A novel runtime empirical bias correction (EBC) has recently been developed and applied to enhance the Canadian Center for Climate Modelling and Analysis' (CCCma) global earth system model CanESM, demonstrating significant improvements in future climate projections, particularly under strong climate change scenarios. The application of EBC to CanESM provides enhanced driving data for dynamical downscaling through regional climate models (RCMs).

This project aims to assess the impact of the improved EBC driving data on two RCMs, namely CanRCM5 (CCCma) and CRCM5 (Ouranos), in order to evaluate the systematic improvement of meteorological variables. Multiple 10-member ensembles are utilized to investigate the added value of employing EBC in driving the RCM simulations. The ensembles consist of three sets: the first set utilizes the original CanESM5 as driving data, the second set incorporates EBC on sea surface temperature (SST) and sea ice concentration (SIC) using the original CanESM5, and the third set employs bias-corrected atmosphere, SST, and SIC data. All three ensembles are compared against ERA5 data as a reference for the historical period.

Results indicate a clear advantage of using EBC, particularly in cases where the initial bias is substantial. For instance, significant improvement in modeling key meteorological phenomena, notably the North American monsoon and the northeasters (extratropical cyclones). These improvements can be attributed not only to the refinement in addressing climatological biases in land and ocean data but also to an enhanced representation of cyclonic activities due to a better representation of overall circulation in our region. Ultimately, this research seeks to contribute to the scientific community by providing a methodology to mitigate uncertainties in downscaled projections of future climate change through the utilization of EBC.

How to cite: Labonté, M.-P., Matte, D., Scinocca, J., Kharin, S., Leduc, M., and Paquin, D.: Enhanced Driving Data for Regional Climate Models: Investigating the Systematic Improvements with GCM Run-time Empirical Bias Correction, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10824, https://doi.org/10.5194/egusphere-egu24-10824, 2024.

Our warming climate enhances both the frequency and intensity of weather extremes. To enable adequate mitigation and adaptation decision-making and planning,  accurate long-term climate projections across scales are essential. Europe has warmed faster than any other World Meteorological Organization region in the last decades, yet a vast majority of RCM simulations does not capture the strong observed temperature rise. This discrepancy is in part related to the widespread use of constant aerosol representations in RCMs, and emerges most clearly during summer, i.e. the period of strong insolation. Thereby it also affects (changes in) heat extremes even more strongly than the mean warming. This warming mismatch is, crucially, not restricted to the past but also affects climate projections. Several European national climate services of several European countries still rely on these simulations and solutions are required.

Here, we present a novel method to adjust the large-scale warming in RCM simulations based on a reference such as observations or other model simulations. In particular, we re-assemble RCM simulations to match the long-term annual mean temperature evolution over Western Europe in state-of-the-art GCMs, which show less (or no) underestimation of the observed summer warming than the RCMs. We demonstrate that our approach preserves the high-resolution information provided by the RCMs, but ensures consistency with respect to both historic as well as projected large-scale warming. It employs existing regional climate information without the use of interpolation methods, and, as the re-assembling is performed based solely on yearly average temperatures, ensures consistency among different climate variables. We show how correcting European warming affects projections of both the mean state as well as weather extremes at the national level, and illustrate results for Switzerland, a small country characterized by complex orography. 

How to cite: Börsig, S., Schumacher, D. L., Hauser, M., Fischer, E., and Seneviratne, S. I.: Bias-adjusting for underestimated large-scale European warming in regional climate model simulations and implications for future extremes, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18186, https://doi.org/10.5194/egusphere-egu24-18186, 2024.

Northern Italy is an area located in the northern part of the Mediterranean region where several factors make the modeling of its climate dynamics particularly challenging. The area is characterized by significant environmental gradients due to the complexity and high orography of  the Alpine arc surrounding the relatively flat area of the Po Valley, while strong air-sea interactions and deep water formation processes in the northern Adriatic Sea are associated with Bora wind episodes flowing over the basin. Moreover, the peculiar West-East oriented topography of the region drives the formation of a complex river network including the Po river, which is one of the major freshwater sources of the Mediterranean Sea. 

Here we assess the performances of a state-of-the-art convection-permitting configuration of the Regional Earth System Model RegCM-ES specifically developed for the northern Italy region. The modeling tool components are : (i) RegCM5  with an horizontal resolution of 3 km and 50 vertical levels for the atmosphere, (ii) MITgcm  with an horizontal resolution of approximately 700 m and 59 vertical levels (non hydrostatic) for the ocean and (iii) CHyM with an horizontal resolution of approximately 1 km for the rivers.

Model performances have been evaluated against observations and reanalysis datasets (available for the region) in a numerical experiment driven at the boundaries by ERA5 for the atmosphere and by the Copernicus Marine Service (CMS) reanalysis for the ocean. The model well captures  the spatial gradients and mean values of land and seawater temperature, precipitation and sea surface salinity. Still some deficiencies are observed such as a warm bias in summer over the western part of the Po Valley and an overestimation of precipitation over the Alps, although the latter is likely linked to the poor coverage of high altitude measurement stations in the area. 

This configuration will be adopted in the future to produce high resolution climate projections for the region.

How to cite: Reale, M., Giuliani, G., Pichelli, E., Garcia-Valdecasas Ojeda, M., Giordano, F., Coppola, E., Querin, S., and Salon, S.: Challenging the complexity: assessing the performances of the convection-permitting configuration of the Regional Earth System Model RegCM-ES over northern Italy, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17308, https://doi.org/10.5194/egusphere-egu24-17308, 2024.

Synoptic-scale cyclones in the southwestern South Atlantic Ocean often result in heavy rainfall, strong winds, sudden temperature drops, and other abrupt changes that affect important metropolitan areas along the south-southeastern coast of Brazil. Despite notable advancements in understanding these systems, numerical simulations continue to face difficulties in accurately reproducing cyclone-induced rainfall and intense winds. Therefore, the aim of this study is to assess the ability of convection-permitting scale simulations (CP) with a spatial resolution of 3 km, to reproduce the mesoscale structures associated with two extratropical cyclones that caused multiple fatalities in the southern Brazil in June and July of 2023. Regional Climate Model (RegCM) version 5 is configured in CP mode and runned continuously from May to July 2023 in a large domain (lon: -81.12 to -33.09; lat: -48.45 to -10.47). The first month is considered a spin-up period. Precipitation simulated in CP mode is compared with locally observed data and with satellite estimates. The comparison with observations indicates that the resolution refinement and the use of cloud microphysics in CP simulation develop many of the cyclone mesoscale structures generating intense precipitation events. The authors thank CNPq, FAPESP and FAPEMIG for their financial support.

How to cite: Reboita, M., da Rocha, R., da Silva, L., Coppola, E., and Raffaele, F.: The impact of convection-permitting simulation on the mesoscale structures of two severe synoptic-scale cyclones on the south-southeast coast of Brazil in 2023 austral winter, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5801, https://doi.org/10.5194/egusphere-egu24-5801, 2024.

Southeastern South America (SESA) stands out as a hotspot on the planet where weather and climate extremes occur with notable frequency, exerting negative impacts on various socio-economic activities. One cause of these extremes are the synoptic-scale cyclones. In this sense, the objective of this study is to evaluate the performance of a convection-permitting scale simulation (CP) with Regional Climate Model version 5 (RegCM5) in simulating the cyclone features (frequency, intensity, lifetime, preferential regions, precipitation, etc.) in the SESA region from January 2018 to December 2021. The CP simulation was driven by ERA5 reanalysis with 3.0 km of horizontal grid spacing in a domain covering from ~ 11°S to 35°S. Cyclones are identified with a tracking algorithm based on relative vorticity at 925 hPa. The cyclone features in the CP simulation are compared with that from ERA5. The mesoscale structure of the simulated precipitation associated with cyclones is compared with satellite estimates. Overall, CP simulation captured the main observed features associated with cyclones.

How to cite: da Silva, M. L., Coppola, E., Reboita, M. S., da Rocha, R. P., and Raffaele, F.: The role of convection-permitting RegCM5 simulations in representing synoptic-scale cyclones over Southeastern South America, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5482, https://doi.org/10.5194/egusphere-egu24-5482, 2024.

Cities play a fundamental role on climate at local to regional scales through modification of heat and moisture fluxes, as well as affecting local atmospheric chemistry and composition, alongside air-pollution dispersion. Vice versa, regional climate change impacts urban areas and is expected to increasingly affect cities and their citizens in the upcoming decades. Indeed, the share of the population living in urban areas is growing, and is projected to reach about 70 % of the world population up to 2050. Urban impact is especially critical in connection to extreme events, for instance heat waves with extremely high temperature exacerbated by the urban heat island effect, in particular during night-time, with significant consequences for human health. Additionally, from the perspective of recent regional climate model development with increasing resolution down to the city scale, proper parameterization of urban processes plays an important role to understand local/regional climate change.  

The inclusion of the individual urban processes affecting energy balance and transport (i.e. heat, humidity, momentum fluxes, emissions) via special urban land-surface interaction parameterization of distinct local processes becomes vital to simulate the urban effects properly. This will enable improved assessment of climate change impacts in the cities and inform adaptation and/or mitigation options by urban decision-makers, as well as adequately prepare for climate related risks (e.g. heat waves, smog conditions etc.). Cities are becoming one of the most vulnerable environments under climate change. Similarly as WCRP, CORDEX community identified cities to be a prime scientific challenge. Therefore, we introduced this topic to the CORDEX platform, within the framework of so-called flagship pilot studies. Main aims of this activity will be presented together with a call for participation in ensemble experiment for selected city following adopted coordinated simulations protocol.

Preliminary results from the analysis of first experiments covering specific case studies, i.e. strong heat wave as an important factor in the urban effects, and strong convective episode to study the convection permitting RCMs performance in urban environment, will be presented. The ensemble of participated teams for selected city – Paris - will be analysed and the first results based on this ensemble will be shown.

How to cite: Halenka, T., Belda, M., Crespo, N., Langendijk, G., and Hoffmann, P.: CORDEX Flagship Pilot Study URB-RCC – Case Studies on Urban Environment Implementation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20302, https://doi.org/10.5194/egusphere-egu24-20302, 2024.

To investigate the effect of climate change on cities and climate adaptation strategies in cities, high-resolution climate model data is needed to resolve urban-rural and intra-urban processes. Due to computational limits, it is currently impossible to create long-term (sub-)kilometric simulations over extended regional domains, such as the EURO-CORDEX domain (Jacob et al., 2020). However, short-term climate simulations at 2.5 km horizontal resolution were performed over Europe with the atmospheric model ALARO which was coupled to the land surface model SURFEX. The model output has been validated against conventional datasets and non-traditional measurements such as those of urban meteorological networks (Caluwaerts et al., 2021). Including non-traditional meteorological measurements is important to verify whether the model captures the urban signature well. Further, a methodology will be presented to obtain climate projections with a more detailed spatial resolution over several European cities. The added value of this new avenue using machine learning to emulate the climatological characteristics from a limited set of cities and generalise this to other cities in the EURO-CORDEX domain will be investigated.

Caluwaerts, S., Top, S., Vergauwen, T., Wauters, G., De Ridder, K., Hamdi, R., Mesuere, B., Van Schaeybroeck, B., Wouters, H. and Termonia, P., 2021. Engaging schools to explore meteorological observational gaps. Bulletin of the American Meteorological Society, 102(6), pp.E1126-E1132.

Jacob, D., Teichmann, C., Sobolowski, S., Katragkou, E., Anders, I., Belda, M., Benestad, R., Boberg, F., Buonomo, E., Cardoso, R.M. and Casanueva, A., 2020. Regional climate downscaling over Europe: perspectives from the EURO-CORDEX community. Regional environmental change, 20, pp.1-20.

How to cite: Top, S., Caluwaerts, S., De Cruz, L., and Hamdi, R.: High-resolution climate model data over EURO-CORDEX with a focus on cities, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3896, https://doi.org/10.5194/egusphere-egu24-3896, 2024.

In the framework of the coordinated regional modeling initiative Med-CORDEX (Coordinated Regional Climate Downscaling Experiment), we present an updated version of the regional Earth System Model ENEA-REG designed to downscale, over the Mediterranean basin, the models used in the Coupled Model Intercomparison Project (CMIP6). The regional ESM includes coupled atmosphere (WRF), ocean (MITgcm), land (Noah-MP, embedded within WRF), and river (HD) components with spatial resolution of 12 km for the atmosphere, 1/12° for the ocean and 0.5° for the river rooting model.

For the present climate, we performed a hindcast (i.e. reanalysis-driven) and a historical simulation (GCM-driven) over the 1980-2014 temporal period. The evaluation shows that the regional ESM reliably reproduces the mean state, spatial and temporal variability of the relevant atmospheric and ocean variables.

In addition, we analyze the future evolution (2015-2100) of the Euro-Mediterranean climate under three different scenarios (SSP1-2.6, SSP2-4.5, SSP5-8.5), focusing on several relevant essential climate variables and climate indicators for impacts. Among others, results highlight how, for the scenarios SSP2-4.5 and SSP5-8.5, the intensity, frequency and duration of marine heat waves continue to increase until the end of the century and anomalies of up to 2°C, which are considered extreme at the beginning of this century, will become the new normal condition of the Mediterranean Sea in less than a hundred years under the SSP5-8.5 scenario.

Overall, our results demonstrate the improvement due to the high-resolution air-sea coupling for the representation of high impact events, such as marine heat waves, and sea-level height.

How to cite: Calmanti, S., Anav, A., Antonelli, M., Carillo, A., Catalano, F., Dell'Aquila, A., Iacono, R., Marullo, S., Napolitano, E., Palma, M., Pisacane, G., Sannino, G., and Struglia, M. V.: Dynamical downscaling of CMIP6 scenarios with ENEA-REG: an impact oriented application for the MED-CORDEX region, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9624, https://doi.org/10.5194/egusphere-egu24-9624, 2024.

An emerging literature has been exploring RCM-GCM differences over Europe and linking many of these differences to the lack (or in a few cases, underestimation) of aerosol “Brightening” in the RCMs (Boe et al, 2019, Gutiérrez et al, 2020, Tarana et al, 2022).  Here we illustrate these differences for mid-century European surface SW, temperature and rainfall projections, with a focus on the spread.  The tendency for the RCMs to underestimate the GCM warming and drying is evident.  For example the RCMs at the lower end show roughly half the warming of the lowest driving CMIP5 GCM and roughly 50% of the RCMs suggest wetter projections that 6 out of 7 driving CMIP5 GCMs.  

We extend this analysis to the wider CORDEX regions and identify where lack of time varying aerosol representation does, and does not matter.   We also use single forcing (AER) CMIP6 experiments, to illustrate how this data can be a useful tool to predict where time varying aerosol representation is (and where it is not) likely to be important in ongoing CMIP6 downscaling.

How to cite: booth, B., Crocker, T., Solis Salas, C., McSweeney, C., and Andrews, T.: Exploring CORDEX and driving GCM differences, in the context of time evolving aerosols , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6495, https://doi.org/10.5194/egusphere-egu24-6495, 2024.

Our study aims to investigate the development of extreme climate indices based on temperature and precipitation over Central Asia using multiple RCMs from CORDEX-CORE. The study area is defined by an overlapping area of the Central Asian (CAS) and the East Asian (EAS) CORDEX domains to increase the number of ensemble members from 4 to 10. This enables us to compare the biases and projections among the ensembles of the different domains as well. We focus on three-time slices of the present (1981-2005), the mid- (2031-2065), and far-future (2071-2095) using the scenario RCP8.5.

For precipitation indices, an increase of consecutive dry days in EAS and a slight to moderate decrease in the northern parts of CAS during the mid-future compared to present values is observed. Consecutive wet days, very heavy precipitation events (R20mm), the maximum one-day precipitation, and very wet days (R95p) are projected to increase in most areas. All indices show a further intensification towards the end of the century over large parts of the domain. For temperature indices, the ensembles project a strong increase over the high mountain regions and southern parts for the consecutive summer days, the heat wave duration index, and the percentage of very hot days (TX90p). Accordingly, the number of consecutive frost days and the percentage of very cold days (TX10p) are projected to decrease. However, some discrepancies in the projected changes prevail among the different RCMs being part of the two CORDEX-domains and in specific landscapes like complex mountain or lake areas.

How to cite: Rai, P., Bangelesa, F., Abel, D., Ziegler, K., Huang, J., Schaffhauser, T., Disse, M., and Paeth, H.: Future projection of extreme precipitation and temperature indices in Central Asia using CORDEX-CORE, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15085, https://doi.org/10.5194/egusphere-egu24-15085, 2024.

We use an ensemble of a regional climate model (RegCM4) projections to assess future changes in surface air temperature, precipitation and Köppen-Trewartha (K-T) climate types in mid-high latitude Northern Asia (NA) under the 1.5-4°C global warming targets. RegCM4 is driven by five CMIP5 global models over an East Asia domain at a grid spacing of 25 km. Validation of the present day (1986-2005) simulations shows that the ensembles of RegCM4 (ensR) and driving GCMs (ensG) reproduce the major characters of the observed temperature, precipitation and K-T climate zones reasonably well. Greater and more realistic spatial detail is found in RegCM4 compared to the driving GCMs. A general warming and overall increases in precipitation are projected over the region, with these changes being more pronounced at higher warming levels. The projected warming by ensR shows different spatial patterns, and is in general lower, compared to ensG in most months of the year, while the percentage increases of precipitation are maximum during the cold months. The future changes in K-T climate zones are characterized by a substantial expansion of Dc (temperature oceanic) and retreat of Ec (sub-arctic continental) over the region, reaching ~20% under the 4°C warming level. The most significant change in climate types in ensR is found over Japan (~60%), followed by Southern Siberia, Mongolia, and the Korea Peninsula (~40%). The largest change in the K-T climate types is found when increasing from 2°C to 3°C.

How to cite: Gao, X., Wu, J., Tang, X., and Giorgi, F.: Projected changes in Köppen-Trewartha climate zones under 1.5–4°C global warming targets over mid-high latitudes of Northern Asia using an ensemble of RegCM4 simulations, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5253, https://doi.org/10.5194/egusphere-egu24-5253, 2024.

Accelerated global warming is anticipated to intensify the frequency and severity of concurrent extremes, leading to negative impacts that surpass those of individual extreme events. Southeastern China has experienced a rise in the occurrence of concurrent hot and dry extremes in recent years, and the expected amplification of such events is likely to worsen economic losses and endanger human well-being. Due to the significant impacts of such concurrent extremes, there is a strong demand for dependable future projections at the local level. However, most studies have focused on univariate analysis of single extremes using coarse-grid global climate models (GCM), which may not fully capture the region-specific climate impacts of global warming. To address this issue, this study will utilize convection-permitting (CP) regional climate modeling and multivariate statistical analysis to evaluate the future changes in concurrent hot and dry extremes. The Weather Research and Forecasting model (WRF) will be used to downscale the bias-corrected CMIP6 GCM projections under the SSP5-8.5 scenario at the convection-permitting scales (4km) over southeastern China. The study aims to investigate the process-based added value of CP projections when assessing future changes in concurrent hot and dry extremes over densely populated regions in China. The high-resolution simulation is expected to enhance the understanding of compound climate extremes and provide quantified insights into prospective climate risks.

[Acknowledgements]

This research was supported by project GRF16308722, which was funded by the Research Grants Council (RGC) of Hong Kong. This research was also partly supported by Korea Environmental Industry & Technology Institute (KEITI) through Water Management Program for Drought Project, funded by Korea Ministry of Environment (MOE) (2022003610003)

How to cite: Zhou, Z., Im, E.-S., and Kwon, H.-H.: Changes in concurrent hot and dry extremes based on convection-permitting projections under the SSP5-8.5 scenario, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3119, https://doi.org/10.5194/egusphere-egu24-3119, 2024.

The impacts of topographic uplift in different areas of the Tibetan Plateau (TP) on the arid climate and dust cycle in the sandy areas on the north and south sides of the plateau are studied using a regional climate model (RCM) by comparing numerical experiments on the uplift of the Pamirs and the northern TP. Simulation results based on tectonic geological records can be used to explain the differences in drought evolution in different areas around the TP. The results show that: (1) The mechanical blocking effect caused by the uplift of the Pamirs has mainly intensified the aridification and desertification of the Taklimakan Desert since the Pliocene, and its uplift has blocked the water vapor channel in the west side of Tarim Basin, causing a 50% reduction in the annual precipitation (mainly winter precipitation) and a 10-30% increase in the atmospheric dust loading in the Taklimakan Deserts. (2) The uplift of the northern Tibetan Plateau mainly intensifies the aridification of the Thar Desert. The uplift of the northern TP controls the position and intensity of the subtropical high center at 700 hPa in the Thar Desert, causing a 50% reduction in summer and annual precipitation and increase in the atmospheric dust loading in the Thar Desert. (3) The aridification of the Gobi Desert and Loess Plateau since the Miocene is also related to the uplift of the northern TP. The uplifting suppresses the East Asian summer monsoon (EASM), causing a 30-50% reduction in summer precipitation in the Gobi Desert and Loess Plateau. Drought further causes a 10-20% increase in the dust loading in the above areas. (4) In view of the limited geological evidence of remarkable tectonic uplift in the northern TP and the Pamirs since the Miocene and Pliocene respectively and based on the current numerical simulation results, it is speculated that the formation of the Thar Desert should be earlier than the Taklimakan Desert.

How to cite: Sun, H. and Liu, X.: Impacts of the uplift of the Pamirs and northern Tibetan Plateau on the aridification of the Taklimakan and Thar deserts since the Miocene, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2880, https://doi.org/10.5194/egusphere-egu24-2880, 2024.

In South Korea, daily weather forecasts are currently limited to a 10-day range, which is insufficient for adequately preparing weather-dependent sectors like agriculture for the impact of meteorological conditions over longer periods. To meet the growing demand for subseasonal-to-seasonal predictions, organizations worldwide provide GCM-driven forecasts that extend several weeks or even months ahead. One example is the Climate Forecast System version 2 (CFSv2) operational forecast by NCEP, which is initialized every 6 hours and extends up to 9 months. Its large pool of available forecast members with different initialization times enables the construction of a time-lagged ensemble, which can improve forecasting accuracy by offering a range of potential future meteorological conditions and accounting for the inherent uncertainty in a single deterministic forecast. In this regard, this study aims to build an optimal time-lagged ensemble for 1-month forecasts in South Korea by employing a systematic method to select suitable members from a pool of hundreds of CFSv2 forecast members. Furthermore, the selected ensemble members will be dynamically downscaled to address limitations arising from their coarse resolution. The integration of the time-lagged ensemble and dynamical downscaling methods will be evaluated from both quantitative and qualitative perspectives to assess their added value in enhancing forecasts. By evaluating the performance of the optimized time-lagged ensemble combined with dynamical downscaling, this study will provide valuable insights into the improvement and practical application of subseasonal-to-seasonal forecasts in South Korea, benefiting various sectors that can leverage the enhanced long-term weather predictions.

Acknowledgments

This study was carried out with the support of “Research Program for Agricultural Science & Technology Development (Project No. PJ014882)”, National Institute of Agricultural Sciences, Rural Development Administration, Republic of Korea.

How to cite: Ha, S., Im, E.-S., Hur, J., Jo, S., and Shim, K.-M.: Enhancing 1-Month Forecasts in South Korea with the Combination of Time-Lagged Ensemble and Dynamical Downscaling, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8433, https://doi.org/10.5194/egusphere-egu24-8433, 2024.

As intermittent meteorological resources become increasingly vital in our energy system, it is crucial to better understand how a changing climate may affect weather variability and, in turn, influence future wind energy production. This study evaluates the performance of climate models in reproducing wind resources over Southern Africa (SA), and in assessing potential impacts of climate change. An ensemble of climate simulations is evaluated over SA, which includes simulations from three Regional Climate Models (RCM; i.e., CCLM4, RegCM4, and REMO2009) that participated in the Coordinated Regional Downscaling Experiment program over Africa (CORDEX-Africa) at a horizontal resolution of approximately 25 km, and the coarser-resolution ones from their four driving General Circulation Models (GCMs; i.e., HadGEM2-ES, MPI-ESM-LR, MPI-ESM-MR, and NorESM1-M) from the Coupled Model Intercomparison Project Phase 5 (CMIP5). The simulated wind is first compared to the reference datasets, derived from ground-based measurements and reanalyses, during 2000-2023 at 3-hour intervals, covering both surface and hub-height (100 m) levels. The performances of both RCMs and GCMs are quantified and compared in terms of their representation of wind resource characteristics, including the mean wind speed and its spatio-temporal variability at hourly-to-annual timescales. Then wind energy potential and capacity factors are also derived for the wind resource assessment in SA, where direct observational data is limited. Finally, the potential impact of climate change on the 100-meter wind energy potential in SA is assessed based on this ensemble of climate projections, up to 2099, under the RCP2.6 and RCP8.5 scenarios. This study helps to understand the performance difference between regional and global models in simulating wind resources and provides insight into how changing climate conditions might affect wind energy production over the long term in SA.

How to cite: Tang, C. and Morel, B.: How do climate models represent wind resources in Southern Africa and predict their future changes?, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8855, https://doi.org/10.5194/egusphere-egu24-8855, 2024.

How to cite: Remedio, A., Weber, T., Engelbrecht, F., Biskop, S., Steinkopf, J., Padvatan, J., van der Waal, C., Wassenaar, T., Banda, K., Tlhalerwa, K., and Perkins, J.: Projected climate change signals for selected hotspot regions using regional climate simulations in southern Africa  , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16077, https://doi.org/10.5194/egusphere-egu24-16077, 2024.

High resolution climate information is critical for the research community and the downstream vulnerability, impacts, adaptation, and climate services communities (VIACS). Coordinated ensembles generated by initiatives such as the World Climate Reseaerch Program's Coodinated Regional Downscaling Experiments (CORDEX) provide consistent and comparable information for the present as well as future scenarios. CORDEX is the initiative responsible for delivering regional climate data over fourteen different domains encompassing all land areas of the globe. And its output forms the basis for downstreams impacts assessments, adaptation planning and climate services development. This talk will focus on the European CORDEX initiative (hereafter EURO-CORDEX), an established framework of European regional climate modelers, and its coordinated effort to build regional climate ensembles for the years to come. In its first phase (2014 until now), EURO-CORDEX produced a rich ensemble of regional climate simulations under different representative concentration pathway scenarios. The EURO-CORDEX dataset is hosted in global and European databases and fed into the Regional Atlas of 6^th IPCC Assessment Report. However, this ensemble and others like it suffered from several shortcomings, which the community seeks to address in the next phase of production. Chief among these is the oft cited criticism that the selection of GCMs that provide input to the regional climate models was not robust and that the resulting ensemble represents an “ensemble of opportunity”. Here we will show how the community has addressed these shortcomings and present a toolkit, which can be used for evaluating the suitability of GCMs for downscaling. The utility of this toolkit extends well beyond the regional climate and VIACS communities to include researchers interested in researching model biases, constraining future change and exploring so-called future storylines. The toolkit is open-source and the community encourages contributions and sugestions for imporvement. Therefore a short tutorial is also included as part of this talk. 

How to cite: Sobolowski, S. and the EURO-CORDEX CMIP6 GCM Selection & Ensemble Task Team: GCM Selection & Ensemble Design: Best Practices and Recommendations from the EURO-CORDEX Community, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1884, https://doi.org/10.5194/egusphere-egu24-1884, 2024.

The process of selecting suitable time periods within the Coupled Model Intercomparison Project Phase 6 (CMIP6) for a specific region and warming level presents notable challenges, such as the incomplete representation of atmospheric dynamics and climate-change uncertainties. This study aims to develop a selection procedure for CMIP6 model periods using a methodology to address both the representation of atmospheric dynamics and the relevant changes in the climate variable of interest. In the first step, the representation of past atmospheric dynamics is evaluated to investigate the model quality. This is then used as a criterion to exclude underperforming models. The second step gives information on interesting model periods and indicates which model periods are relevant for selection.

To eliminate models that did not adequately represent historical atmospheric dynamics, the Lamb Weather Type (LWT) classification was used to assess the representation of large-scale circulation patterns across 33 CMIP6 models and compare these with ERA5. This resulted in the exclusion of three CMIP6 models for the circulation regimes over Belgium. Furthermore, in order to account for the increased occurrence of weather patterns related to extreme heat, while also reducing the number of different weather types of the existing LWT classification, the classification was adapted to incorporate a temperature-dependent classification through the optimization of the clustering of weather types and the associated maximum temperatures. Additionally, a new index, the hot weather type index, was introduced and combined with a set of heat-related metrics to illustrate the selection methodology. The final period selection was made based on the combination of the ranks of the different metrics for each global warming level considered.

The method developed in this study offers a framework for selecting periods within CMIP6 while considering both the uncertainties of the large-scale circulation patterns and changes in the climate-change signal. This framework holds potential to contribute to regional climate modelling and facilitate decisions related to the selection of model periods for dynamical downscaling of relevant climate projections.

How to cite: Serras, F., Vandelanotte, K., Borgers, R., Van Schaeybroeck, B., Demuzere, M., Termonia, P., and van Lipzig, N.: GCM selection based on weather patterns for extreme heat: a case study over Belgium, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7537, https://doi.org/10.5194/egusphere-egu24-7537, 2024.

 Selecting representative climate models for climate change impact studies: A case study of the eastern Omo basin

Tekalegn Ayele Woldesenbet^{a b}, Nadir Ahmed Elagib^a

^a Institute of Geography, Faculty of Mathematics and Natural Sciences, University of Cologne, Cologne, Germany

^b Ethiopian Institute of Water Resources, Addis Ababa University, Addis Ababa, Ethiopia

Abstract

Selecting reliable general climate models (GCMs) is crucial for assessing the impact of climate change in a region. The current study evaluated the performance of 34 GCMs from the Coupled Model Intercomparison Project Phase 6 (CMIP6) in simulating monthly total rainfall. To this end, the case of eastern Omo basin was considered with observed rainfall data at seven meteorological stations for the period 1985–2014. The corresponding GCM-simulated rainfall data were compared using seven performance metrics. Eight performance metrics were selected under four categories as follow: error metrics (mean bias error, mean absolute error, root mean squared error), model efficiency (Nash and Sutcliffe’s model efficiency, absolute model efficiency, Kling-Gupta model efficiency), indices of agreement (modified index of agreement), and goodness of fit (coefficient of determination). Three key results were obtained: 1) There is no single best overall GCM across the meteorological stations using a single performance metric, 2) No single best GCM was found for a meteorological station using all the performance metrics, and 3) Based on all performance metrics across all the meteorological stations, the best GCMs in order of performance are SAM0-UNICON, TaiESM1, INM_CM5_0, AWI_ESM_1_1_LR, and CMCC_CM2_HR4. The selected GCMs can be used for evaluating the impact of climate change on hydrology, water resource availability, ecological flow regime and for climate adaptation and mitigation strategies.

Keywords: performance metrics, CMIP6, rainfall, climate models, ranking, Omo basin,

How to cite: Woldesenbet, T. A. and Elagib, N.: Selecting representative climate models for climate change impact studies: A case study of the eastern Omo basin, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3740, https://doi.org/10.5194/egusphere-egu24-3740, 2024.

As the number of models in Coupled Model Intercomparison Project (CMIP) archives increase from generation to generation, there is a pressing need for guidance on how to interpret and best use the abundance of newly available climate information. Users of the latest CMIP6 seeking to draw conclusions about model agreement must contend with an "ensemble of opportunity" containing similar models that appear under different names. Those who used the previous CMIP5 as a basis for downstream applications must filter through hundreds of new CMIP6 simulations to find several best suited to their region, season, and climate horizon of interest. Here, we present methods to address both issues, model dependence and model subselection, to help users previously anchored in CMIP5 to navigate CMIP6 and multi-model ensembles in general. We refine a definition of model dependence based on climate output to designate discrete model families within CMIP5/6. We show that the increased presence of model families in CMIP6 bolsters the upper mode of the ensemble's bimodal effective Equilibrium Climate Sensitivity (ECS) distribution. Accounting for the mismatch in representation between model families and individual model runs shifts the CMIP6 ECS median and 75th percentile down by 0.43˚C, achieving better alignment with CMIP5's ECS distribution.

 Subsequently, we present a new cost-function minimization-based approach to model subselection, Climate model Selection by Independence, Performance, and Spread (ClimSIPS). We demonstrate ClimSIPS by selecting sets of 2, 3, and 5 CMIP models for European applications, incorporating the new dependence definition along with a performance metric based on agreement with observed mean climate fields and the ensemble spread of projected midcentury change in mean surface air temperature and precipitation. Because different combinations of models are selected by the cost function for different independence, performance, and spread priority balances, we present all selected subsets in ternary contour "subselection triangles". ClimSIPS represents a novel framework to select models in an informed, efficient, and transparent manner and addresses the growing need for guidance and simple tools so those seeking climate services can navigate the increasingly complex CMIP landscape.

How to cite: Merrifield, A., Brunner, L., Lorenz, R., Humphrey, V., and Knutti, R.: Climate model Selection by Independence, Performance, and Spread (ClimSIPS), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-28, https://doi.org/10.5194/egusphere-egu24-28, 2024.

Projecting how temperature variability is likely to change in the future is important for understanding future extreme events. This comes from the fact that such extremes can change due to both changes in the mean climate and its variability. The recent IPCC report found large regions of low model agreement in the change of temperature variability in both December, January, February (DJF) and June, July, August (JJA) when considering 7 Single Model Initial-Condition Large Ensembles (SMILEs). In this study we use the framework described by Suarez-Gutierrez et al, (2021) to constrain future projections of temperature variability by selecting the SMILEs that best represent observed variability.

We consider 11 SMILEs with CMIP5 and CMIP6 forcing over 9 ocean regions and 24 land regions. We find that CESM2-LE, GFDL-SPEAR-MED and MPI-GE-CMIP6 perform best in DJF and CESM2-LE, CESM1-LE and GFDL-SPEAR-MED perform best in JJA. We find that the Southern Ocean is poorly represented in all models and that few models can represent Central America, the Amazon, North-East Brazil, and eastern and southern Asia, with western and eastern Africa poorly represented in JJA and northern Australia poorly represented in DJF. Overall models perform well over the Northern Hemisphere land masses. When we constrain temperature variability estimates, variability is generally lower, particularly over South America, Africa, and Australia – the same regions where the constraint improves projections, and where the projected change is smaller in the constraint. Where hot extremes increase so do cold extremes showing projected changes in all regions as a fattening or thinning of the distribution rather than a change in skewness.

How to cite: Maher, N., Suarez-Gutierrez, L., and Milinski, S.: Constraining temperature variability projections using SMILEs that best represent observed variability, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13517, https://doi.org/10.5194/egusphere-egu24-13517, 2024.

Various approaches have been proposed to combine individual climate models and extract a robust signal from an ensemble, such as the Multi-Model Mean or the weighted Multi-Model Mean. However, they often rely on weights that are applied to models globally, overlooking the fact that individual models often demonstrate strengths in specific regions. This suggests that a more localized approach could improve climate projections based on models ensembles.

So far, the only approach that really exploits the regional strengths of different models over multi-decadal timescales is the graph cuts approach (Thao et al., 2022). It consists in selecting for each grid point the most appropriate model, while at the same time considering the overall spatial consistency of the resulting field. Although this method showed encouraging results, outperforming other combination approaches, it is limited to optimizing for one variable, resulting in an inconsistent model selection for each and thus a loss of the multivariate relationships observed in the models. For instance it is known that precipitation and temperature are physically linked. Therefore having an independent model combination for each variable can result in inconsistencies. Moreover, the method was only applicable to multi-decadal averages, not allowing for retrieving distributional properties of the combined models such as extreme events.

Here we present a series of improvements of graph cuts enabling to combine distributions of daily-means while preserving multivariate relationships, thus better capturing the complete span of climate dynamics. Using the Hellinger distance to measure model performance, we are able to select, at each grid location, the model that best represents the joint distribution of the target variables while minimizing the apparition of unrealistic discontinuities in the resulting fields. The resulting projections display more realistic extremes and compound events representations.

REFERENCES 

Thao, S., Garvik, M., Mariethoz, G., & Vrac, M. (2022). Combining global climate models using graph cuts. Climate Dynamics, February. https://doi.org/10.1007/s00382-022-06213-4

How to cite: Schmutz, L., Thao, S., Vrac, M., Allard, D., and Mariethoz, G.: Better than Multi-Model Means: combining climate models using multivariate graph cuts for improved CMIP6 projections, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19233, https://doi.org/10.5194/egusphere-egu24-19233, 2024.

For a given greenhouse gas emission scenario, climate models are showing very significant differences for the future climate, due to processes complex to simulate. Among them, the Atlantic Meridional Overturning Circulation (AMOC) fate has been shown to be very uncertain, explaining large amount of the differences in climate projections in the North Atlantic region. Indeed, under the ssp2-4.5 scenario, CMIP6 models show an AMOC maximum at 26°N that goes from 18.1 ± 4.1 Sv (1 Sv=10⁶ m³/s, ensemble mean of 30 models one standard deviation) during the period 1850-1900, to 11.6 ± 3,9 Sv in the last decade of 2100, with AMOC ranging from 4.3 to 21.3 Sv depending on the model. There is thus a clear need to improve estimates of the AMOC in the future.

In this respect, methods called emergent or observational constraint (OC) have been recently developed. They combine climate models and observations, by finding and using an emergent relationship between a given climate variable in the future and observable predictors, to refine the best guess and uncertainty estimation of this climate variable. This study proposes to apply them to the case of AMOC projections. Both the choice of the predictor, and of the OC method can be key. What are the best choices to reduce most the uncertainty of the AMOC at the end of the 21^st century? To answer such a question, this study compares two possible cases: it uses either only one predictor, the past AMOC, or a set of predictors, the sea surface temperature and salinity from various regions in the world, which are known to impact on-going and future fate of the AMOC. Moreover, this study compares five different OC methods. The uncertainty is evaluated for each couple of predictors and OC choice, considering cross-validation and observational errors.

The best estimates of future AMOC under ssp2-4.5 scenario, constrained by the observed AMOC over the period 2004-2021, is 11,6 ± 2,5 Sv, using the linear regression method. When constrained by a larger set of predictors, it is 7,9 ± 2,1 Sv, using also the linear regression found here as the best OC method, with a Ridge regularization that limits overfitting. Thus, the future AMOC, in 2091-2100 compared to 1850-1900, is weakening by 56% when constrained by various recent observations that count for AMOC dynamics, in place of 36% when estimated using ensemble mean or AMOC observation only, as done in last IPCC reports. This result might have therefore considerable impacts on future adaptation plans within the North Atlantic regions.

How to cite: portmann, V., Swingedouw, D., Chavent, M., and Khattab, O.: Comparison of different observational constraint methods to reduce the uncertainty in the AMOC at the end of the 21st century, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5341, https://doi.org/10.5194/egusphere-egu24-5341, 2024.

Precipitation is an important aspect of the climate and is expected to change from increasing greenhouse gases. However, model projections of precipitation changes are stubbornly uncertain, particularly in tropical regions. It has been long recognized that much of the uncertainty in tropical precipitation changes is rooted in the uncertainty in sea surface temperature (SST) changes. But unfortunately, constraining SST changes at regional scales has been quite difficult. Here, we advocate that instead of fixating on regional SST changes, it is much more productive to focus on constraining precipitation sensitivity to SST changes (i.e., hydrological sensitivity). We show that local hydrological sensitivity varies widely among the state-of-the-art global climate models, but it can be effectively constrained by honing in on specific aspects of the observed precipitation-SST relationships. We find that despite the large inter-model disagreement, regional hydrological sensitivity in tropical oceans is accurately projected by the CMIP6 multi-model averages.

How to cite: He, J.: Constraining Hydrological Sensitivity in Tropical Oceans, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6803, https://doi.org/10.5194/egusphere-egu24-6803, 2024.

The uncertainty of future climate change is typically estimated by a collection of models from various climate institutions. The Ensemble Dependence Transformation (EDT) method has proven effective in producing ensembles with means that lie closer to the verifying observations and with variances that match the variance of the observations about the ensemble mean. However, EDT does not specifically address temporal variability and persistence attributes within individual models. This limitation can potentially be addressed by the Time Variability Correction (TVC) method, designed to quantify, and correct model variability errors across differing time scales.

In this study, we test and compare four approaches: 1) TVC only; 2) EDT only; 3) TE: Applying TVC to individual models first, followed by EDT; 4) ETE: Applying EDT to obtain the time-varying mean series, using TVC to correct the EDT-transformed series, and applying EDT again to TVC post-processed model series. These methods are employed to post-process 26 CMIP6 (Coupled Model Intercomparison Project Phase 6) daily mean temperature projections across Australia under a model-as-truth setup.

We evaluate the results using verification metrics for assessing both individual models and multi-model ensembles. Findings indicate that, overall, ETE performs better in improving individual model statistics, including variance and lag correlations relative to the time-varying ensemble mean. Additionally, ETE enhances ensemble statistics, notably ensemble standard deviation (ESD) during both in-sample historical and out-of-sample projection periods. TE is particularly effective at improving root mean squared difference (RMSD) between ensemble mean and observations, along with continuous ranked probability skill score (CRPSS).

How to cite: Shao, Y., Bishop, C., Abramowitz, G., and Hobeichi, S.: Improving Multi-model Ensembles of Climate Projections through Time Variability Correction and Ensemble Dependence Transformation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6796, https://doi.org/10.5194/egusphere-egu24-6796, 2024.