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The Earth system model EC-Earth3 for contributions to CMIP6 is documented here, with its flexible coupling framework, major model configurations, a methodology for ensuring the simulations are comparable across different high-performance computing (HPC) systems, and with the physical performance of base configurations over the historical period. The variety of possible configurations and sub-models reflects the broad interests in the EC-Earth community. EC-Earth3 key performance metrics demonstrate physical behavior and biases well within the frame known from recent CMIP models. With improved physical and dynamic features, new Earth system model (ESM) components, community tools, and largely improved physical performance compared to the CMIP5 version, EC-Earth3 represents a clear step forward for the only European community ESM. We demonstrate here that EC-Earth3 is suited for a range of tasks in CMIP6 and beyond. ; The development of EC-Earth3 was supported by the European Union's Horizon 2020 research and innovation program under project IS-ENES3, the third phase of the distributed e-infrastructure of the European Network for Earth System Modelling (ENES) (grant agreement no. 824084, PRIMAVERA grant no. 641727, and CRESCENDO grant no. 641816). Etienne Tourigny and Raffaele Bernardello have received funding from the European Union's Horizon 2020 research and innovation program under Marie Skłodowska-Curie grant agreement nos. 748750 (SPFireSD project) and 708063 (NeTNPPAO project). Ivana Cvijanovic was supported by Generalitat de Catalunya (Secretaria d'Universitats i Recerca del Departament d'Empresa i Coneixement) through the Beatriu de Pinós program. Yohan Ruprich-Robert was funded by the European Union's Horizon 2020 research and innovation program in the framework of Marie Skłodowska-Curie grant INADEC (grant agreement 800154). Paul A. Miller, Lars Nieradzik, David Wårlind, Roland Schrödner, and Benjamin Smith acknowledge financial support from the strategic research area "Modeling the Regional and Global ...

This paper describes the second major release of the Earth System Model Evaluation Tool (ESMValTool), a community diagnostic and performance metrics tool for the evaluation of Earth system models (ESMs) participating in the Coupled Model Intercomparison Project (CMIP). Compared to version 1.0, released in 2016, ESMValTool version 2.0 (v2.0) features a brand new design, with an improved interface and a revised preprocessor. It also features a significantly enhanced diagnostic part that is described in three companion papers. The new version of ESMValTool has been specifically developed to target the increased data volume of CMIP Phase 6 (CMIP6) and the related challenges posed by the analysis and the evaluation of output from multiple high-resolution or complex ESMs. The new version takes advantage of state-of-the-art computational libraries and methods to deploy an efficient and user-friendly data processing. Common operations on the input data (such as regridding or computation of multi-model statistics) are centralized in a highly optimized preprocessor, which allows applying a series of preprocessing functions before diagnostics scripts are applied for in-depth scientific analysis of the model output. Performance tests conducted on a set of standard diagnostics show that the new version is faster than its predecessor by about a factor of 3. The performance can be further improved, up to a factor of more than 30, when the newly introduced task-based parallelization options are used, which enable the efficient exploitation of much larger computing infrastructures. ESMValTool v2.0 also includes a revised and simplified installation procedure, the setting of user-configurable options based on modern language formats, and high code quality standards following the best practices for software development. ; The technical development work for ESValTool v2.0 was funded by various projects, in particular (1) the Copernicus Climate Change Service (C3S) "Metrics and Access to Global Indices for Climate Projections (C3S-MAGIC)" project; (2) the European Union's Horizon 2020 Framework Programme for Research and Innovation "Infrastructure for the European Network for Earth System Modelling (IS-ENES3)" project under grant agreement no. 824084; (3) the European Union's Horizon 2020 Framework Programme for Research and Innovation "Coordinated Research in Earth Systems and Climate: Experiments, kNowledge, Dissemination and Outreach (CRESCENDO)" project under grant agreement no. 641816; (4) the the European Union's Horizon 2020 Framework Programme for Research and Innovation "PRocess-based climate sIMulation: AdVances in high-resolution modelling and European climate Risk Assessment (PRIMAVERA)" project under grant agreement no. 641727; (5) the Helmholtz Society project "Advanced Earth System Model Evaluation for CMIP (EVal4CMIP)"; (6) project S1 (Diagnosis and Metrics in Climate Models) of the Collaborative Research Centre TRR 181 "Energy Transfer in Atmosphere and Ocean" funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) project no. 274762653; and (7) National Environmental Research Council (NERC) National Capability Science Multi-Centre (NCSMC) funding for the UK, Earth System Modelling project (grant no. NE/N018036/1). The article processing charges for this open-access publication were covered by a Research Centre of the Helmholtz Association. ; Peer Reviewed ; Postprint (published version)

Coordinated experimental design and implementation has become a cornerstone of global climate modelling. Model Intercomparison Projects (MIPs) enable systematic and robust analysis of results across many models, by reducing the influence of ad hoc differences in model set-up or experimental boundary conditions. As it enters its 6th phase, the Coupled Model Intercomparison Project (CMIP6) has grown significantly in scope with the design and documentation of individual simulations delegated to individual climate science communities. The Coupled Climate-Carbon Cycle Model Intercomparison Project (C4MIP) takes responsibility for design, documentation, and analysis of carbon cycle feedbacks and interactions in climate simulations. These feedbacks are potentially large and play a leading-order contribution in determining the atmospheric composition in response to human emissions of CO2 and in the setting of emissions targets to stabilize climate or avoid dangerous climate change. For over a decade, C4MIP has coordinated coupled climate-carbon cycle simulations, and in this paper we describe the C4MIP simulations that will be formally part of CMIP6. While the climate-carbon cycle community has created this experimental design, the simulations also fit within the wider CMIP activity, conform to some common standards including documentation and diagnostic requests, and are designed to complement the CMIP core experiments known as the Diagnostic, Evaluation and Characterization of Klima (DECK). C4MIP has three key strands of scientific motivation and the requested simulations are designed to satisfy their needs: (1) pre-industrial and historical simulations (formally part of the common set of CMIP6 experiments) to enable model evaluation, (2) idealized coupled and partially coupled simulations with 1% per year increases in CO2 to enable diagnosis of feedback strength and its components, (3) future scenario simulations to project how the Earth system will respond to anthropogenic activity over the 21st century and beyond. This paper documents in detail these simulations, explains their rationale and planned analysis, and describes how to set up and run the simulations. Particular attention is paid to boundary conditions, input data, and requested output diagnostics. It is important that modelling groups participating in C4MIP adhere as closely as possible to this experimental design. ; CRESCENDO project members (CDJ, PF, LB, VB, TI, SZ) acknowledge funding received from the Horizon 2020 European Union's Framework Programme for Research and Innovation under grant agreement no. 641816. CDJ was supported by the Joint UK BEIS/Defra Met Office Hadley Centre Climate Programme (GA01101). HDG was supported by a Marie Curie Career Integration Grant from the European Commission. JP is supported by the German Research Foundation's Emmy Noether Program (PO 1751/1-1).

Acknowledgements The work of CDJ, JAL, AW was supported by the Joint UK DECC/Defra Met Office Hadley Centre Climate Programme (GA01101). CDJ, JAL, AW, PF, EK, DvV are supported by the European Union's Horizon 2020 research and innovation programme under grant agreement No 641816 (CRESCENDO). GPP was supported by the Norwegian Research Council (project 209701). PC acknowledges support from the European Research Council Synergy grant ERC-2013-SyG-610028 IMBALANCE-P. We acknowledge the World Climate Research Programme's Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modeling groups (listed in Table 1 of this paper) for producing and making available their model output. For CMIP the U.S. Department of Energy's Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in artnership with the Global Organization for Earth system Science Portals. ; Peer reviewed ; Publisher PDF

Stratospheric ozone and water vapor are key components of the Earth system, and past and future changes to both have important impacts on global and regional climate. Here we evaluate long-term changes in these species from the pre-industrial (1850) to the end of the 21st century in CMIP6 models under a range of future emissions scenarios. There is good agreement between the CMIP multi-model mean and observations for total column ozone (TCO), although there is substantial variation between the individual CMIP6 models. For the CMIP6 multi-model mean, global mean TCO has increased from ~300 DU in 1850 to ~305 DU in 1960, before rapidly declining in the 1970s and 1980s following the use and emission of halogenated ozone depleting substances (ODSs). TCO is projected to return to 1960's values by the middle of the 21st century under the SSP2-4.5, SSP3-7.0, SSP4-3.4, SSP4-6.0 and SSP5-8.5 scenarios, and under the SSP3-7.0 and SSP5-8.5 scenarios TCO values are projected to be ~10 DU higher than the 1960's values by 2100. However, under the SSP1-1.9 and SSP1-1.6 scenarios, TCO is not projected to return to the 1960's values despite reductions in halogenated ODSs due to decreases in tropospheric ozone mixing ratios. This global pattern is similar to regional patterns, except in the tropics where TCO under most scenarios is not projected to return to 1960's values, either through reductions in tropospheric ozone under SSP1-1.9 and SSP1-2.6, or through reductions in lower stratospheric ozone resulting from an acceleration of the Brewer-Dobson Circulation under other SSPs. In contrast to TCO, there is poorer agreement between the CMIP6 multi-model mean and observed lower stratospheric water vapour mixing ratios, with the CMIP6 multi-model mean underestimating observed water vapour mixing ratios by ~0.5 ppmv at 70hPa. CMIP6 multi-model mean stratospheric water vapor mixing ratios in the tropical lower stratosphere have increased by ~0.5 ppmv from the pre-industrial to the present day and are projected to increase further by the end of the 21st century. The largest increases (~2 ppmv) are simulated under the future scenarios with the highest assumed forcing pathway (e.g. SSP5-8.5). Tropical lower stratospheric water vapor, and to a lesser extent TCO, show large variations following explosive volcanic eruptions. ; JK and PTG thank NERC for financial support through NCAS (grant no. R8/H12/83/003). BH was supported by the European Union's Horizon 2020 Framework Programme for Research and Innovation "Coordinated Research in Earth Systems and Climate: Experiments, kNowledge, Dissemination and Outreach (CRESCENDO)" project under grant agreement no. 641816. GZ and OM were supported by the NZ Government's Strategic Science Investment Fund (SSIF) through the NIWA programme CACV. MD was supported by the Japan Society for the Promotion of Science (grant no. JP20K04070). GC was supported by the Swiss National Science Foundation within the Ambizione programme (grant no. PZ00P2_180043). The work of SB and OCS was supported by the Energy Exascale Earth System Model (E3SM) project, funded by the US Department of Energy, Office of Science, Office of Biological and Environmental Research. The work of PCS was performed at LLNL under contract no. DE-AC52-07NA27344. The E3SM data were produced using resources of the National Energy Research Scientific Computing Center, a DOE Office of Science User Facility supported by the Office of Science of the US Department of Energy under contract no. DE-AC02-05CH11231.

1. Modelling tools and plausible scenarios in science-policy to improve evidence based decision making for human-well-being -- 2. Basic Introduction to Species Distribution Modelling -- 3. Machine Learning based predictive modelling approaches for better understanding evolutionary history, distribution and niche occupancy: Experience from Western Ghats -- 4. Mapping the impact of climate change on eco-sensitive hotspots using species distribution modelling (SDMs) – Gaps, Challenges and Future Perspectives -- 5. Approaches for modelling the climate change impacts on ecosystems -- 6. Developing a Bayesian Model of Climate-Induced Lake Overturn in Talisay, Taal Lake -- 7. Global sensitivity and uncertainty analysis of MaxEnt model: Implications in species habitat projections -- 8. Tree species diversity and richness patterns reveal high priority areas for conservation in Eswatini -- 9. Improving the conservation status of a threatened tree (Acer sikkimensis Miq. syn. Acer hookeri Miq.) through standardization of seed germination protocol and using ecological niche modeling -- 10. Ecological Niche Modeling of the endemic Himalayan near-threatened treeline conifer Abies spectabilis (D.Don) Mirb. in the Indian Central Himalaya -- 11. Modeling the distribution of a medicinal plant Oroxylum indicum (L.) Kurz for its conservation in Arunachal Pradesh -- 12. Habitat Suitability and Niche Modeling for Conservation and Restoration of Aconitum heterophyllum Wall. in Temperate Himalayan Forest Ecosystem -- 13. Application of species distribution modeling for conservation and restoration of forest ecosystems -- 14. Habitat suitability analysis of Asiatic Elephants (Elephas maximus) in the Tropical Moist Deciduous Forest of Assam using Analytic Hierarchy Process (AHP) -- 15. Factors Affecting the Habitat Suitability of Eastern Swamp Deer (Rucervus duvaucelii ranjitsinhi Groves, 1982) in Manas National Park and Implication for Terai Grassland Restoration -- 16. Evaluating Potential Habitats of Chital, Sloth Bear and Jungle Cat in Selected Areas of Central Indian Landscape -- 17. Habitat suitability modeling of Tor tor (Hamilton, 1822) in the Indian drainage systems using MaxEnt -- 18. Modelling the influence of Marine Fishery Advisories on the reduction of Carbon Dioxide emissions for Odisha under varying Climate Change Scenarios using CMIP models – an evidence-based approach for policymaking -- 19. Impacts of pollution on tropical montane and temperate forests of South Asia: Preliminary studies by post graduate students in India and Sri Lanka -- 20. Selection of strategic sampling sites for river quality assessments near mined areas as a policy handle for low-impact development and biodiversity conservation – a case study of River Godavari -- 21. Ecological niche modeling predicts the potential area for cultivation of Melia dubia Cav. (Meliaceae): a promising tree species for agroforestry in India -- 22. Proportions of change in the airborne particulate matter (PM10) concentrations across selected states in Peninsular India - a study of decadal, pre-pandemic trends for planning restoration -- 23. Decomposition of Sunflower Cuttings and its Impact on Soil Fertility of Rice Terraces (Payoh) in Banaue, Ifugao, Philippines -- 24. Forest ecosystem modeling for policy planning: A review -- 25. Ecological Carrying Capacity Modelling and Sustainability Assessment of the Seven Lakes of San Pablo City, Laguna, Philippines -- 26. Assessment of the contribution of Fresh Water Ecosystem Services to the Hydropower Sector in the Kura-Araz basin -- 27. Eutrophication modelling of Chilika Lagoon using an artificial neural network approach.

We present a long-term assessment of precipitation trends in Southwestern Europe (1850-2018) using data from multiple sources, including observations, gridded datasets and global climate model experiments. Contrary to previous investigations based on shorter records, we demonstrate, using new long-term, quality controlled precipitation series, the lack of statistically significant long-term decreasing trends in precipitation for the region. Rather, significant trends were mostly found for shorter periods, highlighting the prevalence of interdecadal and interannual variability at these time-scales. Global climate model outputs from three CMIP experiments are evaluated for periods concurrent with observations. Both the CMIP3 and CMIP5 ensembles show precipitation decline, with only CMIP6 showing agreement with long term trends in observations. However, for both CMIP3 and CMIP5 large interannual and internal variability among ensemble members makes it difficult to identify a trend that is statistically different from observations. Across both observations and models, our results make it difficult to associate any declining trends in precipitation in Southwestern Europe to anthropogenic forcing at this stage. ; This work was supported by the research projects CGL2017-82216-R, CGL2017-83866-C3-3-R and PCI2019-103631, financed by the Spanish Commission of Science and Technology and FEDER; CROSSDRO project financed by the AXIS (Assessment of Cross(X) - sectoral climate Impacts and pathways for Sustainable transformation), JPI-Climate co-funded call of the European Commission and INDECIS which is part of ERA4CS, an ERA-NET initiated by JPI Climate, and funded by FORMAS (SE), DLR (DE), BMWFW (AT), IFD (DK), MINECO (ES), ANR (FR) with co-funding by the European Union (Grant 690462).

Working across U.S. federal agencies, international agencies, and multiple worldwide data centers, and spanning seven international network organizations, the Earth System Grid Federation (ESGF) allows users to access, analyze, and visualize data using a globally federated collection of networks, computers, and software. Its architecture employs a system of geographically distributed peer nodes that are independently administered yet united by common federation protocols and application programming interfaces (APIs). The full ESGF infrastructure has now been adopted by multiple Earth science projects and allows access to petabytes of geophysical data, including the Coupled Model Intercomparison Project (CMIP)—output used by the Intergovernmental Panel on Climate Change assessment reports. Data served by ESGF not only include model output (i.e., CMIP simulation runs) but also include observational data from satellites and instruments, reanalyses, and generated images. Metadata summarize basic information about the data for fast and easy data discovery. ; This work was supported by the U.S. Department of Energy Office of Science/Office of Biological and Environmental Research under Contract DE-AC52-07NA27344 at Lawrence Livermore National Laboratory. VB is supported by the Cooperative Institute for Climate Science, Princeton University, under Award NA08OAR4320752 from the National Oceanic and Atmospheric Administration, U.S. Department of Commerce. Part of this work was undertaken with the assistance of resources from the National Computational Infrastructure (NCI), which is supported by the Australian Government. Part of this activity was performed on behalf of the Jet Propulsion Laboratory, California Institute of Technology, under a contract with NASA. Part of this activity was performed on behalf of the Goddard Space Flight Center, under a contract with NASA. This work was supported by ANR Convergence project (Grant Agreement ANR-13-MONU-0008). This work was supported by FP7 IS-ENES2 project (Grant Agreement 312979).

Skillful predictions of the frequency of flood events over long lead times (e.g., from one to ten years ahead) are essential for governments and institutions making near‐term flood risk plans. However, little is known about current flood prediction capabilities over annual to decadal time scales. Here we address this knowledge gap at 286 U.S. Geological Survey gaging stations across the U.S. Midwest using precipitation and temperature decadal predictions from the Coupled Model Intercomparison Project (CMIP) phase 5 models. We use the 1‐ to 10‐year predictions of precipitation and temperature as inputs to statistical models that have significant skill in reproducing inter‐annual and decadal changes in the observed frequency of flood events. Our results indicate that the limited skill of basin‐averaged precipitation predictions suppresses the skill of flood event frequency predictions, even at the shortest lead time, but downscaling and bias correction improves predictions across all lead times and especially in spring.

The increasing demand for high-resolution climate information has attracted growing attention to statistical downscaling (SDS) methods, due in part to their relative advantages and merits as compared to dynamical approaches (based on regional climate model simulations), such as their much lower computational cost and their fitness for purpose for many local-scale applications. As a result, a plethora of SDS methods is nowadays available to climate scientists, which has motivated recent efforts for their comprehensive evaluation, like the VALUE initiative (http://www.value-cost.eu, last access: 29 March 2020). The systematic intercomparison of a large number of SDS techniques undertaken in VALUE, many of them independently developed by different authors and modeling centers in a variety of languages/environments, has shown a compelling need for new tools allowing for their application within an integrated framework. In this regard, downscaleR is an R package for statistical downscaling of climate information which covers the most popular approaches (model output statistics ? including the so-called ?bias correction? methods ? and perfect prognosis) and state-of-the-art techniques. It has been conceived to work primarily with daily data and can be used in the framework of both seasonal forecasting and climate change studies. Its full integration within the climate4R framework (Iturbide et al., 2019) makes possible the development of end-to-end downscaling applications, from data retrieval to model building, validation, and prediction, bringing to climate scientists and practitioners a unique comprehensive framework for SDS model development. In this article the main features of downscaleR are showcased through the replication of some of the results obtained in VALUE, placing an emphasis on the most technically complex stages of perfect-prognosis model calibration (predictor screening, cross-validation, and model selection) that are accomplished through simple commands allowing for extremely flexible model tuning, tailored to the needs of users requiring an easy interface for different levels of experimental complexity. As part of the open-source climate4R framework, downscaleR is freely available and the necessary data and R scripts to fully replicate the experiments included in this paper are also provided as a companion notebook. ; We thank the European Union Cooperation in Science and Technology (EU COST) Action ES1102 VALUE (http://www.value-cost.eu) for making publicly available the data used in this article and the tools implementing the comprehensive set of validation measures and indices. We also thank the THREDDS Data Server (TDS) software developed by UCAR/Unidata (https://doi.org/10.5065/D6N014KG, Unidata, 2006) and all R developers and their supporting community for providing free software facilitating open science. We acknowledge the World Climate Research Program's Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the EC-EARTH Consortium for producing and making available their model output used in this paper. For CMIP the U.S. Department of Energy's Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led the development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. We are very grateful to the two anonymous referees participating in the interactive discussion for their insightful comments, helping us to considerably improve the original paper. Financial support. The authors acknowledge partial funding from the MULTI-SDM project (MINECO/FEDER, CGL2015-66583-R) and from the project INDECIS, part of the European Research Area for Climate Services Consortium (ERA4CS) with co-funding by the uropean Union (grant no. 690462).

The distribution of data contributed to the Coupled Model Intercomparison Project Phase 6 (CMIP6) is via the Earth System Grid Federation (ESGF). The ESGF is a network of internationally distributed sites that together work as a federated data archive. Data records from climate modelling institutes are published to the ESGF and then shared around the world. It is anticipated that CMIP6 will produce approximately 20 PB of data to be published and distributed via the ESGF. In addition to this large volume of data a number of value-added CMIP6 services are required to interact with the ESGF; for example the citation and errata services both interact with the ESGF but are not a core part of its infrastructure. With a number of interacting services and a large volume of data anticipated for CMIP6, the CMIP Data Node Operations Team (CDNOT) was formed. The CDNOT coordinated and implemented a series of CMIP6 preparation data challenges to test all the interacting components in the ESGF CMIP6 software ecosystem. This ensured that when CMIP6 data were released they could be reliably distributed. ; This international collaborative work was funded through various agencies. Co-authors at Lawrence Berkeley National Laboratory were funded under contract no. DE-AC02-05CH11231, and co-authors at Lawrence Livermore National Laboratory under contract DE-AC52-07NA27344 with the US Department of Energy. European co-authors were supported by the European Union Horizon 2020 IS-ENES3 project (grant agreement no. 824084). CNRM participants were additionally funded by the French National Research Agency project CONVERGENCE (grant ANR-13-MONU-0008-02). Co-authors from NCI were supported by the National Collaborative Research Infrastructure Strategy (NCRIS)-funded National Computational Infrastructure (NCI) Australia and the Australian Research Data Commons (ARDC). ; Peer Reviewed ; "Article signat per 38 autors: Ruth Petrie, Sébastien Denvil, Sasha Ames, Guillaume Levavasseur, Sandro Fiore, Chris Allen, Fabrizio Antonio, Katharina Berger, Pierre-Antoine Bretonnière, Luca Cinquini, Eli Dart, Prashanth Dwarakanath, Kelsey Druken, Ben Evans, Laurent Franchistéguy, Sébastien Gardoll, Eric Gerbier, Mark Greenslade, David Hassell, Alan Iwi, Martin Juckes, Stephan Kindermann, Lukasz Lacinski, Maria Mirto, Atef Ben Nasser, Paola Nassisi, Eric Nienhouse, Sergey Nikonov, Alessandra Nuzzo, Clare Richards, Syazwan Ridzwan, Michel Rixen, Kim Serradell, Kate Snow, Ag Stephens, Martina Stockhause, Hans Vahlenkamp, and Rick Wagner" ; Postprint (published version)

This is the final version. Available on open access from Nature Research via the DOI in this record ; Data availability: All data used in this manuscript are fully open access and available. The soil data were obtained from a published snapshot derived from the World Soil Information database (https://doi.org/10.17027/isric-wdcsoils.20160003), the long-term climate data are available in the WorldClim version 2.0 database (http://worldclim.org), while the MODIS primary productivity, evapotranspiration, and landcover data are available in the MOD17A3 (https://doi.org/10.5067/MODIS/MOD17A3.006), MOD16A2 (https://doi.org/10.5067/MODIS/MOD16A2.006) and MCD12Q1 (https://doi.org/10.5067/MODIS/MCD12Q1.006) databases respectively. The UKESM data from the sixth coupled model intercomparison project (CMIP6) is available in the public data archive (https://data.ceda.ac.uk/badc/cmip6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL). ; Physical and chemical stabilisation mechanisms are now known to play a critical role in controlling carbon (C) storage in mineral soils, leading to suggestions that climate warming-induced C losses may be lower than previously predicted. By analysing > 9,000 soil profiles, here we show that, overall, C storage declines strongly with mean annual temperature. However, the reduction in C storage with temperature was more than three times greater in coarse-textured soils, with limited capacities for stabilising organic matter, than in fine-textured soils with greater stabilisation capacities. This pattern was observed independently in cool and warm regions, and after accounting for potentially confounding factors (plant productivity, precipitation, aridity, cation exchange capacity, and pH). The results could not, however, be represented by an established Earth system model (ESM). We conclude that warming will promote substantial soil C losses, but ESMs may not be predicting these losses accurately or which stocks are most vulnerable. ; Natural Environment Research Council (NERC) ; Swedish Research Council ; European Union Horizon 2020

The distribution of data contributed to the Coupled Model Intercomparison Project Phase 6 (CMIP6) is via the Earth System Grid Federation (ESGF). The ESGF is a network of internationally distributed sites that together work as a federated data archive. Data records from climate modelling institutes are published to the ESGF and then shared around the world. It is anticipated that CMIP6 will produce approximately 20 PB of data to be published and distributed via the ESGF. In addition to this large volume of data a number of value-added CMIP6 services are required to interact with the ESGF; for example the citation and errata services both interact with the ESGF but are not a core part of its infrastructure. With a number of interacting services and a large volume of data anticipated for CMIP6, the CMIP Data Node Operations Team (CDNOT) was formed. The CDNOT coordinated and implemented a series of CMIP6 preparation data challenges to test all the interacting components in the ESGF CMIP6 software ecosystem. This ensured that when CMIP6 data were released they could be reliably distributed. No. DE-ACO2-05CH11231 and authors at Lawrence Livermore National Laboratory (LLNL) under contract DE-AC52-07NA27344 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).

11 Pags.- 1 Tabl.- 4 Figs. Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence. ; We present a long-term assessment of precipitation trends in Southwestern Europe (1850–2018) using data from multiple sources, including observations, gridded datasets and global climate model experiments. Contrary to previous investigations based on shorter records, we demonstrate, using new long-term, quality controlled precipitation series, the lack of statistically significant long-term decreasing trends in precipitation for the region. Rather, significant trends were mostly found for shorter periods, highlighting the prevalence of interdecadal and interannual variability at these time-scales. Global climate model outputs from three CMIP experiments are evaluated for periods concurrent with observations. Both the CMIP3 and CMIP5 ensembles show precipitation decline, with only CMIP6 showing agreement with long term trends in observations. However, for both CMIP3 and CMIP5 large interannual and internal variability among ensemble members makes it difficult to identify a trend that is statistically different from observations. Across both observations and models, our results make it difficult to associate any declining trends in precipitation in Southwestern Europe to anthropogenic forcing at this stage. ; This work was supported by the research projects CGL2017-82216-R, CGL2017-83866-C3-3-R and PCI2019-103631, financed by the Spanish Commission of Science and Technology and FEDER; CROSSDRO project financed by the AXIS (Assessment of Cross(X)—sectoral climate Impacts and pathways for Sustainable transformation), JPI-Climate co-funded call of the European Commission and INDECIS which is part of ERA4CS, an ERA-NET initiated by JPI Climate, and funded by FORMAS (SE), DLR (DE), BMWFW (AT), IFD (DK), MINECO (ES), ANR (FR) with co-funding by the European Union (Grant 690462). Dhais Peña-Angulo received a 'Juan de la Cierva' postdoctoral contract (FJCI-2017-33652 Spanish Ministry of Economy and Competitiveness, MEC). Conor Murphy was supported by the Irish Environmental Protection Agency (Grant Nos. 2019-CCRP-MS.60). Marco Turco has received funding from the Spanish Ministry of Science, Innovation and Universities through the project PREDFIRE (RTI2018-099711-J-I00), which is co-financed with the European Regional Development Fund (ERDF/FEDER). ; Peer reviewed

The distribution of data contributed to the Coupled Model Intercomparison Project Phase 6 (CMIP6) is via the Earth System Grid Federation (ESGF). The ESGF is a network of internationally distributed sites that together work as a federated data archive. Data records from climate modelling institutes are published to the ESGF and then shared around the world. It is anticipated that CMIP6 will produce approximately 20 PB of data to be published and distributed via the ESGF. In addition to this large volume of data a number of value-added CMIP6 services are required to interact with the ESGF; for example the citation and errata services both interact with the ESGF but are not a core part of its infrastructure. With a number of interacting services and a large volume of data anticipated for CMIP6, the CMIP Data Node Operations Team (CDNOT) was formed. The CDNOT coordinated and implemented a series of CMIP6 preparation data challenges to test all the interacting components in the ESGF CMIP6 software ecosystem. This ensured that when CMIP6 data were released they could be reliably distributed. No. DE-ACO2-05CH11231 and authors at Lawrence Livermore National Laboratory (LLNL) under contract DE-AC52-07NA27344 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan). ; Funding Agencies|US Department of EnergyUnited States Department of Energy (DOE) [DE-AC02-05CH11231, DE-AC52-07NA27344]; European UnionEuropean Commission [824084]; French National Research Agency project CONVERGENCEFrench National Research Agency (ANR) [ANR-13-MONU-0008-02]; National Collaborative Research Infrastructure Strategy (NCRIS)-funded National Computational Infrastructure (NCI) Australia; Australian Research Data Commons (ARDC)