UP3.1

Temperature and humidity are key climatic variables for the assessment of climate variability. This study focuses on the climatic trends of temperature, specific and relative humidity both at the surface and in multiple pressure levels in the atmosphere. We present the first results of the analysis the dynamics of some key climate variables over Europe. The analysis was conducted for Europe, but it is focusing also on Greece. The main purpose of this study is to investigate whether possible changes in the basic climate variables exist over the recent years. 

Data from the ERA5 reanalysis product are used for the period 1979-2018 (40 years) with spatial resolution of 0.25° x 0.25°. The Sen’s slope estimator is used to identify the climate trends at each grid point and the Mann-Kendall statistical test was applied to detect statistically significant spatial and temporal changes for the examined domain. The results indicate statistically significant warming trends at the 99% level over land and sea at surface. Regarding Greece, statistically significant warming trends at the 99% level occur during summer. In addition, positive temperature trends are also presented over land and sea, in the troposphere, over the particular domain. In contrast, in the stratosphere, statistically significant cooling trends at the 99% level are observed. Additionally, the stratospheric cooling trends increase with increasing altitude in the atmosphere. Regarding the climatic trends of the specific humidity, mainly positive values prevail up to mid-troposphere. Finally, the climatic trends of the relative humidity exhibit positive and negative values due to the relationship of humidity and temperature.

How to cite: Tzanis, C., Kourtesiotis, C., and Philippopoulos, K.: Dynamics of key climate variables over Europe, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-497, https://doi.org/10.5194/ems2021-497, 2021.

Since changes in temperatures and precipitation significantly affect the biosphere, viticulture as an important economic branch in the moderate latitudes (e.g., mainly between 35°N and 55°N) is strongly influenced by climate change. The most commonly analysed/modelled phenological phases of grapevines are budburst (beginning of grapevine seasonal growth), flowering (crucial for the reproductive cycle) and veraison (initiation of the ripening). Resent studies indicate that budburst is greatly regulated by temperature. Due to climate change and temperature increase, budburst dates show trends in earlier occurrences at several available stations throughout Croatia which increases the vulnerability of the grapevine to the spring frost.

The aim of this study is to determine trends and changes in budburst date, their statistical characteristics at available stations in period 1961-2020 in Croatia. We focus on four grapevine varieties, two white (Graševina and Chardonnay) and two red (Merlot and Plavac Mali) and performance of statistical models (GDD, Riou’s model and BRIN model) that predict budburst dates in the present climate. For this purpose an effect of the dormancy period and base temperature on the simulated budburst date have been explored. The study is further extended to future climatic conditions using statistical and numerical climate models. Therefore, a daily output from three CORDEX Regional Climate Models’ (RCMs) simulations (CLMcom-CCLM4-8-17, SMHI-RCA4, CNRM-ALADIN5.3) for Croatian domain are used. All RCMs are forced by Global Climate Models (GCMs) with a moderate (RCP4.5) and a high-end (RCP8.5) green-house gass (GHG) scenario(s) and all the simulations have horizontal grid spacing of 0.11°. Results indicate further earlier appearance of budburst regardless of varieties.

How to cite: Omazić, B., Telišman Prtenjak, M., Prša, I., and Karoglan, M.: Analysis of the observed and modelled budburst dates of grapevine in Croatia, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-20, https://doi.org/10.5194/ems2021-20, 2021.

Persistence or sequences of critical weather patterns over Europe can trigger seasonally extreme hydroclimatic conditions in certain regions. In order to better estimate return periods of extremes across Europe, existing time series of sequences of weather-types over Europe were used to train monthly rules for the transition from one situation to another and their duration behaviour. This can be efficiently realized and tested by setting up decision trees and generating up to 10,000 year time series of weather-type sequences.

In an experiment carried out, large-scale weather situation types according to Hess/Brezowsky available from 1961 to 2020 were divided into two time periods and rules for the transition were derived for both by training decision trees. Based on the trained rules of transistions for the periods 1961-1990 and 1991-2020, 10,000-year weather-type sequences were then generated and analysed.

The comparison of the probability density functions of persistence for the 30 different large-scale weather situation types show that omega-like circultion patterns over Europe have a higher tendency to persist in the present time period. In connection with this, the risks of prolonged dry phases in Central Europe have increased. For the translation of different weather-types into local weather-type characteristics, long-term monthly mean daily precipitation values per weather-type was assigned from ERA5 reanalysis data and rearranged in a post-processing step according to the generated weather-type sequences. The analysis of the maximum duration of consecutive dry and wet months in Europe was the main focus and the identified long-term changes in hydroclimatic quantities can be thus exclusively attributed to dynamic factors.

How to cite: Hoffmann, P.: Learning of weather-type transitions for risk assessment, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-382, https://doi.org/10.5194/ems2021-382, 2021.

As a consequence of the ever-increasing global temperature, not only the air, and surface, but also lakes are warming up. This is expressed by steadily increasing base temperatures, but also in increases in the frequency and intensity of lake heatwaves. Land-based organisms may adapt to a changing climate by migrating to more suitable habitats, but this is usually not an option for lake-dwellers. Because many livelihoods depend on the ecosystem services of lakes, understanding the effects of heatwaves on lake composition form  an important input for the assessment of climate change impacts and design of adaptation strategies.

Using satellite data of lake temperature and water quality observations, we here investigate the effects of heatwaves on lake composition by studying the relationship between heatwaves and water quality variables of temperature, chlorophyll-a , colored dissolved organic matter, and suspended particulate matter . The latter can be used to infer effects of heat stress on health and populations of phyto- and zooplankton communities and higher aquatic organisms. Satellite-based data sets provided by the Climate Change Initiative of the European Space Agency,  CCI-Lakes (https://climate.esa.int/en/projects/lakes/) are  used in conjunction with the 2SeaColor model to determine depth-dependent attenuation coefficients and water quality variables.These data are complemented with and compared to data from Copernicus Global Land Services (https://land.copernicus.eu/global/products/). 

The co-occurrence of heatwaves and changes in lake composition is investigated using statistical tools, and the causality is examined by comparison with biophysical models. The results from this study are discussed in light of previously published projected changes in heatwave frequency and intensity.

How to cite: Penning de Vries, M., Salama, S., Mannaerts, C., and van der Wal, D.: Effects of heatwaves on lake composition derived from satellite observations, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-332, https://doi.org/10.5194/ems2021-332, 2021.

It is generally accepted that a climatic data set of meteorological measurements with true sequences and real interdependencies between meteorological variables is needed for a representative climate simulation. In the late 1970s the Typical Meteorological Year (TMY) concept was introduced in USA as a design tool for approximating expected climate conditions at specific locations, at a time when computers were much slower and had less memory than today. A TMY is a collation of selected weather data for a specific location, listing usually hourly values of meteorological and solar radiation elements for one-year period. The values are generated from a data bank much longer than a year in duration, at least 10 years. It is specially selected so that it presents the range of weather phenomena for the location in question, while still giving annual averages that are consistent with the long-term averages for the specific location. Each TMY data file consists of 12 months chosen as most “typical“ among the years present in the long-term data set. Although TMYs do not provide information about extreme events and do not necessarily represent actual conditions at any given time, they still reflect all the climatic information of the location. TMY sets remain in popular use until today providing a relatively concise data set from which system performance estimates can be developed, without the need of incorporating large amounts of data into simulation models. 

TMY sets for 33 locations in Greece distributed all over the country were developed, covering for the first time all climatic zones, for both historical and future periods. Historical TMY sets generation was based on meteorological data collected from the Hellenic National Meteorological Service (HNMS) network in Greece in the period 1985-2014, while the corresponding total solar radiation values have been derived through the Meteorological Radiation Model (MRM).

Moreover, the generation of future TMY sets for Greece was also performed, for all 33 locations. To this aim, bias adjusted daily data for the closest grid point to the HNMS station’s location were employed from the RCA4 Regional Climate Model of the Swedish Meteorological and Hydrological Institute (SMHI) driven by the Earth system model of the Max Planck Institute for Meteorology (MPI-M). Simulations were carried out in the framework of the EURO-CORDEX modeling experiment, with a horizontal RCA4 model resolution of 0.11^o (~12 x 12 km). We used daily data for four periods: the 1985-2014 used as reference period and the 2021-2050, 2046-2070 and 2071-2100 future periods under RCP4.5 and RCP8.5 scenarios. 

This work was carried out in the framework of the “Development of synergistic and integrated methods and tools for monitoring, management and forecasting of environmental parameters and pressures” (KRIPIS-THESPIA-II) Greek national funded project.

How to cite: Psiloglou, B., Kambezidis, H. D., Varotsos, K. V., Kaskaoutis, D. G., Karagiannis, D., Petrinoli, K., Gavriil, A., Kavadias, K., and Giannakopoulos, C.: Historical and Future Typical Meteorological Years for 33 locations in Greece: a handy tool for various applications, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-337, https://doi.org/10.5194/ems2021-337, 2021.

The near-surface zero degree line (ZDL) is a key isotherm in mountain regions worldwide, but a detailed analysis of methods for the ZDL determination, their properties and applicability in a changing climate is missing. We here test different approaches to determine the near-surface ZDL on a monthly scale in the Swiss Alps. A non-linear profile yields more robust and more realistic ZDLs than a linear profile throughout the year and especially in the winter-half year when frequent inversions disqualify a linear assumption. In the period 1871-2019, the Swiss ZDL has risen significantly in every calendar month: In northern Switzerland, the monthly ZDL increases generally amount to 300-400 m with smaller values in April and September (200-250 m) and a larger value in October (almost 500 m). The largest increases of 600-700 m but also very large uncertainties (±400 m, 95% confidence interval) are found in December and January. The trends have accelerated in the last decades especially in spring and summer. The ZDL has increased by ~160 m per °C warming in the summer-half year and up to 340±45 m/°C in winter months. In southern Switzerland, ZDL trends and temperature scalings are somewhat smaller, especially in winter. Sensitivity analyses using a simple shift of the non-linear temperature profile suggest that the winter ZDL-temperature scalings are at a record high today or will reach it in the near future, and are expected to decrease with a strong future warming. Nevertheless, the cumulative ZDL increase for strong warming is considerably larger in winter than in summer. Based on a few key criteria, we also present best practises to determine the ZDL in mountain regions worldwide. The outlined methods lay a foundation for the analysis of further isotherms and to study the future ZDL evolution based on climate scenario data.

How to cite: Scherrer, S. C., Gubler, S., Wehrli, K., Fischer, A. M., and Kotlarski, S.: The Swiss Alpine zero degree line: methods, past evolution, sensitivities, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-201, https://doi.org/10.5194/ems2021-201, 2021.

The Köppen-Geiger system classifies climate into five main classes and thirty sub-types, based on threshold values and seasonality of monthly air temperature and precipitation. Its aim is to map empirically biome distributions around the world. In this paper, we analyze the evolution of this climate classification in the Basque Country for the historical period 1971-2000 and for three periods of future conditions: 2011-2040, 2041-2070, 2071-2100 projected by climate change scenarios.

The baseline data consists of high spatial resolution climate data available in the Basque Country. Different results from the KLIMATEK project on Adaptation to Climate Change are used (promoted by IHOBE -Basque Government-). These results have been generated for the RCP4.5 and RCP8.5 experiments, based on simulations carried out with regional climate models within the framework of the Euro-CORDEX project. Once the indicators were obtained at a spatial resolution of 12km x 12km, they were also obtained at a resolution of 1km x 1km, using the delta method. This process is carried out for each of the Euro-CORDEX models, so that an averaged result is finally provided, together with a statistic on its dispersion.

The evolution of the Köppen-Geiger maps is accompanied by other bioclimatic indices (aridity/continentality) and diagrams, which reinforce the pre-diagnosis of future climate conditions in the Basque Country. In addition, the dispersion of the models used is taken into account in the analysis of results, showing the most and least unfavorable scenarios.

These indices are applicable in impact and vulnerability analysis studies in sectors such as agriculture and landscape. Although they have received less attention than other indices of climate extremes, they nevertheless reflect concepts that are relatively simple to understand by the general public and are therefore also useful in the task of disseminating the consequences of climate change.

How to cite: Hernandez, R., Martija, M., Gomez de Segura, J. D., and Gaztelumendi, S.: Evolution of Köppen-Geiger's climate classification in the Basque Country in the context of climate change, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-248, https://doi.org/10.5194/ems2021-248, 2021.

We analyze recent trends in extreme daily precipitation in the Southwestern Alps. We consider a high-resolution precipitation dataset of 1 x1 km² for the period 1958-2017. A robust method of trend estimation is considered, based on nonstationary extreme value distribution and a homogeneous neighborhood approach. The results show contrasting trends in extreme daily precipitation depending on the season. In autumn, the trends are significantly increasing in most of the Southwestern Greater Alpine Region, with an increase up to 100% the average maxima for the 20-year return level between 1958 and 2017, while the French Alps show mainly decreasing extremes. Knowing that autumn experiences most of the largest maxima, the increase in the Mediterranean area is of concern for risk protection.  In winter, the valleys and medium mountain areas surrounding the Northern French Alps show significant increasing extremes, while the inner French Alps, the Swiss Valais and the Aosta Valley show significant decreasing trends. In the other seasons, the significant trends are mostly negative in the Mediterranean area, while the French Alps show less organized and contrasting trends.  For all seasons, part of the significant changes in extremes can be related to changes in the dominant atmospheric influences generating the maxima, particularly in the Mediterranean influenced region that shows the most organized trends. In particular, the strong positive trends in autumn in Southern France are concomitant with an increase in Mediterranean influence generating the maxima. However some exceptions are notable with counter-intuitive trends in extremes given the trends in dominant influences. 

How to cite: Blanchet, J., Blanc, A., and Creutin, J.-D.: Explaining recent trends in extreme precipitation in the Southwestern Alps by changes in atmospheric influences, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-214, https://doi.org/10.5194/ems2021-214, 2021.

The topographically complex coastal-mountainous region of the eastern Adriatic and Dinaric Alps is one of the rainiest areas in the Mediterranean and particularly vulnerable to climate change. The aim is to estimate the future climate change of precipitation over this region over which research on this subject is still limited. We use the climate projections from the latest EURO-CORDEX ensemble at 0.11° resolution. The ensemble is comprised of 14 regional climate models (RCMs) driven by eight CMIP5 global climate models (GCMs), a total of 68 members. The climate change signal is examined for the far future period (2071-2100) with respect to the historical period (1971-2000) for one greenhouse gases concentration scenario, particularly for RCP8.5. Total precipitation shows a considerable reduction in summer months, while in winter it is projected to increase in the northern part of the region and to decrease in southern parts, displaying the known south-north gradients. Accordingly, the number of rainy days is projected to decrease by the end of the century, especially during summer over the entire region and in winter over the southern parts. However, the precipitation intensity increase can be expected by the end of the century, especially during the winter months, while in the summer there is no clear consensus between different models. Also, an increase in extreme precipitation is projected during the winter months, while during summer months a similar south-north gradient is shown as for total precipitation. A more detailed analysis for multiple future periods and greenhouse gases concentration scenarios, with an emphasis on extreme precipitation, is planned.

How to cite: Ivušić, S., Güttler, I., and Horvath, K.: Future precipitation changes over the eastern Adriatic and Dinaric Alps areas in the latest EURO-CORDEX ensemble, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-12, https://doi.org/10.5194/ems2021-12, 2021.

Ancient lakes throughout the Mediterranean are at risk of disappearing due to a combination of climate change and human impacts. The growing imbalance between water availability and demand is creating unprecedented ecological problems. There is an urgent need for better understanding the patterns of natural lake water variability to improve water resource management and conservation. The incorporation of long-term cycles is particularly important for assessing low frequency – high magnitude trends in lake water levels.

The Ohrid-Prespa Lake system is amongst the oldest permanent lake systems in Europe, with an age of >1 million years, and hosts a globally significant biodiversity. The closed-basin Prespa Lakes are particularly sensitive to climatic variability with long-term water level changes informing on the dynamic balance between [1] runoff and precipitation supplying water to the lakes, and [2] water loss from the lakes by evaporation and underground karst outflow.

The large, ongoing, fall of the Prespa Lakes that started in 1987 threatens the biodiversity and water resources of the interconnected lake system. This decline is caused by climate change, specifically by decreases of 10% in precipitation and 25% in runoff, amplified by water abstraction. There is no precedent for this water level fall in the observational record (1951-present), although geological archives indicate equally low water levels at least twice over the past five millennia. 

Here we present the first quantified estimates of changes in the lake water balance over time that are based on the strong relationship between open water surface area and water loss. This quantification allows direct comparison of lake low- and highstand events across time and assessing magnitudes of regional hydro-climatic changes. This study uses a novel approach that reconstructs absolute lake levels and related open water surface areas for different past periods, using the landform-sediment record.

The hydro-climate of the Prespa catchment shows a drying trend of since the mid Holocene. The recent (2001-2018) lake lowstand is the most significant over the past 700 years in terms of water loss changes. A lake lowstand period of a similar magnitude occurred about 2000 years ago. The most extreme lowstand period over the past 5000 years occurred between 1100-800 years ago during the Medieval Climate Anomaly, when water loss changes were >50% higher compared to the present lowstand. However, the renewed decline in lake level and surface area since 2019 requires close monitoring; if lake level falls a further 2m to 840m.a.s.l. it would become the largest recorded fall over the entire Holocene, with unknown impacts for the wider system.

How to cite: van der Schriek, T. and Giannakopoulos, C.: Providing a long-term hydroclimatic perspective for the recent extreme decline of Lake Prespa (SW Balkans) through quantitative reconstruction of lake water-balance changes over the past 5000 years, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-309, https://doi.org/10.5194/ems2021-309, 2021.

To study climate change, it is essential to analyze extremes as well. The study of extremes can be done on the one hand by examining the time series of extreme climatic events and on the other hand by examining the extremes of climatic time series. In the latter case, if we analyze a single element, the extreme is the maximum or minimum of the given time series. In the present study, we determine the extreme values of climatic time series by examining several meteorological elements together and thus determining the extremes. In general, the main difficulties are connected with the different probability distribution of the variables and the handling of the stochastic connection between them. The first issue can be solved by the standardization procedures, i.e. to transform the variables into standard normal ones. For example, the Standardized Precipitation Index (SPI) uses precipitation sums assuming gamma distribution, or the standardization of temperature series assumes normal distribution. In case of more variables, the problem of stochastic connection can be solved on the basis of the vector norm of the variables defined by their covariance matrix. According to this methodology we have developed a new index in order to examine the precipitation and temperature variables jointly. We present the new index with the mathematical background, furthermore some examples for spatio-temporal examination of these indices using our software MASH (Multiple Analysis of Series for Homogenization; Szentimrey) and MISH (Meteorological Interpolation based on Surface Homogenized Data Basis; Szentimrey, Bihari). For our study, we used the daily average temperature and precipitation time series in Hungary for the period 1870-2020. First of all, our analyses indicate that even though some years may not be considered extreme if only either precipitation or average temperature is taken in to account, but examining the two elements together these years were extreme years indeed. Based on these, therefore, the study of the extremes of multidimensional climate time series complements and thus makes the study of climate change more efficient compared to examining only one-dimensional time series.

How to cite: Izsák, B., Szentimrey, T., Lakatos, M., and Pongrácz, R.: Multidimensional extremes: joint study of precipitation and temperature time series, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-223, https://doi.org/10.5194/ems2021-223, 2021.

Heavy large-scale precipitation events are associated with large negative impacts on human society, mainly as they may trigger floods and landslides. Therefore, it is important to better understand underlying physical mechanisms leading to extremes and how they are reproduced in climate models.

The present study evaluates ability of current climate models to reproduce relationships between large-scale heavy precipitation and atmospheric circulation over central Europe. We use an ensemble of 32 regional climate model (RCM) simulations with the 0.11° resolution, taken from the Euro-CORDEX project. The statistics are compared for the recent climate simulations (1951-2005) against observations from the E-OBS gridded data set to identify main drawbacks of the RCMs. The large-scale heavy precipitation events are defined as days with at least 50% of all grid points over the examined area with heavy precipitation (exceeding the 75th or 90th percentile of the distribution of seasonal rainy days). The association with atmospheric circulation types is investigated through circulation types derived from sea level pressure using airflow indices (direction, strength and vorticity). The analysis is carried out separately for summer (JJA) and winter (DJF) season.

The number of days with large-scale heavy precipitation per season in observations reflects the seasonal precipitation sums (the larger precipitation sum the more days). In winter, the large-scale heavy precipitation is mainly associated with the west, northwest, southwest and cyclonic circulation types while in summer with the cyclonic, north, southwest and undefined types (in the observed data). Some RCM simulations are not able to reproduce the number of days with the large-scale heavy precipitation events and their relationships with circulation, especially in summer.

How to cite: Beranova, R. and Kysely, J.: Large-scale heavy precipitation and its link to atmospheric circulation in climate model outputs over central Europe, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-50, https://doi.org/10.5194/ems2021-50, 2021.

The outputs from regional climate models (RCMs) have to be further downscaled. This is usually done via a bias correction method.  This study presents a novel approach to statistical downscaling of outputs from RCMs. The novelty lies primarily in distinguishing the convective and stratiform precipitation by rain generator operating in 6-hour time step. For this purpose, the technique based on determination of threshold rainfall intensity is used, built on the observation that the convective precipitation amounts follow exponential distribution.

The resulting rain generator operates in the following steps: disaggregation of 6-hour cumulative precipitation into convective and stratiform types, fitting of the first order 3-state discrete time Markov chain to the data, and simulation of long time series of precipitation. Then the mixture of log-normal and Generalized Pareto distribution is fitted to stratiform events and the Generalized extreme value distribution to convective events.

The impact of climate change on precipitation is represented by change factors that are identified for precipitation occurrence (by comparing the transition matrices for the future and control period) and for precipitation amounts (by comparing the scale and location parameters of distributions fitted for the future and control period). The observational data are then altered with the obtained change factors.

From evaluation of observational data it stems that the average volume of a convective event is higher for the western region than the eastern region of the Czech Republic. Additionally, statistically significant trends in the number and volume of convective events were identified for the western region. The analysis of the RCM simulations shows that even though the overall precipitation is projected to be lower in future, the proportion of convective events (versus stratiform ones) would be higher. In a future climate, the number of convective events is projected to be lower while the mean volume of a convective event to be larger.

How to cite: Martinkova, M.: Stochastic Rain Generator Based on Disaggregation of Precipitation into Convective and Stratiform for Climate Change Studies, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-55, https://doi.org/10.5194/ems2021-55, 2021.

A better understanding of the dynamics and impacts of extreme weather events and their changes due to climate change is the subject of the ClimXtreme project (climxtreme.net) funded by the German Federal Ministry of Education and Research. 
The CoDEx project is investigating how data compression techniques can contribute to a better description and understanding of extremes. Various unsupervised learning approaches, such as clustering or principal component analysis, focusing on extremes have been developed recently and will be investigated and compared within the project. 
We use principal component analysis to study the spatial (co-)occurrence during extreme weather events such as heavy precipitation, heat waves or droughts. The focus on extreme events is done by using the tail pairwise dependence matrix (TPDM), proposed by Cooley and Thibaud (2019) as an analogue to the covariance matrix for extremes. Since the simultaneous occurrence of precipitation deficits and high temperature played an important role, especially in heat waves, we explore how Cooley and Thibaud's concept can be used in this regard. We propose an estimation of the TPDM based on pairwise dependencies of two variables. A singular value decomposition gives us insight into the spatial co-occurrence of extreme spatial patterns, which contributes to the understanding of so-called compound events. 
We use daily precipitation and temperature data, including observational stations and regional reanalyses in Germany and Europe. Using this method, we extract spatial patterns over Germany and Europe based on extreme dependencies. In addition, we identify historical events, and examine them in more detail in this context.

How to cite: Szemkus, S. and Friederichs, P.: Extremal dependence as given by the tail pairwise dependence matrix inprecipitation and temperature data, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-335, https://doi.org/10.5194/ems2021-335, 2021.

Projections of the Köppen-Geiger climate classification under future climate change for the Iberian Peninsula (IP) are investigated by using a seven-ensemble mean of regional climate models (RCMs) attained from EURO-CORDEX. Maps with predicted future scenarios for temperature, precipitation and Köppen-Geiger classification are analyzed under RCP4.5 and RCP8.5 in Iberia. Widespread statistically significant shifts in temperature, precipitation and climate regimes are projected between 2041 and 2070, with higher expression under RCP8.5. An overall increase of temperatures and a decrease of precipitation in the south-southeast is predicted. Of the two climate types dry (B) and temperate (C), the dominant one was C in 86% of the Iberian territory for 1961-1990, predicted to decrease by 8.0% towards 2041-2070 under RCP4.5 (9.1% under RCP8.5). The hot-summer Mediterranean climate (CSa) will progressively replaces CSb (warm-summer) type towards north in the northwestern half of Iberia until 2070. This shift, depicted by the SSIM index, is noticeable in Portugal with a projected establishment of the CSa climate by 2041-2070. A predicted retreat of humid subtropical (Cfa) and temperate oceanic (Cfb) areas in the northeast towards Pyrenees region is noteworthy, alongside an increase of desert (BW) and semi-desert (BS) climates (7.8% and 9%) that progressively sets in the southeast (between Granada and Valencia). Climate types BSh and BWh (hot semi-desert and hot-desert, respectively), non-existent in 1961-1990 period, are projected to represent 2.8% of territory in 2041-2070 under RCP4.5 (5% under RCP8.5). The statistically significant projected changes hint at the disappearance of some vegetation species in certain regions of Iberia, with an expected increase of steppe, bush, grassland and wasteland vegetation cover, typical of dry climates in the southeast.

Funding: This research was funded by National Funds by FCT - Portuguese Foundation for Science and Technology, under the project UIDB/04033/2020.

How to cite: Andrade, C. and Contente, J.: Projections for the Köppen-Geiger climate classification under future climate change for the Iberian Peninsula, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-380, https://doi.org/10.5194/ems2021-380, 2021.

Anthropogenic activities contribute to the rising level of greenhouse gas concentrations in the atmosphere at a rate of approximately 1% per year providing a time-dependent external radiative forcing on the climate. In addition to tangible consequences of anthropogenic forcing affecting the climate system, simultaneous, less apparent changes occurring on low-frequency timescales demand effort to deal with. These include changes in natural internal processes of the climate system due to the non-stationary anthropogenic forcing. This represents additional uncertainty affecting future model projections on top of internal variability, scenario and model uncertainty. Here, with the application of state-of-the-art Single Model Initial-condition Large Ensemble (SMILE) simulations – that account for the chaotic behavior of the climate system with perturbed initial condition runs of the same model – we offer a way forward for new perspectives on externally-forced changes in internal variability. In doing so, we utilize an approach for analyzing SMILEs called the snapshot view, which offers a mathematically exact and elegant formulation and the potential to complement previous, time-series-based diagnostics with ensemble-based statistics. We reveal how the snapshot view allows for surprisingly simple practices to detect anthropogenically forced changes in modes of large-scale internal atmospheric circulation variability (so-called “teleconnection patterns”) as well as coupled modes of atmospheric variability with Arctic sea ice. A crucial message of the snapshot view is that all of the traditional, time series-based methods can be reformulated for ensembles and thus, on the one hand, ambiguous results arising from subjective choices of traditional methods (e.g. length and center of time windows) can be avoided, and on the other hand, new perspectives open for detecting forced changes in internal variability.

How to cite: Topal, D., Haszpra, T., and Herein, M.: Forced changes in internal variability: an additional uncertainty to deal with, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-404, https://doi.org/10.5194/ems2021-404, 2021.

Spring precipitation is a key factor determining vegetation growth and strongly affecting soil moisture in spring and summer. Whilst winter precipitation and snow cover as well as summer convective precipitation are frequently researched, only little attention is given to spring precipitation and its temporal variation.

In this contribution we focus on March, April and May daily precipitation at 72 station in the Czechia over 1980-2016. The temporal and spatial variability of precipitation totals, number of wet and dry days and periods is discussed in detail. The altitudes of the stations range from 158 to 1302 m a. s. l. and the mean spring precipitation totals vary from 104 to 327 mm. The highest mean number of dry days (more than 70) is reached at four stations elevated from 241 to 474 m a. s. l.  whilst the lowest number (less than 54) occurs at two stations above 1 km but also at one station in 740 m a. s. l. The duration of severe dry spells is not linked to altitude, either. The longest 43-days dry spell occurred at almost all stations up to 880 m a. s. l. No common trend of precipitation totals as well as dry or wet days exists over the area in studied period.

The analysis indicate that spring precipitation characteristics are rather related to surrounding and position of station with regard to prevailing flow direction than altitude. The hierarchical cluster analysis based on seasonal and monthly precipitation totals, number of wet and dry days, number and duration of wet and dry periods, trends of wet and dry days number and several other characteristics separated individual stations into 4 groups. Except the groups of lowland dry and mountain wet stations, the group associated low elevated wet stations on windward mountain sides and the other one with high elevated dry stations were created.

How to cite: Pokorná, L., Rulfová, Z., and Kuchyňková, J.: Temporal and spatial variability of spring precipitation in the Czech Republic, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-59, https://doi.org/10.5194/ems2021-59, 2021.

The EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF) generates and distributes high quality long-term climate data records (CDR) of energy and water cycle parameters, which are freely available.

In fall 2021, a new version of the “Surface Solar Radiation data set – Heliosat” will be released: SARAH-3. As the previous editions, the SARAH-3 climate data record is based on satellite observations from the first and second METEOSAT generations and provides various surface radiation parameters, including global radiation, direct radiation, sunshine duration, photosynthetic active radiation and others. SARAH-3 covers the time period 1983 to 2020 and offers 30-minute instantaneous data as well as daily and monthly means on a regular 0.05° x 0.05° lon/lat grid.

In this presentation, an overview of the SARAH climate data record and their applications will be provided. A focus will be on the SARAH-3 developments and improvements (i.e. improved consideration of snow-covered surfaces). First validation results of the new Climate Data Record using surface reference observations will be presented. Further, SARAH-3 will be used for the analysis of the climate variability in Europe during the last decades.

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How to cite: Pfeifroth, U., Drücke, J., Trentmann, J., and Hollmann, R.: SARAH-3 - a new satellite-based Cimate Data Record for surface radiation parameters from the CM SAF, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-454, https://doi.org/10.5194/ems2021-454, 2021.

It is already a well known fact that different types of climate datasets (station, gridded, reanalyses) and even individual datasets differ in how they describe statistical properties of climate variables. Here we compare precipitation trends in Europe between station data (taken from the ECA&D database), gridded data (E-OBS and CRU TS), and reanalyses (JRA-55 and NCEP/NCAR) for period 1961-2011, both annually and for individual seasons. Theil-Sen non-parametric trend estimator is used for the quantification of the trend magnitude; Mann-Kendall test is used to evaluate the significance of trends.

On the annual basis, station data indicate precipitation increases in northern Europe and decreases in southern and southeastern Europe. Whereas trends in the gridded datasets roughly agree with station data, reanalyses provide much more negative trends with a different geographical distribution. There is a tendency for reanalyses to overestimate precipitation in the beginning of the period at some places, whereas they underestimate precipitation near the end of the period elsewhere. The disagreement among different data types and datasets is larger in all seasonal analyses except winter. Particularly notable is an excessive drying trend in central, southwestern, and southeastern Europe in NCEP/NCAR in most seasons. Reanalyses thus do not appear to be suitable data sources for estimation of precipitation trends.  

Reasons for the disagreement are varied and are conjectured by a detailed examination of station / point or regional time series: station series may suffer from inhomogeneities; gridded data may be affected by different sets of stations entering the interpolation procedure at different times; while reanalyses may be affected by different kinds of data being assimilated into them in different periods.

How to cite: Huth, R. and Vít, V.: Long-term trends of precipitation in Europe in different datasets, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-257, https://doi.org/10.5194/ems2021-257, 2021.

Environmental restoration has the potential to constrain human-induced land degradation, loss of biodiversity and climate change. Although the practise is increasingly integrated into natural resource and climate mitigation strategies, scientific studies underline that the effectiveness and impact of these restoration projects are currently difficult to monitor and assess. In order to measure the global community’s progress towards the Sustainable Development Goals (SDGs), restoration interventions need to be assessed in a systematic and objective manner. However, the long-term and high-quality data records that are required for this are often lacking in both time and space. Satellite data products that can detect changes in land use, surface temperature and hydrological conditions over time in a consistent manner, can fill this gap.

Over the last few decades, the scientific community has made great efforts to merge different satellites into multi-decadal historical datasets of climate variables. Examples of such long-term climate data records (CDRs) are the soil moisture (from 1978 onwards), land surface temperature (since 1995) and land cover (since 2008) datasets of the European Space Agency Climate Change Initiative (ESA CCI). These consistent datasets, combined with near real-time observations, offer a great opportunity to quantify and monitor the impact of restoration interventions on degraded landscapes. In order to monitor restoration projects affecting areas smaller than the native resolutions of these datasets (up to approximately 25 km), downscaling techniques can be used to increase the spatial level of detail (approximately in the 0.1-1 km range). The resulting monitoring service could help managers of restoration programs and green investment funds to steer decisions and communicate on effectiveness towards their donors. 

The satellite datasets were investigated in space and time in relation to the effects of the restoration projects. For each restoration project area, several surface conditions were monitored and compared to those in an unaffected control area to detect and attribute the effects of the restoration program. The present work focuses on several case studies in which the relevance of satellite-based CDRs for the end users’ operational practises related to impact monitoring is assessed in the context of the SDGs 12 (Responsible production and consumption), 13 (Life on land) and 15 (Climate action).

How to cite: van der Vliet, M., de Jeu, R., Schellekens, J., and van der Schalie, R.: A methodology to quantify and monitor the impact of environmental restoration using climate data records from satellite observations, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-9, https://doi.org/10.5194/ems2021-9, 2021.

The Mediterranean basin is one of the main cyclogenetic regions in the world. This is likely due to the orographic conditions, as well as the thermodynamic
characteristics found over the Mediterranean. Among the large amount of cyclones that develop in this area, cyclones with tropical characteristics called medicanes (“Mediterranean Hurricanes”) eventually develop in the Mediterranean Sea. They have large harmful potential and a correct simulation of their evolution in climate projections is important for an adequate adaptation to climate change. Different studies suggest that ocean–atmosphere coupled models provide a better representation of medicanes, especially in terms of intensity and frequency. In this work, we use the regionally coupled model ROM and its stand-alone atmospheric component (REMO) that in this work is used as uncoupled model to study how air-sea interactions affect the evolution of medicanes in future climate projections. We find that under the RCP8.5 scenario our climate simulations show an overall frequency decrease which is more pronounced in the coupled than in the uncoupled configuration, whereas the intensity displays a different behaviour depending on the coupling. These changes could be explained due to the decrease in the number of extratropical cyclones and the increase of atmospheric stability conditions. In the coupled run, the relative frequency of higher-intensity medicanes increases, but this is not found in the uncoupled simulation. Also, this study indicates that the coupled model simulates better the summer minimum in the occurrence of medicanes, avoiding the reproduction of unrealistically intense events that can be found in summer in the uncoupled model.

How to cite: Gutiérrez-Fernández, J., González-Alemán, J. J., de la Vara, A., Cabos, W., Sein, D. V., and Gaertner, M. A.: Impact of ocean–atmosphere coupling on future projectionof Medicanes in the Mediterranean sea, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-244, https://doi.org/10.5194/ems2021-244, 2021.

The warming climate evokes increasing frequency of extreme precipitation in some region. Analysis of long-term measurements could support the better understanding of the processes that cause extreme precipitation events.

Automatic stations replaced the ombrometer in many places in Hungary, particularly from the late 1990s. The change of the measurement practice do not allow simply merging the data recorded form the registering paper in the past and the recent 10 minutes measurements.   The most intense 5, 10, 20, 30, 60, 180 min sub-totals per rainfall events were recorded from the ombrometer registering paper before atomization, typically until 1993. By contrast, the 10 min precipitation sum from the AWSs are stored in the meteorological database of the Hungarian Meteorological Service from automatization. In order to join together the older and the AWS measurements it was necessary to develop a method to make this possible. Therefore we downscaled the 10 min data in time. The sampling of the AWSs is one minute, although the 1-minute data are available only for some stations in the digital database.  We applied a linear regression model to downscale the 10-miniute data for 1 min. After this, we can derive the most intense sub-totals per events from the AWS data as if they have been measured with the ombrometers.

Thereby a set of sub-daily precipitation indices defined in the INTENSE project (https://research.ncl.ac.uk/intense/aboutintense/ can be computed for longer data series. Some of the indices specified in INTENSE project describes the maximum rainfall totals and timing, the intensity, duration and frequency of heavy precipitation, frequency of rainfall above specific thresholds and some of them is related to diurnal cycle. A few of these indices are analysed for long data series to detect the sub-daily precipitation changes in Hungary.

How to cite: Lakatos, M. and Szentes, O.: Long term changes of the sub-daily precipitation extremes in Hungary, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-251, https://doi.org/10.5194/ems2021-251, 2021.