The estimation of reference evapotranspiration (ETo) holds significant importance for the hydrological cycle, necessitating an extensive understanding of the various climate variables and their influence on ETo variability. This study aims to examine spatio-temporal variations in Penman Monteith based ETo estimations and the factors contributing to their changes over the Indian subcontinent in the historic and future climate change. Using climate variables from the ERA5 reanalysis and CMIP6 simulations this study focuses on the changes in ETo across different aridity zones in the study area. Further, the partial least squares (PLS) regression was employed to determine the relative contribution of different climate variables on ETo trends. Results show that the majority (70%) of the areas in the subcontinent exhibited decreasing ETo trends in the historical past. Zonal analysis of ETo trends revealed all zones except the humid zone exhibited a significant decreasing trend for ETo. Contribution analysis shows that, across the study area, temperature and radiation are the most significant factors influencing ETo, followed by wind speed and relative humidity. Further, temperature and ETo were found to be having opposing tendencies, highlighting an “evapotranspiration paradox” that encompasses the majority of the study area. CMIP6 simulations show that ETo is projected to increase significantly across the Indian subcontinent, especially in the semi-arid and arid regions with temperature and radiation being the dominant factor contributing to increases in ETo.

How to cite: Varghese, F. C. and Mitra, S.: Spatio-temporal variation of reference evapotranspiration and its contributing factors over the Indian subcontinent under historic and future climate change, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-675, https://doi.org/10.5194/egusphere-egu24-675, 2024.

The intricate interplay between land use, climate dynamics, and other contributing factors significantly influences the occurrence of extreme events such as droughts, floods, and heatwaves. Modeling this complex system in a high-dimensional space poses a formidable challenge, given incomplete understanding and limited availability of data. This study explores the application of deep learning approaches, specifically leveraging transformer architectures, to capture long-range dependencies in spatiotemporal data. These mechanisms are then employed to encapsulate the complex interactions between land use, climate, and other factors influencing extreme events. The proposed approach incorporates attention mechanisms, enhancing interpretability by highlighting crucial spatial and temporal features essential for forecasting. To evaluate the effectiveness of this methodology, a case study was conducted on the Godavari River Basin in India. Utilizing vegetation indices as a representation of crop type and land use, alongside climate data spanning from 2000 to 2020, the results provide valuable insights into the driving factors behind land use change and climate extremes in the region. The study not only demonstrates predictive capabilities of the proposed approach but also offers insights into the intricate relationships within the land-atmosphere feedback system. The extracted information is useful for making informed decisions related to land management, climate adaptation, and disaster risk reduction.

How to cite: subhadarsini, S., Kumar, D. N., and Rao, S. G.: Land-Climate Nexus: Unravelling Extremes with Attention Networks, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-878, https://doi.org/10.5194/egusphere-egu24-878, 2024.

How to cite: Lo, M.-H., Lee, T.-H., Hsu, J., Chiang, C.-L., and Kuo, Y.-N.: Forest Canopy Transpiration: A Key Moderator of Hydroclimate Variability and Extreme Rainfall in the Maritime Continent, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1608, https://doi.org/10.5194/egusphere-egu24-1608, 2024.

Flash droughts (FDs) and heatwaves are posing disproportionate biophysical and social losses worldwide, particularly threatening the disadvantaged communities in the Global South. However, the underlying physical mechanisms behind compound heat-flash drought (CHFD) events and their impacts on global socio-ecosystem productivity remain elusive. Here using satellites, reanalysis, reconstructions, and field measurements, we find more dry regions (53%~62%) with above-average ratios of FDs accompanied by extreme heat than humid regions (50%~57%), due to asymmetric effects by synoptic weather systems. The CHFDs associated with strong soil moisture-temperature coupling aggravate the constraint on plant photosynthesis in dry regions, whereas this coupling-related vegetation stress is not significant in humid regions. We further develop a global risk framework that integrates CHFD hazards, population/agriculture exposures, and vulnerability, and find the Global South is the primary region affected by CHFDs, contributing to greater-than-usual carbon uptake reduction, 90%~94% and 76%~86% of risks to world population and agriculture over the past four decades. We reveal the Global South is severely affected by the impacts of CHFDs on socio-ecosystem productivity decline and underscore the importance of efforts to monitor, predict, and mitigate the rise in CHFDs. 

How to cite: Gu, L., Fischer, E., Yin, J., Slater, L., Sippel, S., and Knutti, R.: Global South most affected by socio-ecosystem productivity decline due to compound heat and flash droughts, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1973, https://doi.org/10.5194/egusphere-egu24-1973, 2024.

The land-atmosphere coupling is responsible for flash droughts as the reduced soil moisture increases sensible heat and consequently the lifting condensation level, which ultimately reduces convective precipitation. Meanwhile, the decrease in atmospheric humidity increases the evaporation demand, facilitates the drying of the land surface, and triggers flash droughts with rapid onset and devastating impact. However, whether the role of the land-atmosphere coupling is enhanced or weakened under climate change remains elusive, as previous studies are usually based on unconditional analysis without discriminating dry or wet extremes. Here, we start the investigation from a mega-flash drought occurred over the Yangtze River basin in southern China during the summer of 2022. Both the offline high-resolution land surface model simulations and the CMIP6 climate model data are used for the analysis. It is found that high temperature aggravates the 2022 flash drought onset speed and intensity, highlighting the importance of climate warming. Even under natural climate forcings, the land-atmosphere coupling increases the risks of flash drought intensity and onset speed. The synergy of coupling and anthropogenic climate change would further increase the risks. The synergistic effect on the long-term trends of flash droughts is also being explored, shedding light on the mechanism of flash droughts in a changing climate.

How to cite: Yuan, X.: Synergistic effect of land-atmosphere coupling and climate change on flash droughts, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2848, https://doi.org/10.5194/egusphere-egu24-2848, 2024.

Heatwaves are a significant threat to human health, agriculture, and infrastructure; particularly in India, where they are prevalent during the pre-monsoon months. May is a critical period for heatwave occurrences, severely impacting the Indian subcontinent. This work delves into the underlying mechanisms driving heatwaves in India, specifically focusing on those that occur in May. Utilizing an intermediate complexity earth system model, PLASIM¹, and its adjoint² for sensitivity analysis³, we systematically investigate the causal role of remote soil moisture in heatwave formation. We find that variations in remote soil moisture significantly influence the strength and duration of pre-monsoon heat waves in India. Our analysis shows that at a lead time of 10-15 days, higher soil moisture particularly over the Middle East, can prolong heatwave conditions over India. On the other hand, high soil moisture over India suppresses the development of heatwaves with no lag. The delayed mechanism of remote soil moisture works through the altered atmospheric circulation patterns induced by heat flux forcing modulated by soil moisture anomalies, leading to enhanced subsidence and reduced moisture transport to India. Our study provides valuable insights into the mechanisms driving heatwaves in India, particularly those in May. These insights are crucial for developing effective early warning systems, enhancing disaster preparedness, and implementing mitigation strategies to reduce the adverse impacts of these extreme events.

¹The Planet Simulator (PlaSim): a climate model of intermediate complexity for Earth, Mars and other planets.

²Marotzke, Jochem, et al. "Construction of the adjoint MIT ocean general circulation model and application to Atlantic heat transport sensitivity." Journal of Geophysical Research: Oceans 104.C12 (1999): 29529-29547.

³Köhl, Armin, and Andrey Vlasenko. "Seasonal prediction of northern European winter air temperatures from SST anomalies based on sensitivity estimates." Geophysical Research Letters 46.11 (2019): 6109-6117.

How to cite: Banerjee, A., Koehl, A., and Stammer, D.: Causal analysis of Heatwaves in India: Impact of Remote Soil Moisture, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3079, https://doi.org/10.5194/egusphere-egu24-3079, 2024.

Assessing the impacts of anthropogenic land use and land cover change (LULCC) on climate extremes is of public concern, calling for the use of state-of-the-art experiments and datasets to update our knowledge. Here, we used the CMIP6-LUMIP experiment results to depict the realistic LULCC effects on extreme temperature and extreme precipitation over both historical and future periods. We pointed out some interesting findings over the historical period: Approximately 1^oC decrease in the maximum temperature, and up to nearly 2^oC decrease in the minimum temperature in the mid-high latitude of the North Hemisphere. About 10 annual heatwave days can be avoided by LULCC effects in 10% of specific LULCC-intense regions. Three LULCC-intense regions in the North Hemisphere have experienced cooling effects in intensity, frequency, and duration aspects. The precipitation displayed a clear contrast change between the North Hemisphere (wetter) and the South Hemisphere (drier), especially on light rainy days (R1mm). Results of the future period indicate that the tropical deforestation regions are projected to induce a remarkably hotter and drier trend. However, the climate responses averaged globally to deforestation have no obvious changes due to the colder and wetter compensation responses in other regions. The maximum temperature increase in deforestation regions is prominent in intensity, frequency, and duration aspects, while the drought is mainly manifested by frequency and duration reduction of precipitation. Seasonal cycle of changes in temperature indices can be discovered in the North Hemisphere mid-latitude deforestation region, tropical region shows year-round consistency. Changes in LULCC induced climate extremes are more obvious under the low-emission scenario in general. Our work is devoted to portraying the latest and more realistic picture of LULCC impacts on climate extremes and gives early warning information to policymakers and the public.

How to cite: Zhang, M. and Gao, Y.: Impacts of anthropogenic land use and land cover change on climate extremes based on CMIP6-LUMIP experiments, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4834, https://doi.org/10.5194/egusphere-egu24-4834, 2024.

Warming lead to a surge in extreme climate events, including heatwaves, droughts, flooding, and wildfires. Numerous studies demonstrate that these occurrences have become more frequent, which exerts notable influences on socio-economic development and human health. Besides natural climate changes, land use and land cover changes (LULCC) play a crucial role in shaping extreme climates. As the most extensive land use type globally, forest has experienced great changes since the industrial evolution. Deforestation is one of the most notable global environmental issues. Besides the decrease of the coverage, fragmentation is one of the appearances of deforestation. Many studies have demonstrated that forest distribution shows high agreements with climate regimes generally, however, the relationship between forest fragmentation and extreme climate events remain unclear. This study analyzes the relation between forest fragmentation and main extreme high temperature indices in 2000-2020. Global continental areas are categorized into regions with increased and decreased forest fragmentation index. Regions with increased index, such as the southeast Amazon, Congo Basin, and parts of the Southeast Asia are emphasized. The 11 extreme temperature indices are analyzed responded to the forest fragmentation index change. This study could provide insights for forest management strategies adapting to climate change in the future.

How to cite: Du, R. and Gao, Y.: The relationship between forest fragmentation and extreme high temperature, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5644, https://doi.org/10.5194/egusphere-egu24-5644, 2024.

The debate on the sign of land-atmosphere coupling has not been solved so far. On the one hand, studies using global coarse-resolution climate models have claimed that the land-atmosphere coupling is positive. But, such models use convective parameterizations, which is a source of uncertainty. On the other hand, studies using regional climate models with explicit convection have reported negative coupling. Yet, the large-scale circulation is prescribed in such models, and interactions with the ocean are neglected. In this study, we revisit the land-atmosphere coupling using a global fully coupled storm-resolving simulation that has been integrated at a grid spacing of 5 km over a full seasonal cycle, and we compare these results to a coarse-resolution climate model simulation using parameterized convection. We find that the coupling between soil moisture and precipitation is weaker and more negative in the storm-resolving than in the coarse-resolution simulation. Further analysis indicates that not only the feedback between soil moisture and evapotranspiration but also between evapotranspiration and precipitation is weaker in the storm-resolving simulation, in better agreement with observations. Reasons for the differences will be mentioned.

How to cite: Lee, J. and Hohenegger, C.: How strong is land-atmosphere coupling in global storm-resolving simulations?, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6099, https://doi.org/10.5194/egusphere-egu24-6099, 2024.

The Three Gorges Dam, the world's largest hydropower project, and its impoundment reservoir have notably modified land cover, with potential implications for regional hydroclimate. However, the seasonal dynamic climate feedbacks arising from variations in water body areas managed by the Three Gorges Reservoir (TGR) remains poorly understood. Based on data-driven analysis and regional climate simulations, we depict the impact of the TGR regulation activities on local land surface temperature (LST) and biophysical processes across different spatiotemporal dimensions, determine the spreading extent of this effect to external territories, and further identify the quantitative attributions between regional climate variabilities and the TGR operation. Results indicate that the TGR induces more pronounced daytime cooling from May to October, particularly in June-August (JJA) with -2.41±0.23 K. The influence of TGR on nighttime LST transitions to warming effects in most regions from November to April (NDJFMA). The significantly increased latent heat (LH) from evaporation growth dominates cooling effects, particularly during daytime, while in JJA, the effects of evaporation are constrained to some extent by abundant precipitation. Albedo exerts a comparatively significant dominance on the nighttime LST in NDJFMA. The TGR-induced surroundings LST changes are notably discernible within an approximately 10 km buffer. The simulations amplify the magnitude and extent of the TGR cooling effect. The simulation results reveal significant reductions in LST of 6.08% (-1.42 K, JJA) and 4.58% (-1.04 K, December-January-February, DJF). respectively, TGR-induced LH variations are dominant for cooling (contributions: -52.09% in JJA; -71.98% in DJF, respectively) among the diverse energy components. This study is valuable for providing scientific guidance in reservoir planning under changing climate.

How to cite: li, H., wang, W., and forzieri, G.: The cooling effect induced by the Three Gorges Reservoir operation in observations and model simulations, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7942, https://doi.org/10.5194/egusphere-egu24-7942, 2024.

The skill of climate models partly relies on their ability to represent land–atmosphere feedbacks in a realistic manner, through the coupling with a land surface model. However, these models often suffer from insufficient or erroneous information on soil hydraulic parameters. In this study, the land–atmosphere model WRF-Hydro driven with ERA5 reanalysis is employed to reproduce the regional climate over Central Europe with a horizontal resolution of 4 km, for the period 2017-2020 during which cosmic-ray neutron sensor (CRNS) soil moisture is available at three Terrestrial Environmental Observatories. The soil hydraulic parameter datasets referred to as SoilGrids and EU-SoilHydroGrids, together with Campbell and van Genuchten–Mualem retention curve equations, are used to assess the role of infiltration on modeled land–atmosphere feedbacks. After calibration of the percolation parameter to better capture observed discharge amounts in the observatories, it is found that WRF-Hydro with Campbell and SoilGrids gives the lowest mean temperature and mean precipitation differences compared to the E-OBS product from European Climate Assessment & Dataset, by reducing soil moisture in the rootzone, increasing temperature, and decreasing precipitation through a positive soil moisture–precipitation feedback process. WRF-Hydro with van Genuchten–Mualem and EU-SoilHydroGrids best reproduces CRNS soil moisture daily variations, despite enhanced positive biases that generate a larger proportion of convective precipitation favored over wet soils and spurious discharge peaks. The question remains open whether an infiltration modeling option that better captures CRNS soil moisture dynamics can also lead to a clear improvement of the simulated climate.

How to cite: Arnault, J., Fersch, B., Schrön, M., Bogena, H. R., Hendricks-Franssen, H.-J., and Kunstmann, H.: Role of infiltration on land–atmosphere feedbacks in Central Europe: WRF-Hydro simulations evaluated with cosmic-ray neutron soil moisture, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8546, https://doi.org/10.5194/egusphere-egu24-8546, 2024.

Studying land-atmosphere interactions is important for understanding the mechanisms leading to changes in temperature and precipitation extremes. However, the non-conservation of energy and water in most products and their coarse spatial and temporal resolution hamper the study of land-atmosphere feedbacks. The combination of remote sensing data and modelling frameworks allows to greatly improve the spatial coverage and resolution of data products. Here, we investigate trends in surface fluxes over Europe using the new data product generated with the high-resolution land surface fluxes from satellite and reanalysis data (HOLAPS) framework. HOLAPS is a one dimensional modelling framework that solves the energy and water balance at the land surface, providing consistent surface and soil variables derived from remote sensing data and reanalysis products as forcings. The evaluation of the HOLAPS product against eddy covariance measurements shows slightly better results than other ET and H products at daily scales in summer (KGE > 0.0 for ET and KGE > -0.3 for H) and during hot extremes (KGE > -0.15 for ET and KGE >-0.7 for H), while the state-of-the-art products show KGE > -0.49 for ET and KGE > -1.2 for H in summer and KGE > -0.49 for ET and KGE > -1.5 for H during hot extremes. These results together with the 1D conservation of energy and water in the modeling framework makes this product the perfect tool for the analysis of trends in surface energy and water fluxes during the last decades. Preliminary results for the period 2001-2016 reveals a larger increase in the energy reaching the surface during the hottest month of the year than during summer over central Europe and the Mediterranean coast. This extra energy is released as sensible heat over dry areas during the hottest month of the year. In areas where soil water is available, the extra energy available during the hottest month is released as latent heat flux, adding it to the already large latent heat flux during summer. These results support previous analyses indicating an increase of latent heat flux during hot conditions at monthly scales. However, trends at higher temporal resolutions should be examined to improve the robustness of this conclusion. 

How to cite: García-García, A. and Peng, J.: Analysis of trends in surface energy fluxes under hot conditions using remote sensing products, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9091, https://doi.org/10.5194/egusphere-egu24-9091, 2024.

Land cover changes, notably forest alterations, have been observed across Europe due to extensive land management policies. These changes have significant influence on local climates through diverse biophysical mechanisms, given the crucial role of forests in the land ecosystem. While modeling studies have emphasized the impact of forest changes on regional temperature and precipitation in recent decades, their effects on drought conditions in this region remain largely unexplored. To address this gap, our study analyzes multiple simulations with regional climate models to comprehensively investigate how forest changes impact drought across various timescales in Europe. Specifically, we explored seven models, each simulated two extreme scenarios: maximum forest coverage and grass coverage in the region. The comparison between extreme forest coverage and grass coverage serves to evaluate the impact of deforestation on drought. The Standardized Precipitation Evapotranspiration Index was chosen as our metric to assess drought conditions. Our findings reveal considerable variation among the models in depicting the response to deforestation in terms of drought, particularly notable in Scandinavia and Eastern Europe. Our results suggest an increase in aridity on the Iberian Peninsula following deforestation. In Scandinavia the response varies during the year: winter months tend toward increased dryness, while summer months display a tendency toward greater wetness post-deforestation. Our primary objectives encompass quantifying the potential impacts of deforestation in Europe, identifying resilient model responses, and unraveling the sources of uncertainty within these simulated impacts. Through a meticulous analysis of model responses across regions and timescales, we aim to offer insights into the nuanced effects of forest change on drought conditions. This exploration is crucial in guiding future land management policies and devising strategies to mitigate potential adverse impacts of deforestation on regional drought susceptibility in Europe. Ultimately, our study seeks to contribute to informed decision-making regarding land use practices and their implications for climate and ecosystems.

How to cite: Li, Y., Huang, B., Tan, C., Liu, Y., and Rust, H. W.: Examining the influence of forest changes on drought across time scales in Europe through multiple regional climate model simulations, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11163, https://doi.org/10.5194/egusphere-egu24-11163, 2024.

Extreme climate events such as droughts and heatwaves significantly impact the stability of ecosystem function and are expected to intensify in the future. The mid-high latitude regions of the Northern Hemisphere (23.5° to 90°N) exhibit pronounced seasonality and are highly sensitive to climate variations. However, further research is needed to understand the vegetation decline and its changing trends driven by extreme hydroclimatic and their compound events in this region. This study, based on multi-source data including NDVI, LAI, and GPP from 1982 to 2015 as vegetation growth indicators, amid to identify vegetation decline during the growing season and explore its temporal trends, and to further reveal the seasonal response. The research supported the importance of drought and high temperature compared to extreme wet and cold conditions. Due to the high frequency, wide impact and long duration of impact, independent low SM dominated the cumulative vegetation decline, followed by low SM and high VPD compound events. High VPD caused stronger negative impacts on vegetation growth than high T and that it was more strongly coupled to SM. We further found a turning point in vegetation decline. Because of the significant increase in VPD and its enhanced coupling with low SM, low SM and its compound events, especially SM- & VPD+ & T+ compound events, led to a significant enhancement of the vegetation decline after about the 21st century. Furthermore, the sensitivity of vegetation growth to extreme hydroclimatic has also significantly increased, with stronger intensity of vegetation decline. Seasonally, early growing season vegetation was more vulnerable (with the strongest continuous decline) due to experiencing the longest duration of negative impacts, while summer vegetation was more sensitive to extreme hydroclimatic, with the strongest intensity. Notably, compound events of high VPD and low SM primarily affected summer vegetation growth. Additionally, there was a significant lag time in vegetation response to extreme hydroclimatic, especially to high VPD and high T. In over half of the regions, the vegetation response to high T and high VPD had a lag time exceeding two months, which may be associated with seasonal legacy. In the context of global warming, further investigation is needed to explore the inter-seasonal connections. This research significantly contributes to a deeper understanding of ecosystem responses to extremes hydroclimatic and its future changes.

How to cite: Du, R. and Wu, J.: The turning point in vegetation decline in the Northern Hemisphere driven by hydroclimatic extremes, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11693, https://doi.org/10.5194/egusphere-egu24-11693, 2024.

Precipitation is an important control of soil moisture on land. Thus, many studies have focused on understanding the influences of mean or total precipitation variability on soil moisture. However, the relationship between precipitation intermittency (the temporal distribution of rainfall events) and soil moisture variability remains largely underexplored. This question requires more attention as climate models are known to be deficient in their representation of precipitation intermittency (PI), and PI is projected to increase in a future warmer climate, potentially affecting soil moisture variability. In this study, we examine the associations between seasonal PI and soil moisture (SM) across the globe in observation-based datasets (ERA5, MSWEP, and GLEAM) and model simulations (CESM2 Large Ensembles – LENS2) for the period 1981–2020. As a methodology to quantify the associations between PI and SM, we use a conditional regression analysis of 10cm soil moisture onto a metric of PI (reverted number of wet days in a season) after the removal of the influence of total seasonal precipitation from each variable. 

The result suggests that in many regions, higher PI leads to decreases in SM under the same amount of seasonal precipitation. These associations are explained by increased runoff under higher PI. Therefore, the spatial patterns of the magnitude and sign of the linkage between PI and SM align with the global patterns of PI-runoff interactions. Additionally, the regions where evapotranspiration (ET)–SM correlations are high (>0.5) present higher SM sensitivity to changes in PI. CESM2 exhibits spatial consistency in the PI–SM associations with ERA5, although noticeable differences exist in the magnitudes of the regression coefficients between the two datasets. In general, the PI–SM associations are weaker in CESM2. This disparity is attributed to the different runoff sensitivity to changes in precipitation and PI. CESM2 exhibits reduced runoff sensitivity to PI than ERA5 over the entire globe. This finding implies that how runoff is modeled and constrained in climate models will affect future projections of soil moisture.

How to cite: Kim, W. M., Simpson, I., Deser, C., Lehner, F., and Pendergrass, A.: Links between seasonal precipitation intermittency and soil moisture variability, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14184, https://doi.org/10.5194/egusphere-egu24-14184, 2024.

Land-atmosphere interactions are crucial in both weather and climate extremes. Studies have revealed certain large atmospheric circulation patterns such as amplified circumglobal wave 5 and 7 play important role in generating and maintaining surface extremes. These extremes can occur at the same time but different locations, for example in 2010, the wave 5 pattern was the driver for Russian heatwave and Pakistan flooding. But how soil moisture and land-atmosphere interactions affect the climatology states of jetstreams, amplified waves, and hence persistent extremes still remains unclear.

Here, we employ large ensemble simulations from climate model EC-Earth 3 to study the role of soil moisture in affecting large-scale atmospheric circulation for the period of 2009 to 2016. Three sets of experiments (each set has 100 ensemble members) are carried out with perturbed atmosphere-soil moisture interactions and one reference run (100 members) in which the interaction between the atmosphere and the land is fully interactive. We show that atmosphere-soil moisture interactions strongly influence the climatological mean states of atmospheric circulation in the Northern Hemisphere during the summer season (June to August) and especially in July. With the same soil moisture climatology, the reference run showed an overall land warming that led to poleward migration of jet and a more Arctic front jet state.

 Additionally, West Russia is chosen for the case study area as it is a hotspot for both amplified wave 5 and wave 7 heat extremes. We define the long duration heatwave event as near-surface temperature exceeding 30^oC for at least eight days. The results show that with the soil-atmosphere interaction, the probability of such events increased from 2.2% to 5.8% for wave 5 and 0.47% to 4.5% for wave 7.

How to cite: Luo, F., Selten, F., and Coumou, D.: The role of soil moisture on summer atmospheric circulation climatology and persistent heatwaves in the Northern Hemisphere, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14484, https://doi.org/10.5194/egusphere-egu24-14484, 2024.

Meteorological droughts are increasing in intensity, frequency, and duration due to climate change. These events may have substantial impacts on vegetation productivity that influence the global carbon balance. Effects vary considerably, however, with the intensity of the drought as well as local abiotic and biotic conditions such as vegetation type, soil type, and the timing of the drought. Productivity is primarily reduced because droughts decrease the efficiency with which plants can convert atmospheric CO2 into carbohydrates, largely because of stomatal closure when energy is not limiting. However, another aspect by which droughts can reduce productivity is by shortening the growing season length (GSL). GSL reduction may be particularly pronounced in vegetation communities already sensitive to precipitation variability, in particular, short-rooted grassland and croplands ecosystems. Here, we use evidence from satellite observations of ecosystem activity, meteorological measurements, and data from eddy-covariance flux towers to reveal the impact of several large-scale meteorological droughts on vegetation productivity on natural and managed ecosystems. In particular, we show that the timing of the drought is important, with late droughts being particularly diminishing to productivity. We also demonstrate that while plant physiological responses to drought dominate the reduction in productivity, the diminishment of GSL plays an underappreciated role. These results have wide implications for the future carbon balance under a changing climate, and suggests that ecosystem models could better explain productivity by incorporating the effects of droughts on GSL.

How to cite: Gray, J., Choi, E., Friedl, M., and Griffiths, P.: Drought Changes Growing Season Length and Vegetation Productivity, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14774, https://doi.org/10.5194/egusphere-egu24-14774, 2024.

Ground surface temperature (GST), measured at a depth of around 5 cm below the ground surface, is essential for understanding the climate change impacts in the Earth Critical Zone. Large spatiotemporal variations of GST have been reported in mountain regions due to the heterogeneity of surface cover and topography. This work aims to improve the monitoring of GST using a physical land-surface model driven by satellite-based land surface temperature (LST). In this regard, GST was simulated using the physical GEOtop model at 1500 m elevation in Matsch Valley, north-eastern Italian Alps, from 2014 to 2017 during the phenological cycle, between April and October. The model was forced only by the LST derived from the Terra MODerate resolution Imaging Spectroradiometer (MODIS). The 1-km MODIS LST was first downscaled to a finer spatial resolution of 250-m using data-driven sharpening from random forest algorithm. The simulated GSTs correlate well with the in-situ observations with a Pearson correlation of 0.88 and a coefficient of determination of 0.77. However, the model overestimated the GST for the whole period with a mean bias of 8.72 °C. These overestimations are similar to the differences between in-situ GST and MODIS LST which range from 4.8 to 19 °C with an average of 8.5 °C. They are mainly caused by the low temporal resolution of LST data with only one observation per day which is additionally limited by frequent cloud cover contamination and the low spatial resolution of the MODIS thermal channels. Modelling the damping of the LST signal in the first centimeters of soil to simulate GST in very heterogeneous areas like alpine pastures is still challenging. This is mainly due to the resolution mismatch between ground and remote sensing observations and the poor knowledge of soil and vegetation properties needed to parametrize physical models.

How to cite: Șerban, R.-D., Bertoldi, G., Bartkowiak, P., Castelli, M., and Andreoli, A.: Challenges in simulating ground surface temperature based on remote sensing land surface temperature over mountain grasslands, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15546, https://doi.org/10.5194/egusphere-egu24-15546, 2024.

Climate change causes a global rise in mean air temperature and increased frequency of temperature extremes. Recent studies link sharp temperature changes between consecutive days to increased mortality, reduced economic growth, and negative effects on ecosystems. While climatological analyses predominantly focus on mean temperatures, extreme temperatures have higher impacts on human health. This study assesses the variability of the daily maximum air temperature between two consecutive days (i.e., volatility) across Germany from 1990 to 2022. We used observation-based raster data of the maximum daily temperature assessed volatility regarding: 1) magnitude, 2) seasonality, 3) the direction of temperature change, and 4) trends during the entire period. As changes of land use and land cover have a direct impact on local temperatures, we analysed the land cover changes during the same period and examine its correlation to extreme volatilities.

The results showed a higher magnitude of rapid temperature decreases compared to temperature increases. Extreme volatilities increased with further distance to the coast from north of Germany to south. Overall, abrupt day-to-day temperature changes occurred mostly during the warming half-year (from March to August). During the study period, significant trends of 0.5 °C and 0.2 °C per decade showed a widening range of extreme volatility in spring and autumn. Compared to unchanged areas, changing land cover was predominantly liked to increasing volatilities of up to 0.5 °C. Understanding rapid temperature changes is crucial for climate change mitigation strategies and limiting impacts on human health and on the environment.

How to cite: Jordan, E., Shekhar, A., and Gharun, M.: Assessing extreme temperature volatilities across Germany between 1990 and 2022, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16559, https://doi.org/10.5194/egusphere-egu24-16559, 2024.

In the context of the European Green Deal framework, understanding the intricate and varied impacts of afforestation and deforestation across different regions is paramount. A complex interplay of environmental factors shapes the resulting climate effects. Evaluating these impacts and their spatial variability is crucial for formulating effective and context-specific climate mitigation and adaptation strategies.

This study takes a comprehensive approach, investigating both local and non-local effects of afforestation and deforestation within Europe, with a specific emphasis on the radiative budget and temperature dynamics.  Utilizing the cutting-edge Regional Climate Model (RegCM5) in conjunction with the Community Land Model version 4.5 (CLM4.5), we conducted simulations at a fine-scale, convective-permitting resolution of 5 km. This granular approach allows for an in-depth understanding of climate dynamics, shedding light on the distinct climate responses to forest cover alterations at various locations.

We conducted three simulations spanning the period 2004-2014: a control run and two scenarios involving afforestation and deforestation.  We concentrated on analyzing climatic changes through variables such as land surface temperature, near-surface air temperature, and the energy fluxes at the Earth's surface and the top of the atmosphere (TOA). Results suggest that afforestation/deforestation can yield substantial impacts on the climate system. It underscores the critical importance of evaluating biophysical effects at a high resolution, emphasizing the need to incorporate such considerations into climate change mitigation strategies.

Recognizing the location-dependent nature of afforestation and deforestation climate impacts, combined with the capabilities of advanced modeling tools, underscores the importance of flexible and adaptable land use planning. The practical implications of our findings extend to policymaking, offering insights that can inform sustainable land use decisions. These insights can guide the formulation of resilient and sustainable land use policies, aligning with the ambitious objectives of the European Green Deal.

How to cite: Caporaso, L., Duveiller, G., Piccardo, M., Massaro, E., Roebroek, C., Migliavacca, M., and Cescatti, A.: Investigating the Climate Impacts of Afforestation and Deforestation in Europe via 5 km climate model simulations, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17393, https://doi.org/10.5194/egusphere-egu24-17393, 2024.

Accurate soil moisture representation is crucial in climate modeling, due to its significant role in land-atmosphere interactions. Our study focuses on water storage dynamics and analyzes how soil moisture limitation is represented in simulations from the land component (land-hist experiment) of seven models within the Coupled Model Intercomparison Project phase 6 (CMIP6). We quantified the annual maximum depletion in soil moisture, contrasting model results with observations of terrestrial water storage from the Gravity Recovery and Climate Experiment (GRACE). Our analysis shows that CMIP6 models mostly underestimate these annual extremes in soil moisture reductions, with the Amazon consistently emerging as the most biased region. We further computed the critical soil moisture thresholds and quantified the frequency of soil moisture limitation in CMIP6 simulations, comparing model estimates against solar-induced fluorescence (SIF) and GRACE observations. We found consistent results with the annual maximum depletion in soil moisture, with models almost always overestimating the frequency of soil moisture limitation globally compared to observations. We validated our findings with data from 128 eddy-covariance sites from eight biomes worldwide. Our study illuminates the biases in soil moisture storage and dynamics between CMIP6 models and empirical observations, highlighting the importance of improving the representations of soil moisture and land-atmosphere interactions in Earth System Models.

How to cite: Giardina, F., Padrón, R. S., Stocker, B. D., Schumacher, D. L., and Seneviratne, S. I.: Large biases in soil moisture limitation across CMIP6 models, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17662, https://doi.org/10.5194/egusphere-egu24-17662, 2024.

Soil moisture is recognized as an Essential Climate Variable (ECV), because it is crucial to assess water availability for plants and hence food production. Having long time series of freely available and interoperable soil moisture data with global coverage enables scientists, practitioners (like farmers) and decision makers to detect trends, assess the impacts of climate change and develop adaptation strategies.

The collection, harmonization and archiving of in situ soil moisture data was the motivation to establish the International Soil Moisture Network (ISMN) at the Vienna University of Technology in 2009 as a community effort. Based on several project funding periods by the European Space Agency (ESA), the ISMN became an essential means for validating and improving global land surface satellite products, climate and hydrological models. In December 2022, the ISMN was transferred to a new hosting facility the International Centre for Water Resources and Global Change (ICWRGC) and the German Federal Institute of Hydrology (BfG) in Koblenz (Germany). ISMN data are successfully provided from the new host since then and will be for decades to come as the German government committed to its long-term funding.

This presentation is going to showcase the International Soil Moisture Network (ISMN). Beyond offering comprehensive in situ soil moisture data, ISMN freely disseminates additional environmental variables, including soil temperature, snow depth, snow water equivalent, precipitation, air temperature, surface temperature and soil water potential if they are available from our data providers. With a global reach, ISMN has already accumulated 3000 stations with observations at various depths, while about 1000 stations are updated on a daily basis. Ongoing efforts are concentrated on expanding the database by incorporating additional stations and networks from institutional or governmental sources. Substantial resources are directed towards fortifying the operational system and improve usability to better serve our users. Additional efforts are undertaken to include ISMN in the data-to-value chain by contributing to international initiatives like WMO, FAO and GCOS. One example is the contribution to WMO’s yearly Global State of the Water Resources report.

How to cite: Zink, M., Boehmer, F., Korres, W., Kramer, K., Dietrich, S., and Olarinoye, T.: The International Soil Moisture Network (ISMN): providing a permanent service for earth system sciences, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17860, https://doi.org/10.5194/egusphere-egu24-17860, 2024.

The occurrence of droughts is ruled by the interplay of complex processes with very different natures and spatio-temporal scales. Different modes of climate variability, like the North Atlantic Oscillation or ENSO (El Niño-Southern Oscillation), set the prevalence of distinct weather regimes providing sources of predictability at large-scale. On the other hand,  land-atmosphere feedbacks play a crucial role in climate extremes, and particularly, in the evolution and amplification of droughts. However, the weak predictability of the former large-scale variability in the extratropics together with the poor representation of these feedbacks in current seasonal predictive systems lead to a limited capability of predicting droughts months in advance. In this study (part of the AI4Drought project, funded by ESA), we aim to enhance summer drought prediction in Europe from spring conditions by the combination of state-of-the-art climate simulations and remote sensing.

A hybrid model combining climate simulations and high-resolution remote sensing data is proposed to boost the predictability signal at seasonal time-scale through the integration of two machine learning (ML) models. The first model (model-A) enhances large-scale predictability. It consists of a generative model (conditional variational auto-encoder, based on Pan et al., 2022), which is trained with 10.000s years of CMIP6 climate simulations to empirically learn the probability distributions between global spring fields; e.g., sea surface temperatures and 500 hPa geopotential height; and summer drought conditions (SPEI3). A local-scale model for extremes amplification is developed (model-B). A pixel-based (multi-layer neural network) model aims to capture land-atmosphere feedbacks; integrating local conditions from satellite-based products and reanalysis data, e.g. soil moisture (SM), temperatures and NDVI together with information from the large-scale predictions from model-A in order to predict SM anomalies for the whole summer season.

Preliminary results highlight the significance of local conditions in enhancing drought predictions, particularly in the Mediterranean region, where land-atmosphere feedbacks are pronounced. Experiments conducted under ideal conditions, knowing the future large-scale conditions in advance, demonstrate improved prediction skill when local conditions (e.g., soil moisture, NDVI) are included as predictors.

Moreover, a DeepSHAP analysis (eXplainableAI-based method) is performed to understand which are the most important drivers for the local-scale model prediction of summer SM anomalies. As expected, the spring’s SM anomalies are the most important input features; together with the large-scale conditions described by August SPEI-3. Additionally, temperature anomalies have a relatively high importance when predicting summer drought conditions.

This research underscores the potential of a hybrid approach integrating climate simulations and remote sensing data to advance the understanding and prediction of summer droughts in Europe.

How to cite: Civantos Prieto, D., Peña-Izquierdo, J., Palma, L., Donat, M., Vilella, G., Tufis, M., Nandi, A., Escorihuela, M. J., and Romero, L.: Summer Drought Prediction in Europe combining Climate Simulations and Remote Sensing, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18231, https://doi.org/10.5194/egusphere-egu24-18231, 2024.

Ambient humidity reduces the ability of the body to cool down through sweating, adding to the heat 
stress caused by elevated air temperature alone. Indeed, humid heat waves (HHWs) are already a threat
for humans, livestock and wildlife, and their impacts are projected to increase with global warming.
HHWs result from the combination of thermodynamic and dynamic processes interacting on a range of 
time and space scales and whose relative importance may vary according to location and time of year.

Africa is one continent where HHWs, defined here as extremes of wet-bulb temperature (Twb), are 
expected to become more important under global warming. Local-scale humid heat extremes may occur 
within more moderate larger-scale events across much of the continent. Yet, climatological 
characteristics of these smaller-scale events such as location and timing (in year and day) are poorly 
documented in the current climate, due to a lack of high-resolution data and research focus. Moreover, 
a comprehensive understanding of their meso- to synoptic-scale drivers is still lacking. Here, we explore 
these two issues using a 10-year pan-African convection-permitting model simulation that explicitly 
resolves land-atmosphere interactions, and particularly those involving moist processes that are 
instrumental to HHWs.

We find humid heat extremes in semi-arid regions occurring in the core of the rainy season, on length 
scales down to a few tens of kilometers. During HHWs, Twb peaks several hours 
later than the climatological peak in the late morning. This diurnal cycle shift is likely due to HHWs 
typically developing in the aftermath of a rainfall event: the resulting positive anomaly in soil moisture 
induces increased latent heat fluxes, low level divergence, and a reduced PBL height, all ingredients
displaying sharp spatial gradients conducive to locally high Twb values. These results have implications 
for the improvement of localized HHW predictability based on local soil moisture conditions, a key step 
towards climate change adaptation through e.g., early-warning systems.

How to cite: Chagnaud, G., Taylor, C., Birch, C., Jackson, L., Marsham, J., and Klein, C.: Exploring the influence of land-atmosphere interactions on humid heat extremes in a convection permitting model simulation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19526, https://doi.org/10.5194/egusphere-egu24-19526, 2024.

Deforestation impacts in the Congo Basin remain significantly understudied compared to other tropical regions. The main driver of Congo Basin deforestation is small-scale industrial agriculture, which leads to the formation of the rural complex; a mosaic patch of deforested land comprising small fields at different stages of regrowth being deforested repeatedly. Transition from primary forest to rural complex may induce lesser changes in albedo, Bowen ratio, and surface roughness than primary forest to cropland, suggesting the impacts of deforestation on temperatures in the Congo Basin will differ from those in other rainforest regions. The Basin's long-term warming trend and possible ongoing drying could exacerbate warming due to deforestation. It is therefore essential that we understand how the specific nature of deforestation in the Congo Basin influences temperatures, and how this is affected by changes in the large-scale conditions driven by global climate change.

In this study, we used MODIS satellite data for LST and EVI, CHIRPS2 for rainfall, and the Global Forest Change dataset for deforestation analysis from 2000 to 2019 to assess how observed deforestation is affecting LST in the Congo Basin and how the deforestation-induced warming varies with climate anomalies, LST and rainfall (SPI), and Δ EVI (deforested EVI – surrounding forest EVI). Due to limited data availability, caused by the prevalence of cloud cover throughout much of the year, our focus narrowed to the most data-consistent dry season (DJF), where land-atmosphere interactions are also likely to be strongest.

We found a linear relationship between cumulative deforestation and warming over deforested land, which varied in intensity by month. A typical 1 km rural complex pixel within the region will warm by +0.33 °C in December, +0.85 °C in January, and +1.54 °C in February, relative to the surrounding forest. We then assessed the cause of the strong seasonal differences by looking at the deforestation-induced warming as a factor of the climate anomalies and Δ EVI. The amount of warming of a typical 1 km rural complex pixel did not show a relationship with the LST anomaly or SPI for the individual months. However, when considering all months collectively, a correlation emerged with the LST anomaly, suggesting a seasonal evolution where the LST anomaly acts as a proxy. We then found a link between the warming of a typical 1 km rural complex pixel and Δ EVI which is present for each month; this partially explains the interannual variability of the results, but it doesn’t explain the seasonal evolution. Comprehensive and high-quality observations are needed over the Congo Basin to fully untangle these relationships. Accurate soil moisture data could be crucial in understanding the pronounced seasonal differences in warming. These findings suggest that even though the rural complex differs from cropland, and might be expected to have a smaller impact, the additional warming can still be substantial (+1.54 °C), although it has a strong seasonal dependency.

How to cite: Adams, C. and Garcia-Carreras, L.: Seasonal Variability of Deforestation-Induced Warming in the Congo Basin Using Remote-Sensing Data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13484, https://doi.org/10.5194/egusphere-egu24-13484, 2024.

We have been developing Integrated Land Simulator as a land model for the next generation of the MIROC climate model. Using a general-purpose coupler, ILS couples various land component models with minimum modifications and makes a land model independent from the atmospheric model. The major changes from the previous version of the land model in MIROC6 are the method of coupling land and atmosphere, the independent grid system and spatial resolution for the land model, and the river model. In MIROC6, the land model was part of the physical process of the atmospheric model and was run sequentially, but in the new model (MIROC-ILS), the land and atmospheric models are run in parallel. We have confirmed the MIROC-ILS meets the requirements such as water balance closure and computation time. In the presentation, we will show how the changes of land-atmosphere coupling method and coupling frequency affects the simulated atmosphere field.

How to cite: Nitta, T., Arakawa, T., Takeshima, A., Yamazaki, D., and Yoshimura, K.: Development of a land model for the next generation MIROC climate model and evaluation of its simulated land-atmosphere coupling, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19126, https://doi.org/10.5194/egusphere-egu24-19126, 2024.