Articles | Volume 24, issue 4
https://doi.org/10.5194/nhess-24-1501-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-24-1501-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A satellite view of the exceptionally warm summer of 2022 over Europe
João P. A. Martins
CORRESPONDING AUTHOR
Instituto Português do Mar e da Atmosfera, 1749-049 Lisbon, Portugal
European Centre for Medium-Range Weather Forecasts, Robert-Schuman-Platz 3, 53175 Bonn, Germany
Sara Caetano
Instituto Português do Mar e da Atmosfera, 1749-049 Lisbon, Portugal
Direção-Geral do Território, 1099-052 Lisbon, Portugal
Carlos Pereira
Instituto Português do Mar e da Atmosfera, 1749-049 Lisbon, Portugal
Emanuel Dutra
Instituto Português do Mar e da Atmosfera, 1749-049 Lisbon, Portugal
Instituto Dom Luiz, Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisbon, Portugal
Rita M. Cardoso
Instituto Dom Luiz, Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisbon, Portugal
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Cited articles
Agathangelidis, I., Cartalis, C., Polydoros, A., Mavrakou, T., and Philippopoulos, K.: Can Satellite-Based Thermal Anomalies Be Indicative of Heatwaves? An Investigation for MODIS Land Surface Temperatures in the Mediterranean Region, Remote Sens., 14, 3139, https://doi.org/10.3390/RS14133139, 2022.
Amengual, A., Homar, V., Romero, R., Brooks, H. E., Ramis, C., Gordaliza, M., and Alonso, S.: Projections of heat waves with high impact on human health in Europe, Global Planet. Change, 119, 71–84, https://doi.org/10.1016/J.GLOPLACHA.2014.05.006, 2014.
Barriopedro, D., Fischer, E. M., Luterbacher, J., Trigo, R. M., and García-Herrera, R.: The hot summer of 2010: Redrawing the temperature record map of Europe, Science, 332, 220–224, https://doi.org/10.1126/science.1201224, 2011.
Barriopedro, D., García-Herrera, R., Ordóñez, C., Miralles, D. G., and Salcedo-Sanz, S.: Heat Waves: Physical Understanding and Scientific Challenges, Rev. Geophys., 61, e2022RG000780, https://doi.org/10.1029/2022rg000780, 2023.
Barrios, J. M., Arboleda, A., Dutra, E., Trigo, I., and Gellens-Meulenberghs, F.: Evapotranspiration and surface energy fluxes across Europe, Africa and Eastern South America throughout the operational life of the Meteosat second generation satellite, Geosci. Data J., 1–19, https://doi.org/10.1002/gdj3.235, online first, 2024.
Bieli, M., Pfahl, S., and Wernli, H.: A Lagrangian investigation of hot and cold temperature extremes in Europe, Q. J. Roy. Meteor. Soc., 141, 98–108, https://doi.org/10.1002/QJ.2339, 2015.
Bojinski, S., Verstraete, M., Peterson, T. C., Richter, C., Simmons, A., and Zemp, M.: The concept of essential climate variables in support of climate research, applications, and policy, B. Am. Meteorol. Soc., 95, 1431–1443, https://doi.org/10.1175/BAMS-D-13-00047.1, 2014.
Brunner, L., Schaller, N., Anstey, J., Sillmann, J., and Steiner, A. K.: Dependence of Present and Future European Temperature Extremes on the Location of Atmospheric Blocking, Geophys. Res. Lett., 45, 6311–6320, https://doi.org/10.1029/2018GL077837, 2018.
Cardoso, R. M., Soares, P. M. M., Lima, D. C. A., and Miranda, P. M. A.: Mean and extreme temperatures in a warming climate: EURO CORDEX and WRF regional climate high-resolution projections for Portugal, Clim. Dynam., 52, 129–157, https://doi.org/10.1007/s00382-018-4124-4, 2019.
Chan, P. W., Catto, J. L., and Collins, M.: Heatwave–blocking relation change likely dominates over decrease in blocking frequency under global warming, NPJ Clim. Atmos. Sci., 5, 1–8, https://doi.org/10.1038/s41612-022-00290-2, 2022.
Christidis, N., Jones, G. S., and Stott, P. A.: Dramatically increasing chance of extremely hot summers since the 2003 European heatwave, Nat. Clim. Change, 5, 46–50, https://doi.org/10.1038/NCLIMATE2468, 2015.
Cornes, R. C., van der Schrier, G., van den Besselaar, E. J. M., and Jones, P. D.: An Ensemble Version of the E-OBS Temperature and Precipitation Data Sets, J. Geophys. Res.-Atmos., 123, 9391–9409, https://doi.org/10.1029/2017JD028200, 2018.
Díaz, J., Linares, C., Carmona, R., Russo, A., Ortiz, C., Salvador, P., and Trigo, R. M.: Saharan dust intrusions in Spain: Health impacts and associated synoptic conditions, Environ. Res., 156, 455–467, https://doi.org/10.1016/J.ENVRES.2017.03.047, 2017.
Duveiller, G., Pickering, M., Muñoz-Sabater, J., Caporaso, L., Boussetta, S., Balsamo, G., and Cescatti, A.: Getting the leaves right matters for estimating temperature extremes, Geosci. Model Dev., 16, 7357–7373, https://doi.org/10.5194/gmd-16-7357-2023, 2023.
EUMETSAT: LSA SAF Data Service, MLST-ASv2, https://datalsasaf.lsasvcs.ipma.pt/PRODUCTS/MSG/MLST-ASv2, last access: 26 April 2024a.
EUMETSAT: LSA SAF Data Service, MTFVC, https://datalsasaf.lsasvcs.ipma.pt/PRODUCTS/MSG/MTFVC/, last access: 26 April 2024b.
EUMETSAT: H Saf, RZSM-ASCAT-NRT-10 (H26), https://hsaf.meteoam.it/Products/Detail?prod=H26, last access: 26 April 2024c.
Fairbairn, D. and de Rosnay, P.: Algorithm Theoretical Baseline Document (ATBD) H141 and H142 – Soil Wetness Index in the roots region Data Record and Offline extension, H SAF, 31 pp., https://hsaf.meteoam.it/CaseStudy/GetDocumentUserDocument?fileName=H141_H142_ATBD.pdf&tipo=ATBD (last access: 26 April 2024), 2020.
Fairbairn, D. and de Rosnay, P.: Algorithm Theoretical Baseline Document (ATBD) H26– Soil Wetness Index in the roots region by ASCAT soil moisture assimilation, H SAF, 27 pp., https://hsaf.meteoam.it/CaseStudy/GetDocumentUserDocument?fileName=h26_atbd_v2.pdf&tipo=ATBD (last access: 26 April 2024), 2021.
FAO: Crop Prospects and Food Situation Quarterly Global Report no. 4, FAO, Rome, https://doi.org/10.4060/cc3233en, 2022.
Furusho-Percot, C., Goergen, K., Hartick, C., Poshyvailo-Strube, L., and Kollet, S.: Groundwater Model Impacts Multiannual Simulations of Heat Waves, Geophys. Res. Lett., 49, e2021GL096781, https://doi.org/10.1029/2021GL096781, 2022.
Galanaki, E., Giannaros, C., Kotroni, V., Lagouvardos, K., and Papavasileiou, G.: Spatio-Temporal Analysis of Heatwaves Characteristics in Greece from 1950 to 2020, Climate, 11, 5, https://doi.org/10.3390/CLI11010005, 2022.
García-Haro, F. J., Camacho, F., Martínez, B., Campos-Taberner, M., Fuster, B., Sánchez-Zapero, J., and Gilabert, M. A.: Climate data records of vegetation variables from geostationary SEVIRI/MSG data: Products, algorithms and applications, Remote Sens.-Basel, 11, 2103, https://doi.org/10.3390/rs11182103, 2019.
Garcia-Herrera, R., Díaz, J., Trigo, R. M., Luterbacher, J., and Fischer, E. M.: A Review of the European Summer Heat Wave of 2003, Crit. Rev. Env. Sci. Te., 40, 267–306, https://doi.org/10.1080/10643380802238137, 2010.
Good, E. J., Aldred, F. M., Ghent, D. J., Veal, K. L., and Jimenez, C.: An Analysis of the Stability and Trends in the LST_cci Land Surface Temperature Datasets Over Europe, Earth Space Sci., 9, e2022EA002317, https://doi.org/10.1029/2022EA002317, 2022.
Göttsche, F. M., Olesen, F. S., Trigo, I. F., Bork-Unkelbach, A., and Martin, M. A.: Long term validation of land surface temperature retrieved from MSG/SEVIRI with continuous in-situ measurements in Africa, Remote Sens.-Basel, 8, 410, https://doi.org/10.3390/rs8050410, 2016.
Gouveia, C. M., Martins, J. P. A., Russo, A., Durão, R., and Trigo, I. F.: Monitoring Heat Extremes across Central Europe Using Land Surface Temperature Data Records from SEVIRI/MSG, Remote Sens.-Basel, 14, 3470, https://doi.org/10.3390/RS14143470/S1, 2022.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020.
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 hourly data on pressure levels from 1940 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.bd0915c6, 2023.
Hoek van Dijke, A. J., Mallick, K., Schlerf, M., Machwitz, M., Herold, M., and Teuling, A. J.: Examining the link between vegetation leaf area and land–atmosphere exchange of water, energy, and carbon fluxes using FLUXNET data, Biogeosciences, 17, 4443–4457, https://doi.org/10.5194/bg-17-4443-2020, 2020.
Hoy, A., Hänsel, S., Maugeri, M., and Bergakademie Freiberg, T.: An endless summer: 2018 heat episodes in Europe in the context of secular temperature variability and change, Int. J. Climatol., 40, 15, https://doi.org/10.1002/joc.6582, 2020.
Hulley, G. C. and Ghent, D.: Taking the temperature of the Earth: steps towards integrated understanding of variability and change, edited by: Hulley, G. and Ghent, D., Elsevier Inc., https://doi.org/10.1016/C2017-0-01600-2, 2019.
H SAF: Scatterometer Root Zone Soil Moisture (RZSM) Data Record 10km resolution – Multimission, EUMETSAT SAF on Support to Operational Hydrology and Water Management, https://doi.org/10.15770/EUM_SAF_H_0008, 2020.
Hundhausen, M., Feldmann, H., Laube, N., and Pinto, J. G.: Future heat extremes and impacts in a convection-permitting climate ensemble over Germany, Nat. Hazards Earth Syst. Sci., 23, 2873–2893, https://doi.org/10.5194/nhess-23-2873-2023, 2023.
Johannsen, F., Ermida, S., Martins, J. P. A., Trigo, I. F., Nogueira, M., and Dutra, E.: Cold Bias of ERA5 Summertime Daily Maximum Land Surface Temperature over Iberian Peninsula, Remote Sens., 11, 2570, https://doi.org/10.3390/rs11212570, 2019.
Juza, M., Fernández-Mora, A., and Tintoré, J.: Sub-Regional Marine Heat Waves in the Mediterranean Sea From Observations: Long-Term Surface Changes, Sub-Surface and Coastal Responses, Front. Mar. Sci., 9, 785771, https://doi.org/10.3389/FMARS.2022.785771, 2022.
Katul, G. G., Oren, R., Manzoni, S., Higgins, C., and Parlange, M. B.: Evapotranspiration: A process driving mass transport and energy exchange in the soil-plant-atmosphere-climate system, Rev. Geophys., 50, RG3002, https://doi.org/10.1029/2011RG000366, 2012.
Kornhuber, K., Petoukhov, V., Petri, S., Rahmstorf, S., and Coumou, D.: Evidence for wave resonance as a key mechanism for generating high-amplitude quasi-stationary waves in boreal summer, Clim. Dynam., 49, 1961–1979, https://doi.org/10.1007/S00382-016-3399-6/FIGURES/11, 2017.
Lhotka, O., Kyselý, J., and Farda, A.: Climate change scenarios of heat waves in Central Europe and their uncertainties, Theor. Appl. Climatol., 131, 1043–1054, https://doi.org/10.1007/S00704-016-2031-3/FIGURES/9, 2018.
Li, Z.-L., Tang, B.-H., Wu, H., Ren, H., Yan, G., Wan, Z., Trigo, I. F., and Sobrino, J. A.: Satellite-derived land surface temperature: Current status and perspectives, Remote Sens. Environ., 131, 14–37, https://doi.org/10.1016/j.rse.2012.12.008, 2013.
Lin, C., Kjellström, E., Wilcke, R. A. I., and Chen, D.: Present and future European heat wave magnitudes: climatologies, trends, and their associated uncertainties in GCM-RCM model chains, Earth Syst. Dynam., 13, 1197–1214, https://doi.org/10.5194/esd-13-1197-2022, 2022.
Manning, C., Widmann, M., Bevacqua, E., Van Loon, A. F., Maraun, D., and Vrac, M.: Soil Moisture Drought in Europe: A Compound Event of Precipitation and Potential Evapotranspiration on Multiple Time Scales, J. Hydrometeorol., 19, 1255–1271, https://doi.org/10.1175/JHM-D-18-0017.1, 2018.
Martins, J. P. A. and Dutra, E.: Validation Report for All Sky Land Surface Temperature (MLST-AS, LSA-005), LSA SAF, https://nextcloud.lsasvcs.ipma.pt/s/dYjdyiMXZTt8sP4?path=%2FVR-Validation_Report (last access: 26 April 2024), 2022.
Martins, J. P. A., Trigo, I. I. F., Freitas, S. C., and Simões, N.: Algorithm Theoretical Basis Document for MSG All-Sky Land Surface Temperature (MLST-AS), LSA SAF, 29 pp., https://nextcloud.lsasvcs.ipma.pt/s/dYjdyiMXZTt8sP4?path=%2FATBD-Algorithm_Theoretial_Basis_Document (last access: 26 April 2024), 2018.
Martins, J. P. A., Trigo, I. F., Ghilain, N., Jimenez, C., Göttsche, F.-M., Ermida, S. L., Olesen, F.-S., Gellens-Meulenberghs, F., and Arboleda, A.: An All-Weather Land Surface Temperature Product Based on MSG/SEVIRI Observations, Remote Sens.-Basel, 11, 3044, https://doi.org/10.3390/rs11243044, 2019.
Meehl, G. A. and Tebaldi, C.: More intense, more frequent, and longer lasting heat waves in the 21st century, Science, 305, 994–997, https://doi.org/10.1126/science.1098704, 2004.
Mildrexler, D. J., Zhao, M., and Running, S. W.: A global comparison between station air temperatures and MODIS land surface temperatures reveals the cooling role of forests, J. Geophys. Res.-Biogeo., 116, G03025, https://doi.org/10.1029/2010JG001486, 2011.
Miralles, D. G., Teuling, A. J., Van Heerwaarden, C. C., and De Arellano, J. V. G.: Mega-heatwave temperatures due to combined soil desiccation and atmospheric heat accumulation, Nat. Geosci., 7, 345–349, https://doi.org/10.1038/ngeo2141, 2014.
Miralles, D. G., Gentine, P., Seneviratne, S. I., and Teuling, A. J.: Land–atmospheric feedbacks during droughts and heatwaves: state of the science and current challenges, Ann. NY. Acad. Sci., 1436, 19–35, https://doi.org/10.1111/NYAS.13912, 2019.
Molina, M. O., Sánchez, E., and Gutiérrez, C.: Future heat waves over the Mediterranean from an Euro-CORDEX regional climate model ensemble, Sci. Rep., 10, 8801, https://doi.org/10.1038/s41598-020-65663-0, 2020.
Morlot, M., Russo, S., Feyen, L., and Formetta, G.: Trends in heat and cold wave risks for the Italian Trentino-Alto Adige region from 1980 to 2018, Nat. Hazards Earth Syst. Sci., 23, 2593–2606, https://doi.org/10.5194/nhess-23-2593-2023, 2023.
Muñoz Sabater, J.: ERA5-Land hourly data from 1950 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.e2161bac, 2019.
Muñoz-Sabater, J., Dutra, E., Agustí-Panareda, A., Albergel, C., Arduini, G., Balsamo, G., Boussetta, S., Choulga, M., Harrigan, S., Hersbach, H., Martens, B., Miralles, D. G., Piles, M., Rodríguez-Fernández, N. J., Zsoter, E., Buontempo, C., and Thépaut, J.-N.: ERA5-Land: a state-of-the-art global reanalysis dataset for land applications, Earth Syst. Sci. Data, 13, 4349–4383, https://doi.org/10.5194/essd-13-4349-2021, 2021.
Nogueira, M., Albergel, C., Boussetta, S., Johannsen, F., Trigo, I. F., Ermida, S. L., Martins, J. P. A., and Dutra, E.: Role of vegetation in representing land surface temperature in the CHTESSEL (CY45R1) and SURFEX-ISBA (v8.1) land surface models: a case study over Iberia, Geosci. Model Dev., 13, 3975–3993, https://doi.org/10.5194/gmd-13-3975-2020, 2020.
Nogueira, M., Boussetta, S., Balsamo, G., Albergel, C., Trigo, I. F., Johannsen, F., Miralles, D. G., and Dutra, E.: Upgrading Land-Cover and Vegetation Seasonality in the ECMWF Coupled System: Verification With FLUXNET Sites, METEOSAT Satellite Land Surface Temperatures, and ERA5 Atmospheric Reanalysis, J. Geophys. Res.-Atmos., 126, e2020JD034163, https://doi.org/10.1029/2020JD034163, 2021.
Pérez-Planells, L., Ghent, D., Ermida, S., Martin, M., and Göttsche, F. M.: Retrieval Consistency between LST CCI Satellite Data Products over Europe and Africa, Remote Sens.-Basel, 15, 3281, https://doi.org/10.3390/rs15133281, 2023.
Petrovic, D., Fersch, B., and Kunstmann, H.: Heat wave characteristics: evaluation of regional climate model performances for Germany, Nat. Hazards Earth Syst. Sci., 24, 265–289, https://doi.org/10.5194/nhess-24-265-2024, 2024.
Reiners, P., Sobrino, J., and Kuenzer, C.: Satellite-Derived Land Surface Temperature Dynamics in the Context of Global Change – A Review, Remote Sens., 15, 1857, https://doi.org/10.3390/rs15071857, 2023.
Rousi, E., Kornhuber, K., Beobide-Arsuaga, G., Luo, F., and Coumou, D.: Accelerated western European heatwave trends linked to more-persistent double jets over Eurasia, Nat. Commun., 13 1–11, https://doi.org/10.1038/s41467-022-31432-y, 2022.
Russo, S., Sillmann, J., and Fischer, E. M.: Top ten European heatwaves since 1950 and their occurrence in the coming decades, Environ. Res. Lett., 10, 124003, https://doi.org/10.1088/1748-9326/10/12/124003, 2015.
Schaller, N., Sillmann, J., Anstey, J., Fischer, E. M., Grams, C. M., and Russo, S.: Influence of blocking on Northern European and Western Russian heatwaves in large climate model ensembles, Environ. Res. Lett., 13, 054015, https://doi.org/10.1088/1748-9326/AABA55, 2018.
Schiermeier, Q.: Droughts, heatwaves and floods: How to tell when climate change is to blame, Nature, 560, 20–23, 2018.
Seneviratne, S. I., Zhang, X., Adnan, M., Badi, W., Dereczynski, C., Di Luca, A., Vicente-Serrano, S. M., Wehner, M., and Zhou, B.: Weather and climate extreme events in a changing climate, in: Climate Change 2021: The Physical Science Basis: Working Group I contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, 1513–1766, https://doi.org/10.1017/9781009157896.013, 2021.
Sousa, P. M., Barriopedro, D., Ramos, A. M., García-Herrera, R., Espírito-Santo, F., and Trigo, R. M.: Saharan air intrusions as a relevant mechanism for Iberian heatwaves: The record breaking events of August 2018 and June 2019, Weather Clim. Extrem., 26, 100224, https://doi.org/10.1016/J.WACE.2019.100224, 2019.
Sousa, P. M., Barriopedro, D., García-Herrera, R., Ordóñez, C., Soares, P. M. M., and Trigo, R. M.: Distinct influences of large-scale circulation and regional feedbacks in two exceptional 2019 European heatwaves, Commun. Earth Environ., 1, 1–13, https://doi.org/10.1038/s43247-020-00048-9, 2020.
Suarez-Gutierrez, L., Müller, W. A., Li, C., and Marotzke, J.: Dynamical and thermodynamical drivers of variability in European summer heat extremes, Clim. Dynam., 54, 4351–4366, https://doi.org/10.1007/S00382-020-05233-2/FIGURES/5, 2020.
Sutanto, S. J., Vitolo, C., Di Napoli, C., D'Andrea, M., and Van Lanen, H. A. J.: Heatwaves, droughts, and fires: Exploring compound and cascading dry hazards at the pan-European scale, Environ. Int., 134, 105276, https://doi.org/10.1016/j.envint.2019.105276, 2020.
Trigo, I. F., Dacamara, C. C., Viterbo, P., Roujean, J.-L., Olesen, F., Barroso, C., Camacho-de-Coca, F., Carrer, D., Freitas, S. C., García-Haro, J., Geiger, B., Gellens-Meulenberghs, F., Ghilain, N., Meliá, J., Pessanha, L., Siljamo, N., and Arboleda, A.: The Satellite Application Facility for Land Surface Analysis, Int. J. Remote Sens., 32, 2725–2744, https://doi.org/10.1080/01431161003743199, 2011.
Trigo, I. F., Ermida, S. L., Martins, J. P. A., Gouveia, C. M., Göttsche, F. M., and Freitas, S. C.: Validation and consistency assessment of land surface temperature from geostationary and polar orbit platforms: SEVIRI/MSG and AVHRR/Metop, ISPRS J. Photogramm., 175, 282–297, https://doi.org/10.1016/J.ISPRSJPRS.2021.03.013, 2021.
Wan, Z.: New refinements and validation of the collection-6 MODIS land-surface temperature/emissivity product, Remote Sens. Environ., 140, 36–45, https://doi.org/10.1016/j.rse.2013.08.027, 2014.
Wang, Y. R., Hessen, D. O., Samset, B. H., and Stordal, F.: Evaluating global and regional land warming trends in the past decades with both MODIS and ERA5-Land land surface temperature data, Remote Sens. Environ., 280, 113181, https://doi.org/10.1016/J.RSE.2022.113181, 2022.
Xu, Z., FitzGerald, G., Guo, Y., Jalaludin, B., and Tong, S.: Impact of heatwave on mortality under different heatwave definitions: A systematic review and meta-analysis, Environ. Int., 89, 193–203, https://doi.org/10.1016/j.envint.2016.02.007, 2016.
Zaitchik, B. F., Macalady, A. K., Bonneau, L. R., and Smith, R. B.: Europe's 2003 heat wave: a satellite view of impacts and land–atmosphere feedbacks, Int. J. Climatol., 26, 743–769, https://doi.org/10.1002/JOC.1280, 2006.
Zhang, R., Sun, C., Zhu, J., Zhang, R., and Li, W.: Increased European heat waves in recent decades in response to shrinking Arctic sea ice and Eurasian snow cover, NPJ Clim. Atmos. Sci., 3, 1–9, https://doi.org/10.1038/s41612-020-0110-8, 2020.
Zhang, X., Hegerl, G., Zwiers, F. W., and Kenyon, J.: Avoiding inhomogeneity in percentile-based indices of temperature extremes, J. Climate, 18, 1641–1651, https://doi.org/10.1175/JCLI3366.1, 2005.
Zscheischler, J., Westra, S., Van Den Hurk, B. J. J. M., Seneviratne, S. I., Ward, P. J., Pitman, A., Aghakouchak, A., Bresch, D. N., Leonard, M., Wahl, T., and Zhang, X.: Future climate risk from compound events, Nat. Clim. Change, 8, 469–477, https://doi.org/10.1038/s41558-018-0156-3, 2018.
Zscheischler, J., Martius, O., Westra, S., Bevacqua, E., Raymond, C., Horton, R. M., van den Hurk, B., AghaKouchak, A., Jézéquel, A., Mahecha, M. D., Maraun, D., Ramos, A. M., Ridder, N. N., Thiery, W., and Vignotto, E.: A typology of compound weather and climate events, Nat. Rev. Earth Environ., 1, 333–347, https://doi.org/10.1038/s43017-020-0060-z, 2020.
Short summary
Over Europe, 2022 was truly exceptional in terms of extreme heat conditions, both in terms of temperature anomalies and their temporal and spatial extent. The satellite all-sky land surface temperature (LST) is used to provide a climatological context to extreme heat events. Where drought conditions prevail, LST anomalies are higher than 2 m air temperature anomalies. ERA5-Land does not represent this effect correctly due to a misrepresentation of vegetation anomalies.
Over Europe, 2022 was truly exceptional in terms of extreme heat conditions, both in terms of...
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