CL5.3.3

Cities play 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, because 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. This is especially critical in connection to extreme events, for instance heat waves with extremely high temperatures exacerbated by the urban heat island effect, in particular during night-time, with consequences for human health.

In 2013, the CORDEX community identified cities to be one of the prime scientific challenges. Therefore, we proposed this topic to become an activity at CORDEX platform, within the framework of so called flagship pilot studies, which was accepted and the FPS URB-RCC activity has been started in May 2021.

The main goal of this FPS is to understand the effect of urban areas on the regional climate, as well as the impact of regional climate change on cities, with the help of coordinated experiments with urbanized RCMs. While the urban climate with all the complex processes has been studied for decades, there is a significant gap to incorporate this knowledge into RCMs. This FPS aims to bridge this gap, leading the way to include urban parameterization schemes as a standard component in RCM simulations, especially at  high resolutions.

From the perspective of recent regional climate models development with increasing resolution down to the city scale, proper parameterization of urban processes is important to understand local/regional climate change. The inclusion of the individual urban processes affecting energy balance and transport (i.e. heat, humidity, momentum fluxes) via special urban land-use parameterization of 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, as well as prepare for climate related risks (e.g. heat waves, smog conditions etc.). More detailed discussion of the RCMs simulations available with urban parameterization and methods already in use will be presented.

How to cite: Halenka, T. and Langendijk, G.: CORDEX Flagship Pilot Study on Urbanization - URBan environments and Regional Climate Change (URB-RCC), EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11040, https://doi.org/10.5194/egusphere-egu22-11040, 2022.

Systematic biases are still inherent in the newest generation of regional climate models at convection-permitting scale. This complicates the direct application of such simulation results for impact studies with vegetation models. Here, we investigate the impact of a statistical bias correction method (quantile mapping) after Piani et al. (2010) on the climate change signal (CCS) of extreme climate indices in time and space-distribution from convection-permitting climate simulations based on the Representative Concentration Pathway (RCP) 8.5. In the frame of the Multisectoral analysis of climate and land use change impacts on pollinators, plant diversity and crops yields (MAPPY), transient regional climate model simulations are performed with COSMO-CLM (v5.16) at a spatial horizontal resolution of 3 km over central Europe from 1980 to 2070. CCSs are computed from the ETCCDI set of climate extreme indices for the “near” (2021-2050) and the “far” (2041-2070) future relative to the reference period 1981-2010.

We find that model biases influence the spatial distribution of climate extremes, even though the mean properties are not heavily changed. However important differences are observed for the total precipitation amount and for heavy precipitation indices. Bias-corrected precipitation data show an increase of 3.5% for the “far” future in the annual total precipitation relative to the reference period. Non bias-corrected data would instead suggest a lower increase of 0.7%. The frequency of heavy precipitation days is also enhanced in the bias-corrected data. For example the amount of rainfall which exceeds the 95 and 99 percentiles for the “far” future is 12.7% more than the reference period. The projections from the non bias-corrected data would instead predict an increase of 9.4% and 9.2% respectively.

The bias-corrected simulation data for the temperature parameters suggest generally warmer winters for both the “near” and “far” future periods with a dampening of the extreme temperatures. As an example, the maximum values of the daily maximum temperatures in the “far” future are in average 1.6 °C warmer relative to the reference period. The non bias-corrected data would instead return an higher value of about 1.1 °C (i.e. 2.7 °C). Vice versa the minimum values of the daily minimum temperatures in the “far” future are in average 2.2 °C warmer relative to the reference period, whereas the non bias-corrected data give a lower increase of 1.8 °C. The dampening of extreme temperatures is also consistent with other observations such as the percentage of warm days, where the maximum temperature is above the 90 percentile or the number of frost days, where the minimum temperature is below 0 °C. In both latter cases the bias-corrected data give lower values with respect to the non bias-corrected data with a relative difference of about 30%.

We conclude that systematic biases in regional climate models can have a significant impact on climate change signals both in space distribution and absolute values. Yet the statistical robustness of our results, the seasonal variability of some extremes as well as the dependency on the resolution scale is currently under investigation.

How to cite: Ugolotti, A. and Tölle, M.: Impact of statistical bias correction on the climate change signal of extreme climate indices from convection-permitting climate simulations over central Europe, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-372, https://doi.org/10.5194/egusphere-egu22-372, 2022.

In this study, the latest version of the Abdus Salam International Center for Theoretical Physics (ICTP) Regional Climate Model RegCM4.7.0 is used to simulate climate of Georgia for the period 1986-2005.

Georgia is the mountainous country located in the south-western part of the Greater Caucasus. Its area is 69.875 km². Mountains cover significant part of the territory 54% of them is located at 1,000 m elevation. From the west Georgia is washed by the Black Sea, from the south it borders with Turkey and Armenia, from the south-east – with Azerbaijan and from the north – with the Russian Federation.

Georgia displays diverse climate and vegetation types: there are almost all climate types from high mountains eternal snow and glaciers to steppe continental climate of eastern Georgia and the Black Sea coastal subtropical humid climate.

To simulate climate with high horizontal resolution and represent more special details for the complex terrain of Georgia the double-nested dynamic downscaling method has been used. First, RegCM was driven by ERA-Interim data at a grid spacing of 50 km. For 50 km resolution simulation, we defined central latitude and central longitude of model domain clat=42.27, clon=42.70 degrees as well as 30 number of points in the N/S direction and 60 number of points in the E/W direction. The 12-km resolution RegCM simulation was nested in the simulation at 50 km resolution. For 12 km resolution simulation, we chose central latitude and central longitude of model domain clat=42, clon =43 degrees as well as 48 N/S 100 E/W points. We selected domain size to be large enough to account for the relevant large-scale processes (such as the large-scale flow modulations due to orographic features and water bodies) but at the same time small enough in size to minimize the use of computational resources.  

We have used the default BATS (Biosphere-atmosphere transfer scheme) land surface parameterization scheme, Emanuel cumulus convective parameterization scheme, SUBEX (Sub-grid Explicit Moisture Scheme) moisture scheme and Holstlag planetary boundary layer scheme for the simulations.

The simulated surface annual and seasonal air temperature and precipitation as well as extreme climate events are compared with Climatic Research Unit (CRU), ERA5 reanalysis, GPCP data sets. For extreme events analyzes, we chose and used some indices, defined by the Expert Team on Climate Change Detection and Indices, recommended by the World Meteorological Organization.

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

How to cite: Elizbarashvili, M., Kalmár, T., Tsintsadze, M., and Mshvenieradze, T.: Regional climate modeling for Georgia with RegCM4.7, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2065, https://doi.org/10.5194/egusphere-egu22-2065, 2022.

Climate change is one of the greatest challenges in history. On the one hand, climate models can be useful tools for providing information on climate change, but on the other hand climate model simulations’ outputs are prone to biases compared to observations, which can be somewhat overcome by different bias‑adjustment techniques. Being the European branches of the international initiative called COordinated Regional Downscaling EXperiment (CORDEX): EURO-CORDEX and Med-CORDEX provide regional climate model (RCM) simulations targeting Europe. Present research focuses on precipitation and temperature change over sub-regions within the Carpathian Basin based on raw and bias-adjusted RCM data under the RCP8.5 scenario. The quality controlled and homogenized CARPATCLIM served as reference dataset for the bias-adjustment. The investigations explore temperature and precipitation changes by the end of the century (2070-2099) with respect to 1976-2005. The comparative research seeks answer the question: how climate change will manifest in heavy rainfall and other temperature related climate indices over regions characterized by different topography?

How to cite: Torma, C. Z., Simon, C., and Kis, A.: On the evidence of orographic modulation of regional fine scale climate change signals based on raw and bias-adjusted CORDEX data: The Carpathians, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-845, https://doi.org/10.5194/egusphere-egu22-845, 2022.

The rapid growth of the offshore wind energy sector emphasizes the need for realistic projections of the lifetime energy yield of existing and planned offshore windfarms. Analyses of CMIP5 data show that, even though near-future wind speed changes over Europe and the North Sea are uncertain across models, these changes should be taken into account by wind industries due to the potentially large impact on the energy. Thanks to advances in model design and the increase in computing resources over the past decades, regional climate models (RCMs) can be used for multi-decadal simulations and that at a spatial resolution of a couple of kilometers or less. These high-resolution simulations not only allow for an improved representation of weather systems, but also allow to represent the interactions between windfarms and the atmosphere through a wind farm parametrization. In this way, RCMs can be employed for a detailed wind resource assessment for the coming decades that takes into account the wakes and energy losses induced by upwind arrays and clusters.

In our wind resource assessment, we use the regional climate model COSMO-CLM, extended with the Fitch wind farm parametrization. An evaluation of a 10-year, ERA5-driven simulation at 2.8km resolution against in-situ anemometers, lidar measurements and satellite-borne ASCAT measurements that has been performed for the North Sea will be discussed. For the year 2019, the spatially-variable bias in the mean wind speed was found to be generally within +- 0.4 m/s and the overlap in the wind speed distributions generally larger than 92%. Next, array- and cluster-scale wakes, modelled at horizontal resolutions of 2.8km and 1km were analysed and compared for a present and near-future windfarm layout over the southern North Sea, providing valuable information on how regional changes to the wind farm layout will impact the farm-specific energy yields due to additional upwind wake generation under different atmospheric conditions. This research frames within the FREEWIND project of KU Leuven (freewind-project.eu), which aims to contribute to the scientific research of offshore wind energy and plans to provide wind farm planning and forecasting tools on multiple time scales over the coming years.

How to cite: Borgers, R., Meyers, J., and Van Lipzig, N.: Offshore windfarm modelling over the North Sea with COSMO-CLM: model evaluation and application at kilometer-scale resolution, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7685, https://doi.org/10.5194/egusphere-egu22-7685, 2022.

As part of the UK Climate Projections 2018, a perturbed physics ensemble of regional climate model simulations was created. Each of the 11 member was created using a variant of the Hadley Centre's global and regional models, using an RCP8.5 scenario and a Europe-wide 12km grid. Here we compare the projected climate changes of this ensemble to those projected by the EuroCORDEX multi-model ensemble over the UK. The two ensembles exhibit biases of comparable magnitudes during the historical period, but project increasingly divergent trends in future climate change. We show that for some indices, the ensembles sample the same future space, but in some cases do not even overlap - despite the wide spread in each ensemble. The effects of these diverging trends are illustrated in a case study of compound indices of extreme weather, such as the Fire Weather Index (derived from temperature, wind, humidity and precipitation variables), for which bias correction is known to be particularly problematic. Future GCM projections can be constrained by the fidelity of each ensemble member’s representation of the observed climate. We suggest how this approach might be extended to RCM ensembles, given the ability to match the spatial pattern seen in observations can be determined by the RCM whilst the magnitude of the climate change response is more related to the driving global model.

How to cite: Brierley, C., Barnes, C., Keeping, T., and Chandler, R.: Controls on projected climate extremes in two regional ensembles for the UK, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6042, https://doi.org/10.5194/egusphere-egu22-6042, 2022.

An analysis of the sensitivity of different surface model options in the WRF model was performed. The main goal is to investigate the transition from wet to dry regimes through the analysis of the soil moisture–temperature, and soil moisture–precipitation interactions; and explore the response of the surface climate to different model options. Four simulations with the WRF model were carried out with different land surface model schemes for the 2004-2006 period, driven by ERA5 reanalysis. The WRF model was used for the simulations over the European domain with a horizontal resolution of 0.11 degrees and 50 vertical levels, which follows the CORDEX guidelines. These simulations rely on the same physical parameterisations with different surface model options. For the first experiment, the Noah land surface model was used. For the remaining simulations, the Noah-MP (multi-physics) land surface model was used with different runoff and groundwater options: (1) original surface and subsurface runoff (free drainage), (2) TOPMODEL with groundwater and (3) Miguez-Macho & Fan groundwater scheme.

An extensive evaluation of all simulations against observations was performed, which is an important step to determine the quality of the simulations. In this way, precipitation, maximum and minimum temperatures from all simulations were compared against observations. The new version of the Europe‐wide E‐OBS temperature and precipitation data set was used to compare with the output of the simulations performed. This dataset has a regular grid with 0.1o spatial resolution. The evaluation of temperature and precipitation showed that the 1st and 4th setup have the best agreement against observations. Additionally, for each WRF experiment, the land energy balance and the land water balance were computed. These results showed some differences between simulations, in particular for the land water balance. Additional analysis are being carried out to determine the impact of different groundwater options of LSMs in surface climate.

Acknowledgements. The authors wish to acknowledge the LEADING (PTDC/CTA-MET/28914/2017) project funded by FCT. The authors would like to acknowledge the financial support FCT through project UIDB/50019/2020 – Instituto Dom Luiz.

How to cite: Lima, D. C. A., Cardoso, R. M., and Soares, P. M. M.: Response of the surface climate to different groundwater options using the WRF model, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8210, https://doi.org/10.5194/egusphere-egu22-8210, 2022.

Automated classifications of atmospheric circulation are routinely used to link synoptic-scale circulation with temperature variability. Though powerful in general, classifications have considerable limitations regarding their skill to capture synoptic links to temperature extremes.

We evaluate and optimize several parameters of the popular method of self-organizing maps, in order to make the method better suited for studying central European temperature extremes. Furthermore, two methods of discretizing Sammon projections of atmospheric circulation have been developed to complement the image obtained by SOMs, and all methods have been used to analyse ERA5 SLP fields in relation to winter extremes.

Here, we plan to apply the new optimized classifications to daily winter and summer SLP fields from the outputs of evaluation and historical runs by CORDEX RCMs to study the skill of the methods to identify RCM biases in simulated circulation and their links to biases in temperature extremes. Furthermore, we plan to utilize various indices of low-frequency large-scale circulation to assess to what extent the eventual biases propagate from the driving (reanalysis, GCM) data.

How to cite: Stryhal, J., Plavcová, E., and Lhotka, O.: Evaluation of temperature extremes over central Europe and their links to atmospheric circulation in CORDEX RCMs, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9604, https://doi.org/10.5194/egusphere-egu22-9604, 2022.

Regular update of climate projections is a crucial task in all countries since such data should be a basis for development further adaptation measures on all levels from national down to local. And the more detailed data is available the more reliable and focused measures to combat climate change could be developed. At the moment data of EuroCORDEX initiative with 0.1^o spacing is the most suitable, detailed and freely available dataset for Ukraine. We used bias-adjusted daily and monthly air temperature and precipitation datasets projected by 34 regional climate models (RCMs) for RCP4.5 and RCP8.5 scenarios for 3 future periods: near-term 2021-2040, mid-term 2041-2060 and far-term 2081-2100. Further bias-adjustment by the delta-method has been applied for the RCMs ensemble means when differences in temperatures (or ratio in case of estimations for precipitation) in future periods were added to (or for precipitation - multiplied by) values in the base period 1991-2010, obtained from the E-OBS v20.0e data. The Delta method applied provides more reliable results and allows to effectively exclude systematic biases in RCMs.

At the same time, in response to practical needs not only main climate parameters such as air temperature and precipitation should be projected under different scenarios but specialized climate indices that are different for different sectors. We’ll present as an example a set of indices relevant to forestry which is among the most vulnerable sectors to climate change in Ukraine. They are as follows: daily and annual temperature ranges, continentality, the coldest month temperature, heat and moisture supply during warm season with t>0^o, and types of climates by Worobjov index. All climate indices for the base period 1991-2010 and past WMO period 1961-1990 are estimated from the E-OBS data. To visualize the above indicators Ukraine atlas has been developed in QGIS application which represents 2 past and 3 future periods for 2 scenarios.

How to cite: Krakovska, S., Shpytal, T., Chyhareva, A., Pysarenko, L., Trofimova, I., and Kryshtop, L.: Ensembles of Euro-CORDEX RCMs for assessment of specialized climate indices in Ukraine, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10724, https://doi.org/10.5194/egusphere-egu22-10724, 2022.

Droughts are one of the major natural hazards, affecting the flora and fauna, but also human activities and health. Such events impact water management and agriculture, potentially causing increased mortality and economical losses. Drought analysis is a complex and challenging task, as it is quite difficult to accurately determine the spatial and temporal dimensions of drought events. Synthetic tools, like drought indices mostly based on the climate information, are often used to tackle this problem. Both the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI) were widely used to characterize droughts. These indices usually consider a monthly aggregation of either precipitation or a water balance (precipitation minus evapotranspiration), adjusting the data to a theoretical Probability Density Function (PDF), in order to get a standardized time-series. However, the input of such a small amount of data into the PDFs could potentially lead to uncertainties and it is not the best for some types of applications which respond on a smaller timescale. Nowadays, observational data are more readily available at a daily time-step. Thus, a new daily index is here proposed, relying on an empirical PDF built from the data. This new daily-SPI and daily-SPEI indices were applied to the World Meteorological Organization - Coordinated Regional Climate Downscaling Experiment for the European domain (EURO-CORDEX). In total 13 Regional Climate models were considered for the 1971-2100 period, following the Intergovernmental Panel on Climate Change – Representative Concentration Pathways (RCP) 2.6, 4.5 and 8.5 from 2006 onwards. The versatility of these new indices allows a building of an ensemble PDF, featuring all model data. A timescale of accumulation with 7-, 15-, 90-, 180 and 360- days were considered. The EURO-CORDEX is then used to assess drought projections throughout the 21^st century in terms of intensity, frequency, and mean duration of events for moderate, severe, and extreme droughts. It is projected an increase of intensity along the century, more pronounced for the RCP 8.5. While for the RCP 2.6, the intensity peak occurs for the mid century (2041-2070). As for the frequency of drought events, the timescale of 15 days reveals a noticeable increase, being constantly above other timescales, particularly for the daily SPEI. Moreover, the mean duration of events reveals a higher increase at the longer accumulation periods, while small to no changes occur for the 7 and 15 days.

Acknowledgements

The authors wish to acknowledge the financial support of FCT through project UIDB/50019/2020 – IDL and EEA-Financial Mechanism 2014-2021 and the Portuguese Environment Agency through Pre-defined Project-2 National Roadmap for Adaptation XXI (PDP-2). J. Careto is supported by the Portuguese Foundation for Science and Technology (FCT) with the Doctoral Grant SFRH/BD/139227/2018 financed by national funds from the MCTES, within the Faculty of Sciences, University of Lisbon. 

How to cite: Careto, J. A. M., Soares, P. M. M., Cardoso, R. M., Russo, A., and Lima, D. C. A.: A new ensemble-based SPI and SPEI index to depict droughts projections for the Iberia Peninsula with the EURO-CORDEX, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12405, https://doi.org/10.5194/egusphere-egu22-12405, 2022.

​​Explicitly considering groundwater dynamics in regional climate models (RCMs) can significantly influence the simulation of states and fluxes at the land surface, leading to an altered land-atmosphere coupling. The modified flux partitioning is relevant for the simulation of heatwaves, and their future evolution under climate change. This study compares the representation of heatwaves at climate time scales in an ensemble of RCMs without, and one coupled RCM with explicit groundwater- and subsurface hydrodynamics. The Terrestrial Systems Modelling Platform (TSMP, https://www.terrsysmp.org) as a regional climate system model, couples the atmospheric model COSMO, the Community Land Model, and the hydrologic model ParFlow (https://www.parflow.org) through the OASIS3-MCT coupler. TSMP simulates a closed terrestrial water cycle from the groundwater to the top of the atmosphere with a 3D variably saturated subsurface flow representation and a free-surface overland flow boundary condition. TSMP is run in a EURO-CORDEX compliant setup at 12km resolution over the European EUR-11 domain. First, we compare heatwave area, frequency, duration, and intensity from 13 years of ERA-Interim driven evaluation runs. TSMP is analysed alongside a EURO-CORDEX RCM ensemble, gridded E-OBS observations, and the ERA5-Land reanalysis. Especially for heatwave intensities and the number of heatwave days, TSMP shows a clear tendency towards lower mean absolute deviations from the comparison data. A comparison to GLEAM-based evapotranspiration indicates low deviations in evapotranspiration anomalies. This is linked to an increased evaporative fraction that is affected by a redistribution of soil moisture and groundwater flow in a continuum approach and interactions with the land surface. In a further analysis, 30 years of selected EURO-CORDEX RCM ensemble members from historical simulations, driven by CMIP5 global climate models (GCM), and TSMP driven by the MPI-ESM-LR GCM are compared. Consistent with the evaluation run analysis and with large spatial and temporal heterogeneity, the soil moisture and groundwater treatment in TSMP attenuates hot events and heatwave extremes, in comparison to the RCM ensemble. The duration of heat events in TSMP decreases as the mean number of hot day events (duration > 3 days) and long hot events (duration > 6 days) decreases by a factor of 1.5-2.3; the mean number of short hot events (duration < 3 days) is higher in TSMP. Also, the frequency of heatwaves (heat events exceeding 6 consecutive days) with an amplitude (intensity) larger than 4K compared to the 90th temperature percentile, is decreased by a factor of 2 and more, while, the frequency of heatwaves with low amplitudes is increased. The results suggest that groundwater dynamics, due to their impact on the number of hot day events, and the frequency and intensity of heatwaves, has to be taken into account when analysing heatwave statistics from RCM ensembles.

How to cite: Goergen, K., Furusho-Percot, C., Poshyvailo-Strube, L., Wagner, N., Hartick, C., and Kollet, S.: Revisiting heatwaves in a EURO-CORDEX RCM ensemble in comparison with a coupled regional climate system model with 3D subsurface hydrodynamics, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12064, https://doi.org/10.5194/egusphere-egu22-12064, 2022.

Regional climate projections indicate that the future changes in European summer mean precipitation may be substantial, with significant drying in southern Europe and possible weak increases in at higher latitudes. Model uncertainties and natural variability are however large. Here we quantify the role of future large-scale circulation changes on future precipitation change in a 16-member single-model regional climate-model ensemble for the RCP8.5 emission scenario (Global climate model EC-Earth2.3, dynamically downscaled using the regional climate model RACMO2 for the period 1950-2100). Circulation analogues are used to distinguish three contributions. The first is the precipitation change occurring without circulation change. The second contribution measures the effects of changes in the large-scale mean circulation. It has a different spatial pattern and is closely related to high-pressure development west of Ireland. For a large area east of Ireland (including parts of western Europe), it is the major contributor to the overall drying signal, locally explaining more than 90% of the ensemble-mean change. The high-pressure region west of Ireland also appears in CMIP6 ensemble-mean projections, although it is weaker than in the EC-Earth2.3/RACMO2 ensemble because of model spread in the exact location of the high-pressure region. The third contribution records the net effect on precipitation of changes in the circulation variability. This term has the smallest net contribution, but a relatively large uncertainty. The analogues can be used effectively to partition the ensemble-mean change but describe only up to 40% of the ensemble-spread. This demonstrates that natural variability in precipitation drivers other than the large-scale circulation (e.g., SST, soil-moisture preconditioning) will generally strongly influence regional summer precipitation trends derived from single climate realisations and thereby reemphasises the need for using large ensembles or other methods where signal to noise ratios are high (e.g., pseudo-global warming experiments).

How to cite: de Vries, H., Lenderink, G., van der Wiel, K., and van Meijgaard, E.: European summer precipitation changes and the role of the large-scale circulation, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11495, https://doi.org/10.5194/egusphere-egu22-11495, 2022.

A new metric that quantifies Added Value (AV) was developed that compares the difference within the entire probability distribution functions (PDFs) of the Regional Climate Model (RCM) and its driving General Circulation Model (GCM) with a high-resolution observation source, at every grid point, to obtain a spatial distribution of AV. This is important to assess the validity of the computationally expensive process of downscaling, especially for Convection Permitting Models (CPMs). The method can be adapted to focus on the tail-end of the distribution, since GCMs struggle to resolve precipitation extremes. To achieve this, the threshold value of the percentile of interest (for example, the 95th percentile) is obtained from the observation source and then applied to the PDF data as a filter, after which the corresponding AV can be obtained. This metric can also be adapted to assess the Climate Change Downscaling Signal (CCDS) of climate projections, by comparing to the corresponding historical data-set instead of an observation source.

This method is now being adapted to CPM simulations using a multi-model approach. The analysis is focused on both daily and hourly data from a 14-model ensemble of the ALP-3 domain using 5 high-resolution observation sources (GRIPHO for Italy; EURO4M for large alpine area; COMEPHORE for France; RADKLIM for Germany; and RdisaggH for Switzerland). The primary objective is to assess the added value of the CPM with the driving RCM, but a comparison to the GCM is also included. Preliminary results show that the CPM runs add value over the RCM, with possible emphasis in models/regions of lower RCM AV (requires confirmation by comparing RCMs to the driving GCMs). The analysis is will also focus on the CCDS metric of the near- and far-future simulations of the CPM, and the historical analysis is being replicated using hourly precipitation instead of daily.

How to cite: Ciarlo, J., Coppola, E., Pichelli, E., and Stocchi, P. and the FPS-Conv Team: Spatially distributed Added Value Index and Climate Change Downscaling Signal for convection permitting scale simulations, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2640, https://doi.org/10.5194/egusphere-egu22-2640, 2022.

Fire in the peatlands and forests of Borneo is a significant environmental issue, with far reaching social and climatic consequences. The KaLi project aims to better understand fire risk in the Kalimantan region of Indonesia, and this part of the project focusses on the climatic risk. While fire severity and frequency are generally expected to increase with climate change, the unique climate and geography of Indonesia and the island of Borneo create heterogenous patterns of change. We ran simulations of RCP8.5 with and without deforestation using regional climate model RegCM4 with boundary conditions from a range of CMIP5 model simulations. These simulations provide high-resolution climate simulations that show the relative contribution of climate change and deforestation to the climatic risk of fire using the Fire Weather Index.

We find that climate and fire risk from climate are significantly affected by both climate change and deforestation, though not to the same extent. The surface temperature in the multi-model mean RCP8.5 simulation increases by ~4 degrees, and deforestation further increases the temperature by ~2 degrees. The climate effects of both RCP8.5 and deforestation are affected by the altitude. Precipitation, in particular, is higher above 500m in the deforestation scenario. Both deforestation and RCP8.5 increase the fire risk according to the Fire Weather Index. Deforestation causes a smaller increase than RCP8.5, but is locally controllable in a way that the carbon emissions causing climate change are not. These high-resolution simulations provide a guide to the most vulnerable areas of Borneo from climatic increases in fire risk, and complement efforts to understand the social aspects of fire risk that are a part of the KaLi project.

How to cite: Davies-Barnard, T., Catto, J., and Harper, A.: Modelling the effects of Climate Change and Deforestation on Fire Risk in Tropical Borneo , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5883, https://doi.org/10.5194/egusphere-egu22-5883, 2022.

Simultaneous very large wildland fires present a unique challenge to fire management and firefighting resource allocation.

Here we present potential changes in very large wildland fire simultaneity projected by an ensemble of regional climate model simulations produced for the North American Coordinated Regional climate Downscaling Experiment (NA-CORDEX) over multiple United States (U.S.) Geographic Area Coordination Centers (GACCs), the main regions over which wildland firefighting resources are coordinated.  The NA-CORDEX simulations evaluated used the RCP8.5 future scenario, cover the years 1950-2100, and roughly span the range of climate sensitivity seen in the CMIP5 simulations.

To calculate simultaneity, we fit generalized linear models (GLMs) with a negative binomial response to observational data to predict megafire simultaneity based on multiple fire weather indices per GACC.  These indices include: KBDI (Keetch-Byram Drought Index), CFWI (Canadian Fire Weather Index), mFFWI, (modified Fosberg Fire Weather Index), ERC (Energy Release Component), BI (Burning Index), FM100, and FM1000 (100- and 1000-hour Fuel Moisture).  The resulting GLMs for the best index-based predictors were then applied to the NA-CORDEX simulations. 

Future projections of changes in the probability of different levels of simultaneity centered on multiple future timeslices will be presented, along with the uncertainty associated with the choice of simulation. 

How to cite: Bukovsky, M., McGinnis, S., Kessenich, L., Mearns, L., Podschwit, H., and Cullen, A.: Future changes in Simultaneous Megafires over the United States as projected by NA-CORDEX Simulations, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6135, https://doi.org/10.5194/egusphere-egu22-6135, 2022.

In this study, we analyze the projected changes in mean and extreme precipitation and temperature over subtropical Chile, based on simulations of 3 Regional Climate Models (RCMs) (RegCM4-7, REMO2015, and ETA) from South-America CORDEX-CORE, each one driven by three different CMIP5 GCMs. The RCM’s performance for the present climate is evaluated against the CR2MET (~5km) dataset.

The changes for the end (2070-2099) of the century for the RCP8.5 scenario are assessed with respect to the historical period (1976-2005). Extreme events are expressed in terms of 30-yrs return values, estimated from the GEV distribution fitted to seasonal extremes for extended austral winter and summer.

Accordingly, to GCMs projections, robust mean drying conditions are found among RCMs between 35ºS-40ºS in winter and 35ºS-45ºS in summer over the continental Chile and adjacent ocean. North of 30ºS REMO2015 and ETA show a statistically significant increase of mean winter precipitation over mountain regions. Another robust signal among the RCMs consists in an increase of extreme winter precipitation north of 35ºS over the mountain area. Increases in the maximum and minimum temperatures are significant over all domain for the 3 RCMs, but it is prominent to the north of 35°S (30°S) for REMO2015 and RegCM4-7 for winter (summer), especially over the mountain leeward, while ETA depicts the higher heating in the south of 30°S. The changes are mainly associated with a positive displacement of distribution (changes in location parameter) for temperatures and the amplification of interannual variability (positive changes in scale parameter) for precipitation. The physical processes responsible for these changes are discussed.

How to cite: Torrez Rodriguez, L. and Goubanova, K.: Assessment of climate change impacts on precipitation and temperature over Subtropical Chile based on South America CORDEX-CORE regional simulations, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6330, https://doi.org/10.5194/egusphere-egu22-6330, 2022.

Over the southeast of South America, the extended warm season from October 2009 to March 2010 registered a great number of extreme precipitation events. In this study, we evaluated the ability of the regional climate models and observational datasets to simulate observed features of the precipitation mean diurnal cycle observed during this period. WRF (two versions – 3.8.1 and 3.9.1) and RegCM4.7.1 simulations, with a horizontal grid spacing of 20 km (which uses both convective and large scale precipitation schemes) and 4 km (the precipitation is solved only by the microphysics scheme - convective permitting - CP), were analysed. We also considered six observational gridded precipitation datasets (MSWEP, CMORPH, PERSIAN, TRMM, ERA5 and GSMAP). These data and simulations are compared against 51 local observations of the precipitation every 3 hours. For the 51 stations, the observed diurnal cycle presents a great variety of patterns (time of maximum, minimum, amplitude, and double peaks during the day), but it is noted a slight predominance of more intense peaks at 06, 09 and 12 local time, characterizing the morning precipitation in the region. Comparisons of the six observational gridded datasets with the in situ data indicate a small outperformance of CMORPH and ERA5 to reproduce the main features of the observed diurnal cycle. At 20 km resolution, the simulations are not able to capture the diversity of diurnal cycles shown by in situ data. CP simulations capture better the great variety of the precipitation diurnal cycles shown by in situ observations. Specifically, for WRF-CP there is a shift in the afternoon peak at 15 LT to the morning-early afternoon (from 6 to 12 LT), while in RegCM4-CP there is a decrease in the number of simulated diurnal cycles peaking at dawn and a displacement of some peaks from dawn (03 LT) to morning (09 LT). The increase in the diversity and shift to morning-early afternoon peaks (6 to 12 LT) are the features showing the greatest agreement between CP simulations and in situ observations of the diurnal cycle of precipitation.

How to cite: Porfírio da Rocha, R., Llopart, M., Bettolli, M. L., Solman, S., Fernández, J., Lavin-Gullon, A., Feijoó, M., and Reboita, M.: Diurnal cycles of one season with precipitation extremes in southeastern South America: comparison between models, resolution and observational datasets, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6672, https://doi.org/10.5194/egusphere-egu22-6672, 2022.

CORDEX users are confronted with multiple sources of climate change information in regions where multiple domains overlap. Assessing the consistency of these sources (particularly the consistency of the climate signals) and understanding potential conflicts is a relevant problem with practical implications. For instance, this knowledge will guide on the best use of CORDEX to produce worldwide information merging the results provided by the different CORDEX domains. Two main approaches have been followed in the literature: 1) Mosaic of overlapped domains: The results from different domains are overlaid producing a mosaic where each region is covered by a single domain; this is the procedure typically followed in CORDEX-CORE (Teichmann et. al., 2021), using the domain which is best suited for each region. 2) Grand ensemble (Spinoni et al., 2020): Pooling together all available simulations across domains for each gridbox. This approach maximizes the information but leads to a heterogeneous ensemble with varying size and members across regions which may create spatial artifacts (e.g. border effects).

A preliminary analysis by Legasa et al. (2020) quantified the changes/uncertainty related to the choice of domain in the Mediterranean area, using the Europe and Africa CORDEX domains. They showed that the variability of the climate change signal from the grand ensemble was mostly determined by the models, and less so by the domain choice. Therefore, there is some evidence (at least, in the Mediterranean) that the grand ensemble approach could be appropriate to enlarge the ensembles for specific regions by pooling multi-domain simulations.

Here we extend this analysis by considering all regions where the worldwide CORDEX dataset domains overlap. The new subcontinental climatic regions used in the IPCC AR6 (Iturbide et al., 2020) are used to aggregate the results. We show that, in these areas, precipitation and near-surface air temperature biases and, especially, future climate change projections are systematically similar for simulations performed with the same GCM-RCM pair over different overlapping domains. This consistency provides higher confidence in the regional results (particularly when there are no physical reasons –e.g. different parameterizations– explaining the differences) and supports the use of a grand ensemble, pooling all available simulations in overlap areas covered by small individual CORDEX ensembles.

References

Iturbide, M., et al. (2020) An update of IPCC climate reference regions for subcontinental analysis of climate model data: definition and aggregated datasets. Earth System Science Data, 12, 2959–2970, https://doi.org/10.5194/essd-12-2959-2020

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 

Spinoni, J., et al. (2020). Future global meteorological drought hot spots: A study based on CORDEX data. Journal of Climate, 33 (9), pp. 3635-3661, https://doi.org/10.1175/JCLI-D-19-0084.1

Teichmann, C., et al. (2021). Assessing mean climate change signals in the global CORDEX-CORE ensemble. Climate Dynamics, 57 (5-6), pp. 1269-1292, https://doi.org/10.1007/s00382-020-05494-x

Acknowledgement

The authors would like to thank the Copernicus Climate Change Service (C3S) for funding part of this research. J.F. and A.S.C acknowledge project CORDyS (PID2020-116595RB-I00). J.M.G. and M.I. acknowledge project ATLAS (PID2019-111481RB-I00) funded by MCIN/AEI/10.13039/501100011033 and A.S.C and E.C. acknowledge project IS-ENES3 funded by the EU H2020 (#824084).

How to cite: 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.

The present study aims to analyse future changes in mean maximum and extreme temperature over India using 17 regional climate simulations from  CORDEX-SA experiment RCM ensembles at a spatial resolution of 0.5° x 0.5° for mid-term (2041-2060) and long-term (2081-2099) future under RCP 4.5 and RCP 8.5 scenarios. The regional climate simulations of the three RCM ensembles namely IITM-RegCM4, SMHI-RCA4 and MPI-CSC-REMO2009 were first evaluated against observed seasonal maximum temperature (Mar-Jun) during historical period (1971-2005). The datasets were obtained from CCCR-IITM ESGF data node for the study period. The model performance was assessed using standard performance measure statistics such as mean absolute error, root mean square error, mean bias, percentage bias. The RCMs show warm and cold bias in simulating the climatological mean maximum temperature with mean bias ranging from to 8.43°C(warmest) to -37.29°C (coldest) by CNRM-CM5 RCM of IITM-RegCM4 ensemble and by CSIRO-Mk3.6 RCM of SMHI-RCA4 ensemble respectively over the country. Variance scaling bias correction method was applied to correct the bias associated with the RCMs which significantly reduced the RMSE of RCMs from 11.03 (SMHI-RCA4) and 9.17 (IITM-RegCM4) to around zero after bias correction. Future changes assessed in mean maximum temperature show an increase in the range of 0.5°C to 4.5°C during mid-term (2041-2060) and 0.6°C to 5.84°C long-term (2081-2099) future period while under RCP 8.5 the increase ranges from 0.88° C to 5.40° C for mid-term (2041-2060) and 1.31° C to 12.87° C for long-term (2081-2099) period. The most pronounced increase is observed in the northern, eastern and north-eastern region of the country in which highest rise was simulated by IPSL-CM5A-MR(SMHI-RCA4) in northern region of the country for both the scenarios. The study also analyses changes in different ET-SCI indices as a measure of extreme temperature which have increased multi-fold in the future over the country. The study identifies the regions and magnitude of significant climate change signal expected in the mean maximum and extreme temperature in the future. It enhances the understanding and quantification of inter-model uncertainties within CORDEX-SA experiment RCM simulations. The outcomes of the study have both scientific and societal values in building resilience and informed efforts to avert the severe implications posed by increasing extreme temperature and heat events over India.

Keywords: Future Climate Change, CORDEX-SA, Regional Climate Model, Bias Correction, Extreme weather events

How to cite: Singh, S. and Mall, R. K.: CORDEX-SA Regional Climate Model Ensemble Performance Evaluation and Future Climate Change over India, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-145, https://doi.org/10.5194/egusphere-egu22-145, 2022.

An effort is made to implement a Regional Earth System Model (RESM) over the CORDEX-SA domain to demonstrate its skill in simulating the Indian summer monsoon characteristics. RESM was simulated on climate mode, 1980-2014, and showed good resemblance to observation in simulating mean precipitation, its variability (intraseasonal to interannual), extremes, and associated processes. RESM offers noticeable added value over its standalone atmospheric component (REMO), coupled model intercomparison project (CMIP5 and CMIP6), and regional climate models of CORDEX-SA. The added value is found to vary spatially. The most remarkable improvement is noticed over the Bay of Bengal (BoB), South-Central India, and Indo-Gangetic belt, where most standalone RCMs and coupled GCMs show limitations. The better representation of low-pressure systems both over land and ocean, sea surface temperature, Indian Ocean Dipole, and its underlying mechanism leads to improve mean precipitation. 

Keywords: RESM, CORDEX-SA, Indian summer monsoon, LPS, air-sea coupling

Acknowledgement: This work is jointly supported by the Department of Science and Technology (DST), Govt. of India, grant number DST/INT/RUS/RSF/P-33/G, and the Russian Science Foundation (Project No.: 19-47-02015).

How to cite: Kumar, P., Mishra, A. K., and Sein, D. V.: Demonstrating the air-sea coupling performance of a Regional Earth System Model: Indian summer monsoon a case study, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8351, https://doi.org/10.5194/egusphere-egu22-8351, 2022.

The climate over Central Asia has been investigated using the REMO (v2015) and its recent vegetation coupled version, REMO-iMOVE for the period of 2000-2015 at horizontal resolutions of 0.44° and 0.11°. Model evaluation is performed using the mean monthly bias patterns for temperature, precipitation, and leaf area index along with different statistical matrices. In comparison to the lower resolution of 0.44°, the spatial precipitation pattern at 0.11° is represented better. In the case of mean temperature, higher resolution simulation from both models tends to agree quite well with the validation dataset. Reduced bias in maximum and minimum temperature at 0.11° resolution is also observed over the study domain. There is improved temperature bias in REMO-iMOVE in comparison to the standard REMO version which has static vegetation while in the case of precipitation, the bias is larger in REMO-iMOVE. Since the iMOVE version is coupled to atmospheric and hydrological components, it has a clear advantage in capturing the vegetation cover and leaf area index better in comparison to the standalone REMO version. Overall, iMOVE is able to perform quite similar to REMO in simulating the mean climate of Central Asia but clearly advantageous in simulating vegetation parameters and temperature.

How to cite: Rai, P., Ziegler, K., Abel, D., Pollinger, F., and Paeth, H.: Assessment of REgional MOdel REMO and its coupled version REMO-iMOVE over Central Asia, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4191, https://doi.org/10.5194/egusphere-egu22-4191, 2022.

A correct representation of the planetary boundary layer (PBL) is critical to achieve realistic simulations, especially regarding surface variables for regional climate simulations. In this study we examine the sensitivity of the performance of the Weather Research and Forecast (WRF) model to the use of three widely used PBL schemes with emphasis on heat extremes. This study aims (i) to explore the differences among the WRF simulated air temperature and heat extremes resulting from the choice of PBL schemes, (ii) to investigate the physical causes of model biases via the analysis of different variables and, finally, (iii) to reveal the most suitable scheme for application in the Middle-East - North Africa (MENA) domain. The schemes under evaluation are the Mellor–Yamada–Janjic (MYJ), Yonsei University (YSU), and the asymmetric convective model, version 2 (ACM2). We performed 11-year (2000-2010) simulations over the MENA region at 24km resolution. The simulations have been compared with the ERA5 reanalyses for several variables, including maximum and minimum 2-meter air temperature and indices of extremes. Results indicate that model biases strongly vary according to geographic area, with simulations showing good performance in some regions and substantial biases in others. Analysis of different variables like PBL height, moisture and heat fluxes show that differences among the schemes can be linked to differences in vertical mixing strength and entrainment of air from above the PBL.

How to cite: Ntoumos, A., Hadjinicolaou, P., Zittis, G., and Lelieveld, J.: Evaluation of WRF model PBL schemes in simulating temperature extremes over the Middle-East – North Africa (MENA) region, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6137, https://doi.org/10.5194/egusphere-egu22-6137, 2022.

Models tend to show strong rainfall biases over Southern Africa, especially during the Summer (DJF) months. This study aims to explore and understand why these biases occur. The Angolan Low (AL) and ENSO are two important sources of Summer rainfall variability. Thus, the study explores whether the relationship between the AL, ENSO and rainfall is represented correctly in three different ensembles; the CORDEX-CORE ensemble (CCORE, 0.22 degrees resolution), the lower resolution (0.44 degrees) CORDEX-phase 1 ensemble (C44) and the driving CMIP5 models. From regression analysis and a self organizing map the results show that wetter (drier) than normal DJF seasons usually occur during a strong (weak) AL and a La Nina (El Nino) event. While the models show this to an extent, they also show some differences in these relationships compared to the observed. The study examines some key dynamical features to understand why these differences occur. These results can further the understanding and improvement of the simulated Southern African rainfall in models. 

How to cite: Abba-Omar, S., Raffaele, F., Coppola, E., Jacob, D., Teichmann, C., and Remedio, A.: The representation of the summer Southern African rainfall and its relationship with the Angolan Low and ENSO in the CORDEX models, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6306, https://doi.org/10.5194/egusphere-egu22-6306, 2022.

The Lake Victoria Basin is home to largest freshwater lake (Lake Victoria; LV) in Africa and second largest in the world. Each year hundreds of fisherman are lost on LV during intense night-time thunderstorms. LV is an essential component of the local economy while at the same time being one of the most hazardous lakes in the world. Despite this, until recently, understanding of the processes contributing to heavy rainfall events was very limited. In this study we present a 10-year (2005-2015) convection permitting (3km grid-spacing) simulation (CPS) of the Lake Victoria Basin using the RegCM version 4.7.0. A lake model is utilized in order to couple the lake regions with RegCM, which has been shown to be of great importance for simulating a realistic lake surface temperature (LST) and precipitation over LV. Mesoscale circulations associated with the diurnal cycle over LV are an important driver of intense night-time thunderstorms. An analysis of the diurnal rainfall cycle over LV shows that the CPS well represents the timing of nocturnal rainfall over the lake which is associated with a strong landbreeze, however the daytime peak in rainfall over the land surrounding the lake is too early relative to observed data. The temporal spectrum of lake rainfall shows a dominance in diurnal frequencies while intraseasonal timescales show only a very weak signal. Extreme nocturnal rainfall events exceeding 2STD of the lake rainfall timeseries are composited and separated for further analysis. These events show a clear migration from the previous daytime peak in rainfall over land, westward onto the lake during the night. To understand these diurnal precipitation events we explore mechanisms which may play a role in the events such as, the strength of the lake-landbreeze circulation, and convective instability.

How to cite: Glazer, R. and Coppola, E.: Understanding extreme diurnal convection over Lake Victoria from a convection permitting regional climate simulation, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10158, https://doi.org/10.5194/egusphere-egu22-10158, 2022.