the least deep systems during El Niño. With a budget for the ascent rate within these systems, we illustrate how favorable moisture structure acts as a buoyancy source in the first case and unfavorable circulation acts as a buoyancy sink in the latter case. For the deepest convective systems, precipitation rates >10 mm hr−1become two times more likely during El NiñoThe distribution of net gross moist stability shifts to lower values for these deepest systems during El NiñoDecreased net gross moist stability can be explained by strengthened large‐scale ascent and free tropospheric drying

Stratification can cause turbulence spectra to deviate from Kolmogorov's isotropic −5/3power law scaling in the universal equilibrium range at high Reynolds numbers. However, a consensus has not been reached with regard to the exact shape of the spectra. Here we propose a shape of the turbulent kinetic energy and temperature spectra in horizontal wavenumber for the equilibrium range that consists of three regimes at small Froude number: the buoyancy subrange, a transition region, and the isotropic inertial subrange through dimensional analysis and substantial revision of previous theoretical approximation. These spectral regimes are confirmed by various observations in the atmospheric boundary layer. The representation of the transition region in direct numerical simulations will require large‐scale separation between the Dougherty‐Ozmidov scale and the Kolmogorov scale for strongly stratified turbulence at high Reynolds numbers, which is still challenging computationally. In addition, we suggest that the failure of Monin‐Obukhov similarity theory in the very stable atmospheric boundary layer is due to the fact that it does not consider the buoyancy scale that characterizes the transition region. Turbulent kinetic energy and temperature spectra in horizontal wavenumber consist of three regions in the stable ABLLarge‐scale separation is required for numerical simulations to resolve the transition regionMonin‐Obukhov similarity theory may not apply in the very stable ABL as the buoyancy scale is not considered

Dry deposition of ozone is an important sink of ozone in near‐surface air. When dry deposition occurs through plant stomata, ozone can injure the plant, altering water and carbon cycling and reducing crop yields. Quantifying both stomatal and nonstomatal uptake accurately is relevant for understanding ozone's impact on human health as an air pollutant and on climate as a potent short‐lived greenhouse gas and primary control on the removal of several reactive greenhouse gases and air pollutants. Robust ozone dry deposition estimates require knowledge of the relative importance of individual deposition pathways, but spatiotemporal variability in nonstomatal deposition is poorly understood. Here we integrate understanding of ozone deposition processes by synthesizing research from fields such as atmospheric chemistry, ecology, and meteorology. We critically review methods for measurements and modeling, highlighting the empiricism that underpins modeling and thus the interpretation of observations. Our unprecedented synthesis of knowledge on deposition pathways, particularly soil and leaf cuticles, reveals process understanding not yet included in widely used models. If coordinated with short‐term field intensives, laboratory studies, and mechanistic modeling, measurements from a few long‐term sites would bridge the molecular to ecosystem scales necessary to establish the relative importance of individual deposition pathways and the extent to which they vary in space and time. Our recommended approaches seek to close knowledge gaps that currently limit quantifying the impact of ozone dry deposition on air quality, ecosystems, and climate. The removal of tropospheric ozone at Earth's surface (often called dry deposition) is important for our understanding of air pollution, ecosystem health, and climate. Several processes contribute to dry deposition of ozone. While we have basic knowledge of these processes, we lack the ability to robustly estimate changes in ozone dry deposition through time and from one place to another. Here we review ozone deposition processes, measurements, and modeling and propose steps necessary to close gaps in understanding. A major conclusion revealed by our review is that most deposition processes can be fairly well described from a theoretical standpoint, but the relative importance of the various processes remains uncertain. We suggest that progress can be made by establishing multiyear measurements of ozone dry deposition at a limited set of sites around the world and coordinating these measurements with laboratory and field experiments that can be integrated with theory through carefully designed modeling studies. Ozone dry deposition through pathways other than plant stomata is critical for describing the total terrestrial ozone sinkProcess‐level knowledge of ozone deposition pathways is missing from the models used to quantify deposition impacts on the Earth systemLong‐term ozone flux and related measurements are key for establishing relative importance of individual pathways

Plant stomata couple the energy, water, and carbon cycles. We use the framework of Regional Climate Modeling to simulate the 2003 European heat wave and assess how higher levels of surface CO2may affect such an extreme event through land‐atmosphere interactions. Increased CO2modifies the seasonality of the water cycle through stomatal regulation and increased leaf area. As a result, the water saved during the growing season through higher water use efficiency mitigates summer dryness and the heat wave impact. Land‐atmosphere interactions and CO2fertilization together synergistically contribute to increased summer transpiration. This, in turn, alters the surface energy budget and decreases sensible heat flux, mitigating air temperature rise. Accurate representation of the response to higher CO2levels and of the coupling between the carbon and water cycles is therefore critical to forecasting seasonal climate, water cycle dynamics, and to enhance the accuracy of extreme event prediction under future climate. Stomatal closure reduction under higher CO2concentration conserves soil water during springtime and increases temperatureSpringtime saved water enables increased transpiration and consequent temperature mitigation during the summerInclusion of CO2physiological effect is essential for future heat wave and seasonal prediction as it alters land‐atmosphere feedbacks