The issue of the added value (AV) of high resolution regional climate models is complex and still strongly debated. Here, we approach AV in a perfect model framework within a 16-member single model initial condition ensemble with the regional climate model RACMO2 embedded in the global climate model EC-Earth2.3.  In addition, we also used an ensemble produced by a pseudo global warming (PGW) approach. Results for winter temperature and precipitation are investigated from two different perspectives: i) a signal-to-noise perspective analysing the systematic response to changing emission forcings versus internal climate variability, and ii) a prediction perspective aimed at predicting a 30-year future climate state. Systematic changes in winter temperature and precipitation contain fine-scale response patterns, but in particular for precipitation these patterns are small compared to internal variability.  Therefore, single members of the ensemble provide only limited information on these systematic patterns. However, they can be estimated more reliably from PGW members because of the stronger constraints on internal variability. From the prediction perspective, we analysed AV of fine-scale information by comparing three prediction pairs. This analysis shows that there is AV in the fine-scale information for temperature, yet for precipitation adding fine-scale changes generally deteriorates the predictions. Using only the large-scale change (without fine scales) from a single ensemble member as a delta change on top of the present-day climate state, already provides a robust estimate of the future climate state and therefore can be used as a simple benchmark to measure added value.

How to cite: Lenderink, G., de Vries, H., van Meijgaard, E., van der Wiel, K., and Selten, F.: A perfect model study on the reliability of the added small-scale information in regional climate change projections, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-11868, https://doi.org/10.5194/egusphere-egu23-11868, 2023.

We present a new method to track weather features that is designed to understand and evaluate regional climate processes and hydrological extremes in models. Tracked phenomena include extratropical cyclones, fronts, atmospheric rivers, high-pressure systems, mesoscale convective systems, tropical cyclones, and their relationship to extreme precipitation. The tracker is applied to ERA5 reanalysis data to explore specific historical events and build up climatologies of object frequencies and characteristics such as intensity, size, and trajectories. The key benefit of this multi-feature tracking method is to understand connections across features, such as the location of atmospheric rivers relative to extra-tropical cyclones or the role of blocking high-pressure systems in heat waves and downstream extreme precipitation. This provides a more process-based understanding of hydrologically relevant weather systems than can be obtained through Eulerian-based assessments. Future work will apply this multi-feature tracking method to Euro-CORDEX and Polar CORDEX simulations and compare with the more traditional Eulerian-based skill scores. This work contributes to the Big Data and Climate FRONTIER project that seeks to mitigate future challenges associated with the exponential increase in climate data expected over the next decade using smart design processes and big data methods.

How to cite: Mooney, P., Prein, A., and Done, J.: Understanding precipitation extremes with a multi-feature tracking tool, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-14895, https://doi.org/10.5194/egusphere-egu23-14895, 2023.

One important type of information for stakeholders is the time of emergence (TOE) of a particular climatic impact-driver (CID) in a specific region as reported also in the latest IPCC AR6. The TOE is the time when a certain signal emerges from the natural variability, thus it is an indicator of the magnitude of the climate change signal and it can be very important in a risk framework for mitigation purposes.

There is no single metric for ToE. It depends on user-driven choices of variables, space and time scales, the baseline relative to which changes are measured, and the threshold at which emergence is defined. In the present study, after testing different metrics, we chose the one based on the literature and  we calculated together with ToE, the Global Temperature of Emergence (GToE), where time is replaced with the global mean temperature and there is no more dependence on models differences and emission pathways. 

The GToE is thus defined on the basis of thresholds of temperature, the Global Warming Levels (GWL), expressed as changes in surface global temperature relative to the period 1850-1900.

The probability of reaching a specific GWL threshold is then estimated for each CID and each region of interest. 

In this study, we use GWL 1.0 1.5, 2.0, 3.0 and 4.0 as thresholds and we evaluated the probability of crossing them for several CIDs based on the Euro-CORDEX regional climate projections.

As expected the probability of crossing a certain threshold increases with the increase of GWLs. There are regions where high probability is shown even at lower GWLs and those indicate "CID hot spots" in the domain, such as in the Mediterranean for example, but also in Scandinavia for other specific CIDs.

How to cite: Raffaele, F., Nogherotto, R., Zazulie, N., and Coppola, E.: Hot spots of Global Temperature of Emergence of several CIDs for the European region, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-15193, https://doi.org/10.5194/egusphere-egu23-15193, 2023.

This work investigates the scalability of wet and dry persisting condition patterns over the European domain with global warming levels. For this aim, we have used the EURO-CORDEX ensemble of regional climate projections at 0.11° grid-mesh for daily minimum and maximum temperature and precipitation to analyze future changes in extreme weather events addressing climate warming levels of 1°C, 2°C, and 3°C, respectively. A simple scaling with the annual mean global mean temperature change modeled by the driving GCM is applied. The annual minimum of daily minimum temperature (TNN) is found to increase more compared to the annual maximum of daily maximum temperature (TXX) at the end of the century. We also identify the emergence of the scaled patterns of minimum and maximum temperatures and wet and dry persisting conditions about certain extreme weather indices. The emergence of the scaled patterns of TNN occurs from around 2040, whereas TXX pattern is emerging around 2050. Individual GCM-RCM pairs tend to have stable spatial patterns since then for both indices. The ensemble mean patterns are emerging earlier than the individual models.

Acknowledgments 
This study was funded by the Scientific and Technological Research Council of Turkey (TUBITAK) ARDEB 3501 Grant No 121Y587.

How to cite: Ozturk, T., Canbaz, E., Bilgin, B., Matte, D., Kurnaz, M. L., and Christensen, J. H.: The emergence of projected scaled patterns of extreme weather events over Europe, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-15038, https://doi.org/10.5194/egusphere-egu23-15038, 2023.

The global climate change resulting from natural and growing anthropogenic factors of particular importance for the territory of Georgia as the frequency and intensity of extreme weather events (extreme high temperatures, heavy precipitation levels, and agricultural and ecological droughts) are increasing in the territory. Georgia’s complex orography and proximity to the Black and Caspian Seas necessitates the use of high-resolution models, such as regional climate models, to assess future climate change hazards. In this study, we analyse the output from high-resolution simulation of mean and extreme precipitation and temperature using the Abdus Salam International Centre for Theoretical Physics Regional Climate Model version 4.7.1 for the period of 2010–1014 as an initial assessment of model performance for the territory. The simulation is performed at a 12 km horizontal grid spacing using ERA5 data as boundary conditions. Comparison with observed station data shows that the model performs better in simulating the monthly mean and extreme values of temperature than precipitation. In some mountain stations, the biases between observation and simulated precipitation are high, partly due to the mountainous terrain, when the horizontal resolution of the model (12 km) can lead to a significant discrepancy between the model's points and the locations of weather stations.  

This study represents the first step of Georgia’s first high resolution assessment to better understand how climate change will impact the territory required to climate change policy and decision-making.

This work was supported by Shota Rustaveli National Science Foundation of Georgia (SRNSFG) № FR-19-8110.

How to cite: Elizbarashvili, M., Mikuchadze, G., Kalmár, T., and Pal, J.: Comparison of Regional Climate Model Simulations to Observational Data for Georgia, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-3828, https://doi.org/10.5194/egusphere-egu23-3828, 2023.

The Mediterranean basin is well recognized as one of the main climate change hotspots; besides, this region is one the most active cyclogenetic area of the Northern Hemisphere with a large number of intense cyclones occurring every year mainly during winter and fall. The climatology of Mediterranean cyclones has been deeply investigated in the past years, leading to a high agreement on the tracks density, seasonal cycle and favorite locations of cyclogenesis. Nevertheless, open questions still remain on the future evolution of Mediterranean cyclogenesis and associated impacts. Mediterranean cyclones typically present weaker intensities, smaller sizes and shorter lifetimes than tropical cyclones or other mid-latitude cyclones that develop over open oceans. However, they are often responsible for extreme precipitation and wind events leading to severe socio-economic and environmental impacts especially over densely populated regions and coastal areas. Thus, studying the feedbacks of air-sea interactions on Mediterranean cyclones will bring to a better understanding of both the contribution of cyclones to the variability in and extremes of the regional climate and the impacts on the marine ecosystems as well as the associated risks in maritime transportation and coastal structures.

This study aims to investigate the added values of the ocean-atmosphere coupling in regional climate models in reproducing Mediterranean cyclones. To this end, two simulations are performed using the ENEA-REG regional earth system model at 12 km over the Med-CORDEX domain. The first experiment uses the mesoscale WRF model with prescribed Sea Surface Temperature (SST), while in the second WRF is coupled to the MITgcm ocean model. Different tracking methods, based on sea level pressure, are used to account for the uncertainties linked with mathematical and physical definitions of cyclone itself. The simulations are validated against ERA5 reanalysis dataset in terms of their ability to reproduce the Mediterranean cyclone climatology as well as to represent sub-daily fields linked to the cyclones (e.g. SST, heat fluxes, wind, precipitation). Here we show how the coupling of the atmosphere with an interactive ocean model affects the number of cyclones along with their intensity and duration.

How to cite: Chericoni, M., Fosser, G., and Anav, A.: Impact of the Ocean-Atmosphere coupling on extratropical cyclones around the Mediterranean basin, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-1616, https://doi.org/10.5194/egusphere-egu23-1616, 2023.

Urban areas will not be spared from the effect of climate change and will face more and more extreme weather events (e.g., heatwaves and floods). Such changes already cause severe environmental issues, economics damages, and many casualties, especially in cities where most activities and populations are concentrated. Evaluating the impacts of future extreme events on urban areas is a major challenge to prepare the adaptation.

Urban areas are very complex systems that require specific modeling tools. The latest advances in regional climate modeling allow simulations to be performed over longer time periods with finer horizontal resolutions of up to few kilometers. The scientific community emphasizes the considerable improvements in using Convection-Permitting Models (CPM), especially for the representation of small-scale phenomena. Also, CPMs offer an interesting modeling framework for studying the interaction between regional climate and urban effects (especially urban heat island) and city-scale impacts through the explicit coupling of the atmospheric climate model with a dedicated urban surface model.

In this study, the CNRM-AROME CPM, coupled with the Town Energy Balance (TEB) urban-canopy model, is used at 2.5-km horizontal resolution. Climate simulations were performed on an extended France domain (northwestern Europe) as part of the EUropean Climate Prediction system (EUCP) project over an historical period (1986-2005) and two future periods (mid-term, 2041-2050 and long-term, 2080-2099) using the RCP8.5 emission scenario. Here, scientific objectives are (1) to evaluate the urban heat island evolution for some metropolitan French cities, and (2) to quantify the evolution of specific meteorological hazards on cities and population. With this aim, we selected indicators related to heatwaves (based on Ouzeau et al. (2016) methodology) and heavy precipitation event, considered as some of the most relevant extreme meteorological events. These indicators were tested and evaluated on the evaluation simulation, and then analyzed in a changing climate to quantify the impacts on the cities. The multi-city approach makes it possible to investigate the variability that can exist between cities depending on their geographical and climatic contexts.

How to cite: Michau, Y., Lemonsu, A., Lucas-Picher, P., and Caillaud, C.: Projecting the evolution of the urban climate of metropolitan French cities using a convection-permitting model, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-16395, https://doi.org/10.5194/egusphere-egu23-16395, 2023.

An important field of application of climate classifications is the study of climate change. Feddema’s climate classification scheme, which is a simplified water-budget based Thornthwaite-type method set out to better define climate parameters to interpret climate change processes for both research and educational purposes. However, pre-defined classes can limit the analysis. For example, grid points with values of the variables required for climate classification close to the climate boundaries can switch to a value belonging to another climate type interval without significant change in the climate variables, and similarly, a significant change is not necessarily will change the category. Feddema's method is suitable not only for determining the direction and extent of expected shifts in the climate and seasonality type changes, but also through the shift of indices expressing annual and seasonal characteristics: annual heat and water availability, seasonal variability, and the seasonally changing variable.

In this study, projected annual and seasonal changes of the Larger Carpathian Region (LCR)'s climate during the 21^st century are analysed based on the Feddema-continuous approach. Observational data are taken from the CarpatClim which is the best open access dataset for the region. Projected changes are estimated using an RCA4-EC-EARTH simulation with EUR-11 resolution, following the RCP8.5 scenario from EURO-CORDEX.  According to the results, the Feddema-continuous method allows to show the magnitude and direction of changes of an area classified in a specific climate or seasonality type based on the Feddema-indices determined per grid point. Moreover, it is also presented how the newly occurred warm thermal-category developed according to the changes in the water-budget. The method also allows to determine the trajectory of changes in different climate characteristics during the 21^st century.

How to cite: Szabó, A. I., Breuer, H., Belda, M., and Ács, F.: Studying the process of climate change using the continuous form of the Feddema climate classification, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-8847, https://doi.org/10.5194/egusphere-egu23-8847, 2023.

Weather and climate strongly influence the hydrology of natural and managed river basins. Changes in these forcings affect the water resources and hydrological extremes, i.e., floods and droughts, in time and space. Attributing the effect of climatic drivers on present and future runoff allows to understand the hydrological response of river basin to changing climate at the local and regional scale. Here, we investigated the runoff changes, including hydrological extremes, across Europe in the early (2010-2040), mid (2041-2070) and late (2070-2099) century. We used runoff simulations from the E-HYPE hydrological model and the bias-adjusted EURO-CORDEX climate model projections. The sensitivity of runoff changes to the climatic factors (precipitation and evapotranspiration) compared to the reference period (1981-2000) was evaluated with the climatic elasticity method through a Budyko approach. In addition, to address lack of robustness in our insights, we assessed the spatial consistency and uncertainty of the runoff changes due to the ensemble variability. Results showed that the sensitivity of runoff changes to climate change varies depending on the climatic gradient and basin physiographic properties. These results are a step towards enhanced hydro-climate services that allow attribution of (extreme) events to climate change.

How to cite: Clemenzi, I., Du, Y., and Pechlivanidis, I.: Attributing runoff changes to present and future climate projections across Europe, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-14063, https://doi.org/10.5194/egusphere-egu23-14063, 2023.

Reindeer herding is a culturally and economically significant livelihood of local communities in the circumpolar North, strongly depending on environmental conditions. Providing climate information for such a target group requires brining together local knowledge and climate model projections.

In this study, information collected in interviews with reindeer herders on what makes a year good or bad for them (critical conditions) was used as a basis for defining indices that can be calculated from climate model projections. In this process, we associated the critical condition to meteorological variables, for example “temperatures above 20°C in June and July” were related to an index tasmax20, which counts the number of days in June and July with daily maximum temperatures above 20°C. In this way, we identified three types of critical conditions/indices (1) indices that can be calculated relatively easily, some of them conforming to climate extreme indices common in climate change analysis; (2) indices that need either more specific information from herders (eg to make a condition “July and August should not be cold”, a specific definition of “cold” is needed) or scientific background knowledge (e.g. “abundance of mosquitos” needs information on the necessary meteorological variables and their thresholds) so they can be calculated; and (3) indices that cannot be calculated from the output climate models commonly provide, because they require for example variables like daily soil temperature (rain on frozen ground) or river ice.

We have focussed our analysis on Fennoscandia and northwestern Russia, using data from the CMIP6, EURO CORDEX and Polar CORDEX data bases to calculate in a first step indices from category (1) for different RCP futures. For example, exposure multiplication factors reveal that for tasmax20, increases by the end of the century are up to a factor of 10 for RCP4.5 and a factor of 20 for RCP8.5, with obvious latitudinal patterns.

How to cite: Matthes, H., Habeck, J. O., Laptander, R., Komu, T., Istomin, K., Horstkotte, T., Tømmervik, H., Rasmus, S., Eronen, J. T., and Forbes, B. C.: Using climate model projections to provide relevant climate information to Arctic reindeer herding communities, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-14434, https://doi.org/10.5194/egusphere-egu23-14434, 2023.

Global climate models (GCMs) represent invaluable instrument for various purposes, most prominently studying climate system dynamics, evolution of past climates and climate change projections. The newest set of GCM simulations has been produced under CMIP6 initiative coordinated by the the World Climate Research Programme’s (WCRP) Working Group on Coupled Modelling. Due to their coarse spatial resolution, some kind of downscaling is necessary for applications of GCM outputs on regional and local scales, with dynamical downscaling using regional climate models (RCMs) being a common solution of this issue. Clearly, the outputs of RCMs are influenced by the boundary conditions provided at the lateral boundaries of the integration domain. The magnitude of this influence is subject of ongoing research and depends on various aspects including the geographical region, temporal scale, climatic variable etc. Nevertheless, previous studies proved that an analysis of boundary conditions is needed for for proper RCM evaluation. Especially with regard to potential error propagation.

              In present study we evaluate the simulation of CNRM-ESM2-1, one of the CMIP6 GCMs, and compare it to other CMIP6 ensemble members. The CNRM-ESM2-1 is being used as driving model for convective-permitting simulation of Aladin-CLIMATE RCM within Czech national project PERUN aimed at creation of updated climate change scenarios for the Czech Republic. We concentrate on historical GCM simulations in the period of 1990-2014, and use reanalysis ERA5 as the reference. The analysis is conducted over the boundaries of Aladin-CLIMATE/CZ intergration domain, which covers Central Europe. The deviations of the driving GCM (CNRM-ESM2-1) from reanalysis are compared to other CMIP6 GCMs. We also evaluate uncertainty arising from natural climate variability using perturbed initial conditions ensemble of CNRM-ESM2-1.

How to cite: Holtanova, E., Belda, M., and Halenka, T.: Evaluation of CMIP6 GCMs: the perspective of RCM boundary conditions, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-11741, https://doi.org/10.5194/egusphere-egu23-11741, 2023.

Current climate change is characterised by a growing number of extreme weather events and their impacts, which can have a significant impact on energy production, transmission and supply systems, with the potential for severe damage to assets and permanent or intermittent supply disruptions. In order to identify the likely regional climate impacts on specific elements of solar power generation systems, a complex analysis of the expected changes, the sensitivity of infrastructure and the possibilities to adapt to these changes is needed.

Accordingly, one of the objectives of the research is to identify and characterize the climatological information and impacts that may pose a risk to solar energy generation facilities in the context of climate change. We investigate the role and significance of extreme weather events and their long-term projected changes for solar power systems in climate impact assessments. The changes of relevant meteorological events due to climate change and their impact on solar energy systems will be evaluated for Hungary based on data from regional climate model simulations available in EURO-CORDEX.

How to cite: Fejes, L., Czira, T., Pongrácz, R., Molnár, S., and Talamon, A.: Examination of the effects of extreme weather events on solar power systems, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-13648, https://doi.org/10.5194/egusphere-egu23-13648, 2023.

The convection permitting (CP) version of Regional Climate Model (RegCM) has been applied to investigate extreme events in southeastern South America in the context of CORDEX collaborative network Flagship Pilot Studies. In this context, we developed a long-term RegCM simulation (from June 2018 to June 2021) at CP resolution (4 km of horizontal grid spacing). RegCM simulation was forced by ERA5 reanalysis and its domain extends from center to southeast (from ~ 15o to 35oS) of South America. In this assessment, simulated daily climatology was compared with both ERA5 reanalysis and with a great number of local stations. These comparisons will help us to understand if CP simulation aggregates relevant information to the ERA5 reanalysis in reproducing local aspects of the climate.  Using the winds from stations,  the ability of CP simulation in reproducing the local circulations, such as sea-breeze, is also investigated. We also discuss how well long-term CP simulation captures long dry and wet periods that were observed in southeastern South America.

How to cite: Porfírio da Rocha, R., Silva, L., Bettolli, M. L., Reboita, M., and Llopart, M.: Assessing long-term climatology of convection permitting simulation of precipitation and associated local circulation over southeastern South America, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-16270, https://doi.org/10.5194/egusphere-egu23-16270, 2023.

The selection of the Global Climate Models (GCMs) needed to provide the initial and boundary conditions for the Regional Climate Model (RCM) downscaling is the first research question to answer for each RCM simulation in a given domain. The methodology is not unique and depends on the second research question that is typically behind any study based on dynamical downscaling, which is the physical process we want to study and which are the mechanisms that are relevant for it, or in a more general way which is the storyline of large-scale circulation we are interested in. In the South America region there are several large-scale circulation features that are relevant to describe the climate of this region together with the specific geographical characteristics and local scale processes and their interaction. In this work we will focus on two different large-scale circulation features: the South America Monsoon and the extratropical cyclones. For each of the two a criterion needs to be established to select which CMIP6 ensemble is more suitable for one or the other or for both, and to represent the uncertainty in their future evolution. To this aim we have defined and used a series of indicators derived from the comparison between the observation and the model and based on precipitation in specific regions of interest like La Plata and Amazon basin, and Southeastern Brazil region. A second set of indicators were based on the mean sea level pressure and wind at two different levels (850 hPa and 200 hPa) aimed at characterizing the main large scale circulation patterns (South American Low-Level jets, South Atlantic Subtropical High, South Pacific Subtropical High, trade winds, Bolivian High, cyclonic circulation vortex and upper-level jet stream). By mean of these indicators a sub set of CMIP6 models has been identified for each of the large-scale circulation features and the fitness for purpose of the whole ensemble evaluated. It is common to use different model climate sensitivities to represent the uncertainty in future evolution. Here we take a different approach and represent the uncertainty in terms of the influence of a small set of large-scale drivers, including both tropical and polar regions, which are known to affect the local circulation feature of interest and which optimally span the range of local circulation response uncertainty. The uncertainty in climate sensitivity is handled by expressing these dynamical storylines of change in terms of global warming levels. In this way, a small set of CMIP6 models can be chosen which is both fit for purpose and which avoids missing plausible worst-case scenarios for regional climate risk.

How to cite: Andrade Cardoso, A., Coppola, E., Mindlin, J., and G. Shepherd, T.: A storyline approach to select the CMIP6 model ensemble to be downscaled for the South America domain, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-15548, https://doi.org/10.5194/egusphere-egu23-15548, 2023.

The latest advances in climate change science were recently summarised in the sixth assessment report of the IPCC, including the contribution of CMIP6 models to understand changes in future climate. The CMIP6 is composed of hundreds of simulations, where the same model can have a few dozen different realizations for a single shared socio-economic pathway (SSP). This wealth of environmental data can be challenging for end-users interested in selecting ensemble runs to perform downscaling and impact assessments. Here, we assess the performance of the CMIP6 historical ensemble runs against observational data (Australian Water Availability Project – AWAP) and reanalysis (ERA-Interim) using a combination of metrics such as the Kling Gupta efficiency (KGE) over Australian regions. The assessment was based on precipitation, minimum and maximum temperature and sea surface temperature for the period 1995-2014, accounting for seasonal, monthly and daily time steps. We also assessed the climate change signal for precipitation and temperature for mid-century and end-of-the-century and developed an algorithm to automatically select the best-ranked ensemble runs and represent the spread in the climate change signal – that is the Skill Spread Selection. The results are presented as a performance score ranging from 0 to 100 which can be used to rank and select ensemble runs with distinct future climate signals. The analysis has great potential to inform scientists and practitioners on the strengths and limitations of individual ensemble runs and offers a robust and practical solution for the selection of CMIP6 realizations.

How to cite: Trancoso, R., Syktus, J., Toombs, N., and Chapman, S.: Assessing and selecting CMIP6 GCMs ensemble runs based on their ability to represent historical climate and future climate change signal, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-11412, https://doi.org/10.5194/egusphere-egu23-11412, 2023.

High-resolution climate change projections are increasingly required to inform climate policy and adaptation planning. Downscaling of global climate models (GCMs) is required to simulate the climate at the spatial scale relevant for local impacts. Here, we dynamically downscaled 15 CMIP6 GCMs to a 10 km resolution over Australia using CCAM (Conformal Cubic Atmospheric model) for SSPs 126, 245 and 370. We compared the host CMIP6 models and downscaled simulations to the AGCD observational dataset, and evaluated performance using Kling-Gupta efficiency, and the Perkins skill score. The new added value index was derived by asssing the daily, monthly (annual cycle and amplitude) and seasonal climate for 1981-2100 period by comparing combined skill of CMIP6 host models and CCAM downscaled simulations. In addition to assesing the Perkins score for entire PDF,  the 5 and 95 percentlile for mean, minimum and maximum teperatures and fraction of dry days and 95 percentile of precipitation were considered. The combined skill score index/added value of downscaling was normalised and relative skill score of individual models can be compared for major IPCC regions and local gavernment planninga areas in Australia. 

Downscaling CMIP6 models improved performance for seasonal  and annual cycles for temperature (10% and 6%) and precipitation (43% and 13%). CCAM downscaling also improved the fraction of dry days, reducing the bias for too many low rain days by nearly half. The largest improvements were found in extremes, with improvements to extreme minimum temperatures in all seasons (varying from 142 to 201%), and improvements of 52% to extreme precipitation in Austral winter (JJA) and 47% in summer (DJF). The ensemble average overall skill score improved by 19% with downscaling. Temperature and precipitation biases were also reduced in mountainous and coastal areas. CCAM downscaling therefore adds value to the CMIP6 models,  so this dataset will be a valuable resource for understanding future climate changes in Australia. The new CMIP6 dataset is the largest in term of number of simulations and resolution for Australia and will contributed to CORDEX and Australian Climate Service database. Work is continuing on deriving climate extreme indices such as drought, heatwaves, fire weather, tropical cycliones and convective extrems, incliuding speciifc focus on climate hazards.

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How to cite: Syktus, J., Chapman, S., and Trancoso, R.: Evaluation of dynamically downscaled CMIP6 models using CCAM over Australia. New appraoch to added value of downscaling using information on climate extremes, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-11689, https://doi.org/10.5194/egusphere-egu23-11689, 2023.

New evaluation simulations with the ERA5 boundary conditions have been completed with the new model RegCM5 for all CORDEX-CORE domains at 25 km resolution. Model performances are satisfactory for all evaluation metrics for both the mean climate and extreme climate and for temperature, precipitation and cloud variables. For some of the domains the new model is able to remove some well know biases like the dry bias in the Amazon region in the CAM domain or the warm bias in the La Plata basin for the SAM domain. In other domains the RegCM5 performs consistently with the previous model version.  One simulation at convection permitting resolution (CP) has been completed for the first time for the whole Euro-CORDEX domain thanks to the new semi implicit dynamical core implemented in the RegCM5 that allow the model to remain stable at such resolution even with time steps of 30 seconds at 3 km resolution.   The evaluation of the CP simulation is comparable with the previous model evaluation over the ALPS domain with a tendency to improve both the dry and wet bias in summer and winter respectively. Over the whole Euro-CORDEX domain validation of the sub daily statistic for the precipitation frequency, intensity and diurnal cycle confirm the fitness for purposes of this new model version to run at such resolution for such extended region. A further CP configuration is also tested with two overlapping longitudinal stripe domains covering the Euro-CORDEX domain and the comparison is shown between the two CP simulations. 

How to cite: Coppola, E., Giuliani, G., Emanuela, P., Nogherotto, R., Raffaele, F., Ciarlò, J., Abba Omar, S., Zazulie, N., Lu, C., Vargas, L., and Andrade Cardoso, A.: Validation of RegCM5 at convection parametrized resolution over the CORDEX-CORE domain and at convection permitting resolution over the Euro-CORDEX domain, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-8528, https://doi.org/10.5194/egusphere-egu23-8528, 2023.

Jordan is a Middle Eastern, developing country at significant risk for the adverse effects of human-induced climate change, including drought incidents. The region is highly vulnerable to drought due to being a water-scarce nation that shares limited water resources, the arid and semi-arid climate conditions, and the population explosion owing to the surrounding conflicts. Using regional climate model simulations from the Coordinated Regional Downscaling Experiment (CORDEX) project, a robust study has been developed to analyze current and future drought conditions in Jordan. The country is included in more than one CORDEX domain. This poses the question, as how sensitive is downscaling to the choice of domain? The regional climate models (RCMs) and the global climate models (GCMs) are two other sources of uncertainty. The goal of this first step study is to address the separate contributions of domain selection, downscaling regional models, and forcing global models to simulation uncertainty. This information will direct the best use of CORDEX to produce climate change signals by combining the results from the various CORDEX domains and enhancing the drought analysis. 

 Legasa et al. (2020) and Diez-Sierra et al. (2022) assessed the uncertainty related to the choice of domain as opposed to that of models combining the uncertainty related to RCMs and GCMs. Legasa et al.'s work was conducted over the Mediterranean region. They concluded that the domain selection effect is negligible. 

In this study, seasonal and monthly climatology data of temperature and precipitation variables from MENA (Middle East and North Africa) and Africa CORDEX domains were considered to construct Taylor diagrams and apply the analysis of variance method over Jordan’s region. The results of the two variables show that the domain contributes the least to the variance and is negligible, whereas the GCMs-related uncertainty contributes the most. This result supports the procedure of building a grand ensemble of various model simulations relating to overlapping domains, which will be used to enhance the drought analysis and assist policymakers with planning mitigation and adaptation procedures.

References:

Legasa, M.N., et al. (2020) Assessing multidomain overlaps and grand ensemble generation in CORDEX regional projections. Geophys. Res. Lett. 47, https://doi.org/10.1029/2019GL086799

Diez-Sierra, J., Iturbide, M., Gutiérrez, J. M., Fernandez, J., Milovac, J., Cofiño, A. S., and Cimadevilla, E.: Assessing the consistency of CORDEX multidomain projections in overlapping regions worldwide, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6059, https://doi.org/10.5194/egusphere-egu22-6059, 2022

How to cite: Alkhasoneh, H. and Rowe, C.: Assessing the separate contribution of the domain, RCM, and GCM to the uncertainty in CORDEX simulations over the overlapped regions that include Jordan, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-10668, https://doi.org/10.5194/egusphere-egu23-10668, 2023.

East Asian Winter Monsoon (EAWM) is an important system, which has impact on extreme winter phenomena, in East Asia including the Korean Peninsula. EAWM is characterized by the Siberian high with cold air and the Aleutian low with warm air, low-level northerly wind between them, East Asian trough in the middle troposphere, and East Asian jet stream in the upper troposphere. According to some previous studies, the variability of EAWM, which can be influenced by human activity and climate change, is strongly correlated with the occurrence of cold waves in East Asia. Additionally, EAWM variability has been shown to affect the precipitation during winter on the Korean Peninsula. Extreme boreal winter phenomena such as cold waves and heavy snow related to EAWM can induce disruptions to regular life and socio-economic damage. Regional climate models (RCMs) have added value in the simulations of extreme climate phenomena, but also have internal variability, which can increase uncertainty in future climate projections. Therefore, in this study, to closely understand EAWM and reduce the uncertainty in future climate projection, the performance of two RCMs (SNURCM and WRF) for EAWM simulation was evaluated. The results showed that two RCMs had the ability to capture the interannual variability and climatological spatial pattern of EAWM despite the systematic biases of EAWM in the lower troposphere (e.g., comparatively low correlation and larger temporal-spatial variability than reanalysis). Additionally, two RCMs presented significant cold bias over the Manchuria and wet bias over the Kuroshio Current. In further analysis, their systematic errors were positively correlated with EAWM, which had a larger pressure gradient between the Siberian high and the Aleutian low and colder advection over Manchuria than those of the ERA5. Wet biases over the Kuroshio Current could be related to unreasonable simulation EAWM induced by uncoupled air-sea interaction in RCMs.

How to cite: Shin, S.-W., Lee, M., Park, C.-Y., Kim, E., Mun, T., Juzbašić, A., and Cha, D.-H.: Systematic errors in regional climate simulation of East Asian winter monsoon, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-15005, https://doi.org/10.5194/egusphere-egu23-15005, 2023.

Tropical cyclones (TCs), which often form over the western North Pacific (WNP), have a large socioeconomic impact and result in destructive damages in East Asian countries. Therefore, it is critical to estimate future changes in TCs characteristics under the global warming. In this study, future characteristics of TCs using five regional climate models (RCMs) (i.g., RegCM4, GRIMs, WRF, CCLM, and HadGEM3-RA) were investigated in Coordinated Regional Climate Downscaling Experiment (CORDEX) East Asia domain. Five RCMs were forced by the UK Earth System Model (UKESM) under the historical and two Shared Socioeconomic Pathways (SSP) scenarios (SSP1-2.6 and SSP5-8.5). The simulation experiments were conducted at about 25-km horizontal resolution. The multi-RCM ensemble mean was used for analysis to reduce the uncertainty of a single RCM. In the historical period (1985-2014) during the TC season (June-November), the ensembled RCMs properly reproduced the TC genesis over the WNP comparable to the best track data. In the future climate, the RCMs tended to migrate the core region of TCs genesis more northward compared in the present climate. This migration could be related to weakening of vertical wind shear over the mid-latitudes, due to decreased meridional sea surface temperature gradient.

How to cite: Kim, E., Kim, T., Mun, T., Shin, S.-W., Lee, M., Cha, D.-H., Chang, E.-C., Ahn, J.-B., Min, S.-K., Kim, J. W., and Byun, Y. H.: Future characteristics of tropical cyclones over CORDEX East Asia domain in Multi-RCMs under the SSP scenarios, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-15733, https://doi.org/10.5194/egusphere-egu23-15733, 2023.

Changes in summer heat extremes have been recorded all over the globe in recent years. East Asia is one of the most vulnerable areas for climate change, owing to a combination of natural and anthropogenic factors. The human experience of heat, however, does not only depend on the temperature itself but rather the combination of factors that regulate the exchange of heat with the environment. The present study uses Net Effective Temperature (NET), an index that combines the effects of temperature, humidity, and wind, to assess heat stress perception and its potential changes by the end of the century over East Asia. The data used in the study are maximums calculated from the three-hourly output of the 10 Coupled General Circulation Model (CGCM) - Regional Climate Model (CGM) chains participating in the Coordinated Regional Climate Downscaling Experiment (CORDEX)-East Asia phase 2. As human beings can acclimate to their environment, not only maximum values but also 95th percentiles of maximum values have been used. The assessment of the models showed that all of the models reproduced the current climate reasonably. The present study utilized two different scenarios, RCP8-5, and SSP5-8.5. In both scenarios, the increase in averages and 95th percentiles of both maximum temperatures and NETs over the whole domain has been projected. The increase in NET was projected to be higher than the increase in temperature itself, and the increase in the SSP5-8.5 scenario was projected to be higher than the increase in the RCP8.5 scenario, but the details of the warming patterns were dependent on the choice of model. Additionally, the Korean Peninsula and Japan have been shown to have the largest difference in the increase between heat stress and temperature, while the highest overall increase of both temperature and NET was projected over the North-Western part of the domain in both scenarios.

How to cite: Juzbasic, A., Cha, D.-H., and Ahn, J.-B.: Future changes in heat stress over CORDEX East Asia phase 2 domain by the end of the century, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-16016, https://doi.org/10.5194/egusphere-egu23-16016, 2023.

In this study, the performance of the output data from the latest high-resolution Coordinated Regional Climate Downscaling Experiment-Coordinated Output for Regional Evaluations (CORDEX-CORE) model simulations is assessed with respect to the corresponding gridded Indian Meteorological Department (IMD) and ERA5 observations in representing the monsoonal precipitation over northeast India (NEI) for the period 1979-2005. Here, in this study, three different RCM model simulations (COSMO, RegCM4.7 and REMO) downscaled over the South Asian CORDEX domain are used. To understand how these RCMs perform over a data scarce region like NEI, various statistical techniques were used to evaluate their performance. Our results show that the COSMO model experiments perform better (moderate bias) with respect to both the observations as compared to other two RCMs. In contrast, the RegCM model experiments show very wet bias, whereas the REMO model experiments show more dry bias across a significant part of the study area. Quantitatively, the COSMO suite of models exhibits a little overestimation (~7 to 13%) in comparison to IMD, while an underestimation (~14 to 18%) is seen with ERA5. However, with respect to both the observations, the monsoon precipitation is overestimated (~15 to 80%) by the RegCM model and is underestimated (~15 to 50%) by the REMO model experiments. Overall, the output data from the CORDEX-CORE model experiments reproduces the monsoon precipitation over the study region but with biases that vary spatially over the study region. Finally, the results from this study offer a thorough assessment of the output data from the CORDEX-CORE model experiments over the largely unexplored NEI and hence can be considered a good tool to study climatological processes over the data-scare regions.

How to cite: Ahamed, M. R. A., Sharma, A., Wani, J. M., and Dimri, A. P.: The representation of summer monsoon precipitation over northeast India: Assessing the performance of CORDEX-CORE model experiments, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-607, https://doi.org/10.5194/egusphere-egu23-607, 2023.

It has been found that large lakes, a common component of the North American (NA) landscape, can affect the water cycle and modulate temperatures in the surrounding regions. The purpose of this study is to evaluate different lake representations in regional climate model simulations. We use the Weather Research and Forecasting (WRF) model, a widely-used regional climate model, forced by the ERA5 reanalysis product. The study is performed for a 40 year historic period (1979-2019) at a resolution of 12 km. The lakes of concern include the Laurentian Great Lakes, which straddle the US-Canada border; the Great Slave and Great Bear Lakes of the Northwest Territories; and the Lakes Winnipeg and Winnipegosis. Alongside the default lake model, two new column lake models are employed: FLake, a more widely used model, and GL25, a recent, physics-based model. These models have been somewhat successful at alleviating inadequacies of the default model by introducing additional process representations, such as a more realistic surface albedo formulation and a better parameterization for vertical overturning. Additionally, we consider the effect of vertical eddy diffusivity and lake stratification on the model performance. While no column lake model is expected to perform perfectly, our goal here is to identify when (seasonally) and where (geographically) a model produces reliable results, including ice cover distribution and near-surface temperature. The effect of the lake representation on the surrounding regions (e.g., lake-effect precipitation) is also evaluated. We find that the two new lake models perform reasonably well, but there are significant differences in seasonal biases with GL25 performing better in summer and FLake performing better in winter; in fact, FLake reproduces the ice cover of the Great Lakes very well. Furthermore, the biases do not seem to be affected by surface wind induced circulation, and hence 3D modelling may not be a requirement for lake modelling. This study can help with the selection of lake models for regional climate modelling in the NA region and inform the interpretation of the predictions.

How to cite: Mahdinia, M., Erler, A., Huo, Y., and Peltier, W. R.: The representation of large lakes in high-resolution regional climate models, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-9292, https://doi.org/10.5194/egusphere-egu23-9292, 2023.