Suchergebnisse

Arctic sea-ice loss is a consequence of anthropogenic global warming and can itself be a driver of climate change in the Arctic and at lower latitudes, with sea-ice minima likely favoring extreme events over Europe and North America. Yet the role that the sea-ice plays in ongoing climate change remains uncertain, partly due to a limited understanding of whether and how the exact geographical distribution of sea-ice loss impacts climate. Here we demonstrate that the climate response to sea-ice loss can vary widely depending on the pattern of sea-ice change, and show that this is due to the presence of an atmospheric feedback mechanism that amplifies the local and remote signals when broader scale sea-ice loss occurs. Our study thus highlights the need to better constrain the spatial pattern of future sea-ice when assessing its impacts on the climate in the Arctic and beyond. ; X.J.L. has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Grant Agreement H2020-MSCA-COFUND-2016-754433 and from the H2020 project APPLICATE (Grant 727862). I.C. was supported by Generalitat de Catalunya (Secretaria d'Universitats i Recerca del Departament d'Empresa i Coneixement) through Beatriu de Pinós 2017 programme. M.G.D. and P.O. are grateful for funding by the Spanish Ministry for the Economy, Industry and Competitiveness, respectively, for the Grant references RYC-2017-22964 and RYC-2017-22772. E.T. has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Grant Agreement No. 748750 (SPFireSD project). Experiments were completed on the Marenostrum IV supercomputer at the Barcelona Supercomputing Center (BSC), and support was provided by BSC's Computational Earth Sciences (CES) department. ; Peer Reviewed ; Postprint (published version)

Quantitative estimate of observational uncertainty is an essential ingredient to correctly interpret changes in climatic and environmental variables such as wildfires. In this work we compare four state-of-the-art satellite fire products with the gridded, ground-based EFFIS dataset for Mediterranean Europe and analyse their statistical differences. The data are compared for spatial and temporal similarities at different aggregations to identify a spatial scale at which most of the observations provide equivalent results. The results of the analysis indicate that the datasets show high temporal correlation with each other (0.5/0.6) when aggregating the data at resolution of at least 1.0° or at NUTS3 level. However, burned area estimates vary widely between datasets. Filtering out satellite fires located on urban and crop land cover classes greatly improves the agreement with EFFIS data. Finally, in spite of the differences found in the area estimates, the spatial pattern is similar for all the datasets, with spatial correlation increasing as the resolution decreases. Also, the general reasonable agreement between satellite products builds confidence in using these datasets and in particular the most-recent developed dataset, FireCCI51, shows the best agreement with EFFIS overall. As a result, the main conclusion of the study is that users should carefully consider the limitations of the satellite fire estimates currently available, as their uncertainties cannot be neglected in the overall uncertainty estimate/cascade that should accompany global or regional change studies and that removing fires on human-dominated land areas is key to analyze forest fires estimation from satellite products. ; The authors thank EFFIS (European Forest Fire Information System of the European Commission Joint Research Centre, http://effis.jrc.ec.europa.eu) for providing access to fire series EFFIS. M.T. and E.T. have received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 740073 (CLIM4CROP project) and grant agreement No. 748750 (SPFireSD project), respectively. The work of A.P. has been supported by the European Union's Horizon 2020 ECOPOTENTIAL project (grant agreement No. 641762).

Atlantic multidecadal variability (AMV) has been linked to the observed slowdown of global warming over 1998–2012 through its impact on the tropical Pacific. Given the global importance of tropical Pacific variability, better understanding this Atlantic–Pacific teleconnection is key for improving climate predictions, but the robustness and strength of this link are uncertain. Analyzing a multi-model set of sensitivity experiments, we find that models differ by a factor of 10 in simulating the amplitude of the Equatorial Pacific cooling response to observed AMV warming. The inter-model spread is mainly driven by different amounts of moist static energy injection from the tropical Atlantic surface into the upper troposphere. We reduce this inter-model uncertainty by analytically correcting models for their mean precipitation biases and we quantify that, following an observed 0.26 °C AMV warming, the equatorial Pacific cools by 0.11 °C with an inter-model standard deviation of 0.03 °C. ; Y.R.-R. was founded by the European Union's Horizon 2020 Research and Innovation Program in the framework of the Marie Skłodowska-Curie grant INADEC (Grant agreement 800154). E.M.-C. acknowledges funding from the European Commission's Horizon 2020 projects PRIMAVERA (Grant Agreement 641727). X.L. has received funding from the European Union's Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement H2020-MSCA-COFUND-2016-754433. A.B. and D.N. acknowledge funding from the European Commission's Horizon 2020 project EUCP (Grant agreement 776613). F.C. and G.D. were supported by the US National Science Foundation (NSF) under the Collaborative Research EaSM2 Grant OCE-1243015 to NCAR and by the US National Oceanic and Atmospheric Administration (NOAA) Climate Program Office under the Climate Variability and Predictability Program Grant NA13OAR4310138. NCAR is a major facility sponsored by the US NSF under Cooperative Agreement 1852977. Acknowledgments are made for the use of ECMWF's computing and archive facilities in this research, in particular, P.D. thanks ECMWF for providing computing time in the framework of the special project SPITDAVI. R.E., N.D., L.H., and D.S. were supported by the Met Office Hadley Center 522 Climate Program funded by BEIS and Defra and by the European Commission Horizon 2020 EUCP 523 project (GA 776613). J.L.-P. was funded by the European Union's Horizon 2020 Research and Innovation Program in the framework of the PRIMAVERA project (Grant Agreement 641727). J.R. and D.H. were funded by NERC via NCAS and the ACSIS project (NE/N018001/1), and JR was also funded by the NERC SMURPHS project (NE/N006054/1). M.M.-R. was funded by the European Union's Horizon 2020 Research and Innovation Program in the framework of the Marie Skłodowska-Curie grant FESTIVAL (Grant agreement 797236). E.T. has received funding from the European Union's Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No. 748750 (SPFireSD project). The analysis and plots of this paper were performed with the NCAR Command Language (Version 6.6.2; 2019)67. ; Peer Reviewed ; "Article signat per 25 autors/es: Yohan Ruprich-Robert, Eduardo Moreno-Chamarro, Xavier Levine, Alessio Bellucci, Christophe Cassou, Frederic Castruccio, Paolo Davini, Rosie Eade, Guillaume Gastineau, Leon Hermanson, Dan Hodson, Katja Lohmann, Jorge Lopez-Parages, Paul-Arthur Monerie, Dario Nicolì, Said Qasmi, Christopher D. Roberts, Emilia Sanchez-Gomez, Gokhan Danabasoglu, Nick Dunstone, Marta Martin-Rey, Rym Msadek, Jon Robson, Doug Smith & Etienne Tourigny " ; Postprint (author's final draft)

Many nations responded to the corona virus disease‐2019 (COVID‐19) pandemic by restricting travel and other activities during 2020, resulting in temporarily reduced emissions of CO2, other greenhouse gases and ozone and aerosol precursors. We present the initial results from a coordinated Intercomparison, CovidMIP, of Earth system model simulations which assess the impact on climate of these emissions reductions. 12 models performed multiple initial‐condition ensembles to produce over 300 simulations spanning both initial condition and model structural uncertainty. We find model consensus on reduced aerosol amounts (particularly over southern and eastern Asia) and associated increases in surface shortwave radiation levels. However, any impact on near‐surface temperature or rainfall during 2020–2024 is extremely small and is not detectable in this initial analysis. Regional analyses on a finer scale, and closer attention to extremes (especially linked to changes in atmospheric composition and air quality) are required to test the impact of COVID‐19‐related emission reductions on near‐term climate. ; C. D. Jones, P. Nabat, R. Séférian acknowledge support from the European Union's Horizon 2020 research and innovation program under grant agreement No 641816 (CRESCENDO). R. D. Lamboll, P. M. Forster, J. Rogelj, R. B. Skeie, P. Nolan, R. Séférian acknowledge support from the European Union's Horizon 2020 research and innovation program under grant agreement No 820829 (CONSTRAIN). E. Tourigny, T. Ilyina and H. Li acknowledge support from the European Union's Horizon 2020 research and innovation program under grant agreement No 821003 (4C). C. Timmreck is supported from the Deutsche Forschungsgemeinschaft DFG (FOR2820, TI 344/2–1). MPI‐ESM simulations were performed at the German Climate Computing Center (DKRZ). We acknowledge DKRZ colleague Martin Schupfner for cmorizing and publishing the MPI‐ESM model simulations. S. T. Rumbold was funded by the National Environmental Research Council (NERC) national capability grant for the UK Earth System Modeling project, grant NE/N017951/1. M. Wu, H. Wang and K. Calvin acknowledge support by the U.S. Department of Energy (DOE), Office of Science, Office of Biological and Environmental Research, Earth and Environmental System Modeling program as part of the Energy Exascale Earth System Model (E3SM) project. The Pacific Northwest National Laboratory (PNNL) is operated for DOE by Battelle Memorial Institute under contract DE‐AC05‐76RLO1830. N. Oshima, T. Koshiro, and M. Deushi were supported by the Japan Society for the Promotion of Science (grant numbers: JP18H03363, JP18H05292, JP19K12312, and JP20K04070), the Environment Research and Technology Development Fund (JPMEERF20202003 and JPMEERF20205001) of the Environmental Restoration and Conservation Agency of Japan, the Integrated Research Program for Advancing Climate Models (TOUGOU) grant number JPMXD0717935561 from the Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan, and the Arctic Challenge for Sustainability II (ArCS II), Program Grant Number JPMXD1420318865. S.F. acknowledges funding for the Hans‐Ertel‐Center for Weather Research "Climate Monitoring and Diagnostic" (ID: BMVI/DWD 4818DWDP5A, https://www.herz.uni-bonn.de) and the Collaborative Research Center "Earth, evolution at the dry limit" (ID: DFG 68236062, https://sfb1211.uni-koeln.de). D. Olivié and J. Tjiputra acknowledge the Research Council of Norway funded projects INES (270061) and KeyClim (295046). Simulations of MIROC‐ES2L are supported by the TOUGOU project "Integrated Research Program for Advancing Climate Models" (grant number: JPMXD0717935715) of the Ministry of Education, Culture, Sports, Science, and Technology of Japan (MEXT). MIROC‐team acknowledges JAMSTEC for use of the Earth Simulator supercomputer. Simulations of UKESM1 and analysis of data were supported by the Joint UK BEIS/Defra Met Office Hadley Center Climate Program (GA01101). We gratefully acknowledge help from Martine Michou for setting up the model configuration used in this work and for processing of data from CNRM‐ESM2‐1. P. Nabat, C. Cassout and R. Séférian, thank the support of the team in charge of the CNRM‐CM climate model. Supercomputing time was provided by the Meteo‐France/DSI supercomputing center. Simulations of GISS‐E2‐1‐G were supported by NASA's Rapid Response and Novel Research in Earth Science program. Resources supporting this work were provided by the NASA High‐End Computing (HEC) Program through the NASA Center for Climate Simulation (NCCS) at Goddard Space Flight Center. We gratefully acknowledge Susanne Bauer, Gregory Faluvegi, Kenneth Lo, and Reto Ruedy for their assistance in preparing simulations and processing output. Y. Yang acknowledges the National Key Research and Development Program of China (Grant 2019YFA0606800 and 2020YFA0607803). S. Yang acknowledges support from the Danish National Center for Climate Research (Nationalt Center for Klimaforskning, NCKF). ; Peer Reviewed ; "Article signat per 49 autors/es: Chris D. Jones, Jonathan E. Hickman, Steven T. Rumbold, Jeremy Walton, Robin D. Lamboll , Ragnhild B. Skeie, Stephanie Fiedler, Piers M. Forster, Joeri Rogelj, Manabu Abe, Michael Botzet, Katherine Calvin, Christophe Cassou, Jason N.S. Cole, Paolo Davini, Makoto Deushi, Martin Dix, John C. Fyfe, Nathan P. Gillett, Tatiana Ilyina, Michio Kawamiya, Maxwell Kelley, Slava Kharin, Tsuyoshi Koshiro, Hongmei Li, Chloe Mackallah, Wolfgang A. Müller, Pierre Nabat, Twan van Noije, Paul Nolan, Rumi Ohgaito, Dirk Olivié, Naga Oshima, Jose Parodi, Thomas J. Reerink, Lili Ren, Anastasia Romanou, Roland Séférian, Yongming Tang, Claudia Timmreck , Jerry Tjiputra, Etienne Tourigny , Kostas Tsigaridis, Hailong Wang, Mingxuan Wu, Klaus Wyse,r Shuting Yang, Yang Yang, Tilo Ziehn" ; Postprint (published version)

We examine the influence of increased resolution on four long-standing biases using five different climate models developed within the PRIMAVERA project. The biases are the warm eastern tropical oceans, the double Intertropical Convergence Zone (ITCZ), the warm Southern Ocean, and the cold North Atlantic. Atmosphere resolution increases from ∼100–200 to ∼25–50 km, and ocean resolution increases from (eddy-parametrized) to (eddy-present). For one model, ocean resolution also reaches ∘ (eddy-rich). The ensemble mean and individual fully coupled general circulation models and their atmosphere-only versions are compared with satellite observations and the ERA5 reanalysis over the period 1980–2014. The four studied biases appear in all the low-resolution coupled models to some extent, although the Southern Ocean warm bias is the least persistent across individual models. In the ensemble mean, increased resolution reduces the surface warm bias and the associated cloud cover and precipitation biases over the eastern tropical oceans, particularly over the tropical South Atlantic. Linked to this and to the improvement in the precipitation distribution over the western tropical Pacific, the double-ITCZ bias is also reduced with increased resolution. The Southern Ocean warm bias increases or remains unchanged at higher resolution, with small reductions in the regional cloud cover and net cloud radiative effect biases. The North Atlantic cold bias is also reduced at higher resolution, albeit at the expense of a new warm bias that emerges in the Labrador Sea related to excessive ocean deep mixing in the region, especially in the ORCA025 ocean model. Overall, the impact of increased resolution on the surface temperature biases is model-dependent in the coupled models. In the atmosphere-only models, increased resolution leads to very modest or no reduction in the studied biases. Thus, both the coupled and atmosphere-only models still show large biases in tropical precipitation and cloud cover, and in midlatitude zonal winds at higher resolutions, with little change in their global biases for temperature, precipitation, cloud cover, and net cloud radiative effect. Our analysis finds no clear reductions in the studied biases due to the increase in atmosphere resolution up to 25–50 km, in ocean resolution up to 0.25∘, or in both. Our study thus adds to evidence that further improved model physics, tuning, and even finer resolutions might be necessary. ; This research has been supported by the Horizon2020 project PRIMAVERA (H2020 GA 641727) and IS-ENES3 (H2020 GA 824084). Eduardo Moreno-Chamarro acknowledges funding from the Spanish Science and Innovation Ministry (Ministerio de Ciencia e Innovación) via the STREAM project (PID2020-114746GB-I00) and from the ESA contract CMUG-CCI3-TECHPROP. Etienne Tourigny has received funding from the European Union's Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement no. 748750 (SPFireSD project). ; Peer Reviewed ; "Article signat per 13 autors/es: Eduardo Moreno-Chamarro, Louis-Philippe Caron, Saskia Loosveldt Tomas, Javier Vegas-Regidor, Oliver Gutjahr, Marie-Pierre Moine, Dian Putrasahan, Christopher D. Roberts, Malcolm J. Roberts, Retish Senan, Laurent Terray, Etienne Tourigny, and Pier Luigi Vidale" ; Postprint (published version)

Dynamical forecast systems have low to moderate skill in continental winter predictions in the extratropics. Here we assess the multimodel predictive skill over Northern Hemisphere high latitudes and midlatitudes using four state‐of‐the‐art forecast systems. Our main goal was to quantify the impact of the Arctic sea ice state during November on the sea level pressure (SLP), surface temperature, and precipitation skill during the following winter. Interannual variability of the November Barents and Kara Sea ice is associated with an important fraction of December to February (DJF) prediction skill in regions of Eurasia. We further show that skill related to sea ice in these regions is accompanied with enhanced skill of DJF SLP in western Russia, established by a sea ice‐atmosphere teleconnection mechanism. The teleconnection is strongest when atmospheric blocking conditions in Scandinavia/western Russia in November reduce a systematic SLP bias that is present in all systems. ; This work was funded by the European Union projects APPLICATE (Grant 727862), PRIMAVERA (Grant 641727), INTAROS (Grant 727890), and ESA/CMUG‐CCI3. We acknowledge PRACE for awarding us access to MareNostrum IV at Barcelona Supercomputing Center (BSC), Spain. J. C. A. N. received financial support from the Spanish Ministerio de Ciencia,Innovación y Universidades through a Juan de la Cierva personal grant (FJCI‐2017‐34027). E. T. received funding from the European Union's Horizon 2020 research and innovation program under the Marie Skłodowska‐Curie grant Agreement 748750 (SPFireSD project). V. G. received funding from the Agence Nationale de la Recherche through the Make Our Planet Great Again Grant ANR‐17‐MPGA‐003. The data from EC‐Earth3.2 and CNRM‐CM6‐1 are publicly available (at https://applicate.eu/data/data‐portal). GloSea5 (v13) and SEAS5 data are publicly available (at https://cds.climate.copernicus.eu/cdsapp#!/dataset/seasonal‐ monthly‐single‐levels?tab=overview). All the data were downloaded from their original source, converted to NetCDF in a format designed for efficient analysis with the tools mentioned before, and quality checked at several levels. ; Peer Reviewed ; Postprint (published version)

Representing the rainy season of the maritime continent is a challenge for global and regional climate models. Here, we compare regional climate models (RCMs) based on the coupled model intercomparison project phase 5 (CMIP5) model generation with high-resolution global climate models with a comparable spatial resolution from the HighResMIP experiment. The onset and the total precipitation of the rainy season for both model experiments are compared against observational datasets for Southeast Asia. A realistic representation of the monsoon rainfall is essential for agriculture in Southeast Asia as a delayed onset jeopardizes the possibility of having three annual crops. In general, the coupled historical runs (Hist-1950) and the historical force atmosphere run (HighresSST) of the high-resolution model intercomparison project (HighResMIP) suite were consistently closer to the observations than the RCM of CMIP5 used in this study. We find that for the whole of Southeast Asia, the HighResMIP models simulate the onset date and the total precipitation of the rainy season over the region closer to the observations than the other model sets used in this study. High-resolution models in the HighresSST experiment showed a similar performance to their low-resolution equivalents in simulating the monsoon characteristics. The HighresSST experiment simulated the anomaly of the onset date and the total precipitation for different El Niño-southern oscillation conditions best, although the magnitude of the onset date anomaly was underestimated. ; Indonesia Endowment Fund for Education (LPDP), Grant/Award Number: S-353/LPDP.3/2019; H2020 Marie Skłodowska-Curie, Grant/Award Number: 748750; European Union's Horizon 2020 Research and Innovation Programme, Grant/Award Number: 641727 ; Peer Reviewed ; Postprint (published version)

In this paper, we present and evaluate the skill of an EC-Earth3.3 decadal prediction system contributing to the Decadal Climate Prediction Project – Component A (DCPP-A). This prediction system is capable of skilfully simulating past global mean surface temperature variations at interannual and decadal forecast times as well as the local surface temperature in regions such as the tropical Atlantic, the Indian Ocean and most of the continental areas, although most of the skill comes from the representation of the external radiative forcings. A benefit of initialization in the predictive skill is evident in some areas of the tropical Pacific and North Atlantic oceans in the first forecast years, an added value that is mostly confined to the south-east tropical Pacific and the eastern subpolar North Atlantic at the longest forecast times (6–10 years). The central subpolar North Atlantic shows poor predictive skill and a detrimental effect of initialization that leads to a quick collapse in Labrador Sea convection, followed by a weakening of the Atlantic Meridional Overturning Circulation (AMOC) and excessive local sea ice growth. The shutdown in Labrador Sea convection responds to a gradual increase in the local density stratification in the first years of the forecast, ultimately related to the different paces at which surface and subsurface temperature and salinity drift towards their preferred mean state. This transition happens rapidly at the surface and more slowly in the subsurface, where, by the 10th forecast year, the model is still far from the typical mean states in the corresponding ensemble of historical simulations with EC-Earth3. Thus, our study highlights the Labrador Sea as a region that can be sensitive to full-field initialization and hamper the final prediction skill, a problem that can be alleviated by improving the regional model biases through model development and by identifying more optimal initialization strategies. ; The work in this paper was supported by the European Commission H2020 projects EUCP (grant no. 776613), APPLICATE (grant no. 727862), INTAROS (grant no. 727890) and PRIMAVERA (grant no. 641727); a Spanish project funded by the Spanish Ministry of Economy, Industry and Competitiveness (CLINSA, grant no. CGL2017-85791-R); a FRS-FNRS/FWO-funded Belgian project (PARAMOUR, grant no. EOS-30454083); and an ESA contract (grant no. CMUG-CCI3-TECHPROP). The climate simulations analysed in the paper were performed using the internal computing resources available at MareNostrum and additional resources from PRACE (HiResNTCP, project 3: grant no. 2017174177) and the Red Española de Supercomputación (AECT-2019-2-0003 and AECT-2019-3-0006 projects) as well as technical support provided by the Barcelona Supercomputing Center. In addition, several co-authors have been supported by personal grants: Yohan Ruprich-Robert, Etienne Tourigny and Simon Wild received funding from the European Union Horizon 2020 research and innovation programme (grant agreement nos. 800154, 748750 and 754433 respectively); Ivana Cvijanovic was supported by Generalitat de Catalunya (Secretaria d'Universitats i Recerca del Departament d'Empresa i Coneixement) through the Beatriu de Pinós programme; Juan Acosta-Navarro was supported by the Spanish Ministry of Science, Innovation and Universities through a Juan de la Cierva personal grant (grant no. FJCI-2017-34027); Rubén Cruz-García was funded by the Spanish Ministry of Education, Culture and Sports with an FPU grant (grant no. FPU15/01511); and Markus Donat and Pablo Ortega were funded by the Spanish Ministry of Economy, Industry and Competitiveness through the Ramon y Cajal grants RYC-2017-22964 and RYC-2017-22772. We also want to thank Panos Athanasidis, Stephen Yeager and Gerald Meehl for their very helpful comments when reviewing the paper. ; Postprint (published version)

11 pages, 6 figures, supplementary information https://doi.org/10.1038/s41612-021-00188-5.-- Data availability; The data generated and analyzed during the current study are available from the corresponding author YRR on reasonable request ; Atlantic multidecadal variability (AMV) has been linked to the observed slowdown of global warming over 1998–2012 through its impact on the tropical Pacific. Given the global importance of tropical Pacific variability, better understanding this Atlantic–Pacific teleconnection is key for improving climate predictions, but the robustness and strength of this link are uncertain. Analyzing a multi-model set of sensitivity experiments, we find that models differ by a factor of 10 in simulating the amplitude of the Equatorial Pacific cooling response to observed AMV warming. The inter-model spread is mainly driven by different amounts of moist static energy injection from the tropical Atlantic surface into the upper troposphere. We reduce this inter-model uncertainty by analytically correcting models for their mean precipitation biases and we quantify that, following an observed 0.26 °C AMV warming, the equatorial Pacific cools by 0.11 °C with an inter-model standard deviation of 0.03 °C ; Y.R.-R. was founded by the European Union's Horizon 2020 Research and Innovation Program in the framework of the Marie Skłodowska-Curie grant INADEC (Grant agreement 800154). E.M.-C. acknowledges funding from the European Commission's Horizon 2020 projects PRIMAVERA (Grant Agreement 641727). X.L. has received funding from the European Union's Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement H2020-MSCA-COFUND-2016-754433. A.B. and D.N. acknowledge funding from the European Commission's Horizon 2020 project EUCP (Grant agreement 776613). F.C. and G.D. were supported by the US National Science Foundation (NSF) under the Collaborative Research EaSM2 Grant OCE-1243015 to NCAR and by the US National Oceanic and Atmospheric Administration (NOAA) Climate Program Office under the Climate Variability and Predictability Program Grant NA13OAR4310138. NCAR is a major facility sponsored by the US NSF under Cooperative Agreement 1852977. [.] R.E., N.D., L.H., and D.S. were supported by the Met Office Hadley Center 522 Climate Program funded by BEIS and Defra and by the European Commission Horizon 2020 EUCP 523 project (GA 776613). J.L.-P. was funded by the European Union's Horizon 2020 Research and Innovation Program in the framework of the PRIMAVERA project (Grant Agreement 641727). J.R. and D.H. were funded by NERC via NCAS and the ACSIS project (NE/N018001/1), and JR was also funded by the NERC SMURPHS project (NE/N006054/1). M.M.-R. was funded by the European Union's Horizon 2020 Research and Innovation Program in the framework of the Marie Skłodowska-Curie grant FESTIVAL (Grant agreement 797236). E.T. has received funding from the European Union's Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No. 748750 (SPFireSD project) ; With the funding support of the 'Severo Ochoa Centre of Excellence' accreditation (CEX2019-000928-S), of the Spanish Research Agency (AEI) ; Peer reviewed