The DANish regional atmospheric ReAnalysis (DANRA) is a novel high-resolution (2.5 km) reanalysis dataset covering Denmark and its surrounding regions over a 34-year period (1990-2023). Denmark's complex coastline, with over 400 islands and an extensive 7400 km coastline, means that most municipalities experience mixed land-sea variability. This complexity requires a regional climate reanalysis system that can resolve fine-scale coastal and inland features, as well as their impact on climate variability. DANRA is based on the HARMONIE-AROME Numerical Weather Prediction (NWP) model and assimilates a comprehensive set of observations, with a particular focus on Denmark. Compared to global reanalyses such as the European Centre for Medium-range Weather Forecast (ECMWF) Reanalysis v5 (ERA5), DANRA demonstrates superior performance in representing essential climate variables, including near-surface weather parameters during both extreme and ordinary conditions. We illustrate these improvements in the representation of several extreme weather cases over Denmark, such as the December 1999 hurricane-force storm, the July 2022 national temperature record, and the August 2007 cloudburst in South Jutland. DANRA is made to support climate adaptation, impact modelling, and the training of next-generation data-driven atmospheric forecasting models. DANRA is distributed as Zarr dataset freely accessible from an object store (10.5281/zenodo.17294179, ), maximizing its usability for climate adaptation, impact modelling, and data-driven research.

In this study, the downscaling modeling chain for prediction of weather and atmospheric composition is described and evaluated against observations. The chain consists of interfacing models for forecasting at different spatiotemporal scales that run in a semi-operational mode. The forecasts were performed for European (EU) regional and Danish (DK) subregional-urban scales by the offline coupled numerical weather prediction HIRLAM and atmospheric chemical transport CAMx models, and for Copenhagen city-street scale by the online coupled computational fluid dynamics M2UE model.The results showed elevated NOx and lowered O-3 concentrations over major urban, industrial, and transport land and water routes in both the EU and DK domain forecasts. The O-3 diurnal cycle predictions in both these domains were equally good, although O-3 values were closer to observations for Denmark. At the same time, the DK forecast of NOx and NO2 levels was more biased (with a better prediction score of the diurnal cycle) than the EU forecast, indicating a necessity to adjust emission rates. Further downscaling to the street level (Copenhagen) indicated that the NOx pollution was 2-fold higher on weekends and more than 5 times higher during the working day with high pollution episodes. Despite high uncertainty in road traffic emissions, the street-scale model effectively captured the NOx and NO2 diurnal cycles and the onset of elevated pollution episodes.The demonstrated downscaling system could be used in future online integrated meteorology and air quality research and operational forecasting, as well as for impact assessments on environment, population, and decision making for emergency preparedness and safety measures planning.

Copernicus Arctic Regional Reanalysis covers the recent three decades for the European arctic at 2.5 km grid. The region is characterised by extensive areas with complex orography, cold surfaces and weather with strong local variability. It has a sparse conventional observation network. The reanalysis system is adapted from the HARMONIE-AROME NWP system, focusing on improved treatment of cold surfaces and enhancement on data input for physiographic databases, ocean and sea ice, snow and use of remote sensing data. A large amount of surface observations, including those over icecap, has been collected with quality control, resulting in significantly more use of surface data than in ERA5. High resolution, gap free albedo data over permafrost regions in the region have been used to improve physical realism. Efforts have been spent to enhance representation of the background error covariance model, and for optimal use of large scale information from the lateral boundary model in the analysis. From verification intercomparison, it appears that high resolution and the above mentioned enhancement in enhancement on data input, assimilation algorithm and treatment for cold surface have all contributed to the added values in the reanalysis. In this talk, we also discuss about weakness of assimilation system as found in some of the analysis bust cases. The work is part of the Copernicus Climate Change Service. ECMWF implements this Service on behalf of the European Commission.

The development of the HARMONIE model system has led to huge advances in numerical weather prediction, including over Greenland where a numerical weather prediction (NWP) model is used to forecast daily surface mass budget over the Greenland ice sheet as presented on polarportal.dk. The new high resolution Copernicus Arctic Reanalysis further developed the possibilities in HARMONIE with full 3DVar data assimilation and extended use of quality-controlled local observations. Here, we discuss the development and current status of the climate version of the HARMONIE Climate model (HCLIM). The HCLIM system has opened up the possibility for flexible use of the model at a range of spatial scales using different physical schemes including HARMONIE-AROME, ALADIN and ALARO for different spatial and temporal resolutions and assimilating observations, including satellite data on sea ice concentration from ESA CCI+, to improve hindcasts. However, the range of possibilities means that documenting the effects of different physics and parameterisation schemes is important before widespread application.Here, we focus on HCLIM performance over the Greenland ice sheet, using observations to verify the different plausible set-ups and investigate biases in climate model outputs that affect the surface mass budget (SMB) of the Greenland ice sheet.The recently funded Horizon 2020 project PolarRES will use the HCLIM model for very high resolution regional downscaling, together with other regional climate models in both Arctic and Antarctic regions, and our analysis thus helps to optimise the use of HCLIM in the polar regions for different modelling purposes.

The Copernicus Climate Change Service (C3S) regional reanalysis for the Arctic consists of two datasets of Essential Climate Variables (ECVs) for the 24 year period from 1997 to 2021. The high resolution (2.5x2.5 km2) datasets cover Greenland, Iceland, Svalbard, the Barents Sea and Northern Scandinavia. Several islands in the Russian Arctic and a few islands in the Canadian Arctic are also covered. The produced datasets are freely available to all. A first subset of the data has been published on the Copernicus Data Store (CDS) in early 2021. The reanalysis is perfomed with state-of-the-art data assimilation techniques that include many local quality-controlled observations that have not been included in previously published reanalysis datasets. The weather forecasting model HARMONIE-AROME cy40h1.1.1 has been used to produce the dataset. The model computations have additionally been optimized for processes essential in the Arctic. Estimated uncertainty data have been produced at atmospheric pressure levels, and validation statistics have been made for synoptic weather stations.

Contexte : en Europe, une partie importante de l'energie produite est utilisee pour le chauffage domestique et pour la climatisation. La qualite de l'isolation des bâtiments a ainsi un impact significatif sur la pollution de l'air.Objectifs : modeliser et calculer les effets d'une amelioration importante de l'isolation des bâtiments existants en Europe sur les niveaux de pollution de l'air, sur la sante et sur l'economie.Methodes : l'energie utilisee dans deux scenarios differents a ete comparee entre 2005 et 2020 : un scenario d’un programme de l'isolation des bâtiments existants en Europe et un scenario de statu quo. Les variations des emissions issues de ces deux scenarios ont ete integrees dans un modele de la qualite de l'air (the Comprehensive Air-Quality Model with extensions). Les variations annuelles moyennes des principaux polluants atmospheriques ont ete calculees pour chaque pays. Des donnees venant de l’Organisation Mondiale de la Sante (OMS) et de l'Union Europeenne (UE) sur les populations et sur les impacts des polluants ont ete utilisees pour deduire quels sont les effets sur la sante et l’economie. La qualite de l'air interieur ne faisait pas partie de l’etude.Resultats : avec le programme de l'isolation des bâtiments existants en Europe, les niveaux moyens annuels de la pollution atmospherique particulaire fine (PM2,5) variaient de -0,008 µg/m3 (Finlande) a -0,538 µg/m3 (Belgique). Le nombre moyen d'annees de vie gagne par annee par 100 000 adultes etait de 24,3 (intervalle de confiance 95 % de 0,9 a 54,5). Le nombre total d'annees de vie gagnees chaque annee variait, selon les pays, entre 31 en Finlande a 22 524 en Allemagne. Le nombre total d'annees de vie gagnees etait de 78 678 en Europe. Un total de 7 173 cas de bronchite chronique pourrait etre evite chaque annee. Plusieurs autres effets sur la sante etaient ameliores de facon similaire. Les couts pour la societe s’elevaient a 6,64 milliards d’euros par an.Conclusions : en plus de la reduction des emissions de carbone, un programme de l'isolation des bâtiments existants en Europe aurait des avantages substantiels sur la sante grâce a l’amelioration de la pollution atmospherique. Les effets sur la sante et sur l’economie peuvent contrebalancer de facon significative les couts d'investissement et devraient etre pris en compte lors de l'evaluation des strategies d'attenuation du rechauffement climatique.

The MUD project addresses assessment of uncertainties of atmospheric dispersion model predictions, as well as optimum presentation to decision makers. Previously, it has not been possible to estimate such uncertainties quantitatively, but merely to calculate the 'most likely' dispersion scenario. However, recent developments in numerical weather prediction (NWP) include probabilistic forecasting techniques, which can be utilised also for atmospheric dispersion models. The ensemble statistical methods developed and applied to NWP models aim at describing the inherent uncertainties of the meteorological model results. These uncertainties stem from e.g. limits in meteorological observations used to initialise meteorological forecast series. By perturbing the initial state of an NWP model run in agreement with the available observational data, an ensemble of meteorological forecasts is produced. In MUD, corresponding ensembles of atmospheric dispersion are computed from which uncertainties of predicted radionuclide concentration and deposition patterns are derived. Abstract max. 2000 characters The MUD project addresses assessment of uncertainties of atmospheric dispersion model predictions, as well as optimum presentation to decision makers. Previously, it has not been possible to estimate such uncertainties quantitatively, but merely to calculate the 'most likely' dispersion scenario. However, recent developments in numerical weather prediction (NWP) include probabilistic forecasting techniques, which can be utilised also for atmospheric dispersion models. The ensemble statistical methods developed and applied to NWP models aim at describing the inherent uncertainties of the meteorological model results. These uncertainties stem from e.g. limits in meteorological observations used to initialise meteorological forecast series. By perturbing the initial state of an NWP model run in agreement with the available observational data, an ensemble of meteorological forecasts is produced. In MUD, corresponding ensembles of atmospheric dispersion are computed from which uncertainties of predicted radionuclide concentration and deposition patterns are derived.

In this study, three Danish sites having the longest (1990)(1991)(1992)(1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004) time-series of ozone measurements were analysed on inter-annual, monthly and diurnal cycle variability as well as elevated and lowered ozone concentration events were identified.The atmospheric trajectory (HYSPLIT) and dispersion (HIRLAM + CAMx) models were employed to study dominating atmospheric transport patterns associated with elevated events and to evaluate spatio-temporal variability of ozone specific episode and typical seasonal patterns for Denmark.It was found that generally inter-annual variability has a positive trend, and events with low ozone concentration (≤10 μg/m 3 ) continued to diminish.On a monthly scale, the highest and lowest mean concentrations are observed in May and November-December, respectively.The elevated concentrations (≥120 μg/m 3 ) are observed during March-September.On a diurnal cycle, it is observed mostly during 13-16 of local time, and more frequent (ten-fold) compared with nighttime-early morning hours.For ozone elevated events, several sectors (or pathways of atmospheric transport) were identified depending on the sites' positions, showing the largest (39%) number of such events associated with the north-western sector, and lowest (13% each)-southwestern and northern sectors.For each site, less than 60 events showed very high concentrations (≥180 µg/m 3 ).Among 12 episodes, one longest elevated episode (19-21 Jun 2000) simultaneously registered at all sites and characterized by dominating transport from the south-southwestern sector, low wind speed, clear-sky, and multiple inversions was studied using modelling tools.For this episode, both measurements and modeling (trajectory and dispersion) results showed a relatively good agreement.

The effects of building insulation on ground-level concentration levels of air pollutants are considered. We have estimated regionally averaged reductions in energy consumption between 2005 and 2020 by comparing a business as usual with a very low energy building scenario for the EU-25. The corresponding reductions in air pollutant emissions were calculated using emission factors. Annual simulations with an air-quality model, where only the emission reductions due to insulation was accounted for, were compared for the scenarios, and statistically significant changes in ground-level mass concentration of main air pollutants were found. Emission reductions of up to 9% in particulate matter and 6.3% for sulphur dioxide were found in north-western Europe. Emission changes were negligible for volatile organic compounds, and carbon monoxide decreased by 0.6% over southern Europe while nitrogen oxides changed by up to 2.5% in the Baltic region. Seasonally and regionally averaged changes in ground-level mass concentrations showed that sulphur dioxide decreased by up to 6.2% and particulate matter by up to 3.6% in north-western Europe. Nitrogen oxide concentrations decreased by 1.7% in Poland and increases of up to 0.6% were found for ozone. Carbon monoxide changes were negligible throughout the modelling domain.