National climate change assessments and scenarios have become an essential requirement for decision-making at international, national and sub-national levels. Over recent years, many European countries have set up quasi-operational climate services informing on the current and future state of the climate in the respective country on a regular basis (e.g. KNMI'14 and KNMI'23 in the Netherlands, UKCP18 in the UK, CH2018 and CH2025 in Switzerland, ÖKS15 and ÖKS26 in Austria, National and federal states Climate Reports in Germany). However, the underpinning science to generate actionable climate information in a user-tailored approach differs from country to country. This session aims at an international exchange on these challenges focusing on:
- Practical challenges and best practices in developing national, regional and global climate projections and predictions to support adaptation action and impact assessments.
- Developments in dynamical and statistical downscaling techniques, process-based model evaluations and quality assessments.
- Methods to quantify uncertainties from climate model ensembles, combination of climate predictions and projections to provide seamless user information.
- Examples of tailoring information for climate impacts and risk assessments to support decision-making and demonstration on evaluation steps taken to monitor the uptake of climate information.
The temperature is increasing historically and is projected to rise in future scenarios. This trend is evident both globally and locally in Norway. In Norway, the temperature has increased by about 1.3 degrees Celsius since 1900 and is expected to continue increasing in the future.
Changing temperature, and the impacts on various climate indices, are affecting all parts of the society. Here different temperature indices are studied. Changing temperature affects, among other things, infrastructure, such as roads and buildings. This leads to changes in for instance design values, which again affects the requirements for buildings, such as insulation, cooling and heating.
To study temperature indices, observation based and bias-adjusted daily climate projections with a 1km grid resolution that covers mainland Norway are used. For the two historical normal periods 1961-1990 and 1991-2020, and for the future climate in the periods 2041-2070 and 2071-2100, for three scenarios RCP2.6, RCP4.5 and SSP3-7.0.
The result shows for example fewer days with freezing temperatures and more summer days in Norway. Other temperature indices that are studied are climatological seasons, growing season, heat waves, tropical days and days with zero crossings. Mainland Norway stretches from about 58 °N to 71 °N, has a long coastline in the west, high mountains and steep valleys, this leads to huge variations throughout the country and seasons. This affects the temperature indices and are clearly visible in the maps.
The Norwegian Centre for Climate Services (NCCS) provides information for climate adaptation and helps municipalities to be robust in a changing climate. Information about changes in temperature indices can be of help and background information for climate adaptation. All authors in this abstract are connected to NCCS.
How to cite: Tajet, H. T. T., Hanssen-Bauer, I., Gangstø, R., Verpe Dyrrdal, A., Brox Nilsen, I., Tveito, O. E., Dobler, A., and Hygen, H. O.: Temperature indices in Norway, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-519, https://doi.org/10.5194/ems2024-519, 2024.
With ongoing climate change, national climate scenarios based on current scientific knowledge are indispensable for developing regional and local mitigation and adaptation strategies. In this context, the project Klima CH2025 currently develops the upcoming edition of Swiss climate change scenarios, facilitated by collaborative efforts involving the Swiss Federal Office of Meteorology and Climatology MeteoSwiss, ETH Zurich and further partners. Klima CH2025 will provide an updated basis of user-relevant climate information and related products containing regional and local assessments of future climate change in Switzerland. The backbone of the data production chain within Klima CH2025 is a comprehensive ensemble of CMIP-driven EURO-CORDEX climate simulations. These simulations are bias-adjusted and downscaled through quantile mapping, ensuring the reliability and accuracy of the derived climate scenarios on a localized level. By combining model simulations and observations through quantile mapping, also regional to local scale heat indicators representing the current and future climates are computed. These heat indicators portraying both current climatic conditions and projected future trends are presented in a global warming level framework. The significance of understanding heat-related extremes within a changing climate cannot be overstated. Such extremes exert direct and indirect impacts on several levels such as the human well-being and critical infrastructure. Thus, gaining insights into changes of heat extremes is crucial for formulating effective adaptation strategies and provides an important basis for decision-making across different sectors. In this contribution, our aim is to validate the representation of different heat extreme indicators formulated within the framework of Klima CH2025 and present their projected future changes for different global warming levels.
How to cite: Herrmann, M., Rajczak, J., Mülchi, R., and Kotlarski, S.: Regional climate scenario products in a global warming level perspective. Heat indicators in Switzerland., EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-417, https://doi.org/10.5194/ems2024-417, 2024.
In the frame of the worrying pace and effects of human induced climate change, mountainous regions are warming about twice as fast as the global average. Modeling climate and climate change scenarios over highly complex topography regions like the Alps still represents a great challenge to regional climate modeling: better characterizing the sources of models biases remains a major issue. We analyze the sources of the bias affecting the near surface temperature (TAS) of regional climate simulations, focusing on a region with complex orography, namely, the Friulian Alps. The temperature measurements consist of weather station records, while model TAS outputs belong to an ensemble of EURO-CORDEX models.
Starting from the vertical structure of the atmospheric thermal profiles, we provide a description of the origin of TAS biases in climate simulations and suggest the presence of three main bias components. The first is linked to the driving GCM ability to reproduce the free atmosphere temperatures at a given level (e.g. 500 hPa); the second bias component depends on the models performance in reproducing the thermal structure between the free atmosphere and the boundary layer top: we show that, under the environmental lapse rate approximation, this in turn depends on the orographic error and on the error in the boundary layer thickness. The last bias component accounts for the poor performance of the boundary layers parameterization schemes in reproducing land-atmosphere processes.
Applying this bias analysis to the temperature measurements and EURO-CORDEX simulations that are available for the study area, we evaluated the goodness of the common practice to remove the effects of orographic discrepancies, by means of the environmental lapse rate approximation, frequently adopted in climate projections and model evaluation studies. We show that, after the orographic correction of GCM-RCM TAS outputs, substantial biases still persist, especially during cold months. According to the existing literature, we argue that CMIP5 might contribute to the overall TAS bias, with a cold bias of about 2 degrees, explaining the free atmosphere level component of the bias, and we discuss the influence of the errors in reproducing the boundary layer thickness.
How to cite: Zarabara, F. and Giaiotti, D.: Sources of temperature biases in regional climate simulations over complex terrain, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-225, https://doi.org/10.5194/ems2024-225, 2024.
Downscaling approaches are a key to translate global climate change projections to regional and local climate scenarios required in adaptation planning. A cutting-edge statistical method was developed under the framework of the European DISTENDER project to produce climate scenarios (1981-2050) of a large number of variables, including precipitation and temperature, at a daily scale for two extensive regions (Austria and the Spanish-Portuguese EURAF region) and at a hourly scale for three small regions: Guimaraes (Portugal), Metropolitan City of Turin (CMTo, Italy) and The North-east of The Netherlands (HUAS). Our method consisted of three steps: 1) Parametric quantile mapping; 2) hourly transfer function; and 3) Geostatistical downscaling. In the first step, a parametric quantile mapping (Monjo et al. 2014) was used to locally transfer reference probability distribution to the Historical and the four main SSP experiments (SSP1-2.6, SSP2-4.5, SSP3-6.0, SSP5-8.5) of three CMIP6 Earth System Models at a daily scale. To make this, ERA5-Land reanalysis data (0.073°×0.073°) was used as a reference. In the second step, for each modeled (targeted) day, the most analogous past day was selected from the reanalysis by comparing their spatial thermal patterns to each targeted day of every climate projection (from the first step) and then linear transfer functions were applied from the maximum/minimum values of each projected day to the hourly curve of its analogous day, so producing a hourly climate projection of that targeted day at the reference 0.073°×0.073° grid. The third step is a purely geostatistical technique with multi-linear AIC-based stepwise regression, fitting high-resolution predictors (land-use, geographical and topographical parameters), with a final bilinear interpolation for the residual errors. The generated climate simulations adequately passed the Kolmogorov-Smirnov test for the historical period. Projections showed a reduction of total precipitation amount in summer (CMTo) and autumn (HUAS and Austria) between 10 and 20%, even decreasing more than 40% during the warmest months in Guimaraes and EURAF. Spring will experience an increase of precipitation up to 20% in CMo, while winter will be 10-30% wettest during the winter season in Guimaraes and HUAS. Maximum temperature will increase up to +3.5ºC in Guimaraes and Austria, and up to +5ºC in CMTo, HUAS and Euraf during summer under the SSP5-8.5 scenario. The developed techniques are ready to be transferred to future projects on climate modeling and environmental applications.
How to cite: Monjo, R., Royé, D., Redolat, D., Paradinas, C., Prado-López, C., San José, R., Pérez-Camanyo, J. L., Ahrens, B., and Ribalaygua, J.: Daily and hourly statistical downscaling of CMIP6 climate scenarios for DISTENDER case studies, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-334, https://doi.org/10.5194/ems2024-334, 2024.
Data from the TRANSLATE project was used to examine the behaviour of standard climate indices associated with prolonged deficits in precipitation, particularly those that rely on the property of “consecutiveness”. Ireland has a temperature oceanic climate and usually gets a large amount of rain, but it is not uncommon for shortfalls to occur also. Having access to reliable climate information about these phenomena is important for planning of future infrastructure and for water-reliant sectors, such as agriculture and utilities.
The TRANSLATE dataset is a bias-corrected ensemble of regional climate model projections over Ireland, with quantile-mapping used to produce the bias-corrections. The regional climate model data comes from a combination of the EURO-CORDEX simulations and an ensemble of COSMO and WRF downscaled simulations over Ireland, both of which were driven by CMIP5 global model data. Bias-correction techniques, such as quantile-mapping, can successfully adjust daily time series to remove overall biases, but generally do not account for “consecutiveness”, and so can introduce occasional wet-day interruptions into otherwise dry periods in the detrended and bias-corrected daily precipitation time series. This has the potential to produce inconsistent results between the raw and bias-corrected projections if the bias-corrected daily time series is used to calculate indices which rely on this property.
Results for the standard climate index Consecutive Dry Days (CDD), the related “Dry Periods” (count of periods of more than 5 consecutive dry days) and the Standardised Precipitation Index (SPI) are presented to highlight where these inconsistencies may arise, and the steps taken to address them. It was found that calculating the indices first, using the raw projections, and then applying the bias-correction to this index data resolved this apparent contradiction. Meanwhile results based on single-day extremes and occurrence frequencies were unaffected by the choice of technique. This research helps to provide the best representation of future projected dry periods in Ireland, while adequately highlighting the uncertainty in these projections.
How to cite: Griffin, S., O'Brien, E., Duffy, C., and Nolan, P.: Resolving inconsistencies in the dry period properties of bias-corrected daily precipitation timeseries: an example from the TRANSLATE climate projections for Ireland., EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-12, https://doi.org/10.5194/ems2024-12, 2024.
Precipitation plays a pivotal role as a climatic indicator across various domains, particularly in the context of climate change studies. However, its accurate simulation and projection remain challenging due to its inherent stochastic nature. Climate models frequently exhibit a tendency to overestimate the frequency of light precipitation events and underestimate the magnitude of extreme precipitation totals, a phenomenon commonly referred to as the 'drizzle bias'. Consequently, while the total precipitation amounts may be adequately represented, discrepancies often emerge in the frequency distribution of rainy days. This discrepancy challenges the model’s ability to capture the precipitation patterns, impacting climate related assessments and predictions.
This study seeks to mitigate the 'drizzle bias' in simulated precipitation, by introducing and implementing two distinct statistical methodologies aimed at improving the precision of simulated and projected rainy-day counts within the broader Euro-Mediterranean region. The first approach, which mimics the convention, involves adjusting the number of rainy days based on the assumption that the relationship between observed and simulated rainy days remains constant over time (thresholding). In contrast, the second approach employs a machine learning model specifically Random Forests (RF), to statistically minimize the drizzle bias based on a function of several simulated climate variables.
The findings suggest that utilizing a multivariate approach yields outcomes comparable to traditional thresholding techniques when adjusting for sub-periods characterized by similar climatic patterns. However, the efficacy of the RF method becomes apparent when addressing periods marked by extreme bias, distinguished by substantially different frequencies of rainy days. These notable deviations predominantly manifest within the Mediterranean domain, particularly in the extremely arid regions. Specifically, within the Mediterranean area, simulated rainy-day counts exceed 100% (relative proportion), with percentages in the African segment of Mediterranean reaching up to 200%.
Furthermore, the study reveals that while the prevalence of the thresholding method is prominent in Eastern Europe and select Mediterranean locations, the RF method demonstrates superior performance for stations exhibiting significant disparities between the two methodologies, notably in the Balkan Peninsula.
Concluding, by employing innovative statistical techniques, this study enhances our understanding of precipitation modelling and highlights the importance of tailored methodologies, particularly in regions characterized by distinct climatic characteristics, such as the Mediterranean.
How to cite: Lazoglou, G., Economou, T., Anagnostopoulou, C., Zittis, G., Tzyrkalli, A., Georgiades, P., and Lelieveld, J.: Tackling Drizzle Bias in the Euro-Mediterranean region: The Multivariate Adjustment Solution, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-13, https://doi.org/10.5194/ems2024-13, 2024.
As the climate keeps changing, it is essential to develop effective solutions to support adaptation and mitigation to the observed and projected climate change. It is imperative that our long term strategies rely on in-depth understanding of the prospective range for expected climate scenarios.
The Coupled Model Intercomparison Project (CMIP) framework was developed in response to this need, integrating an extensive collection of global climatic models developed by different modelling groups, each of them with a focus on specific physical and atmospherical processes. These models generate valuable future climate projections that could be integrated in decision-support solutions to foster the delivery of key ecosystem services in water-dependent habitats and improve their resilience to climate change.
This study focuses on the Danube Basin, a representative climate change hotspot at European level, which integrates the joint impacts of a wide range of socio-economic factors (i.e., urbanization, land use change). This region is subject to the ecosystemic and biodiversity threats arising from increased temperatures, decreased precipitation and reduced river flow in both present and future climate, challenging the management of restoration actions. This study is aimed to provide an improved understanding of the expected future climate change signals for the timeframe 2015 - 2100 in the top priority CMIP6 scenarios (i.e., SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5) at the Danube Basin scale, using bias-corrected data. The model outputs are calibrated on the historical period 1961–2014 with E-OBS gridded dataset at 0.1° spatial resolution.
Our approach includes a performance comparison of several bias-correction methods (e.g., univariate - quantile delta mapping, multivariate – R2D2, MBCn) used within a spatial disaggregation algorithm, to minimize the uncertainty induced by systematic errors in the projection data. The bias-corrected climate projections of air temperature and precipitation will feed the Restore4Life decision-support system for wetland restauration.
Acknowledgements
This research received funds from the project “Restoration of wetland complexes as life supporting systems in the Danube Basin (Restore4Life)” funded by the European Union Horizon Europe programme, under Grant agreement n° 101112736.
How to cite: Smău, R. and Dumitrescu, A.: A bias-corrected CMIP6 climate projection dataset for the Danube Basin , EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-668, https://doi.org/10.5194/ems2024-668, 2024.
In climate change (CC) impact studies, various weather-dependent models (e.g. crop growth models or rainfall-runoff models) are employed to assess possible CC impacts on crop yields, river flows, as well as on various climatological and other indices. To perform such experiments, one has to decide what meteorological data (representing present & future climate) will be used as inputs. Two most commonly used approaches at hand are Regional Climate Models (RCMs) and Stochastic Weather Generators (WGs). While one might say, that these two approaches are the rivals which compete for getting the main role in providing the weather data for CC impact studies, we want to demonstrate that these two approaches could be rather allies which could be very effective while being used together.
In our presentation, we show three ways of such co-operation: (1) To produce weather series representing the future climate by the WG, WG parameters must be modified by CC scenarios, which may be derived by comparing RCM simulations of present vs. future climates. (2) To assess separate effects of changes (projected by RCMs) in individual weather variables (e.g. temperature or precipitation) and their statistical characteristics (means, variabilities, spatial and/or temporal correlations), WG may be used: only selected WG parameters representing chosen variable and its statistical characteristic may be modified before producing the synthetic series. (3) To assess statistical significance of the changes derived from a given RCM simulation, WG may be also used: the significancy of projected changes may be based on analyzing the spread of the changes derived by comparing multiple pairs of synthetic time series produced by WG calibrated with RCM simulations for future vs. present time slices.
In the experiment, we use: (a) our multi-site multi-variate parametric weather generator SPAGETTA, (b) E-OBS data to calibrate the generator for the present climate conditions in 8 European regions, and (c) outputs from ensemble of 19 RCM simulations for present and future climate (CORDEX database) in these regions.
Acknowledgement: The experiment was made within the frame of the PERUN project funded by the Technological Agency of the Czech Republic (project no. SS0203004000).
How to cite: Dubrovsky, M., Meitner, J., Skalak, P., and Plavcova, E.: The Weather Generator and The Regional Climate Model: The Rivals or The Allies?, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-903, https://doi.org/10.5194/ems2024-903, 2024.
National climate scenarios reflecting the current scientific state of knowledge are an indispensable basis for public and private sectors to plan and design adaptation and mitigation measures. Regional or even local assessments of future climate change are therefore an important climate service. The next generation of climate scenarios for Switzerland is currently under development in the project Klima CH2025. Similar to previous climate scenario generations, the new project is a joint effort involving the Federal Office of Meteorology and Climatology MeteoSwiss, ETH Zurich, C2SM and further partners from academia and administration. The main goal of Klima CH2025 is to develop, update and provide the physical basis of climate change in Switzerland and related products. Two main scientific questions will be addressed: 1) How can we better merge observations and model-based climate scenarios in order to provide consistent and temporally seamless information to best serve user needs?; 2) What is the projected evolution of impact-relevant climate extremes in Switzerland and what are their underlying processes? Guided by these two questions, we will develop a range of new products, engage with stakeholders, and plan active communication and dissemination.
We build our scenarios upon the existing CMIP5-based EURO-CORDEX simulations but combine them with CMIP6 GCM information using a variant of a pattern scaling approach. Our approach bridges the gap between different CMIP and CORDEX generations and at the same time merges models and observations to provide consistent information on climate change for the past, the present and the future. In this presentation, the general approach of the Klima CH2025 framework will be presented together with the method applied to bridge models and observations and to integrate CMIP6 evidence into CMIP5-based EURO-CORDEX simulations. In addition, first results of the project and planned products will be presented.
How to cite: Mülchi, R., Anet, J., Stefanie, B., Laura, B., Mischa, C.-M., Erich M., F., Lilja, J., Reto, K., Sven, K., Mark, L., Ruth, L., Anna, M., Jan, R., Christoph, S., Simon C., S., Christina, S. P., Dominik, S., Cornelia, S., and Sonia I., S.: Climate scenarios for Switzerland: How to provide seamless information by bridging models and observations?, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-51, https://doi.org/10.5194/ems2024-51, 2024.
The TRANSLATE project has already generated a first set of standardised climate projections, based on a selection of CMIP5 global model projections using 3 different forcing scenarios (RCP 2.6, 4.5 and 8.5). For each scenario, a 6-member ensemble of CMIP5 simulations were dynamically downscaled to high-resolution (4 km) over Ireland using the COSMO and WRF regional models, while a larger ensemble (up to 30 members, depending on scenario) were downscaled to 12 km by the EURO-CORDEX project. The rest of the 21st century was divided into three 30-year periods (2021-2050, 2041-2070, and 2071-2100), and for each of these the downscaled simulations were detrended and bias-corrected (using quantile-delta mapping), and further statistically downscaled to a high-resolution observational grid. This project is now adding several more parameters like wind speed and direction, relative humidity, and downward shortwave radiation at the surface. The method remains much the same as used by O’Brien E and Nolan P (2023). These variables may be combined with temperature projections produced earlier to compute projections of derived indices such as evapotranspiration or renewable energy potential.
A high resolution (2km X 2km) reanalysis dataset is used in lieu of reference observations from 1981 to 2010. These data were generated using the WRF regional model down-scaling the ERA Interim dataset. These 30 year daily observations were used to validate the corresponding variables in regional climate models output for the same historical period (1981-2010). Ensembles of reconstructed i.e., detrended, bias-corrected, and further downscaled daily timeseries for all three parameters are now completed. Using these data, future climate change scenarios from both COSMO and Cordex ensembles are analysed relative to the historical baseline period. The most interesting selected results will be presented.
We also plan to repeat these analyses using CMIP6 based projections.
How to cite: Samala, B. K., O'Brien, E., and Nolan, P.: Additional parameters in TRANSLATE: A standardized Climate change dataset for Ireland, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-237, https://doi.org/10.5194/ems2024-237, 2024.
The heat index metric (a function of temperature and humidity) is used often in the United States as a quantitative measure associated with human heat exposure. Heat index values are part of warm season forecasts issued by the US National Weather Service, incorporated into heat warning systems used by governmental entities, and appear in many scientific publications, especially interdisciplinary studies linking public health with weather and climate. Yet, there is more than one way to calculate heat index values from observational data and from model predictions and projections. Not surprisingly, the choice of input data sets, computational algorithms used, and other methodological choices can lead to different quantitative results and hence uncertainties. Our experiences suggest that some sources of uncertainties are usually considered (e.g., scenario and model uncertainty, using the nomenclature of Hawkins and Sutton [2009]) by those without formal climate science training, whereas other uncertainties are not necessarily appreciated. Neglecting sources of uncertainty can lead to overconfidence in quantitative results produced in a particular study. Here we present information developed during a research project investigating historical and projected daily maximum heat index values for the northeastern United States, with a focus on the city of Philadelphia. We illustrate how, in some cases, uncertainties associated with the choice of downscaling or bias correction methods, the observational data product used when bias correcting, the temporal resolution of weather and climate data, and common estimation methods used in some calculations, can lead to uncertainties whose magnitudes can rival those of scenario and model uncertainty. Comments will be offered regarding the practical challenges of seeking to promote improved practices when limited by data availability or other resources.
Hawkins, E., and R. Sutton, 2009: The Potential to Narrow Uncertainty in Regional Climate Predictions. Bulletin of the American Meteorological Society.
How to cite: Dixon, K., Adams-Smith, D., Le Roy, B., and Zenes, N.: Questions Arising While Co-producing Climate Projection Information Relevant to Philadelphia Public Health Extreme Heat Interests, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-379, https://doi.org/10.5194/ems2024-379, 2024.
With strong local autonomy, Norwegian municipalities hold a significant responsibility for adapting to climate change at the local level (Gram-Hanssen et al., 2023).
The Norwegian Centre for Climate Services (NCCS) provides national fine-scale climate projections, as the knowledge base for climate change adaptation in Norway. With the aim to support local decision makers to implement adaptation measures, NCCS has developed county-specific fact sheets, describing future changes in climate, hydrology, and effects on natural hazards (Nilsen et al., 2022). The fact sheets contain recommendations for climate change allowances for flooding, heavy rainfall and sea level rise, and constitutes an important part of the knowledge base to be used in local and regional planning ("Statlige planretningslinjer for klima- og energiplanlegging og klimatilpasning," 2018).
However, previous feedback from users indicates a gap between the services provided by NCCS and the user needs of Norwegian municipalities.
In the wake of IPCC AR6, the fine-scale climate projections for Norway are updated, and will be published in the report “Climate in Norway 2100” (exp. 2025). In the development of new and updated services, the NCCS-project Klimakverna (The climate mill), aims to make climate projections easier to find and easier to use.
Our research examines the utilization of climate projections in adaptation efforts, among small and medium-sized Norwegian municipalities. In-depth interviews have been conducted, with 9 municipalities with populations under 20,000. To complement the municipalities' perspectives, 4 directorates have also been interviewed, as well as the The Norwegian Association of Local and Regional Authorities (KS), all of which supports municipalities in their climate adaptation efforts, through mediation or authority.
The presentation will focus on how the scientific resources for climate adaptation are conveyed and employed, and how the accessibility and applicability of climate services can be improved to meet the municipalities requirements.
By bridging the gap between scientific knowledge and local implementation, NCCS aims to empower municipalities to build more resilient communities in the face of climate change.
References
Gram-Hanssen, I., Aall, C., Drews, M., Juhola, S., Jurgilevich, A., Klein, R. J. T., Mikaelsson, M. A., & Lyngtorp Mik-Meyer, V. (2023). Comparison and analysis of national climate change adaptation policies in the Nordic region.
Nilsen, I. B., Hanssen-Bauer, I., Dyrrdal, A. V., Hisdal, H., Lawrence, D., Haddeland, I., & Wong, W. K. (2022). From Climate Model Output to Actionable Climate Information in Norway. Frontiers in climate, 4. https://doi.org/10.3389/fclim.2022.866563
Statlige planretningslinjer for klima- og energiplanlegging og klimatilpasning, Kommunal- og distriktsdepartementet (2018). (In Norwegian - Central government planning guidelines) https://lovdata.no/pro/SF/forskrift/2018-09-28-1469
How to cite: Garvin, K. and Verpe Dyrrdal, A.: Bridging the gap between scientific knowledge and local implementation of climate change adaptation. Results from a user survey of Norwegian climate services., EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-775, https://doi.org/10.5194/ems2024-775, 2024.
Climate services provide tailored information to support climate adaptation at the local level. One common form of climate service is the provision of downscaled climate projections, often bias-corrected using local observations and customised to meet the needs of local society based on extensive stakeholder engagement. Well-established examples of such services already exist in many European countries, while others are currently in the process of developing their own. While each such instance has its own peculiarities, there is also a high degree of overlap and duplication between these services. For example, the Danish “Klimaatlas”, the Norwegian “Climate in Norway 2100” report and the Swedish “Advanced Climate Change Scenario Service” all start from the EURO-CORDEX ensemble, bias-correct against local datasets, and produce comparable indicators, albeit entirely independently of each other. Recognising the potential to reduce duplication, to learn from each other and to enable the development of climate services in new regions, we established the KAPy (Klimaatlases in Python) project and network. KAPy builds on an open-source software stack centred on the Python language, leveraging the extensive tools already developed in this programming community. The use of workflow control tools from the field of bioinformatics enables reproducibility and scalability, while the open-source approach drives both collaboration and transparency. We illustrate the capability of this tool to produce climate service information using, as an example, ongoing work in Ghana, with a detailed analysis of the efforts required to produce climate-service ready indicators starting from scratch: after downloading of data was complete, configuration, bias-correction and indicator production was possible in a matter of a few hours. We also detail experiments showing how bandwidth limitations can be circumvented using cloud computing, further increasing the productivity and enabling implementation in resource limited situations, such low and low-middle income countries. We conclude with an open invitation to all to join the KAPy network as both users and developers, and thereby contribute to making climate services more transparent and widely accessible.
How to cite: Payne, M. R., Nangombe, S. S., Abbey, P.-W., Ansah, S. O., Björnsson, H., Bredvold, K. L. H., Boberg, F., Dobler, A., Düsterhus, A., Ólafsdóttir, A. H., Poropat, L., Quaye, D., Stensballe, J., Tunheim, K., Tómasdóttir, K. A., and Zaqout, T. A. M.: KAPy – a community-based approach to the production of climate services, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-731, https://doi.org/10.5194/ems2024-731, 2024.
Climate change is one of the greatest challenges of our time. Despite multiple efforts to reduce GHG emissions, reductions are too small to keep global warming well below 2 K, preferably at 1.5 K. Instead, the increase probably reaches 2.9 K by the end of this century [1]. Therefore, adaptation to climate change is essential to reduce the negative consequences.
Urban areas with their high population and infrastructure density are specifically vulnerable to extreme weather. The urban fabric increases its vulnerability to (severe) weather events. For example, land-sealing hinders the infiltration of rain water and thus increases the probability for flash floods; three-dimensional buildings with their high heat storage capacity lead to higher temperatures in the city at night compared to surrounding areas. Values might be about 3 K higher in the summer climate average [2] but can reach values of up to 10 K in specific situations [3]. The German Climate Adaptation law states that from 1st of July 2024 adaptation by already occurring or foreseeable climate changes has to be considered in planning and decisions by institutions with public responsibilities. The law names several sectors to act on, for example local heat island effects. The law also states that technical laws or accepted standards are to be taken into account. This requires joint efforts for considering adaptation by the committees working on newly developed or updated standards.
The approach followed by VDI and other organisations for standardisation is briefly introduced, and new standards, relevant for assessing climate and its changes (e.g. calculation of climate indicators, how to perform urban climate simulations, heat action plan) are presented.
[1] UNEP: Emissions Gap Report 2023. https://www.unep.org/resources/emissions-gap-report-2023, last used 30.03.2024
[2] WMO (2023): Guidance on Measuring, Modelling and Monitoring the Canopy Layer Urban Heat Is-land (CL‑UHI). K.H. Schlünzen, S. Grimmond, A. Baklanov (edts.), World Meteorological organisa-tion, WEATHER CLIMATE WATER. 2023 edition. WMO-No. 1292, pp.88. https://library.wmo.int/idurl/4/58410 last used 12.04.2024.
[3] Kuttler W.; Weber S. (2023): Characteristics and phenomena of the urban climate. Meteorol. Z. (Contrib. Atm. Sci.), 32, No. 1, 15–47, doi: 10.1127/metz/2023/1153.
How to cite: Schlünzen, K. H. and Fröhling, C.: Addressing climate change in standards, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-502, https://doi.org/10.5194/ems2024-502, 2024.
In the construction sector in particular, climate change is increasingly being factored into planning due to the long service life of structures. Among other things, standards aim to prevent damage through their specifications. Many of these are dependent on weather parameters. They are regularly reviewed and this offers the opportunity to incorporate more up-to-date or newly available data, to apply modernize the used methods, and to take climate change into account.
MeteoSwiss has been working closely with the Swiss standardisation associations for many years with the aim of supporting optimal foundations for forward-looking standardisation and adaptation to climate change in the construction sector. This not only involves drawing on existing data, but also developing new foundations and co-designing climate services in joint projects.
In these interactions, MeteoSwiss often takes on a coordinating role as well as providing the scientific expertise for the climate data and the statistical evaluations. In addition, as a national weather service, the experience as a provider of reliable operational services is key. On the stakeholder side, the knowhow about the specific problem, the risk landscape and the needs of the end-users are essential for the creation of the products and services.
In this presentation, some of these collaborations and their results will be presented. These include the heating and cooling of buildings, hail protection and property drainage. These examples will also be used to reflect on which elements are important for the long-term interaction between a national weather and climate service and stakeholders, how uncertainties can be dealt with by engaing into a risk dialogue and what lessons have been learned by both sides over the years in order to put actionable climate services into practice.
How to cite: Schwierz, C., Schlegel, T., Aller, D., Wehrli, K., Kotlarski, S., Fukutome, S., Schröer, K., and Croci-Maspoli, M.: National climate services to support standardisation - examples from Switzerland, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-919, https://doi.org/10.5194/ems2024-919, 2024.
Assessing renewable energy resources under future climate scenarios has been highlighted in recent years to analyze and understand potential impacts of future change in renewable generation on the power sector. Solar energy is well-known as the most plentiful among various renewable resources and usually converted to electricity using photovoltaics (PV) technologies, and the global deployment of PV technology has increased rapidly in recent decades. In this study, we develop a statistical technique to downscale the future projection of solar irradiance for PV energy-related applications. A set of Regional Climate Model (RCM)-based projections obtained from the North American Coordinated Regional Climate Downscaling Experiment (NA-CORDEX) are used as inputs to statistical methods to generate high-resolution global horizontal irradiance (GHI) over the contiguous United States (CONUS). The main steps of the statistical downscaling method include (1) regridding RCM output (0.22 degree and daily resolutions) to handle the modeled-observed data sets on a common grid, (2) correcting bias of RCM GHI using satellite-derived observation, and (3) implementing temporal and spatial downscaling to generate GHI at 8-km and hourly resolution. Basically, complex physical processes and interactions between solar radiation and various atmospheric constituents lead solar irradiance to be highly variable and uncertain. Underrepresentation of clouds from the RCM parameterizations is the main source of error and uncertainty in modeling solar irradiance. Thus, we adapt and use the high-quality satellite-derived data from the National Solar Radiation Database (NSRDB) to analyze the bias and error of RCM GHI as well as estimate the statistical parameters for spatial and temporal downscaling. This presentation will summarize the comprehensive analysis conducted to produce and assess the results under two climate scenarios (RCP4.5 and RCP8.5). We will also present a detailed validation demonstrating the strengths of the proposed downscaling method and future extension of this research.
How to cite: Sengupta, M., Yang, J., Bailey, M., Habte, A., Xie, Y., Nychka, D., and Bandyopadhyay, S.: An Unbiased High-Resolution Climate Dataset for Solar Energy Applications, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-968, https://doi.org/10.5194/ems2024-968, 2024.
Decadal climate predictions are a source of climate information to anticipate the evolution of the climate system from 1 to 10 years ahead. Whereas both climate projections and decadal predictions contain information about external forcings, their main difference is that decadal predictions also include information on the phase of the internal climate variability. To achieve this, decadal climate models are initialised once per year with observation-based initial conditions.
This study assesses the effectiveness of various statistical downscaling methods applied to multi-model decadal predictions of mean near-surface air temperature and precipitation for the forecast years 1-5 over the Iberian Peninsula. The multi-model ensemble combines predictions from 13 forecast systems contributing to the Decadal Climate Prediction Project (DCPP) component of the Coupled Model Intercomparison Project Phase 6 (CMIP6). The performance of the different downscaling methods is determined by comparing their forecast quality against the raw, coarse-resolution predictions using four deterministic or probabilistic metrics: the Anomaly Correlation Coefficient (ACC), Root Mean Square error Skill Score (RMSSS), Ranked Probability Skill Score (RPSS) and Continuous Ranked Probability Skill Score (CRPSS). The downscaling and forecast quality assessment are carried out using the high-resolution ERA5Land reanalysis as the reference dataset, and it is performed in leave-one-out cross-validation mode in order to emulate real-time conditions and not to overestimate the actual skill.
Three kinds of downscaling methods have been examined. The first type is based on calibrating the interpolated raw predictions (i.e. correcting biases in the mean value or variance, among others). The second involves building linear regressions using different predictors: (i) large-scale decadal indices as (e.g. the Atlantic Multi-decadal Variability, AMV; or the North Atlantic Oscillation, NAO) (ii) interpolated model data (basic linear regression) or (iii) a combination of the 9 nearest neighbours of model data. Finally, the third approach involves the search for past analog days in the high-resolution reference dataset.
The results show that the skill estimates primarily depend on the calibration or linear regression approaches, with small differences from the interpolation method used during the downscaling. While the first type of methods maintains the spatial distribution of the skill compared to the raw predictions, the second and third types can change it. For temperature, the raw predictions show high skill, which is maintained after applying calibration, basic linear regressions or 9 nearest neighbours linear regression. However, the skill is reduced after calculating the linear regressions with external predictors. For precipitation, the skill of the raw predictions is rather low, and the calibration methods do not generally increase such skill. On the other hand, the linear regression method using the AMV index as a predictor is the one that shows the most improvement in skill compared to the raw predictions in some regions.
How to cite: Moreno-Montes, S., Delgado-Torres, C., Duzenli, E., Pérez-Zanón, N., Marcos-Matamoros, R., and Soret, A.: A Comparative Analysis of Downscaled Multi-model Decadal Climate Predictions over the Iberian Peninsula, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-434, https://doi.org/10.5194/ems2024-434, 2024.
Despite its critical importance in the global cycles of energy, water, and carbon, the extent of global wetlands remains highly uncertain. A straightforward approach to estimating global wetland areas is to relate the topographic wetness index (TWI) with groundwater or climate variables. In this study, we examined multiple TWI-based methods for estimating wetland areas across historical and future timeframes. First, we identified inland wetlands using global TWI data combined with soil moisture data from an advanced reanalysis system. For comparison, we also generated three other sets of wetland area estimates using monthly precipitation and potential evaporation (Ep) data from the same system, applying three different Ep formulas to assess the sensitivity of wetland areas to changes in surface resistance. We extended this methodology to multiple Earth system models (ESMs) from the Coupled Model Intercomparison Project phase 6 (CMIP6) to explore the impact of atmospheric CO2 concentrations on wetland dynamics. Our findings indicated that increases in historical atmospheric CO2 had a minimal impact on TWI-based wetland areas. However, their influence is expected to become more significant even under a sustainable emission scenario by the mid-2050s. We also found that CMIP6 ESM soil moisture projections show smaller changes in wetland areas compared to those estimated using a traditional Ep formula. This highlights potential inconsistencies between future wetland projections based on climatic variables and those derived from soil moisture projections.
Acknowledgements: This work was supported by Korea Environmental Industry & Technology Institute (KEITI) through Wetland Ecosystem Value Evaluation and Carbon Absorption Value Promotion Technology Development Project (2022003640001), funded by Korea Ministry of Environment (MOE).
How to cite: Kim, D. and Kim, E.: Diverging estimates of future global wetland areas under warming scenarios, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-627, https://doi.org/10.5194/ems2024-627, 2024.
ClimRisk.eu is a new web portal about climate change projections and underlying data needed for climate proofing. ClimRisk.eu is designed for investors planning their investments to withstand the future climate conditions. It is intended to help the public organizations to formulate their policies and plan specific adaptation measures. It is also a tool for companies and private entities to comply with the EU requirements to assess the sustainability of investments (EU Regulation 2021/1060, the so-called EU Taxonomy). ClimRisk.eu also serves as the data source for individuals and broad public and their climate-related personal interests.
ClimRisk.eu works over two domains: Czech Republic and Central Europe. Information for the Czech Republic is based on more detailed and accurate data inputs, e.g. climate observations and it is derived from high resolution data grid of 0.5 km step. The data on the Central European domain is built on the less detailed data sources and thus the spatial resolution reaches only 10 km. Climate projections are delivered for four selected Shared Socioeconomic Pathways (SSP) scenarios and ensemble of seven Coupled Model Intercomparison Project Phase 6 global climate models (GCMs). The set of seven GCMs was taken from a larger ensemble consisted of more than 20 CMIP6 GCMs and its choice was done to reduce the effort needed to process all data while keeping the statistical properties of the original ensemble of more than 20 GCMs.ClimRisk.eu offers long-term means of meteorological parameters (air temperature, precipitation, wind speed, humidity, solar radiation) as well as important climate indices including those focused on extremes. Information on the range of uncertainty related to climate projection for a given territory is also included at ClimRisk.eu.
Analysis of future climate conditions is based on simulations of the most recent generation of global climate models (GCM). Given the outputs of climate models are associated with systematic errors (due to the necessary simplification of the complex real-world processes), they need to be corrected in order to obtain meaningful results about the simulated properties of the climate system. In case of GCM it is not possible to apply correction methods suitable for RCMs, like quantile mapping. In this case this is made possible by the Advanced Delta Change ("ADC") method.
How to cite: Trnka, M., Stepanek, P., Skalák, P., Balek, J., Zahradníček, P., Meitner, J., and Farda, A.: ClimRisk.eu – new climate proofing tool for Central Europe, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-1014, https://doi.org/10.5194/ems2024-1014, 2024.
