Abstract. The co-occurrence of (not necessarily extreme) precipitation and surge can lead to extreme inland water levels in coastal areas. In a previous work the positive dependence between the two meteorological drivers was demonstrated in a managed water system in the Netherlands by empirically investigating an 800-year time series of water levels, which were simulated via a physical-based hydrological model driven by a regional climate model large ensemble. In this study, we present an impact-focused multivariate statistical framework to model the dependence between these flooding drivers and the resulting return periods of inland water levels. This framework is applied to the same managed water system using the aforementioned large ensemble. Composite analysis is used to guide the selection of suitable predictors and to obtain an impact function that optimally describes the relationship between high inland water levels (the impact) and the explanatory predictors. This is complex due to the high degree of human management affecting the dynamics of the water level. Training the impact function with subsets of data uniformly distributed along the range of water levels plays a major role in obtaining an unbiased performance. The dependence structure between the defined predictors is modelled using two- and three-dimensional copulas. These are used to generate paired synthetic precipitation and surge events, transformed into inland water levels via the impact function. The compounding effects of surge and precipitation and the return water level estimates fairly well reproduce the earlier results from the empirical analysis of the same regional climate model ensemble. Regarding the return levels, this is quantified by a root-mean-square deviation of 0.02 m. The proposed framework is able to produce robust estimates of compound extreme water levels for a highly managed hydrological system. Even though the framework has only been applied and validated in one study area, it shows great potential to be transferred to other areas. In addition, we present a unique assessment of the uncertainty when using only 50 years of data (what is typically available from observations). Training the impact function with short records leads to a general underestimation of the return levels as water level extremes are not well sampled. Also, the marginal distributions of the 50-year time series of the surge show high variability. Moreover, compounding effects tend to be underestimated when using 50-year slices to estimate the dependence pattern between predictors. Overall, the internal variability of the climate system is identified as a major source of uncertainty in the multivariate statistical model.

<p>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.</p><p>Georgia is the mountainous country located in the south-western part of the Greater Caucasus. Its area is 69.875 km<sup>2</sup>. 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.</p><p>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.</p><p>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)<em> </em>but at the same time small enough in size to minimize the use of computational resources.  </p><p>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.</p><p>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.</p><p>This work was supported by Shota Rustaveli National Science Foundation of Georgia (SRNSFG) № FR-19-8110.</p><p> </p>

The current study focuses on the RegCM4.5 model and specifically on a comparison of hydrostatic and non-hydrostatic approaches as well as on different microphysical parameterisations and planetary boundary layer (PBL) schemes. The main goal of the paper is to simulate the historical regional precipitation characteristics of the Carpathian region as reliably as possible. For this purpose, seven different model experiments at a 10 km horizontal resolution were completed for a 10-year period (1981-1990) using ERA-Interim reanalysis data (with 0.75 resolution) as initial and boundary conditions. Our simulation matrix consists of hydrostatic and non-hydrostatic runs together with different treatments of moisture, namely, the SUBEX and the NogTom schemes. In addition, two PBL schemes are tested, the Holtslag and the UW-PBL scheme. In this detailed validation study, RegCM outputs (e.g., temperature, global radiation, cloud cover, precipitation) are compared to the homogenized, gridded CarpatClim data (available with 0.1 resolution) that are based on measurements at regular meteorological station sites. The validation considers seasonal and monthly means, as well as extreme climatic events. On the basis of the results we can conclude that the role of the non-hydrostatic core can be clearly recognized for precipitation, particularly over mountains. Moreover, it was also found that the UW-PBL scheme performs with a negative bias regarding atmospheric boundary layer thickness and temperature and it reduces the wet/dry biases of the Holtslag PBL scheme. Regarding microphysical schemes, the NogTom scheme performs better than the SUBEX scheme, but the modified SUBEX (SUB4.3) can also reduce the precipitation over mountainous areas.

Abstract. The co-occurrence of (not necessarily extreme) precipitation and surge can lead to extreme inland water levels in coastal areas. In a previous work the positive dependence between the two meteorological drivers was demonstrated in a case study in the Netherlands by empirically investigating an 800-year time series of water levels, which were simulated via a physical-based hydrological model driven by a regional climate model large ensemble. In this study, we present and test a multivariate statistical framework to replicate the demonstrated dependence and the resulting return periods of inland water levels. We use the same 800-year data series to develop an impact function, which is able to empirically describe the relationship between high inland water levels (the impact) and its driving variables (precipitation and surge). In our study area, this relationship is complex because of the high degree of human management affecting the dynamics of the water level. By event sampling and conditioning the drivers, an impact function was created that can reproduce the water levels maintaining an unbiased performance at the full range of simulated water levels. The dependence structure between the driving variables is modeled using two- and three-dimensional copulas. These are used to generate paired synthetic precipitation and surge events, transformed into inland water levels via the impact function. The compounding effects of surge and precipitation and the return water level estimates fairly well reproduce the earlier results from the empirical analysis of the same regional climate model ensemble. The proposed framework is therefore able to produce robust estimates of compound extreme water levels for a highly managed hydrological system. In addition, we present a unique assessment of the uncertainty when using only 50 years of data (what is typically available from observations). Training the impact function with short records leads to a general underestimation of the return levels as water level extremes are not well sampled. Also, the marginal distributions of the 50-year time series of the surge show high variability. Moreover, compounding effects tend to be underestimated when using 50 year slices to estimate the dependence pattern between predictors. Overall, the internal variability of the climate system is identified as a major source of uncertainty in the multivariate statistical model.

<p>The global climate change resulting from natural and growing anthropogenic factors of particular importance for the territory of Georgia as the frequency and intensity of extreme weather events (extreme high temperatures, heavy precipitation levels, and agricultural and ecological droughts) are increasing in the territory. Georgia’s complex orography and proximity to the Black and Caspian Seas necessitates the use of high-resolution models, such as regional climate models, to assess future climate change hazards. In this study, we analyse the output from high-resolution simulation of mean and extreme precipitation and temperature using the Abdus Salam International Centre for Theoretical Physics Regional Climate Model version 4.7.1 for the period of 2010–1014 as an initial assessment of model performance for the territory. The simulation is performed at a 12 km horizontal grid spacing using<strong> </strong>ERA5 data as boundary conditions. Comparison with observed station data shows that the model performs better in simulating the monthly mean and extreme values of temperature than precipitation. In some mountain stations, the biases between observation and simulated precipitation are high, partly due to the mountainous terrain, when the horizontal resolution of the model (12 km) can lead to a significant discrepancy between the model's points and the locations of weather stations.  </p> <p>This study represents the first step of Georgia’s first high resolution assessment to better understand how climate change will impact the territory required to climate change policy and decision-making.</p> <p>This work was supported by Shota Rustaveli National Science Foundation of Georgia (SRNSFG) № FR-19-8110.</p>