Balancing EC-Earth3 improving the performance of EC-Earth CMIP6 configurations by minimizing the coupling cost

Earth System Models (ESMs) are complex systems used in weather and climate studies generally built from different independent components responsible for simulating a specific realm (ocean, atmosphere, biosphere, etc.). To replicate the interactions between these processes, ESMs typically use coupling libraries that manage the synchronization and field exchanges between the individual components, which run in parallel as a Multi-Program, Multiple-Data application. As ESMs get more complex (increase in resolution, number of components, configurations, etc.), achieving the best performance when running in High-performance Computing platforms has become increasingly challenging and of major concern. One of the critical bottlenecks is the load-imbalance, where the fastest components will have to wait for the slower ones. Finding the optimal number of processing elements to assign to each of the multiple independent constituents to minimize the performance loss due to synchronizations and maximize the overall parallel efficiency is impossible without the right performance metrics, methodology, and tools. This paper presents the results of balancing multiple Coupled Model Intercomparison Project phase 6 configurations for the EC-Earth3 ESM. We will show that intuitive approaches can lead to suboptimal resource allocations and propose new setups up to 25% fasters while reducing the computational cost by 72%. We prove that new methods are needed to deal with the load-balance of ESMs and hope that our study will serve as a guide to optimize any other coupled system.The research leading to these results has received co-funding from the National Research Agency through OEMES (PID2020-116324RA-I00).Peer ReviewedPostprint (published version

A comparison of remotely-sensed and inventory datasets for burned area in Mediterranean Europe

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)

Extratropical transition of tropical cyclones in a multiresolution ensemble of atmosphere-only and fully coupled global Climate Models

Tropical cyclones undergo extratropical transition (ET) in every ocean basin. Projected changes in ET frequency under climate change are uncertain and differ between basins, so multimodel studies are required to establish confidence. We used a feature-tracking algorithm to identify tropical cyclones and performed cyclone phase-space analysis to identify ET in an ensemble of atmosphere-only and fully coupled global model simulations, run at various resolutions under historical (1950–2014) and future (2015–50) forcing. Historical simulations were evaluated against five reanalyses for 1979–2018. Considering ET globally, ensemble-mean biases in track and genesis densities are reduced in the North Atlantic and western North Pacific when horizontal resolution is increased from ∼100 to ∼25 km. At high resolution, multi-reanalysis-mean climatological ET frequencies across most ocean basins as well as basins’ seasonal cycles are reproduced better than in low-resolution models. Skill in simulating historical ET interannual variability in the North Atlantic and western North Pacific is ∼0.3, which is lower than for all tropical cyclones. Models project an increase in ET frequency in the North Atlantic and a decrease in the western North Pacific. We explain these opposing responses by secular change in ET seasonality and an increase in lower-tropospheric, pre-ET warm-core strength, both of which are largely unique to the North Atlantic. Multimodel consensus about climate change responses is clearer for frequency metrics than for intensity metrics. These results help clarify the role of model resolution in simulating ET and help quantify uncertainty surrounding ET in a warming climate.All authors received financial support from the PRIMAVERA project (European Commission Horizon2020 Grant Agreement 641727) with data access via JASMIN (https://jasmin.ac.uk) supported by IS-ENES3 (Grant Agreement 824084). AJB also received support from National Environmental Research Council (NERC) national capability grant for the North Atlantic Climate System: Integrated study (ACSIS) program (Grants NE/N018001/1, NE/N018044/1, NE/N018028/1, and NE/N018052/1). KL received funding from the German Federal Ministry of Education and Research (BMBF) through JPI Climate/JPI Oceans NextG-Climate Science-ROADMAP (FKZ: 01LP2002A). The authors are grateful to the editor and to three anonymous reviewers, whose recommendations improved this paper. AJB, PLV, RJH, and MJR conceived the study. Simulations were performed by MJR, ET, KL, CDR, and LT. Output data were managed by JS. MJR performed the cyclone tracking. BV computed the Eady growth rate. AJB undertook cyclone phase-space analysis and all other data analyses, figure preparation, and wrote the manuscript. All authors provided input in interpreting results and approved the final manuscript. The authors declare no competing interests.Peer Reviewed"Article signat per 13 autors/es: Alexander J. Baker, Malcolm J. Roberts, Pier Luigi Vidale, Kevin I. Hodges, Jon Seddon, Benoît Vannière, Rein J. Haarsma, Reinhard Schiemann, Dimitris Kapetanakis, Etienne Tourigny, Katja Lohmann, Christopher D. Roberts, and Laurent Terray"Postprint (published version

WMO global annual to decadal climate update: a prediction for 2021–25

As climate change accelerates, societies and climate-sensitive socioeconomic sectors cannot continue to rely on the past as a guide to possible future climate hazards. Operational decadal predictions offer the potential to inform current adaptation and increase resilience by filling the important gap between seasonal forecasts and climate projections. The World Meteorological Organization (WMO) has recognized this and in 2017 established the WMO Lead Centre for Annual to Decadal Climate Predictions (shortened to “Lead Centre” below), which annually provides a large multimodel ensemble of predictions covering the next 5 years. This international collaboration produces a prediction that is more skillful and useful than any single center can achieve. One of the main outputs of the Lead Centre is the Global Annual to Decadal Climate Update (GADCU), a consensus forecast based on these predictions. This update includes maps showing key variables, discussion on forecast skill, and predictions of climate indices such as the global mean near-surface temperature and Atlantic multidecadal variability. it also estimates the probability of the global mean temperature exceeding 1.5°C above preindustrial levels for at least 1 year in the next 5 years, which helps policy-makers understand how closely the world is approaching this goal of the Paris Agreement. This paper, written by the authors of the GADCU, introduces the GADCU, presents its key outputs, and briefly discusses its role in providing vital climate information for society now and in the future.LH and AS were supported by the EUCP project funded by the European Commission’s Horizon 2020 programme, Grant Agreement 776613 and supported by the U.K.–China Research and Innovation Partnership Fund through the Met Office Climate Science for Service Partnership (CSSP) China as part of the Newton Fund. AS, DS, and MS were supported by the Met Office Hadley Centre Climate Programme funded by BEIS and Defra. The EC-Earth simulations at SMHI were performed on resources provided by the Swedish National Infrastructure for Computing (SNIC) at PDC and NSC and have been performed as part of the NordForsk-funded Nordic Centre of Excellence project (Award 76654) Arctic Climate Predictions: Pathways to Resilient, Sustainable Societies (ARCPATH). SY and TT were supported by the ARCPATH (NordForsk Award 76654) and the Danish state-funded National Centre for Climate Research [Nationalt Center for Klimaforskning (NCFK)]. SY was also partly supported by the EUCP project (Horizon 2020 Grant Agreement 776613). The EC-Earth simulations at BSC were performed using resources from the Red Española de Supercomputación. HP and WM received funding from the German Federal Ministry of Education and Research (BMBF) project MiKlip (FKZ 01LP1519A). Takahito Kataoka and Hiroaki Tatebe were supported by the Integrated Research Program for Advancing Climate Models (TOUGOU) Grant JPMXD0717935457 from the Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan. MIROC simulations were performed on the Earth Simulator at JAMSTEC. NK, YW, FC, and IB were supported by the Trond Mohn Foundation (Grant BFS2018TMT01; Bjerknes Climate Prediction Unit).Peer Reviewed"Article signat per 45 autors/es: Leon Hermanson, Doug Smith, Melissa Seabrook, Roberto Bilbao, Francisco Doblas-Reyes, Etienne Tourigny, Vladimir Lapin, Viatcheslav V. Kharin, William J. Merryfield, Reinel Sospedra-Alfonso, Panos Athanasiadis, Dario Nicoli, Silvio Gualdi, Nick Dunstone, Rosie Eade, Adam Scaife, Mark Collier, Terence O’Kane, Vassili Kitsios, Paul Sandery, Klaus Pankatz, Barbara Früh, Holger Pohlmann, Wolfgang Müller, Takahito Kataoka, Hiroaki Tatebe, Masayoshi Ishii, Yukiko Imada, Tim Kruschke, Torben Koenigk, Mehdi Pasha Karami, Shuting Yang, Tian Tian, Liping Zhang, Tom Delworth, Xiaosong Yang, Fanrong Zeng, Yiguo Wang, François Counillon, Noel Keenlyside, Ingo Bethke, Judith Lean, Jürg Luterbacher, Rupa Kumar Kolli, and Arun Kumar"Postprint (published version

Impact of increased resolution on long-standing biases in HighResMIP-PRIMAVERA climate models

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

A comparison of the spatial heterogeneities of surface fluxes simulated by INLAND model with observations at a valley and a nearby plateau stations in Central Amazon Forest

An improved version of the Integrated Land Surface Model (INLAND), incorporating the physical, ecological and hydrological parameters and processes pertaining to two subclasses of tropical forest in the central Amazon basin, a poorly drained flat plateau and a well-drained adjacent broad valley, is used to simulate the hydrological, energy and CO2 fluxes. The model is forced with observed meteorological data. The experimental output data from the model runs are compared with observational data at the two locations. The seasonal variabilities of water table depth at the valley site and the soil moisture at the plateau site are satisfactorily simulated. The two locations exhibit large differences in energy, carbon and water fluxes, both in the simulations and in the observations. Results validate the INLAND model and indicate the need for incorporating sub-grid scale variability in the relief, soil type and vegetation type attributes to improve the representation of the Amazonian ecosystems in land-surface models.This work was supported by the São Paulo Research Foundation (FAPESP) Grant Number 2017/22269-2. The first author was funded by The National Council of Scientific and Technological Development (CNPq) and National Coordination for High Level Education and Training (CAPES). The second author was supported by CNPq Grant Number 314780/2020-3. The fourth author was supported by Grant Number 2308.019802/2018-7, PVNS (National Senior Visiting Professor) program by CAPES in Brazil and CNPq in Brazil for research Grant number PQ 306595/2013-3.Peer ReviewedPostprint (published version

WMO Global Annual to Decadal Climate Update A Prediction for 2021-25

Under embargo until: 2022-10-01As climate change accelerates, societies and climate-sensitive socioeconomic sectors cannot continue to rely on the past as a guide to possible future climate hazards. Operational decadal predictions offer the potential to inform current adaptation and increase resilience by filling the important gap between seasonal forecasts and climate projections. The World Meteorological Organization (WMO) has recognized this and in 2017 established the WMO Lead Centre for Annual to Decadal Climate Predictions (shortened to “Lead Centre” below), which annually provides a large multimodel ensemble of predictions covering the next 5 years. This international collaboration produces a prediction that is more skillful and useful than any single center can achieve. One of the main outputs of the Lead Centre is the Global Annual to Decadal Climate Update (GADCU), a consensus forecast based on these predictions. This update includes maps showing key variables, discussion on forecast skill, and predictions of climate indices such as the global mean near-surface temperature and Atlantic multidecadal variability. it also estimates the probability of the global mean temperature exceeding 1.5°C above preindustrial levels for at least 1 year in the next 5 years, which helps policy-makers understand how closely the world is approaching this goal of the Paris Agreement. This paper, written by the authors of the GADCU, introduces the GADCU, presents its key outputs, and briefly discusses its role in providing vital climate information for society now and in the future.publishedVersio

The EC-Earth3 Earth system model for the Coupled Model Intercomparison Project 6

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.Peer reviewe

Atmospheric feedback explains disparate climate response to regional Arctic sea-ice loss

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 ReviewedPostprint (published version