Earth System Research Articles

Contrasting Southern Ocean Sea Level Responses to Surface Flux Changes in Eddy-Rich and Eddy-Parameterized Climate Model Configurations

Abstract The impact of ocean model resolution on sea level projections in the Southern Ocean is investigated using eddy-rich (ER) and eddy-parameterized configurations of the Max Planck Institute Earth System Model under the Shared Socioeconomic Pathway (SSP) 5-8.5 scenario. We employ the Flux-Anomaly Forced Model Intercomparison Project (FAFMIP) experiment—heat, stress, and freshwater perturbations—at both resolutions to pinpoint the sources of these differences. South of 55°S, we found that the changes in thermosteric and halosteric sea levels vary substantially between resolutions due to different responses to freshwater perturbations. In the eddy-parameterized model, the resulting increase in stratification suppresses the mixing of salt and heat from the Circumpolar Deep Water with surface layers. These cause differences in the response of surface fluxes and meridional transports yielding an increase in thermosteric sea levels and a decrease in halosteric sea levels. In the eddy-rich configuration, the main driver of eddy-induced warming and salinification between 40° and 44°S is wind stress perturbations. The efficiency of direct eddy effects in ER is restricted to small areas such as the Agulhas Retroflection, the Brazil–Malvinas confluence zone, the Tasman Sea, and, to some extent, the Antarctic Circumpolar Current (ACC). Contrary to expectations, ACC transport increases in the eddy-rich model while decreasing in the eddy-parameterized model under the SSP5-8.5 scenario. FAFMIP results reveal that this decrease is a result of the overcompensation of wind-induced changes by freshwater flux forcing. These results underscore the critical importance of high-resolution models for capturing the processes in sea level projections in the Southern Ocean and beyond. Significance Statement We studied how ocean model resolution affects sea level projections in the Southern Ocean using Max Planck Institute Earth System Model simulations. Higher-resolution models provide a more accurate representation of ocean circulation and its response to changing forcings. We examined how surface heat, momentum, and water fluxes, both separately and combined, shape ocean dynamics. In a strong global warming scenario, significant differences in steric sea level change were observed south of 55°S between the model that simulates eddies and the one that has their effects parameterized. The response to surface freshwater forcing is the primary cause of these differences. Our findings emphasize the critical role of ocean model resolution in accurately understanding and predicting future sea level changes, which is essential for effectively addressing our needs for adaptation.

Performance and bias of the Community Integrated Earth System Model in simulating the impact of the North Pacific Meridional Mode on the El Niño-Southern Oscillation

Performance and bias of the Community Integrated Earth System Model in simulating the impact of the North Pacific Meridional Mode on the El Niño-Southern Oscillation

Emulating Wildfire Plume Injection Using Machine Learning Trained by Large Eddy Simulation (LES).

Wildfires have a major influence on the Earth system, with costly impacts on society. Despite decades of research, wildfires are still challenging to represent in air quality and chemistry-climate models. Wildfire plume rise (injection) is one of those poorly resolved processes and is also a major source of uncertainty in evaluating the wildfire impacts on air quality. Studies have shown that current plume rise models are subject to large uncertainties, including the Freitas Scheme, a widely used 1-dimensional, cloud-resolving subgrid model. In this work, a new machine learning-based plume rise emulator is presented, trained using a high-resolution, turbulence-resolving large eddy simulation (LES) model coupled with microphysics. The preliminary results show that this machine learning emulator outperforms the benchmark model, the Freitas scheme, in both accuracy and computational efficiency. Furthermore, a bagging ensemble is built to further increase the robustness and to battle internal variability. Efforts have been made to ensure that the machine learning emulator is robust, transparent, and not overtrained, and the results are interpretable and physically sound. Overall, this Plume Rise Emulating System using Machine Learning (PRESML) is a promising solution for regional and global air quality and chemistry-climate models.

Rapid assessment of the levels, nature, and dynamics of sustainability for the United Kingdom 1992–2015 from the doughnut model perspective

The Doughnut model's application to determine sustainability outcomes has thus far been primarily based upon indirect quantitative-based approaches. Therefore, the challenge has been how to directly determine and evaluate sustainability outcomes from a Doughnut-based assessment. In this paper, a direct determination and evaluation of the sustainability of the United Kingdom's environment-human system for the period 1992–2015 from a Doughnut-based assessment is conducted, based upon the application of the Sustainability Dynamics Framework (SDF). The fundamental methodology adopted consisted of: (i). Development of a new scoring criteria framework to convert the indicator-threshold ratios of biophysical boundaries and social threshold indicators into scores; (ii). Determination of the annual indicated values of the Environment (E), Human Needs and Interests (HNI), and Sustainability (S); (iii). Fourier analysis to indicatively determine the dynamics of the UK environment-human system; and (iv). Dynamic modelling of the dynamics of Sustainability (S), Total capital (K), the Earth System (N-A) and dE/dt occurring. The results indicate that the UK's environment-human system was consistent with very weak sustainability throughout the specified period, based upon obtained S-values which were within the range of [+0.075 ≤ S(t) ≤ +0.158] over the specified period. This was due to a declining and fundamentally compromised environmental system caused by the significant exceedance of the biophysical boundaries, and the near-total to total fulfilment of the social thresholds. These outcomes were further supported by the Fourier transformation plots, which indicated increasing dynamic perturbations occurring within the UK environment-human system over the specified period. The paper concludes that the UK will not achieve a state consistent with operating within the safe and just operating space (SJOS), if it continues along its current development pathway. Without fundamental and transformative actions, then there is the realistic possibility of a systemic crisis occurring due to the UK's operating considerably beyond its SJOS.

Proxy and Model-Estimated Coupled Megadroughts in the Southwestern Regions of North and South America

Abstract The North American Southwest (NASW) and South American Southwest (SASW) are regions susceptible to prolonged and intense droughts that can span a decade or more (i.e., megadroughts). Although the drivers and impacts of megadroughts in each region and their co-occurrence have been examined in paleoclimate reconstructions, it is not known whether climate models simulate co-occuring megadroughts in these regions with characteristics and drivers that are similar to the real world. We compare the temporal characteristics of concurrent megadroughts and the Pacific Ocean conditions associated with these events in the Paleo Hydrodynamics Data Assimilation Product (PHYDA) and the Community Earth System Model Last Millennium Ensemble (CESM-LME). We find that concurrent megadroughts in PHYDA and CESM-LME have similar temporal characteristics, but the relationship between hydroclimate conditions in the NASW and SASW is different between proxy-based estimates and the climate model. Further analyses reveal that changes in the tropical Pacific Ocean are weaker during concurrent megadroughts in the CESM-LME compared to PHYDA, and that their teleconnection patterns and strengths are different. Reconstruction methodology is also found to be a factor in how the relationship between the tropical Pacific and each region is characterized. These results together indicate that while the CESM-LME simulates concurrent megadroughts with temporal characteristics similar to PHYDA, it does so for different reasons; this result leaves open the question of whether climate models used for future projections can accurately capture the risk of concurrent megadroughts in future projections.

Earth system responses to different levels of greenhouse gas emissions mitigation

Anthropogenic carbon dioxide (CO2) emissions are the main driver of climate change, with global warming increasing almost linearly with cumulative CO2 emissions. Hence, future warming will primarily result from future emissions of CO2 with contributions from other greenhouse gases (mostly CH4 and N2O) and aerosols. Climate projections of the 21st century, such as those assessed by the IPCC, are provided from comprehensive climate models, also called Earth System models, driven by scenarios of the 21st century evolution of emissions from those climate forcers. While it seems now inevitable that the world will reach 1.5°C of warming above pre-industrial levels by the early 2030s, the extent to which we exceed this warming level and how quickly we may be able to reduce temperatures again depends strongly on global activity taken now to limit emissions. In this paper, we review the current understanding on Earth system changes under two highly contrasted possible future worlds. We first focus on high-end scenarios, where anthropogenic emissions continue to increase over the course of the 21st century, leading to large warming levels, associated impacts on all components of the Earth System, and increased risks of triggering tipping points. We then assess low-end scenarios, where anthropogenic emissions rapidly decline, reaching net zero and potentially becoming net negative before the end of the 21st century. Such “overshoot” scenarios lead to a peak in global warming followed by a slow decline in global temperature, with some degree of reversibility in the global carbon cycle and key Earth system components. We also review paleoclimatic information relevant to these two contrasting future worlds. Paleoclimate evidence for geo-biosphere interactions shows that stabilizing feedbacks operate on millennial or longer timescales, whereas destabilizing feedbacks and tipping cascades occurred also on shorter timescales.

Unveiling spatiotemporal patterns of wildfire risk: a transformer-based earth system analysis

Unveiling spatiotemporal patterns of wildfire risk: a transformer-based earth system analysis

Impacts of land-use and land-cover changes on temperature-related mortality.

Land-use and land-cover change (LULCC) can substantially affect climate through biogeochemical and biogeophysical effects. Here, we examine the future temperature-mortality impact for two contrasting LULCC scenarios in a background climate of low greenhouse gas concentrations. The first LULCC scenario implies a globally sustainable land use and socioeconomic development (sustainability). In the second LULCC scenario, sustainability is implemented only in the Organisation for Economic Cooperation and Development countries (inequality). Using the Multi-Country Multi-City (MCC) dataset on mortality from 823 locations in 52 countries and territories, we estimated the temperature-mortality exposure-response functions (ERFs). The LULCC and noLULCC scenarios were implemented in three fully coupled Earth system models (ESMs): Community Earth System Model, Max Planck Institute Earth System Model, and European Consortium Earth System Model. Next, using temperature from the ESMs' simulations and the estimated location-specific ERFs, we assessed the temperature-related impact on mortality for the LULCC and noLULCC scenarios around the mid and end century. Under sustainability, the multimodel mean changes in excess mortality range from -1.1 to +0.6 percentage points by 2050-2059 across all locations and from -1.4 to +0.5 percentage points by 2090-2099. Under inequality, these vary from -0.7 to +0.9 percentage points by 2050-2059 and from -1.3 to +2 percentage points by 2090-2099. While an unequal socioeconomic development and unsustainable land use could increase the burden of heat-related mortality in most regions, globally sustainable land use has the potential to reduce it in some locations. However, the total (cold and heat) impact on mortality is very location specific and strongly depends on the underlying climate change scenario due to nonlinearity in the temperature-mortality relationship.

A moving target: trade-offs between maximizing carbon and minimizing hydraulic stress for plants in a changing climate.

Observational evidence indicates that tree leaf area may acclimate in response to changes in water availability to alleviate hydraulic stress. However, the underlying mechanisms driving leaf area changes and consequences of different leaf area allocation strategies remain unknown. Here, we use a trait-based hydraulically enabled tree model with two endmember leaf area allocation strategies, aimed at either maximizing carbon gain or moderating hydraulic stress. We examined the impacts of these strategies on future plant stress and productivity. Allocating leaf area to maximize carbon gain increased productivity with high CO2, but systematically increased hydraulic stress. Following an allocation strategy to avoid increased future hydraulic stress missed out on 26% of the potential future net primary productivity in some geographies. Both endmember leaf area allocation strategies resulted in leaf area decreases under future climate scenarios, contrary to Earth system model (ESM) predictions. Leaf area acclimation to avoid increased hydraulic stress (and potentially the risk of accelerated mortality) was possible, but led to reduced carbon gain. Accounting for plant hydraulic effects on canopy acclimation in ESMs could limit or reverse current projections of future increases in leaf area, with consequences for the carbon and water cycles, and surface energy budgets.

Advanced precipitation peak offsets middle growing-season drought in impacting grassland C sink.

Redistribution of precipitation across seasons is a widespread phenomenon affecting dryland ecosystems globally. However, the impacts of shifting seasonal precipitation patterns on carbon (C) cycling and sequestration in dryland ecosystems remain poorly understood. In this study, we conducted a 10-yr (2013-2022) field manipulative experiment that altered the timing of growing-season precipitation peaks in a semi-arid grassland. We found that the delayed precipitation peak suppressed plant growth and thus reduced gross ecosystem productivity, ecosystem respiration, and net ecosystem productivity due to middle growing-season water stress. Surprisingly, shifting more precipitation to the early growing season can advance plant development, increase the dominance of drought-tolerant forbs, and thus compensate for the negative impacts of middle growing-season water stress on ecosystem C cycling, leading to a neutral change in grassland C sink. Our findings indicate that greater precipitation and plant development in spring could act as a crucial mechanism, maintaining plant growth and stabilizing ecosystem C sink. This underscores the urgent need to incorporate precipitation seasonality into Earth system models, which is crucial for improving projections of terrestrial C cycling and sequestration under future climate change scenarios.

Unravelling nitrate transformation mechanisms in karst catchments through the coupling of high-frequency sensor data and machine learning

Nitrate dynamics within a catchment are critical to the earth's system process, yet the intricate details of its transport and transformation at high resolutions remain elusive. Hydrological effects on nitrate dynamics in particular have not been thoroughly assessed previously and this knowledge gap hampers our understanding and effective management of nitrogen cycling in watersheds. Here, machine learning (ML) models were employed to reconstruct the annual variation trend in nitrate dynamics and isotopes within a typical karst catchment. Random forest model demonstrates promising potential in predicting nitrate concentration and its isotopes, surpassing other ML models (including Long Short-term Memory, Convolutional Neural Network, and Support Vector Machine) in performance. The ML-modeled NO3−-N concentrations, δ15N-NO3−, and δ18O-NO3− values were in close agreement with field data (NSE values of 0.95, 0.80, and 0.53, respectively), which are notably challenging to achieve for process models. During the transition from dry to wet period, approximately 23.0% of the annual precipitation (∼269.1 mm) was identified as the threshold for triggering a rapid response in the wet period. The modeled nitrate isotope values were significantly supported by the field data, suggesting seasonal variations of nitrogen sources, with precipitation as the primary driving force for fertilizer sources. Mixing of multiple sources appeared to be the main control of the transport and transformation of nitrate during the rising limb in the wet period, whereas process control (denitrification) took precedence during the falling limb, and the fate of nitrate was controlled by biogeochemical processes during the dry period.

A generalized bilevel optimization model for large-scale task scheduling in multiple agile earth observation satellites

Scheduling large-scale tasks within a multi-agile earth observation satellite system poses a formidable challenge. Given the complexity of this optimization problem, the responding solving strategy should exhibit both efficiency and effectiveness in terms of computational time and solution quality. The current strategies can broadly be categorized into all-in-one and two-stage methodologies. The latter, more conducive to real-world scenarios, owing to reducing the problem complexity and practical operational flexibility, dissects this challenge into task allocation and single-satellite scheduling. However, existing two-stage strategies still consider the objectives and constraints at the equivalent level and only adapt to specific algorithms. In this way, the pivotal issues concerning the solution complexity and strategy compatibility remain fundamentally unaddressed. To address this limitation, we proposed a generalized bilevel optimization model called Question-and-Answer model, which establishes two distinct optimization model with different contains and objectives. In this model, the upper questions are highly indispensable in the response from the lower level and constraints are considered separately at two stages. To ascertain the generalization of this framework, we conduct a comprehensive range of experiments employing various proposed strategies and algorithms in two stages. Diverse algorithms ranging from heuristic principles, and evolutionary strategies, to reinforcement learning can be seamlessly combined and integrated within this framework. The results demonstrate that the scale of scenarios does not affect the effectiveness of any amalgamated algorithms within this framework. Furthermore, the transition of upper questions according to the lower answers indeed improves the objectives but concurrently intensify the computational time.

Economic irrigation water productivity of wheat and potato: An earth observation perspective on policy implications in the Litani Basin, Lebanon

Efficient water management is critical in addressing the challenges posed by water scarcity and resource sustainability in agriculture. However, the utilization of remote sensing technology to monitor and enhance water productivity in data-scarce environments remains an open question. This study underscores the importance of integrating earth observation systems into agricultural water management policies, elucidating their potential to shape crucial policy decisions and investment strategies, particularly in regions facing data limitations. To address this issue, we provide a comprehensive framework that blends remote sensing data with field-based economic information, to develop the concept of economic productivity of irrigation water. Leveraging FAO’s Water Productivity Open-access portal (WaPOR) data, we spatially assess yields, water use, crop water productivity, economic water productivity, and economic irrigation water productivity (EIWP) for wheat and potatoes in the Upper Litani Basin of Lebanon's Bekaa Valley. Early season potatoes outperform irrigated wheat significantly, providing up to ten times higher returns on irrigation water ($6/m3 vs. $0.6/m3). Spatial analysis shows that southern areas have 30 % lower potato EIWP than northern areas of the valley but 55 % higher wheat EIWP, mainly due to higher rainfall. Our findings reveal that merely considering crop water productivity is insufficient when deciding what to plant, as it fails to account for profitability. While transitioning to crops or practices with greater EIWP can enhance economic productivity on the short-term, it may simultaneously lead to increased water consumption. As a result, the shift towards highly water-productive systems may require measures such as controlling irrigated areas or implementing restrictions on water withdrawals to maintain stable water consumption levels. This study offers valuable insights for water resource management, agricultural policy, and sustainable water utilization, especially in semi-arid regions like the Litani Basin, providing a foundation for informed decision-making in these resource-constrained environments.

The Role of Sea Ice Insulation Effects on the Probability of AMOC Transitions

Abstract Recent simulations performed with the Community Earth System Model (CESM) have suggested a crucial role of sea ice processes in Atlantic meridional overturning circulation (AMOC) hysteresis behavior under varying surface freshwater forcing. Here, we further investigate this issue using additional CESM simulations and a novel conceptual ocean–sea ice box model. The CESM simulations suggest that the presence of sea ice gives rise to the existence of statistical equilibria with a weak AMOC strength. This is confirmed in the conceptual model, which captures similar AMOC hysteresis behavior as in the CESM simulation and where steady states are computed versus freshwater forcing parameters. In the conceptual model, transition probabilities between the different equilibrium states are determined using rare event techniques. The transition probabilities from a strong AMOC state to a weak AMOC state increase when considering sea ice insulation effects and indicate that sea ice promotes these transitions. On the other hand, sea ice insulation effects strongly reduce the probabilities of the reverse transition from a weak AMOC state to a strong AMOC state and this implies that sea ice also limits AMOC recovery. The results here indicate that sea ice effects play a dominant role in AMOC hysteresis width and influence transition probabilities between the different equilibrium states. Significance Statement We develop a novel conceptual ocean–sea ice box model to explain AMOC hysteresis behavior recently found in a modern complex climate model (the Community Earth System Model) and determine how sea ice insulation effects influence the hysteresis width and the probability of AMOC transitions.

Is soil contamination a missing driver of soil heterotrophic respiration in land surface models? A study case with copper –

Land Surface Models (LSMs) are crucial elements of Earth System Models used to estimate the effects of anthropogenic greenhouse gas (GHG) emissions on Earth's climate. Nevertheless, as well as land use change and direct GHG emissions, anthropogenic activities are also associated with contaminant emissions and depositions. Although contamination has a recognized impact on soil processes such as GHG emissions, soil contamination is currently not considered as an important process to consider into LSMs.In this study we hypothesized that soil contamination has significant impact on soil CO2 emissions such as heterotrophic respiration (Rh). To this end, we analyzed how soil contamination may account for residuals of modeled Rh from 11 LSMs as compared to Rh products derived from observations over Europe. We used generalized least squared mixed models to evaluate the primary factors driving of the model residuals. Among contaminants, we focused on copper (Cu), which is widely used in industry or agriculture, causing significant diffuse contamination. Additionally, research demonstrated a strong correlation between soil Rh and Cu availability to soil fauna and various soil pedological and climatic parameters. Hence, we completed our analysis by including pedo-environmental parameters and by analyzing Rh against a proxy of bioavailable Cu.Our findings indicate that Cu is a non-negligible variable in explaining the Rh models inaccuracy considering either total or free Cu forms. Therefore, it can be concluded that Cu should not be disregarded as a key factor in predicting Rh.

Building high-resolution projections of temperature potential changes using statistical downscaling for the future period 2026-2100 in the Highland region of Yemen – A supportive approach for empowering environmental planning and decision-making

Environmental resources and ecological systems are significantly affected by the steady rise of the global temperature. However, the degree of temperature change at the regional and local levels is uncertain. The uncertainty arises from various factors, but mostly due to the short length of ground data and dependency of local studies on the large-scale and spatially coarse output of Global Climate Models (GCMs). Therefore, the output of GCM cannot be directly used in impact assessment studies at a regional and local level. In this study, the Statistical Down-Scaling Model (SDSM) is employed to investigate the magnitude of temperature changes (Minimum and Maximum Temperature) for the future period 2026-2100. The SDSM builds relationships between large-scale predictors and local climate variables, allowing for finer-resolution projections at a regional level. The study utilized the Climate Hazard Infra-Red Temperature with Station (CHIRTS-daily) to complete daily missing records in more than 90 ground stations. Additionally, predictors of the National Center for Environmental Prediction (NCEP) for the historical period (1961-2010) and the Canadian Earth System Model (CanESM2) for the future period (2026-2100) are employed to calibrate SDSM and to build finer-resolution scenarios under two representative concentration pathways; RCP2.6 and RCP8.5. The methodology additionally involved validating the SDSM performance using observed historical data before applying it to future projections. The findings indicate that both minimum and maximum temperatures (T-min and T-max) will increase, with a more pronounced rise in minimum temperature (T-min). Over the future period (2026-2100), the projected average temperature rise is 1.10°C (T-max) and 1.43°C (T-min) under RCP2.6. For RCP8.5, the projected average increases are 1.56°C and 2.3°C for T-max and T-min, respectively. Overall, the most significant increase is projected to occur in the 2090s (2076-2100) under RCP8.5, particularly in the lowlands and wadis of Al Mahwit and Raymah governorate. In these areas, the minimum temperature (T-min) exhibited an increased absolute value of up to 3.2°C. This high rise in temperatures is expected to result in increased evapotranspiration, prolonged droughts, and possibly breakouts of some plant diseases and pests. This would require effective adaptation measures such as harvesting rainwater and growing short-time and heat-resistance crops. Engaging in field visits and social discussions added depth to the study by introducing various traditional methods and indigenous practices. Valuable resources for future efforts to mitigate the potential impacts of climate change are offered by these insights.

A flexible class of priors for orthonormal matrices with basis function-specific structure

Statistical modeling of high-dimensional matrix-valued data motivates the use of a low-rank representation that simultaneously summarizes key characteristics of the data and enables dimension reduction. Low-rank representations commonly factor the original data into the product of orthonormal basis functions and weights, where each basis function represents an independent feature of the data. However, the basis functions in these factorizations are typically computed using algorithmic methods that cannot quantify uncertainty or account for basis function correlation structure a priori. While there exist Bayesian methods that allow for a common correlation structure across basis functions, empirical examples motivate the need for basis function-specific dependence structure. We propose a prior distribution for orthonormal matrices that can explicitly model basis function-specific structure. The prior is used within a general probabilistic model for singular value decomposition to conduct posterior inference on the basis functions while accounting for measurement error and fixed effects. We discuss how the prior specification can be used for various scenarios and demonstrate favorable model properties through synthetic data examples. Finally, we apply our method to two-meter air temperature data from the Pacific Northwest, enhancing our understanding of the Earth system’s internal variability.

Accurate and Efficient Numerical Simulation of Land Models Using SUMMA With SUNDIALS.

Numerical simulation of land models without error control can be highly inaccurate. We present the incorporation of the Suite of Nonlinear and Differential-Algebraic Equation Solvers (SUNDIALS) package to solve the equations that simulate thermodynamics and hydrologic processes in the Structure for Unifying Multiple Modeling Alternatives (SUMMA) land model. The algorithmic features of SUNDIALS, such as error estimation and adaptive order and step-size control, result in a SUMMA-SUNDIALS model that delivers substantially improved accuracy and relative computational efficiency compared to integration with the previous SUMMA model, which uses the low-order backward Euler method with no rigorous error control. The results are demonstrated through simulations over the North American continent with more than 500,000 spatial elements. Compared to the previous SUMMA model, we find that the simulations produced by the SUMMA-SUNDIALS model are orders of magnitude closer to converged solutions for the same computational cost. Being able to efficiently perform more reliable simulations makes the SUMMA-SUNDIALS model a powerful tool for improving our understanding of the terrestrial component of the Earth System.

Incorporating changes in land surface temperature improves BESS evapotranspiration estimates under water-deficit conditions: A case study for US Midwest and Great Plains grasslands

Evapotranspiration (ET) is a critical climate and ecosystem variable that interconnects water, energy, and carbon cycles. Breathing Earth System Simulator (BESS) is one of the state-of-the-art biophysical models capable of producing spatio-temporal continuous ET results. However, we found that since the BESS model does not use an explicit constraint on soil moisture (SM), it has a relatively lower performance under drier conditions. Given that changes in land surface temperature (LST) are closely associated with surface water status and sensible heat energy, we hypothesize that integrating LST changes could explicitly add the soil moisture constraints and thus enhance BESS’s ability to estimate ET. Here we used the morning rise rate of LST (Trate) as a proxy of LST change because of the low noise level in Trate as well as Trate’s close relationship with daily mean sensible heat. To test the hypothesis, this study first assessed whether the performance of BESS ET can be explained by the LST change, targeting grassland sites of the AmeriFlux network in the US Midwest and Great Plains. Specifically, the ET deviation (i.e., the difference between BESS-modeled ET and field-measured ET) and Trate deviation, as well as their relationships, were investigated under different conditions of precipitation, SM, and vapor pressure deficit (VPD) at the AmeriFlux sites. Results indicated that BESS ET exhibited consistently higher performance under well-watered conditions than water-deficit conditions. Also, the deviations of ET and Trate became more negatively correlated under water-deficit conditions. Leveraging the empirical relationship between ET and Trate deviations, this study developed a new way to calibrate BESS ET based on Trate calculated from LST diurnal observations, particularly under soil or atmospheric water-deficit conditions. After calibrating BESS ET, the statistical indicators between the calibrated ET and the ground measurements showed meaningful improvements relative to those before calibration. Specifically, in the Midwest (Great Plains), R2 increased from 0.42 to 0.51 (from 0.45 to 0.46), and RMSE and absolute bias decreased by 12% and 42% (11% and 45%), respectively. This study highlights that the morning rise rate of LST can effectively constrain the ET models that have no SM constraints under water-deficit conditions.

Land surface scheme TerM: the model formulation, code architecture and applications

Abstract This paper presents the INM RAS–MSU land surface scheme, extracted from the INM RAS Earth system model into an independent software complex and supplemented with several modules to reproduce new components and processes of the Earth system. The resulting software product is referred to as TerM (Terrestrial Model). The physical and mathematical foundations of the model, the main features of the software implementation, and examples of applications in reproducing components of the terrestrial hydrological and carbon cycles are briefly outlined. Separating the land surface block into a standalone software complex significantly saves computational resources when assessing the impact of global and regional climate changes on natural resources (including hydrological ones), ecosystem dynamics, and emissions of climate-relevant substances with high spatial detalization. Within the TerM modelling complex, the development, validation, and calibration of new parameterizations of physical and biogeochemical processes are being conducted in an autonomous mode for subsequent implementation into the full INM RAS Earth system model.