Articles | Volume 15, issue 12
https://doi.org/10.5194/essd-15-5371-2023
© 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/essd-15-5371-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
30 Nov 2023
Data description paper |
| 30 Nov 2023
| 30 Nov 2023
CHESS-SCAPE: high-resolution future projections of multiple climate scenarios for the United Kingdom derived from downscaled United Kingdom Climate Projections 2018 regional climate model output
UK Centre for Ecology & Hydrology, Wallingford, UK
Chris Huntingford
UK Centre for Ecology & Hydrology, Wallingford, UK
Valyaveetil Shamsudheen Semeena
UK Centre for Ecology & Hydrology, Wallingford, UK
James M. Bullock
UK Centre for Ecology & Hydrology, Wallingford, UK
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Emma L. Robinson, Matthew J. Brown, Alison L. Kay, Rosanna A. Lane, Rhian Chapman, Victoria A. Bell, and Eleanor M. Blyth
Earth Syst. Sci. Data, 15, 4433–4461, https://doi.org/10.5194/essd-15-4433-2023, https://doi.org/10.5194/essd-15-4433-2023, 2023
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This work presents two new Penman–Monteith potential evaporation datasets for the UK, calculated with the same methodology applied to historical climate data (Hydro-PE HadUK-Grid) and an ensemble of future climate projections (Hydro-PE UKCP18 RCM). Both include an optional correction for evaporation of rain that lands on the surface of vegetation. The historical data are consistent with existing PE datasets, and the future projections include effects of rising atmospheric CO2 on vegetation.
Ewan Pinnington, Javier Amezcua, Elizabeth Cooper, Simon Dadson, Rich Ellis, Jian Peng, Emma Robinson, Ross Morrison, Simon Osborne, and Tristan Quaife
Hydrol. Earth Syst. Sci., 25, 1617–1641, https://doi.org/10.5194/hess-25-1617-2021, https://doi.org/10.5194/hess-25-1617-2021, 2021
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Land surface models are important tools for translating meteorological forecasts and reanalyses into real-world impacts at the Earth's surface. We show that the hydrological predictions, in particular soil moisture, of these models can be improved by combining them with satellite observations from the NASA SMAP mission to update uncertain parameters. We find a 22 % reduction in error at a network of in situ soil moisture sensors after combining model predictions with satellite observations.
Gemma Coxon, Nans Addor, John P. Bloomfield, Jim Freer, Matt Fry, Jamie Hannaford, Nicholas J. K. Howden, Rosanna Lane, Melinda Lewis, Emma L. Robinson, Thorsten Wagener, and Ross Woods
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We present the first large-sample catchment hydrology dataset for Great Britain. The dataset collates river flows, catchment attributes, and catchment boundaries for 671 catchments across Great Britain. We characterise the topography, climate, streamflow, land cover, soils, hydrogeology, human influence, and discharge uncertainty of each catchment. The dataset is publicly available for the community to use in a wide range of environmental and modelling analyses.
Paul D. L. Ritchie, Chris Huntingford, and Peter M. Cox
Earth Syst. Dynam., 16, 1523–1526, https://doi.org/10.5194/esd-16-1523-2025, https://doi.org/10.5194/esd-16-1523-2025, 2025
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Climate tipping points are not committed upon crossing critical thresholds in global warming, as is often assumed. Instead, it is possible to temporarily overshoot a threshold without causing tipping, provided the duration of the overshoot is short. In this Idea, we demonstrate that restricting the time over 1.5 °C would considerably reduce tipping point risks.
Joseph Clarke, Chris Huntingford, Paul David Longden Ritchie, Rebecca Varney, Mark Williamson, and Peter Cox
EGUsphere, https://doi.org/10.5194/egusphere-2025-3703, https://doi.org/10.5194/egusphere-2025-3703, 2025
This preprint is open for discussion and under review for Earth System Dynamics (ESD).
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An increase in CO2 in the atmosphere warms the climate through the greenhouse effect, but also leads to uptake of CO2 by the land and ocean. However, the warming is also expected to suppress carbon uptake. If this suppression were strong enough, it could overwhelm the uptake of carbon, leading to a runaway feedback loop causing severe global warming. We find it is possible that this runaway could be relevant in complex climate models and even at the end of the last ice age.
Chris Huntingford, Andrew J. Nicoll, Cornelia Klein, and Jawairia A. Ahmad
Earth Syst. Dynam., 16, 475–495, https://doi.org/10.5194/esd-16-475-2025, https://doi.org/10.5194/esd-16-475-2025, 2025
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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.
Camilla Mathison, Eleanor J. Burke, Gregory Munday, Chris D. Jones, Chris J. Smith, Norman J. Steinert, Andy J. Wiltshire, Chris Huntingford, Eszter Kovacs, Laila K. Gohar, Rebecca M. Varney, and Douglas McNeall
Geosci. Model Dev., 18, 1785–1808, https://doi.org/10.5194/gmd-18-1785-2025, https://doi.org/10.5194/gmd-18-1785-2025, 2025
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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.
Emma L. Robinson, Matthew J. Brown, Alison L. Kay, Rosanna A. Lane, Rhian Chapman, Victoria A. Bell, and Eleanor M. Blyth
Earth Syst. Sci. Data, 15, 4433–4461, https://doi.org/10.5194/essd-15-4433-2023, https://doi.org/10.5194/essd-15-4433-2023, 2023
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This work presents two new Penman–Monteith potential evaporation datasets for the UK, calculated with the same methodology applied to historical climate data (Hydro-PE HadUK-Grid) and an ensemble of future climate projections (Hydro-PE UKCP18 RCM). Both include an optional correction for evaporation of rain that lands on the surface of vegetation. The historical data are consistent with existing PE datasets, and the future projections include effects of rising atmospheric CO2 on vegetation.
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
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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.
Louise J. Slater, Chris Huntingford, Richard F. Pywell, John W. Redhead, and Elizabeth J. Kendon
Earth Syst. Dynam., 13, 1377–1396, https://doi.org/10.5194/esd-13-1377-2022, https://doi.org/10.5194/esd-13-1377-2022, 2022
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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.
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
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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.
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.
Ewan Pinnington, Javier Amezcua, Elizabeth Cooper, Simon Dadson, Rich Ellis, Jian Peng, Emma Robinson, Ross Morrison, Simon Osborne, and Tristan Quaife
Hydrol. Earth Syst. Sci., 25, 1617–1641, https://doi.org/10.5194/hess-25-1617-2021, https://doi.org/10.5194/hess-25-1617-2021, 2021
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Land surface models are important tools for translating meteorological forecasts and reanalyses into real-world impacts at the Earth's surface. We show that the hydrological predictions, in particular soil moisture, of these models can be improved by combining them with satellite observations from the NASA SMAP mission to update uncertain parameters. We find a 22 % reduction in error at a network of in situ soil moisture sensors after combining model predictions with satellite observations.
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.
Gemma Coxon, Nans Addor, John P. Bloomfield, Jim Freer, Matt Fry, Jamie Hannaford, Nicholas J. K. Howden, Rosanna Lane, Melinda Lewis, Emma L. Robinson, Thorsten Wagener, and Ross Woods
Earth Syst. Sci. Data, 12, 2459–2483, https://doi.org/10.5194/essd-12-2459-2020, https://doi.org/10.5194/essd-12-2459-2020, 2020
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We present the first large-sample catchment hydrology dataset for Great Britain. The dataset collates river flows, catchment attributes, and catchment boundaries for 671 catchments across Great Britain. We characterise the topography, climate, streamflow, land cover, soils, hydrogeology, human influence, and discharge uncertainty of each catchment. The dataset is publicly available for the community to use in a wide range of environmental and modelling analyses.
Cited articles
Allen, R. G., Trezza, R., and Tasumi, M.: Analytical integrated functions for daily solar radiation on slopes, Agr. Forest Meteorol., 139, 55–73, https://doi.org/10.1016/j.agrformet.2006.05.012, 2006. a
Ashraf Vaghefi, S., Iravani, M., Sauchyn, D., Andreichuk, Y., Goss, G., and Faramarzi, M.: Regionalization and Parameterization of a Hydrologic Model Significantly Affect the Cascade of Uncertainty in Climate-Impact Projections, Clim. Dynam., 53, 2861–2886, https://doi.org/10.1007/s00382-019-04664-w, 2019. a
Ayar, P. V., Vrac, M., and Mailhot, A.: Ensemble bias correction of climate simulations: preserving internal variability, Sci. Rep., 11, 3098, https://doi.org/10.1038/s41598-021-82715-1, 2021. a
Barros, V. R., Field, C. B., Dokken, D. J., Mastrandrea, M. D., Mach, K. J., Bilir, T. E., Chatterjee, M., Ebi, K. L., Estrada, Y. O., Genova, R. C., Girma, B., Kissel, E. S., Levy, A. N., MacCracken, S., Mastrandrea, P. R., and White, L. L. (Eds.): Climate Change 2014: Impacts, Adaptation and Vulnerability: Part B: Regional Aspects: Working Group II Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, 1–1820, Cambridge University Press, ISBN 978-1-107-68386-0, 2014. a, b
Betts, R. A., Alfieri, L., Bradshaw, C., Caesar, J., Feyen, L., Friedlingstein, P., Gohar, L., Koutroulis, A., Lewis, K., Morfopoulos, C., Papadimitriou, L., Richardson, K. J., Tsanis, I., and Wyser, K.: Changes in climate extremes, fresh water availability and vulnerability to food insecurity projected at 1.5 C and 2 C global warming with a higher-resolution global climate model, Philos. T. Roy. Soc. A, 376, 20160452, https://doi.org/10.1098/rsta.2016.0452, 2018. a
Bierkens, M. F. P., Bell, V. A., Burek, P., Chaney, N., Condon, L. E., David, C. H., de Roo, A., Döll, P., Drost, N., Famiglietti, J. S., Flörke, M., Gochis, D. J., Houser, P., Hut, R., Keune, J., Kollet, S., Maxwell, R. M., Reager, J. T., Samaniego, L., Sudicky, E., Sutanudjaja, E. H., van de Giesen, N., Winsemius, H., and Wood, E. F.: Hyper-resolution global hydrological modelling: what is next?, Hydrol. Process., 29, 310–320, https://doi.org/10.1002/hyp.10391, 2015. a
Bock, L., Lauer, A., Schlund, M., Barreiro, M., Bellouin, N., Jones, C., Meehl, G. A., Predoi, V., Roberts, M. J., and Eyring, V.: Quantifying Progress Across Different CMIP Phases With the ESMValTool, J. Geophys. Res.-Atmos., 125, e2019JD032321, https://doi.org/10.1029/2019JD032321, 2020. a
Brown, C., Seo, B., Alexander, P., Burton, V., Chacón-Montalvàn, E. A., Dunford, R., Merkle, M., Harrison, P. A., Prestele, R., Robinson, E. L., and Rounsevell, M.: Agent-Based Modeling of Alternative Futures in the British Land Use System, Earth's Future, 10, e2022EF002905, https://doi.org/10.1029/2022EF002905, 2022. a
Ciais, P., Sabine, C., Bala, G., Bopp, L., Brovkin, V., Canadell, J., Chhabra, A., DeFries, R., Galloway, J., Heimann, M., Jones, C., Le Quéré, C., Myneni, R. B., Piao, S., and Thornton, P.: Carbon and other biogeochemical cycles, in: Climate change 2013: the physical science basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, 465–570, ISBN 978-1-107-66182-0, 2014. a
Collins, W. J., Bellouin, N., Doutriaux-Boucher, M., Gedney, N., Halloran, P., Hinton, T., Hughes, J., Jones, C. D., Joshi, M., Liddicoat, S., Martin, G., O'Connor, F., Rae, J., Senior, C., Sitch, S., Totterdell, I., Wiltshire, A., and Woodward, S.: Development and evaluation of an Earth-System model – HadGEM2, Geosci. Model Dev., 4, 1051–1075, https://doi.org/10.5194/gmd-4-1051-2011, 2011. a
COP21: Adoption of The Paris Agreement. Proposal by the President, in: Paris Climate Change Conference – November 2015, COP 21, United Nations Framework Convention on Climate Change, Paris, FCCC/CP/2015/L.9/Rev.1, https://unfccc.int/resource/docs/2015/cop21/eng/l09r01.pdf (last access: 15 November 2023), 2015. a
Dilley, A. C. and O'Brien, D. M.: Estimating downward clear sky long-wave irradiance at the surface from screen temperature and precipitable water, Q. J. Roy. Meteor. Soc., 124, 1391–1401, https://doi.org/10.1002/qj.49712454903, 1998. a
Dupont-Doaré, C. and Alagador, D.: Overlooked effects of temporal resolution choice on climate-proof spatial conservation plans for biodiversity, Biol. Conserv., 263, 109330, https://doi.org/10.1016/j.biocon.2021.109330, 2021. a
Ehret, U., Zehe, E., Wulfmeyer, V., Warrach-Sagi, K., and Liebert, J.: HESS Opinions “Should we apply bias correction to global and regional climate model data?”, Hydrol. Earth Syst. Sci., 16, 3391–3404, https://doi.org/10.5194/hess-16-3391-2012, 2012. a
EPSG: EPSG:29902 TM65/Irish Grid, https://epsg.io/29902 (last access: 15 November 2023), b. a
European Environment Agency: European Digital Elevation Model version 1.1, http://land.copernicus.eu/pan-european/satellite-derived-products/eu-dem/eu-dem-v1.1/view (last access: 24 November 2022), 2021. a
Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., and Taylor, K. E.: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization, Geosci. Model Dev., 9, 1937–1958, https://doi.org/10.5194/gmd-9-1937-2016, 2016. a, b, c
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, https://www.metoffice.gov.uk/pub/data/weather/uk/ukcp18/science-reports/UKCP-Convection-permitting-model-projections-report.pdf (last access: 15 November 2023), 2019. a, b, c, d
Gill, A. E.: Atmosphere-ocean Dynamics, Academic Press, San Diego, California, USA, ISBN 978-0-12-283522-3, 1982. a
Gudmundsson, L., Bremnes, J. B., Haugen, J. E., and Engen-Skaugen, T.: Technical Note: Downscaling RCM precipitation to the station scale using statistical transformations – a comparison of methods, Hydrol. Earth Syst. Sci., 16, 3383–3390, https://doi.org/10.5194/hess-16-3383-2012, 2012. a
Hassell, D., Gregory, J., Blower, J., Lawrence, B. N., and Taylor, K. E.: A data model of the Climate and Forecast metadata conventions (CF-1.6) with a software implementation (cf-python v2.1), Geosci. Model Dev., 10, 4619–4646, https://doi.org/10.5194/gmd-10-4619-2017, 2017. a
Hawkins, E. and Sutton, R.: The Potential to Narrow Uncertainty in Regional Climate Predictions, B. Am. Meteorol. Soc., 90, 1095–1108, https://doi.org/10.1175/2009BAMS2607.1, 2009. a, b
Hayashi, M. and Shiogama, H.: Assessment of CMIP6-Based Future Climate Projections Selected for Impact Studies in Japan, SOLA, 18, 96–103, https://doi.org/10.2151/sola.2022-016, 2022. a
Herger, N., Sanderson, B. M., and Knutti, R.: Improved pattern scaling approaches for the use in climate impact studies, Geophys. Res. Lett., 42, 3486–3494, https://doi.org/10.1002/2015GL063569, 2015. a
Hough, M., Palmer, S., Weir, A., Lee, M., and Barrie, I.: The Meteorological Office Rainfall and Evaporation Calculation System: MORECS version 2.0, An update to Hydrological Memorandum 45, Met Office, https://digital.nmla.metoffice.gov.uk/IO_9d68dec6-8ad2-420b-a971-806f7a6987d8/ (last access: 22 September 2023), 1997. a, b, c
IPCC: Climate Change 2007: Synthesis Report. Contribution of Working Groups I, II and III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, IPCC, Geneva, Switzerland, ISBN 92-9169-122-4, 2007a. a
IPCC: Report of the 26th session of the IPCC, Bangkok, 30 April–4 May 2007, Tech. rep., Geneva, Switzerland, https://www.ipcc.ch/site/assets/uploads/2018/05/final_report_26.pdf (last access: 15 November 2023), 2007b. a
IPCC: Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, IPCC, Geneva, Switzerland, ISBN 978-92-9169-143-2, 2014. a
IPCC: 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, https://doi.org/10.1017/9781009157896, 2021. a
IPCC: Climate Change 2022: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, https://doi.org/10.1017/9781009325844, 2022. a
Ito, R., Shiogama, H., Nakaegawa, T., and Takayabu, I.: Uncertainties in climate change projections covered by the ISIMIP and CORDEX model subsets from CMIP5, Geosci. Model Dev., 13, 859–872, https://doi.org/10.5194/gmd-13-859-2020, 2020. a
Jain, S., Scaife, A. A., Shepherd, T. G., Deser, C., Dunstone, N., Schmidt, G. A., Trenberth, K. E., and Turkington, T.: Importance of internal variability for climate model assessment, npj Clim. Atmos. Sci., 6, 68, https://doi.org/10.1038/s41612-023-00389-0, 2023. a
Jakob Themeßl, M., Gobiet, A., and Leuprecht, A.: Empirical-statistical downscaling and error correction of daily precipitation from regional climate models, Int. J. Climatol., 31, 1530–1544, https://doi.org/10.1002/joc.2168, 2011. a
James, R., Washington, R., Schleussner, C.-F., Rogelj, J., and Conway, D.: Characterizing half-a-degree difference: a review of methods for identifying regional climate responses to global warming targets, WIREs Clim. Change, 8, e457, https://doi.org/10.1002/wcc.457, 2017. a, b
Johnson, F. and Sharma, A.: A nesting model for bias correction of variability at multiple time scales in general circulation model precipitation simulations, Water Resour. Res., 48, W01504, https://doi.org/10.1029/2011WR010464, 2012. a
Karthikeyan, L., Chawla, I., and Mishra, A. K.: A review of remote sensing applications in agriculture for food security: Crop growth and yield, irrigation, and crop losses, J. Hydrol., 586, 124905, https://doi.org/10.1016/j.jhydrol.2020.124905, 2020. a
Keller, V. D. J., Tanguy, M., Prosdocimi, I., Terry, J. A., Hitt, O., Cole, S. J., Fry, M., Morris, D. G., and Dixon, H.: CEH-GEAR: 1 km resolution daily and monthly areal rainfall estimates for the UK for hydrological and other applications, Earth Syst. Sci. Data, 7, 143–155, https://doi.org/10.5194/essd-7-143-2015, 2015. a
Kendon, M., McCarthy, M., Jevrejeva, S., Matthews, A., Williams, J., Sparks, T., and West, F.: State of the UK Climate 2022, Int. J. Climatol., 43, 1–83, https://doi.org/10.1002/joc.8167, 2023. a
Kennedy-Asser, A. T., Andrews, O., Mitchell, D. M., and Warren, R. F.: Evaluating heat extremes in the UK Climate Projections (UKCP18), Environ. Res. Lett., 16, 014039, https://doi.org/10.1088/1748-9326/abc4ad, 2021. a
Kimball, B. A., Idso, S. B., and Aase, J. K.: A model of thermal radiation from partly cloudy and overcast skies, Water Resour. Res., 18, 931–936, https://doi.org/10.1029/WR018i004p00931, 1982. a
Leander, R. and Buishand, T. A.: Resampling of regional climate model output for the simulation of extreme river flows, J. Hydrol., 332, 487–496, https://doi.org/10.1016/j.jhydrol.2006.08.006, 2007. a
Lenderink, G., Buishand, A., and van Deursen, W.: Estimates of future discharges of the river Rhine using two scenario methodologies: direct versus delta approach, Hydrol. Earth Syst. Sci., 11, 1145–1159, https://doi.org/10.5194/hess-11-1145-2007, 2007. a
Maraun, D.: Bias Correcting Climate Change Simulations – a Critical Review, Current Climate Change Reports, 2 211–220, https://doi.org/10.1007/s40641-016-0050-x, 2016. a
Masui, T., Matsumoto, K., Hijioka, Y., Kinoshita, T., Nozawa, T., Ishiwatari, S., Kato, E., Shukla, P. R., Yamagata, Y., and Kainuma, M.: An emission pathway for stabilization at 6Wm−2 radiative forcing, Climatic Change, 109, 59, https://doi.org/10.1007/s10584-011-0150-5, 2011. a
McSweeney, C. F. and Jones, R. G.: How representative is the spread of climate projections from the 5 CMIP5 GCMs used in ISI-MIP?, Climate Services, 1, 24–29, https://doi.org/10.1016/j.cliser.2016.02.001, 2016. a
McSweeney, C. F., Jones, R. G., Lee, R. W., and Rowell, D. P.: Selecting CMIP5 GCMs for downscaling over multiple regions, Clim. Dynam., 44, 3237–3260, https://doi.org/10.1007/s00382-014-2418-8, 2014. a
Meinshausen, M., Smith, S. J., Calvin, K., Daniel, J. S., Kainuma, M. L. T., Lamarque, J.-F., Matsumoto, K., Montzka, S. A., Raper, S. C. B., Riahi, K., Thomson, A., Velders, G. J. M., and van Vuuren, D. P. P.: The RCP greenhouse gas concentrations and their extensions from 1765 to 2300, Climatic Change, 109, 213, https://doi.org/10.1007/s10584-011-0156-z, 2011. a
Melsen, L. A., Teuling, A. J., Torfs, P. J. J. F., Uijlenhoet, R., Mizukami, N., and Clark, M. P.: HESS Opinions: The need for process-based evaluation of large-domain hyper-resolution models, Hydrol. Earth Syst. Sci., 20, 1069–1079, https://doi.org/10.5194/hess-20-1069-2016, 2016. a
Met Office, Hollis, D., McCarthy, M., Kendon, M., Legg, T., and Simpson, I.: HadUK-Grid gridded and regional average climate observations for the UK, http://catalogue.ceda.ac.uk/uuid/4dc8450d889a491ebb20e724debe2dfb (last access: 15 November 2023), 2021. a
Met Office Hadley Centre: UKCP Local Projections by Administrative Regions over the UK for 1980–2080, https://catalogue.ceda.ac.uk/uuid/e7b0165f3b57409998ca2632dad7a1a3 (last access: 3 June 2020), 2018a. a
Met Office Hadley Centre: UKCP18 Probabilistic Projections by Administrative Regions over the UK for 1961–2100, https://catalogue.ceda.ac.uk/uuid/8eca5b80ee244d9486162e699c5197f5 (last access: 3 June 2020), 2018b. a
Met Office Hadley Centre: UKCP18 Regional Projections on a 12 km grid over the UK for 1980–2080, https://catalogue.ceda.ac.uk/uuid/589211abeb844070a95d061c8cc7f604 (last access: 3 June 2020), 2019a. a
Met Office Hadley Centre: UKCP18 land projections – 12 km regional climate model, surface altitude (m) over Europe, NERC EDS Centre for Environmental Data Analysis [data set], https://data.ceda.ac.uk/badc/ukcp18/data/land-rcm/ancil/orog (last access: 22 September 2023), 2019b. a
Mitchell, D., James, R., Forster, P. M., Betts, R. A., Shiogama, H., and Allen, M.: Realizing the Impacts of a 1.5∘C Warmer World, Nature Climate Change, 6, 735–737, https://doi.org/10.1038/nclimate3055, 2016. a
Mitchell, T. D.: Pattern Scaling: An Examination of the Accuracy of the Technique for Describing Future Climates, Climatic Change, 60, 217–242, https://doi.org/10.1023/A:1026035305597, 2003. a
Morris, D. G. and Flavin, R. W.: A digital terrain model for hydrology, Proc. 4th International Symposium on Spatial Data Handling, vol. 1, 23–27 July 1990, Zürich, 250–262, 1990. a
Muller, J.-P., López, G., Watson, G., Shane, N., Kennedy, T., Yuen, 35 P., Lewis, P., Fischer, J., Guanter, L., Domench, C., Preusker, R., North, P., Heckel, A., Danne, O., Krämer, U., Zühlke, M., Brockmann, C., and Pinnock, S.: The ESA GlobAlbedo Project for mapping the Earth’s land surface albedo for 15 years from European sensors, Geophys. Res. Abstr., vol. 13, p. 10969, 2012. a
Muneer, T. and Munawwar, S.: Potential for improvement in estimation of solar diffuse irradiance, Energ. Convers. Manage., 47, 68–86, https://doi.org/10.1016/j.enconman.2005.03.015, 2006. a
Murphy, J. M., Harris, G. R., Sexton, D. M. H., Kendon, E. J., Bett, P. E., Brown, S. J., Clark, R. T., Eagle, K., Fosser, G., Fung, F., Lowe, J. A., McDonald, R. E., McInnes, R. N., McSweeney, C. F., Mitchell, J. F. B., Rostron, J., Thornton, H. E., Tucker, S., and Yamazaki, K.: UKCP18 Land Projections: Science Report, https://www.metoffice.gov.uk/pub/data/weather/uk/ukcp18/science-reports/UKCP18-Land-report.pdf (last access: 15 November 2023), 2018. a, b, c, d, e, f, g, h
Nadeau, C. P., Urban, M. C., and Bridle, J. R.: Coarse climate change projections for species living in a fine-scaled world, Glob. Change Biol., 23, 12–24, https://doi.org/10.1111/gcb.13475, 2017. a
Nakicenovic, N. and Swart, R.: Special Report on Emissions Scenarios, Cambridge University Press, UK, ISBN 92-9169-113-5, https://escholarship.org/uc/item/9sz5p22f (last access: 15 November 2023), 2000. a
Navarro-Racines, C., Tarapues, J., Thornton, P., Jarvis, A., and Ramirez-Villegas, J.: High-resolution and bias-corrected CMIP5 projections for climate change impact assessments, Sci. Data, 7, 7, https://doi.org/10.1038/s41597-019-0343-8, 2020. a
O'Neill, B. C., Kriegler, E., Riahi, K., Ebi, K. L., Hallegatte, S., Carter, T. T., Mathur, R., and van Vuuren, D. P.: A new scenario framework for climate change research: the concept of shared socioeconomic pathways, Climatic Change, 122, 387–400, https://doi.org/10.1007/s10584-013-0905-2, 2014. a
O'Neill, B. C., Tebaldi, C., van Vuuren, D. P., Eyring, V., Friedlingstein, P., Hurtt, G., Knutti, R., Kriegler, E., Lamarque, J.-F., Lowe, J., Meehl, G. A., Moss, R., Riahi, K., and Sanderson, B. M.: The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6, Geosci. Model Dev., 9, 3461–3482, https://doi.org/10.5194/gmd-9-3461-2016, 2016. a
O'Neill, B. C., Kriegler, E., Ebi, K. L., Kemp-Benedict, E., Riahi, K., Rothman, D. S., van Ruijven, B. J., van Vuuren, D. P., Birkmann, J., Kok, K., Levy, M., and Solecki, W.: The roads ahead: Narratives for shared socioeconomic pathways describing world futures in the 21st century, Global Environ. Chang., 42, 169–180, https://doi.org/10.1016/j.gloenvcha.2015.01.004, 2017. a
Open Government Licence: https://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/ (last access: 9 December 2022), 2022. a
Ordnance Survey of Northern Ireland: OSNI Open Data – 50 m DTM, https://www.opendatani.gov.uk/dataset/osni-open-data-50m-dtm (last access: 30 July 2019), 2019. a
Pedde, S., Harrison, P. A., Holman, I. P., Powney, G. D., Lofts, S., Schmucki, R., Gramberger, M., and Bullock, J. M.: Enriching the Shared Socioeconomic Pathways to co-create consistent multi-sector scenarios for the UK, Sci. Total Environ., 756, 143172, https://doi.org/10.1016/j.scitotenv.2020.143172, 2021. a, b, c
Prata, A. J.: A new long-wave formula for estimating downward clear-sky radiation at the surface, Q. J. Roy. Meteor. Soc., 122, 1127–1151, https://doi.org/10.1002/qj.49712253306, 1996. a
Prein, A. F., Langhans, W., Fosser, G., Ferrone, A., Ban, N., Goergen, K., Keller, M., Tölle, M., Gutjahr, O., Feser, F., Brisson, E., Kollet, S., Schmidli, J., van Lipzig, N. P. M., and Leung, R.: A review on regional convection-permitting climate modeling: Demonstrations, prospects, and challenges, Rev. Geophys., 53, 323–361, https://doi.org/10.1002/2014RG000475, 2015. a
Priestley, M. D. K. and Catto, J. L.: Improved Representation of Extratropical Cyclone Structure in HighResMIP Models, Geophys. Res. Lett., 49, e2021GL096708, https://doi.org/10.1029/2021GL096708, 2022. a
Priestley, M. D. K., Ackerley, D., Catto, J. L., Hodges, K. I., McDonald, R. E., and Lee, R. W.: An Overview of the Extratropical Storm Tracks in CMIP6 Historical Simulations, J. Climate, 33, 6315–6343, https://doi.org/10.1175/JCLI-D-19-0928.1, 2020. a
Ranasinghe, R., Ruane, A. C., Vautard, R., Arnell, N., Coppola, E., Cruz, F. A., Dessai, S., Islam, A. S., Rahimi, M., Ruiz Carrascal, D., Sillmann, J., Sylla, M. B., Tebaldi, C., Wang, W., and Zaaboul, R.: Climate Change Information for Regional Impact and for Risk Assessment, 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, https://doi.org/10.1017/9781009157896.014, 2021. a
Riahi, K., Rao, S., Krey, V., Cho, C., Chirkov, V., Fischer, G., Kindermann, G., Nakicenovic, N., and Rafaj, P.: RCP 8.5 – A scenario of comparatively high greenhouse gas emissions, Climatic Change, 109, 33–57, https://doi.org/10.1007/s10584-011-0149-y, 2011. a
Riahi, K., van Vuuren, D. P., Kriegler, E., Edmonds, J., O'Neill, B. C., Fujimori, S., Bauer, N., Calvin, K., Dellink, R., Fricko, O., Lutz, W., Popp, A., Cuaresma, J. C., KC, S., Leimbach, M., Jiang, L., Kram, T., Rao, S., Emmerling, J., Ebi, K., Hasegawa, T., Havlik, P., Humpenöder, F., Da Silva, L. A., Smith, S., Stehfest, E., Bosetti, V., Eom, J., Gernaat, D., Masui, T., Rogelj, J., Strefler, J., Drouet, L., Krey, V., Luderer, G., Harmsen, M., Takahashi, K., Baumstark, L., Doelman, J. C., Kainuma, M., Klimont, Z., Marangoni, G., Lotze-Campen, H., Obersteiner, M., Tabeau, A., and Tavoni, M.: The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview, Global Environ. Chang., 42, 153–168, https://doi.org/10.1016/j.gloenvcha.2016.05.009, 2017. a
Richards, J. M.: A simple expression for the saturation vapour pressure of water in the range −50 to 140 ∘C, J. Phys. D, 4, L15–L18, https://doi.org/10.1088/0022-3727/4/4/101, 1971. a, b
Roberts, M. J., Vidale, P. L., Senior, C., Hewitt, H. T., Bates, C., Berthou, S., Chang, P., Christensen, H. M., Danilov, S., Demory, M.-E., Griffies, S. M., Haarsma, R., Jung, T., Martin, G., Minobe, S., Ringler, T., Satoh, M., Schiemann, R., Scoccimarro, E., Stephens, G., and Wehner, M. F.: The Benefits of Global High Resolution for Climate Simulation: Process Understanding and the Enabling of Stakeholder Decisions at the Regional Scale, B. Am. Meteorol. Soc., 99, 2341–2359, https://doi.org/10.1175/BAMS-D-15-00320.1, 2018. a
Roberts, M. J., Camp, J., Seddon, J., Vidale, P. L., Hodges, K., Vannière, B., Mecking, J., Haarsma, R., Bellucci, A., Scoccimarro, E., Caron, L.-P., Chauvin, F., Terray, L., Valcke, S., Moine, M.-P., Putrasahan, D., Roberts, C. D., Senan, R., Zarzycki, C., Ullrich, P., Yamada, Y., Mizuta, R., Kodama, C., Fu, D., Zhang, Q., Danabasoglu, G., Rosenbloom, N., Wang, H., and Wu, L.: Projected Future Changes in Tropical Cyclones Using the CMIP6 HighResMIP Multimodel Ensemble, Geophys. Res. Lett., 47, e2020GL088662, https://doi.org/10.1029/2020GL088662, 2020. a
Robinson, E., Blyth, E., Clark, D., Comyn-Platt, E., and Rudd, A.: Climate hydrology and ecology research support system meteorology dataset for Great Britain (1961–2017) [CHESS-met], NERC EDS Environmental Information Data Centre [data set], https://doi.org/10.5285/2AB15BF0-AD08-415C-BA64-831168BE7293, 2020. a, b, c
Robinson, E., Huntingford, C., Semeena, V., and Bullock, J.: CHESS-SCAPE: Future projections of meteorological variables at 1 km resolution for the United Kingdom 1980–2080 derived from UK Climate Projections 2018, NERC EDS Centre for Environmental Data Analysis [data set], https://doi.org/10.5285/8194b416cbee482b89e0dfbe17c5786c, 2022. a, b, c
Robinson, E. L., Blyth, E. M., Clark, D. B., Finch, J., and Rudd, A. C.: Trends in atmospheric evaporative demand in Great Britain using high-resolution meteorological data, Hydrol. Earth Syst. Sci., 21, 1189–1224, https://doi.org/10.5194/hess-21-1189-2017, 2017. a, b, c, d
Rudd, A. C. and Kay, A. L.: Use of very high resolution climate model data for hydrological modelling: estimation of potential evaporation, Hydrol. Res., 47, 660–670, https://doi.org/10.2166/nh.2015.028, 2016. a
Schleussner, C.-F., Lissner, T. K., Fischer, E. M., Wohland, J., Perrette, M., Golly, A., Rogelj, J., Childers, K., Schewe, J., Frieler, K., Mengel, M., Hare, W., and Schaeffer, M.: Differential climate impacts for policy-relevant limits to global warming: the case of 1.5 °C and 2 °C, Earth Syst. Dynam., 7, 327–351, https://doi.org/10.5194/esd-7-327-2016, 2016. a
Schmidli, J., Frei, C., and Vidale, P. L.: Downscaling from GCM precipitation: a benchmark for dynamical and statistical downscaling methods, Int. J. Climatol., 26, 679–689, https://doi.org/10.1002/joc.1287, 2006. a
Schwarzwald, K. and Lenssen, N.: The importance of internal climate variability in climate impact projections, P. Natl. Acad. Sci. USA, 119, e2208095119, https://doi.org/10.1073/pnas.2208095119, 2022. a
Sexton, D. M. H., McSweeney, C. F., Rostron, J. W., Yamazaki, K., Booth, B. B. B., Murphy, J. M., Regayre, L., Johnson, J. S., and Karmalkar, A. V.: A Perturbed Parameter Ensemble of HadGEM3-GC3.05 Coupled Model Projections: Part 1: Selecting the Parameter Combinations, Clim. Dynam., 56, 3395–3436, https://doi.org/10.1007/s00382-021-05709-9, 2021. a, b
Slingo, J., Bates, P., Bauer, P., Belcher, S., Palmer, T., Stephens, G., Stevens, B., Stocker, T., and Teutsch, G.: Ambitious Partnership Needed for Reliable Climate Prediction, Nat. Clim. Change, 12, 499–503, https://doi.org/10.1038/s41558-022-01384-8, 2022. a
Sunter, M.: MIDAS Data User Guide for UK Land Observations, v20200921, Zenodo [data set], https://doi.org/10.5281/zenodo.7357325, 2020. a
Tanguy, M., Dixon, H., Prosdocimi, I., Morris, D., and Keller, V.: Gridded estimates of daily and monthly areal rainfall for the United Kingdom (1890–2017) [CEH-GEAR], NERC EDS Environmental Information Data Centre [data set], https://doi.org/10.5285/EE9AB43D-A4FE-4E73-AFD5-CD4FC4C82556, 2019. a
Taylor, K. E., Stouffer, R. J., and Meehl, G. A.: An Overview of CMIP5 and the Experiment Design, B. Am. Meteorol. Soc., 93, 485–498, https://doi.org/10.1175/BAMS-D-11-00094.1, 2012. a, b, c
Teutschbein, C. and Seibert, J.: Bias correction of regional climate model simulations for hydrological climate-change impact studies: Review and evaluation of different methods, J. Hydrol., 456–457, 12–29, https://doi.org/10.1016/j.jhydrol.2012.05.052, 2012. a, b, c
Thomson, A. M., Calvin, K. V., Smith, S. J., Kyle, G. P., Volke, A., Patel, P., Delgado-Arias, S., Bond-Lamberty, B., Wise, M. A., Clarke, L. E., and Edmonds, J. A.: RCP4.5: a pathway for stabilization of radiative forcing by 2100, Climatic Change, 109, 77, https://doi.org/10.1007/s10584-011-0151-4, 2011. a
Trigo, R. M., Osborn, T. J., and Corte-Real, J. M.: The North Atlantic Oscillation influence on Europe: climate impacts and associated physical mechanisms, Clim. Res., 20, 9–17, 2002. a
van Vuuren, D. P., Edmonds, J., Kainuma, M., Riahi, K., Thomson, A., Hibbard, K., Hurtt, G. C., Kram, T., Krey, V., Lamarque, J.-F., Masui, T., Meinshausen, M., Nakicenovic, N., Smith, S. J., and Rose, S. K.: The representative concentration pathways: an overview, Climatic Change, 109, 5, https://doi.org/10.1007/s10584-011-0148-z, 2011a. a
van Vuuren, D. P., Stehfest, E., den Elzen, M. G. J., Kram, T., van Vliet, J., Deetman, S., Isaac, M., Klein Goldewijk, K., Hof, A., Mendoza Beltran, A., Oostenrijk, R., and van Ruijven, B.: RCP2.6: exploring the possibility to keep global mean temperature increase below 2 ∘C, Climatic Change, 109, 95, https://doi.org/10.1007/s10584-011-0152-3, 2011b. a
van Vuuren, D. P., Kriegler, E., O'Neill, B. C., Ebi, K. L., Riahi, K., Carter, T. R., Edmonds, J., Hallegatte, S., Kram, T., Mathur, R., and Winkler, H.: A new scenario framework for Climate Change Research: scenario matrix architecture, Climatic Change, 122, 373–386, https://doi.org/10.1007/s10584-013-0906-1, 2014. a
Watanabe, S., Kanae, S., Seto, S., Yeh, P. J.-F., Hirabayashi, Y., and Oki, T.: Intercomparison of bias-correction methods for monthly temperature and precipitation simulated by multiple climate models, J. Geophys. Res.-Atmos., 117, D23114, https://doi.org/10.1029/2012JD018192, 2012. a
Widmann, M., Bretherton, C. S., and Salathé Jr, E. P.: Statistical precipitation downscaling over the northwestern United States using numerically simulated precipitation as a predictor, J. Climate, 16, 799–816, 2003. a
Wilby, R. L. and Dessai, S.: Robust adaptation to climate change, Weather, 65, 180–185, https://doi.org/10.1002/wea.543, 2010. a, b
Wood, E. F., Roundy, J. K., Troy, T. J., van Beek, L. P. H., Bierkens, M. F. P., Blyth, E., de Roo, A., Döll, P., Ek, M., Famiglietti, J., Gochis, D., van de Giesen, N., Houser, P., Jaffé, P. R., Kollet, S., Lehner, B., Lettenmaier, D. P., Peters-Lidard, C., Sivapalan, M., Sheffield, J., Wade, A., and Whitehead, P.: Hyperresolution global land surface modeling: Meeting a grand challenge for monitoring Earth's terrestrial water, Water Resour. Res., 47, https://doi.org/10.1029/2010WR010090, 2011. a
Zappa, G. and Shepherd, T. G.: Storylines of Atmospheric Circulation Change for European Regional Climate Impact Assessment, J. Climate, 30, 6561–6577, https://doi.org/10.1175/jcli-d-16-0807.1, 2017. a
Zelazowski, P., Huntingford, C., Mercado, L. M., and Schaller, N.: Climate pattern-scaling set for an ensemble of 22 GCMs – adding uncertainty to the IMOGEN version 2.0 impact system, Geosci. Model Dev., 11, 541–560, https://doi.org/10.5194/gmd-11-541-2018, 2018. a
Short summary
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.
CHESS-SCAPE is a suite of high-resolution climate projections for the UK to 2080, derived from...
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