Articles | Volume 13, issue 3
https://doi.org/10.5194/esd-13-1377-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/esd-13-1377-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
29 Sep 2022
Research article |
| 29 Sep 2022
| 29 Sep 2022
Resilience of UK crop yields to compound climate change
Louise J. Slater
CORRESPONDING AUTHOR
School of Geography and the Environment, University of Oxford, Oxford, OX1 3QY, UK
Chris Huntingford
UK Centre for Ecology and Hydrology, Wallingford, Oxfordshire, OX10 8BB, UK
Richard F. Pywell
UK Centre for Ecology and Hydrology, Wallingford, Oxfordshire, OX10 8BB, UK
John W. Redhead
UK Centre for Ecology and Hydrology, Wallingford, Oxfordshire, OX10 8BB, UK
Elizabeth J. Kendon
Met Office, FitzRoy Road, Exeter, Devon, EX1 3PB, UK
Bristol University, Faculty of Science, BS8 1UH, UK
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AI is impacting science, providing key data insights, but most algorithms are statistical requiring cautious "out-of-sample" extrapolation. Yet climate research concerns predicting future climatic states. We consider a new method of AI-led equation discovery. Equations offer process interpretation and more robust predictions. We recommend this method for climate analysis, suggesting illustrative application to atmospheric convection, land–atmosphere CO2 flux, and global ocean circulation models.
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Geert Lenderink, Nikolina Ban, Erwan Brisson, Ségolène Berthou, Virginia Edith Cortés-Hernández, Elizabeth Kendon, Hayley J. Fowler, and Hylke de Vries
Hydrol. Earth Syst. Sci., 29, 1201–1220, https://doi.org/10.5194/hess-29-1201-2025, https://doi.org/10.5194/hess-29-1201-2025, 2025
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Future extreme rainfall events are influenced by changes in both absolute and relative humidity. The impact of increasing absolute humidity is reasonably well understood, but the role of relative humidity decreases over land remains largely unknown. Using hourly observations from France and the Netherlands, we find that lower relative humidity generally leads to more intense rainfall extremes. This relation is only captured well in recently developed convection-permitting climate models.
Colin G. Jones, Fanny Adloff, Ben B. B. Booth, Peter M. Cox, Veronika Eyring, Pierre Friedlingstein, Katja Frieler, Helene T. Hewitt, Hazel A. Jeffery, Sylvie Joussaume, Torben Koenigk, Bryan N. Lawrence, Eleanor O'Rourke, Malcolm J. Roberts, Benjamin M. Sanderson, Roland Séférian, Samuel Somot, Pier Luigi Vidale, Detlef van Vuuren, Mario Acosta, Mats Bentsen, Raffaele Bernardello, Richard Betts, Ed Blockley, Julien Boé, Tom Bracegirdle, Pascale Braconnot, Victor Brovkin, Carlo Buontempo, Francisco Doblas-Reyes, Markus Donat, Italo Epicoco, Pete Falloon, Sandro Fiore, Thomas Frölicher, Neven S. Fučkar, Matthew J. Gidden, Helge F. Goessling, Rune Grand Graversen, Silvio Gualdi, José M. Gutiérrez, Tatiana Ilyina, Daniela Jacob, Chris D. Jones, Martin Juckes, Elizabeth Kendon, Erik Kjellström, Reto Knutti, Jason Lowe, Matthew Mizielinski, Paola Nassisi, Michael Obersteiner, Pierre Regnier, Romain Roehrig, David Salas y Mélia, Carl-Friedrich Schleussner, Michael Schulz, Enrico Scoccimarro, Laurent Terray, Hannes Thiemann, Richard A. Wood, Shuting Yang, and Sönke Zaehle
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We propose a number of priority areas for the international climate research community to address over the coming decade. Advances in these areas will both increase our understanding of past and future Earth system change, including the societal and environmental impacts of this change, and deliver significantly improved scientific support to international climate policy, such as future IPCC assessments and the UNFCCC Global Stocktake.
Peter Levy, Laura Bentley, Peter Danks, Bridget Emmett, Angus Garbutt, Stephen Heming, Peter Henrys, Aidan Keith, Inma Lebron, Niall McNamara, Richard Pywell, John Redhead, David Robinson, and Alexander Wickenden
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We collated a large data set (15 790 soil cores) on soil carbon stock in different land uses. Soil carbon stocks were highest in woodlands and lowest in croplands. The variability in the effects was large. This has important implications for agri-environment schemes seeking to sequester carbon in the soil by altering land use because the effect of a given intervention is very hard to verify.
Rutong Liu, Jiabo Yin, Louise Slater, Shengyu Kang, Yuanhang Yang, Pan Liu, Jiali Guo, Xihui Gu, Xiang Zhang, and Aliaksandr Volchak
Hydrol. Earth Syst. Sci., 28, 3305–3326, https://doi.org/10.5194/hess-28-3305-2024, https://doi.org/10.5194/hess-28-3305-2024, 2024
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Climate change accelerates the water cycle and alters the spatiotemporal distribution of hydrological variables, thus complicating the projection of future streamflow and hydrological droughts. We develop a cascade modeling chain to project future bivariate hydrological drought characteristics over China, using five bias-corrected global climate model outputs under three shared socioeconomic pathways, five hydrological models, and a deep-learning model.
Solomon H. Gebrechorkos, Julian Leyland, Simon J. Dadson, Sagy Cohen, Louise Slater, Michel Wortmann, Philip J. Ashworth, Georgina L. Bennett, Richard Boothroyd, Hannah Cloke, Pauline Delorme, Helen Griffith, Richard Hardy, Laurence Hawker, Stuart McLelland, Jeffrey Neal, Andrew Nicholas, Andrew J. Tatem, Ellie Vahidi, Yinxue Liu, Justin Sheffield, Daniel R. Parsons, and Stephen E. Darby
Hydrol. Earth Syst. Sci., 28, 3099–3118, https://doi.org/10.5194/hess-28-3099-2024, https://doi.org/10.5194/hess-28-3099-2024, 2024
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This study evaluated six high-resolution global precipitation datasets for hydrological modelling. MSWEP and ERA5 showed better performance, but spatial variability was high. The findings highlight the importance of careful dataset selection for river discharge modelling due to the lack of a universally superior dataset. Further improvements in global precipitation data products are needed.
Mark S. Williamson, Peter M. Cox, Chris Huntingford, and Femke J. M. M. Nijsse
Earth Syst. Dynam., 15, 829–852, https://doi.org/10.5194/esd-15-829-2024, https://doi.org/10.5194/esd-15-829-2024, 2024
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Emergent constraints on equilibrium climate sensitivity (ECS) have generally got statistically weaker in the latest set of state-of-the-art climate models (CMIP6) compared to past sets (CMIP5). We look at why this weakening happened for one particular study (Cox et al, 2018) and attribute it to an assumption made in the theory that when corrected for restores there is a stronger relationship between predictor and ECS.
Marcus Buechel, Louise Slater, and Simon Dadson
Hydrol. Earth Syst. Sci., 28, 2081–2105, https://doi.org/10.5194/hess-28-2081-2024, https://doi.org/10.5194/hess-28-2081-2024, 2024
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Afforestation has been proposed internationally, but the hydrological implications of such large increases in the spatial extent of woodland are not fully understood. In this study, we use a land surface model to simulate hydrology across Great Britain with realistic afforestation scenarios and potential climate changes. Countrywide afforestation minimally influences hydrology, when compared to climate change, and reduces low streamflow whilst not lowering the highest flows.
Bailey J. Anderson, Manuela I. Brunner, Louise J. Slater, and Simon J. Dadson
Hydrol. Earth Syst. Sci., 28, 1567–1583, https://doi.org/10.5194/hess-28-1567-2024, https://doi.org/10.5194/hess-28-1567-2024, 2024
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Elasticityrefers to how much the amount of water in a river changes with precipitation. We usually calculate this using average streamflow values; however, the amount of water within rivers is also dependent on stored water sources. Here, we look at how elasticity varies across the streamflow distribution and show that not only do low and high streamflows respond differently to precipitation change, but also these differences vary with water storage availability.
Jiabo Yin, Louise J. Slater, Abdou Khouakhi, Le Yu, Pan Liu, Fupeng Li, Yadu Pokhrel, and Pierre Gentine
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Emma L. Robinson, Chris Huntingford, Valyaveetil Shamsudheen Semeena, and James M. Bullock
Earth Syst. Sci. Data, 15, 5371–5401, https://doi.org/10.5194/essd-15-5371-2023, https://doi.org/10.5194/essd-15-5371-2023, 2023
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CHESS-SCAPE is a suite of high-resolution climate projections for the UK to 2080, derived from United Kingdom Climate Projections 2018 (UKCP18), designed to support climate impact modelling. It contains four realisations of four scenarios of future greenhouse gas levels (RCP2.6, 4.5, 6.0 and 8.5), with and without bias correction to historical data. The variables are available at 1 km resolution and a daily time step, with monthly, seasonal and annual means and 20-year mean-monthly time slices.
Louise J. Slater, Louise Arnal, Marie-Amélie Boucher, Annie Y.-Y. Chang, Simon Moulds, Conor Murphy, Grey Nearing, Guy Shalev, Chaopeng Shen, Linda Speight, Gabriele Villarini, Robert L. Wilby, Andrew Wood, and Massimiliano Zappa
Hydrol. Earth Syst. Sci., 27, 1865–1889, https://doi.org/10.5194/hess-27-1865-2023, https://doi.org/10.5194/hess-27-1865-2023, 2023
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Hybrid forecasting systems combine data-driven methods with physics-based weather and climate models to improve the accuracy of predictions for meteorological and hydroclimatic events such as rainfall, temperature, streamflow, floods, droughts, tropical cyclones, or atmospheric rivers. We review recent developments in hybrid forecasting and outline key challenges and opportunities in the field.
Chris Huntingford, Peter M. Cox, Mark S. Williamson, Joseph J. Clarke, and Paul D. L. Ritchie
Earth Syst. Dynam., 14, 433–442, https://doi.org/10.5194/esd-14-433-2023, https://doi.org/10.5194/esd-14-433-2023, 2023
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Emergent constraints (ECs) reduce the spread of projections between climate models. ECs estimate changes to climate features impacting adaptation policy, and with this high profile, the method is under scrutiny. Asking
What is an EC?, we suggest they are often the discovery of parameters that characterise hidden large-scale equations that climate models solve implicitly. We present this conceptually via two examples. Our analysis implies possible new paths to link ECs and physical processes.
Gang Liu, Shushi Peng, Chris Huntingford, and Yi Xi
Geosci. Model Dev., 16, 1277–1296, https://doi.org/10.5194/gmd-16-1277-2023, https://doi.org/10.5194/gmd-16-1277-2023, 2023
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Due to computational limits, lower-complexity models (LCMs) were developed as a complementary tool for accelerating comprehensive Earth system models (ESMs) but still lack a good precipitation emulator for LCMs. Here, we developed a data-calibrated precipitation emulator (PREMU), a computationally effective way to better estimate historical and simulated precipitation by current ESMs. PREMU has potential applications related to land surface processes and their interactions with climate change.
Rebecca J. Oliver, Lina M. Mercado, Doug B. Clark, Chris Huntingford, Christopher M. Taylor, Pier Luigi Vidale, Patrick C. McGuire, Markus Todt, Sonja Folwell, Valiyaveetil Shamsudheen Semeena, and Belinda E. Medlyn
Geosci. Model Dev., 15, 5567–5592, https://doi.org/10.5194/gmd-15-5567-2022, https://doi.org/10.5194/gmd-15-5567-2022, 2022
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We introduce new representations of plant physiological processes into a land surface model. Including new biological understanding improves modelled carbon and water fluxes for the present in tropical and northern-latitude forests. Future climate simulations demonstrate the sensitivity of photosynthesis to temperature is important for modelling carbon cycle dynamics in a warming world. Accurate representation of these processes in models is necessary for robust predictions of climate change.
Thomas Lees, Steven Reece, Frederik Kratzert, Daniel Klotz, Martin Gauch, Jens De Bruijn, Reetik Kumar Sahu, Peter Greve, Louise Slater, and Simon J. Dadson
Hydrol. Earth Syst. Sci., 26, 3079–3101, https://doi.org/10.5194/hess-26-3079-2022, https://doi.org/10.5194/hess-26-3079-2022, 2022
Short summary
Short summary
Despite the accuracy of deep learning rainfall-runoff models, we are currently uncertain of what these models have learned. In this study we explore the internals of one deep learning architecture and demonstrate that the model learns about intermediate hydrological stores of soil moisture and snow water, despite never having seen data about these processes during training. Therefore, we find evidence that the deep learning approach learns a physically realistic mapping from inputs to outputs.
Manuela I. Brunner and Louise J. Slater
Hydrol. Earth Syst. Sci., 26, 469–482, https://doi.org/10.5194/hess-26-469-2022, https://doi.org/10.5194/hess-26-469-2022, 2022
Short summary
Short summary
Assessing the rarity and magnitude of very extreme flood events occurring less than twice a century is challenging due to the lack of observations of such rare events. Here we develop a new approach, pooling reforecast ensemble members from the European Flood Awareness System to increase the sample size available to estimate the frequency of extreme flood events. We demonstrate that such ensemble pooling produces more robust estimates than observation-based estimates.
Thomas Lees, Marcus Buechel, Bailey Anderson, Louise Slater, Steven Reece, Gemma Coxon, and Simon J. Dadson
Hydrol. Earth Syst. Sci., 25, 5517–5534, https://doi.org/10.5194/hess-25-5517-2021, https://doi.org/10.5194/hess-25-5517-2021, 2021
Short summary
Short summary
We used deep learning (DL) models to simulate the amount of water moving through a river channel (discharge) based on the rainfall, temperature and potential evaporation in the previous days. We tested the DL models on catchments across Great Britain finding that the model can accurately simulate hydrological systems across a variety of catchment conditions. Ultimately, the model struggled most in areas where there is chalky bedrock and where human influence on the catchment is large.
Louise J. Slater, Bailey Anderson, Marcus Buechel, Simon Dadson, Shasha Han, Shaun Harrigan, Timo Kelder, Katie Kowal, Thomas Lees, Tom Matthews, Conor Murphy, and Robert L. Wilby
Hydrol. Earth Syst. Sci., 25, 3897–3935, https://doi.org/10.5194/hess-25-3897-2021, https://doi.org/10.5194/hess-25-3897-2021, 2021
Short summary
Short summary
Weather and water extremes have devastating effects each year. One of the principal challenges for society is understanding how extremes are likely to evolve under the influence of changes in climate, land cover, and other human impacts. This paper provides a review of the methods and challenges associated with the detection, attribution, management, and projection of nonstationary weather and water extremes.
Anna B. Harper, Karina E. Williams, Patrick C. McGuire, Maria Carolina Duran Rojas, Debbie Hemming, Anne Verhoef, Chris Huntingford, Lucy Rowland, Toby Marthews, Cleiton Breder Eller, Camilla Mathison, Rodolfo L. B. Nobrega, Nicola Gedney, Pier Luigi Vidale, Fred Otu-Larbi, Divya Pandey, Sebastien Garrigues, Azin Wright, Darren Slevin, Martin G. De Kauwe, Eleanor Blyth, Jonas Ardö, Andrew Black, Damien Bonal, Nina Buchmann, Benoit Burban, Kathrin Fuchs, Agnès de Grandcourt, Ivan Mammarella, Lutz Merbold, Leonardo Montagnani, Yann Nouvellon, Natalia Restrepo-Coupe, and Georg Wohlfahrt
Geosci. Model Dev., 14, 3269–3294, https://doi.org/10.5194/gmd-14-3269-2021, https://doi.org/10.5194/gmd-14-3269-2021, 2021
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We evaluated 10 representations of soil moisture stress in the JULES land surface model against site observations of GPP and latent heat flux. Increasing the soil depth and plant access to deep soil moisture improved many aspects of the simulations, and we recommend these settings in future work using JULES. In addition, using soil matric potential presents the opportunity to include parameters specific to plant functional type to further improve modeled fluxes.
Garry D. Hayman, Edward Comyn-Platt, Chris Huntingford, Anna B. Harper, Tom Powell, Peter M. Cox, William Collins, Christopher Webber, Jason Lowe, Stephen Sitch, Joanna I. House, Jonathan C. Doelman, Detlef P. van Vuuren, Sarah E. Chadburn, Eleanor Burke, and Nicola Gedney
Earth Syst. Dynam., 12, 513–544, https://doi.org/10.5194/esd-12-513-2021, https://doi.org/10.5194/esd-12-513-2021, 2021
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We model greenhouse gas emission scenarios consistent with limiting global warming to either 1.5 or 2 °C above pre-industrial levels. We quantify the effectiveness of methane emission control and land-based mitigation options regionally. Our results highlight the importance of reducing methane emissions for realistic emission pathways that meet the global warming targets. For land-based mitigation, growing bioenergy crops on existing agricultural land is preferable to replacing forests.
Douglas I. Kelley, Chantelle Burton, Chris Huntingford, Megan A. J. Brown, Rhys Whitley, and Ning Dong
Biogeosciences, 18, 787–804, https://doi.org/10.5194/bg-18-787-2021, https://doi.org/10.5194/bg-18-787-2021, 2021
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Initial evidence suggests human ignitions or landscape changes caused most Amazon fires during August 2019. However, confirmation is needed that meteorological conditions did not have a substantial role. Assessing the influence of historical weather on burning in an uncertainty framework, we find that 2019 meteorological conditions alone should have resulted in much less fire than observed. We conclude socio-economic factors likely had a strong role in the high recorded 2019 fire activity.
Cited articles
AHDB: Wheat growth guide, Kenilworth, Warwickshire,
https://cereals.ahdb.org.uk/media/185687/g66-wheat-growth-guide.pdf (last access: 1 January 2021), 2018.
AHDB: AHDB Harvest Report, Report 6 – Week 13, Week ending 6 October,
https://ahdb.org.uk/cereals-oilseeds/gb-harvest-progress (last access: 22 September 2022), 2020.
AHDB: The growth stages of cereals, Kenilworth,
Warwicksh,
https://ahdb.org.uk/knowledge-library/the-growth-stages-of-cereals, last access: 22 September 2022.
Ainsworth, E. A. and Long, S. P.: 30 years of free-air carbon dioxide
enrichment (FACE): What have we learned about future crop productivity and
its potential for adaptation?, Glob. Chang. Biol., 27, 27–49,
https://doi.org/10.1111/gcb.15375, 2021.
Andrews, T., Andrews, M. B., Bodas-Salcedo, A., Jones, G. S., Kuhlbrodt, T.,
Manners, J., Menary, M. B., Ridley, J., Ringer, M. A., Sellar, A. A.,
Senior, C. A., and Tang, Y.: Forcings, Feedbacks, and Climate Sensitivity in
HadGEM3-GC3.1 and UKESM1, J. Adv. Model. Earth Syst., 11, 4377–4394,
https://doi.org/10.1029/2019MS001866, 2019.
Arnell, N. W. and Freeman, A.: The effect of climate change on agro-climatic
indicators in the UK, Clim. Change, 165, 40,
https://doi.org/10.1007/s10584-021-03054-8, 2021.
Arnell, N. W., Freeman, A., Kay, A. L., Rudd, A. C., and Lowe, J. A.:
Indicators of climate risk in the UK at different levels of warming,
Environ. Res. Commun., 3, 095005, https://doi.org/10.1088/2515-7620/ac24c0, 2021.
Barlow, K. M., Christy, B. P., O'Leary, G. J., Riffkin, P. A., and Nuttall,
J. G.: Simulating the impact of extreme heat and frost events on wheat crop
production: A review, F. Crop. Res., 171, 109–119,
https://doi.org/10.1016/j.fcr.2014.11.010, 2015.
Ben-Ari, T., Boé, J., Ciais, P., Lecerf, R., Van Der Velde, M., and
Makowski, D.: Causes and implications of the unforeseen 2016 extreme yield
loss in the breadbasket of France, Nat. Commun., 9, 1–10,
https://doi.org/10.1038/s41467-018-04087-x, 2018.
Brisson, N., Gate, P., Gouache, D., Charmet, G., Oury, F. X., and Huard, F.:
Why are wheat yields stagnating in Europe? A comprehensive data analysis for
France, F. Crop. Res., 119, 201–212, https://doi.org/10.1016/j.fcr.2010.07.012,
2010.
Chaves, M. M., Maroco, J. P., and Pereira, J. S.: Understanding plant
responses to drought – From genes to the whole plant, Funct. Plant Biol.,
30, 239–264, https://doi.org/10.1071/FP02076, 2003.
Chen, D., Rojas, M., Samset, B. H., Cobb, K., Diongue Niang, A., Edwards,
P., Emori, S., Faria, S. H., Hawkins, E., Hope, P., Huybrechts, P.,
Meinshausen, M., Mustafa, S. K., Plattner, G. K., and Tréguier, A. M.:
Framing, Context, and Methods, in: Climate Change 2021: The Physical Science
Basis. Contribution of Working Group I to the Sixth Assessment Report of the
Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 14–286, https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_FullReport.pdf (last access: 22 September 2022), 2021.
DEFRA: Farming Statistics –
Final Crop Areas, Yields, Livestock Populations and Agricultural Workforce
at 1 June 2012, United Kingdom,
https://assets.publishing.service.gov.uk/file/183200/defra-stats-foodfarm (last access: 22 September 2022),
2012.
DEFRA: United Kingdom Cereal Yields, 2020.
DEFRA: Agriculture in the United Kingdom 2018, 1–111,
https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/741062/AUK-2017-18sep18.pdf (last access: 22 September 2022), 2018a.
DEFRA: Farming Statistics – Final Crop Areas, Yields, Livestock Populations and Agricultural Workforce at 1 June 2018, United Kingdom, 1–24, https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/869062/structure-jun2018final-uk-28feb20.pdf (last access: 22 September 2022), 2018b.
DEFRA: Farming Statistics –
Final Crop Areas, Yields, Livestock Populations and Agricultural Workforce
at June 2019, United Kingdom, 1–22,
https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/865769/structure-jun2019final-uk-22jan20-rev_v2.pdf (last access: 22 September 2022), 2019.
European Commission: Eurostat NUTS Administrative statistical units,
https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units/nuts (last access: 22 September 2022),
2010.
Ewert, F., Rodriguez, D., Jamieson, P., Semenov, M. A., Mitchell, R. A. C.,
Goudriaan, J., Porter, J. R., Kimball, B. A., Pinter, P. J., Manderscheid,
R., Weigel, H. J., Fangmeier, A., Fereres, E., and Villalobos, F.: Effects of
elevated CO2 and drought on wheat: Testing crop simulation models for
different experimental and climatic conditions, Agric. Ecosyst. Environ.,
93, 249–266, https://doi.org/10.1016/S0167-8809(01)00352-8, 2002.
FAO: FAOSTAT crops,
http://www.fao.org/faostat/en/#data/QC (last access: 22 September 2022), 2018.
Frich, P., Alexander, L. V., Della-Marta, P., Gleason, B., Haylock, M., Tank
Klein, A. M. G., and Peterson, T.: Observed coherent changes in climatic
extremes during the second half of the twentieth century, Clim. Res., 19,
193–212, https://doi.org/10.3354/cr019193, 2002.
Gagic, V., Kleijn, D., Báldi, A., Boros, G., Jørgensen, H. B., Elek,
Z., Garratt, M. P. D., de Groot, G. A., Hedlund, K.,
Kovács-Hostyánszki, A., Marini, L., Martin, E., Pevere, I., Potts,
S. G., Redlich, S., Senapathi, D., Steffan-Dewenter, I., Świtek, S.,
Smith, H. G., Takács, V., Tryjanowski, P., van der Putten, W. H., van
Gils, S., and Bommarco, R.: Combined effects of agrochemicals and ecosystem
services on crop yield across Europe, Ecol. Lett., 20, 1427–1436,
https://doi.org/10.1111/ele.12850, 2017.
Grassini, P., Eskridge, K. M., and Cassman, K. G.: Distinguishing between
yield advances and yield plateaus in historical crop production trends, Nat.
Commun., 4, 1–11, https://doi.org/10.1038/ncomms3918, 2013.
Harkness, C., Semenov, M. A., Areal, F., Senapati, N., Trnka, M., Balek, J.,
and Bishop, J.: Adverse weather conditions for UK wheat production under
climate change, Agric. For. Meteorol., 107862, 282–283,
https://doi.org/10.1016/j.agrformet.2019.107862, 2020.
Hausfather, Z. and Peters, G. P.: Emissions - the “business as usual”
story is misleading, Nature, 577, 618–620, 2020.
Hillocks, R. J.: Farming with fewer pesticides: EU pesticide review and
resulting challenges for UK agriculture, Crop Prot., 31, 85–93,
https://doi.org/10.1016/j.cropro.2011.08.008, 2012.
Hochman, Z., Gobbett, D. L., and Horan, H.: Climate trends account for
stalled wheat yields in Australia since 1990, Glob. Chang. Biol., 23,
2071–2081, https://doi.org/10.1111/gcb.13604, 2017.
Hollis, D., McCarthy, M., Kendon, M., Legg, T., and Simpson, I.:
HadUK-Grid – A new UK dataset of gridded climate observations, Geosci. Data
J., 6, 151–159, https://doi.org/10.1002/gdj3.78, 2019 (data available at: https://dap.ceda.ac.uk/badc/ukmo-hadobs/data/insitu/MOHC/HadOBS/HadUK-Grid/, last access: 22 September 2022).
Holman, I. P., Hess, T. M., Rey, D., and Knox, J. W.: A Multi-Level Framework
for Adaptation to Drought Within Temperate Agriculture, Front. Environ.
Sci., 8, 1–14, https://doi.org/10.3389/fenvs.2020.589871, 2021.
Hunt, M. L., Blackburn, G. A., Carrasco, L., Redhead, J. W., and Rowland, C.
S.: High resolution wheat yield mapping using Sentinel-2, Remote Sens.
Environ., 233, 111410, https://doi.org/10.1016/j.rse.2019.111410, 2019.
Iizumi, T. and Ramankutty, N.: Changes in yield variability of major crops
for 1981–2010 explained by climate change, Environ. Res. Lett., 11, 034003,
https://doi.org/10.1088/1748-9326/11/3/034003, 2016.
Kahiluoto, H., Kaseva, J., Balek, J., Olesen, J. E., Ruiz-Ramos, M., Gobin,
A., Kersebaum, K. C., Takáč, J., Ruget, F., Ferrise, R., Bezak, P.,
Capellades, G., Dibari, C., Mäkinen, H., Nendel, C., Ventrella, D.,
Rodríguez, A., Bindi, M., and Trnka, M.: Decline in climate resilience
of European wheat, Proc. Natl. Acad. Sci. USA, 116, 123–128,
https://doi.org/10.1073/pnas.1804387115, 2019.
Kendon, E., Short, C., Pope, J., Chan, S., Wilkinson, J., Tucker, S., Bett,
P., and Harris, G.: Update to UKCP Local (2.2 km) projections, Science Report,
Met Office Hadley Centre, Exeter, UK,
https://www.metoffice.gov.uk/pub/data/weather/uk/ukcp18/science-reports/ukcp18_local_update_report_2021.pdf (last access: 22 September 2022),
2021.
Kendon, E., Fosser, G., Murphy, J., Chan, S., Clark, R., Harris, G., Lock, A., Lowe, J., Martin, G., Pirret, J., Roberts, N., Sanderson, M., and Tucker, S.: UKCP
Convection-permitting model projections, Science report, Met Office Hadley
Centre,
https://www.metoffice.gov.uk/pub/data/weather/uk/ukcp18/science-reports/UKCP-Convection-permitting-model-projections-report.pdf (last access: 22 September 2022),
2019 (data available at: https://www.metoffice.gov.uk/research/approach/collaboration/ukcp/data/index, last access: 22 September 2022).
Kendon, E. J., Roberts, N. M., Fosser, G., Martin, G. M., Lock, A. P.,
Murphy, J. M., Senior, C. A., and Tucker, S. O.: Greater future U.K. winter
precipitation increase in new convection-permitting scenarios, J. Clim.,
33, 7303–7318, https://doi.org/10.1175/JCLI-D-20-0089.1, 2020.
Knight, S., Kightley, S., Bingham, I., Hoad, S., Lang, B., Philpott, H.,
Stobart, R., Thomas, J., Barnes, A., and Ball, B.: Desk study to evaluate
contributory causes of the current “yield plateau” in wheat and oilseed
rape, AHDB Project Report No. 502, 2012.
Lesk, C., Coffel, E., and Horton, R.: Net benefits to US soy and maize yields
from intensifying hourly rainfall, Nat. Clim. Chang., 10, 819–822,
https://doi.org/10.1038/s41558-020-0830-0, 2020.
Lesk, C., Coffel, E., Winter, J., Ray, D., Zscheischler, J., Seneviratne, S.
I., and Horton, R.: Stronger temperature–moisture couplings exacerbate the
impact of climate warming on global crop yields, Nat. Food, 2, 683–691,
https://doi.org/10.1038/s43016-021-00341-6, 2021.
Lobell, D. B., Hammer, G. L., McLean, G., Messina, C., Roberts, M. J., and
Schlenker, W.: The critical role of extreme heat for maize production in the
United States, Nat. Clim. Chang., 3, 497–501, https://doi.org/10.1038/nclimate1832,
2013.
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. N.Y. Acad. Sci., 1436, 19–35,
https://doi.org/10.1111/nyas.13912, 2019.
Ortiz-Bobea, A., Ault, T. R., Carrillo, C. M., Chambers, R. G., and Lobell,
D. B.: Anthropogenic climate change has slowed global agricultural
productivity growth, Nat. Clim. Chang., 11, 306–312,
https://doi.org/10.1038/s41558-021-01000-1, 2021.
Rahmstorf, S. and Coumou, D.: Increase of extreme events in a warming world,
P. Natl. Acad. Sci. USA, 108, 17905–17909, https://doi.org/10.1073/pnas.1101766108,
2011.
Ray, D. K., West, P. C., Clark, M., Gerber, J. S., Prishchepov, A. V., and
Chatterjee, S.: Climate change has likely already affected global food
production, PLoS One, 14, 1–18, https://doi.org/10.1371/journal.pone.0217148, 2019.
Reynolds, M. P.: Climate change and crop production, CABI Climate Change Series, CABI, ISBN: 9781845936341, 2010.
Rigden, A. J., Mueller, N. D., Holbrook, N. M., Pillai, N., and Huybers, P.:
Combined influence of soil moisture and atmospheric evaporative demand is
important for accurately predicting US maize yields, Nat. Food, 1,
127–133, https://doi.org/10.1038/s43016-020-0028-7, 2020.
Rosenzweig, C., Iglesias, A., Yang, X. B., Epstein, P. R., and Chivian, E.:
Implications for food production, plant diseases, and pests, Glob. Chang.
Hum. Heal., 2, 90–104,
https://doi.org/10.1023/A:1015086831467, 2001.
Shortridge, J.: Observed trends in daily rainfall variability result in more
severe climate change impacts to agriculture, Clim. Change, 157,
429–444, https://doi.org/10.1007/s10584-019-02555-x, 2019.
Slater, L., Arnal, L., Boucher, M.-A., Chang, A. Y.-Y., Moulds, S., Murphy, C., Nearing, G., Shalev, G., Shen, C., Speight, L., Villarini, G., Wilby, R. L., Wood, A., and Zappa, M.: Hybrid forecasting: using statistics and machine learning to integrate predictions from dynamical models, Hydrol. Earth Syst. Sci. Discuss. [preprint], https://doi.org/10.5194/hess-2022-334, in review, 2022.
Slater, L. J., Anderson, B., Buechel, M., Dadson, S., Han, S., Harrigan, S., Kelder, T., Kowal, K., Lees, T., Matthews, T., Murphy, C., and Wilby, R. L.: Nonstationary weather and water extremes: a review of methods for their detection, attribution, and management, Hydrol. Earth Syst. Sci., 25, 3897–3935, https://doi.org/10.5194/hess-25-3897-2021, 2021.
Sultan, B., Defrance, D., and Iizumi, T.: Evidence of crop production losses
in West Africa due to historical global warming in two crop models, Sci.
Rep., 9, 1–15, https://doi.org/10.1038/s41598-019-49167-0, 2019.
Swann, A. L. S., Hoffman, F. M., Koven, C. D., and Randerson, J. T.: Plant
responses to increasing CO2 reduce estimates of climate impacts on drought
severity, P. Natl. Acad. Sci. USA, 113, 10019–10024,
https://doi.org/10.1073/pnas.1604581113, 2016.
Thomas, M. R., Cook, R. J., and King, J. E.: Factors affecting development of
Septoria tritici in winter wheat and its effect on yield, Plant Pathol.,
38, 246–257, https://doi.org/10.1111/j.1365-3059.1989.tb02140.x, 1989.
Tilman, D., Balzer, C., Hill, J., and Befort, B. L.: Global food demand and
the sustainable intensification of agriculture, P. Natl. Acad. Sci. USA, 108, 20260–20264, https://doi.org/10.1073/pnas.1116437108, 2011.
Trnka, M., Rötter, R. P., Ruiz-Ramos, M., Kersebaum, K. C., Olesen, J.
E., Žalud, Z., and Semenov, M. A.: Adverse weather conditions for
European wheat production will become more frequent with climate change,
Nat. Clim. Chang., 4, 637–643, https://doi.org/10.1038/nclimate2242, 2014.
Wesseler, J., Bonanno, A., Drabik, D., Materia, V. C., Malaguti, L., Meijer,
M., and Venus, T. J.: Overview of the agricultural inputs sector in the EU,
European Parliament, Policy Department B: Structural and Cohesion Policies
European, Brussels, https://policycommons.net/artifacts/1336081/overview-of-the-agricultural-inputs-sector-in-the-eu/1942999/ (last access: 22 September 2022), 2015.
Williams, K. D., Copsey, D., Blockley, E. W., Bodas-Salcedo, A., Calvert,
D., Comer, R., Davis, P., Graham, T., Hewitt, H. T., Hill, R., Hyder, P.,
Ineson, S., Johns, T. C., Keen, A. B., Lee, R. W., Megann, A., Milton, S.
F., Rae, J. G. L., Roberts, M. J., Scaife, A. A., Schiemann, R., Storkey,
D., Thorpe, L., Watterson, I. G., Walters, D. N., West, A., Wood, R. A.,
Woollings, T., and Xavier, P. K.: The Met Office Global Coupled Model 3.0 and 3.1 (GC3.0 and GC3.1) Configurations, J. Adv. Model. Earth Syst., 10,
357–380, https://doi.org/10.1002/2017MS001115, 2018.
Xu, Y.: hyfo: Hydrology and Climate Forecasting, R package version 1.4.2,
https://cran.r-project.org/package=hyfo (last access: 22 September 2022), 2020.
Zampieri, M., Ceglar, A., Dentener, F., and Toreti, A.: Wheat yield loss
attributable to heat waves, drought and water excess at the global, national
and subnational scales, Environ. Res. Lett., 12, 064008,
https://doi.org/10.1088/1748-9326/aa723b, 2017.
Zhang, X., Alexander, L., Hegerl, G. C., Jones, P., Tank, A. K., Peterson,
T. C., Trewin, B., and Zwiers, F. W.: Indices for monitoring changes in
extremes based on daily temperature and precipitation data, WIREs Clim. Change, 2, 851–870, https://doi.org/10.1002/wcc.147, 2011.
Zheng, B., Chenu, K., Fernanda Dreccer, M., and Chapman, S. C.: Breeding for
the future: What are the potential impacts of future frost and heat events
on sowing and flowering time requirements for Australian bread wheat
(Triticum aestivium) varieties?, Glob. Chang. Biol., 18, 2899–2914,
https://doi.org/10.1111/j.1365-2486.2012.02724.x, 2012.
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
This work considers how wheat yields are affected by weather conditions during the three main wheat growth stages in the UK. Impacts are strongest in years with compound weather extremes across multiple growth stages. Future climate projections are beneficial for wheat yields, on average, but indicate a high risk of unseen weather conditions which farmers may struggle to adapt to and mitigate against.
This work considers how wheat yields are affected by weather conditions during the three main...
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