Meteorology-normalized impact of the COVID-19 lockdown upon NO2 pollution in Spain

The spread of the new coronavirus SARS-CoV-2 that causes COVID-19 forced the Spanish Government to implement extensive lockdown measures to reduce the number of hospital admissions, starting on 14 March 2020. Over the following days and weeks, strong reductions in nitrogen dioxide (NO2) pollution were reported in many regions of Spain. A substantial part of these reductions was obviously due to decreased local and regional anthropogenic emissions. Yet, the confounding effect of meteorological variability hinders a reliable quantification of the lockdown's impact upon the observed pollution levels. Our study uses machine-learning (ML) models fed by meteorological data along with other time features to estimate the “business-as-usual” NO2 mixing ratios that would have been observed in the absence of the lockdown. We then quantify the so-called meteorology-normalized NO2 reductions induced by the lockdown measures by comparing the estimated business-as-usual values with the observed NO2 mixing ratios. We applied this analysis for a selection of urban background and traffic stations covering the more than 50 Spanish provinces and islands. The ML predictive models were found to perform remarkably well in most locations, with an overall bias, root mean square error and correlation of +4 %, 29 % and 0.86, respectively. During the period of study, from the enforcement of the state of alarm in Spain on 14 March to 23 April, we found the lockdown measures to be responsible for a 50 % reduction in NO2 levels on average over all provinces and islands. The lockdown in Spain has gone through several phases with different levels of severity with respect to mobility restrictions. As expected, the meteorology-normalized change in NO2 was found to be stronger during phase II (the most stringent phase) and phase III of the lockdown than during phase I. In the largest agglomerations, where both urban background and traffic stations were available, a stronger meteorology-normalized NO2 change is highlighted at traffic stations compared with urban background sites. Our results are consistent with foreseen (although still uncertain) changes in anthropogenic emissions induced by the lockdown. We also show the importance of taking the meteorological variability into account for accurately assessing the impact of the lockdown on NO2 levels, in particular at fine spatial and temporal scales. Meteorology-normalized estimates such as those presented here are crucial to reliably quantify the health implications of the lockdown due to reduced air pollution.This project has received funding from the European Union's Horizon 2020 (H2020) research and innovation program under the Marie Skłodowska-Curie Actions (grant no. H2020-MSCA-COFUND-2016-754433) and the H2020 ACTRIS IMP project (grant no. 871115). We also acknowledge support from the European Research Council (grant no. 773051; FRAGMENT); the AXA Research Fund; the Spanish Ministry of Science, Innovation and Universities (grant nos. RYC-2015-18690, CGL2017-88911-R, RTI2018-099894-B-I00 and Red Temática ACTRIS España CGL2017-90884-REDT); and the BSC-CNS “Centro de Excelencia Severo Ochoa 2015-2019” program (grant no. SEV-2015-0493). The authors are grateful to PRACE for awarding us access to MareNostrum Supercomputer in the Barcelona Supercomputing Center.Peer ReviewedPostprint (published version

Using EC-Earth for climate prediction research

Climate prediction at the subseasonal to interannual time range is now performed routinely and operationally by an increasing number of institutions. The feasibility of climate prediction largely depends on the existence of slow and predictable variations in the ocean surface temperature, sea ice, soil moisture and snow cover, and on our ability to model the atmosphere’s interactions with those variables. Climate prediction is typically performed with statistical-empirical or process-based models. The two methods are complementary. Although forecasting systems using global climate models (GCMs) have made substantial progress in the last few decades (Doblas-Reyes et al., 2013), systematic errors and misrepresentations of key processes still limit the value of dynamical prediction in certain areas of the globe. At the same time, model initialisation, ensemble generation, understanding the processes at the origin of predictability, forecasting extremes, bias adjustment and model evaluation are all challenging aspects of the climate prediction problem. Addressing them requires both a large base of researchers with expertise in physics, mathematics, statistics, high-performance computing and data analysis interested in climate prediction issues and a tool for them to work with. This article illustrates how one of these tools, the EC-Earth climate model (Box A), has been used to train scientists in climate prediction and to address scientific challenges in this field. The use of model components from ECMWF’s Integrated Forecasting System (IFS) in EC-Earth means that some of the results obtained with EC-Earth can feed back into ECMWF’s activities. EC-Earth has been run extensively on ECMWF’s high-performance computing facility (HPCF), among a range of HPCFs across Europe and North America. The availability of ECMWF’s HPCF to EC-Earth partners, including the use of the successful ECMWF Special Project programme, means that a substantial amount of EC-Earth’s collaborative work, both within the consortium and with ECMWF, takes place on this platform.Postprint (published version

Analysis, Developments and optimizations on the NMMB/BSC model

Aquest projecte final de Màster s'emmarca dins de la feina realitzada en el departament de Ciències de la Terra del Barcelona Supercomputing Center. És un recull dels diversos desenvolupaments i anàlisi duts a terme a partir del Model MMB/BSC-CTM per posar-lo en producció

Analysis, Developments and optimizations on the NMMB/BSC model

Aquest projecte final de Màster s'emmarca dins de la feina realitzada en el departament de Ciències de la Terra del Barcelona Supercomputing Center. És un recull dels diversos desenvolupaments i anàlisi duts a terme a partir del Model MMB/BSC-CTM per posar-lo en producció

Super-resolution for downscaling climate data

A common task in Earth Sciences is to infer climate information at local and regional scales from global climate models. An alternative to running expensive numerical models at high resolution is to use statistical downscaling techniques. Statistical downscaling aims at learning empirical links be- tween the large-scale and local-scale climate, i.e., a mapping from a low-resolution gridded variable to a higher-resolution grid that incorporates observational data. Seasonal climate predictions can forecast the climate vari- ability up to several months ahead and support a wide range of societal activities. The coarse spatial resolution of seasonal forecasts needs to be downscaled or refined to the local scale for specific applications. In this study, we present super-resolution (SR) techniques for the task of downscaling climate variables with a focus on temperature over Catalonia. Our models are trained using high and medium resolution ( ~ 5 and ~ 25 km) gridded climate datasets with the ultimate goal of increasing the resolution of coarse resolution ( ~100 km) seasonal forecasting systems. Taking the gridded data from ~100 to ~5 km implies a 20x upscaling factor. It is worth pointing out that handling such large upsampling factor is not typical in computer vision, where most applications factors while 16x is considered as extreme SR

Coordinating an operational data distribution network for CMIP6 data

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

Time-resolved emission reductions for atmospheric chemistry modelling in Europe during the COVID-19 lockdowns

We quantify the reductions in primary emissions due to the COVID-19 lockdowns in Europe. Our estimates are provided in the form of a dataset of reduction factors varying per country and day that will allow the modelling and identification of the associated impacts upon air quality. The country- and daily-resolved reduction factors are provided for each of the following source categories: energy industry (power plants), manufacturing industry, road traffic and aviation (landing and take-off cycle). We computed the reduction factors based on open-access and near-real-time measured activity data from a wide range of information sources. We also trained a machine learning model with meteorological data to derive weather-normalized electricity consumption reductions. The time period covered is from 21 February, when the first European localized lockdown was implemented in the region of Lombardy (Italy), until 26 April 2020. This period includes 5 weeks (23 March until 26 April) with the most severe and relatively unchanged restrictions upon mobility and socio-economic activities across Europe. The computed reduction factors were combined with the Copernicus Atmosphere Monitoring Service's European emission inventory using adjusted temporal emission profiles in order to derive time-resolved emission reductions per country and pollutant sector. During the most severe lockdown period, we estimate the average emission reductions to be −33 % for NOx, −8 % for non-methane volatile organic compounds (NMVOCs), −7 % for SOx and −7 % for PM2.5 at the EU-30 level (EU-28 plus Norway and Switzerland). For all pollutants more than 85 % of the total reduction is attributable to road transport, except SOx. The reductions reached −50 % (NOx), −14 % (NMVOCs), −12 % (SOx) and −15 % (PM2.5) in countries where the lockdown restrictions were more severe such as Italy, France or Spain. To show the potential for air quality modelling, we simulated and evaluated NO2 concentration decreases in rural and urban background regions across Europe (Italy, Spain, France, Germany, United-Kingdom and Sweden). We found the lockdown measures to be responsible for NO2 reductions of up to −58 % at urban background locations (Madrid, Spain) and −44 % at rural background areas (France), with an average contribution of the traffic sector to total reductions of 86 % and 93 %, respectively. A clear improvement of the modelled results was found when considering the emission reduction factors, especially in Madrid, Paris and London where the bias is reduced by more than 90 %. Future updates will include the extension of the COVID-19 lockdown period covered, the addition of other pollutant sectors potentially affected by the restrictions (commercial and residential combustion and shipping) and the evaluation of other air quality pollutants such as O3 and PM2.5. All the emission reduction factors are provided in the Supplement.The research leading to these results has received funding from the Copernicus Atmosphere Monitoring Service (CAMS), which is implemented by the European Centre for Medium-Range Weather Forecasts (ECMWF) on behalf of the European Commission. We acknowledge support from the Ministerio de Ciencia, Innovación y Universidades (MICINN) as part of the BROWNING project RTI2018-099894-B-I00 and NUTRIENT project CGL2017-88911-R, the Agencia Estatal de Investigacion (AEI) as part of the VITALISE project (PID2019-108086RA-I00/AEI/0.13039/501100011033), the AXA Research Fund, and the European Research Council (grant no. 773051, FRAGMENT). We also acknowledge PRACE and RES for awarding access to Marenostrum4 based in Spain at the Barcelona Supercomputing Center through the eFRAGMENT2 and AECT-2020-1-0007 projects. This project has also 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. Carlos Pérez García-Pando also acknowledges support received through the Ramón y Cajal programme (grant RYC-2015-18690) of the MICINN.Peer ReviewedPostprint (published version

ETP4HPC’s SRA 5 strategic research agenda for High-Performance Computing in Europe 2022: European HPC research priorities 2023-2027

This document feeds research and development priorities devel-oped by the European HPC ecosystem into EuroHPC’s Research and Innovation Advisory Group with an aim to define the HPC Technology research Work Programme and the calls for proposals included in it and to be launched from 2023 to 2026. This SRA also describes the major trends in the deployment of HPC and HPDA methods and systems, driven by economic and societal needs in Europe, taking into account the changes ex-pected in the technologies and architectures of the expanding underlying IT infrastructure. The goal is to draw a complete picture of the state of the art and the challenges for the next three to four years rather than to focus on specific technologies, implementations or solutions.Peer ReviewedArticle signat per 140 autors/autores: Estela Suarez,JSC; Nico Mittenzwey, MEGWARE; Laurent Cargemel, Atos; Alessandro Russo, Leonardo; Andy Forrester, HypeAccelerator Solutions Limited; Carlo Cavazzoni, Leonardo; Craig Prunty, SiPearl; Daniele Cesarini, Cineca; David Tur, Do IT Now; Fabrizio Magugliani, E4 Computer Engineering SpA; Marc Casas, BSC; Rene Oertel, MEGWARE Computer Vertrieb und Service GmbH; Sergio Sánchez, Vicomtech; Thierry Porcher, Do IT Now; Marc Duranton, CEA / HiPEAC; Sakir Sezer, NVIDIA; Craig Prunty, SIPEARL; Alessandro Russo, Leonardo; Dirk Pleiter, KTH; Luigi Capone, Leonardo; Marco Cicala, E4 Computer Engineering SpA; Osman Unsal, BSC; Paolo Amato, Micron; Petar Radojkovic, BSC; Rod Evans, NVIDIA; Thierry Porcher, Do IT Now; Xavier Martorell, BSC; Manolis Marazakis, FORTH; Maria Perez, UPM / BDVA; Pascale Rossé-Laurent, Atos; Alberto Miranda, BSC; Alberto Scionti, LINKS Foundation; Alessandro Russo, Leonardo; David Tur, Do IT Now; Denis Maggi, Vicomtech; Georgios Goumas, ICCS; Ina Schmitz, ParTec; Jordi Guitart, BSC; Julita Corbalan, BSC; Michele Martone, LRZ; Nico Mittenzwey, MEGWARE; Nicolo Magini, Leonardo; Paolo Viviani, LINKS Foundation; Rene Oertel, MEGWARE Computer Vertrieb und Service GmbH; Sebastien Varrette, University of Luxembourg; Thierry Porcher, Do IT Now; Thomas Moschny, Par-Tec, Guy Lonsdale, SCAPOS; Paul Carpenter, BSC; Gabriel Antoniu, INRIA (BDVA); Alexander Costan, INSA Rennes/Inria; Ani Anciaux Sedrakian, IFPEN; Antonio Peña, BSC; Antonio Sciarappa, Leonardo; Christian Perez, INRIA; Francesco Iannone, ENEA; Jose Gracia, HLRS; Leonardo Arturo Bautista Gomez, BSC; Luigi Capone, Leonardo; Miguel Vasquez, BSC; Olivier Marsden, ECMWF; Paolo Viviani, LINKS Foundation; Patrick Carribault, CEA; Sebastien Varrette, University of Luxembourg; Vicenc Beltran, BSC; Xavier Martorell, BSC; Andre Brinkmann, JGU – Mainz; Sai Narasimhamurthy, Seagate; Anna Queralt, BSC; Jean-Thomas Acquaviva, DDN Storage; Jesús Carretero Pérez, UC3M; Nicolo Magini, Leonardo Labs; Paolo Amato, Micron; Philippe Deniel, CEA; Ramon Nou, BSC; Tiago Quintino, ECMWF; Dirk Pleiter, KTH; Utz-Uwe Haus, HPE; Adrian Tate, NAG; Ani Anciaux Sedrakian, IFPEN; Antonio Sciarappa, Leonardo Labs; Chiara Vercellino, LINKS Foundation; Dario Garcia-Gasulla, BSC; Giovanni Samaey, KU Leuven; Luigi Capone, Leonardo; Marcin Chrust, ECMWF; Maximilian Behr, Northern Data AG, Olivier Beaumont, INRIA; Ricard Borrell, BSC; Ulrich Ruede, CERFACS/FAU; Ward Melis, KU Leuven; Erwin Laure, MPCDF; Andreas Wierse, SICOS; Bruno Raffin, INRIA; Carlo Cavazzoni, Leonardo; Guillaume Houzeaux, BSC; Ioan Hadade, ECMWF; Kim Serradell Maronda, BSC; Luigi Capone, Leonardo; Miguel Vasquez, BSC; Ricard Borrell, BSC; Sabri Pllana; Sinead Ryan, Trinity College Dublin; Vicence Beltran, BSC; Hans-Christian Hoppe, ParTec; Jens Krueger, FRAUNHOFER; Alberto Scionti, LINKS Foundation; Ander Garcia, Vicomtech; Anna Queralt, BSC; Benjamin Depardon, UCit; Craig Prunty, SIPEARL; Daniela Ghezzim, Leonardo S.p.a.; Daniele Piccarozzi, Arm; David Carrera, BSC; Philippe Bricard, UCit; Thierry Goubier, CEA; Venkatesh Kannan, ICHEC; Valeria Bartsch, FRAUNHOFER; Cyril Allouche, Atos; Kristel Michelsen, JSC; Andrea Scarabosio, Links Foundation; Artur Garcia, BSC; Chayma Bouazza, PASQAL; Daniele Dragoni, Leonardo; Daniele Gregori, E4 Computer Engineering SpA; Daniele Ottaviani, Cineca; David Bowden, Dell Technologies; David Tur, Do IT Now; Dennis Hoppe, High-Performance Computing Center Stuttgart; Fabrizio Magugliani,E4 Computer Engineering SpA; Filippo Palombi, ENEA; Giacomo Vitali, LINKS Foundation; Guillaume Colin de Verdière, CEA; Jean-Philippe Nominé, CEA; Leonardo Arturo Bautista Gomez, BSC; Mikael Johansson, CSC; Olivier Terzo, LINKS Foundation; Osman Unsal, BSC; Vekatesh KannanI, CHEC; Erwin Laure, MPCDF; Andreas Wierse, SICOS; Bruno Raffin, INRIA; Carlo Cavazzoni, Leonardo; Guillaume Houzeaux, BSC; Ioan Hadade, ECMWF; Kim Serradell Maronda, BSC; Luigi Capone, Leonardo; Miguel Vasquez, BSC; Ricard Borrell, BSC; Sabri Pllana Sinead Ryan, Trinity College Dublin; Vicence Beltran, BSC; Hans-Christian Hoppe, ParTec; Jens Krueger, FRAUNHOFER; Alberto Scionti, LINKS Foundation; Ander Garcia, Vicomtech; Anna Queralt, BSC; Benjamin Depardon, UCit; Craig Prunty, SIPEARL; Daniela Ghezzi, Leonardo S.p.a.; Daniele Piccarozzi, Arm; David Carrera, BSC; Philippe Bricard, UCit; Thierry Goubier, CEA; Venkatesh Kannan, ICHEC; Valeria Bartsch, FRAUNHOFER; Cyril Allouche, Atos; Kristel Michelsen, JSC; Andrea Scarabosio, Links Foundation; Artur Garcia, BSC; Chayma Bouazza, PASQAL; Daniele Dragoni, Leonardo Daniele Gregori, E4 Computer Engineering SpA; Daniele Ottaviani, Cineca; David Bowden, Dell Technologies; David Tur, Do IT Now; Dennis Hoppe, High-Performance Computing Center Stuttgart; Fabrizio Magugliani, E4 Computer Engineering SpA; Filippo Palombi, ENEA; Giacomo Vitali, LINKS Foundation; Guillaume Colin de Verdière, CEA; Jean-Philippe Nominé, CEA Leonardo; Arturo Bautista Gomez, BSC, Mikael Johansson, CSC; Olivier Terzo, LINKS Foundation; Osman Unsal, BSC; Vekatesh KannanI; Utz-Uwe Haus (HPE); Sai Narasimhamurthy (Seagate); Maria S. Perez (UPM); Dirk Pleiter (KTH); Andreas Wierse (SICOS); Paul Carpenter (BSC); Utz-Uwe Haus (HPE); Erwin Laure (MPCDF); Sai Narasimhamurthy (Seagate Systems); Estela Suarez (Forschungszentrum Jülich); Manolis Marazakis (FORTH); Marc Duranton (CEA); Dirk Pleiter (KTH); Giuliano Taffoni (INAF); Hans-Christian Hoppe (Scapos AG); Gabriel Antoniu (Inria); Patrick Valduriez (Inria); Hans-Christian Hoppe (SCAPOS); Jens Krüger (Fraunhofer ITWM); Andreas Wierse (Sicos BW); François Bodin (Inria); Sagar Dolas (SURF); Damien Gratadour (Université Paris Diderot); Michael Malms (ETP4HPC); Leonieke Mevus (SURF); Pascale Rossé-Laurent (Atos); Jean-Robert Bacou (Atos); Carlos Puchol (BSC); Kristel Michielsen (JSC); Tiina Leiponen (CSC); Carlo Cavazzoni (Leonardo), Ivan Spisso (Leonardo); Maike Gilliot (ETP4HPC); Hans-Christian Hoppe (Scapos); Guy Lonsdale (ETP4HPC Steering Board / Fraunhofer / Scapos / FocusCoE); Fabrizio Magugliani (ETP4HPC Steering Board / E4 Computer Engineering); Jean-Philippe Nominé (ETP4HPC Steering Board / CEA); Andreas Wierse (SICOS); Pascal Bouvry (University of Luxembourg); Daniela Posch (SICOS); Gilles Civario (Intel); Henri Calandra (TotalEnergies & PRACE IAC); Vincent Galinier (Airbus & PRACE IAC); Xavier Vigouroux (Atos); Alban Rousset (LuxProvide); Michael Schlottke-Lakemper (HLRS); Carolina Berucci (Leonardo), Erwin Laure, Carolina Berucci (Leonardo)Postprint (published version