Global Energy Cycle Research Articles

The Global Atmospheric Energy Cycle in TaiESM1: Present and Future

AbstractThe Lorenz Energy Cycle (LEC) in the Taiwan Earth System Model Version 1 (TaiESM1) historical simulation is calculated and compared with ERA5 to evaluate the model performance from the thermodynamic aspect. The future change of LEC is accessed by comparing the SSP5‐8.5 and historical simulations in TaiESM1. TaiESM1 reasonably simulates the global mean, seasonal cycle, and spatial patterns of the energy reservoirs with larger values in the mean energy components and smaller in the eddy energy components. The energy cycle in TaiESM1 is about 35%–45% stronger than ERA5, except from December to February. The impact of global warming on the LEC is different at the vertical levels. The influence of meridional temperature gradient change is the dominant factor in the intensity of the energy cycle, and the change in static stability only contributes to the lower troposphere. Lifting the tropopause in the tropics increases the meridional temperature gradient and produces more zonal mean potential energy (PM) in the upper troposphere. PM is the primary driver of the LEC and leads to a more active energy cycle in the upper troposphere. As the tropical tropospheric depth increases and the mid‐latitude eddy activities become more active, more (less) energy is stored in the upper (lower) troposphere, and the energy conversion processes tend to become stronger (weaker) in the upper (lower) troposphere.

Turbulent Energy and Carbon Fluxes in an Andean Montane Forest—Energy Balance and Heat Storage

High mountain rainforests are vital in the global energy and carbon cycle. Understanding the exchange of energy and carbon plays an important role in reflecting responses to climate change. In this study, an eddy covariance (EC) measurement system installed in the high Andean Mountains of southern Ecuador was used. As EC measurements are affected by heterogeneous topography and the vegetation height, the main objective was to estimate the effect of the sloped terrain and the forest on the turbulent energy and carbon fluxes considering the energy balance closure (EBC) and the heat storage. The results showed that the performance of the EBC was generally good and estimated it to be 79.5%. This could be improved when the heat storage effect was considered. Based on the variability of the residuals in the diel, modifications in the imbalances were highlighted. Particularly, during daytime, the residuals were largest (56.9 W/m2 on average), with a clear overestimation. At nighttime, mean imbalances were rather weak (6.5 W/m2) and mostly positive while strongest underestimations developed in the transition period to morning hours (down to −100 W/m2). With respect to the Monin–Obukhov stability parameter ((z − d)/L) and the friction velocity (u*), it was revealed that the largest overestimations evolved in weak unstable and very stable conditions associated with large u* values. In contrast, underestimation was related to very unstable conditions. The estimated carbon fluxes were independently modelled with a non-linear regression using a light-response relationship and reached a good performance value (R2 = 0.51). All fluxes were additionally examined in the annual course to estimate whether both the energy and carbon fluxes resembled the microclimatological conditions of the study site. This unique study demonstrated that EC measurements provide valuable insights into land-surface–atmosphere interactions and contribute to our understanding of energy and carbon exchanges. Moreover, the flux data provide an important basis to validate coupled atmosphere ecosystem models.

The reconstructed solar-induced chlorophyll fluorescence dataset reveals the almost ubiquitous close relationship between vegetation transpiration and solar-induced chlorophyll fluorescence

The precise estimation of vegetation transpiration (T) holds significant implications for enhancing our understanding of global carbon, water, and energy cycles. Nonetheless, vegetation transpiration stands out as one of the most challenging hydrological variables to capture on a global scale. In recent years, the newly introduced solar-induced chlorophyll fluorescence (SIF) has gained widespread application in the monitoring of photosynthesis within terrestrial ecosystems. It holds promise as a potential parameter for the precise estimation of vegetation transpiration. At various scales (leaf, canopy, and ecosystem), the relationship between SIF and vegetation transpiration has garnered widespread attention. Previous investigations predominantly relied on tower-based SIF or coarse-resolution satellite observations of SIF to assess the association between SIF and vegetation transpiration. The reconstituted SIF data (continuous SIF, CSIF) offer heightened spatial resolution, presenting novel opportunities for elucidating the SIF-T relationship. Nevertheless, it remains uncertain whether the relationship between SIF and vegetation transpiration is universally applicable or contingent upon specific land cover types. This study globally analyzes the relationship between SIF and vegetation transpiration utilizing reconstituted SIF data (CSIF) in conjunction with vegetation transpiration data acquired from 109 flux tower sites worldwide, encompassing 11 distinct land cover types. The results indicate that: (1) CSIF exhibits strong correlations with site T on both 8-day and monthly temporal scales, with the correlation between CSIF and site T surpassing that of traditional MODIS vegetation indices and site T. (2) Across the 11 land cover types, the majority of the slopes in the CSIF-T linear relationships are essentially consistent, indicating that the relationship between SIF and T is nearly universal rather than specific to particular land cover types. (3) The slope of the CSIF-T linear relationship in C4 vegetation is higher than that in C3 vegetation, indicating that the SIF-T relationship is contingent upon the photosynthetic pathway. (4) A robust linear relationship between CSIF and T emerges under favorable environmental conditions, while under extreme conditions such as water stress, the linear correlation between SIF and T diminishes. Our research findings enhance our understanding of the SIF-T relationship, offering a scientific basis for reliable T estimation on a global scale.

Global reduction in sensitivity of vegetation water use efficiency to increasing CO2

Ecosystem water use efficiency (WUE), which measures the ratio of carbon gain to water loss, is crucial to the global water, energy, and carbon cycles. It is an important metric in understanding how efficiently an ecosystem uses water to assimilate carbon and is affected by various environmental factors, including CO2 levels. However, the temporal dynamics of CO2-induced changes in WUE (CO2_WUE) remain uncertain. After isolating CO2_WUE from total changes in WUE, we assessed historical changes in global CO2_WUE and future projected trends. We found that the increasing atmospheric CO2 concentration enhanced global WUE by 5.65 ± 1.14 % 100 ppm−1 during 1982–2015. However, CO2-driven gains in WUE exhibit a significant declining trend across most of the Earth’s land surface during 1982–2015 (−1.38 ± 0.19 % 100 ppm−1 yr−1). This widespread CO2_WUE decline is projected to continue throughout the remainder of the 21st century. The decline in CO2_WUE implies that the global land surface is likely to witness a higher water cost to maintain the land carbon sink. This finding suggests a heightened difficulty of mitigating global warming and an elevated risk of global water scarcity. The global CO2_WUE decline is primarily driven by rising vapor pressure deficit (VPD) reducing photosynthesis and increasing total evaporation. Our findings highlight the key role of VPD in the coupling of global water and carbon cycles and reveal a continuous increase in the water cost of the global land carbon sink.

Capturing Snowmelt Runoff Onset Date under Different Land Cover Types Using Synthetic Aperture Radar: Case Study of Sierra Nevada Mountains, USA

Snow plays a crucial role in the global water and energy cycles, and its melting process can have a series of impacts on hydrological or climatic systems. Accurately capturing the timing of snowmelt runoff is essential for the utilization of snow resources and the early warning of snow-related disasters. A synthetic aperture radar (SAR) offers an effective means for capturing snowmelt runoff onset dates (RODs) over large areas, but its accuracy under different land cover types remains unclear. This study focuses on the Sierra Nevada Mountains and surrounding areas in the western United States. Using a total of 3117 Sentinel-1 images from 2017 to 2023, we extracted the annual ROD based on the Google Earth Engine (GEE) platform. The satellite extraction results were validated using the ROD derived from the snow water equivalent (SWE) data from 125 stations within the study area. The mean absolute errors (MAEs) for the four land cover types—tree cover, shrubland, grassland, and bare land—are 24, 18, 18, and 16 d, respectively. It indicates that vegetation significantly influences the accuracy of the ROD captured from Sentinel-1 data. Furthermore, we analyze the variation trends in the ROD from 2017 to 2023. The average ROD captured by the stations shows an advancing trend under different land cover types, while that derived from Sentinel-1 data only exhibits an advancing trend in bare land areas. It indicates that vegetation leads to a delayed trend in the ROD captured by using Sentinel-1 data, opposite to the results from the stations. Meanwhile, the variation trends of the average ROD captured by both methods are not significant (p > 0.05) due to the impact of the extreme snowfall in 2023. Finally, we analyze the influence of the SWE on RODs under different land cover types. A significant correlation (p < 0.05) is observed between the SWE and ROD captured from both stations and Sentinel-1 data. An increase in the SWE causes a delay in the ROD, with a greater delay rate in vegetated areas. These findings will provide vital reference for the accurate acquisition of the ROD and water resources management in the study area.

White spruce presence increases leaf miner effects on aspen growth in interior Alaska

Alaska’s boreal forests are experiencing rapid changes in climate that may favor deciduous-dominated systems, with important implications for global biogeochemical and energy cycles. However, aspen (Populus tremuloides Michx.) has experienced substantial defoliation from the aspen leaf miner (Phyllocnistis populiella Cham., hereafter ALM) in Alaska, resulting in significant growth reductions. We conducted a tree-ring and Δ13C study to test the hypothesis that moisture limitation may have predisposed aspen to leaf miner damage. Contrary to our hypothesis, differences in climate-growth correlations between relatively severely and lightly affected trees were negligible during the pre-outbreak decades. Stands with greater summer precipitation had more limited ALM impact, however differences among models were small and multiple climate variables were suitable predictors of ALM impact. The strong negative relationship we detected between tree-ring Δ13C and basal area increment (BAI) suggested that interannual variation in Δ13C was driven primarily by variation in photosynthesis, limiting the utility of Δ13C as a tool to detect stomatal responses to moisture-limitation. Instead, we found that larger, faster-growing individuals on gentler slopes showed a stronger absolute reduction in BAI (pre-ALM BAI−post-ALM BAI), but were similar in relative BAI reduction (pre-ALM BAI/post-ALM BAI), with smaller, slower growing trees. Older trees and stands with greater relative abundance of white spruce [Picea glauca (Moench) Voss] had greater relative ALM impact whereas slower growing trees on steeper slopes were less affected. The significant effect of white spruce abundance on ALM impact was likely due to favorable leaf miner overwintering habitat provided beneath white spruce trees, which can lead to increased leaf miner survival and thus greater reductions in aspen growth. Our results illustrate the subtle but complex biotic interaction between microclimate and pest physiology in determining ALM-induced aspen growth reductions, adding important nuance to a hypothesized increase in deciduous tree cover in Alaska’s boreal forest.

Comparative analysis of precipitable water vapour data in the Tarim Basin, China

Atmospheric water vapour is an important contributor to global energy and water cycles and its detection is necessary for precipitation forecasting and climate change research. To explore the applicability of reanalysis data for determining precipitable water vapour (PWV) content in the Tarim Basin, China, this study compared the reliability of two reanalysis PWV products, ERA-5 (PWERA) and MERRA-2 (PWMER), using data from four ground-based global navigation satellite system stations representing basin ecosystems (Oasis, Gobi Desert, Central Desert, and Alpine; dataset: PWGNSS) as reference values. We conducted correlation analysis between PWGNSS and both reanalysis PWV products at different time scales to verify the products’ accuracy. The results showed that the reanalysis PWV products were mostly overestimated at the Desert station, while they were mostly underestimated at the Oasis station. The applicability of PWERA and PWMER varied by season and location, with better applicability at the Oasis station from spring to autumn. The applicability of reanalysis PWV products was lower during precipitation periods than during non-precipitation, and varied by location. During non-precipitation situation, the PWMER had better applicability at the Oasis station and at the Central Desert station when the PWGNSS exceeded 25 mm. Meanwhile, PWERA had better applicability in precipitation situation at the Gobi Desert station from April to June and at Oasis station from August to September when PWGNSS was above 30 mm, at the Central Desert station from May to August, and when the PWGNSS exceeded 30 mm.

Precipitation data in Seoul, Korea during 1778–1907

Precipitation plays a crucial role in the global energy and water cycle and has important implications for food, water, and energy security. To enhance our understanding of the water cycle, it is invaluable to have a comprehensive historical record of precipitation. However, obtaining such records, especially for the period before the Industrial Revolution, can be challenging. During the Joseon Dynasty, Korea established a network for measuring rainfall and recorded this information in historical documents known as Seungjeongwon Ilgi and Ilseongnok. Recently, these documents have been digitized, providing us with daily precipitation data for Seoul spanning 130 years, from 1778 to 1907. By combining and analyzing these two documents, we were able to address inconsistencies found in previous studies and improve the quality of the data. Notably, this dataset is free of any missing values, making it the longest daily precipitation record in the world before the Industrial Revolution. Its availability to the public holds great potential for climate research in East Asia during the late Little Ice Age.

Soil Moisture Profiles of Ecosystem Water Use Revealed With ECOSTRESS

AbstractWhile remote sensing has provided extensive insights into the global terrestrial water, carbon, and energy cycles, space‐based retrievals remain limited in observing the belowground influence of the full soil moisture (SM) profile on ecosystem function. We show that this gap can be addressed when coupling 70 m resolution ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station retrievals of land surface temperature (LST) with in‐situ SM profile measurements. These data sets together reveal that ecosystem water use decreases with depth with 93% of sites showing significant LST coupling with SM shallower than 20 cm while 34% of sites have interactions with SM deeper than 50 cm. Furthermore, the median depth of peak ecosystem water use is estimated to be 10 cm, though forests have more common peak interactions with deeper soil layers (50–100 cm) in 37% of cases. High spatial resolution remote sensing coupled with field‐level data can thus elucidate the role of belowground processes on land surface behavior.

Ocean eddy detection based on YOLO deep learning algorithm by synthetic aperture radar data

Ocean eddies play a crucial role in the global energy cycle and significantly impact the transport of heat, salt, and nutrients in the global ocean. Spaceborne synthetic aperture radar (SAR), with its high spatial resolution (O(20 m)) and wide coverage, is an important tool for studying ocean eddies. In this paper, we propose a deep-learning network, named EOLO, to automatically detect ocean Eddies observed in C-band spaceborne SAR imagery, based on the You-Only-Look-Once (YOLO) deep learning algorithm. A Sentinel-1 (S1) SAR data-based ocean eddy dataset (named EddyDataset) is established to train the EOLO network. To effectively improve the performance of the EOLO network, we introduced a channel attention mechanism, adopted the up-sampling operator with the larger receptive field, and improved feature fusion method, anchor box size, and loss function. With the support of a high-performance detection network, the geographic information extraction module based on the affine geographic transformation model and a data preprocessing module were added, making EOLO an application-level framework. Our experiment results on EddyDataset demonstrate that EOLO achieves a high quality of eddy detection with 91.5% average precision. We further applied EOLO to entire scenes of S1 images to detect eddies in the Red Sea, the Baltic Sea, and the Western Mediterranean Sea, achieving 96.6%, 98.8% and 98.9% precision, respectively. Moreover, EOLO was used to extract size and location information of ocean eddies based on 6135 S1 scenes acquired in 2021 over the Western Mediterranean Sea. Their spatial characteristics were derived and compared with the eddies extracted from radar altimeter data, presenting interesting discrepancies between sub-mesoscale and mesoscale eddies in the ocean.

Seasonal aridity regulates drivers and temporal variability of wood phenology: A meta-analysis of dendrometer monitoring data across the Northern Hemisphere

The secondary growth phenology of trees, also known as wood phenology, plays a vital role in assessing the timing of carbon sequestration in forests. However, its spatio-temporal variability and determinants in the Northern Hemisphere remain debated. Here, we presented a meta-analysis using secondary growth phenology data based on dendrometer monitoring of 59 tree species and 84 sites to explore the drivers and temporal connections of phenology across seasonal arid and humid regions in the Northern Hemisphere. We found spring temperatures and precipitation co-regulated the start of secondary growth (SOG) in spring arid regions, and SOG advanced with the increase in spring temperatures and precipitation. However, SOG was only influenced by spring temperatures in spring humid regions. The end of secondary growth (EOG) indicated strong correlations with autumn temperatures only in autumn arid regions but not in humid regions. Interestingly, the earlier SOG promoted advancement of the time of maximum secondary growth rate (MOG) and EOG both in seasonal humid and arid regions, while the end of secondary growth in the previous year (prEOG) negatively affected SOG only in seasonal arid regions. The legacy effects of previous phenological events on subsequent ones should be integrated into phenological prediction models to help accurately assess global carbon, water, and energy cycles.

Better estimation of evapotranspiration and transpiration using an improved modified Priestly-Taylor model based on a new parameter of leaf senescence in a rice field

Evapotranspiration (ET) is at the heart of the global water, energy, and carbon cycles. As ET is difficult and expensive to measure, it is crucial to develop estimation models that can be widely applied. Currently, an improved Priestley-Taylor (PT) model considers soil moisture stress, temperature constraints, and leaf senescence; however, its parameter (fs) for simulating crop senescence is based on empirical values, making it difficult to apply to different varieties and complex external conditions and thus challenging to generalize. We improved the parameters fs in the original model based on the chlorophyll decomposition that accompanies crop senescence through easily observable SPAD values (Soil-Plant Analysis Development readings) in the field. We validated the improved model by obtaining ET of different rice varieties in 2022 and 2023 using the energy balance residual method at the Free Air Concentration Enrichment Experimental (FACE) Facility located in Yangzhou City, China. The results showed that the simulation of leaf senescence using SPAD values was feasible and could be extended to different varieties. The new model using improved leaf senescence parameter for estimating ET and transpiration (T) in three plots (2022 and 2023) exhibited slightly enhanced accuracy, particularly at the later stages of crop growth. Moreover, the higher the T/ET ratio of the cropland, the more significant the improvement. This new development enhances the ability of PT models to estimate ET and T using readily available field observations and provides some suggestions for wider application in the field for other crop species.

Ubiquitous filter feeders shape open ocean microbial community structure and function.

The mechanism of mortality plays a large role in how microorganisms in the open ocean contribute to global energy and nutrient cycling. Salps are ubiquitous pelagic tunicates that are a well-known mortality source for large phototrophic microorganisms in coastal and high-latitude systems, but their impact on the immense populations of smaller prokaryotes in the tropical and subtropical open ocean gyres is not well quantified. We used robustly quantitative techniques to measure salp clearance and enrichment of specific microbial functional groups in the North Pacific Subtropical Gyre, one of the largest ecosystems on Earth. We discovered that salps are a previously unknown predator of the globally abundant nitrogen fixer Crocosphaera; thus, salps restrain new nitrogen delivery to the marine ecosystem. We show that the ocean's two numerically dominant cells, Prochlorococcus and SAR11, are not consumed by salps, which offers a new explanation for the dominance of small cells in open ocean systems. We also identified a double bonus for Prochlorococcus, wherein it not only escapes salp predation but the salps also remove one of its major mixotrophic predators, the prymnesiophyte Chrysochromulina. When we modeled the interaction between salp mesh and particles, we found that cell size alone could not account for these prey selection patterns. Instead, the results suggest that alternative mechanisms, such as surface property, shape, nutritional quality, or even prey behavior, determine which microbial cells are consumed by salps. Together, these results identify salps as a major factor in shaping the structure, function, and ecology of open ocean microbial communities.

Uniform upscaling techniques for eddy covariance FLUXes (UFLUX)

ABSTRACT Data-driven techniques that scale up eddy covariance (EC) fluxes from tower footprints with satellite observations and machine learning algorithms significantly advance our understanding of global carbon, water, and energy cycles. However, few upscaling approaches take a consistent approach to upscaling both carbon and energy fluxes. A lack of uniformity in the upscaling approach could lead to inconsistencies in global interannual variability of fluxes and between types of carbon and energy fluxes. Hence, this study aims to identify obstacles in flux upscaling and propose a uniform upscaling framework UFLUX for gross primary productivity (GPP), ecosystem respiration (Reco), net ecosystem exchange (NEE), sensible heat (H), and latent energy (LE). The key findings are as follows: 1) The upscaling performance exhibits a limited improvement from the use of more advanced machine learning approaches (e.g. <0.3 in R2 improvements while using deep neural networks). 2) The spatial density of EC towers is the primary factor determining the effectiveness of upscaling, explaining >50% of the upscaling uncertainty. 3) The UFLUX framework considered the interconnection between fluxes and achieved a competitive validation precision (daily R2 = 0.7 on average of five flux types) when compared with products that upscaled a subset of the fluxes. UFLUX effectively preserved the ecosystem light-use efficiency (0.83 of linear regression slope and the same after), Bowen ratio (0.8), and particularly, the water-use efficiency (0.81), when compared to the only other product (i.e. FLUXCOM) to upscale both carbon and water.

Estimation of Above-Ground Forest Biomass in Nepal by the Use of Airborne LiDAR, and Forest Inventory Data

Forests play a significant role in sequestering carbon and regulating the global carbon and energy cycles. Accurately estimating forest biomass is crucial for understanding carbon stock and sequestration, forest degradation, and climate change mitigation. This study was conducted to estimate above-ground biomass (AGB) and compare the accuracy of the AGB estimating models using LiDAR (light detection and ranging) data and forest inventory data in the central Terai region of Nepal. Airborne LiDAR data were collected in 2021 and made available by Nepal Ban Nigam Limited, Government of Nepal. Thirty-two metrics derived from the laser-scanned LiDAR point cloud data were used as predictor variables (independent variables), while the AGB calculated from field data at the plot level served as the response variable (dependent variable). The predictor variables in this study were LiDAR-based height and canopy metrics. Two statistical methods, the stepwise linear regression (LR) and the random forest (RF) models, were used to estimate forest AGB. The output was an accurate map of AGB for each model. The RF method demonstrated better precision compared to the stepwise LR model, as the R2 metric increased from 0.65 to 0.85, while the RMSE values decreased correspondingly from 105.88 to 60.9 ton/ha. The estimated AGB density varies from 0 to 446 ton/ha among the sample plots. This study revealed that the height-based LiDAR metrics, such as height percentile or maximum height, can accurately and precisely predict AGB quantities in tropical forests. Consequently, we confidently assert that substantial potential exists to monitor AGB levels in forests effectively by employing airborne LiDAR technology in combination with field inventory data.

Research into land atmosphere interactions supports the sustainable development agenda

Abstract Non-technical summary Greenhouse gas emissions and land use change – from deforestation, forest degradation, and agricultural intensification – are contributing to climate change and biodiversity loss. Important land-based strategies such as planting trees or growing bioenergy crops (with carbon capture and storage) are needed to achieve the goals of the Paris Climate Agreement and to enhance biodiversity. The integrated Land Ecosystems Atmospheric Processes Study (iLEAPS) is an international knowledge-exchange and capacity-building network, specializing in ecosystems and their role in controlling the exchange of water, energy and chemical compounds between the land surface and the atmosphere. We outline priority directions for land–atmosphere interaction research and its contribution to the sustainable development agenda. Technical summary Greenhouse-gas emissions from human activities and land use change (from deforestation, forest degradation, and agricultural intensification) are contributing to climate change and biodiversity loss. Afforestation, reforestation, or growing bioenergy crops (with carbon capture and storage) are important land-based strategies to achieve the goals of the Paris Climate Agreement and to enhance biodiversity. The effectiveness of these actions depends on terrestrial ecosystems and their role in controlling or moderating the exchange of water, heat, and chemical compounds between the land surface and the atmosphere. The integrated Land Ecosystems Atmospheric Processes Study (iLEAPS), a global research network of Future Earth, enables the international community to communicate and remain up to date with developments and concepts about terrestrial ecosystems and their role in global water, energy, and biogeochemical cycles. Covering critically important topics such as fire, forestry, wetlands, methane emissions, urban areas, pollution, and climate change, the iLEAPS Global Research Programme sits center stage for some of the most important environmental questions facing humanity. In this paper, we outline the new challenges and opportunities for land–atmosphere interaction research and its role in supporting the broader sustainable development agenda. Social Media Summary Future directions for research into land–atmosphere interactions that supports the sustainable development agenda

Uncertainty Analysis and Data Fusion of Multi-Source Land Evapotranspiration Products Based on the TCH Method

Evapotranspiration (ET) is a very important variable in the global water cycle, carbon cycle, and energy cycle. However, there are still some uncertainties in existing ET products. Therefore, this paper evaluates the uncertainty of three widely used global ET products (ERA5-Land, GLDAS-Noah, and MERRA-2) based on the three-cornered hat (TCH) method, and generates a new ET product based on this. The new product is a long-series global monthly ET dataset with a spatial resolution of 0.25° × 0.25° and a time span of 21 years. The results show that ERA5-Land (8.46 mm/month) has the lowest uncertainty among the three ET products, followed by GLDAS-Noah (8.81 mm/month) and MERRA-2 (11.78 mm/month). The new product (TCH) captures ET trends in different regions as well as validating against in situ flux observations, and it exhibits better performance than the re-analysis dataset (ERA5-Land) in vegetation classifications such as evergreen needle-leaf forest, grassland, open shrubland, savanna, and woody savanna. The linear trend analysis of the new product shows a significant decreasing trend in south-eastern South America and southwestern parts of Africa, and an increasing trend in almost all other regions, including eastern North America, north-eastern South America, western Europe, north-central Africa, southern Asia, and south-eastern Oceania.

High uncertainty of evapotranspiration products under extreme climatic conditions

Terrestrial Evapotranspiration (ET) is an important process for understanding regional or global water, energy, and carbon cycles, and with the development of satellite observations and increased research investment, grid ET products covering a broad spatial extent are becoming more readily available. However, as global warming becomes a reality and extreme climate events occur worldwide, existing studies do not go far enough to verify that these grid products can still be used under extreme climate conditions. This study evaluates nine global ET products, including land surface reanalysis products (GLDAS_CLSM, GLDAS_NOAH, ERA5, and FLDAS), remote sensing (RS) products (GLEAM_v3.6b, MOD16A2, and PMLv2), and multi-source data fusion products (REA and Synthesized), using observations from 153 flux towers worldwide. The objective is to evaluate their performance in estimating ET under extreme climatic conditions (high temperature, high vapor pressure deficit (VPD), and drought). The results indicate that the estimation accuracy of all ET products is significantly reduced under extreme climatic conditions, showing high uncertainty, and this impact is most severe in the Americas (AM) region. Overall, multi-source data fusion products showed the best estimation performance and were less affected by extreme climatic conditions. Among the remote sensing modeling products, GLEAM_v3.6b showed the best performance, while MOD16A2 has the lowest estimation accuracy. Land surface reanalysis products were most affected by extreme conditions, with CLSM and NOAH showing similar performance and ERA5 having the largest errors (RMSE_ERA5 = 1.699 mm/d, MAE_ERA5 = 1.294 mm/d). The ET products show significant error fluctuations and overestimation (PBias > 0.5) in most of North America, and there is a decline in simulation accuracy in arid and semi-arid regions near 30°N, with most ET products showing overestimation. The most significant errors were observed in cropland areas (CRO) and deciduous broadleaf forest areas (DBF), with significant overestimation in mixed forest areas (MF). The results of this study provide valuable insights for researchers in selecting ET products under extreme climatic conditions and encourage product developers to consider uncertainty under such conditions, thereby improving product accuracy.

ET-WB: water-balance-based estimations of terrestrial evaporation over global land and major global basins

Abstract. Evaporation (ET) is one of the crucial components of the water cycle, which serves as the nexus between global water, energy, and carbon cycles. Accurate quantification of ET is, therefore, pivotal in understanding various earth system processes and subsequent societal applications. The prevailing approaches for ET retrievals are either limited in spatiotemporal coverage or largely influenced by the choice of input data or simplified model physics, or a combination thereof. Here, using an independent mass conservation approach, we develop water-balance-based ET datasets (ET-WB) for the global land and the selected 168 major river basins. We generate 4669 probabilistic unique combinations of the ET-WB leveraging multi-source datasets (23 precipitation, 29 runoff, and 7 storage change datasets) from satellite products, in situ measurements, reanalysis, and hydrological simulations. We compare our results with the four auxiliary global ET datasets and previous regional studies, followed by a rigorous discussion of the uncertainties, their possible sources, and potential ways to constrain them. The seasonal cycle of global ET-WB possesses a unimodal distribution with the highest (median value: 65.61 mm per month) and lowest (median value: 36.11 mm per month) values in July and January, respectively, with the spread range of roughly ±10 mm per month from different subsets of the ensemble. Auxiliary ET products illustrate similar intra-annual characteristics with some over- or underestimation, which are completely within the range of the ET-WB ensemble. We found a gradual increase in global ET-WB from 2003 to 2010 and a subsequent decrease during 2010–2015, followed by a sharper reduction in the remaining years primarily attributed to the varying precipitation. Multiple statistical metrics show reasonably good accuracy of monthly ET-WB (e.g., a relative bias of ±20 %) in most river basins, which ameliorates at annual scales. The long-term mean annual ET-WB varies within 500–600 mm yr−1 and is consistent with the four auxiliary ET products (543–569 mm yr−1). Observed trend estimates, though regionally divergent, are evidence of the increasing ET in a warming climate. The current dataset will likely be useful for several scientific assessments centering around water resources management to benefit society at large. The dataset is publicly available in various formats (NetCDF, Mat, and Shapefile) at https://doi.org/10.5281/zenodo.8339655 (Xiong et al., 2023).

Spatial patterns and recent temporal trends in global transpiration modelled using eco-evolutionary optimality

Transpiration from vegetation accounts for about two thirds of land evapotranspiration (ET), and exerts important effects on of global water, energy, and carbon cycles. Resistance-based ET partitioning models using remote sensing data are one of the main methods to estimate global land transpiration, overcoming the limitation by the sparse distribution and short observation periods of site-level measurements. However, the uncertainties of estimated transpiration for these models mainly come from the resistance parameterization based on specific empirical parameters across different plant functional types (PFT). A model based on eco-evolutionary optimization (P model) has recently been proposed to simulate stomatal conductance without the need of calibrated parameters. Here, we calculated global long-term (1982–2018) monthly transpiration with the Penman-Monteith (PM) equation using canopy conductance estimated by the P model (PM-P) and Ball-Berry-Leuning model (PM-BBL). Using the observations of SAPFLUXNET and FLUXNET sites as reference, the performance of PM-P was comparable with that of PM-BBL and Global Land Evaporation Amsterdam model (GLEAM). Multi-year mean and trends in growing season transpiration estimated by GLEAM and the PM-P model revealed a similar spatial distribution globally. Both GLEAM and the PM-P model showed a widespread increasing trend of growing season transpiration over 72.06%∼80.38% of global land, especially for some main greening hotspots with >3.0 mm/year. The good performance of the P model indicated that it could avoid the uncertainties emerging from the resistance parameterization with too many empirical parameters and had the potential to accurately estimate global transpiration.