Articles | Volume 16, issue 24
https://doi.org/10.5194/gmd-16-7311-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-16-7311-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Modeling and evaluating the effects of irrigation on land–atmosphere interaction in southwestern Europe with the regional climate model REMO2020–iMOVE using a newly developed parameterization
Christina Asmus
CORRESPONDING AUTHOR
Climate Service Center Germany (GERICS), Helmholtz-Zentrum Hereon, Hamburg, Germany
Peter Hoffmann
Climate Service Center Germany (GERICS), Helmholtz-Zentrum Hereon, Hamburg, Germany
Joni-Pekka Pietikäinen
Climate Service Center Germany (GERICS), Helmholtz-Zentrum Hereon, Hamburg, Germany
Jürgen Böhner
Center for Earth System Research and Sustainability (CEN), Universität Hamburg, Hamburg, Germany
Diana Rechid
Climate Service Center Germany (GERICS), Helmholtz-Zentrum Hereon, Hamburg, Germany
Related authors
Joni-Pekka Pietikäinen, Kevin Sieck, Lars Buntemeyer, Thomas Frisius, Christine Nam, Peter Hoffmann, Christina Pop, Diana Rechid, and Daniela Jacob
EGUsphere, https://doi.org/10.5194/egusphere-2025-1586, https://doi.org/10.5194/egusphere-2025-1586, 2025
Short summary
Short summary
This paper introduces REMO2020, a modernized version of the well-known and widely used REMO regional climate model. We demonstrate why REMO2020 will be our new model version for future dynamical downscaling activities. It outperforms our previous model version in many analyzed areas and is the biggest update to REMO so far. It also supports climate service needs based developments through new more modular structure.
Peter Hoffmann, Vanessa Reinhart, Diana Rechid, Nathalie de Noblet-Ducoudré, Edouard L. Davin, Christina Asmus, Benjamin Bechtel, Jürgen Böhner, Eleni Katragkou, and Sebastiaan Luyssaert
Earth Syst. Sci. Data, 15, 3819–3852, https://doi.org/10.5194/essd-15-3819-2023, https://doi.org/10.5194/essd-15-3819-2023, 2023
Short summary
Short summary
This paper introduces the new high-resolution land use and land cover change dataset LUCAS LUC for Europe (version 1.1), tailored for use in regional climate models. Historical and projected future land use change information from the Land-Use Harmonization 2 (LUH2) dataset is translated into annual plant functional type changes from 1950 to 2015 and 2016 to 2100, respectively, by employing a newly developed land use translator.
Peter Hoffmann, Vanessa Reinhart, Diana Rechid, Nathalie de Noblet-Ducoudré, Edouard L. Davin, Christina Asmus, Benjamin Bechtel, Jürgen Böhner, Eleni Katragkou, and Sebastiaan Luyssaert
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2021-252, https://doi.org/10.5194/essd-2021-252, 2021
Manuscript not accepted for further review
Short summary
Short summary
This paper introduces the new high-resolution land-use land-cover change dataset LUCAS LUC historical and future land use and land cover change dataset (Version 1.0), tailored for use in regional climate models. Historical and projected future land use change information from the Land-Use Harmonization 2 (LUH2) dataset is translated into annual plant functional type changes from 1950 to 2015 and 2016 to 2100, respectively, by employing a newly developed land use translator.
Joni-Pekka Pietikäinen, Kevin Sieck, Lars Buntemeyer, Thomas Frisius, Christine Nam, Peter Hoffmann, Christina Pop, Diana Rechid, and Daniela Jacob
EGUsphere, https://doi.org/10.5194/egusphere-2025-1586, https://doi.org/10.5194/egusphere-2025-1586, 2025
Short summary
Short summary
This paper introduces REMO2020, a modernized version of the well-known and widely used REMO regional climate model. We demonstrate why REMO2020 will be our new model version for future dynamical downscaling activities. It outperforms our previous model version in many analyzed areas and is the biggest update to REMO so far. It also supports climate service needs based developments through new more modular structure.
Florian Knutzen, Paul Averbeck, Caterina Barrasso, Laurens M. Bouwer, Barry Gardiner, José M. Grünzweig, Sabine Hänel, Karsten Haustein, Marius Rohde Johannessen, Stefan Kollet, Mortimer M. Müller, Joni-Pekka Pietikäinen, Karolina Pietras-Couffignal, Joaquim G. Pinto, Diana Rechid, Efi Rousi, Ana Russo, Laura Suarez-Gutierrez, Sarah Veit, Julian Wendler, Elena Xoplaki, and Daniel Gliksman
Nat. Hazards Earth Syst. Sci., 25, 77–117, https://doi.org/10.5194/nhess-25-77-2025, https://doi.org/10.5194/nhess-25-77-2025, 2025
Short summary
Short summary
Our research, involving 22 European scientists, investigated drought and heat impacts on forests in 2018–2022. Findings reveal that climate extremes are intensifying, with central Europe being most severely impacted. The southern region showed resilience due to historical drought exposure, while northern and Alpine areas experienced emerging or minimal impacts. The study highlights the need for region-specific strategies, improved data collection, and sustainable practices to safeguard forests.
Jan Wohland, Peter Hoffmann, Daniela C. A. Lima, Marcus Breil, Olivier Asselin, and Diana Rechid
Earth Syst. Dynam., 15, 1385–1400, https://doi.org/10.5194/esd-15-1385-2024, https://doi.org/10.5194/esd-15-1385-2024, 2024
Short summary
Short summary
We evaluate how winds change when humans grow or cut down forests. Our analysis draws from climate model simulations with extreme scenarios where Europe is either fully forested or covered with grass. We find that the effect of land use change on wind energy is very important: wind energy potentials are twice as high above grass as compared to forest in some locations. Our results imply that wind profile changes should be better incorporated in climate change assessments for wind energy.
Peter Hoffmann, Vanessa Reinhart, Diana Rechid, Nathalie de Noblet-Ducoudré, Edouard L. Davin, Christina Asmus, Benjamin Bechtel, Jürgen Böhner, Eleni Katragkou, and Sebastiaan Luyssaert
Earth Syst. Sci. Data, 15, 3819–3852, https://doi.org/10.5194/essd-15-3819-2023, https://doi.org/10.5194/essd-15-3819-2023, 2023
Short summary
Short summary
This paper introduces the new high-resolution land use and land cover change dataset LUCAS LUC for Europe (version 1.1), tailored for use in regional climate models. Historical and projected future land use change information from the Land-Use Harmonization 2 (LUH2) dataset is translated into annual plant functional type changes from 1950 to 2015 and 2016 to 2100, respectively, by employing a newly developed land use translator.
Lennart Marien, Mahyar Valizadeh, Wolfgang zu Castell, Christine Nam, Diana Rechid, Alexandra Schneider, Christine Meisinger, Jakob Linseisen, Kathrin Wolf, and Laurens M. Bouwer
Nat. Hazards Earth Syst. Sci., 22, 3015–3039, https://doi.org/10.5194/nhess-22-3015-2022, https://doi.org/10.5194/nhess-22-3015-2022, 2022
Short summary
Short summary
Myocardial infarctions (MIs; heart attacks) are influenced by temperature extremes, air pollution, lack of green spaces and ageing population. Here, we apply machine learning (ML) models in order to estimate the influence of various environmental and demographic risk factors. The resulting ML models can accurately reproduce observed annual variability in MI and inter-annual trends. The models allow quantification of the importance of individual factors and can be used to project future risk.
Swantje Preuschmann, Tanja Blome, Knut Görl, Fiona Köhnke, Bettina Steuri, Juliane El Zohbi, Diana Rechid, Martin Schultz, Jianing Sun, and Daniela Jacob
Adv. Sci. Res., 19, 51–71, https://doi.org/10.5194/asr-19-51-2022, https://doi.org/10.5194/asr-19-51-2022, 2022
Short summary
Short summary
The main aspect of the paper is to obtain transferable principles for the development of digital knowledge transfer products. As such products are still unstandardised, the authors explored challenges and approaches for product developments. The authors report what they see as useful principles for developing digital knowledge transfer products, by describing the experience of developing the Net-Zero-2050 Web-Atlas and the "Bodenkohlenstoff-App".
Priscilla A. Mooney, Diana Rechid, Edouard L. Davin, Eleni Katragkou, Natalie de Noblet-Ducoudré, Marcus Breil, Rita M. Cardoso, Anne Sophie Daloz, Peter Hoffmann, Daniela C. A. Lima, Ronny Meier, Pedro M. M. Soares, Giannis Sofiadis, Susanna Strada, Gustav Strandberg, Merja H. Toelle, and Marianne T. Lund
The Cryosphere, 16, 1383–1397, https://doi.org/10.5194/tc-16-1383-2022, https://doi.org/10.5194/tc-16-1383-2022, 2022
Short summary
Short summary
We use multiple regional climate models to show that afforestation in sub-polar and alpine regions reduces the radiative impact of snow albedo on the atmosphere, reduces snow cover, and delays the start of the snowmelt season. This is important for local communities that are highly reliant on snowpack for water resources and winter tourism. However, models disagree on the amount of change particularly when snow is melting. This shows that more research is needed on snow–vegetation interactions.
Vanessa Reinhart, Peter Hoffmann, Diana Rechid, Jürgen Böhner, and Benjamin Bechtel
Earth Syst. Sci. Data, 14, 1735–1794, https://doi.org/10.5194/essd-14-1735-2022, https://doi.org/10.5194/essd-14-1735-2022, 2022
Short summary
Short summary
The LANDMATE plant functional type (PFT) land cover dataset for Europe 2015 (Version 1.0) is a gridded, high-resolution dataset for use in regional climate models. LANDMATE PFT is prepared using the expertise of regional climate modellers all over Europe and is easily adjustable to fit into different climate model families. We provide comprehensive spatial quality information for LANDMATE PFT, which can be used to reduce uncertainty in regional climate model simulations.
Giannis Sofiadis, Eleni Katragkou, Edouard L. Davin, Diana Rechid, Nathalie de Noblet-Ducoudre, Marcus Breil, Rita M. Cardoso, Peter Hoffmann, Lisa Jach, Ronny Meier, Priscilla A. Mooney, Pedro M. M. Soares, Susanna Strada, Merja H. Tölle, and Kirsten Warrach Sagi
Geosci. Model Dev., 15, 595–616, https://doi.org/10.5194/gmd-15-595-2022, https://doi.org/10.5194/gmd-15-595-2022, 2022
Short summary
Short summary
Afforestation is currently promoted as a greenhouse gas mitigation strategy. In our study, we examine the differences in soil temperature and moisture between grounds covered either by forests or grass. The main conclusion emerged is that forest-covered grounds are cooler but drier than open lands in summer. Therefore, afforestation disrupts the seasonal cycle of soil temperature, which in turn could trigger changes in crucial chemical processes such as soil carbon sequestration.
Peter Hoffmann, Vanessa Reinhart, Diana Rechid, Nathalie de Noblet-Ducoudré, Edouard L. Davin, Christina Asmus, Benjamin Bechtel, Jürgen Böhner, Eleni Katragkou, and Sebastiaan Luyssaert
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2021-252, https://doi.org/10.5194/essd-2021-252, 2021
Manuscript not accepted for further review
Short summary
Short summary
This paper introduces the new high-resolution land-use land-cover change dataset LUCAS LUC historical and future land use and land cover change dataset (Version 1.0), tailored for use in regional climate models. Historical and projected future land use change information from the Land-Use Harmonization 2 (LUH2) dataset is translated into annual plant functional type changes from 1950 to 2015 and 2016 to 2100, respectively, by employing a newly developed land use translator.
Kevin Sieck, Christine Nam, Laurens M. Bouwer, Diana Rechid, and Daniela Jacob
Earth Syst. Dynam., 12, 457–468, https://doi.org/10.5194/esd-12-457-2021, https://doi.org/10.5194/esd-12-457-2021, 2021
Short summary
Short summary
This paper presents new estimates of future extreme weather in Europe, including extreme heat, extreme rainfall and meteorological drought. These new estimates were achieved by repeating model calculations many times, thereby reducing uncertainties of these rare events at low levels of global warming at 1.5 and 2 °C above
pre-industrial temperature levels. These results are important, as they help to assess which weather extremes could increase at moderate warming levels and where.
Marcus Breil, Edouard L. Davin, and Diana Rechid
Biogeosciences, 18, 1499–1510, https://doi.org/10.5194/bg-18-1499-2021, https://doi.org/10.5194/bg-18-1499-2021, 2021
Short summary
Short summary
The physical processes behind varying evapotranspiration rates in forests and grasslands in Europe are investigated in a regional model study with idealized afforestation scenarios. The results show that the evapotranspiration response to afforestation depends on the interplay of two counteracting factors: the transpiration facilitating characteristics of a forest and the reduced saturation deficits of forests caused by an increased surface roughness and associated lower surface temperatures.
Cited articles
Abel, D. K.-J.: Weiterentwicklung der Bodenhydrologie des regionalen Klimamodells REMO, PhD thesis, Universität Würzburg, https://doi.org/10.25972/OPUS-31146, 2023. a
Asmus, C.: Modeling and evaluating the effects of irrigation on land-atmosphere interaction in southwestern Europe with the regional climate model REMO2020-iMOVE using a newly developed parameterization, Zenodo [data set], https://doi.org/10.5281/zenodo.7867328, 2023. a
Asmus, C. and Buntemeyer, L.: Modeling and evaluating the effects of irrigation on land-atmosphere interaction in southwestern Europe with the regional climate model REMO2020-iMOVE using a newly developed parameterization, Zenodo [code], https://doi.org/10.5281/zenodo.7889384, 2023. a
Balmer, C. and Amante, A.: Analysis: Wasted water saps battle against Italy's worst drought in decades, Reuters, https://www.reuters.com/world/europe/wasted-water-saps-battle-against-italys-worst-drought-decades-2022-07-19/ (last access: 30 November 2023), 2022. a
Bjorneberg, D.: IRRIGATION | Methods, in: Reference Module in Earth Systems and Environmental Sciences, Elsevier, ISBN 978-0-12-409548-9, https://doi.org/10.1016/B978-0-12-409548-9.05195-2, 2013. a
Boucher, O., Myhre, G., and Myhre, A.: Direct human influence of irrigation on atmospheric water vapour and climate, Clim. Dynam., 22, 597–603, https://doi.org/10.1007/s00382-004-0402-4, 2004. a
Desiato, F., Fioravanti, G., Fraschetti, P., Perconti, W., and Toreti, A.: Climate indicators for Italy: calculation and dissemination, Adv. Sci. Res., 6, 147–150, https://doi.org/10.5194/asr-6-147-2011, 2011 (data available at: http://193.206.192.214/servertsutm/serietemporali400.php, last access: 8 December 2023). a, b
de Vrese, P. and Hagemann, S.: Uncertainties in modelling the climate impact of irrigation, Clim. Dynam., 51, 2023–2038, https://doi.org/10.1007/s00382-017-3996-z, 2018. a, b, c, d
Doell, P. and Siebert, S.: A Digital Global Map of Irrgated Areas, Report A9901, Center for Environmental Systems Research, University of Kassel, Kurt Wolters Strasse 3, 34109 Kassel, Germany, https://doi.org/10.13140/2.1.2726.2080, 1999. a
Dümenil, L. and Todini, E.: Chapter 9 – A rainfall–runoff scheme for use in the Hamburg climate model, in: Advances in Theoretical Hydrology, edited by: O'Kane, J. P., European Geophysical Society Series on Hydrological Sciences, Elsevier, Amsterdam, 129–157, https://doi.org/10.1016/B978-0-444-89831-9.50016-8, 1992. a
Eggert, B.: Auswirkungen der Oberflächeneigenschaften in REMO auf die Simulation der unteren Atmosphäre, CSC Report, 8, https://epub.sub.uni-hamburg.de/epub/volltexte/2013/23967/pdf/csc_report8.pdf (last access: 30 November 2023), 2011. a
Giorgi, F. and Avissar, R.: Representation of heterogeneity effects in Earth system modeling: Experience from land surface modeling, Rev. Geophys., 35, 413–437, https://doi.org/10.1029/97RG01754, 1997. a
Giuffrida, A.: More than 100 towns in Italy’s Po valley asked to ration water, The Guardian, https://www.theguardian.com/world/2022/jun/15/italy-drought-po-valley-ration-water (last access: 30 November 2023), 2022. a
Goettel, H.: Einfluss der nichthydrostatischen Modellierung und der Niederschlagsverdriftung auf die Ergebnisse regionaler Klimamodellierung, PhD thesis, MPI für Meteorologie, Hamburg, https://doi.org/10.17617/2.994076, 2009. a
Hagemann, S.: An Improved Land Surface Parameter Dataset for Global and Regional Climate Models, MPI Report 336, Max-Planck-Institut fuer Meteorologie, 21 pp., https://doi.org/10.17617/2.2344576, 2002. a
Hagemann, S., Botzet, M., Dümenil, L., and Machenhauer, B.: Derivation of global GCM boundary conditions 10 from 1 km land use satellite date, MPI Report No. 289, Max-Planck-Institut fuer Meteorologie, 34 pp., https://pure.mpg.de/rest/items/item_1562156_5/component/file_1562155/content (last access: 30 November 2023), 1999. a, b
Hoffmann, P., Reinhart, V., Rechid, D., de Noblet-Ducoudré, N., Davin, E. L., Asmus, C., Bechtel, B., Böhner, J., Katragkou, E., and Luyssaert, S.: High-resolution land use and land cover dataset for regional climate modelling: historical and future changes in Europe, Earth Syst. Sci. Data, 15, 3819–3852, https://doi.org/10.5194/essd-15-3819-2023, 2023. a
Im, E.-S., Coppola, E., Giorgi, F., and Bi, X.: Validation of a High-Resolution Regional Climate Model for the Alpine Region and Effects of a Subgrid-Scale Topography and Land Use Representation, J. Climate, 23, 1854–1873, https://doi.org/10.1175/2009JCLI3262.1, 2010. a
IPCC: Summary for Policymakers, in: Climate Change and Land: an IPCC special report on climate change, desertification, land degradation, sustainable land management, food security, and greenhouse gas fluxes in terrestrial ecosystems, edited by: Shukla, P. R., Skea, J., Calvo Buendia, E., Masson-Delmotte, V., Pörtner, H.-O., Roberts, D. C., Zhai, P., Slade, R., Connors, S., van Diemen, R., Ferrat, M., Haughey, E., Luz, S., Neogi, S., Pathak, M., Petzold, J., Portugal Pereira, J., Vyas, P., Huntley, E., Kissick, K., Belkacemi, M., and Malley, J., https://doi.org/10.1017/9781009157988.001, 2019. a
Jacob, D.: A note to the simulation of the annual and inter-annual variability of the water budget over the Baltic Sea drainage basin, Meteorol. Atmos. Phys., 77, 61–73, https://doi.org/10.1007/s007030170017, 2001. a
Jacob, D.and Podzun, R.: Sensitivity studies with the regional climate model REMO, Meteorol. Atmos. Phys., 63, 119–129, https://doi.org/10.1007/BF01025368, 1997. a
Jia, G., Shevliakova, E., Artaxo, P., De Noblet-Ducoudré, N., Houghton, R., House, J., Kitajima, K., Lennard, C., Popp, A., Sirin, A., Sukumar, R., and Verchot, L.: Land–climate interactions, in: Climate Change and Land: an IPCC special report on climate change, desertification, land degradation, sustainable land management, food security, and greenhouse gas fluxes in terrestrial ecosystems, edited by: Shukla, P. R., Skea, J., Calvo Buendia, E., Masson-Delmotte, V., Pörtner, H.-O., Roberts, D., Zhai, P., Slade, R., Connors, S., van Diemen, R., Ferrat, M., Haughey, E., Luz, A., Neogi, S., Pathak, M., Petzold, J., Portugal Pereira, J., Vyas, P., Huntley, E., Kissick, K., Belkacemi, M., and Malley, J., book section Chapter 2, Intergovernmental Panel on Climate Change, https://www.ipcc.ch/site/assets/uploads/sites/4/2022/11/SRCCL_Chapter_2.pdf (last access: 30 November 2023), in press, 2019. a, b
Kew, S. F., Philip, S. Y., van Oldenborgh, G. J., van der Schrier, G., Otto, F. E. L., and Vautard, R.: The Exceptional Summer Heat Wave in Southern Europe 2017, B. Am. Meteorol. Soc., 100, S49–S53, https://doi.org/10.1175/BAMS-D-18-0109.1, 2019. a, b
Lawrence, D. M., Fisher, R. A., Koven, C. D., Oleson, K. W., Swenson, S. C., Bonan, G., Collier, N., Ghimire, B., van Kampenhout, L., Kennedy, D., Kluzek, E., Lawrence, P. J., Li, F., Li, H., Lombardozzi, D., Riley, W. J., Sacks, W. J., Shi, M., Vertenstein, M., Wieder, W. R., Xu, C., Ali, A. A., Badger, A. M., Bisht, G., van den Broeke, M., Brunke, M. A., Burns, S. P., Buzan, J., Clark, M., Craig, A., Dahlin, K., Drewniak, B., Fisher, J. B., Flanner, M., Fox, A. M., Gentine, P., Hoffman, F., Keppel-Aleks, G., Knox, R., Kumar, S., Lenaerts, J., Leung, L. R., Lipscomb, W. H., Lu, Y., Pandey, A., Pelletier, J. D., Perket, J., Randerson, J. T., Ricciuto, D. M., Sanderson, B. M., Slater, A., Subin, Z. M., Tang, J., Thomas, R. Q., Val Martin, M., and Zeng, X.: The Community Land Model Version 5: Description of New Features, Benchmarking, and Impact of Forcing Uncertainty, J. Adv. Model. Earth Sy., 11, 4245–4287, https://doi.org/10.1029/2018MS001583, 2019. a, b
Leng, G., Huang, M., Tang, Q., and Leung, L. R.: A modeling study of irrigation effects on global surface water and groundwater resources under a changing climate, J. Adv. Model. Earth Sy., 7, 1285–1304, https://doi.org/10.1002/2015MS000437, 2015. a
Leng, G., Leung, L. R., and Huang, M.: Significant impacts of irrigation water sources and methods on modeling irrigation effects in the ACME Land Model, J. Adv. Model. Earth Sy., 9, 1665–1683, https://doi.org/10.1002/2016MS000885, 2017. a
Luyssaert, S., Jammet, M., Stoy, P., Estel, S., Pongratz, J., Ceschia, E., Churkina, G., Don, A., Erb, K.-H., Ferlicoq, M., Gielen, B., Grünwald, T., Houghton, R., Klumpp, K., Knohl, A., Kolb, T., Kuemmerle, T., Laurila, T., Lohila, A., and Dolman, H. A.: Land management and land-cover change have impacts of similar magnitude on surface temperature, Nat. Clim. Change, 4, 389–393, https://doi.org/10.1038/nclimate2196, 2014. a, b
Majewski, D.: The EUROPA-modell of the Deutscher Wetterdienst, in: Seminar on Numerical Methods in Atmospheric Models, 9–13 September 1991, vol. II, ECMWF, ECMWF, Shinfield Park, Reading, 147–193, https://www.ecmwf.int/node/10940 (last access: 1 December 2023), 1991. a
Patanè, C.: Leaf Area Index, Leaf Transpiration and Stomatal Conductance as Affected by Soil Water Deficit and VPD in Processing Tomato in Semi Arid Mediterranean Climate, J. Agron. Crop Sci., 197, 165–176, https://doi.org/10.1111/j.1439-037X.2010.00454.x, 2011. a
Peterson, J. B., Robinson, B. F., and Beck, R. H.: Predictability of Change in Soil Reflectance on Wetting, LARS Symposia, Paper 279, http://docs.lib.purdue.edu/lars_symp/279 (last access: 1 December 2023), 1979. a
Pietikäinen, J.-P., Markkanen, T., Sieck, K., Jacob, D., Korhonen, J., Räisänen, P., Gao, Y., Ahola, J., Korhonen, H., Laaksonen, A., and Kaurola, J.: The regional climate model REMO (v2015) coupled with the 1-D freshwater lake model FLake (v1): Fenno-Scandinavian climate and lakes, Geosci. Model Dev., 11, 1321–1342, https://doi.org/10.5194/gmd-11-1321-2018, 2018. a, b, c
Puma, M. and Cook, B.: Effects of irrigation on global climate during the 20th century, J. Geophys. Res, 115, D16120, https://doi.org/10.1029/2010JD014122, 2010. a, b
Raddatz, T. J., Reick, C. H., Knorr, W., Kattge, J., Roeckner, E., Schnur, R., Schnitzler, K.-G., Wetzel, P., and Jungclaus, J.: Will the tropical land biosphere dominate the climate–carbon cycle feedback during the twenty-first century?, Clim. Dynam., 29, 565–574, https://doi.org/10.1007/s00382-007-0247-8, 2007. a
Rai, P., Ziegler, K., Abel, D., Pollinger, F., and Paeth, H.: Performance of a regional climate model with interactive vegetation (REMO-iMOVE) over Central Asia, Theor. Appl. Climatol., 150, 1385–1405, https://doi.org/10.1007/s00704-022-04233-y, 2022. a
Rechid, D. and Jacob, D.: Influence of monthly varying vegetation on the simulated climate in Europe, Meteorol. Z., 15, 99–116, https://doi.org/10.1127/0941-2948/2006/0091, 2006. a, b
Reinhart, V., Hoffmann, P., Rechid, D., Böhner, J., and Bechtel, B.: High-resolution land use and land cover dataset for regional climate modelling: a plant functional type map for Europe 2015, Earth Syst. Sci. Data, 14, 1735–1794, https://doi.org/10.5194/essd-14-1735-2022, 2022. a
Roeckner, E., Arpe, K., Bengtsson, L., Christoph, M., Claussen, M., Dümenil, L., Esch, M., Giorgetta, M., Schlese, U., and Schulzweida, U.: The Atmospheric General Circulation Model ECHAM-4: Model Description and Simulation of Present Day, Climate MPI Report No. 218, Max-Planck-Institut für Meteorologie, Hamburg, Germany, ISSN 0937-1060, https://esdynamics.geo.uni-tuebingen.de/wiki/files/modelling/pdf/MPI-Report_218.pdf (last access: 1 December 2023), 1996. a
Sacks, W. J., Cook, B. I., Buenning, N., Levis, S., and Helkowski, J. H.: Effects of global irrigation on the near-surface climate, Clim. Dynam., 33, 159–175, https://doi.org/10.1007/s00382-008-0445-z, 2009. a, b, c, d
Saeed, F., Hagemann, S., and Jacob, D.: Impact of irrigation on the South Asian summer monsoon, Geophys. Res. Lett., 36, L20711, https://doi.org/10.1029/2009GL040625, 2009. a, b, c, d
Sánchez-Benítez, A., García-Herrera, R., Barriopedro, D., Sousa, P. M., and Trigo, R. M.: June 2017: The Earliest European Summer Megaheatwave of Reanalysis Period, Geophys. Res. Lett., 45, 1955–1962, https://doi.org/10.1002/2018GL077253, 2018. a
Semmler, T.: Der Wasser- und Energiehaushalt der arktischen Atmosphäre, PhD Thesis, Universität Hamburg, Hamburg, https://pure.mpg.de/rest/items/item_995430_6/component/file_995429/content (last access: 1 December 2023), 2002. a
Siebert, S., Henrich, V., Frenken, K, and Burke, J.: Global Map of Irrigation Areas version 5, Rheinische Friedrich-Wilhelms-University, Bonn, Germany/Food and Agriculture Organization of the United Nations, Rome, Italy, https://www.fao.org/aquastat/en/geospatial-information/global-maps-irrigated-areas/latest-version (last access: 1 December 2023), 2013a. a, b, c
Siebert, S., Henrich, V., Frenken, K., and Burke, J.: Update of the Digital Global Map of Irrigation Areas to Version 5; Food and Agriculture Organization of the United Nations (FAO), Rome, Italy, https://doi.org/10.13140/2.1.2660.6728, 2013b. a, b
Szilagyi, J. and Franz, T. E.: Anthropogenic hydrometeorological changes at a regional scale: observed irrigation–precipitation feedback (1979–2015) in Nebraska, USA, Sustainable Water Resources Management, 6, 1, https://doi.org/10.1007/s40899-020-00368-w, 2020. a
Thiery, W., Visser, A., Fischer, E., Hauser, M., Hirsch, A., Lawrence, D., Lejeune, Q., Davin, E., and Seneviratne, S.: Warming of hot extremes alleviated by expanding irrigation, Nat. Commun., 11, 290, https://doi.org/10.1038/s41467-019-14075-4, 2020. a, b, c
Tuinenburg, O. A., Hutjes, R. W. A., Stacke, T., Wiltshire, A., and Lucas-Picher, P.: Effects of Irrigation in India on the Atmospheric Water Budget, J. Hydrometeorol., 15, 1028–1050, https://doi.org/10.1175/JHM-D-13-078.1, 2014. a
Valmassoi, A. and Keller, J.: A review on irrigation parameterizations in Earth system models, Frontiers in Water, 4, 906664, https://doi.org/10.3389/frwa.2022.906664, 2022. a
Valmassoi, A., Dudhia, J., Di Sabatino, S., and Pilla, F.: Irrigation impact on precipitation during a heatwave event using WRF-ARW: The summer 2015 Po Valley case, Atmos. Res., 241, 104951, https://doi.org/10.1016/j.atmosres.2020.104951, 2020a. a
Valmassoi, A., Dudhia, J., Di Sabatino, S., and Pilla, F.: Regional Climate Impacts of Irrigation in Northern Italy Using a High Resolution Model, Atmosphere, 11, 72, https://doi.org/10.3390/atmos11010072, 2020b. a, b, c
Yao, Y., Vanderkelen, I., Lombardozzi, D., Swenson, S., Lawrence, D., Jägermeyr, J., Grant, L., and Thiery, W.: Implementation and Evaluation of Irrigation Techniques in the Community Land Model, J. Adv. Model. Earth Sy., 14, e2022MS003074, https://doi.org/10.1029/2022MS003074, 2022. a, b, c
Zucaro, R.: Atlas of Italian Irrigation systems, Tech. rep., Istituto Nazionale di Economia Agraria (INEA), https://sigrian.crea.gov.it/wp-content/uploads/2019/11/Atlas_Italian_irrigation_2014_INEA.pdf (last access: 1 December 2023), 2014. a
Short summary
Irrigation modifies the land surface and soil conditions. The effects can be quantified using numerical climate models. Our study introduces a new irrigation parameterization, which simulates the effects of irrigation on land, atmosphere, and vegetation. We applied the parameterization and evaluated the results in terms of their physical consistency. We found an improvement in the model results in the 2 m temperature representation in comparison with observational data for our study.
Irrigation modifies the land surface and soil conditions. The effects can be quantified using...