bluuebunny/crossref_metadata_2025 huggingface dataset:
Uni- and multivariate bias-adjustment on a 1 km grid over Norway
Authors: Dobler Andreas , Kwok Wong Wai , Haddeland Ingjerd , Lawrence Deborah , Haugen Jan Erik , Verpe Dyrrdal Anita
Published: June 2022

The national climate assessment report series ‘Klima i Norge – 2100’ by the Norwegian Centre for Climate Services provides information on past and future changes in meteorological parameters and derived indices, hydrological components and effects on natural hazards. As a consequence, one of the key-user of national weather and climate data in downstream applications, namely hydrologists, are directly involved in the process of framing the report, including the selection of simulations and the application of bias-adjustment methods.

For the upcoming update of the report (expected to be published in 2024), a set of nine variables from an ensemble of regional climate model (RCM) projections will be bias-adjusted on a 1x1 km grid covering the complete Norwegian mainland. To this end, different methods have been implemented, including empirical quantile mapping, which has already been used in the former reports, quantile delta mapping and multivariate bias-adjustment. Applying the methods to a set of RCMs yields a variety of datasets. These datasets, by design of the methods, reproduce the reference data with various accuracy, for instance in terms of spatial, temporal and inter-variable consistencies. Furthermore, the bias-adjusted climate projections differ on how they inherit specific climate change signals from the RCMs. Compared to the raw data, they may or may not preserve monthly or seasonal trends, changes in the quantiles or in the dependency structures. Since it is not always clear whether all the changes in the RCMs are physically plausible and relevant, selecting a single method may not be appropriate. And even if it’s clear, the corresponding bias-adjustment method may have other undesirable shortcomings.

In this presentation we will show examples of how the different methods can affect the projected climate change signals in various aspects, and thus are adding a level of uncertainty. We will further emphasise the point of investigating these uncertainties in climate change assessments when bias-adjustment is involved. By feeding the data into a downstream application, the projected changes may differ substantially.
When the Earth goes white: the Snowball Earth attractor
Authors: Herein Matyas , Haszpra Timea , Kaszas Balint
Published: June 2022

Using an intermediate complexity climate model we investigate the so-called snowball Earth transition. For certain values (including its current value) of the solar constant, the climate system allows two different stable states: one of them is the snowball Earth, covered by ice and snow, and the other one is today's climate. In our setup, we consider the case when the climate system starts from its warm attractor (the stable climate we experience today), and the solar constant is changed according to the following scenario: it is decreased continuously and abruptly, over one year, to a state, where only the Snowball Earth's attractor remains stable. This induces an inevitable transition, or climate tipping from the warm climate. The reverse transition is also discussed. Increasing the solar constant back to its original value in a similar way, in individual simulations, depending on the rate of the solar constant reduction we find that either the system stays stuck in the snowball state or returns to a warm climate. However, using ensemble methods i.e., using an ensemble of climate realizations differing only slightly in their initial conditions we show that the transition from the snowball Earth to the warm climate is also possible with a certain probability which depends on the specific scenario used. From the point of view of dynamical systems theory, we can say that the system's snapshot attractor splits between the warm climate's and the snowball Earth's attractor. Despite the limitations of an intermediate complexity climate model, all this reveals that incautious geoengineering (e.g. overdone solar radiation shielding) could even result in a snowball-like climate.
Instrumental noise and its impact on mean wind measurements
Authors: Kayser Markus , Lehmann Volker , Päschke Eileen , Detring Carola , Knist Christine , Leinweber Ronny , Beyrich Frank
Published: June 2022

Fibre-optic based Doppler wind lidars (DL) are able to retrieve vertical profiles of kinematic quantities across the lower atmosphere with high spatio-temporal resolution. Especially short-term forecasting would benefit from assimilating their data which renders these compact systems promising candidates for operational use in future observing networks of meteorological and environmental services. Therefore, DWD includes the assessment of DLs in the effort to evaluate ground-based remote sensing systems for their operational readiness, called “Pilotstation”. Besides tests focusing on aspects such as technical reliability, uncertainty characterization, scanning strategies, and the verification of the retrieved mean wind speed and direction with the help of independent reference data from a 482 MHz radar wind profiler (RWP) and 6-hourly radiosonde (RS) ascents, DWD developed a standardized retrieval assuring a high-quality Level-2 product.

However, a prerequisite for operational applications is the robust detection of atmospheric return signals in the presence of instrumental noise. While the most common approach filters data via a fixed signal-to-noise ratio (SNR) threshold, we find the non-linear consensus method (CNS), already operational in the data processing chain of radar wind profilers, to be more efficient in the weak signal regime where it increases data availability without reducing data quality.

Here, we present results from a long-term assessment at the Lindenberg Meteorological Observatory using the RWP and RS as references and from a side-by-side comparison of eight Halo Photonics “Streamline” DLs during the FESSTVaL 2021 field experiment. We focus on the characterization of the instrumental noise and show its impact on the derived winds and the data availability.
Analysis of the Impacts of Small-Scale Orography on the Atmospheric Boundary Layer: Developing ICON-LES for the Perdigão Field Experiment
Authors: Quimbayo-Duarte Julian , Schmidli Juerg , Köhler Martin , Schlemmer Linda
Published: June 2022

The response of the lower atmosphere to resolved versus parametrized orographic drag over moderately complex terrain is investigated. The larger terrain scales may trigger propagating gravity waves and generate flow blocking, while the smaller scales (smaller than 5 km) may modify the turbulent atmospheric boundary layer leading to turbulent orographic form drag (TOFD). We perform high-resolution numerical simulations to evaluate the ability of a TOFD parametrization to reproduce the impact of small-scale orographic features on the flow over complex terrain. The tool selected to perform the simulations is the Icosahedral Nonhydrostatic (ICON) numerical model, a unified modelling system for global numerical weather prediction (NWP) and climate studies. In the present study, the model is used in its limited-area mode. In the TOFD parametrization, the surface stress and its vertical distribution are formulated in terms of the orography spectrum, meaning that it only depends on the orography characteristics in the box (more specifically, only on the variance of the sub-grid scale orography).

As a first step, simulations using different grid spacings, from the km scale (NWP mode) to the O100 m scale (large-eddy simulations, LES), are carried out to reproduce the intensive observational period (IOP) of the Perdigão field experiment. The high-resolution LES is used to assess the performance of the TOFD parametrization (used in NWP mode) in simulating the surface stress, the near-surface atmosphere and to highlight possible issues resulting in a miss-representation of the flow over moderately complex terrain. The km-scale simulations are run continuously for the complete 49-day IOP using the ERA5 reanalysis dataset for initial and boundary conditions. LES at 130 m grid spacing, are run for the whole IOP nested into the NWP runs (offline nesting). Initial results of the NWP control simulation show good performance compared to the tower wind observations for the whole IOP. The model performance was especially good for the second part of the IOP when a high-pressure regime and weak synoptic forcing were observed.

Larger-scale thermally driven flows from the mountain ranges surrounding the Perdigão site impact the wind flow system at the double ridge, and when decomposed into along- and cross-valley, the performance of the model in the cross-valley direction tends to be very good. However, the model failed to reproduce the transition between anabatic- and katabatic-slope flow in the Serra da Estrela mountain range (NE of Perdigão). The latter negatively impacted the reproduction of the along-valley flow during the afternoon hours at the Perdigão site, when the most important deviations from observations were observed.
Optimization of Soil Texture to Improve the Soil Moisture in the Land Surface Model
Authors: Lim Sujeong , Park Seon Ki , Cassardo Claudio
Published: June 2022

Soil moisture is a key variable in the hydrologic cycle and affects weather and climate; thus, accurate soil moisture prediction is essential in the land surface modeling. In this study, we have used a land surface model, called the University of Torino land surface Process model for Interaction in the Atmosphere (UTOPIA), to predict the soil moisture. The UTOPIA is a one-dimensional model representing the interactions among atmosphere, land surface, vegetation and soil layers. Being UTOPIA a multilayer soil model, the user can discretize the soil into a certain number of layers: each layer needs specific physical properties related to soil moisture and temperature depending on the soil texture type. The soil texture allows to infer a useful information about the soil, such as the wilting point, field capacity, heat capacity, and eventually soil moisture. However, it is hard to obtain the accurate information of soil texture, especially in deep soil layers, due to insufficient and/or uncertain observation. Therefore, we have implemented the micro-genetic algorithm (micro-GA) within UTOPIA to optimize the soil textures by comparing the model-generated soil moistures versus the in-situ observations. The micro-GA is a global optimization algorithm based on the natural selection or survival of fitness to evolve the best potential solution. As a preliminary result, we anticipate that the optimal soil textures within the multiple layers lead a substantial improvement on soil moisture prediction. Furthermore, we will investigate the changes in latent and sensible heat fluxes which can be affect to the atmosphere model from the soil moisture improvements. 
Recent improvements in the E-OBS gridded data set for daily mean wind speed over Europe in the period 1980-2021 
Authors: de Baar Jouke , Nhat Luu Linh , van der Schrier Gerard , van den Besselaar Else , Garcia-Marti Irene
Published: June 2022

The provision of authoritative information about the past, present, and future climate in Europe is one of the principal objectives of the Copernicus Climate Change Service (C3S). In the past decades, the KNMI has been actively contributing to this endeavor through the ECA&D and E-OBS initiatives, which, among responsibilities, periodically release via C3S European-wide gridded observational datasets (E-OBS) of the principal weather variables (e.g. temperature, precipitation) that are fundamental for climate research.  Recently, the KNMI has released E-OBS version 25.0e, which includes the first release of gridded daily mean wind speed that covers most of Europe.   

The E-OBS wind speed data set covers the period 1980 – 2021. This newly developed gridded dataset applies a combination of spatial regression methods with local uncertainty estimates using a 20-member ensemble. When creating this gridded data set, the daily mean wind speed is modeled using an array of auxiliary variables (e.g. topography, distance to coast, surface roughness) and the ensemble ensures that a measure of uncertainty is provided along with the mean wind speed. In this presentation, we focus on three main parts: 1) general characteristics of the wind dataset; 2) improvements with respect to the previous version (e.g. spatial coverage); and 3) how the use of auxiliary variables characterization of the surroundings of the monitoring station with environmental variables improves the spatial resolution of the grid. 

Nevertheless, a pertinent question that follows the creation of the E-OBS daily mean wind dataset is: how does it compare to other well-consolidated data products stemming from numerical weather prediction? In this work, we carry out different statistical comparisons between the daily wind speed provided by E-OBS and ERA5-Land to gain insights about the observed similarities and differences. One of the aspects we consider are possible climatological trends in wind speed over Europe. For example, in the E-OBS data set we see a reduction in yearly mean wind speed around 1990. Is this effect significant, and do we also observe it in ERA5-Land? The comparison of E-OBS and ERA5-Land data for wind speed can provide useful information about their strengths and weaknesses, suggesting directions for future model improvement and improvement of the analysis of observations. Furthermore, this information may be valuable for the proper interpretation of both ERA5-Land and E-OBS data by various users. 
Predictability of the North Atlantic European region and the role of Indian Ocean during early winter
Authors: Abid Muhammad Adnan , Kucharski Fred , Molteni Franco
Published: June 2022

The North Atlantic European (NAE) regional circulation anomalies variability and predictability during the boreal winter season is modulated through both stratospheric and tropospheric pathways on sub-seasonal to seasonal timescales. El Niño-Southern Oscillation (ENSO) through the atmospheric teleconnections is one of the dominating forcing that modulates the global climate variability and Predictability during the boreal winter season. However, recent studies indicate the intra-seasonality in the ENSO teleconnections, where through the inter-basin interactions the Indian Ocean (IO) bridges the ENSO response to the NAE region in the early winter, while the direct ENSO response dominates in latter half of the boreal winter. Therefore, in the current study we analyzed the predictability of the NAE circulation anomalies and the tropical Indian Ocean precipitation anomalies during the early winter season using the ECMWF System-5 seasonal (SEAS5) dataset. We noted the boreal Autumn Indian Ocean Dipole (IOD) conditions are the pre-courser for the early winter precipitation anomalies in the Tropical Western-Central Indian Ocean (TWCIO) region, which dominates the ENSO response to the IO precipitation anomalies during the earlier half of the winter season. These TWCIO precipitation anomalies are well predicted by the ECMWF-SEAS5 prediction system during early winter. Furthermore, we noted the positive TWCIO heating anomalies tend to favor the positive North Atlantic Oscillation (NAO) condition in the North Atlantic region. This leads to the above normal Surface Air temperature (SAT) conditions, indicating to the mild early winter conditions over the European continent. The ECMWF-SEAS5 system shows a significant prediction skill of the SAT anomalies over the NAE region.
A new hydro-meteorological precipitation and flood forecasting system for small catchments
Authors: Winterrath Tanja , Chwala Christian , Grundmann Jens , Philipp Andy , Six Achim
Published: June 2022

Small, fast responding catchments are prone to flooding induced by local convective precipitation events. In such cases, the availability of high-quality information as basis for target-oriented warnings and effective protective measures in potentially affected regions is essential. More precisely, they rely on high-quality precipitation data, reliable weather and flood forecasting and last but not least information and education of persons in charge for prevention of hazards.

Within the project ‘HoWa-innovativ’ funded by the German Federal Ministry of Education and Research (BMBF) a new hydro-meteorological processing chain has been established to improve flood forecasting and warnings for small catchments. Study regions were located in the federal state of Saxony. The project consisted of three focal topics: first, the improvement of precipitation estimates by adding data from Commercial Microwave Links (CML) to the DWD gauge-adjustment of radar-based Quantitative Precipitation Estimates (QPE); second, the development of a hydrological ensemble prediction system designed for small catchments including coupling to DWD meteorological ensemble data; third, the development of a demonstrator with tailored visual information on precipitation analyses and forecasts as well as flood forecasts and its introduction to clients in disaster and flood management in specific workshops.

Small scale heavy precipitation events are difficult to detect and quantified with conventional measurements. DWD combines the areal reflectivity measurements of the radar network with quantitative gauge measurements throughout Germany to provide QPE in near real-time to clients in flood risk management. This so-called radar online adjustment (RADar-OnLine-ANeichung, RADOLAN) performs well for hourly precipitation sums. However, for small-scale, rapidly evolving convective cells the availability of corresponding gauge information is limited. In contrast to precipitation gauges, CMLs are by far more numerous and have the potential to provide near-ground precipitation information with good spatial coverage, high temporal resolution and very low latency. Within the project, the retrieval of precipitation information from CML has been optimized and prepared for automatic processing. We use CML data for the adjustment of radar-based precipitation estimates with focus on high spatial accuracy and the potential for higher temporal resolution.

This contribution gives an overview of the project results comprising the whole processing chain, while the focus will be on the new multi-sensor precipitation QPE system pyRADOLAN.
Identifying high-wind features within extratropical cyclones using a probabilistic random forest
Authors: Eisenstein Lea , Schulz Benedikt , Qadir Ghulam A. , Knippertz Peter , Pinto Joaquim G.
Published: June 2022

Strong winds associated with extratropical cyclones are one of the most dangerous natural hazards in Europe. These high winds are mostly connected with five mesoscale dynamical features, namely the warm (conveyor belt) jet (WJ), the cold (conveyor belt) jet (CJ), (post) cold-frontal convective gusts (CFC), strong cold sector winds (CS) and – at least in some storms – the sting jet (SJ). While all these have strong winds in common, the timing, location and some further characteristics tend to differ and hence likely also the forecast errors occurring in association with them.

Here we present a novel objective identification approach for the features listed above, based on a probabilistic random forest using each feature’s most important characteristics in wind, rainfall, pressure and temperature evolution. However, as CJ and SJ turn out to be difficult to distinguish in surface observations alone, we decided to consider the two features together. This identification can then be used to generate a climatology for Central Europe, to analyse forecast errors specific to individual features, and to ultimately improve forecasts of high wind events through feature-dependent statistical post-processing. To achieve this, we strive to identify the features in irregularly spaced surface observations and in gridded analyses and forecasts in a consistent way, thus making it independent of spatial dependencies and gradients.

To train the probabilistic random forest, we subjectively identify the four storm features in twelve winter storm cases between 2015 and 2020 in both hourly surface observations and high-resolution reanalyses of the German COSMO model over Europe, using an interactive data analysis and visualisation tool. Results show that mean sea-level pressure (tendency), potential temperature, precipitation amount and wind direction are most important for the distinction between the features. From the random forest we get occurrence probabilities for each feature at every station, which can be converted into areal information using Kriging.

The results show a satisfactory identification for all features, especially for WJ and CFC. We encounter, however, some difficulties to clearly distinguish the CJ and CS, which are dynamically similar. A climatology is currently being compiled for both surface observations and the reanalyses over a period of around 20 years using the trained probabilistic random forests and further for high-resolution COSMO ensemble forecasts, for which we want to analyse forecast errors and develop feature-dependent postprocessing procedures.
Evaluation of Northern Hemisphere snow water equivalent in CMIP6 models during 1982-2014
Authors: Kouki Kerttu , Räisänen Petri , Luojus Kari , Luomaranta Anna , Riihelä Aku
Published: June 2022

Seasonal snow cover of the Northern Hemisphere (NH) is a major factor in the global climate system, which makes snow cover an important variable in climate models. However, climate models have had difficulties in correctly reproducing the seasonal snow and its recent trends. A recent bias-correction method significantly reduces the uncertainty of NH snow water equivalent (SWE) estimation, which enables a more reliable analysis of the climate models’ ability to describe the snow cover. In this study, we have intercompared CMIP6 (Coupled Model Intercomparison Project Phase 6) and observation-based SWE estimates north of 40° N for the period 1982-2014 and analyzed whether temperature (T) and precipitation (P) biases could explain the SWE biases. We analyzed separately SWE in winter and SWE change rate in spring. For SWE reference data, we used bias-corrected SnowCCI data for non-mountainous regions and the mean of Brown, MERRA-2 and Crocus v7 datasets for the mountainous regions. The analysis shows that CMIP6 models tend to overestimate SWE, but large variability exists between models. In winter, the SWE model biases are mainly positive, while in spring, the variability between models increases. In winter, P is the dominant factor causing SWE discrepancies especially in the northern and coastal regions. T contributes to SWE biases mainly in regions, where T is close to 0℃ in winter. In spring, the importance of T in explaining the snowmelt rate discrepancies increases. This is to be expected, because the increase in T is the main factor that causes snow to melt as spring progresses. However, the results also showed that biases in T or P cannot explain all model biases either in SWE in winter or in the snowmelt rate in spring. Other factors, such as observation uncertainty or deficiencies in model parameterizations, also contribute to SWE biases.
Attribution of pre-selected climate indicators in summer over the Carpathian basin
Authors: Szabó Péter , Bartholy Judit , Pongrácz Rita
Published: June 2022

While anthropogenic global warming is well-accepted and thoroughly described in the climate community, the vast majority of the general public is still not always fully-certain whether to associate specific local weather or climate event with this global phenomenon. It is of utmost importance to raise public awareness by providing easily understood but scientifically sound information, and to explain how the anthropogenic activity contributes to specific weather or climate events. Although it is getting to be widely assessed now in Western Europe, only a few case studies for past high-impact local weather events were assessed in Hungary, and the systematic analyses of long-term trends of such indicators with their linkage to anthropogenic activity are missing in this region. Our local attribution project (started in September 2021) aims to fill this gap in Hungary: the results of the analyses of seasonally relevant indices are published in each season at around a time of an event occurrence or absence. Dissemination is executed via an already established, online Hungarian platform (https://masfelfok.hu/, meaning “1.5 degrees”), since it is reaching the general public with its readily understood climate change information through its broad media coverage and a growing social media network.

The analysis uses several freely-available data sets: (1) an ensemble selection of CMIP6 global climate model simulations with both natural-only forcings and historical runs, (2) a fit-for-purpose ensemble of regional climate model simulations from the Euro-CORDEX on 0.11° resolution, including the RCP2.6, RCP4.5 and RCP8.5 future scenarios, (3) a fine-resolution, homogenized observation-based gridded data for Hungary, compiled by the Hungarian Meteorological Service, (4) the newest E-OBS database for the area outside of Hungary. Within the project, we selected seasonally relevant extreme, less extreme but well-known events, and these pre-selected indices are attributed to anthropogenic activity through their detected intensity, duration, or frequency changes. For instance, third-level heatwave days, hot days, tropical nights, annual temperature maxima and return values, meteorological droughts, and heavy precipitation days are evaluated for summer, as they have the largest public interest and impacts in our target region.
Sensitivity of the LES model PALM in the urban environment: a case study in Prague
Authors: Belda Michal , Jaroslav Resler , Jan Geletič , Pavel Krč , Bjorn Maronga , Matthias Suhring , Mona Kurppa , Vladimír Fuka
Published: June 2022

Numerical models using the large-eddy simulation technique (LES) are particularly useful in the urban environment in which their high resolution allows for capturing small-scale features of built-up areas such as buildings, roads, pavements or urban greenery. However, good model performance is not only the result of an accurate representation of the relevant physical processes. It also largely depends on the availability of proper input data describing the urban setup, namely the building and land-surface properties. In this study an LES-based modeling system PALM 6.0 featuring an improved urban surface model (USM) was used to assess sensitivity with respect to land-surface and building properties in a densely built-up residential area in Prague, Czech Republic, particularly in the vicinity of a typical crossroads. Two types of scenario simulations were employed. First, a set of synthetic scenarios changing surface and material parameters such as albedo or emissivity, by which the sensitivity of the model simulations to potentially erroneous input data was tested. These showed the highest sensitivity to the correct setting of surface parameters used in radiation balance equations. Second, a set of urbanistic scenarios was designed to assess the limits of effects of commonly considered urban-heat-island mitigation measures such as greening of the streets or altering surface materials. In this case, urban greenery is confirmed to be the most effective measure, especially when considering both physical and biophysical temperature indicators. On the other hand, analysis of air quality, specifically with respect to PM2.5 dispersion, confirmed the opposite behavior to that of thermal indicators; i.e. improved thermal comfort brings deterioration of PM2.5 concentrations.
Evaluating the potential of MeteoSwiss forecast strategies when constructing weather warnings
Authors: Allen Sam , Bhend Jonas , Martius Olivia , Ziegel Johanna
Published: June 2022

To mitigate the impacts associated with adverse weather conditions, meteorological services issue weather warnings to the general public. These warning systems rely heavily on forecasts issued by underlying prediction systems. When deciding which prediction system(s) to utilise when constructing warnings, it is important to compare systems in their ability to forecast the occurrence and severity of extreme weather events: if a warning system has access to more accurate forecasts for extreme weather, then it has the potential to generate more useful warnings. However, evaluating forecasts for extreme events is known to be a challenging task. This is exacerbated further by the fact that high-impact weather often manifests as a result of several confounding features, a realisation that has led to considerable recent research on so-called compound weather events. Both univariate and multivariate methods are therefore required to evaluate forecasts for high-impact weather. In this work, we review weighted verification tools, which allow particular outcomes to be emphasised during forecast evaluation, and demonstrate how these can be used to verify forecasts for compound weather events. We compare different approaches to construct weighted scoring rules, and exploit recent developments to apply these scores in a multivariate setting. Additionally, we leverage existing results on weighted scores to introduce weighted probability integral transform (PIT) histograms, allowing forecast calibration to be assessed conditionally on particular outcomes having occurred. To illustrate the practical benefit afforded by these weighted verification tools, they are applied to forecasts for extreme heat events issued by the Swiss Federal Office of Meteorology and Climatology (MeteoSwiss). Extreme heat events are defined in terms of MeteoSwiss heat warning criteria, and these verification methods therefore assess the forecasts' potential to generate useful weather warnings.
Lagrangian characterization of heat waves by Eulerian tracer advection with relaxation
Authors: Mayer Amelie , Wirth Volkmar
Published: June 2022

Diagnostics of Lagrangian information about the atmospheric flow are usually obtained through the computation of trajectories. Trajectories provide detailed information along the pathways of individual parcels, but one has to consider a vast amount of them in order to obtain continuous-in-time, volume-filling information. As a consequence, analysing trajectories can be a painstaking task. To overcome this problem, we developed an alternative method allowing one to continuously diagnose Lagrangian information about the atmospheric flow on a Eulerian grid. The method is based on the advection of passive tracer fields and includes a relaxation term. Thus, it provides a field-based view on Lagrangian properties of the flow. The convenient output format allows one to analyse large data sets such as reanalysis data in a straightforward manner. Here, we make use of this method to quantify the processes of horizontal advection, downwelling, and diabatic heating associated with heat waves in Europe. More specifically, we compute the recent meridional and vertical parcel displacement and the recent parcel-based diabatic heating for each grid point at any time step to correlate these to the occurrence of temperature anomalies in Europe. In this way, we characterize European heat waves in terms of their Lagrangian bevahior. The analysis focuses on the recent decade, which experienced several severe heat waves in Europe, and encompasses case studies of recent major European heat waves as well as a more comprehensive statistical analysis. Our study furthers our knowledge on important mechanisms and drivers of European heat waves, which in turn will help to improve their forecasts.
Climate change detection and attribution in extreme precipitation using compact representations 
Authors: Szemkus Svenja , Friederichs Petra , Buschow Sebastian , Pscheidt Ieda
Published: June 2022

In the BMBF project climXtreme CoDEx, we analyze and explore methods for data compression to detect and attribute changes in the frequency and severity of extreme weather events with respect to climate change. Particularly for local processes on the atmospheric mesoscale, climate change signals are often masked by additional variability, resulting in poor signal-to-noise ratios. Appropriate reduction of degrees of freedom can improve the signal-to-noise ratio, increasing the potential to detect less strong signals in this setting. Our focus is on unsupervised learning approaches such as principal component analysis that are specifically designed for extremes.

We focus on extreme precipitation in Germany and analyze how different data compression techniques can be used in a detection and attribution (D&A) study. Among others, we use the approach of Cooley and Thibaud (2019), who decompose the pairwise dependence matrix as an analogue to the covariance matrix for extreme dependencies. We use principal component analysis and find an index that accounts for precipitation extremes due to common extremes in the first principal components. We use this index for a D&A study.

We further suggest that changes in extreme precipitation may depend on the spatial scale, e.g., when thinking about stratiform and convective precipitation events caused by different atmospheric conditions. Therefore, separate investigation of different event types might be necessary for robust and reliable D&A. This motivates our approach to filter different event types using the dual-tree wavelet transform, as previously applied by Buschow and Friederichs (2021). We propose a scale-dependent D&A study based on the filtered precipitation data.
Homogenisation of relative humidity data series
Authors: Izsak Beatrix , Szentes Olivér , Lakatos Mónika
Published: June 2022

The MASH (Multiple Analysis of Series for Homogenization) system was developed to complete, control and homogenize long-term monthly and daily data series. The data series homogenised by MASH sytem are the basis of other systems, since long data series represent the spatial and temporal samples for climate information. The daily average relative humidity data series are homogenised with using the MASHv3.03 software operationally at the Hungarian Meteorological Service.

In this study we present the verification statistics of the station data series and we show the representativity values of the station network. The data quality control part of MASH works efficiently for daily relative humidity values. We show the quality control results, including cases when the suspicious values are acceptable. Doubtful measurements come out due to the existence of e.g. inversion situations.

Recently we have homogenised not only the daily data but also the 6-hourly measurements. As the mathematical background of the MASH software allows us to use the homogenisation results of the past years, we homogenise these data series with using the results of the daily data. Since the hourly data series are highly inhomogeneous, moreover, their inhomogeneities are not the same as those of the daily series, therefore we cannot ignore the daily trend of the inhomogeneities.

Consequently, we decided to homogenise the 6-hourly series separately, using the usual MASH procedure. When homogenising the 6-hourly data series with the MASH system, the breakpoints detected in the daily data series can be automatically used as metadata. The results of the homogenisation of the 6-hourly values are also presented. These sub-daily data series are important for the accuracy of hazard warning, e.g. to study foggy situations.
Too hot for air conditioning. Increased energy demand during heat waves in Poland. 
Authors: Graczyk Dariusz , Pińskwar Iwona , Choryński Adam
Published: June 2022

Climate change observed in recent decades is manifested by an increasing occurrence of extreme meteorological phenomena, which have  impact significantly various sectors of the economy and society. One of the most vulnerable sectors is energy production and distribution.

Over the past 20 years, we have observed a steady increase in the demand for electricity in Poland. It increases both in individual months and for each season of the year. However, it can be seen that the changes are not identical for winter and summer. In the scale of the whole country, for January the increase in energy consumption between the first and the second decade of the 21st century is below 10%, while for July it is almost 15%. During the hottest months, energy consumption is already similar to the winter months.

One of the factors analyzed by the authors responsible for such a large change are the more frequent, longer, and more intense heat waves. One of such waves, occurred in 2015, led to limitations in energy supplies to industrial recipients. The aim of the presented study is to estimate the impact of heat waves on the demand for electricity in Poland on a time scale: from hourly and daily to monthly demand. The 20-year time series of hourly electricity consumption in Poland provided by the national operator of the energy grid (Polskie Sieci Elektroenergetyczne) will be compared with a similar time series of meteorological data such as the maximum, minimum, and average daily air temperature and hourly temperature values. Energy consumption during 5 selected heat waves from 2002 to 2021 will also be analyzed. This will allow to assess whether the limitations in energy supplies may repeat in the future and what are the meteorological factors behind them.

Acknowledgements: This research has been funded by the National Science Center of Poland under the grant number: 2018/31/B/HS4/03223
Statistical analysis and classification of cyclogenesis events in the Mediterranean
Authors: Carniel Cosimo Enrico , Ferretti Rossella , Ricchi Antonio , Curci Gabriele , Serafini Piero , Wellmeyer Evan David , Zardi Dino
Published: June 2022

The Mediterranean Sea is a mid-latitude fairly temperate marine basin, strongly influenced by the North-Atlantic atmospheric circulations. In this semi-enclosed basin, a wide variety of cyclogenesis mechanisms are known to develop, including baroclinic waves coming from the Atlantic, Mediterranean cyclogenesis originating from the cut-off of baroclinic waves, Tropical-Like Cyclones (TLC), Rapid-Cyclogenesis (RC) and Intense Mediterranean Cyclones (IMC). Depending on the cyclone type, the characteristic frequency of appearance can vary, ranging from tens per month to 1.5 per year, as in the TLC case. RCs are among the rarest and probably most intense and destructive cyclogenesis events that can develop within the Mediterranean basin; they usually originate at high latitudes, during wintertime, and mainly over the sea, preferring areas with high Sea Surface Temperature (SST) gradients. It is generally accepted that these events are determined by 12 different parameters, among which the most relevant one is the quick drop of pressure, close to 1hPa/hr for 24 hours, within the eye of the cyclone. RCs formation is an extremely complicated process, and in the Mediterranean basin it is mostly driven by air intrusions from the stratosphere and by the presence of Atmospheric Rivers. Using ERA5 dataset, we firstly conducted a physical and dynamical analysis of the 30 most intense cyclogenesis events occurred in the Mediterranean basin in the period 1979-2020, identifying factors which triggered and generated cyclones and contributed to the intensification of such events; this allowed to classify them as TLC, RC or IMC. Further analysis has been undertaken to determine the cyclones' phase and their main morphological characteristics, as well as their statistical distribution, seasonality and correlation with relevant indexes such as NAO, EA and SCAND, as well as  SST anomalies exhibited by the Central Mediterranean Basin.
A new dataset for climate change studies in complex alpine terrain (in the making)
Authors: Sedlmeier Katrin , Lotz Annette , Nitsche Oliver , Heiser Sebastian , Paunovic Ivan , Bock Lothar , Mühlbacher Gudrun
Published: June 2022

The Berchtesgaden National Park located in the Bavarian Alps in Southeastern Germany is a highly interesting study field due to its extreme topography (~ 600 - 2700 m.a.s.l.) and locally most variable climate conditions. 

Due to the research objective of the national park and the cooperation with the German Meteorological Service (DWD) we find a dense measurement infrastructure compared to other alpine regions. Ground measurements of temperature, relative humidity, precipitation and wind conducted by both partners go back as long as the 1980s with stations in different altitudes covering the complex terrain. Altogether, data of almost 100 stations is available, albeit with different record lengths (some only have short records of a few years). However, up to now, the datasets of the two institutions have only been used separately and lack a stringent quality control which takes into account the challenges of measurements in complex terrain.

New efforts are now underway to create a quality controlled, homogenized dataset for the Berchtesgaden National Park which will be openly available for the research community. Additionally, a gridding of the station data is planned which is of high importance to cover complex high mountain areas as adequately as less complex regions. The efforts also include the assessment of uncertainties, both of the measurement devices and of the digitization of thermohygrograph charts, as well as the compilation of metadata. Data from the operational Networks of the German and Austrian Meteorological Services, as well as other available station networks in the region are included in the processing of data.

This contribution will introduce this new dataset which is currently in the making and which in future can be used as common historical reference e.g. for climate change studies, as boundary conditions for impact models or for the validation of climate models in complex terrain. It will also highlight some of the challenges of measurements in complex terrain and their data processing.
A new urban parameterisation for the ICON atmospheric model
Authors: Schulz Jan-Peter , Mercogliano Paola , Adinolfi Marianna , Apreda Carmela , Bassani Francesca , Bucchignani Edoardo , Campanale Angelo , Cinquegrana Davide , De Lucia Carmine , Dumitrache Rodica , Fedele Giusy , Garbero Valeria , Interewicz Witold , Iriza-Burca Amalia , Jaczewski Adam , Khain Pavel , Levi Yoav , Maco Bogdan , Mandal Alan , Milelli Massimo ,   COSMO PP CITTA' team
Published: June 2022

A new urban canopy scheme for the ICON atmospheric model is presented. Increasing the resolution of atmospheric models for numerical weather prediction (NWP) or climate simulations allows, among others, for a more realistic description of the processes at the land surface. Here, one field of growing interest are the processes in urban areas. Beside their relevance for the meteorological modelling, there is a general trend in most countries that the number of people living in towns is significantly increasing. During the recent years, an urban canopy parameterization was developed for the multi-layer land surface scheme TERRA of the Consortium for Small-scale Modeling (COSMO) mesoscale atmospheric model. This parameterisation, TERRA_URB, originally developed for the climate version of COSMO and then ported to the NWP version, was shown to be able to reproduce the key urban meteorological features for different European cities. In the framework of the transition of the COSMO Consortium to the ICON model, TERRA_URB needs to be implemented in ICON. Furthermore, an updated set of urban canopy parameters needs to be provided, for describing the urban characteristics down to a mesh size of 1 km, and below. For these purposes, the COSMO Consortium organises the dedicated Priority Project CITTA’. First results are presented for TERRA_URB in the ICON limited-area model ICON-LAM for different cities of interest of the CITTA’ partners. The preliminary results indicate already that urban features like the urban heat island effect are well represented. This is in agreement with the experiences with TERRA_URB in the COSMO model, both the climate as well as the NWP version.
Irrigation nudging in COSMO-REA6
Authors: Rosell Christian , Valmassoi Arianna , Keller Jan , Friederichs Petra
Published: June 2022

Irrigation, predominantly carried out in southern Europe, has a considerable impact on the state of the atmosphere, but is not yet included in numerical models used for producing reanalyses. A common approach to correct the soil moisture (and, thus, also lower boundary condition in the atmospheric model) is to alter the soil moisture field to fit 2m temperature observations in a data assimilation scheme. In this way, water is added to or removed from the soil in the form of assimilation increments. This raises the question whether this assimilation process also recognizes soil moisture changes from irrigation and adjusts the model state accordingly.

To answer this question, we analyze the effects in the Po valley in northern Italy – a region where irrigated fields are the predominant land use. Here we compare three years in the late irrigation period July and August with each other, one particular rainy year, one drought year with irrigation and one drought year when irrigation was prohibited by law.
For this purpose we conduct a spatial analysis of the soil moisture increments in the neighbourhood of assimilated stations with respect to irrigation and precipitation bias. The resulting findings are then interpreted in terms of their spatial relation to other stations and their values of irrigation and precipitation bias.

The results indicate that the assimilation process is able to compensate for the deviations caused by irrigation. However, the effictiveness of this process also depends on the spatio-temporal density of observations. Since the vast majority of observing sites are located in non-irrigated areas, considerable model deviations in soil moisture and corresponding atmospheric parameters are still expected to be present in irrigated areas.