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This paper presents an analysis of the relationships between soil moisture, cloud cover, solar radiation, air temperature and humidity, and precipitation during the warm half of the year in East‐Central Europe over the years 1971–2020. The temporal and spatial variability of these meteorological elements is presented in association with the occurrence of anticyclonic blocking events over the study area. It demonstrates that changes in soil moisture in East‐Central Europe point to the combined influence of many meteorological factors resulting from the atmospheric circulation, and are an indicator of the comprehensive relationships among those factors. The main factors affecting soil water content are precipitation and evapotranspiration, which in turn depend on air humidity, cloudiness, intensity of solar radiation and air temperature. The increase in the frequency and duration of sequences of days with blocking events in East‐Central Europe has contributed to an increased probability of longer periods with soil moisture negative anomalies. [ABSTRACT FROM AUTHOR]

We investigated positive temperature (warm) and negative precipitation (dry) biases in convection-permitting model (CPM) simulations for Europe (2.2 km grid spacing) that were considerably larger than in equivalent regional climate model (RCM) simulations (12 km grid spacing). We found that improvements in dry biases could be made by (1) using a more complex runoff scheme which takes into account topography and groundwater, (2) delaying the onset of water stress in vegetation to enhance transpiration, (3) changing the microphysics scheme to CASIM (Cloud AeroSol Interacting Microphysics) which also decreases heavy rainfall and increases light rainfall. Increasing soil moisture to the critical point can remove dry precipitation biases in southern Europe but not in northern areas, indicating that soil moisture limitation is a key contributor to precipitation biases in the south only. Instead, in the north, changing the cloud scheme of the model has more impact on precipitation biases. We found that the more intense and intermittent nature of rainfall in the CPM, which is more realistic leads to different canopy interception compared to the RCM. This can impact canopy evaporation, evapotranspiration and feed back on precipitation. Increasing rainfall storage in the canopy only leads to small improvements in warm biases, since it still fills rapidly with intense CPM rainfall, suggesting the need for an additional moisture store via improved groundwater modelling or surface pooling. Overall, this work highlights the challenge of correctly capturing land surface feedbacks in CPMs, which play an important role in future climate projections in some regions. [ABSTRACT FROM AUTHOR]

Since 1980, both the intensity and duration of summer heatwaves in the middle and high latitudes of the Northern Hemisphere have significantly increased, leading this region to become a critical area for a significant increase in the frequency of intense and long-lived extreme heatwaves. We found that stronger and more persistent high-pressure systems and lower soil moisture before the events were the main drivers of intense and long-lived extreme heatwaves in western Europe and the middle and high latitudes of North America. However, in eastern Europe and Siberia, lower cloud cover before events is also a main driver of this type of extreme heatwave, in addition to the above drivers. These factors are coupled with each other and can change heatwave intensity and duration by influencing surface radiation processes during events. Using the self-organizing map classification method, we found that 6 weather patterns with increased frequency, intensity, and duration were the main dynamic reasons leading to the increase in intense and long-lived extreme heatwaves after 1980. In addition, the decrease in summer average soil moisture in most areas of the mid-high latitudes of the Northern Hemisphere and the decrease in average cloud cover in eastern Europe and Siberia are found to be the main thermodynamic reasons leading to the increase in these extreme heatwaves. [ABSTRACT FROM AUTHOR]

Land surface models (LSMs) are an important tool for advancing our knowledge of the Earth system. LSMs are constantly improved to represent the various terrestrial processes in more detail. High-quality data, freely available from various observation networks, are being used to improve the prediction of terrestrial states and fluxes of water and energy. To optimize LSMs with observations, data assimilation methods and tools have been developed in the past decades. We apply the coupled Community Land Model version 5 (CLM5) and Parallel Data Assimilation Framework (PDAF) system (CLM5-PDAF) for 13 forest field sites throughout Europe covering different climate zones. The goal of this study is to assimilate in situ soil moisture measurements into CLM5 to improve the modeled evapotranspiration fluxes. The modeled fluxes will be evaluated using the predicted evapotranspiration fluxes with eddy covariance (EC) systems. Most of the sites use point-scale measurements from sensors placed in the ground; however, for three of the forest sites we use soil water content data from cosmic-ray neutron sensors, which have a measurement scale closer to the typical land surface model grid scale and EC footprint. Our results show that while data assimilation reduced the root-mean-square error for soil water content on average by 56 % to 64 %, the root-mean-square error for the evapotranspiration estimation is increased by 4 %. This finding indicates that only improving the soil water content (SWC) estimation of state-of-the-art LSMs such as CLM5 is not sufficient to improve evapotranspiration estimates for forest sites. To improve evapotranspiration estimates, it is also necessary to consider the representation of leaf area index (LAI) in magnitude and timing, as well as uncertainties in water uptake by roots and vegetation parameters. [ABSTRACT FROM AUTHOR]

Many studies have focused on understanding the drivers of soil moisture droughts in Europe when the events have already intensified. Still, how atmospheric circulation changes throughout the entire life cycle of droughts, particularly during the transition periods to drought initiation and termination, has not been thoroughly investigated. Therefore, this study investigates temporal characteristics and atmospheric circulation associated with onsets (a transition period from a normal condition to drought) and terminations (a transition period from drought to a normal condition) of soil moisture droughts in Europe during 1980–2020. The typical duration, preferred seasons of occurrence, and atmospheric circulation during onsets and terminations are examined. The regions of study are central (CEU) and Mediterranean (MED) Europe, and soil moisture from ERA5-Land, GLEAM version 3, SoMo.ml, Noah-LSM, and a simulation from the Community Land Model version 5 (CLM-TRENDY) are utilized. Our findings indicate that the duration of onsets and terminations depends on the dataset: the five soil moisture datasets exhibit different mean duration of onsets and terminations, with ERA5-Land showing shorter and GLEAM longer duration across Europe. Nevertheless, within the same dataset, onsets and terminations exhibit similar durations, implying that onsets and terminations can occur at the same speed. Regarding the preferred seasons for onsets and terminations, onsets occur more during the wet seasons, specifically summer in CEU and autumn and winter in MED. Nevertheless, the frequencies of occurrence during these seasons only slightly exceed that during other seasons. Terminations tend to occur more during the driest seasons. For atmospheric circulation, onsets come with large-scale anticyclonic atmospheric circulation patterns. Terminations do not exhibit a dominant mean circulation pattern over the region where terminations occur but instead show geopotential height anomalies with reduced magnitudes in Europe and a high-pressure system over the North Atlantic. The anticyclonic circulation during onsets is anomalously persistent and shows linkages with the North Atlantic Oscillation (NAO). Positive NAO occurs much more frequently during onsets compared to other drought phases. This finding implies an important role for this large-scale mode of variability in initiating dry periods by reducing soil moisture and its potential to serve as an early warning for droughts during the period prior to the events. [ABSTRACT FROM AUTHOR]

Species-specific tree traits substantially impact precipitation partitioning, soil infiltration, retention, and overall water management. With the changing climate, the evidence of drought-induced tree mortality is increasing, indicating the need for a shift to more resilient species. Therefore, the knowledge of tree species' effect on soil–water management is needed. We used a total of 54 stations for monitoring the soil moisture regimes of silt loam soil in Central Bohemia under three common and different tree species (spruce, larch, and beech). For the dry and warm part of the year 2022, the soil moisture in winter and early spring was significantly lowered by high canopy interception and gradually depleted by early spring transpiration of evergreen spruce, where mean values were more than 15% lower than for beech. Since March, the soil moisture under spruce was already below the limit of easily available water. In contrast, the deciduous beech and larch did not show a significant decrease until the foliage emergence in early May. The beech stands showed the highest soil moisture levels, which we attributed to a more effective water recharge mechanism induced by its structural traits. This adaptive strategy is vital as soil–water storage is critical for coping with climate change. In general, the results show a distinct advantage in the water recharge ability of deciduous trees and that the selection of tree species can distinctly affect soil moisture conditions, especially during drought periods in areas characterized by tight hydrological balance. [ABSTRACT FROM AUTHOR]

In the 2022 summer, western–central Europe and several other regions in the northern extratropics experienced substantial soil moisture deficits in the wake of precipitation shortages and elevated temperatures. Much of Europe has not witnessed a more severe soil drought since at least the mid-20th century, raising the question whether this is a manifestation of our warming climate. Here, we employ a well-established statistical approach to attribute the low 2022 summer soil moisture to human-induced climate change using observation-driven soil moisture estimates and climate models. We find that in western–central Europe, a June–August root zone soil moisture drought such as in 2022 is expected to occur once in 20 years in the present climate but would have occurred only about once per century during preindustrial times. The entire northern extratropics show an even stronger global warming imprint with a 20-fold soil drought probability increase or higher, but we note that the underlying uncertainty is large. Reasons are manifold but include the lack of direct soil moisture observations at the required spatiotemporal scales, the limitations of remotely sensed estimates, and the resulting need to simulate soil moisture with land surface models driven by meteorological data. Nevertheless, observation-based products indicate long-term declining summer soil moisture for both regions, and this tendency is likely fueled by regional warming, while no clear trends emerge for precipitation. Finally, our climate model analysis suggests that under 2 ∘ C global warming, 2022-like soil drought conditions would become twice as likely for western–central Europe compared to today and would take place nearly every year across the northern extratropics. [ABSTRACT FROM AUTHOR]

The joint probability of precipitation and soil moisture is here investigated over Europe with the goal to extrapolate meaningful insights into the potential joint use of these variables for the detection of agricultural droughts within a multivariate probabilistic modeling framework. The use of copulas is explored, being the framework often used in hydrological studies for the analysis of bivariate distributions. The analysis is performed for the period 1996–2020 on the empirical frequencies derived from ERA5 precipitation and LISFLOOD soil moisture datasets, both available as part of the Copernicus European Drought Observatory. The results show an overall good correlation between the two standardized series (Kendall's τ= 0.42±0.1) but also clear spatial patterns in the tail dependence derived with both non-parametric and parametric approaches. About half of the domain shows symmetric tail dependence, well reproduced by the Student's t copula, whereas the rest of the domain is almost equally split between low- and high-tail dependences (both modeled with the Gumbel family of copulas). These spatial patterns are reasonably reproduced by a random forest classifier, suggesting that this outcome is not driven by chance. This study stresses how a joint use of standardized precipitation and soil moisture for agriculture drought characterization may be beneficial in areas with strong low-tail dependence and how this behavior should be carefully considered in multivariate drought studies. [ABSTRACT FROM AUTHOR]

Heavy rainfall is the main driver of soil erosion by water, which is a threat to soil and water resources across the globe. As a consequence of climate change, precipitation – especially extreme precipitation – is increasing in a warmer world, leading to an increase in rainfall erosivity. However, conventional global climate models struggle to represent extreme rain events and cannot provide precipitation data at the high spatiotemporal resolution that is needed for an accurate estimation of future rainfall erosivity. Convection-permitting simulations (CPSs), on the other hand, provide high-resolution precipitation data and a better representation of extreme rain events, but they are mostly limited to relatively small spatial extents and short time periods. Here, we present, for the first time, rainfall erosivity in a large modeling domain such as central Europe based on high-resolution CPS climate data generated with the regional climate model COSMO-CLM using the Representative Concentration Pathway 8.5 (RCP8.5) emission scenario. We calculated rainfall erosivity for the past (1971–2000), present (2001–2019), near future (2031–2060) and far future (2071–2100). Our results showed that future increases in rainfall erosivity in central Europe can be up to 84 % in the region's river basins. These increases are much higher than previously estimated based on regression with mean annual precipitation. We conclude that despite remaining limitations, CPSs have an enormous and currently unexploited potential for climate impact studies on soil erosion. Thus, the soil erosion modeling community should closely follow the recent and future advances in climate modeling to take advantage of new CPSs for climate impact studies. [ABSTRACT FROM AUTHOR]

Groundwater and climate interact in a two-way manner. Precipitation ultimately controls groundwater recharge and, conversely, groundwater may influence climate through evapotranspiration. Yet very few global climate models or Earth system models actually simulate groundwater flows. And while the expected impacts of climate change on groundwater resources are the subject of a growing concern, global-scale groundwater–climate feedbacks have received very little attention so far. Here we show that the integration of unconfined aquifers in a global climate model can regionally affect the climate change signal on temperatures and precipitation. We assess the impact of groundwater under preindustrial and 4xCO2 conditions (after climate stabilization). In both cases, we find that groundwater has a cooling and a wetting effect in certain regions of the world. In eastern Europe, both these impacts are stronger in the warmer climate (4xCO2 forcing) where the presence of groundwater reduces the frequency of summer heatwaves by 40%, compared to a 15% reduction in the preindustrial world. This work constitutes one of the very first global assessments of the potential feedbacks of groundwater on climate change. Our results support the idea that groundwater should be represented in global climate models and Earth system models, as it does indeed play an active role in the climate system. [ABSTRACT FROM AUTHOR]

Hedera helix L. is an evergreen vine native to Europe. Nowadays, it is commonly recognized as an expansive species, posing danger to biodiversity of natural ecosystems. Obtaining the relationships between morphological variability, productive parameters of vegetative individuals, and variations in environmental factors (on the example of the Kórnik Arboretum) is important for understanding the successful adaptation and dispersion of H. helix. Thus, the issue of our studies was to discover the link between environmental factors and morphometric measurements of H. helix. We collected data on leaf length, leaf width, leaf petiole length, plant stem length, and the number of leaves per stem. We assessed values of ecological plasticity and correlation between the studied parameters. The results indicated the tolerance of ivy to wide variation in edaphic conditions. The limiting environmental factors for growth of the studied subpopulations were soil moisture and light intensity. We found large variability of leaf morphometric parameters and shoot length. Close correlation links were detected between leaf length/leaf width, leaf length/leaf petiole length, and leaf width/leaf petiole length. The length of the average leaf and the length of leaf petiole were the most variable parameters under different values of light intensity. Weak relationships were found between light intensity at the soil surface and plant stem length/the number of leaves per shoot. A high level of index of morphological integration of H. helix individuals and a close relationship between the quality index/subpopulation compositions were established in this study. A direct relationship between light intensity and specific leaf area values was established. The value of specific leaf area decreased linearly with increasing light intensity at the soil surface. [ABSTRACT FROM AUTHOR]

The CMIP6 projections constitute the basis of our latest understanding of the climate response to anthropogenic forcing. However, there is still considerable uncertainty in the projections, especially at the regional scale. One way to constrain the uncertainty is by comparing the models historical climate change signals against observations and investigate the physical reasons for divergences. Here, we assess the signal‐to‐noise ratio (S/N) of surface air temperature (SAT), precipitation (PREC) and soil moisture (SM) over Europe for a set of CMIP6 historical simulations and compare them against the E‐OBS observational product and the ERA5 reanalysis. We found considerable divergences between the CMIP6 ensemble mean S/N and that of E‐OBS and ERA5, as well as between ERA5 and E‐OBS. The latter indicates that the S/N is affected by data coverage. We show that the differences among model signals are associated with different atmospheric circulation responses. We also investigate the potential relationships between the models' signals and climatological biases, and we found evidence that the models with a warm climatological bias in southern Europe tend to have smaller SAT signals (warm less). Finally, we found no apparent relationship between SM biases and the warming signal, suggesting that the mechanism by which SM–atmosphere interactions affect climate variability does not explain the mean changes. However, there is a tendency for models with higher SM to dry faster than models with lower SM. [ABSTRACT FROM AUTHOR]

Water stress is the main risk facing sugar beet production in Europe and is expected to worsen with climate change. Therefore, future production essentially depends on the traits that sustain growth during water shortages. In this study, we investigated the interplay of stomatal conductance and transpiration rate as well as the root characteristics of six sugar beet cultivars in a climate chamber experiment under environmental conditions progressing from a non-stressed initial state toward high atmospheric water demand, followed by reduced soil water supply and then by a combination of high demand and low supply. Stomatal conductance quickly responded to changing conditions, dropping from 406.4 to 42.5 mmol m−2 s−1, one order of magnitude, with the transition to reduced soil water availability. The transpiration rate showed a slightly delayed response compared with stomatal conductance, while we observed an inverse influence on the subsequent stomatal behavior exerted by the depletion/conservation of available soil water. The rooting pattern substantially differed among genotypes, predominantly at depths between 60 and 80 cm, where 50.5% of the root length was allocated. Longer roots buffered the effects of the reduction in stomatal conductance at the onset of water supply limitation, with 5.4 mmol m−2 s−1 higher conductance per 100 cm of root length. Therefore, breeding and/or management measures targeting root system vigor are the key to the growth maintenance of sugar beet during dry periods. [ABSTRACT FROM AUTHOR]

Every application of soil erosion models brings the need of proper parameterisation, that is, finding physically or conceptually plausible parameter values that allow a model to reproduce measured values. No universal approach for model parameterisation, calibration and validation exists, as it depends on the model, spatial and temporal resolution and the nature of the datasets used. We explored some existing options for parameterisation, calibration and validation for erosion modelling exemplary with a specific dataset and modelling approach. A new Morgan‐Morgan‐Finney (MMF)‐type model was developed, representing a balanced position between physically‐based and empirical modelling approaches. The resulting model termed 'calculator for soil erosion' (CASE), works in a spatially distributed way on the timescale of individual rainfall events. A dataset of 142 high‐intensity rainfall experiments in Central Europe (AT, HU, IT, CZ), covering various slopes, soil types and experimental designs was used for calibration and validation with a modified Monte‐Carlo approach. Subsequently, model parameter values were compared to parameter values obtained by alternative methods (measurements, pedotransfer functions, literature data). The model reproduced runoff and soil loss of the dataset in the validation setting with R2adj of 0.89 and 0.76, respectively. Satisfactory agreement for the water phase was found, with calibrated saturated hydraulic conductivity (ksat) values falling within the interquartile range of ksat predicted with 14 different pedotransfer functions, or being within one order of magnitude. The chosen approach also well reflected specific experimental setups contained in the dataset dealing with the effects of consecutive rainfall and different soil water conditions. For the sediment phase of the tested model agreement between calibrated cohesion, literature values and field measurements were only partially in line. The methods we explored may specifically be interesting for use with other MMF‐type models, or with similar datasets. [ABSTRACT FROM AUTHOR]

Surface soil moisture (SSM) and root-zone soil moisture (RZSM) are key hydrological variables for the agricultural water cycle and vegetation growth. Accurate SSM and RZSM forecasting at sub-seasonal scales would be valuable for agricultural water management and preparations. Currently, weather model-based soil moisture predictions are subject to large uncertainties due to inaccurate initial conditions and empirical parameterization schemes, while the data-driven machine learning methods have limitations in modeling long-term temporal dependences of SSM and RZSM because of the lack of considerations in the soil water process. Thus, here, we innovatively integrate the model-based soil moisture predictions from a sub-seasonal-to-seasonal (S2S) model into a data-driven stacked deep learning model to construct a hybrid SSM and RZSM forecasting framework. The hybrid forecasting model is evaluated over the Yangtze River Basin and parts of Europe from 1- to 46-day lead times and is compared with four baseline methods, including the support vector regression (SVR), random forest (RF), convolutional long short-term memory (ConvLSTM) and the S2S model. The results indicate substantial skill improvements in the hybrid model relative to baseline models over the two study areas spatiotemporally, in terms of the correlation coefficient, unbiased root mean square error (ubRMSE) and RMSE. The hybrid forecasting model benefits from the long-lead predictive skill from S2S and retains the advantages of data-driven soil moisture memory modeling at short-lead scales, which account for the superiority of hybrid forecasting. Overall, the developed hybrid model is promising for improved sub-seasonal SSM and RZSM forecasting over global and local areas. [ABSTRACT FROM AUTHOR]

In this study, soil moisture retrievals of the combined active–passive ESA Climate Change Initiative (CCI) soil moisture product are assimilated into the Noah-MP land surface model over Europe using a one-dimensional ensemble Kalman filter and an 18-yr study period. The performance of the data assimilation (DA) system is evaluated by comparing it with a model-only experiment (at in situ sites) and by assessing statistics of innovations and increments as DA diagnostics (over the entire domain). For both assessments, we explore the impact of three design choices, resulting in the following insights. 1) The magnitude of the assumed observation errors strongly affects the skill improvements evaluated against in situ stations and internal diagnostics. 2) Choosing between climatological or monthly cumulative distribution function matching as the observation bias correction method only has a marginal effect on the in situ skill of the DA system. However, the internal diagnostics suggest a more robust system parameterization if the observations are rescaled monthly. 3) The choice of atmospheric reanalysis dataset to force the land surface model affects the model-only skill and the DA skill improvements. The model-only skill is higher with input from the MERRA-2 than with input from the ERA5 reanalysis, resulting in larger DA skill improvements for the latter. Additionally, we show that the added value of the DA strongly depends on the quality of the satellite retrievals and land cover, with the most substantial soil moisture skill improvements occurring over croplands and skill degradation occurring over densely forested areas. [ABSTRACT FROM AUTHOR]

This study examines the European heatwaves' predictability at subseasonal timescales. Land surface feedbacks and tropical convection, due to their variability at intraseasonal timescales, are taken into consideration and their potential role in extending the predictability beyond the medium range (10 days) is explored. A classification of European heatwaves into five heatwave types is used to discriminate the effects of surface feedbacks and of tropical variability among the different heatwave types. The classification is computed in terms of circulation patterns. By inferring the near‐surface temperature through atmospheric circulation, we aim to identify the predictable component of the heatwave events. All five heatwave circulation patterns are characterized by persistent anticyclonic anomalies located over the region with maximum temperatures. We show that soil moisture deficit is not a required precondition for the occurrence of heatwaves over most of Europe. However, heatwave events over southern Europe exhibit some sensitivity to dry conditions. We use a simplified index to describe the dominant mode of tropical convection at intraseasonal timescales. The index, based on precipitation anomalies, represents the evolution of the Boreal Summer Intraseasonal Oscillation (BSISO). We find that episodes with strong BSISO amplitudes characterized by enhanced convection over India, Bay of Bengal and China sea favour the occurrence of heatwave events over Russia. The results highlight the role of tropical intraseasonal variability in enhancing the predictability of some extreme temperature events over Europe. [ABSTRACT FROM AUTHOR]

In the 2022 summer, West-Central Europe and several other northern-hemisphere mid-latitude regions experienced substantial soil moisture deficits in the wake of precipitation shortages and elevated temperatures. Much of Europe has not witnessed a more severe soil drought since at least the mid-20th century, raising the question whether this is a manifestation of our warming climate. Here, we employ a well-established statistical approach to attribute the low 2022 summer soil moisture to human-induced climate change, using observation-driven soil moisture estimates and climate models. We find that in West-Central Europe, a June-August root-zone soil moisture drought such as in 2022 is expected to occur once in 20 years in the present climate, but would have occurred only about once per century during pre-industrial times. The entire northern extratropics show an even stronger global warming imprint with a 20-fold soil drought probability increase or higher, but we note that the underlying uncertainty is large. Reasons are manifold, but include the lack of direct soil moisture observations at the required spatiotemporal scales, the limitations of remotely sensed estimates, and the resulting need to simulate soil moisture with land surface models driven by meteorological data. Nevertheless, observationbased products indicate long-term declining summer soil moisture for both regions, and this tendency is likely fueled by regional warming, while no clear trends emerge for precipitation. Finally, our climate model analysis suggests that in a 2 °C world, 2022-like soil drought conditions would become twice as likely for West-Central Europe compared to today, and would take place nearly every year across the northern extratropics. [ABSTRACT FROM AUTHOR]

Temperature rise and more severe and frequent droughts will alter forest transpiration, thereby affecting the global water cycle. Yet, tree responses to increased atmospheric vapour pressure deficit (VPD) and reduced soil water content (SWC) are not fully understood due to long‐term tree adjustments to local environmental conditions that modify transpiration responses to short‐term VPD and SWC changes.We analysed sap flux density (SFD) of Fagus sylvatica, Picea abies, Pinus sylvestris and Quercus ilex from 25 sites across Europe to understand how daily variation in SWC affects the sensitivity of SFD to VPD (βVPD) and the maximum SFD (S95). Furthermore, we tested whether long‐term adjustments to site climatic conditions and stand characteristics affect βVPD and S95.The studied species showed contrasting βVPD and S95 with the largest values in F. sylvatica, followed by Q. ilex, which surpassed the two conifers that showed low βVPD and low S95. We observed that βVPD and S95 dropped during days of low SWC in F. sylvatica, P. sylvestris and Q. ilex, but not in P. abies. Both βVPD and S95 were driven by tree height, and the temperature and precipitation at the sites. However, stand basal area was the most important driver of βVPD and S95, explaining 30% of their total variance.Synthesis and applications: A future warmer and drier climate will restrict tree transpiration and thereby heavily affect the soil–plant‐atmosphere coupling. However, the effect of basal area, being the largest driver of tree transpiration sensitivity to vapour pressure deficit across a broad range of conditions, provides the opportunity to pre‐adapt European forests to future climate conditions. While stand thinning can increase the soil water availability for remaining trees, it also increases transpiration sensitivity to high air temperatures and may thereby amplify tree vulnerability to heat and drought. [ABSTRACT FROM AUTHOR]

Projections of changes in extreme droughts under future climate conditions are associated with large uncertainties, owing to the complex genesis of droughts and large model uncertainty in the atmospheric dynamics. In this study we investigate the impact of global warming on soil moisture drought severity in west-central Europe by employing pseudo global warming (PGW) experiments, which project the 1980–2020 period in a globally warmer world. The future analogues of present-day drought episodes allow for investigation of changes in drought severity conditional on the historic day-to-day evolution of the atmospheric circulation. The 2018 west-central European drought is the most severe drought in the 1980–2020 reference period in this region. Under 1.5, 2 and 3 ∘ C global warming, this drought episode experiences strongly enhanced summer temperatures but a fairly modest soil moisture drying response compared to the change in climatology. This is primarily because evaporation is already strongly moisture-constrained during present-day conditions, limiting the increase in evaporation and thus the modulation of the temperature response under PGW. Increasing precipitation in winter, spring and autumn limits or prevents an earlier drought onset and duration. Nevertheless, the drought severity, defined as the cumulative soil moisture deficit volume, increases considerably, with 20 % to 39 % under 2 ∘ C warming. The extreme drought frequency in the 1980–2020 period strongly increases under 2 ∘ C warming. Several years without noticeable droughts under present-day conditions show very strong drying and warming. This results in an increase in 2003-like drought occurrences, compounding with local summer temperature increases considerably above 2 ∘ C. Even without taking into account a (potentially large) dynamical response to climate change, drought risk in west-central Europe is strongly enhanced under global warming. Owing to increases in drought frequency, severity and compounding heat, a reduction in recovery times between drought episodes is expected to occur. Our physical climate storyline provides evidence complementing conventional large-ensemble approaches and is intended to contribute to the formulation of effective adaptation strategies. [ABSTRACT FROM AUTHOR]

Earth is a complex non-linear dynamical system. Despite decades of research and considerable scientific and methodological progress, many processes and relations between Earth system variables remain poorly understood. Current approaches for studying relations in the Earth system rely either on numerical simulations or statistical approaches. However, there are several inherent limitations to existing approaches, including high computational costs, uncertainties in numerical models, strong assumptions about linearity or locality, and the fallacy of correlation and causality. Here, we propose a novel methodology combining deep learning (DL) and principles of causality research in an attempt to overcome these limitations. On the one hand, we employ the recent idea of training and analyzing DL models to gain new scientific insights into relations between input and target variables. On the other hand, we use the fact that a statistical model learns the causal effect of an input variable on a target variable if suitable additional input variables are included. As an illustrative example, we apply the methodology to study soil-moisture–precipitation coupling in ERA5 climate reanalysis data across Europe. We demonstrate that, harnessing the great power and flexibility of DL models, the proposed methodology may yield new scientific insights into complex non-linear and non-local coupling mechanisms in the Earth system. [ABSTRACT FROM AUTHOR]

The endangered continental Black-tailed Godwit (Limosa limosa limosa) is a migratory ground-nesting wader breeding in a wide variety of open, wet habitats across Europe. Conservation research has concentrated on the causes of population decline, but we know surprisingly little about whether any resources limit local breeding populations and if so, whether these are resources for the adults or the chicks. We collected data from 63 key breeding sites in five countries across Europe to test whether, after correcting for differences in surveyed areas, the size of Godwit breeding populations was related to environmental variables (vegetation biomass, soil moisture) or food resources for adult birds (soil invertebrates) or chicks (vegetation dwelling arthropods) measured during different times of the reproductive cycle. We found the number of Godwit territories to be positively related to arthropod abundance during the chick-hatching period. We found additional, weaker support for a positive relation between Godwit territory numbers and the abundance of soil-dwelling invertebrates (mostly earthworms) at clutch laying, but not at chick-hatching. These relationships were observed across countries, while we found little support for relationships within countries, possibly due to the smaller range in conditions that exist within countries. Both vegetation growth and soil moisture weren't related to Godwit territory numbers. Our results suggest that food abundance for chicks, and to a lesser extent adult birds, are key factors determining the size of local Godwit breeding populations. Conservation management aiming to enhance local Godwit populations should therefore consider the impacts of management strategies on the arthropod prey of chicks. [ABSTRACT FROM AUTHOR]

High-resolution large-scale predictions of hydrologic states and fluxes are important for many multi-scale applications, including water resource management. However, many of the existing global- to continental-scale hydrological models are applied at coarse resolution and neglect more complex processes such as lateral surface and groundwater flow, thereby not capturing smaller-scale hydrologic processes. Applications of high-resolution and physically based integrated hydrological models are often limited to watershed scales, neglecting the mesoscale climate effects on the water cycle. We implemented an integrated, physically based coupled land surface groundwater model, ParFlow-CLM version 3.6.0, over a pan-European model domain at 0.0275 ∘ (∼3 km) resolution. The model simulates a three-dimensional variably saturated groundwater-flow-solving Richards equation and overland flow with a two-dimensional kinematic wave approximation, which is fully integrated with land surface exchange processes. A comprehensive evaluation of multiple hydrologic variables including discharge, surface soil moisture (SM), evapotranspiration (ET), snow water equivalent (SWE), total water storage (TWS), and water table depth (WTD) resulting from a 10-year (1997–2006) model simulation was performed using in situ and remote sensing (RS) observations. Overall, the uncalibrated ParFlow-CLM model showed good agreement in simulating river discharge for 176 gauging stations across Europe (average Spearman's rank correlation (R) of 0.77). At the local scale, ParFlow-CLM model performed well for ET (R>0.94) against eddy covariance observations but showed relatively large differences for SM and WTD (median R values of 0.7 and 0.50, respectively) when compared with soil moisture networks and groundwater-monitoring-well data. However, model performance varied between hydroclimate regions, with the best agreement to RS datasets being shown in semi-arid and arid regions for most variables. Conversely, the largest differences between modeled and RS datasets (e.g., for SM, SWE, and TWS) are shown in humid and cold regions. Our findings highlight the importance of including multiple variables using both local-scale and large-scale RS datasets in model evaluations for a better understanding of physically based fully distributed hydrologic model performance and uncertainties in water and energy fluxes over continental scales and across different hydroclimate regions. The large-scale, high-resolution setup also forms a basis for future studies and provides an evaluation reference for climate change impact projections and a climatology for hydrological forecasting considering the effects of lateral surface and groundwater flows. [ABSTRACT FROM AUTHOR]

Land surface models (LSM) are an important tool for advancing our knowledge of the Earth system. LSM are constantly improved to represent the various terrestrial processes in more detail. High quality data, freely available from various observation networks, are providing being used to improve the prediction of terrestrial states and fluxes of water and energy. To optimize LSM with observations, data assimilation methods and tools have been developed in the past decades. We apply the coupled Community Land Model version 5 (CLM5) and Parallel Data Assimilation Framework (PDAF) system (CLM5-PDAF) for thirteen forest field sites throughout Europe covering different climate zones. The goal of this study is to assimilate in-situ soil moisture measurements into CLM5 to improve the modeled evapotranspiration fluxes. The modeled fluxes will be evaluated using the predicted evapotranspiration fluxes with eddy covariance (EC) systems. Most of the sites use point scale measurements from, however for three of the forest sites we use soil water content data from cosmic-ray neutron sensors, which have a measurement scale closer to the typical land surface model grid scale and EC footprint. Our results show that while data assimilation reduced the root-mean-square error for soil water content on average by 56 to 64 %, the root-mean-square error for the evapotranspiration estimation is increased by 4 %. This finding indicates that state-of-the-art LSM such as CLM5 still suffer from uncertainties in the representation of soil hydrological processes in forests, e.g. deep root water uptake, or highly uncertain vegetation parameters. [ABSTRACT FROM AUTHOR]

Soil moisture is central to local climate on land. In situ soil moisture observations are vital for observing vegetation‐relevant root‐zone soil moisture. However, stations included in the International Soil Moisture Network are sparse in regions with strong land‐atmosphere coupling. We apply a machine‐learning‐based procedure for informing future station placement using virtual soil moisture stations in future CMIP6 projections. Stations are placed where the climate is currently most under‐represented. This strategy outperforms random station placement and station placement according to geographical distance. Doubling the current number of stations using this method alleviates the uneven global distribution of stations, increases the skill in the estimation of inter‐annual variability and trends in dry‐season soil moisture, and reduces its differences across climates in future projections. Stations are predominantly placed in tropical climates, especially when optimizing for drying trends. The results can inform future station placement to support climate change mitigation efforts. Plain Language Summary: Water stored in the ground as soil moisture is the main water supply for ecosystems and can influence local weather. Ground observations of soil moisture can measure water in deeper soil layers that roots can access. Many stations are collected in the International Soil Moisture Network database. However, this database mainly contains observation stations from the US and Europe. If we were to put additional stations, we should focus on regions and climates that the current network does not sufficiently cover. Locations in model projections of future climate that currently contain a station ("virtual stations") are used to build a machine‐learning model that predicts soil moisture at locations without stations. We then place new virtual stations where the prediction error of dry‐season soil moisture variability and dry‐season trends is largest. This strategy outperforms randomly placing new stations or placing stations where they are most distant from existing ones. Stations are placed predominantly in the tropics, but prediction improves in all regions. Furthermore, stations are more evenly distributed across the globe, and differences in skill between regions become smaller. This tool can be used to decide where to place future soil moisture stations, which are important for monitoring climate change. Key Points: In situ soil moisture observations are distributed unequally across the globe and sparse in regions with strong land‐atmosphere couplingWe present a machine‐learning based procedure for informing future station placement using virtual soil moisture stationsDoubling the number of stations with this strategy reduces error in soil moisture trends by 0.2–1.4 kg m−2 and reduces global differences [ABSTRACT FROM AUTHOR]

Europe witnessed a strong increase in climate variability and enhanced climate-induced extreme events, such as hot drought periods, mega heat waves, and persistent flooding and flash floods. Intensified land degradation, land use, and landcover changes further amplified the pressure on the environmental system functionalities and fuelled climate change feedbacks. On the other hand, global satellite observations detected a positive spectral greening trend—most likely as a response to rising atmospheric CO2 concentrations and global warming. But which are the engines behind such shifts in surface reflectance patterns, vegetation response to global climate changes, or anomalies in the environmental control mechanisms? This article compares long-term environmental variables (1948–2021) to recent vegetation index data (Normalized Difference Vegetation Index (NDVI), 2001–2021) and presents regional trends in climate variability and vegetation response across Europe. Results show that positive trends in vegetation response, temperature, rainfall, and soil moisture are accompanied by a strong increase in climate anomalies over large parts of Europe. Vegetation dynamics are strongly coupled to increased temperature and enhanced soil moisture during winter and the early growing season in the northern latitudes. Simultaneously, temperature, precipitation, and soil moisture anomalies are strongly increasing. Such a strong amplification in climate variability across Europe further enhances the vulnerability of vegetation cover during extreme events. [ABSTRACT FROM AUTHOR]

Although prolonged heat periods have become a recurring feature of European climate, little knowledge is available on dominant spatial patterns of heatwaves and their influence on moisture-related processes. Increased knowledge will help to improve heatwave and drought prediction and mitigation. This study uses hierarchical agglomerative clustering to derive nine dominating spatial heatwave patterns from a 50-member regional climate model (Canadian Regional Climate Model version 5, CRCM5-LE). The heatwave patterns correspond well with clusters derived from an observational data set (E-OBS) and with extreme historical heatwave events. Moreover, we analyse the occurrence of heatwaves in the identified spatial patterns regarding a soil moisture deficit present before and after the event. We show that negative soil moisture anomalies in the preceding winter/spring (JFMA) can serve as a predictor for heatwaves in South Europe. For North Europe, we find a negative correlation between the number of heatwave days in summer and autumn (OND) soil moisture content. [ABSTRACT FROM AUTHOR]

Radial tree growth is sensitive to environmental conditions, making observed growth increments an important indicator of climate change effects on forest growth. However, unprecedented climate variability could lead to non‐stationarity, that is, a decoupling of tree growth responses from climate over time, potentially inducing biases in climate reconstructions and forest growth projections. Little is known about whether and to what extent environmental conditions, species, and model type and resolution affect the occurrence and magnitude of non‐stationarity. To systematically assess potential drivers of non‐stationarity, we compiled tree‐ring width chronologies of two conifer species, Picea abies and Pinus sylvestris, distributed across cold, dry, and mixed climates. We analyzed 147 sites across the Europe including the distribution margins of these species as well as moderate sites. We calibrated four numerical models (linear vs. non‐linear, daily vs. monthly resolution) to simulate growth chronologies based on temperature and soil moisture data. Climate–growth models were tested in independent verification periods to quantify their non‐stationarity, which was assessed based on bootstrapped transfer function stability tests. The degree of non‐stationarity varied between species, site climatic conditions, and models. Chronologies of P. sylvestris showed stronger non‐stationarity compared with Picea abies stands with a high degree of stationarity. Sites with mixed climatic signals were most affected by non‐stationarity compared with sites sampled at cold and dry species distribution margins. Moreover, linear models with daily resolution exhibited greater non‐stationarity compared with monthly‐resolved non‐linear models. We conclude that non‐stationarity in climate–growth responses is a multifactorial phenomenon driven by the interaction of site climatic conditions, tree species, and methodological features of the modeling approach. Given the existence of multiple drivers and the frequent occurrence of non‐stationarity, we recommend that temporal non‐stationarity rather than stationarity should be considered as the baseline model of climate–growth response for temperate forests. [ABSTRACT FROM AUTHOR]

This study was focused on a key aspect of drought monitoring that has not been systematically studied in the literature: evaluation of the capacity of evapotranspiration data retrieved using geostationary meteorological satellites for use as a water stress precursor. The work was methodologically based on comparisons between constructed indexes of vegetation water stress (evapotranspiration drought index (ETDI) and evaporative stress ratio (ESR)) derived from the EUMETSAT LSASAF METREF and DMET satellite products and soil moisture availability (SMA) from a SVAT model. Long-term (2011–2021) data for regions with strong land–atmosphere coupling in Southeastern Europe (Bulgaria) were used. Stochastic graphical analysis and Q–Q (quantile–quantile) analyses were performed to compare water stress metrics and SMA. Analyses confirmed the consistency in the behavior of vegetation water-stress indexes and SMA in terms of their means, spatiotemporal variability at monthly and annual levels, and anomalous distributions. The biophysical aspects of the drought evaluation confirmed the complementary and parallel interaction of potential (METREF) and actual (DMET) evapotranspiration (in view of the Bouchet hypothesis) for the studied region. Anomalies in evapotranspiration stress indexes can provide useful early signals of agricultural/ecological drought, and the results confirm the validity of using their satellite-based versions to characterize SMA in the root zone and drought severity. [ABSTRACT FROM AUTHOR]

Global soil water availability is challenged by the effects of climate change and a growing population. On average, 70 % of freshwater extraction is attributed to agriculture, and the demand is increasing. In this study, the effects of climate change on the evolution of the irrigation water requirement to sustain current crop productivity are assessed by using the Food and Agriculture Organization (FAO) crop growth model AquaCrop version 6.1. The model is run at 0.5∘lat×0.5∘long resolution over the European mainland, assuming a general C3-type of crop, and forced by climate input data from the Inter-Sectoral Impact Model Intercomparison Project phase three (ISIMIP3). First, the AquaCrop surface soil moisture (SSM) forced with two types of ISIMIP3 historical meteorological datasets is evaluated with satellite-based SSM estimates in two ways. When driven by ISIMIP3a reanalysis meteorology, daily simulated SSM values have an unbiased root mean square difference of 0.08 and 0.06 m3m-3 , with SSM retrievals from the Soil Moisture Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) missions, respectively, for the years 2015–2016 (2016 is the end year of the reanalysis data). When forced with ISIMIP3b meteorology from five global climate models (GCMs) for the years 2015–2020, the historical simulated SSM climatology closely agrees with the satellite-based SSM climatologies. Second, the evaluated AquaCrop model is run to quantify the future irrigation requirement, for an ensemble of five GCMs and three different emission scenarios. The simulated net irrigation requirement (Inet) of the three summer months for a near and far future climate period (2031–2060 and 2071–2100) is compared to the baseline period of 1985–2014 to assess changes in the mean and interannual variability of the irrigation demand. Averaged over the continent and the model ensemble, the far future Inet is expected to increase by 22 mm per month (+ 30 %) under a high-emission scenario Shared Socioeconomic Pathway (SSP) 3–7.0. Central and southern Europe are the most impacted, with larger Inet increases. The interannual variability in Inet is likely to increase in northern and central Europe, whereas the variability is expected to decrease in southern regions. Under a high mitigation scenario (SSP1–2.6), the increase in Inet will stabilize at around 13 mm per month towards the end of the century, and interannual variability will still increase but to a smaller extent. The results emphasize a large uncertainty in the Inet projected by various GCMs. [ABSTRACT FROM AUTHOR]

Compound events occur when multiple drivers or hazards occur in the same region or on the same time scale, hence amplifying their impacts. Compound events can cause large economic damage or endanger human lives. Thus, a better understanding of the characteristics of these events is needed in order to protect human lives. This study investigates the drivers and characteristics of floods in Europe and North America from the compound event perspective. More than 100 catchments across Europe and North America were selected as case study examples in order to investigate characteristics of floods during a 1979–2019 period. Air temperature, precipitation, snow thickness, snow liquid water equivalent, wind speed, vapour pressure, and soil moisture content were used as potential drivers. Annual maximum floods were classified into several flood types. Predefined flood types were snowmelt floods, rain-on-snow floods, short precipitation floods and long precipitation floods that were further classified into two sub-categories (i.e., wet and dry initial conditions). The results of this study show that snowmelt floods were often the dominant flood type in the selected catchments, especially at higher latitudes. Moreover, snow-related floods were slightly less frequent for high altitude catchments compared to low- and medium-elevation catchments. These high-altitude areas often experience intense summer rainstorms that generate the highest annual discharges. On the other hand, snowmelt-driven floods were the predominant flood type for the lower elevation catchments. Moreover, wet initial conditions were more frequent than the dry initial conditions, indicating the importance of the soil moisture for flood generation. Hence, these findings can be used for flood risk management and modelling. [ABSTRACT FROM AUTHOR]

Machine learning (ML) has emerged as a novel tool for generating large-scale land surface data in recent years. ML can learn the relationship between input and target, e.g. meteorological variables and in-situ soil moisture, and then estimate soil moisture across space and time, independently of prior physics-based knowledge. Here we develop a high-resolution (0.1°) daily soil moisture dataset in Europe (SoMo.ml-EU) using Long Short-Term Memory trained with in-situ measurements. The resulting dataset covers three vertical layers and the period 2003–2020. Compared to its previous version with a lower spatial resolution (0.25°), it shows a closer agreement with independent in-situ data in terms of temporal variation, demonstrating the enhanced usefulness of in-situ observations when processed jointly with high-resolution meteorological data. Regional comparison with other gridded datasets also demonstrates the ability of SoMo.ml-EU in describing the variability of soil moisture, including drought conditions. As a result, our new dataset will benefit regional studies requiring high-resolution observation-based soil moisture, such as hydrological and agricultural analyses. Measurement(s) wetness of soil Technology Type(s) machine learning Sample Characteristic - Environment wetness of soil Sample Characteristic - Location Europe [ABSTRACT FROM AUTHOR]

Variations in individual phenological events in response to global change have received considerable attentions. However, the development of phenological stages is relatively neglected, especially based on in situ observation data. In this study, the rate of vegetation greenup (Vgreenup) across the Northern Hemisphere was examined for different plant functional types (PFTs) by using eddy covariance flux data from 40 sites (417 site-years). Then, the controls of climatic variables on the spatial distribution of Vgreenup across PFTs were further investigated. The mean Vgreenup was 0.22 ± 0.11 g C m−2 day−2 across all sites, with the largest and lowest values observed in cropland and evergreen needle-leaf forest, respectively. A strong latitude dependence by Vgreenup was observed in both Europe and North America. The spatial variations of Vgreenup were jointly regulated by the duration of greenup (Dgreenup) and the amplitude of greenup (Agreenup). However, the predominant factor was Dgreenup in Europe, which changed to Agreenup in North America. Spring climatic factors exerted significant influences on the spatial distribution of Vgreenup across PFTs. Specifically, increasing temperature tended to shorten Dgreenup and promote Agreenup simultaneously, resulting in an acceleration of Vgreenup. Dryness had a depression effect on Vgreenup for the whole study area, as exhibited by a lower Vgreenup with increasing vapor pressure deficit or decreasing soil moisture. However, Vgreenup in North America was only significantly and positively correlated with temperature. Without the limitation of other climatic factors, the temperature sensitivity of Vgreenup was higher in North America (0.021 g C m−2 day−2 °C−1) than in Europe (0.015 g C m−2 day−2 °C−1). This study provides new cognitions for Vgreenup dynamics from in situ observations in complement to satellite observations, which can improve our understanding of terrestrial carbon cycles. [ABSTRACT FROM AUTHOR]

Spike characteristics include spike length, total spikelet number per spike, number of fertile flowers, spike density, spike fertility, grain number, thousand kernel weight, the number of spikes per square meter, harvest index and the grain yield during the flowering and ripening stage. The six winter and one facultative variety differed in earliness, derived in part from the allele of the Ppd-D1 gene and phenological observation. The two sites significantly differed in the soil moisture, which varied during continual microclimate monitoring. The spike architecture of winter wheat was affected by drought. The plant samples from the site FIELD 2 (more drought stressed) showed a higher reduction in spike characteristics such as a lower spike length, total spikelet number, number of fertile flowers and spike fertility, leading to a lower yield than the site FIELD 1. Both early and late varieties possess compensatory abilities to create the grain yield during drought stress; however, the timing and duration of exposure to drought determine the application and success of the compensatory ability. In our experiment, the late varieties (photoperiod sensitive) performed better in yield than the early varieties during both growing seasons. That is at odds with the generally recommended "drought escape strategy" (early varieties) and suggests a possible direction for variety selection and breeding in arid areas in Central Europe. [ABSTRACT FROM AUTHOR]

Many definitions and delineation methods exist for identifying flash droughts (FDs), which are events of rapid and unusual large depletion of root-zone soil moisture, in comparison to average moisture conditions, due to climatic compound conditions over a short period of several weeks. Six FD identification methods were compared to analyse their functioning using data from several experimental cropland sites across Central Europe. Co- and misidentification of the FD time series were assessed using confusion and synchronicity metrics on a local scale. Even though a large degree of synchronicity of individual FD events was observed, some divergence in drought periods was detected, which was related to four intrinsic differences in the underlying FD definitions: (1) type of critical variable; (2) velocity of drought intensification; (3) pre-set threshold values for final depletion and/or (4) minimum length of the duration of FDs. To balance the strengths and weaknesses of those methods that are not based on soil moisture, we suggest using an ensemble approach for event identification, which is validated in this study for the temperate central European region. In doing so, the current unclearly defined sub-types of FDs can be detected, regardless of the different combinations of compound drivers and differences in intensification dynamics. All methods were implemented in an R package and are available as a Shiny app for the public. [ABSTRACT FROM AUTHOR]

Long-term exposure to polycyclic aromatic hydrocarbons (PAHs) and their nitrated (NPAHs) and oxygenated (OPAHs) derivatives can cause adverse health effects due to their carcinogenicity, mutagenicity and oxidative potential. The distribution of PAH derivatives in the terrestrial environment has hardly been studied, although several PAH derivatives are ubiquitous in air and long-lived in soil and water. We report the multi-annual variations in the concentrations of NPAHs, OPAHs and PAHs in soils sampled at a semi-urban (Mokrá, Czech Republic) and a regional background site (Košetice, Czech Republic) in central Europe. The concentrations of the Σ18NPAHs and the Σ11+2OPAHs and O-heterocycles were 0.31 ± 0.23 ng g−1 and 4.03 ± 3.03 ng g−1, respectively, in Košetice, while slightly higher concentrations of 0.54 ± 0.45 ng g−1 and 5.91 ± 0.45 ng g−1, respectively, were found in soil from Mokrá. Among the 5 NPAHs found in the soils, 1-nitropyrene and less so 6-nitrobenzo(a)pyrene were most abundant. The OPAHs were more evenly distributed. The ratios of the PAH derivatives to their parent PAHs in Košetice indicate that they were long-range transported to the background site. Our results show that several NPAHs and OPAHs are abundant in soil and that gas-particle partitioning is a major factor influencing the concentration of several semi-volatile NPAHs and OPAHs in the soils. Complete understanding of the long-term variations of NPAH and OPAH concentrations in soil is limited by the lack of kinetic data describing their formation and degradation. [ABSTRACT FROM AUTHOR]

Forest transpiration is controlled by the atmospheric water demand, potentially constrained by soil moisture availability, and regulated by plant physiological properties. During summer periods, soil moisture availability at sites with thin soils can be limited, forcing the plants to access moisture stored in the weathered bedrock. Land surface models (LSMs) have considerably evolved in the description of the physical processes related to vegetation water use, but the effects of bedrock position and water uptake from fractured bedrock have not received much attention. In this study, the Community Land Model version 5.0 (CLM 5) is implemented at four forested sites with relatively shallow bedrock and located across an environmental gradient in Europe. Three different bedrock configurations (i.e., default, deeper, and fractured) are applied to evaluate if the omission of water uptake from weathered bedrock could explain some model deficiencies with respect to the simulation of seasonal transpiration patterns. Sap flow measurements are used to benchmark the response of these three bedrock configurations. It was found that the simulated transpiration response of the default model configuration is strongly limited by soil moisture availability at sites with extended dry seasons. Under these climate conditions, the implementation of an alternative (i.e., deeper and fractured) bedrock configuration resulted in a better agreement between modeled and measured transpiration. At the site with a continental climate, the default model configuration accurately reproduced the magnitude and temporal patterns of the measured transpiration. The implementation of the alternative bedrock configurations at this site provided more realistic water potentials in plant tissues but negatively affected the modeled transpiration during the summer period. Finally, all three bedrock configurations did not show differences in terms of water potentials, fluxes, and performances on the more northern and colder site exhibiting a transition between oceanic and continental climate. Model performances at this site are low, with a clear overestimation of transpiration compared to sap flow data. The results of this study call for increased efforts into better representing lithological controls on plant water uptake in LSMs. [ABSTRACT FROM AUTHOR]

To study the combined impact of soil moisture and microphysical perturbations on convective clouds and precipitation over Central Europe, an ensemble of five dozen real‐world weather prediction forecasts was conducted with the COnsortium for Small‐scale MOdeling (COSMO) model at convection‐permitting resolution for a case with weak large‐scale forcing (6 June 2016). We find a large sensitivity of precipitation, ranging from +10% to −$$ - $$23% in 12‐hr precipitation totals. While the homogeneous soil‐moisture bias of ±$$ \pm $$25% primarily controls the timing of convection initiation and the amount of surface rainfall, the number of cloud condensation nuclei and width of the cloud droplet size distribution mainly control the number, size, and lifetime of convective clouds. In moisture‐limited conditions, mainly positive couplings are acting. Drier soils, cleaner air, and a broader cloud droplet size distribution result in less rainfall. Wetter soils and more polluted conditions lead to fewer, but larger, cloud clusters. Since microphysical process rates depend systematically on the sign of the perturbations, but rainfall does not, there are compensating effects at work that buffer microphysical perturbations directly and impact the cloud condensate amount and the rainfall at the ground. [ABSTRACT FROM AUTHOR]

Key message: Potential CH4 oxidation in the top soil layer increased with decreasing soil water content in spring but was inhibited during severe summer drought in naturally-regenerated oak-dominated temperate deciduous forest stands regardless of their age. No direct effect of mineral nitrogen on soil CH4 oxidation was found. Soil CH4 oxidation in temperate forests could be reduced by extreme climatic events. Context: The oxidation of atmospheric methane (CH4) by methanotrophic bacteria in forest soils is an important but overlooked ecosystem service. Aim: Our objective was to determine which factors drive variations in soil CH4 oxidation in oak-dominated temperate deciduous forest stands of different ages. Methods: Soil samples were collected in 16 stands aged 20 to 143 years in periods of high and low soil water content (SWC). The potential rate of soil CH4 oxidation was measured by incubating the first five centimetres of intact soil cores at 20 °C. Results: SWC was the main driver accounting for variations in CH4 oxidation. In spring, a two-fold reduction in SWC greatly increased CH4 oxidation. But when the soil was dry in late summer, a further reduction in SWC led to a decrease in CH4 oxidation in the top soil layer. No direct effect of mineral nitrogen on soil CH4 oxidation was found. Conclusions: With regard to soil CH4 oxidation, naturally regenerated forest stands contribute equally to climate change mitigation regardless of their age. Considering future climate scenarios for Europe, soil CH4 sink in temperate forests could be reduced, due to both an increase in the number of flooding episodes in spring and drier summers. [ABSTRACT FROM AUTHOR]

Three-hourly net ecosystem exchange (NEE) is estimated at spatial scales of 0.25 ∘ over the European continent, based on the pre-operational inverse modelling framework "CarboScope Regional" (CSR) for the years 2006 to 2019. To assess the uncertainty originating from the choice of a priori flux models and observational data, ensembles of inversions were produced using three terrestrial ecosystem flux models, two ocean flux models, and three sets of atmospheric stations. We find that the station set ensemble accounts for 61 % of the total spread of the annually aggregated fluxes over the full domain when varying all these elements, while the biosphere and ocean ensembles resulted in much smaller contributions to the spread of 28 % and 11 %, respectively. These percentages differ over the specific regions of Europe, based on the availability of atmospheric data. For example, the spread of the biosphere ensemble is prone to be larger in regions that are less constrained by CO 2 measurements. We investigate the impact of unprecedented increase in temperature and simultaneous reduction in soil water content (SWC) observed in 2018 and 2019 on the carbon cycle. We find that NEE estimates during these 2 years suggest an impact of drought occurrences represented by the reduction in net primary productivity (NPP), which in turn leads to less CO 2 uptake across Europe in 2018 and 2019, resulting in anomalies of up to 0.13 and 0.07 PgC yr -1 above the climatological mean, respectively. Annual temperature anomalies also exceeded the climatological mean by 0.46 ∘ C in 2018 and by 0.56 ∘ C in 2019, while Standardised Precipitation–Evaporation Index (SPEI) anomalies declined to -0.20 and -0.05 SPEI units below the climatological mean in both 2018 and 2019, respectively. Therefore, the biogenic fluxes showed a weaker sink of CO 2 in both 2018 and 2019 (-0.22 ± 0.05 and -0.28 ± 0.06 PgC yr -1 , respectively) in comparison with the mean -0.36 ± 0.07 PgC yr -1 calculated over the full analysed period (i.e. 14 years). These translate into a continental-wide reduction in the annual sink by 39 % and 22 %, respectively, larger than the typical year-to-year standard deviation of 19 % observed over the full period. [ABSTRACT FROM AUTHOR]

This study explores the uncertainty of future summer warming over Iberia using storylines constructed from climate model simulations of the Climate Model Intercomparison Project Phase 6. Unlike prior storyline approaches focusing on remote drivers and global teleconnections of atmospheric circulation, we use regional factors that directly influence summer temperatures: ridging activity, soil moisture and Mediterranean sea surface temperature. These drivers explain a substantial portion of the observed variability across climate models, with ridging activity and soil moisture showing the strongest influence on Iberian warming. Under a high radiative forcing scenario (SSP5–8.5), the storylines of Iberian warming based on these two drivers range between 7 and 9 °C for the end of the 21st century. The storyline leading to the largest warming is characterised by a drying out of the soil conditions and an increase in the anticyclonic activity over Iberia. We find similar conclusions for simple extreme heat indicators, though the approach struggles with more complex heatwave metrics. We also propose a novel modification of the storyline approach to increase the data sample of climate responses by using different time intervals throughout the 21st century. This modification would allow the application of more complex statistical models, the exploration of non-linear relationships and the identification of other drivers shaping the regional climate projections. • Soil moisture & ridging explain most multi-model spread of Iberian warming (IW). • Storylines of IW range between ∼1.6–2.1 °C per degree of global warming. • The influence of Mediterranean sea surface temperatures on IW is of secondary importance. • Similar findings on extreme temperatures, but complex metrics pose challenges. • Novel modification of the storyline approach: extending data via moving windows. [ABSTRACT FROM AUTHOR]

The Earth system is a complex non-linear dynamical system. Despite decades of research, many processes and relations between Earth system variables are still poorly understood. Current approaches for studying relations in the Earth system may be broadly divided into approaches based on numerical simulations and statistical approaches. However, there are several inherent limitations to current approaches that are, for example, high computational costs, reliance on the correct representation of relations in numerical models, strong assumptions related to linearity or locality, and the fallacy of correlation and causality. Here, we propose a novel methodology combining deep learning (DL) and principles of causality research in an attempt to overcome these limitations. The methodology combines the recent idea of training and analyzing DL models to gain new scientific insights in the relations between input and target variables with a theorem from causality research. This theorem states that a statistical model may learn the causal impact of an input variable on a target variable if suitable additional input variables are included. As an illustrative example, we apply the methodology to study soil moisture-precipitation coupling in ERA5 climate reanalysis data across Europe. We demonstrate that, harnessing the great power and flexibility of DL models, the proposed methodology may yield new scientific insights into complex, nonlinear and non-local coupling mechanisms in the Earth system. [ABSTRACT FROM AUTHOR]

Extreme weather events are triggered by atmospheric circulation patterns and shaped by slower components, including soil moisture and sea surface temperature, and by the background climate. This separation of factors is exploited by the storyline approach in which an atmospheric model is nudged toward the observed dynamics using different climate boundary conditions to explore their influence. The storyline approach disregards uncertain climatic changes in the frequency and intensity of dynamical conditions, focusing instead on the thermodynamic influence of climate on extreme events. Here we demonstrate an advanced storyline approach that employs a coupled climate model (AWI-CM-1-1-MR) in which the large-scale free-troposphere dynamics are nudged toward ERA5 data. Five-member ensembles are run for present-day (2017–19), preindustrial, +2-K, and +4-K climates branching off from CMIP6 historical and scenario simulations of the same model. In contrast to previous studies, which employed atmosphere-only models, feedbacks between extreme events and the ocean and sea ice state, and the dependence of such feedbacks on the climate, are consistently simulated. Our setup is capable of reproducing observed anomalies of relevant unconstrained parameters, including near-surface temperature, cloud cover, soil moisture, sea surface temperature, and sea ice concentration. Focusing on the July 2019 European heat wave, we find that the strongest warming amplification expands from southern to central Europe over the course of the twenty-first century. The warming reaches up to 10 K in the 4-K-warmer climate, suggesting that an analogous event would entail peak temperatures around 50°C in central Europe. Significance Statement: This work explores a new storyline method to determine the impact of climate change on specific recent extreme events. The observed evolution of the large-scale atmospheric circulation is imposed in a coupled climate model. Variations in climate parameters, including ocean temperatures and sea ice, are well reproduced. By varying the background climate, including CO2 concentrations, it is demonstrated how the July 2019 European heat wave could have evolved in preindustrial times and in warmer climates. For example, up to 10°C warmer peak temperatures could occur in central Europe in a 4°C warmer climate. The method should be explored for other types of extreme events and has the potential to make climate change more tangible and to inform adaptation measures. [ABSTRACT FROM AUTHOR]

Understanding the critical soil moisture (SM) threshold (θcrit) of plant water stress and land surface energy partitioning is a basis to evaluate drought impacts and improve models for predicting future ecosystem condition and climate. Quantifying the θcrit across biomes and climates is challenging because observations of surface energy fluxes and SM remain sparse. Here, we used the latest database of eddy covariance measurements to estimate θcrit across Europe by evaluating evaporative fraction (EF)‐SM relationships and investigating the covariance between vapor pressure deficit (VPD) and gross primary production (GPP) during SM dry‐down periods. We found that the θcrit and soil matric potential threshold in Europe are 16.5% and −0.7 MPa, respectively. Surface energy partitioning characteristics varied among different vegetation types; EF in savannas had the highest sensitivities to SM in water‐limited stage, and the lowest in forests. The sign of the covariance between daily VPD and GPP consistently changed from positive to negative during dry‐down across all sites when EF shifted from relatively high to low values. This sign of the covariance changed after longer period of SM decline in forests than in grasslands and savannas. Estimated θcrit from the VPD–GPP covariance method match well with the EF–SM method, showing this covariance method can be used to detect the θcrit. We further found that soil texture dominates the spatial variability of θcrit while shortwave radiation and VPD are the major drivers in determining the spatial pattern of EF sensitivities. Our results highlight for the first time that the sign change of the covariance between daily VPD and GPP can be used as an indicator of how ecosystems transition from energy to SM limitation. We also characterized the corresponding θcrit and its drivers across diverse ecosystems in Europe, an essential variable to improve the representation of water stress in land surface models. [ABSTRACT FROM AUTHOR]

Xylem embolism resistance has been identified as a key trait with a causal relation to drought-induced tree mortality, but not much is known about its intra-specific trait variability (ITV) in dependence on environmental variation. We measured xylem safety and efficiency in 300 European beech (Fagus sylvatica L.) trees across 30 sites in Central Europe, covering a precipitation reduction from 886 to 522 mm year−1. A broad range of variables that might affect embolism resistance in mature trees, including climatic and soil water availability, competition, and branch age, were examined. The average P50 value varied by up to 1 MPa between sites. Neither climatic aridity nor structural variables had a significant influence on P50. However, P50 was less negative for trees with a higher soil water storage capacity, and positively related to branch age, while specific conductivity (Ks) was not significantly associated with either of these variables. The greatest part of the ITV for xylem safety and efficiency was attributed to random variability within populations. We conclude that the influence of site water availability on P50 and Ks is low in European beech, and that the high degree of within-population variability for P50, partly due to variation in branch age, hampers the identification of a clear environmental signal. [ABSTRACT FROM AUTHOR]

Climate change increases the occurrence and severity of droughts due to increasing temperatures, altered circulation patterns, and reduced snow occurrence. While Europe has suffered from drought events in the last decade unlike ever seen since the beginning of weather recordings, harmonized long-term datasets across the continent are needed to monitor change and support predictions. Here we present soil moisture data from 66 cosmic-ray neutron sensors (CRNSs) in Europe (COSMOS-Europe for short) covering recent drought events. The CRNS sites are distributed across Europe and cover all major land use types and climate zones in Europe. The raw neutron count data from the CRNS stations were provided by 24 research institutions and processed using state-of-the-art methods. The harmonized processing included correction of the raw neutron counts and a harmonized methodology for the conversion into soil moisture based on available in situ information. In addition, the uncertainty estimate is provided with the dataset, information that is particularly useful for remote sensing and modeling applications. This paper presents the current spatiotemporal coverage of CRNS stations in Europe and describes the protocols for data processing from raw measurements to consistent soil moisture products. The data of the presented COSMOS-Europe network open up a manifold of potential applications for environmental research, such as remote sensing data validation, trend analysis, or model assimilation. The dataset could be of particular importance for the analysis of extreme climatic events at the continental scale. Due its timely relevance in the scope of climate change in the recent years, we demonstrate this potential application with a brief analysis on the spatiotemporal soil moisture variability. The dataset, entitled "Dataset of COSMOS-Europe: A European network of Cosmic-Ray Neutron Soil Moisture Sensors", is shared via Forschungszentrum Jülich: 10.34731/x9s3-kr48 (Bogena and Ney, 2021). [ABSTRACT FROM AUTHOR]

During the period 2018–2020, Europe experienced a series of hot and dry weather conditions with significant socioeconomic and environmental consequences. Yet, the extremity of these multi‐year dry conditions is not recognized. Here, we provide a comprehensive spatio‐temporal assessment of the drought hazard over Europe by benchmarking past exceptional events during the period from 1766 to 2020. We identified the 2018–2020 drought event as a new benchmark having an unprecedented intensity that persisted for more than 2 years, exhibiting a mean areal coverage of 35.6% and an average duration of 12.2 months. What makes this event truly exceptional compared with past events is its near‐surface air temperature anomaly reaching +2.8 K, which constitutes a further evidence that the ongoing global warming is exacerbating present drought events. Furthermore, future events based on climate model simulations Coupled Model Intercomparison Project v5 suggest that Europe should be prepared for events of comparable intensity as the 2018–2020 event but with durations longer than any of those experienced in the last 250 years. Our study thus emphasizes the urgent need for adaption and mitigation strategies to cope with such multi‐year drought events across Europe. Plain Language Summary: This manuscript demonstrates that the 2018–2020 multi‐year drought event constitutes a new benchmark in Europe, with an unprecedented level of intensity over the past 250 years. What makes this event truly exceptional compared with past events is its temperature anomaly reaching +2.8 K. This finding provides new evidence that the ongoing global warming exacerbates current drought events. The key message of this study is that the projected future events across the European continent will have a comparable intensity as the 2018–2020 drought but exhibit considerably longer durations than any of those observed during the last 250 years. Our analysis also shows that these exceptional temperature‐enhanced droughts significantly negatively impact commodity crops across Europe. Key Points: The 2018–2020 multi‐year drought shows unprecedented level of intensity during the past 250 yearsThe 2018–2020 event reached record‐breaking +2.8 K temperature anomaly and negatively impacted major cropsFuture drought events reach comparable intensity of 2018–2020 but with considerably longer durations [ABSTRACT FROM AUTHOR]

Despite the importance of vegetation uptake of atmospheric gaseous elemental mercury (Hg(0)) within the global Hg cycle, little knowledge exists on the physiological, climatic, and geographic factors controlling stomatal uptake of atmospheric Hg(0) by tree foliage. We investigate controls on foliar stomatal Hg(0) uptake by combining Hg measurements of 3569 foliage samples across Europe with data on tree species' traits and environmental conditions. To account for foliar Hg accumulation over time, we normalized foliar Hg concentration over the foliar life period from the simulated start of the growing season to sample harvest. The most relevant parameter impacting daily foliar stomatal Hg uptake was tree functional group (deciduous versus coniferous trees). On average, we measured 3.2 times higher daily foliar stomatal Hg uptake rates in deciduous leaves than in coniferous needles of the same age. Across tree species, for foliage of beech and fir, and at two out of three forest plots with more than 20 samples, we found a significant (p<0.001) increase in foliar Hg values with respective leaf nitrogen concentrations. We therefore suggest that foliar stomatal Hg uptake is controlled by tree functional traits with uptake rates increasing from low to high nutrient content representing low to high physiological activity. For pine and spruce needles, we detected a significant linear decrease in daily foliar stomatal Hg uptake with the proportion of time during which water vapor pressure deficit (VPD) exceeded the species-specific threshold values of 1.2 and 3 kPa, respectively. The proportion of time within the growing season during which surface soil water content (ERA5-Land) in the region of forest plots was low correlated negatively with foliar Hg uptake rates of beech and pine. These findings suggest that stomatal uptake of atmospheric Hg(0) is inhibited under high VPD conditions and/or low soil water content due to the regulation of stomatal conductance to reduce water loss under dry conditions. Other parameters associated with forest sampling sites (latitude and altitude), sampled trees (average age and diameter at breast height), or regional satellite-observation-based transpiration product (Global Land Evaporation Amsterdam Model: GLEAM) did not significantly correlate with daily foliar Hg uptake rates. We conclude that tree physiological activity and stomatal response to VPD and soil water content should be implemented in a stomatal Hg model to assess future Hg cycling under different anthropogenic emission scenarios and global warming. [ABSTRACT FROM AUTHOR]

The land surface is a potential source of climate predictability over the Northern Hemisphere midlatitudes but has received less attention than sea surface temperature in this regard. This study quantified the degree to which realistic land initialization contributes to interannual climate predictability over Europe based on a coupled climate system model named FGOALS-g2. The potential predictability provided by the initialization, which incorporates the soil moisture and soil temperature of a land surface reanalysis product into the coupled model with a dimension-reduced projection four-dimensional variational data assimilation (DRP-4DVar)-based weakly coupled data assimilation (WCDA) system, was analyzed first. The effective predictability (i.e., prediction skill) of the hindcasts by FGOALS-g2 with realistic and well-balanced initial conditions from the initialization were then evaluated. Results show an enhanced interannual prediction skill for summer surface air temperature and precipitation in the hindcast over Europe, demonstrating the potential benefit from realistic land initialization. This study highlights the significant contributions of land surface to interannual predictability of summer climate over Europe. [ABSTRACT FROM AUTHOR]

How summertime temperature variability will change with warming has important implications for climate adaptation and mitigation. CMIP5 simulations indicate a compound risk of extreme hot temperatures in western Europe from both warming and increasing temperature variance. CMIP6 simulations, however, indicate only a moderate increase in temperature variance that does not covary with warming. To explore this intergenerational discrepancy in CMIP results, we decompose changes in monthly temperature variance into those arising from changes in sensitivity to forcing and changes in forcing variance. Across models, sensitivity increases with local warming in both CMIP5 and CMIP6 at an average rate of 5.7 ([3.7, 7.9]; 95% c.i.) × 10−3°C per W m−2 per °C warming. We use a simple model of moist surface energetics to explain increased sensitivity as a consequence of greater atmospheric demand (∼70%) and drier soil (∼40%) that is partially offset by the Planck feedback (∼−10%). Conversely, forcing variance is stable in CMIP5 but decreases with warming in CMIP6 at an average rate of −21 ([−28, −15]; 95% c.i.) W2 m−4 per °C warming. We examine scaling relationships with mean cloud fraction and find that mean forcing variance decreases with decreasing cloud fraction at twice the rate in CMIP6 than CMIP5. The stability of CMIP6 temperature variance is, thus, a consequence of offsetting changes in sensitivity and forcing variance. Further work to determine which models and generations of CMIP simulations better represent changes in cloud radiative forcing is important for assessing risks associated with increased temperature variance. Plain Language Summary: CMIP5 models show that, in the Northern Hemisphere midlatitudes, summertime temperature variability increases as the surface warms, indicating a compound risk of extreme hot months that have important implications for climate adaptation and mitigation. CMIP6 models, however, show only a moderate increase in temperature variability that is unrelated to warming. To understand this intergenerational discrepancy in CMIP results, we develop a framework to decompose changes in temperature variability into contributions from changes in the variability of external forcing and changes in the sensitivity of temperature to that forcing. We find that both CMIP5 and CMIP6 models show consistent increases in sensitivity as the surface warms, which we demonstrate to arise mainly from warming and drying using a simple diagnostic model. Changes in forcing variability, however, differ between CMIP5 and CMIP6. Whereas, forcing variability is stable in CMIP5, it decreases substantially with warming in CMIP6 and offsets the effect of sensitivity growth. Hence, although midlatitude land surface tends to become more sensitive in all models, whether temperature variability will increase with warming remains uncertain and relies on how forcing variability changes. Key Points: Summer temperature variance increases with warming in Northern Hemisphere midlatitudes in CMIP5 but not in CMIP6We develop a decomposition framework and a simple model to explain differences in variance changes across modelsTemperature sensitivity increases in both CMIP5 and CMIP6 but is offset by lower forcing variance in CMIP6 [ABSTRACT FROM AUTHOR]