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Flash poster on configuration of regional NWP systems at the Arctic Earth System Modelling Workshop

Flash poster on verification of winter precipitation at Arctic Earth System Modelling workshop

There are a number of collaborations between APPLICATE partners and groups in the USA and Canada. Effective clustering for these activities happens both in terms of science coordination as well as at scientific levels. As part of WP8 on Clustering scientific collaborations that are directly or indirectly related to the project activities have been tracked. This document, the second of three reports, contains a non-exhaustive list of activities as reported by APPLICATE partners on their current and most significant engagements with collaborators from North America.

Increased human activity in the Arctic calls for accurate and reliable weather predictions. This study presents an intercomparison of operational and/or high-resolution models in an attempt to establish a baseline for present-day Arctic short-range forecast capabilities for near-surface weather (pressure, wind speed, temperature, precipitation, and total cloud cover) during winter. One global model [the highresolution version of the ECMWF Integrated Forecasting System (IFS-HRES)], and three high-resolution, limited-area models [Applications of Research to Operations at Mesoscale (AROME)-Arctic, Canadian Arctic Prediction System (CAPS), and AROME with Météo-France setup (MF-AROME)] are evaluated. As part of the model intercomparison, several aspects of the impact of observation errors and representativeness on the verification are discussed. The results show how the forecasts differ in their spatial details and how forecast accuracy varies with region, parameter, lead time, weather, and forecast system, and they confirm many findings from mid- or lower latitudes. While some weaknesses are unique or more pronounced in some of the systems, several common model deficiencies are found, such as forecasting temperature during cloudfree, calm weather; a cold bias in windy conditions; the distinction between freezing and melting conditions; underestimation of solid precipitation; less skillful wind speed forecasts over land than over ocean; and difficulties with small-scale spatial variability. The added value of high-resolution limited area models is most pronounced for wind speed and temperature in regions with complex terrain and coastlines. However, forecast errors grow faster in the high-resolution models. This study also shows that observation errors and representativeness can account for a substantial part of the difference between forecast and observations in standard verification.

There are a number of collaborations between APPLICATE partners and groups in the USA and Canada. Effective clustering for these activities happens both in terms of science coordination as well as at scientific levels. As part of WP8 on Clustering scientific collaborations that are directly or indirectly related to the project activities have been tracked. This document contains a non-exhaustive list of activities as reported by APPLICATE partners on their current and most significant engagements with collaborators from North America.

Som en del av Bedre byluft-prosjektet er det utviklet en prognosemodell for detaljerte beregninger av meteorologi og luftkvalitet i byene Oslo, Bergen, Trondheim, Drammen og Stavanger. Hensikten med prognosemodellen har vært å gi kvantitative prognoser av NO2, PM2,5 og PM10 i vintersesongen (perioden 1. november til 1. mai) for å støtte den lokale varslingen i hver enkelt by. Prognosene er ferdigstilt i perioden kl. 07 til kl. 09 om morgenen (lokal vintertid) og er gyldige for neste dag. De lokale myndigheter har dermed den nødvendige tid for å anvende prognosene i varslingen for neste dag

Presentation on a NWP forecast inter-comparison during YOPP-SOP1-NH.

Presentation on a NWP forecast inter-comparison during YOPP-SOP1-NH.

Flash poster on verificationof winter precipitation at Arctic Earth System Modelling workshop

Presentation at Arctic Frontiers about high resolution Arctic NWP.

This document provides an overview of individual impacts of improved description of Arctic processes, model resolution and ensemble generation in weather and climate predictions based on work in APPLICATE work package 5.3. The report provides recommendations on which of the developments to be included in the enhanced (stream 2) predictions performed for Deliverable 5.4, whose purpose is to assess the added-value of APPLICATE on weather and climate predictions. This document shows that for short- and medium-range predictions enhancement in prediction skill can be achieved by introducing a sea ice model in the Meteo France weather prediction systems (improve near-surface temperature) and a multi-layer snow scheme in the European Center for Medium Weather Forecasting (ECMWF) forecast systems (improve near-surface temperature and snow depth). These changes are recommended to be included in their respective systems. The superiority of surface assimilation compared to dynamical downscaling of global forecasts in regional forecast systems further underlines the importance of the surface processes. Regional high-resolution systems will benefit from both a further increase in resolution and introduction of Ensemble Prediction Systems (EPSs) to account for the uncertainty in the predictions. Both these enhancements are recommended, but require a substantial increase in operational computer power and should therefore be considered depending on the use of the prediction systems. Improving the oceanic and atmospheric resolution in seasonal predictions give rather inconclusive results. Both positive and negative impacts are found in different systems. More studies are needed before recommendations on operational use of increased computer power can be given. In climate prediction systems finer oceanic resolution can improve the representation of the Arctic ocean with realistic atmospheric forcing. However, large-scale atmospheric circulation biases in the fully coupled system deny such an improvement. No substantial improvements and small sensitivity in seasonal prediction skill is found by introducing a more realistic description of sea ice melt ponds. This may be related to the existing tuning of the sea ice albedo which is reduced to account for the missing melt ponds. It is therefore not recommended to include a more realistic melt pond description without any further tuning of the sea ice models. The description of the sea ice thickness distribution in seasonal prediction systems has also been studied. A sensitivity is found on the number of sea ice categories, but no evident benefit from including additional categories beyond the default configuration (5 levels) which it is recommended to keep. Stochastic perturbations to represent the uncertainty in a seasonal prediction system show a neutral impact in the ocean model, but a deterioration in the atmosphere. It is therefore not recommended to use these perturbations scheme as more work, e.g. on tuning, is needed.

Presentation at Arctic Frontiers about high resolution Arctic NWP.

This document provides an overview of individual impacts of improved description of Arctic processes, model resolution and ensemble generation in weather and climate predictions based on work in APPLICATE work package 5.3. The report provides recommendations on which of the developments to be included in the enhanced (stream 2) predictions performed for Deliverable 5.4, whose purpose is to assess the added-value of APPLICATE on weather and climate predictions. This document shows that for short- and medium-range predictions enhancement in prediction skill can be achieved by introducing a sea ice model in the Meteo France weather prediction systems (improve near-surface temperature) and a multi-layer snow scheme in the European Center for Medium Weather Forecasting (ECMWF) forecast systems (improve near-surface temperature and snow depth). These changes are recommended to be included in their respective systems. The superiority of surface assimilation compared to dynamical downscaling of global forecasts in regional forecast systems further underlines the importance of the surface processes. Regional high-resolution systems will benefit from both a further increase in resolution and introduction of Ensemble Prediction Systems (EPSs) to account for the uncertainty in the predictions. Both these enhancements are recommended, but require a substantial increase in operational computer power and should therefore be considered depending on the use of the prediction systems. Improving the oceanic and atmospheric resolution in seasonal predictions give rather inconclusive results. Both positive and negative impacts are found in different systems. More studies are needed before recommendations on operational use of increased computer power can be given. In climate prediction systems finer oceanic resolution can improve the representation of the Arctic ocean with realistic atmospheric forcing. However, large-scale atmospheric circulation biases in the fully coupled system deny such an improvement. No substantial improvements and small sensitivity in seasonal prediction skill is found by introducing a more realistic description of sea ice melt ponds. This may be related to the existing tuning of the sea ice albedo which is reduced to account for the missing melt ponds. It is therefore not recommended to include a more realistic melt pond description without any further tuning of the sea ice models. The description of the sea ice thickness distribution in seasonal prediction systems has also been studied. A sensitivity is found on the number of sea ice categories, but no evident benefit from including additional categories beyond the default configuration (5 levels) which it is recommended to keep. Stochastic perturbations to represent the uncertainty in a seasonal prediction system show a neutral impact in the ocean model, but a deterioration in the atmosphere. It is therefore not recommended to use these perturbations scheme as more work, e.g. on tuning, is needed.

Flash poster on verification of winter precipitation at Arctic Earth System Modelling workshop

© Copyright 2022 American Meteorological Society (AMS). For permission to reuse any portion of this Work, please contact permissions@ametsoc.org. Any use of material in this Work that is determined to be “fair use” under Section 107 of the U.S. Copyright Act (17 U.S. Code § 107) or that satisfies the conditions specified in Section 108 of the U.S. Copyright Act (17 USC § 108) does not require the AMS’s permission. Republication, systematic reproduction, posting in electronic form, such as on a website or in a searchable database, or other uses of this material, except as exempted by the above statement, requires written permission or a license from the AMS. All AMS journals and monograph publications are registered with the Copyright Clearance Center (https://www.copyright.com). Additional details are provided in the AMS Copyright Policy statement, available on the AMS website (https://www.ametsoc.org/PUBSCopyrightPolicy). The Arctic environment is changing, increasing the vulnerability of local communities and ecosystems, and impacting its socio-economic landscape. In this context, weather and climate prediction systems can be powerful tools to support strategic planning and decision-making at different time horizons. This article presents several success stories from the H2020 project APPLICATE on how to advance Arctic weather and seasonal climate prediction, synthesizing the key lessons learned throughout the project and providing recommendations for future model and forecast system development. The results discussed in this article were supported by the project APPLICATE (727862), funded by the European Union's Horizon 2020 research and innovation programme. PO was additionally supported by the Spanish fellowship RYC-2017-22772. Peer Reviewed Article signat per 29 autors/es: Pablo Ortega (1), Edward W. Blockley (2), Morten Køltzow (3), François Massonnet (4), Irina Sandu (5), Gunilla Svensson (6), Juan C. Acosta Navarro (1), Gabriele Arduini (5), Lauriane Batté (7), Eric Bazile (7), Matthieu Chevallier (8), Rubén Cruz-García (1), Jonathan J. Day (5), Thierry Fichefet (4), Daniela Flocco (9), Mukesh Gupta (4), Kerstin Hartung (6,10), Ed Hawkins (9), Claudia Hinrichs (11), Linus Magnusson (5), Eduardo Moreno-Chamarro (1), Sergio Pérez-Montero (1), Leandro Ponsoni (4), Tido Semmler (11), Doug Smith (2), Jean Sterlin (4), Michael Tjernström (6), Ilona Välisuo (7,12), and Thomas Jung (11,13) // (1) Barcelona Supercomputing Center, Barcelona, Spain | (2) Met Office, Exeter, UK | (3) Norwegian Meteorological Institute, Oslo, Norway | (4) Université catholique de Louvain, Earth and Life Institute, Georges Lemaître Centre for Earth and Climate Research, Louvain-la-Neuve, Belgium | (5) European Centre for Medium-Range Weather Forecasts, Reading, UK | (6) Department of Meteorology, Stockholm University, Stockholm, Sweden | (7) CNRM, Université de Toulouse, Météo-France, CNRS, Toulouse, France | (8) Météo-France, Toulouse, France | (9) National Centre for Atmospheric Science, Department of Meteorology, University of Reading, Reading, UK. | (10) Now at: Deutsches Zentrum für Luft- und Raumfahrt, Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany | (11) Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, Germany | (12) Now at: Meteorology Unit, Finnish Meteorological Institute, Helsinki, Finland | (13) Department of Physics and Electrical Engineering, University of Bremen, Bremen, Germany