CL5.2.4

EPISODES is an empirical statistical downscaling (ESD) method, which has been initiated and developed by the German Weather Service (DWD). Having resulted in good evaluation scores for Germany, the methodology it is also set-up and adapted for Austria at ZAMG and, hence, for an alpine territory with complex topography.

ESD methods are sparing regarding computational costs compared to dynamical downscaling models. Due to this advantage ESD can be applied in a short time frame and in a demand-based manner. It enables, e.g., processing ensembles of downscaled climate projections, which can be assessed either as stand-alone data set or to enhance ensembles based on dynamical methods. This helps improve the robustness of climatological statements for the purpose of climate impact research.

Preconditions for achieving high-quality results by EPISODES are long-term, temporally consistent observation data sets and a best possible realistic reproduction of relevant large-scale weather conditions by the GCMs. Given these requirements, EPISODES produces high-quality multivariate and spatially/temporally consistent synthetic time series on regular grids or station locations. The output is provided for daily time steps and, at maximum, for the resolution of underlying observation data.

The EPISODES method consists on mainly two steps: At first stage, univariate time series are produced on a coarse grid based on the analogue method and linear regression. It means that coarse scale atmospheric conditions of each single day as described by the GCM projections are assigned to a selection of at most similar daily weather situations of the observed past. From this selection new values are determined by linear regression for each day.

The second stage of the EPISODES method works like a weather generator. Short-term anomalies based on first stage results, on the one hand, and on observations, on the other hand, are matched selecting the most similar day for all used meteorological parameters and coarse grid points at the same time. Together with the high-resolution climatological background of observations and the climatological shift as described by GCM projections the short-term variability are combined to synthetic daily values for each target grid point. This approach provides the desired characteristics of the downscaled climate projections such as multivariability and spatio-temporal consistency.

Recent EPISODES evaluation results for daily precipitation and daily mean temperature are presented for the Austrian federal territory. Performance of the EPISODES ensemble will also be discussed in relation to existing ensembles based on dynamical methods which have already been widely used in climate impact studies in Austria: EURO-CORDEX and ÖKS15.

How to cite: Schellander-Gorgas, T., Kreienkamp, F., Lorenz, P., Matulla, C., and Tordai, J.: Empirical statistical downscaling with EPISODES in Austria, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1224, https://doi.org/10.5194/egusphere-egu21-1224, 2021.

Careful planning of the use of water resources is critical in the semi-arid eastern Mediterranean region. The relevant areas are characterized by complex terrain and coastlines, and exhibit large spatial variability in seasonal precipitation. Global climate models provide only partial information on local‐scale phenomenon, such as precipitation, primarily due to their coarse resolution. In this study, statistical downscaling algorithms, based on both synoptic scale past weather regimes and  analogues and their associated observed precipitation at rain gauges, are operated for eighteen Israeli rain gauges in four hydrological basins with an altitude ranging between ‐200 and ~1000 m ASL. In order to project seasonal precipitation over Israel and its hydrologic basins, the algorithms are applied to six Coupled Model Inter‐comparison Project Phase 5 (CMIP5) models for the end of the 21st century, according to the RCP4.5 and RCP8.5 scenarios. The downscaled models are able to capture quite well the seasonal precipitation distribution. All models display a significant reduction of seasonal precipitation for the 21st century of up to ~50% with variations depending on the scenario, algorithm and hydrological basin. The reduction is less acute when applying the weather regimes algorithm as it relies on past  daily mean precipitation values per regime, while the analogues downscaling algorithm relies on the daily precipitation of the individual past analogues and therefore better captures the tails of the distribution. Moreover, the analogues downscaling algorithm projects a significant increase of outliers in the right tale of the distribution i.e. increase in extreme precipitation events. The reduction in seasonal precipitaton is due to both decrease in the frequency of the synoptic systems responsible for precipitation as well as reduction in the daily precipitation amounts at the stations. While the percentage of reduction is quite similar among stations (same reduction in the precipitating synoptic systems that affect the whole area), the reduced amounts are different as they are characterized by different seasonal precipitation amounts. In some cases reductions in precipitation can lead to transition of some areas to semi-arid and arid climates. The statistical downscaling methods applied in this study can be easily transferred to other regions where long‐term datasets of observed precipitation are available.

How to cite: Rostkier-Edelstein, D., Hochman, A., Kunin, P., Alpert, P., Harpaz, T., and Saaroni, H.: Weather regimes and analogues downscaling of seasonal precipitation for the 21st century; A case study over Israel, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1574, https://doi.org/10.5194/egusphere-egu21-1574, 2021.

General Circulation Models (GCMs) are the primary source of knowledge for constructing climate scenarios and provide the basis for quantifying the climate change impacts at multi-scales and from local to global. However, the climate model simulations have a lower resolution than the desired watershed or hydrologic scale. Different downscaling methodologies are adopted to transform the global scale (coarser resolution) climate information to the local scale (finer resolution). One of the drawbacks of the GCM simulations is the systematic bias relative to historical observations. Bias correction is thus required to adjust the simulated values to reflect the observed distribution and statistics. In this study, the effect of bias correction is evaluated on the statistical downscaling models' performance to predict the temperature. Three statistical downscaling models are used: (i) Multi-linear Regression (MLR); (ii) Generalized Regression Neural Network (GRNN); and (iii) Cascade Neural Network (CasNN). The average daily temperature simulations generated by 25 GCMs of Coupled Model Intercomparison Project Phase-5 (CMIP5) are used in the study. The analysis is carried out at 22 stations of the Upper Thames River Basin (UTRB) in Canada during the baseline period of 1950 to 2005. The downscaling models' performance is evaluated using the Pearson Correlation Coefficient (CC) and Nash Sutcliffe Efficiency (NSE). The results indicated that bias correction had improved all the downscaling models' performance at all stations of UTRB. The respective increase in CC and NSE values for (i) MLR is 8% and 10%; (ii) GRNN is 4% and 7%; and (iii) CasNN is 4% and 8%. Among the three downscaling models, multi-linear regression and cascade neural network models have shown similar performance.

How to cite: Wagh, P. and Srivastav, R.: Statistical Downscaling of Temperature Using Global Climate Model Outputs - Effect of Bias correction, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7261, https://doi.org/10.5194/egusphere-egu21-7261, 2021.

The available state-of-the art Regional Climate Model (RCM) simulations from the Euro-Cordex initiative have an horizontal resolution of about 12km which although is adequate for assessing regional climate change impacts is still coarse for studying the climate change impacts in an urban environment such as the Greater Athens Area (GAA). To this aim we propose a hybrid dynamical-statistical downscaling approach that produces high resolution, in the order of 1km, climate change projections for two future periods and under two RCP scenarios. To produce the higher resolution climate projections we combine the results of the Weather Research and Forecasting model (WRF) - Version 3.9.1 -including a single-layer urban canopy model to represent the urban tile- with available RCMs simulations obtained from the Euro-Cordex database.

Initially an annual WRF, ERA interim driven, simulation for a year identified as a “representative year” for the period 1971-2000 in the GAA is performed at an horizontal resolution of 1km. Subsequently the spatial signal of the WRF simulation is passed to the ERA interim driven RCM simulations for the period 1971-2000 using the unbiasing bias adjusting method which maintains the absolute trend as well as the variability of the RCM simulated data at all time scales. In a second step the donwscaled RCM evaluation simulations are used to bias adjust the transient RCM simulations using the empirical quantile method (EQM). EQM works by matching the transient simulations empirical cumulative distributions to the evaluation ones. This is achieved by establishing a quantile-dependent correction function between them during the reference period. The correction functions are then applied to both the historical and the future periods.

In this study we present the results for temperature and precipitation but the methodology can be extended to other variables of interest assuming that the WRF and the evaluation RCM simulations adequately reproduce their spatial and temporal variability, respectively.

How to cite: Varotsos, K. V., Dandou, A., Papangelis, G., Roukounakis, N., Tombrou, M., and Giannakopoulos, C.: A hybrid dynamical-statistical downscaling approach for climate change impacts analysis on high resolution in the Greater Athens Area, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8759, https://doi.org/10.5194/egusphere-egu21-8759, 2021.

ADRIADAPT is a project inside the framework of the Italy-Croatia Interreg Cooperation Programme. Focused to contrast impacts over Adriatic coastal areas, which are particularly exposed to climate changes, it aims to provide a resilience information platform, suitable for performing vulnerability analysis and making decisions. In this work, climate projections are computed for some Emilia-Romagna coastal areas throughout a statistical downscaling scheme, based on the canonical correlation analysis between local climate indices (predictands) and large-scale fields (predictors). Firstly, the scheme has been calibrated and validated at a seasonal time scale for minimum and maximum temperature, tropical nights, heatwave duration, frost days, obtained by the ERACLITO observation gridded dataset for Emilia-Romagna, and large-scale fields (mean sea level pressure, 500hPa geopotential height, and 850hPa temperature) of the ECMWF-ERA40 and ERA-interim re-analysis data set. Calibration is performed over the 1961-1985 and 2006-2010 periods, while validation concerns the 1986-2005 period. Correlation coefficients, bias, and root mean square errors are taken as skill measures. Secondly, large-scale field data simulated by four global climate models from CMIP5 experiments (CMCC-CM, MPI ESM-MR, CNRM -CM5, Can-ESM2) in the framework of two emission scenarios (RCP4.5 and RCP8.5) has been treated as input to the statistical downscaling scheme to obtain local climate indices for the next four 20-year periods: 2021-2040, 2041-2060, 2061-2080, 2081-2100. Changes respect to the 1986-2005 period, taken as climatic reference, are evaluated. A Poor Man’s ensemble technique is applied to reduce uncertainties and give more statistical robustness to the results. The minimum and maximum temperature projections show a significant increase could be expected to occur for all seasons and both RCPs. The magnitude of changes is higher for the maximum temperature, especially during the summer season when changes up to 4°C for RCP4.5 and 8°C for RCP8.5 are expected at the end of the century. As regards extreme temperature indices, the seasonal tropical nights and heatwaves duration are projected to increase while frost days to decrease over all the four-time periods and for both emission scenarios.
This work has been performed in the framework of Italy-Croatia Interreg Cooperation Programme – ADRIADAPT Project (https://www.italy-croatia.eu/web/adriadapt/).

How to cite: Tomozeiu, R., Monti, R., and Nerozzi, F.: Statistically downscaled climate projections as a support for adaptation tools, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15438, https://doi.org/10.5194/egusphere-egu21-15438, 2021.

The NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) archive contains statistically downscaled projections at 0.25-degree horizontal resolution and a daily timestep for eight variables from 1950 to 2099. The original version of NEX-GDDP is based on an ensemble of three experiments (historical, RCP4.5, and RCP 8.5) from the larger CMIP5 archive, while a new version currently under development is based on an ensemble of three comparable experiments (historical, SSP245, and SSP585) from the recently released CMIP6 archive. While the methodology used in the creation of both versions is the same (daily bias-corrected spatial disaggregation), we will explain the nuanced differences between the two executions of that method. In addition, we will present examples of differences and similarities in output between the two versions.

How to cite: Wang, W., Thrasher, B., Michaelis, A., Nemani, R., and Lee, T.: The NASA Earth Exchange Global Daily Downscaled Projections, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1012, https://doi.org/10.5194/egusphere-egu21-1012, 2021.

Floods are highly destructive natural hazards causing widespread impacts on socio-ecosystems. This hazard could be further amplified with the ongoing climate change, which will likely alter magnitude and frequency of floods. Estimating how flood regimes could change in the future is however not straightforward. The classical approach is to estimate future hydrological regimes from hydrological simulations forced by time series scenarii of weather variables for different future climate scenarii. The development of relevant weather scenarii for this is often critical. To be adapted to the critical space and time scales of the considered basins, weather scenarii are thus typically produced from climate models with downscaling models (either dynamic or statistical).

In this study, we aim to evaluate the capacity of such a simulation chain to reproduce floods observed in the upper Rhône River (10900 km², European Alps) over the last century. The modeling chain is made up of (i) the atmospheric reanalysis ERA-20C (1900-2010), (ii) the statistical downscaling model Analog, and (iii) the glacio-hydrological model GSM-SOCONT (Glacier and Snowmelt SOil CONTribution model; Schaefli et al., 2005). To assess the performance of this modeling chain, the simulated scenarii of mean areal precipitation and temperature are compared to the observed time series over the common period (1961-2010), whereas the discharge scenarii are compared to the reference time series (1920-2010).

In this presentation, we will discuss (i) the results obtained by the basic Analog method, namely a flood events underestimation due to an underestimation of extreme precipitation values, in particular 3-day and 5-day extreme precipitation, and (ii) the enhanced results obtained by the improved version of Analog SCAMP (Sequential Constructive Atmospheric Analogues for Multivariate weather Predictions; Raynaud et al., 2020) combined to the Schaake Shuffle method.

References:

Schaefli, B., Hingray, B., M. Niggli, M., Musy, A. (2005). A conceptual glacio-hydrological model for high mountainous catchments. Hydrology and Earth System Sciences Discussions, European Geosciences Union, 9, 95-109.

Raynaud, D., Hingray, B., Evin, G., Favre, A.-C., Chardon, J. (2020). Assessment of meteorological extremes using a synoptic weather generator and a downscaling model based on analogues. Hydrology and Earth System Sciences Discussions, European Geosciences Union, 24(9), 4339-4352.

How to cite: Legrand, C., Hingray, B., and Wilhelm, B.: Simulating catchment scale river discharges and flood events from large scale atmospheric information: Example of the Upper Rhône River (European Alps), EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7843, https://doi.org/10.5194/egusphere-egu21-7843, 2021.

Regional climate projections using ultra-high resolution convection-permitting (CP) models are now increasingly available, with recent endeavours also focussing on vulnerable tropical regions. A number of recent studies have examined a pair of pan-Africa integrations of the Met Office CP model (CP4A), run at 4.4km resolution with 10 years of both a present-day simulation and a circa-2100 projection. However, experience from inter-disciplinary discussions has revealed different perspectives on the value of such experiments, with climate scientists emphasising the importance of an improved representation of convection, whereas applied scientists emphasise the importance of the unprecedented spatial scale of the available climate data. This raises critical questions about the usable spatial scales of such projections. Can CP models really provide robust information about future climate change at finer scales than parameterised regional climate models? We address this question with a focus on projected changes in rainfall, both seasonal means and daily extremes, both of which may be expected to exhibit heterogeneous climate responses in regions of large surface forcing. Although the capacity for statistically significant detail is found to be small in this short projection, detectable sub-25km variability is indeed apparent in regions of high topographic variability. Coastal regions, such as lakes and marine bays are also examined, along with urban boundaries. Furthermore, where no significant fine-scale detail is apparent (spatial heterogeneity is only due to sampling variability), we also examine the extent to which the robustness of climate information (better signal-to-noise ratios) can be enhanced for users by the spatial aggregation of model data.

How to cite: Rowell, D. and Berthou, S.: Fine-Scale Climate Projections for Africa: What Additional Robust Spatial Detail is Provided by a Convection-Permitting Model? , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1283, https://doi.org/10.5194/egusphere-egu21-1283, 2021.

The availability of in situ atmospheric observations decreases with elevation and topographic complexity. Data sets based on numerical atmospheric modeling, such as reanalysis data sets, represent an alternative source of information, but often suffer from inaccuracies, e.g., due to insufficient spatial resolution. In this contribution, we investigate the added value of sDoG, a reanalysis data postprocessing and downscaling tool designed to extend short-term and/or interrupted weather station data from high mountain sites to the baseline climate. sDoG is applied to ERA interim predictors to produce a retrospective forecast of daily air temperature at the Vernagtbach climate monitoring site (2640 MSL) in the Central European Alps. sDoG training and cross-validation is based on observations from 2002 to 2012. The availability of observations at the Vernagtbach climate monitoring site further back in time allows us to perform a true evaluation: "true evaluation" in contrast to cross-validation, by assessing the performance of the sDoG retrospective forecast for the period 1979 to 2001.

We demonstrate the ability of sDoG in modeling air temperature in the true evaluation period for different temporal scales. sDoG adds significant value over a selection of reference data sets, including state-of-the-art global and regional reanalysis data sets, output by a regional climate model, and an observation-based gridded product (SPARTACUS). However, we identify limitations of sDoG in modeling summer air temperature variations, most probably related to changes of the microclimate around the Vernagtbach climate monitoring site that violate the stationarity assumption underlying sDoG. Comparing the performance of the considered reference data sets reveals that higher resolution data sets do not necessarily add value over data sets with lower spatial resolution. For example, the global reanalyses ERA5 (31 km resolution) and ERA interim (80 km resolution) both clearly outperform the higher resolution surface analyses ERA5-Land (11 km resolution), HARMONIE (11 km resolution), and UERRA MESCAN-SURFEX (5.5 km resolution). Performance differences amongst ERA5 and ERA interim, by contrast, are comparably small. The results highlight the importance of station-scale uncertainty assessments of atmospheric numerical model output and downscaling products for high mountain areas, both for data users and model developers.

How to cite: Hofer, M. and Horak, J.: The added value of downscaling for high mountain sites, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10656, https://doi.org/10.5194/egusphere-egu21-10656, 2021.

The evaluation of models in general is a non-trivial task. Even a well established model may yield correct results for the wrong reasons, i.e. by a different chain of processes than found in observations. While guidelines and strategies exist to maximize the chances that results match measurements for the right reasons, these are mostly applicable to full-physics models, such as numerical weather prediction models. The Intermediate Complexity Atmospheric Research (ICAR) model is a comparatively novel atmospheric model employed to downscale atmospheric fields. ICAR uses linear mountain wave theory to represent the wind field and advects atmospheric quantities, such as temperature and moisture in this wind field. Additionally a microphysics scheme is applied to represent the formation of clouds and precipitation.

We conducted an in-depth process-based evaluation of ICAR, employing idealized simulations to increase the understanding of the model and develop recommendations to improve its results. We contrast the ICAR simulations to Weather Research and Forecasting (WRF) model simulations and asses the impact of our recommendations with a case study for the South Island of New Zealand.

Our results suggest two key aspects relevant for ICAR to obtain the correct results for the right reasons. Firstly, the representation of the wind field within the domain improves when the dry and the moist Brunt-Väisälä frequencies are calculated in accordance to linear mountain wave theory from the unperturbed base state rather than from the time-dependent perturbed atmosphere. Secondly, the results show that there is a lowest possible model top elevation that should not be undercut to avoid influences of the model top on cloud and precipitation processes within the domain. We analysed the causes for the differences between the idealized ICAR and WRF simulations and attribute them to the non-linearities in the WRF wind field and additional simplifications in the governing equations of ICAR. With our recommended ICAR setup applied to the real case study we find an upwind spatial shift of the precipitation maximum in comparison to the results obtained with the original ICAR setup. Additionally our results show that when model skill is evaluated from statistical metrics based on comparisons to surface observations only, such analysis may not reflect the skill of the model in capturing atmospheric processes such as gravity waves and cloud formation.

Overall our findings have consequences for the interpretation of past results obtained with ICAR and suggest improvements to ICAR in future studies.

How to cite: Horak, J., Hofer, M., Gohm, A., and Rotach, M. W.: Better downscaling results for the right reasons - A process based evaluation of the ICAR model, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14971, https://doi.org/10.5194/egusphere-egu21-14971, 2021.