CL4.1

Temperature-based events such as heatwaves and compound dry hot extremes impact the socio-economic sectors of a nation. In this study, the differential rates of temperature intensification across different seasons and regions in India coupled with dry/ wet climatologies are studied. The analysis is done for both historical observations and future CMIP6 simulations. Further, the temperature intensification rates were linked to established atmospheric feedback mechanisms. Results show that observed temperature intensification rates are positive/negative during dry/wet climatology relative to average climatology. Analysis of feedback mechanisms showed that cooling temperature trends are associated with a decrease in atmospheric aridity (vapor pressure deficit) and an increase in relative humidity. While in southern India, temperature trends are similar for all three climatologies (average, dry, and wet), albeit with different rates of intensification, in northern India, the temperature intensification shows notable contrasting trends during dry and wet climatologies. The highly irrigated Indo-Gangetic Plain region in northern India is found to experience significant cooling temperature trends during dry climatology and these trends are much more prominent during the agricultural Rabi season. Climate change analysis using CMIP6 simulations indicates further exacerbation of temperatures across all regions in the Indian subcontinent and foresees an increased probability of compound extremes in the future.

How to cite: Prabhakar, A. and Mitra, S.: Modulation of Dry and Wet Period Temperatures in India, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-234, https://doi.org/10.5194/egusphere-egu22-234, 2022.

Measurements of global solar and net radiation fluxes were made above a grass-covered surface at DACCIWA site in a tropical location, Ile-Ife, southwest Nigeria for a period of three years (2017 - 2019). The radiation data sets were obtained from a four-component net radiometer (model NR01). Observations were made for cases of clear sky and cloudy conditions during the measurement period. The results showed considerable fluctuations of both radiation fluxes occurring during the period of measurements at the location. For clear sky conditions, the magnitudes of global and net radiation fluxes were higher than those observed for cloudy conditions due to attenuation by clouds and aerosols. For the period of observation, the highest radiation flux values occurred in 2018 while the lowest were observed in 2017. The daily surface albedo (α) values ranged from 0.16 to 0.22 at the site. Empirical relationships obtained for global solar and net radiation are  R_N = 0.754 R_G – 17.4 Wm^-2 and  R_N = 0.657 R_G – 32.7 Wm^-2 for wet and dry seasons respectively. Based on the empirical relationships, daily net and global solar radiation can be obtained when measurements like these are not available. Linear relationships between R_N  and R_G indicate that for all days (cloudy and clear sky conditions), average R_N  is about 65 % of R_G , and about 50 % of  R_G for clear sky conditions at the location

How to cite: Ajao, A., Abiye, O., and Agboola, A.: Analysis of global and net radiation fluxes in relation to surface albedo at DACCIWA site in Ile-Ife, southwest Nigeria, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1283, https://doi.org/10.5194/egusphere-egu22-1283, 2022.

The change of land cover affects regional and global climate through the surface energy budget and the water cycle, which determine the interactions between the terrestrial biosphere and the atmosphere. Land cover change not only affects the climate but is also influenced by it. The projected climate change and the occurrence of extreme climate events will profoundly affect the land cover, crop production, as well as water and food security. Yet, the complex interactions between land cover changes and climate variability are not fully understood. Previous studies have shown that land cover change influences the mean and extreme values of climate variables such as temperature. However, most research focused on specific types of land cover change such as deforestation or urbanisation and looked at only one climate variable (e.g., temperature). A comprehensive multivariate analysis relating multiple land cover changes and climate variables at the global scale is still missing. Here, we take an observation-based approach that analyses the complex interactions between different types of land cover change and the joint effect of temperature and humidity variability at the global scale. We analyse almost three decades of remotely sensed land cover and climate data to investigate the complex coupling between the patterns of different types of land cover change and the variability of temperature and relative humidity across the globe. Our analysis identifies hotspots of change on a global scale and correlations which will help to devise necessary action plans for sustainable land management and climate change mitigation measures crucial to the achievement of the United Nations Sustainable Development Goals.

How to cite: Hemshorn de Sánchez, A. L., Stevens, B., D’Odorico, P., and Shokri, N.: Interactions between land cover change and temperature-humidity variability on a global scale, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1526, https://doi.org/10.5194/egusphere-egu22-1526, 2022.

In tropical convective climates, where numerical weather prediction of rainfall has high uncertainty, nowcasting provides essential alerts of extreme events several hours ahead. In principle, short-term prediction of intense convective storms could benefit from knowledge of the slowly-evolving land surface state in regions where soil moisture controls surface fluxes. Here we explore how near-real time (NRT) satellite observations of the land surface and convective clouds can be combined to aid early warning of severe weather in the Sahel on time scales of up to 12 hours. Using Land Surface Temperature (LST) as a proxy for soil moisture deficit, we characterise the state of the surface energy balance in NRT. We identify the most convectively-active parts of Mesoscale Convective Systems (MCSs) from spatial filtering of cloud-top temperature imagery.

We find that predictive skill provided by LST data is maximised early in the rainy season, when soils are drier and vegetation less developed. Land-based skill in predicting intense convection extends well beyond the afternoon, with strong positive correlations between daytime LST and MCS activity persisting as far as the following morning in more arid conditions. For the Science for Weather Information and Forecasting Techniques (SWIFT) Forecasting Testbed event during September 2021, we developed a simple technique to translate LST data into NRT maps quantifying the likelihood of convection based solely on land state. We used these maps in combination with convective features to nowcast the tracks of existing MCSs, and predict likely new initiation locations. This is the first time to our knowledge that nowcasting tools based principally on land observations have been developed. The strong sensitivity of Sahelian MCSs to soil moisture, in combination with MCS life times of typically 6-18 hours, opens up the opportunity for nowcasting of hazardous weather well beyond what is possible from atmospheric observations alone, and could be applied elsewhere in the semi-arid tropics.

How to cite: Taylor, C. M., Klein, C., Dione, C., Parker, D. J., Marsham, J., Abdoulahat Diop, C., Fletcher, J., Saidou Chaibou, A. A., Nafissa, D. B., Semeena, V. S., Cole, S., and Anderson, S.: Nowcasting Tracks of Severe Convective Storms in West Africa from Observations of Land Surface State, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2915, https://doi.org/10.5194/egusphere-egu22-2915, 2022.

Results: Soil temperature is a crucial variable in Land Surface Models (LSMs) because it affects the fractions of frozen and unfrozen water content in the soil. For example, getting the coupling between below-ground heat- and water transfer correct in LSMs is very important in permafrost regions because these are particularly sensitive to climate change. Poor predictions of the energy- and water balance in these regions will lead to large uncertainties in predicted carbon fluxes, and related land-atmosphere feedbacks. Also, simulated near-surface soil temperatures can be used to diagnose and explain model differences in skin temperatures and soil heat fluxes, both of which are pivotal in the prediction of the surface energy balance.

Soil temperature is generally under-researched as part of LSM intercomparisons. Here we present an analysis of the spatial distribution (including the vertical distribution along the soil profile) and seasonal evolution of soil temperature simulated by eight LSMs as part of the Soil Parameter Model Intercomparison Project (SP-MIP). We found large inter-model differences in key metrics of the annual soil temperature wave, including the amplitude, phase shift and damping depth, which were partly attributed to diversity in hydraulic as well as thermal soil properties. Soil layer discretisation also played a role.

Methods: Via manipulation of model soil hydraulic properties, and the soil texture inputs required to calculate these properties, controlled multi-model experiments have been conducted as part of SP-MIP, this MIP was originally proposed at the GEWEX-SoilWat workshop held in Leipzig (June 2016).

The model experiments closely followed the LS3MIP protocol (van den Hurk et al. 2016). Eight land models (CLM5, ISBA, JSBACH, JULES, MATSIRO, MATSIRO-GW, NOAH-MP and ORCHIDEE) were run globally on 0.5° with GSWP3 forcing, from 1980-2010, for vertically homogeneous soil columns. There were 4 model experiments, leading to 7 model runs: Experiment 1. Global soil hydraulic parameter maps provided by SP-MIP; Experiment 2. Soil-hydraulic parameters derived from common soil textural properties, provided by SP-MIP, using model-specific pedotransfer functions (PTFs); Experiment 3. Reference run with all models applying their default soil hydraulic settings (including their own soil maps to derive the parameters); Experiment 4: four runs using spatially uniform soil hydraulic parameters for the whole globe (loamy sand, loam, clay and silt) provided by SP-MIP.

Differences between the model experiments will allow the assessment of the inter-model variability that is introduced by the different stages of preparing model parameters. Soil parameters for Experiments 1 and soil textures for Experiment 2 at 0.5° resolution were prepared from dominant soil classes of the 0-5 cm layer of SoilGrids (Hengl et al. 2014) at 5 km resolution. Brooks and Corey hydraulic parameters come from Table 2 of Clapp and Hornberger (1978), Mualem-Van Genuchten hydraulic parameters are ROSETTA class average hydraulic parameters (Schaap et al. 2001), and soil textures are from Table 2 of Cosby et al. (1984). Experiments 4 a-d use the USDA soil classes, using the same PTFs for Brooks and Corey and Mualem-van Genuchten parameters as in Experiment 1.

How to cite: Verhoef, A., Zeng, Y., Cuntz, M., Gudmundsson, L., Thober, S., McGuire, P. C., Bergner, H., Boone, A., Ducharne, A., Ellis, R., Kim, H., Koirala, S., Lawrence, D., Oleson, K., Swenson, S., Tafasca, S., de Vrese, P., Seneviratne, S., Or, D., and Vereecken, H.: Assessing the variability of soil temperatures in Land Surface Models using outputs from the Soil Parameter Model Intercomparison Project (SP-MIP), EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4349, https://doi.org/10.5194/egusphere-egu22-4349, 2022.

Different land covers present contrasting changes in energy budgets as a response to heatwaves and droughts and thus the land feedback is expected to vary over the landscape. To date, the study of the biotic determinants of land-atmosphere feedbacks during heatwaves has been restricted to the consideration of different plant functional types. We used improved vegetation structural measurements at organizational levels lower than plant functional types (inter– and intra–specific) to estimate the impact of forests on the surface thermal balance.

We combined space-borne measurements of the temperature of plants (ECOSTRESS) and the land surface (MODIS) with ground-based meteorological data to estimate the thermal balance of the surface (∆T) at a resolution of 70x70m in 615 forest plots, dominated by 28 different species. In each plot, forest structural variables were determined through LiDAR. We then analysed the spatiotemporal drivers of ∆T by quantifying the contribution of topographical, landscape, meteorological and forest structural variables on ∆T both during normal conditions and heatwave episodes.

Canopy temperatures fluctuated according to changes in air temperature and were on average 1˚C warmer than the air. During heatwaves, canopies were relatively cooler than the air, compared to normal conditions in all but Mediterranean coniferous forests. The thermal response of canopies to heatwaves strongly varied as a function of environmental variables. Forests in rainy areas and in steep slopes presented the lowest ∆T, whereas forests in arid areas and flat terrain had the highest ∆T. Interestingly, there was a strong effect of forest structure, since forests with larger biomass kept a cooler thermal balance (lower ∆T). Indeed, the total effect of forest structural variables on ∆T was of equal magnitude as that of topography or meteorological conditions.

The thermal balance of the surface (∆T) was not only different among the main forest types, but also, it strongly varied within forests dominated by the same species. Because ∆T is an important component of the surface energy budget, our results on its dependence on forest structure imply that forest management could be employed to modify the surface energy budget to promote negative (mitigating) feedbacks of forests during heatwave episodes. Further efforts concentrate on estimating changes in aerodynamic conductance between forests and their surroundings, and their potential influence on the land–atmosphere coupling and the feedback of forests on local temperatures.

How to cite: Barbeta, A., Miralles, D. G., Mendiola, L., Gimeno, T. E., Sabaté, S., Pou, A., and Carnicer, J.: Drivers of the spatiotemporal variability in the thermal balance of forests during heatwaves and normal conditions., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5646, https://doi.org/10.5194/egusphere-egu22-5646, 2022.

The major concern of land-atmosphere interactions (L-A) is the evolutionary process between the land surface and the planet boundary layer during the daytime, however many relevant studies had to use entire-day-mean daily time series to perform investigation due to lack of sub-daily data. Yet it is unclear whether the inclusion of nighttime data would alter the results or obscure the L-A interactive processes. To address this question, we generated daytime-only-mean (D) and entire-day-mean (E) daily data based on the ERA5 (5th ECMWF reanalysis) hourly product, and evaluated the strength of L-A coupling through a two-legged metrics, which assessed the coupling strength by the causality as well as the impact magnitude through two segments (land-fluxes and fluxes-atmosphere). The results demonstrated significant differences between the D- and E-based diagnoses as large as 67% (median 20.7%), which strongly depended on the season and the region. More importantly, for the first time, two special L-A coupling mechanisms were revealed. One was the advection-dominant L-A mechanism in tropical hyper-arid regions. The other was the soil moisture and sensible heat flux coupling mechanism during the cooling process over the nighttime. Both processes may play important roles during the night, andweaken the signal of L-A coupling if E was applied. To improve our knowledge of L-A interactions, we call attention to the urgent need for more high frequency data for relevant diagnoses. Meanwhile, we propose two approaches to resolve the dilemma of huge storage for high frequency data: (1) integration of L-A metrics in Earth System Model outputs, and (2) production of daily datasets based on different averaging algorithms.

How to cite: Yin, Z., Findell, K., Dirmeyer, P., Shevliakova, E., Malyshev, S., Ghannam, K., Raoult, N., and Tan, Z.: Daytime-only-mean data can enhance our understanding of land-atmosphere coupling, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6467, https://doi.org/10.5194/egusphere-egu22-6467, 2022.

Heat wave is one of the most severe natural disasters in the mid-latitude regions. Due to climate change and urbanization, heat waves have been intensified in the past, and are projected to be more severe in the future. Droughts and heat waves usually occur simultaneously, which are referred to as compound extreme events. Antecedent or simultaneous droughts enhance heat waves through local land-atmosphere interaction, but a few case studies show that upwind droughts can have a significant impact on heat waves through sensible heat advection. In order to systematically study the impact of upwind droughts on heat waves, this study uses a Lagrangian integrated trajectory model driven by reanalysis data to analyze the heat wave events in northern part of Eastern China from 1979 to 2019. We find that half of the heat waves are enhanced by upwind droughts. For the related heat waves, the upwind droughts contributed to 67.9% of the heat anomalies. The impact of flash drought on heat waves in Eastern China is also being explored, with particular interest to extract heat wave signals from antecedent flash drought to provide early warning for extreme heat waves over downwind areas.

How to cite: Zhou, S. and Yuan, X.: Upwind droughts enhance heat waves in Eastern China, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6753, https://doi.org/10.5194/egusphere-egu22-6753, 2022.

Morocco as many semi-arid Mediterranean and north African countries is facing strong pressure on water resources exacerbated by climate change. Assessing the representation and variability of the Moroccan climate by using the climate models is of major importance to strengthen the reliability of future scenarios and anticipate the water cycle evolutions.

The aim of this study is to evaluate and improve the representation of the surface-atmosphere coupling, and the boundary-layer dynamics over the Haouz plain by the IPSL-CM Earth System Model. The Haouz plain is one of the most important agricultural and touristic regions of Morocco. It is located in the Tensift watershed and limited with the Atlas mountains, and it has been equipped with a network of meteorological stations. We set a simulation configuration up with a model grid refined over the Haouz plain and with a nudging towards atmospheric reanalysis outside the plain, making it possible to concomitantly compare the model outputs with in-situ data. 

A first evaluation of the control simulation reveals an overall good agreement between the observed daily mean temperature and the simulated one despite some cold biases. Simulated near-surface relative humidity is generally low-biased (up to 20%) while precipitation is overestimated (up to 50% of observed daily precipitation). Those biases are further deciphered through a careful evaluation of the different terms of the surface energy and water budgets. Complementary analyses conditioned to the direction of the large scale flow also investigate how model’s performances over the plain depend on the representation of the orographic flow over the Atlas. This evaluation work is a preliminary and an important step to identify which and how LMDZ parameterizations have to be improved for semi-arid African regions. 

How to cite: Arjdal, K., Driouech, F., Vignon, É., Chéruy, F., Sima, A., Drobinski, P., Chehbouni, A., and Er-Raki, S.: Modeling the surface-atmosphere coupling in the Moroccan semi-arid plains in the context of climate change, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8163, https://doi.org/10.5194/egusphere-egu22-8163, 2022.

The Arabian Peninsula exhibits extreme hot summers and has one of the world's largest population growth. We use satellite observations and reanalysis as well as climate model projections to analyze morning and evening land surface temperatures (LST), to refer to processes at the surface, and wet bulb temperatures (WBT) to measure human heat stress. We focus on three regions: The Persian Gulf and Gulf of Oman, the inland capital of Saudi Arabia, Riyadh and the irrigated agricultural region in Al-Jouf, Saudi Arabia. This study shows that the time of the day is important when studying LST and WBT, with current and future WBT higher in the early summer evenings. It also shows that the effect of humidity brought from waterbodies or through irrigation can significantly increase heat stress.

Over the coasts of the Peninsula, humidity decreases LST but increases heat stress via WBT values higher than 25°C in the evening. Riyadh, located in the heart of the Peninsula has lower WBT of 15°C to 17.5°C and LST reaching 42.5°C. Irrigation in the Al-Jouf province decreases LST by up to 10° with respect to its surroundings, while it increases WBT by up to 2.5°. Climate projections over the Arabian Peninsula suggest that global efforts will determine the survivability in this region. Even under the sustainability scenario, the projected increase in LST and WBT reaches +10° and +5°C respectively in the Persian Gulf and Riyadh by 2100 posing significant risk on human survivability in the Peninsula.

How to cite: Safieddine, S., Whitburn, S., Clarisse, L., and Clerbaux, C.: Present and future land surface and wet bulb temperatures in the Arabian Peninsula, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8601, https://doi.org/10.5194/egusphere-egu22-8601, 2022.

Droughts are impactful climate extremes with proven dramatic consequences on economy, ecosystems and society. Numerous research has been devoted to exploring land surface controls on meteorological drought onset and evolution. However, the importance of land conditions may be equally important for drought termination, yet the latter remains much less understood. Drought demise is often abrupt, can lead to extreme rainfall and floods, and is generally hard to capture using traditional monthly drought metrics. A better predictability of the end of a drought can not only help better anticipate the duration of droughts, but also significantly improve risk assessment and water resource management during dry extremes.

In this study, we explore the existence of a positive or negative feedback between the decreasing soil moisture and the probability of drought termination. As test cases, multiple droughts in Belgium during the period of 1981–2015 are selected. As a first step, we compose a data set of past droughts based on precipitation and soil moisture from ECMWF reanalysis data and identify the drought termination days. Next, multiple simulations of the drought termination days are executed with the CLASS4GL mixed-layer model framework, in which the influence of changing soil moisture conditions is evaluated. Finally, the sensitivity of drought demise to soil moisture is assessed based on multiple soil moisture–atmosphere coupling metrics and revealed sensitivity relationships. The obtained results highlight the importance of realistic representation of land–atmosphere feedbacks and soil moisture for drought evolution and termination, and could be used to inform drought prediction efforts or pave the way for effective geoengineering solutions designed to mitigate the increasing risk of dry climate extremes in the future.

How to cite: De Vestele, D., Yu. Petrova, I., and G. Miralles, D.: Land surface controls on drought termination in Belgium, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9810, https://doi.org/10.5194/egusphere-egu22-9810, 2022.

To simulate the extreme precipitation events through GCMs has become a challenge due to discrepancies in spatio-temporal resolution, physics, and parameterization schemes of the models along with deficiencies in the observed datasets. In this study, the performance of 27 CMIP6 models and their Multi model mean (MMM) in simulating extreme precipitation indices has been compared to the observed precipitation datasets (APHRODITE and IMD) over India during JJAS for 1975-2014. Meanwhile, the MMM shows a close agreement in simulating the indices derived from APHRODITE with PCC >0.6 for all indices with higher skill score (0.54), lower NRMSE than IMD. However, the MMM over- (under)-estimate the number of consecutive wet days (total precipitation) with a median relative error of 64% and 160% (5% and 20%) respectively, as compared to APHRODITE and IMD. Which inferred that similar biases still persist in the newly released CMIP6 GCMs with inter-observation dissimilarity in reproducing the indices. In general, the MMM is unable to replicate the very heavy precipitation (R20mm), with negative median relative errors. However, for all three aforementioned precipitation indices the extent of over- and under-estimation is less while comparing against the APHRODITE than IMD. For consecutive dry days (CDD), the MMM over- (under)-estimate over the North west (northern tip and peninsular as well as lee side of Western Ghat) parts of India, where the biases relative to APHRODITE (IMD) is large (less). The MMM simulates precipitation indices well, instead of using individual model. Whereas, the variation of NRMSE values of individual models are less with the exception of CDD and CWD, where the disagreement between the models with observation is large with larger interquartile model range. Comparing the relative errors between the different homogenous regions of India, all the regions are marginally performing good in simulating the different indices except the NW region, which is appended with larger relative error. It was worth noting that the models having higher spatial resolutions simulate the indices realistically with high (low) PCC (NRMSE), whereas the reversal is not valid for the worst performing models.

Key Words: Extreme Precipitation, CMIP6, MMM, IMD, APHRODITE

How to cite: Bhuyan, D. P., Salunke, P., and Mishra, S. K.: Assessment of Extreme Precipitation Indices over India by CMIP6 Models, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11121, https://doi.org/10.5194/egusphere-egu22-11121, 2022.