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Abstract Modeling evaporationEfrom inland water bodies is challenging largely due to the uncertainties of input data, particularly surface water temperature that plays a key role in the available energy, i.e., net radiationR_nminus rate of water heat storage changeG. The equilibrium temperature approach (ETA) for estimating water surface temperature offers an alternative method to calculateR_nandGusing standard meteorological data. This study evaluates the global lakeEestimates from the widely used Penman model (PM) coupled with the ETA (PM-ETA) against field observations and model simulations from the Lake, Ice, Snow, and Sediment Simulator (LISSS). Our analysis reveals that the PM-ETA tends to overestimateEby approximately 36% and 24% compared to observations and the LISSS simulations, respectively, despite being driven by the same input data. The biases of the PM-ETAEare more pronounced in the cold and polar regions with distinct seasonality ofR_nandG. Furthermore, theEtrends from the PM-ETA deviate from the LISSS simulations over the period of 2001–16 due to the bias trends in the available energy. By incorporating the LISSS-simulatedR_nandGinto the PM, the bias inEis reduced to less than ±5% compared to the LISSS results. This study highlights the need to improve the available energy input of the PM to improve the estimates of global lakeEfor better water resource management and planning. Significance StatementThis study addresses a crucial challenge in modeling evaporationEfrom inland water bodies—uncertainties in surface water temperature and available energy inputs, particularly net radiationR_nand rate of heat storage changeG. By evaluating the widely used Penman model (PM) coupled with the equilibrium temperature approach (ETA), we reveal a tendency for the PM-ETA to overestimateEglobally, with the largest biases observed in cold and polar regions. Incorporating higher-qualityR_nandGestimates from the Lake, Ice, Snow, and Sediment Simulator (LISSS) significantly reduces these biases. These findings highlight the importance of alternative higher-quality data products for available energy inputs for improvingEestimates by the PM. 

While extensive research has focused on evapotranspiration (ET) from land surface, the spatial distributions of ET of the woodland and forest understorey remain poorly understood. This study developed a method for estimating spatially distributed understorey ET by integrating the Maximum Entropy Production model with airborne thermal imagery. Validation against ground-truth estimation showed good model performance (R2 = 0.93, RMSE = 0.03 mm/h), confirming its efficacy across different land cover types, including open and understory areas. The results revealed significant spatial heterogeneity in understory ET with varying vegetation cover and topographic attributes, and distinct responses to wetting events. This method provides a new tool for estimating the important understory water consumption in forests and woodlands, contributing to assessing ecosystem water use efficiency and improving water resource and vegetation management strategies. 

Abstract The inverse temperature layer (ITL) beneath water‐atmosphere interface within which temperature increases with depth has been observed from measurement of water temperature profile at an inland lake. Strong solar radiation combined with moderate wind‐driven near‐surface turbulence leads to the formation of a pronounced diurnal cycle of the ITL predicted by a physical heat transfer model. The ITL only forms during daytime when solar radiation intensity exceeds a threshold while consistently occurs during nighttime. The largest depth of the ITL is comparable to thee‐fold penetration depth of solar radiation during daytime and at least one order of magnitude deeper during nighttime. The dynamics of the ITL depth variation simulated by a physical model forced by observed water surface solar radiation and temperature is confirmed by the observed water temperature profile in the lake. 

Abstract Improved modeling of permafrost active layer freeze‐thaw plays a crucial role in understanding the response of the Arctic ecosystem to the accelerating warming trend in the region over the past decades. However, modeling the dynamics of the active layer at diurnal time scale remains challenging using the traditional models of freeze‐thaw processes. In this study, a physically based analytical model is formulated to simulate the thaw depth of the active layer under changing boundary conditions of soil heat flux. Conservation of energy for the active layer leads to a nonlinear integral equation of the thaw depth using a temperature profile approximated from the analytical solution of the heat transfer equation forced by ground heat flux. Temporally variable ground heat flux is estimated using non‐gradient models when field observations are not available. Validation of the proposed model conducted against field data obtained from three Arctic forest and tundra sites demonstrates that the model is able to simulate both thaw depth and soil temperature profiles accurately. The model has the potential to estimate regional variability of the thaw depth for permafrost related applications. 

Abstract Ground heat flux (G₀) is a key component of the land‐surface energy balance of high‐latitude regions. Despite its crucial role in controlling permafrost degradation due to global warming,G₀is sparsely measured and not well represented in the outputs of global scale model simulation. In this study, an analytical heat transfer model is tested to reconstructG₀across seasons using soil temperature series from field measurements, Global Climate Model, and climate reanalysis outputs. The probability density functions of ground heat flux and of model parameters are inferred using availableG₀data (measured or modeled) for snow‐free period as a reference. When observedG₀is not available, a numerical model is applied using estimates of surface heat flux (dependent on parameters) as the top boundary condition. These estimates (and thus the corresponding parameters) are verified by comparing the distributions of simulated and measured soil temperature at several depths. Aided by state‐of‐the‐art uncertainty quantification methods, the developedG₀reconstruction approach provides novel means for assessing the probabilistic structure of the ground heat flux for regional permafrost change studies. 

Abstract Science, engineering, and society increasingly require integrative thinking about emerging problems in complex systems, a notion referred to as convergence science. Due to the concurrent pressures of two main stressors—rapid climate change and industrialization, Arctic research demands such a paradigm of scientific inquiry. This perspective represents a synthesis of a vision for its application in Arctic system studies, developed by a group of disciplinary experts consisting of social and earth system scientists, ecologists, and engineers. Our objective is to demonstrate how convergence research questions can be developed via a holistic view of system interactions that are then parsed into material links and concrete inquiries of disciplinary and interdisciplinary nature. We illustrate the application of the convergence science paradigm to several forms of Arctic stressors using the Yamal Peninsula of the Russian Arctic as a representative natural laboratory with a biogeographic gradient from the forest‐tundra ecotone to the high Arctic. 

Abstract Heat storage change (HSC) is a crucial component of lake's thermal energy budget. Conventional temperature profile based models of HSC require location specific parameters such as lakebed topography. Based on the half‐order time‐derivative formula of heat fluxes, an analytical model was formulated for estimating HSC from water surface temperature and solar radiation without using geography dependent parameters. The proposed model was tested against field measurements at Poyang Lake, a shallow inland lake, which has pronounced seasonal variations in water level and lake area. Our analysis indicates that the model accurately simulates diurnal HSC with a coefficient of determination of 0.94 and a root mean squared error (RMSE) of 77.5 ± 21.6 Wm⁻²for the study period. Larger nighttime RMSE (75.0 ± 26.8 Wm⁻²) than the daytime value (55.1 ± 19.7 W m⁻²) is attributable to larger measurement errors of nighttime turbulent fluxes. The estimation of HSC independent of temperature profile and lake‐specific parameters by the proposed model facilitates remote sensing monitoring the HSC of global water bodies. 

Abstract Due to lack of a unified description of the Earth surface temperature, a generic dynamic equation is postulated as an inference from the special case of snow. Solar radiation is explicitly included in the formulation for transparent media such as snow, ice and water while implicitly through (conductive) surface heat flux for non‐transparent media such as soil. The physical parameters of the equation are medium thermal inertia, thermal and radiative diffusivity. The equation for transparent media reduces to the familiar force‐restore model of soil surface temperature when the penetration depth of solar radiation tends to zero. Proof‐of‐concept validation for snow surface temperature as a paradigm of transparent media at three sites in the Arctic and Antarctica confirms the postulated equation as a generic description of the dynamics of surface temperature.