Abstract. Over the last century, our societies have experienced a sharp increase in urban population and fossil-fuelled transportation, turning air pollution into a critical issue. It is therefore key to accurately characterize the spatiotemporal variability of surface air pollution in order to understand its effects upon the environment, knowledge that can then be used to design effective pollution reduction policies. Global atmospheric composition reanalyses offer great capabilities towards this characterization through assimilation of satellite measurements. However, they generally do not integrate surface measurements and thus remain affected by significant biases at ground level. In this study, we thoroughly evaluate two global atmospheric composition reanalyses, the Copernicus Atmosphere Monitoring Service (CAMSRA) and the Modern-Era Retrospective Analysis for Research and Applications v2 (MERRA-2), between 2003 and 2020, against independent surface measurements of O3, NO2, CO, SO2 and particulate matter (PM; both PM10 and PM2.5) over the European continent. Overall, both reanalyses present significant and persistent biases for almost all examined pollutants. CAMSRA clearly outperforms MERRA-2 in capturing the spatiotemporal variability of most pollutants, as shown by generally lower biases (all pollutants except for PM2.5), lower errors (all pollutants) and higher correlations (all pollutants except SO2). CAMSRA also outperforms MERRA-2 in capturing the annual trends found in all pollutants (except for SO2). Overall, CAMSRA tends to perform best for O3 and CO, followed by NO2 and PM10, while poorer results are typically found for SO2 and PM2.5. Higher correlations are generally found in autumn and/or winter for reactive gases. Compared to MERRA-2, CAMSRA assimilates a wider range of satellite products which, while enhancing the performance of the reanalysis in the troposphere (as shown by other studies), has a limited impact on the surface. The biases found in both reanalyses are likely explained by a combination of factors, including errors in emission inventories and/or sinks, a lack of surface data assimilation, and their relatively coarse resolution. Our results highlight the current limitations of reanalyses to represent surface pollution, which limits their applicability for health and environmental impact studies. When applied to reanalysis data, bias-correction methodologies based on surface observations should help to constrain the spatiotemporal variability of surface pollution and its associated impacts.

Abstract. Over the last century, our societies have experienced a sharp increase in urban population and fossil-fueled transportation, turning air pollution into one of the most critical issues of our time. It is therefore fundamental to accurately characterize the spatiotemporal variability of surface air pollution, in order to understand its effects upon human health and the environment, knowledge that can then be used to design effective pollution reduction policies. Global atmospheric composition reanalyses offer great capabilities towards this characterization through assimilation of satellite measurements. However, they do not integrate surface measurements and thus remain affected by significant biases at ground-level. In this study, we thoroughly evaluate two global atmospheric composition reanalyses, CAMSRA and MERRA-2, between 2003 and 2020, against independent surface measurements of O3, NO2, CO, SO2, PM10 and PM2.5 over the European continent. Overall, both reanalyses present significant and persistent biases for almost all examined pollutants. CAMSRA clearly outperforms MERRA-2 in capturing the spatiotemporal variability of O3, CO, PM10 and PM2.5 surface concentrations. Despite its higher spatial resolution and focus on aerosol representation, MERRA-2 only performs better than CAMSRA for SO2. CAMSRA also outperforms MERRA-2 in capturing the annual trends found in all pollutants. Both reanalyses show a better performance in summer (JJA), in terms of biases and errors, than in winter (DJF), when pollutant concentrations peak, with the exception of O3. Higher correlations are not necessarily found in JJA, particularly for reactive gases, which show greater correlation values in autumn (SON) and winter. Compared to MERRA-2, CAMSRA assimilates a wider range of satellite products which, while enhancing the performance of the reanalysis in the troposphere (as shown by other studies), has a limited impact on the surface. The biases found in both reanalyses are likely explained by a combination of factors, including errors in emission inventories and/or sinks, a lack of surface data assimilation and their relatively coarse resolution. Our results highlight the current limitations of reanalyses to represent surface pollution, which limits their applicability for health and environmental impact studies. When applied to reanalysis data, bias-correction methodologies based on surface observations should help constraining the spatiotemporal variability of surface pollution and its associated impacts.

<p>Current climate projections point towards a severe increase in the intensity, duration and frequency of heat waves under climate change conditions. Such changes are not homogeneous, with certain regions of the planet presenting a higher vulnerability to these extreme events and, therefore, greater adaptation challenges. Among the areas affected by heat waves, urban environments are particularly susceptible to their impacts due to the urban heat island (UHI) effect, which magnifies the severity of heat waves inside cities and significantly increases the health-related risks associated with heat stress. </p><p>Simulations produced by Global Climate Models (GCMs) (e.g. CMIP) are of crucial importance to better understand how the Earth’s climate system will evolve in the coming decades. Unfortunately, their coarse resolution, typically above 100 x 100 km, makes them unable to resolve fine-scale physical processes, including urban-scale phenomena such as the UHI. High-resolution simulations are therefore required to accurately represent physical processes that remain hidden to models with coarser representations of the climate system. GCMs with km-scale grids and sub-hourly output frequency provide the ability to study heat waves at global, mesoscale or even local level, together with an enhanced (i.e. better in physical terms) representation of the large-scale circulation systems (e.g. Rossby waves) that give rise to heat waves. </p><p>In the framework of Destination Earth (DestinE), we are developing an urban use case for the Climate Adaptation Digital Twin (ClimateDT) that focuses on the climate impacts produced by extreme temperatures in urban environments. We will present the background and the current state of development of the use case, together with its associated challenges. Given the high-resolution simulations envisioned for the ClimateDT are not yet available, we will use NextGEMS cycle 2-3 data, which have similar characteristics, to present several climate indicators related to heat waves and human thermal comfort (e.g. UTCI, HWMI, EHF), with a particular focus on large metropolitan areas and their immediate surroundings, though results at global scale will be also assessed. Nonetheless, the previously mentioned high spatial and temporal resolutions imply unprecedented volumes of data, which, due to limited storage capacity, need to be streamed at model runtime, without the users ever having access to the full model output, but only to a fraction of it over a limited period of time. Therefore, the innovative streaming framework introduced by DestinE requires the use of one-pass algorithms to create statistical summaries of the simulated climate fields, which in turn places particular constraints on the development of the use case.</p><p>Together with other relevant statistics, these indicators will allow us to study the spatial and temporal variability of heat waves inside urban areas, a significant knowledge gap in current climate projections. The ultimate goal of our work is to provide useful knowledge to urban planners, both in terms of storylines and climate data, which can be of use towards designing more resilient cities that are better adapted to the impacts of heat waves.</p>

<p><span>The Destination Earth Climate Adaptation Digital Twin (Climate DT) will design and implement a climate information system running on pre-exascale high-performance computing platforms to support climate adaptation efforts. An overview of the overall Climate DT will be given by Kontkanen et al. (this session).</span></p><p><span>The Climate DT will provide global climate data for both the historical period and the near-term future with unprecedented spatial and temporal resolution. The downstream applications will access the full model state vector (MSV) at runtime. This will </span><span>lead to an interactive system where applications harnessing the MSV can be added, removed, or modified as required by the user. The climate MSV, which contains both the prognostic and a large number of diagnosed variables, will be continuously streamed (understood as all the user-requested variables being available in a federated and curated repository for a limited period of time before being erased) at both high frequency and native resolution. </span><span>The applications will be able to consume this data at runtime as it is streamed. This is equivalent to applications using all the model data they require in a similar manner as one observes a physical system with all the necessary detail to satisfy specific requirements. Additional functionalities will be provided to help the data consumers access relevant statistics, to speed up the data processing and facilitate the data reduction (e.g., on-the-fly bias adjustment). This approach reduces the entry-level requirements for applications to participate in this completely new approach to access climate information data sources. The applications have the possibility of not only interacting with the model to extract the required climate data and indicators on real time, but also iteratively contribute to the design of the experimental set up and request additional variables and indicators.</span></p><p><span>To illustrate the broadest possible applicability of the Climate DT concept, five different pilot use cases were selected for the co-design, implementation, user feedback and evaluation of the Climate DT. The selected use cases focus on wildfires, urban climate, river discharge, wind energy, and hydrometeorological applications. Another consumer of the MSV will be the climate model evaluation. Furthermore, the use cases will present technical recipes for users to access the data and link their applications or impact models to the digital twin.</span></p><p><span>Each use case has identified specific key users. A close exchange with these key users is foreseen to meet the user requirements. To ensure transferability of the work to other users, an exchange with a wider circle of users is foreseen at a more advanced phase at dedicated stakeholder meetings.</span></p><p><span>An overview of the use cases, the technical concepts and the ongoing user engagement and co-design activities will be given to demonstrate the novelty, potential and advantages the digital twin offers. The use cases will illustrate the progress beyond current practices that is possible with these new climate simulation workflow compared to the traditional way of delivering climate simulation.</span></p>

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