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Downwelling longwave radiation (Rld) is a dominant term in the surface energy balance and is central to global warming. It is influenced by the radiative properties in the whole atmospheric column, particularly greenhouse gases, water vapor, clouds, and atmospheric heat storage. To reveal the leading terms responsible for the spatiotemporal climatological variations in Rld, we use the semi-empirical equation derived by Brutsaert (1975, “B75”), which only needs near-surface observations of air temperature and humidity. We first evaluated B75 and its extension by Crawford and Duchon (1999, "C&D99") with FLUXNET observations, NASA-CERES satellite data, and ERA5 reanalysis. We found a strong agreement, with R2 being 0.87, 0.97, and 0.99, respectively. We then used the equations to show that diurnal and seasonal variations in Rld are predominantly controlled by changes in atmospheric heat storage. Variations in atmospheric emissivity form a secondary contribution to the variation of Rld, and are mostly controlled by anomalies in cloud cover. We also found that with increased aridity, the contributions by changes in atmospheric heat storage and emissivity acted to compensate each other (20~30 W/m2 and ~-40 W/m2, respectively), thus explaining the relatively little variation in Rld with aridity (-20~-10 W/m2). The equations further indicate that under global warming, the amplification of water vapor is stronger in arid regions because clear-sky conditions are more sensitive to an increase in greenhouse gases. These equations thus provide a firm, physical basis to understand the spatiotemporal variability of downwelling longwave radiation at the surface. This should be helpful to better understand and interpret climatological changes, for instance those associated with global warming and extreme events.

The diurnal air temperature range (DTR) is strongly shaped by solar radiation but is modulated by hydrologic cycling through changes in atmospheric (clouds) and land-surface (evaporation) characteristics. Here, we aim to determine the distinct patterns in DTR over dry and wet periods and identify their respective controls. To do this, we develop a simple energy balance model that constrains the land-atmosphere exchange using the thermodynamic limit of maximum power. In this framework, we explicitly account for changes in radiative conditions due to clouds and changes in boundary layer heat storage associated with surface water limitation, both of which affect the maximum power limit. Using observations of radiative forcings and surface evaporation, our model predicts DTR reasonably well across 81 FLUXNET sites in North America, Europe, and Australia. We show that DTR is primarily shaped by the trade-off between the heat gain due to solar absorption and heat lost at the surface due to evaporation. Radiation remains a primary control on DTR over very dry and wet conditions where evaporation is either close to zero or limited by available energy. Over these regions, changes in DTR are strongly modulated by clouds which alters the radiative conditions. DTR becomes coupled to the land surface during the transition regime where changes in surface water availability directly control the evaporation rates. Over these regions, increased soil moisture results in more evaporation and reduced DTR. These responses were consistent in both, observations and maximum power estimates. We then apply our framework to quantify the response of DTR to global warming. Our model projects a decrease in DTR by 0.18K for a 1K rise in global temperature, which is consistent with the current observed response. Our findings imply that the predominant controls on DTR are set by clouds and evaporation as they directly modulate the diurnal heating of the lower atmosphere and can be further altered by increased greenhouse forcing.

Energy scenarios envision installation of up to 230 GW of wind capacity over available areas within the German onshore by 2050. The associated technical wind energy potential is typically derived assuming that the electricity generated by the wind turbines does not affect the wind resource. Consequently, future capacity factors, the ratio of generation to installed capacity, are implicitly assumed to be independent of the extent to which the wind resource is depleted. However, capacity factors reduce as wind capacity is increased. This is because kinetic energy (KE) removal lowers wind speeds that result in lower generation from the turbines. To assess the relevance of this resource depletion effect on capacity factors, we simulated electricity generation by wind turbines with a range of hypothetical and planned deployment scenarios using the Weather Research and Forecasting (WRF) model that incorporates the effects of atmosphere - turbine interactions and compared these to estimates derived from a simple, momentum-balance approach (VKE). Despite potential biases in modelled wind speeds, we find that for a typical planned scenario of ~200 GW deployed over 13.8% of land area, mean annual wind speeds reduce by an average of 0.4 m s-1 compared to the case where the impact of atmospheric - turbine interactions is excluded. Associated reductions in capacity factor are up to 20% in regions of high installed capacities. To isolate the key atmospheric influence, we compare the simulated range of wind speeds and capacity factors with those from the VKE model that only accounts for KE removal effects. We find that the KE removal effects play the dominant role in shaping the reductions in wind speeds and capacity factors, thus providing a simple tool to capture these effects. We conclude that with increased deployment of wind energy in the context of the energy transition, these wind resource depletion effects need to be taken into account, but this can be done in a comparatively simple and physical way.

A dominant term in the surface energy balance and central to global warming is downwelling longwave radiation (Rld). It is influenced by radiative properties of the atmospheric column, in particular by greenhouse gases, water vapour, clouds and differences in atmospheric heat storage. We use the semi-empirical equation derived by Brutsaert (1975) to identify the leading terms responsible for the spatio-temporal climatological variations in Rld. This equation requires only near-surface observations of air temperature and humidity. We first evaluated this equation and its extension by Crawford and Duchon (1999) with observations from FLUXNET, the NASA-CERES dataset , and the ERA5 reanalysis. We found a strong agreement with r2 ranging from 0.87 to 0.99 across the datasets for clear-sky and all-sky conditions. We then used the equations to show that diurnal and seasonal variations in Rld are predominantly controlled by changes in atmospheric heat storage. Variations in the emissivity of the atmosphere form a secondary contribution to the variation in Rld, and are mainly controlled by anomalies in cloud cover. We also found that as aridity increases, the contributions from changes in emissivity and atmospheric heat storage tend to offset each other (−40 W m−2 and 20−30 W m−2, respectively), explaining the relatively small decrease in Rld with aridity (−(10−20) W/m−2). These equations thus provide a solid physical basis for understanding the spatio-temporal variability of surface downwelling longwave radiation. This should help to better understand and interpret climatological changes, such as those associated with extreme events and global warming.

The diurnal variations of surface and air temperature are closely related, but their different responses to evaporative conditions can inform us about land–atmosphere interactions. Here, we evaluate the responses of the diurnal ranges in surface (ΔTs) and air (ΔTa) temperature to evaporative fraction at 160 FLUXNET sites and in the ERA5 reanalysis. We show that the sensitivity of ΔTs to evaporative fraction depends on vegetation type, whereas ΔTa does not. On days with low evaporative fraction, ΔTs in FLUXNET is enhanced by up to ∼20 K (∼30 K in ERA5) in short vegetation, but only by up to ∼10 K (∼10 K in ERA5) in forests. Note that ΔTa responds rather similarly to evaporative fraction irrespective of vegetation type (∼5 K in FLUXNET, ∼10 K in ERA5). We find a systematic bias in ERA5’s ΔT response to evaporative conditions, showing a stronger sensitivity to evaporative fraction than in FLUXNET. We then demonstrate with a simple atmospheric boundary layer (SABL) model that the weak response of ΔTa to evaporative fraction can be explained by greater boundary layer growth under dry conditions, which increases the heat storage capacity and reduces the response of air temperature to evaporative fraction. Additionally, using a simplified surface energy balance (SSEB) model we show that ΔTs mainly responds to solar radiation, evaporative fraction, and aerodynamic conductance. We conclude that the dominant patterns of diurnal temperature variations can be explained by fundamental physical concepts, which should help us to better understand the main controls of land–atmosphere interactions. Significance Statement Generally, air temperature is used more widely than the surface temperature, and often they are assumed to be equivalent. However, we show that their responses to changes in vegetation type and evaporative conditions are quite different. Using FLUXNET observations, ERA5 reanalysis, and two simple physical models, we found that these responses are much stronger in surface temperature, especially in short vegetation, and relatively weaker in air temperature. Despite being measured just 2 m above the surface, air temperature carries lesser imprints of evaporation and vegetation than the surface temperature because of boundary layer dynamics. These findings suggest the importance of coupled land–atmosphere processes in shaping surface and air temperature differently and provide insights on their distinctive responses to global changes.

Optimality concepts related to energy and entropy have long been proposed in Earth system science, yet they remain obscure, seem contradictory regarding their goal to either maximize or minimize, and have so far only played marginal roles. This review aims to clarify the role of thermodynamics and optimality in Earth system science by showing that it plays a pivotal role in how, and how much, work can be derived from the solar forcing, and that this imposes a major constraint to the dynamics of dissipative structures of the Earth system. This is, however, not as simple as it may sound. It requires a consistent formulation of Earth system processes in thermodynamic terms, including their linkages and interactions. Thermodynamics then constrains the ability of the Earth system to derive work and generate free energy from the solar radiative forcing, which limits the ability to maintain motion, mass transport, geochemical cycling, and biotic activity. It thus limits directly the generation of atmospheric motion and other processes indirectly through their need for transport, such as hydrologic cycling or biotic activity. I demonstrate the application of this thermodynamic Earth system view by deriving first-order estimates associated with atmospheric motion, hydrologic cycling, and terrestrial productivity that agree very well with observations. This supports the notion that the emergent simplicity and predictability inherent in observed climatological variations can be attributed to these processes working as hard as they can, reflecting thermodynamic limits directly or indirectly. I discuss how this thermodynamic interpretation is consistent with established theoretical concepts in the respective disciplines, interpret other optimality concepts in light of this thermodynamic Earth system view, and describe its utility for Earth system science.

Climate models predict an intensification of precipitation extremes as a result of a warmer and moister atmosphere at the rate of 7 % K−1. However, observations in tropical regions show contrastingly negative precipitation–temperature scaling at temperatures above 23–25 ∘C. We use observations from India and show that this negative scaling can be explained by the radiative effects of clouds on surface temperatures. Cloud radiative cooling during precipitation events make observed temperatures covary with precipitation, with wetter periods and heavier precipitation having a stronger cooling effect. We remove this confounding effect of clouds from temperatures using a surface energy balance approach constrained by thermodynamics. We then find a diametric change in precipitation scaling with rates becoming positive and coming closer to the Clausius–Clapeyron (CC) scaling rate (7 % K−1). Our findings imply that the intensification of precipitation extremes with warmer temperatures expected with global warming is consistent with observations from tropical regions when the radiative effect of clouds on surface temperatures and the resulting covariation with precipitation is accounted for.

We employ the approach of Roderick and Farquhar (2011) to assess the sensitivity of runoff (R) given changes in precipitation (P), potential evapotranspiration (Ep ), and other properties that change the partitioning of P (n) by estimating coefficients that predict the weight of each variable in the relative change of R. We use this framework using different data sources and products for P, actual evapotranspiration (E), and Ep within the Amazon River basin to quantify the uncertainty of the hydrologic response at the subcatchment scale. We show that when estimating results from the different combinations of datasets for the entire river basin (at Obidos), a 10% increase in P would increase R on average 16%, while a 10% increase in Ep would decrease R about 6%. In addition, a 10% change in the parameter n would affect the hydrological response of the entire basin around 5%. However, results change from catchment to catchment and are dependent on the combination of datasets. Finally, results suggest that enhanced estimates of E and Ep are needed to improve our understanding of the future scenarios of hydrological sensitivity with implications for the quantification of climate change impacts at the regional (subcatchment and subbasin) scale in Amazonia.

Natural processes within the Earth system have been shown to organise themselves to achieve a state of thermodynamic optimality. Here we test these physical principles for convective flux exchange within the surface – atmosphere system. We propose an idealised modelling framework where the convective exchange is conceptualised as the outcome of a heat engine operated between the hotter Earth’s surface and the cooler atmosphere. We use the first and second law of thermodynamics in conjunction with the surface energy balance which give rise to thermodynamic constraints on turbulent flux exchange. This new constraint is associated with the maximum power that can be generated within the heat engine to sustain convective motion. We use daily radiative forcing from NASA-CERES dataset as the input to our approach and estimated the surface energy partitioning on land into turbulent fluxes and emitted longwave radiation. The former is closely related to convective exchange within the atmosphere driving the hydrologic cycling while the latter directly relates to the surface temperature of the Earth. We compare our estimates of surface temperatures, latent and sensible heat fluxes with observation based datasets and found a very good agreement over land at a global scale. Our findings show that physical principles of thermodynamics alone can explain the surface energy partitioning to a large extent. We further show an application of this approach in removing the cloud radiative effects (CRE) from surface temperatures. We used clear-sky fluxes from the NASA-CERES dataset as a forcing to our thermodynamically constrained energy balance model and estimated "clear-sky" temperatures. These temperatures removes the effect of radiative cooling by clouds on surface temperatures and can be used as useful variable to infer the hydrological sensitivity from observations. Our work implies that thermodynamically constrained idealised models can be used to identify the dominant physical controls on climate system to better understand land-atmosphere interactions and climate sensitivities.

Policy and economic analyses use simple estimates of regional and global wind energy potential, or technical wind energy potential, to evaluate low carbon energy transition pathways. They neglect the reduction in mean wind speeds due to the extraction of kinetic energy (KE) from the lower atmosphere as a means to reduce the computational complexity. However, climatological analyses of proposed regional wind turbine deployments with capacity densities ranging from 2-10 MW km-2 show that this assumption leads to significant overestimation of wind energy potentials because the removal of KE does reduce wind speeds and turbine yields. This gap between policy focussed and climatology based estimates implies the need for the former to use a more climatologically descriptive, yet simple, approach to estimating technical potential. Framing regional wind energy potential within the context of a fixed lower atmosphere KE budget which uses variation in mean boundary layer height to define the KE budgets can improve estimates without increasing computational complexity. We evaluate this hypothesis by analysing a set of previously published Weather Research and Forecasting simulations of a hypothetical large scale wind turbine deployment in Kansas, US, which showed that nighttime yields were lower than daytime, despite wind speeds being 40% higher at night. We assess this seemingly counter-intuitive result by estimating day and nighttime yields while constraining them with separate day and night KE budgets. Daytime budgets are defined by higher mean boundary layer heights (2000m) while nighttime budgets by lower heights (900m). The combination of wind speeds boundary layer variations during day and night result in similar budgets. This means that turbines extract more KE from the atmosphere at night than daytime, leading to the nighttime budgets being depleted faster and thus lower deployment yields. Using the standard approach, which discounts the effect of KE removal by wind turbines, leads to a 180% and 600% overestimation in day and nighttime yields, respectively, relative to the weather model simulations. The KE budget approach , in contrast, leads to a significant improvement with day and nighttime bias being reduced to 20% and 60%, respectively. Using this approach, we revaluate existing technical wind energy potentials using the net area available for wind energy deployment in Kansas to show that yield expectations of 2000-3000 TWh yr-1 could be reduced by almost 50%. Despite this reduction, the technical potential remains almost 3-5 times higher than the state’s 2018 primary energy consumption. We show that framing the yield from regional wind turbine deployments within a fixed lower atmosphere KE budget framework that uses mean boundary layer variations to define the KE budget leads to more climatologically representative estimates of technical potential without increasing computational complexity. These estimates are likely to improve further with the inclusion of nighttime stability effects. Climatologically representative estimates of technical wind energy potential will enable the development of more robust renewable energy transition policy.

I use thermodynamics and an Earth system approach to determine how much kinetic energy the atmosphere is physically capable of generating at large scales from the solar radiative forcing. The work done to generate and maintain large-scale atmospheric motion can be seen as the consequence of an atmospheric heat engine, which is driven by the difference in solar radiative heating between the tropics and the poles. The resulting motion transports heat, which depletes this differential solar heating and the associated, large-scale temperature difference, which drives this energy conversion in the first place. This interaction between the thermodynamic driver (temperature difference) and the resulting dynamics (heat transport) is critical for determining the maximum power that can be generated. It leads to a maximum in the global mean generation rate of kinetic energy of about 1.7 W m-2, which matches rates inferred from observations of about 2.1 - 2.5 W m-2 very well. This represents less than 1% of the total absorbed solar radiation that is converted into kinetic energy. Although it would seem that the atmosphere is extremely inefficient in generating motion, thermodynamics shows that the atmosphere works as hard as it can to generate the energy contained in the winds. I then show that this view of atmospheric dynamics is essentially the same as a maximised generation rate of Available Potential Energy (APE) for the Lorenz energy cycle, and that it is also consistent with the outcome of the proposed principle of Maximum Entropy Production (MEP) while representing a more physically interpretable approach. This supports the notion that Earth system processes evolve to and operate near their thermodynamic limit, which permits the use of this constraint to do climate science analytically with less empirical input.

Klimamodelle simulieren klare Erwärmungsmuster als Folge des verstärkten Treibhauseffekts, der durch die Verbrennung fossiler Energie entsteht. Sie liefern aber keine einfache Erklärung mit, was die grundlegenden Mechanismen sind, die diese verursachen. Hier zeige ich mithilfe von einer einfachen, aber physikalisch basierten Formulierung der Energiebilanz der Erdoberfläche auf, dass Änderungen des Treibhauseffekts zu grundlegend anderen Erwärmungsmustern führen als Änderungen der Solarstrahlung. So kann man mithilfe der Energiebilanz aufzeigen, dass Änderungen des Treibhauseffekts sich insbesondere dann bemerkbar machen, wenn die Solarstrahlung wenig zur Erwärmung der Erdoberfläche beiträgt. Dies führt zu einer verstärkten Erwärmung in der Nacht über Land sowie in den hohen Breiten im Winter. Mithilfe von Satellitendatensätzen von Strahlungsflüssen des gegenwärtigen Klimazustands (NASA-CERES) kann man diese unterschiedlichen Sensitivitäten bestimmen und zeigen, dass diese mit denen von Kllimamodellsimulationen sehr gut übereinstimmen. Die grundlegenden Muster des Klimawandels lassen sich somit auch einfach und physikalisch verstehen und berechnen, und man ist dafür nicht auf komplexe Klimamodelle angewiesen.

Recent developments in hydrology have led to a new perspective on runoff processes, extending beyond the classical mass dynamics of water in a catchment. For instance, stream flow has been analysed in a thermodynamic framework, which allows the incorporation of two additional physical laws and enhances our understanding of catchments as open environmental systems. Related investigations suggested that energetic extremal principles might constrain hydrological processes, because the latter are associated with conversions and dissipation of free energy. Here we expand this thermodynamic perspective by exploring how hillslope structures at the macro- and microscale control the free energy balance of Hortonian overland flow. We put special emphasis on the transitions of surface runoff processes at the hillslope scale, as hillslopes energetically behave distinctly different in comparison to fluvial systems. To this end, we develop a general theory of surface runoff and of the related conversion of geopotential energy gradients into other forms of energy, particularly kinetic energy as the driver of erosion and sediment transport. We then use this framework at a macroscopic scale to analyse how combinations of typical hillslopes profiles and width distributions control the spatial patterns of steady-state stream power and energy dissipation along the flow path. At the microscale, we analyse flow concentration in rills and its influence on the distribution of energy and dissipation in space. Therefore, we develop a new numerical method for the Catflow model, which allows a dynamical separation of Hortonian surface runoff between a rill- and a sheet flow domain. We calibrated the new Catflow-Rill model to rainfall simulation experiments and observed overland flow in the Weiherbach catchment and found evidence that flow accumulation in rills serves as a means to redistribute energy gradients in space, therefore minimizing energy expenditure along the flow path, while also maximizing overall power of the system. Our results indicate that laminar sheet flow and turbulent rill flow on hillslopes develop to a dynamic equilibrium that corresponds to a maximum power state, and that the transition of flow from one domain into the other is marked by an energy maximum in space.

With the use of the appropriate technology, such as photovoltaics and seawater desalination, humans have the ability to increase sustainably their production of food and energy while minimising detrimental impacts on the Earth system. Focussing on energy conversion and dissipation allows us to compare human activity with Earth system processes at a very general level. Here, I emphasise three points: (i) human activity consumes free energy at orders of magnitude not far away from other Earth system processes; (ii) the most detrimental aspects of human activity relate to its consumption of free energy from the Earth system, rather than generating it; and (iii) some forms of technology, in particular photovoltaics, allow to convert solar radiation into free energy more efficiently than Nature does, which provides a basis for a sustainable future. In summary, instead of consuming free energy, human societies should turn into producers of free energy by the use of technology, thus minimising their impact on the planetary environment.

Surface topography is an important source of information about the functioning and form of a hydrological landscape. Because of its key role in explaining hydrological processes and structures, and also because of its wide availability at good resolution in the form of digital elevation models (DEMs), it is frequently used to inform hydrological analyses. Not surprisingly, several hydrological indices and models have been proposed for linking geomorphic properties of a landscape with its hydrological functioning; a widely used example is the “height above the nearest drainage” (HAND) index. From an energy-centered perspective HAND reflects the gravitational potential energy of a given unit mass of water located on a hillslope, with the reference level set to the elevation of the nearest corresponding river. Given that potential energy differences are the main drivers for runoff generation, HAND distributions provide important proxies to explain runoff generation in catchments. However, as expressed by the second law of thermodynamics, the driver of a flux explains only one aspect of the runoff generation mechanism, with the driving potential of every flux being depleted via entropy production and dissipative energy loss. In fact, such losses dominate when rainfall becomes runoff, and only a tiny portion of the driving potential energy is actually transformed into the kinetic energy of streamflow. In recognition of this, we derive a topographic index called reduced dissipation per unit length index (rDUNE) by reinterpreting and enhancing HAND following a straightforward thermodynamic argumentation. We compare rDUNE with HAND, and with the frequently used topographic wetness index (TWI), and show that rDUNE provides stronger discrimination of catchments into groups that are similar with respect to their dominant runoff processes. Our analysis indicates that accounting for both the driver and resistance aspects of flux generation provides a promising approach for linking the architecture of a system with its functioning and is hence an appropriate basis for developing similarity indices in hydrology.

To understand changes in land surface energy balance partitioning due to tropical deforestation, we use a physically based analytical formulation of the surface energy balance. Turbulent heat fluxes are constrained by the thermodynamic maximum power limit and a formulation for diurnal heat redistribution within the land‐atmosphere system. The derived turbulent fluxes of sensible and latent heat compare very well to in situ observations for sites with intact rainforest and soybean land cover in southeastern Amazonia. The equilibrium partitioning into sensible and latent heat flux compares well with observations for both sites, except for the soybean site during the dry season where water limitation needs to be explicitly accounted for. Our results show that tropical deforestation primarily affects the absorption of solar radiation and the water limitation of evapotranspiration, but not the overall magnitude of turbulent heat fluxes that is set by the thermodynamic maximum power limit. Tropical deforestation impacts the local energy and water exchange between land surface and atmosphere, typically resulting in regionally warmer and drier climates. General circulation models still disagree in reproducing these changes and little has been done to derive them from first principles. Here, we present an alternative approach to describe the effects of tropical land conversion from forest to soy agriculture, based on a physical theory of land‐atmosphere interactions. We view land‐atmosphere exchange as the result of a heat engine strongly shaped by turbulent heat exchange. This provides a framework to derive analytical expressions of the turbulent fluxes from the limit by how much work this engine can maximally perform. By comparing these with observations from a tropical rainforest and a soybean field in Amazonia, we find that the diurnal variations of turbulent fluxes are very well estimated. This means that turbulent land‐atmosphere exchange is primarily constrained by the thermodynamic limit, irrespective of surface roughness and evapotranspiration, and suggests that one can estimate the primary impacts of tropical land use change from physical principles. Thus, using thermodynamic limits represents an alternative approach to investigate the highly complex nature of land‐atmosphere interactions and global change from first principles.

Land-atmosphere interactions are typically evaluated using numerical simulation models of increasingly greater complexity. But what are the key, major constraints that determine the first-order controls of the land-atmosphere system? Here, we present an alternative approach that is solely based on energetic and thermodynamic constraints of the coupled land-atmosphere system and show that this approach can reproduce observations at the diurnal scale very well. The key concept we use is that turbulent heat fluxes are predominantly the result of an atmospheric heat engine that is driven by the heat input from the surface and that operates at the thermodynamic limit of maximum power. This provides a closure for the magnitude of turbulent fluxes in the surface energy balance. Interactions enter this approach mainly in two ways: First, the cooling effect of turbulent heat fluxes on surface temperature lowers the engine's efficiency, thereby setting the maximum power limit, and second, by heat storage changes in the lower atmosphere, which represent an entropy term inside the heat engine and alter the thermodynamic limit for power output. Both effects are, however, well constrained by energy balances, yielding analytical solutions for energy balance partitioning during the day without the need for empirical parameters. The further partitioning into sensible and latent heat fluxes is obtained from the assumption of thermodynamic equilibrium at the surface where heat and moisture is added to the atmosphere (if sufficient soil water is accessible). We then show that this approach works remarkably well in reproducing FluxNet observations over the diurnal cycle. What this implies is that these physical constraints determine the first-order dynamics of the land-atmosphere system, enabling us to derive simple, physics-based estimates of climate, the dominant effects of vegetation, and the response of the coupled system to global climate change.

One direct effect of climate warming on hydrology is the increase in moisture holding capacity of atmosphere at the rate of 7%/°C as suggested by the Clausius Clapeyron equation. Extreme precipitation largely depends on the amount of precipitable water in the atmospheric column and is thus expected to scale with temperature at the same rate. Observations, however, show significant variability in precipitation - temperature scaling rates, with negative scaling dominating in the tropical regions. These scaling relationships assume a one way causality, i.e. temperature is independent of precipitation. However, we show here that temperatures strongly co-vary with precipitation through the effect that clouds have on surface radiation. The presence of clouds associated with precipitation events result in lower solar isolation at the surface, further leading to reduced temperatures. This induces a two-way causality and thus temperature is no longer independent of precipitation. To remove this cooling effect of clouds, we used a surface energy balance model with a thermodynamic constraint to derive clear sky temperatures during precipitation events. We then show using observations from India, that extreme precipitation scaled with clear sky temperatures shows an increase consistent with the CC rate. On contrary, the negative scaling obtained using observed temperatures misrepresent the precipitation response to warming as a result of the co-variation with the cloud radiative effect. Our findings reveal that scaling relationships not only show how precipitation changes with temperature but also how atmospheric conditions associated with precipitation affect temperature. Thus, this covariation needs to be taken into account when using observations to derive scaling relationships that are then used to infer the extreme precipitation response to climate change.

Extreme precipitation is expected to increase at the rate of 7% per degree rise in temperature as suggested by the Clausius-Clapeyron equation (also known as CC scaling). Observations however, show deviations from the CC rate, with mostly negative precipitation - temperature scaling in warm tropical regions. Here we explain the negative precipitation scaling in the tropics with the cloud radiative effect on surface temperatures. Temperatures are shaped by the surface energy balance, which is affected by clouds, and hence temperatures are not independent of precipitation. We used observations from India and found negative scaling rates over most regions as extreme precipitation scaling tends to breakdown at temperatures of about 23◦to 25◦C. We show that these negative scaling rates arise from the radiative cooling of clouds associated with precipitation events which is predominant in India during the summer monsoon season. To test our hypothesis, we used an energy balance model constrained by assumption that convective exchange within atmosphere works at its thermodynamic limit of maximum power. Using the NASA-CERES radiation product, we calculated surface temperatures for “All sky” and “Clear sky” conditions to include/exclude the effect of cloud radiative forcing. Our results show a diametric change in precipitation scaling after removing the cooling effect of clouds on surface temperatures. Negative precipitation scaling (-4% /◦C) was found when using “All sky” conditions, but these come close to the CC rate (7% to 9% /◦C) when estimated using temperatures derived from “Clear sky” conditions. The breakdown in extreme precipitation scaling at high temperatures also disappeared forthe “Clear sky” temperatures. This implies that the breakdown in scaling may not relate to changes in aridity or the lack of moisture, but rather to the associated changes in cloud cover. Negative scaling rates derived from observations are thus likely to misrepresent the response of extreme precipitation to global warming in tropical regions. Our findings suggest that an intensification of precipitation extremes at CC rate with global warming is consistent with observations.Keywords: Extreme Precipitation, CC scaling, Maximum Power, Indian Mon-soon

The European Commission’s net zero decarbonization scenarios estimate that up to 450GW of offshore wind capacity could be installed in Europe by 2050. German energy scenarios estimate that 50 to 70 GW of this could be installed in the German Bight in the North Sea and yield about 4000 full load hours (FLH) per year of power. However, these assume that wind speeds and yields are not reduced by the increased extraction of kinetic energy from the regional atmospheric flow by large wind farms. Our initial assessment of these assumptions, using two different approaches - the simple Kinetic Energy Budget of the Atmosphere model (KEBA) and the Weather Research and Forecasting model with Explicit Wake Parameterization (WRF-EWP), showed that emplacing such a large turbine capacity within the German Bight may lower expected yield down to 3300-3000 FLH. Here, we identify the major factors leading to this reduction. We use the two models to evaluate the role of atmospheric variables like wind directions, atmospheric stability, boundary layer height and surface friction in constraining large scale offshore wind energy generation. We test the KEBA model concept of limited kinetic energy fluxes through the boundary layer determining generation potential, and investigate deviations between the models to identify limitations of the simpler approach.The WRF model sets our ”best guess” of energy yield from regional wind turbine deployments (at scales of 104km2) since the scale of deployments that we assess are not in operation yet. Our analysis will provide insights about key atmospheric variables that shape regional offshorewind energy potential of the German Bight. We propose that estimating regional wind energy potential should account for atmospheric response.

To understand the interaction between vegetation and the climate, Dynamic Global Vegetation Models (DGVMs) have been coupled with Earth system models (ESMs). In DGVMs, global vegetation is commonly empirically categorized into a few discrete plant functional types (PFTs) by differentiating their phenological, morphophysiological, and bioclimatic properties. Although the PFT-approach is useful to capture the large-scale general features of plants, growing evidence from the ecology community has challenged the use of the plant-type specific parametrization in models and the omission of the intra-variation of PFTs. Modelling studies have also shown that the local climate is highly sensitive to the selection and combination of PFTs. Therefore, a less parameter-dependent approach, of which the results should be robust to the empirical selection of parameters, is critical to address vegetation-climate interaction in climate models.Based on a process-based plant functioning trade-off scheme developed by Kleidon and Mooney (2000), we have set up a new vegetation model JeDi-BACH and have implemented the new model into the land component of the ICON-Earth System Model (ICON-ESM). The advantage of this new model is to obtain plant distribution as a result of environmental filtering. Plants are represented based on several well-known fundamental functional trade-offs that link the plant functions to abiotic and biotic attributes. For example, plants which partition more biomass to roots could improve their soil-water uptake and thereby reduce the stress from water shortage. Each plant functional aspect is defined by a set of plant-trait parameters that is randomly generated for each plant species. Hence, every parameter set realizes a plant growth strategy with different functional capabilities. Using a large number of randomly generated plant growth strategies, plants are allowed to ‘grow’ everywhere; but the environment will select the survivors. In such a way, plants dynamically adjust to the changing environment and meanwhile influence climate. We have done several simulations of present-day climate with JeDi-BACH and coupled to the atmosphere component of the ICON-ESM to investigate how such an adaptive ecosystem interacts with regional and global climate. Future investigations will focus on non-analogue climates (eg. Eocene with tropical vegetation at high latitudes) where in contrast to PFT-based DGVMs the new model allows vegetation to adjust consistently with climate because of its dynamic selection of plant traits by environmental filtering.Kleidon, A. and Mooney, H. A.: A global distribution of biodiversity inferred from climatic constraints: Results from a process-based modelling study, Glob. Chang. Biol., 6(5), 507–523, doi:10.1046/j.1365-2486.2000.00332.x, 2000.

The diurnal variations of surface and air temperature are related but their different responses to evaporative conditions can inform us about land-atmosphere interactions, extreme events, and their response to global change. Here, we evaluate the sensitivity of the diurnal ranges of surface (DTsR) and air (DTaR) temperature to evaporative fraction, across short vegetation, savanna, and forests at 106 Fluxnet observational sites and in the ERA5 global reanalysis. We show that the sensitivity of DTsR to evaporative fraction depends on vegetation type, whereas for DTaR it does not. Using FLUXNET data we found that on days with low evaporative fraction, DTsR is enhanced by up to 20 °C (30 °C in ERA5) in short vegetation, whereas only by 8 °C (10 °C in ERA5) in forests. Particularly, in short vegetation, ERA5 shows stronger responses, which is attributable to a negative bias on days with the high evaporative fraction. ERA5 also tends to have lower shortwave and longwave radiation input when compared to FLUXNET data. Contrary to DTsR, DTaR responds rather similarly to evaporative fraction irrespective of vegetation type (8 °C in FLUXNET, 10 °C in ERA5). To explain this, we show that the DTaR response to the evaporative fraction is compensated for differences in atmospheric boundary layer height by up to 2000 m, which is similar across vegetation types. We demonstrate this with a simple boundary layer heat storage calculation, indicating that DTaR is primarily shaped by changes in boundary layer heat storage whereas DTsR mainly responds to solar radiation, evaporation, and vegetation. Our study reveals some systematic biases in ERA5 that need to be considered when using its temperature products for understanding land-atmosphere interactions or extreme events. To conclude, this study demonstrates the importance of vegetation and the dynamics of the atmospheric boundary layer in regulating diurnal variations in surface and air temperature under different evaporative conditions.

Optimality concepts have been used to successfully infer ecophysiological properties and functioning of terrestrial vegetation from the leaf- to ecosystem scale. In many cases this implies, roughly speaking, that vegetation is as productive as it can possibly be. However, when vegetation activity is looked at in terms of its energy conversion from the radiant energy in sunlight to the chemical energy stored in carbohydrates, it has a very low conversion efficiency of about 1% or less. This is much less than what would be expected from thermodynamics applied to the photochemical conversion process. How do these two seemingly contradictory views fit together? Here I suggest that thermally-driven gas exchange between vegetation canopies and the lower atmosphere represents the major bottleneck, explaining the low thermodynamic efficiency of carbon uptake and setting a strong constraint to any form of vegetation optimality. Gas exchange intimately links the carbon taken up by vegetation from the atmosphere for photosynthesis during the day with the water loss by evaporation, with evaporation being a major component of the surface energy balance. The magnitude of this exchange is, however, not externally set by atmospheric conditions, but predominantly determined by the local heating of the surface, creating buoyancy and thus this exchange. Thermodynamics sets a strong constraint on the magnitude of this locally generated exchange by the maximum power that can be derived from the absorption of solar radiation to generate the associated kinetic energy. I use global, observation-based radiation and precipitation datasets and this thermodynamic constraint to quantify surface energy balance partitioning over land as well as the associated rate of evaporation at the climatological scale. I then use a typical value for the water use efficiency observed in vegetation to convert this evaporative flux to a carbon uptake flux by vegetation and show that the derived fluxes of water and carbon compare very well to observation-based estimates across regions. This means that the low thermodynamic efficiency of terrestrial carbon uptake should not be attributed to an inefficient use of light, but rather to the low efficiency by which radiative heating generates gas exchange that is needed to supply canopies with carbon dioxide and that maintains evaporation. This interpretation has broad implications for the role of vegetation in the Earth system. It implies that physically-driven gas exchange with the atmosphere - and not energy directly - is a major constraint on vegetation activity, shaping its geographic patterns. Given this constraint, vegetation may then maximize its carbon uptake for the given evaporative flux, but it has comparatively little control over evaporation and surface energy balance partitioning if sufficient water is available. Applied to global warming, this then implies that the response of evaporation is mostly determined by changes in the radiative forcing and water availability, and not by stomatal responses.