Leaves are the main interface between terrestrial ecosystems and the atmosphere. They govern the exchange of carbon, water and energy between vegetation and the atmospheric boundary layer. They are the surface designed to capture light and transform it to sugars via photosynthesis, but they also regulate how much water they transpire through their stomata. Their colour, density and orientation will affect their albedo, which determines how much energy is reflected back to the atmosphere, while their overall configuration within the canopy structure can affect the roughness length of the surface.

When we manage landscapes, be it by planting crops or cutting down forests, we are typically changing the quantity and type of leaves covering the surface of the land. By doing so, we can modify the land-atmosphere interactions and thereby have an effect on the climate. For instance, a substantial local cooling effect could be attained by using cover crops in winter, especially with highly reflective chlorophyll deficient mutants. Increasing forest cover appears to lead to more cloud cover, which itself could affect albedo at the top of the atmosphere. But the amount of leaves in the landscape can further affect extremes.

Here I will illustrate how leaves affect land-atmosphere interactions in the context of extreme events with two studies. The first study looks at the known biophysical effect of land use change on local surface temperature, but extends it to explore its sensitivity across the globe during the extremes observed in 20 years of satellite remote sensing records. The second study shows how much getting leaves right matters within the reanalysis records of ERA5 and ERA5-Land, where prescribed seasonal cycles of leaf area index (LAI) lead to biases in modelling land surface temperature (LST), thereby underestimating the intensity of heat waves over Europe.

How to cite: Duveiller, G.: Leaves, land-atmosphere interactions and extremes, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9777, https://doi.org/10.5194/egusphere-egu23-9777, 2023.

The land surface is a key source of predictability for forecasts at the subseasonal-to-seasonal (S2S; 2 weeks to 2 months) timescale, since variables such as root zone soil moisture and leaf area vary more slowly than the atmospheric state. Previous work has mostly focused on the predictability gained from realistic soil moisture initialisations. Considering observable land surface variables, vegetation shows more persistent changes than surface soil moisture following subseasonal rainfall events, and therefore has the potential to provide predictability at longer lead times. We therefore perform the first investigation of vegetation feedbacks onto near-surface air temperatures using global daily data, to ascertain in which regions and seasons these feedbacks can provide S2S predictability. We use daily datasets of Vegetation Optical Depth (VOD, from the VODCA X-band product) and 2m temperature (from ERA5) at 0.25° horizontal resolution, and compute lagged correlations to identify where spatial structures in VOD anomalies are associated with similar structure in 2m temperature anomalies. Using daily data allows us to investigate how the correlations decay as a function of lead time within the S2S timescale. At zero lag, water-limited regions exhibit negative correlations, indicating that an increase in vegetation water content is associated with increased evapotranspiration and reduced sensible heat, leading to cooler near-surface air temperatures. We find extensive regions in the semi-arid tropics and sub-tropics where at certain times of year VOD anomaly patterns are anti-correlated with temperature patterns 2 weeks ahead. These periods tend to occur outside of the wettest time of year. In some regions, e.g. southern Africa in MAM,  predictability of temperature from VOD anomalies extends to lags of 30 days, suggesting that incorporating vegetation variability can improve S2S forecasting. We develop a model for the strength and persistence of vegetation feedbacks to near-surface temperatures based on seasonal cycles of rainfall and vegetation.

How to cite: Taylor, C. and Harris, B.: Global observations highlight regions where vegetation can enhance S2S predictability, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15403, https://doi.org/10.5194/egusphere-egu23-15403, 2023.

Hot temperature extremes are changing in intensity and frequency. Quantifying these changes is key for developing adaptation and mitigation strategies. The conventional approach to study changes in hot extremes is based on air temperatures. However, many biogeochemical processes, i.e. decomposition of organic material and release of CO2, are triggered by soil temperature and it remains unclear whether it changes as does air temperature. Here, we demonstrate that soil hot extremes are intensifying and becoming even more frequent faster than air hot extremes over central eastern and western Europe. Based on existing model simulations, we also show that the increase in hot soil extremes could amplify or spread future heat waves by releasing sensible heat during hot days. We find an increase of 3 (7) % in the number of hot days with a contribution of heat from the soil under a warming level of 2.0 (3.0) °C than under a warming level of 1.5 °C. Furthermore, defining intensity and frequency extreme indices based on soil and air temperatures leads to a difference of more than 1 °C in intensity and 10% in frequency regionally during the last decades of the 21st century under the SPP5 8.5 emission scenario. In light of these results, maximum soil temperatures should be included in ecological risk studies as a complementary perspective to the conventional approach using extreme indices based on air temperatures.

How to cite: García-García, A., Cuesta-Valero, F. J., Miralles, D. G., Mahecha, M. D., Quaas, J., Reichstein, M., Zscheischler, J., and Peng, J.: Soil Hot Extremes are Increasing Faster than Air Hot Extremes Regionally, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5961, https://doi.org/10.5194/egusphere-egu23-5961, 2023.

Terrestrial vegetation is largely mediated by vegetation-climate coupling. Growing conditions control vegetation growth, which in turn feeds back to climate through changes in biophysical and biogeochemical properties and processes, such as canopy structure and carbon and water exchanges. The vegetation-climate coupling is thus highly variable in space and time. However, little is known on how the large-scale vegetation-climate coupling varies within growing season, and how vegetation responds to climate extremes. In this contribution, we present some recent findings on seasonal and intra-seasonal vegetation-climate coupling and vegetation sensitivity to droughts using multiple remote sensing products including MODIS EVI, GIMMS3g NDVI and VIP EVI2. We account for the differences in phenological stages of growing seasons affected by both climate and landscape heterogeneity. Based on a novel analytical framework incorporating meteorological and vegetation conditions to locally defined vegetation growing seasons, we analyse vegetation-climate couplings using both local climate conditions and teleconnection indices (e.g., Jet Latitude Index). In addition, vegetation sensitivity to droughts and post-drought vegetation changes are assessed. Our results highlight the importance of considering vegetation phenology in understanding sub-seasonal land-atmosphere interaction and vegetation dynamics. The developed analytical framework is suggested to be an effective approach for evaluating vegetation and climate dynamics simulated by Earth System Models.

How to cite: Wu, M., Messori, G., Vico, G., Manzoni, S., Cai, Z., Tang, J., Tagesson, T., and Duan, Z.: Vegetation-climate coupling and vegetation sensitivity to climate extremes in growing seasons, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9838, https://doi.org/10.5194/egusphere-egu23-9838, 2023.

Transpiration (T), the component of evaporation (E) controlled by vegetation, dominates terrestrial Evaporation, but measurements are highly uncertain. In the light of the importance of evaporation for studying the terrestrial water cycle, hydro-climatic extremes such as droughts and heatwaves and land-atmospheric interactions, there is a strong demand on novel approaches to reliably estimate T. Currently available approaches to estimate T mostly rely on its relationship with photosynthesis, but parameterizing this relationship is difficult and estimates of T strongly disagree among each other in terms of magnitude. Moreover, in-situ measurements are scarce and and evaporation cannot be measured directly from space.

We developed a hybrid Priestley-Taylor (PT) model using Deep Learning to learn the relationship between T and state variables such as soil moisture, vapor pressure deficit and the fraction of photosynthetic active radiation for different plant functional types (PFTs). We use globally available variables from reanalysis and remote sensing data as forcing to train an artificial neural network on the PT-coefficient α obtained by inverting the PT model on sap flow based ecosystem T. In this way, we can predict Transpiration at local scales independently from hard-to-obtain fluxes like E or vegetation parameters such as stomatal conductance. We evaluate our algorithm against T estimates from flux partitioning methods based on water use efficiency at eddy covariance sites for different PFTs and regions. Also, we compare our estimates with other available products of transpiration like GLEAM, PML-V2 and ERA5-Land. Preliminary results of this research showed that the developed model can learn the relationship between T and few influencing variables, without incorporating variables such as net radiation or GPP. Our findings contribute to dissolving the scarcity of T estimates in forest ecosystems based on actual observations. Future work is needed to apply our method to the larger scale for studying spatial patterns of T, e.g. across the European continent.

How to cite: Hannemann, M., García-García, A., and Peng, J.: Transpiration in forest ecosystems based on deep learning and sap flow observations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14104, https://doi.org/10.5194/egusphere-egu23-14104, 2023.

Climate model predictions of land hydroclimate changes show large geographic heterogeneity, and differences between models are large. We introduce a new process-oriented phase space that reduces the dimensionality of the problem but preserves (and emphasizes) the mechanistic relations between variables. This transform from geographical space to climatological aridity index (AI) and daily soil moisture (SM) percentiles allows for interpretation of local, daily mechanistic relations between the key hydroclimatic variables in the context of time-mean and/or global-mean energetic constraints and the wet-get-wetter/dry-get-drier paradigm. Focusing on the tropics (30S-30N), we show that simulations from 16 different CMIP models exhibit coherent patterns of change in the AI/SM phase space that are aligned with the established soil-moisture/evapotranspiration regimes. Results indicate the need to introduce an active-rain regime as a special case of the energy-limited regime. In response to CO₂-induced warming, rainfall only increases in this regime, and this temporal rainfall repartitioning is reflected in an overall decrease in soil moisture. Consequently, the regimes where SM constrains evapotranspiration become more frequently occupied, and hydroclimatic changes align with the position of the critical soil moisture value in the AI/SM phase space. Analysis of land hydroclimate changes in CMIP6 historical simulations in the AI/SM phase space reveal the very different impact of CO₂ forcing and aerosol forcing. CESM2 Single Forcing Large Ensemble Experiments are used to understand their roles.

How to cite: Duan, S., Findell, K., and Fueglistaler, S.: Mechanistic patterns of land hydroclimate changes in a changing climate, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10118, https://doi.org/10.5194/egusphere-egu23-10118, 2023.

The response of vegetation canopy conductance (g_c) to changes in moisture availability (ϒ_gc) during drought is a major source of uncertainty in climate projections. Representing ϒ_gc accurately in Earth System Models (ESMs) is particularly problematic because no regional scale g_c observations exist with which to evaluate it. Here, we overcome this challenge by deriving an emergent constraint on ϒ_gc across ESMs from Phase 6 of the Coupled Model Intercomparison Project (CMIP6). We leverage an ensemble of satellite, reanalysis and station-based estimates of surface temperatures, which are physically and statistically linked to ϒ_gc due to the local cooling effect of g_c. We find that models systemically underestimate ϒ_gc by ~50%, particularly in semi-arid grasslands, croplands, and savannas. Based on the mediating effect of g_c on carbon, water and energy fluxes through land-atmosphere interactions, the underestimation of modeled ϒ_gc in these regions contributes to biases in temperature, transpiration and gross primary production. Our results provide a novel benchmark to improve model representation of vegetation dynamics and land-atmosphere feedbacks in these regions, thus improving forecasting ability of climate extremes under future climate change scenarios.

How to cite: Green, J. K., Zhang, Y., Luo, X., and Keenan, T.: An emergent constraint exposes widespread underestimation of drought impacts by Earth System Models, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9421, https://doi.org/10.5194/egusphere-egu23-9421, 2023.

This work presents the first observationally-based study over subtropical South America linking the spatial location of convection and drier soil patches of the order of tens of kilometers, as well as observational evidence of the control of background flow on the sign of SM-PPT feedbacks at convective scales. Using satellite data from multiple infrared and microwave radiometers, we track nascent, daytime convective clouds over subtropical South America and quantify the underlying, antecedent (morning), SM heterogeneity. We find that convection initiates preferentially on the dry side of strong dry-wet SM boundaries that are associated with spatially drier and warmer patches of tens of kilometers scale consistent with findings in other parts of the world. This preference maximizes during weak background low-level wind, high convective available potential energy, low convective inhibition and low vegetation density when analyzing surface gradients of 30 km length scale. On the other hand, surface gradients of 100 km length scale are significantly associated with afternoon convection during convectively unfavorable synoptic conditions and strong background flow, unlike previous studies. The location of the precipitation maxima following CI onset is most sensitive to the lower tropospheric background flow at the time of CI. The wind profile during weak background flow does not support propagation of convective features away from the dry regions and rainfall accumulates over the dry patch. Convection during strong background flow leads to greater rainfall hundreds of kilometers away from the CI location. 

How to cite: Dominguez, F., Chug, D., Taylor, C., Klein, C., and Nesbitt, S.: Mesoscale Gradients in Soil Moisture over South America Lead to Enhanced Convection, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11538, https://doi.org/10.5194/egusphere-egu23-11538, 2023.

The land surface supplies heat and moisture to the atmosphere influencing the regional climate during the convective season. Availability of soil moisture for evapotranspiration, vegetation phenology and atmospheric conditions influence the strength of the land surface impact on the atmosphere, and the mechanisms predominating the heat and moisture exchange. As both the synoptic conditions as well as the vegetation state vary on sub-seasonal to interannual time scales, the strength of land-atmosphere (L-A) interaction is expected to fluctuate on these time scales.

Up to now, research typically either focuses on case studies to understand the mechanisms of how land surface and atmosphere interact, or on climatic time scales to quantify co-variances in the climate system based on a sufficient sample size. Timescales in between remain rarely considered in land-atmosphere feedback studies.

In our study, we applied various L-A coupling measures to evaluate land surface impacts on the atmosphere and quantify interactions associated with the triggering of convective precipitation and droughts for all summers between 1991 and 2022 over Europe based on ERA5 data.

Our results highlight that differently strong L-A interactions evolve in dependence of atmospheric wetness, temperature, and the circulation pattern, as well as the root zone soil moisture and vegetation cover. Under warm and dry conditions such as in 2003, 2018 and 2022, soil moisture availability imposed limits for evapotranspiration not only in Southern Europe, but also in Central and Eastern Europe, interfering with vegetation growth and atmospheric moisture supply. Limited moisture and excessive heat supply amplified the already high temperatures and low near-surface moisture, which finally aggravated the unfavorable conditions for local precipitation and caused extreme drought conditions. On the contrary, warm and wet conditions such as in 2021 provided well-suited conditions for vegetation growth, which enhanced the moisture supply to the atmosphere. Together with stronger atmospheric instability, this provided more favorable preconditions for convective precipitation. Generally, most L-A interactions perform as an intensifier of persisting anomalies, particularly under warm and dry atmospheric conditions over Europe.

How to cite: Jach, L., Schwitalla, T., Wulfmeyer, V., and Warrach-Sagi, K.: Interannual Variation of Land-Atmosphere Interactions and their Connection with Extremes over Europe, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12925, https://doi.org/10.5194/egusphere-egu23-12925, 2023.

China has shown a world-leading vegetation greening trend since 2000, which may exert biophysical effects on near-surface air temperature (SAT). However, such effects remain largely unknown because prior studies either focus on land surface temperature, which differs from SAT, or rely on simulations, which are limited by model uncertainties. As a widely used metric in climate and extremes research, SAT is more relevant to human health and terrestrial ecosystem functions. Therefore, it is necessary to explore impacts of greening on SAT and extremes based on observations. Here, we investigate the greening effects on SAT and subsequent extremes over 2003–2014 in China based on high-resolution SAT observations combined with satellite datasets. We find that greening can cause cooling effects on the mean SAT and more pronounced cooling effects on SAT extremes over semiarid regions. Such cooling effects are attributed to enhanced evapotranspiration caused by greening and strong coupling between evapotranspiration and SAT in semiarid regions. Semiarid regions in China are the transitional zone of both climate and ecosystem and deeply influenced by human agricultural and pastoral activities. These factors make the ecosystem of these regions fragile and extremely vulnerable to climate change. Our results reveal a considerable climate benefit of greening to natural and human systems in semiarid regions, and have significant implications for on-going revegetation programs implemented in these regions of China.

How to cite: Cao, Y., Guo, W., Ge, J., Liu, Y., Chen, C., Luo, X., and Yang, L.: Greening vegetation alleviates hot extremes in the semiarid region of China, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3780, https://doi.org/10.5194/egusphere-egu23-3780, 2023.

Numerous cyclones develop in the Bay of Bengal during the pre-monsoon and post-monsoon seasons. The heavy rain associated with these cyclones causes devastating damage to life and property during landfall. The modern numerical weather prediction models and high temporal satellite observation data have significantly increased the accuracy of cyclone prediction in recent years. However, accurately predicting rainfall intensity and its dissipation after landfall is still challenging. Previous studies have indicated that land-based evapotranspiration plays an essential role in determining the intensity and decay of cyclones post-landfall. In this study, we quantify the contribution of land-based evapotranspiration to the rainfall associated with cyclones and the impact of land conditions on the speed and track of cyclones originating in the Bay of Bengal. For this purpose, we employed the Weather Research Forecasting (WRF) model upgraded with Eulerian water tagging capabilities to track evapotranspiration from land. The tagging model will tag the evapotranspiration originating on land and track it throughout the atmosphere till it precipitates or moves out of the domain. We simulated six cyclones of varying intensities, with three during the pre and three during the post-monsoon seasons. We conducted sensitivity experiments with dry and wet initial soil moisture conditions to determine the impact of perturbed soil moisture on TC. To account for the model's internal variability, we simulated an ensemble with four members for the control simulation. The ensemble is created by changing each member's model initialization time by six hours. This ensemble helped identify the magnitude of the model's internal variability, which was less than the variability due to soil moisture changes. The study revealed that soil moisture conditions prior to TC formation have an impact on its evolution. By analyzing the latent heat, temperature, and wind pattern, we found that the initial soil moisture during the pre and post-monsoon seasons alters the synoptic features over the Indian subcontinent, resulting in variations in the TC evolution. The relatively low-intensity TC tracks are more sensitive to the initial soil moisture conditions. The rainfall originating from land-based evapotranspiration is more significant as the cyclone approaches land. Therefore, land-based evapotranspiration plays a crucial role in the end phase of the cyclone (from just before landfall till its decay). For post-monsoon cyclones, the rainfall from land-based evapotranspiration is as high as 20% to 30% after landfall, whereas, for pre-monsoon cyclones, the land contribution is around 5% to 10%. In addition to soil moisture, factors such as proximity to land, track length over land, and TC intensity also have a role in determining the quantity of precipitation originating from the land for a TC.

How to cite: Lanka, K. and Navale, A.: Influence of Soil Moisture on the Evolution of Landfalling Tropical Cyclones during pre and post-monsoon seasons, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15299, https://doi.org/10.5194/egusphere-egu23-15299, 2023.

Heavy precipitation (HP) events can be preceded by moist heatwaves (HWs; i.e., hot and humid weather), and both can be intensified by urbanization. However, the effect of moist HWs on increasing urban HP remains unknown. Based on statistical analyses of daily weather observations and ERA5 reanalysis data, we investigate the effect of moist HWs on urban-intensified HP by dividing summer HP events into NoHW- and HW-preceded events in the Yangtze River delta (YRD) urban agglomeration of China. During the period 1961–2019, the YRD has experienced more frequent, longer-lasting, and stronger intense HP events in the summer season (i.e., June–August), and urbanization has contributed to these increases (by 22.66%–37.50%). In contrast, urban effects on HP are almost absent if we remove HW-preceded HP events from all HP events. Our results show that urbanization-induced increases in HP are associated with, and magnified by, moist HWs in urban areas of the YRD region. Moist HWs are conducive to an unstable atmosphere and stormy weather, and they also enhance urban heat island intensity, driving increases in HP over urban areas.

How to cite: Gu, X., Li, C., and Slater, L.: Urbanization-Induced Increases in Heavy Precipitation are Magnified by Moist Heatwaves in an Urban Agglomeration of East China, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1814, https://doi.org/10.5194/egusphere-egu23-1814, 2023.

Plant roots act as critical pathways of moisture from subsurface sources to the atmosphere. Moreover, deep plant roots allow vegetation to meet water demand during seasonally dry periods by taking up moisture from accessible groundwater. This is an important resilience mechanism in the Amazon, a hydrologically and ecologically significant region. However, most regional land-atmosphere computational models do not adequately capture the link between deep roots and groundwater. This study details the implementation of a dynamic rooting scheme in the Noah-Multiparameterization (Noah-MP) land surface model, a widely used tool for studying the exchange of energy and moisture between the land and atmosphere. The rooting scheme is a first-order representation of dynamic rooting depth based on the soil water profile and includes quantification of deep root water uptake (RWU). The scheme is easily scalable and ideal for regional or continental-scale climate simulations. It is used in conjunction with a groundwater scheme which captures high-resolution spatial groundwater variations, allowing us to capture the critical link between deep roots and groundwater. We perform 10-year simulations with and without the root scheme for a test region in the Amazon to validate the enhanced model. We analyze time series of soil moisture, RWU, and evapotranspiration for points with differing vegetation cover and elevation. This allows us to demonstrate functionality of the root scheme and ensure it behaves properly for varying conditions. Representation of deep RWU is critical for realistic simulation of the soil-plant-atmosphere system. As the land surface is an important component of atmospheric predictability, inclusion of deep RWU can contribute to improved prediction of atmospheric variables such as precipitation.

How to cite: Bieri, C., Dominguez, F., Miguez-Macho, G., and Fan, Y.: Exploring deep root water uptake, soil moisture, and land surface fluxes in the Amazon, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16444, https://doi.org/10.5194/egusphere-egu23-16444, 2023.

Summer 2022 has been the second hottest summer after 2003 in France since 1900, with 33 cumulative days of heatwaves. It has also been one of the 10 driest summers in France since 1959. The average precipitation deficit reached 20% compared to the 1991-2020 period, exceeding 60% in some regions, even though June 2022 broke the monthly record of storm occurrences.

These extreme climate conditions led to water restrictions and fostered the development of many wildfires. In particular, so called “megafires” burnt more than 28,000 hectares of the Landes forest in the Nouvelle-Aquitaine region, in the South-West of France.

Starting from the 18th century, this swampy region has been dried out by planting maritime pines and digging ditches to drain away excess water. Due to recent events, these land management practices are questioned : the record-breaking soil dryness of summer 2022 enabled fire to propagate underground and resurface further away, making firemen’s work extremely difficult.

By controlling ditch drainage, is it possible to reduce soil dryness and thus fire risk in summer, as well as mitigate heavy precipitation impacts in this flood prone area ? To answer this question, this work first aims at characterizing and interpreting local climate evolution during the last decades, in terms of trends, changes in the seasonal cycle and extreme events, using  ERA 5 reanalysis, the E-Obs dataset, and MODIS satellite observations. CORDEX regional climate projections are also analysed. Nouvelle-Aquitaine will experience both more frequent and intense heatwaves and droughts and an increase in heavy precipitations. Landes forest management thus has to be adapted.

The perspective of this work is to develop a conceptual ditch drainage model and quantify the drought and flood risk reduction potential using storylines based on plausible short and long term climate conditions in Nouvelle-Aquitaine.

In a broader perspective, the objective of this work is to develop a methodology replicable in other regions of the world to analyse the impacts of climate change at a local scale and explore how climate science can provide quantitative information to help decision making.

How to cite: Lanet, M., Li, L., and Le Treut, H.: Characterisation and interpretation of local climate evolution in the South-West of France, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3211, https://doi.org/10.5194/egusphere-egu23-3211, 2023.