Physikalisch-Meteorologisches Observatorium Davos

Some major volcanic eruptions, such as the one of Mt. Pinatubo in 1991, can inject large amounts of sulfur dioxide (SO2) into the stratosphere, leading to the formation of a volcanic aerosol cloud. This dense aerosol cloud induces radiative heating of the stratosphere, causing ozone and water vapour changes, thereby altering middle atmospheric dynamics and chemistry. The scale of these impacts on stratospheric temperature anomalies is still highly uncertain.In this study we analyse data from the Historical Eruptions SO2 Emission Assessment Protocol (HErSEA) under the Interactive Stratospheric Aerosol Model Intercomparison Project (ISA-MIP). The results from eight global interactive-aerosol models confirm our general understanding of the stratospheric aerosol forcing due to SO2 injection following a volcanic eruption. As direct observations are sparse we compare models to three widely used reanalyses (ERA5, MERRA2, and JRA55). This analysis shows that while the multi-model mean temperature anomalies agree well with reanalyses, differences among individual models can be large. Our study shows that agreement in the median occurs through error compensation when averaging across models. The analysis and the sensitivity tests for model selection presented here highlight that by far the most important factor driving both magnitude and spread of the multi-model distribution in temperature response to volcanic aerosol forcing is model choice. Differences in transport, radiative transfer, and microphysics as well as the characterization of aerosol size distributions play a crucial role for the simulated spread in the temperature response.Another candidate to explain the spread in the ISA-MIP models, is the use of interactive aerosol schemes. To test this hypothesis, we compared the ISA-MIP multi-model distribution with those obtained from CCMI-2022 and CMIP6-AMIP model intercomparisons, which use prescribed SADs. If indeed interactive aerosol treatment would be a key contributor, one would expect smaller multi-model temperature anomaly distributions from CCMI-2022 and CMIP6-AMIP. Interestingly, this hypothesis has to be rejected, as no reduction in the multi-model spread is found. Hence, we argue for caution in attribution studies and the interpretation of stratospheric aerosol injection experiments relying on individual or few models.

Floods can have enhanced impacts when they occur in close succession with streamflow droughts. Despite the increased likelihood of impacts during such drought-to-flood transition events, substantial gaps remain in the understanding of the hydrological processes and drivers leading to their development. Specifically, it remains unclear how changes in hydrological conditions (IHCs) after drought and meteorological forcings during the transition towards flood conditions propagate to flood characteristics, such as duration and peak flow. We address this research gap by running three stress test experiments at the event scale: (1) changes in IHC based on perturbations applied to the meteorological forcing (e.g., temperature and precipitation) during the drought; (2) perturbation of observed forcing during the transition; and (3) modifications to both IHC and forcings during the event (i.e., a combination of cases 1 and 2). To do so, we calibrate the bucket-type GR4J hydrological model across a quasi-global dataset encompassing 2003 catchments. Using the results of all synthetic experiments conducted across all catchments, we estimate the isolated and joint sensitivities of both transition and flood characteristics to changes in drought characteristics (e.g., duration, intensity, severity) and to meteorological forcings during the transition. By analyzing the joint sensitivity of a given attribute (e.g., peak flow) to IHCs and meteorological forcings, we estimate the relative importance of each in driving the attribute's overall sensitivity (i.e., total effect of all individual sensitivities). Our findings indicate that, regardless of whether the system is stressed in isolation (i.e., experiment 1 or 2) or jointly (i.e., experiment 3), meteorological forcings - primarily temperature - are the main drivers affecting changes in transition and flood characteristics across hydroclimatic regions. Overall, clear differences between sensitivities, e.g., of peak flow to precipitation during the transition, emerge when comparing snowmelt- and rainfall-influenced catchments. Furthermore, hydroclimatic and streamflow regimes, along with catchment storage dynamics, are the main proxies for understanding the sensitivity of transition attributes to changes in IHC and meteorological forcings. Additionally, we show that IHCs are more important than meteorological forcing in explaining the variability of transition characteristics. Consequently, although IHCs control the baseline state, small changes in variables such as temperature during transitions can significantly alter flood characteristics, largely independently of the IHCs. Ultimately, we show that warming, rather than variations in drought severity (i.e., antecedent conditions) or precipitation, is likely to shape future transitional floods. Our results enhance our understanding of drought-to-flood transitions and their sensitivities to hydrometeorological conditions, thereby generating clearer insights into how droughts and meteorology influence floods and their impacts.

For many critical energy-related applications, such as reliable PV power production, accurate Global Horizontal Irradiance (GHI) short-term forecasts are crucial. Forecasts of GHI, as analyzed in this study, are produced by the state-of-the-art, high-resolution forecasts developed within the Destination Earth initiative, the ECMWF Digital Twin Engine using the ExtremeDT (Weather-Induced Extremes Digital Twin) dataset, that produces daily global simulations at resolutions of 4.4 km kilometres up to four days ahead. However the GHI ExtremesDT forecasts do not take into account the aerosol effects, which may introduce systematic biases, especially during periods of high aerosol load events. We investigated the impact of aerosols on DT GHI forecasts and the associated PV power predictions within the context of the DestinE Destination Renewable Energy (DRE) use case.We analyze almost one year of data comprising two-day-ahead DT GHI forecasts and the corresponding aerosol optical depth (AOD) forecasts from the Copernicus Atmosphere Monitoring Service (CAMS). The DT GHI forecasts are corrected for aerosol effects using fast radiative transfer model techniques utilising the lidRadtran [1, 2] package.Within the DRE project, and based on co-design activities with the project's end user, site-specific PV power production forecasts tailored to the user’s needs and infrastructure were developed, using DT GHI forecasts and a machine learning (ML) model that was trained on the user’s historical data. The impact of aerosol correction was also evaluated by comparing PV power production forecasts derived from GHI forecasts with and without aerosol correction against the actual PV power production of the plant.A machine learning (ML) model, trained on historical, site-specific production data from the QUEST PV park, was created within the DRE project to convert predicted solar irradiance into PV park power output in order to evaluate the impact on power production. The ML model propagates the original and aerosol-corrected GHI forecasts, which are then compared to actual production.The results show how CAMS-based aerosol correction of GHI forecasts can reduce bias and consistently improve PV power prediction. Results show the value of incorporating atmospheric composition data with ML-based power conversion models for operational energy applications, as well as the significance of aerosol representation in solar forecasting.AcknowledgmentsDRE project has received funding from the European Space Agency under the DESTINATION EARTH USE CASES – DESP USE CASES - ROUND 1. The duration of the project is 12 months (November 2023 - November 2024).We would like also to acknowledge the COST Action HARMONIA (International network for harmonization of atmospheric aerosol retrievals from ground based photometers), CA21119.Bibliography(1) Mayer, B.; Kylling, A. Atmospheric Chemistry and Physics 2005, 5, 1855–1877.(2) Emde, C.; Buras-Schnell, R.; Kylling, A.; Mayer, B.; Gasteiger, J.; Hamann, U.; Kylling, J.; Richter, B.; Pause, C.; Dowling, T.; Bugliaro, L. Geoscientific Model Development 2016, 9, 1647–1672.

Examining catchment response to precipitation at event scale is useful for understanding how various hydrological systems store and release water. Many of such event scale characteristics, for example event runoff coefficient and event time scale are also important engineering metrics used for design. However, deriving these characteristics requires identification of discrete precipitation-streamflow events from continuous hydrometeorological time series.Event identification is not at all a trivial task. It becomes even more challenging when working with very large datasets that encompass a wide range of spatial and temporal dynamics. Approaches range from visual expert judgement to baseflow-separation-based methods and objective methods based on the coupled dynamics of precipitation and streamflow. Here, we would like to present our experience in the quest to devise the “ideal” method for large datasets – and trust us, we tried, a lot. We demonstrate that expert-based methods can be seriously flawed simply by changing a few meta parameters, such as the length of displayed periods, baseflow-separation-based methods deliver completely opposite results when different underlying separation methods are selected, and objective methods suddenly fail when dynamics with different temporal scales are simultaneously present.Ultimately, we realized that finding a one-size-fits-all method was not possible and that compromises had to be made to select sufficiently representative events across large datasets. Therefore, we advocate for pragmatic case-specific evaluation criteria and for transparency in event identification to make study results reproducible and fit for purpose, if not perfect.

Climate change is expected to alter floods in complex ways. Understanding changes in flood frequency and driving processes under changing climatic conditions is urgently needed to develop sustainable adaptation measures to floods in rivers such as the Swiss section of the Rhône, which has been hit by a severe flood in June 2024. The investigation of future flood characteristics and driving processes is, however, challenging because these events exhibit large interannual variability and are, among other processes, caused by localized intense precipitation, which is not optimally represented in global climate model simulations. High-resolution initial condition large ensembles and convection-resolving climate simulations can be used to address these challenges. This work leverages these two types of climate ensembles and process-based hydrologic modeling to quantify long-term changes in peak flows in the Swiss section of the Rhône until the end of the century, and their uncertainty. We will use these simulations to investigate the driving processes underlying future floods and how these differ from those relevant for flood generation today. In doing so, we provide information crucial for decision-making related to future flood protection and adaptation.

The EarthCARE (ECA) satellite is currently in its second year in orbit, collecting new data everyday that could play a crucial role in advancing climate science. However, due to the advancedtechnologies and retrieval approaches used in EarthCARE, the credibility of each instrument andof their synergetic products must be verified. Significant effort has been devoted to this topicboth currently and in the past. Nevertheless, a substantial amount of publicly available datathat could improve validation has not yet been used. In this study, we use the ground-basedradiation measurements from the Baseline Surface Radiation Network (BSRN) to validate1D surface solar radiation estimates from the EarthCARE ACM_RT product. Cloud effects areanalyzed separately using the cloud modification factor approach. Values from BSRN stationsare used if the station has less than 50 km distance to the satellite ground track. In addition,an intercomparison with Copernicus Atmospheric Monitoring Service (CAMS) satellite basedsurface solar radiation estimations has been performed. For the comparison with CAMS,ECA values are averaged over time to obtain collocated grid cells. Due to limited gridded dataavailability of the CAMS radiation service, this comparison is restricted to September-December2024.The ECA surface solar irradiance exhibits a Mean Bias Error (MBE) of −10.4 Wm-2 and aRoot Mean Square Error (RMSE) of 191.7 Wm-2 against ground based (BSRN) measurements.Relative to CAMS, ECA surface solar irradiance exhibits a MBE of −23.3 Wm-2 and a RMSEof 103.3 Wm-2. While some parts of South America, Northern Africa and Western Asia tend tohave higher EarthCARE irradiance, most of the available regions show higher CAMS irradiance.This is especially the case in Oceania, middle part of Africa and Europe. Approximately 69%of the difference between EarthCARE and CAMS can be contributed to differences in cloudestimation, while 31% can be contributed to differences in clear-sky irradiance.Future data releases from BSRN and CAMS are expected to enable a more robust assessment.This analysis offers valuable insights relevant to the solar energy community.Acknowledgements:The authors acknowledge the project RACE-ECV (SBFI-633.4-2021-2024/PMOD - EarthCARE 202/2), supported by SBFI, the project Observe: Optimising 3D RT Earthcare product using geostationary observations and AI, ESA Contract No. 4000147848/25/I/AG and the CERTAINTY (Cloud aERosol inTeractions & their impActs IN The earth sYstem) project funded from the Horizon Europe programme under Grant Agreement No 10113768

Shallow landslides pose a significant threat to people and infrastructure in mountainous regions and can occur abruptly on steep soil slopes. To assess their hazard potential, data-driven landslide susceptibility mapping aims to predict the spatial likelihood of such events. In recent decades, machine learning approaches and high-quality spatial information have continuously improved landslide susceptibility assessment. Nevertheless, discrepancies between predicted susceptibility and observed landslide occurrence seem to remain unavoidable. In simple terms, this mismatch between predicted and observed patterns can have two causes: 1) the information on covariates controlling landslide triggering is limiting (the predicted susceptibility is ‘wrong’) or 2) the observed time scale is too short to capture the failure of more areas with similar (correctly predicted) susceptibilities. To explore these two options, we first developed a landslide susceptibility map for Switzerland based on a wide range of spatial datasets and machine-learning methods. Next, we evaluated its performance against an independent inventory which contains detailed field information of 763 landslides. Information from soil profiles collected at the head scarps of these landslides allowed us to assess the specific conditions that lead to slope instabilities which large-scale spatial models are not capable of addressing. In a third step, we performed field investigations at selected past landslide sites and compared their subsurface structure (deduced from electrical resistivity tomography) with nearby locations that had not yet failed but exhibited similar predicted susceptibility values. These measurements revealed significant differences in the subsurface. Our approach highlights the critical role of subsurface complexity in controlling hydrological flow paths that ultimately govern slope failure. In particular, variations in soil texture, soil development, soil type and soil depth strongly influence the mechanical and hydrological conditions affecting slope stability. These findings provide new insights into the limitations of large-scale susceptibility mapping and emphasize the importance of subsurface hydrology in understanding shallow landslide initiation.

We present observations of impulsive solar energetic particle events on 2025 March 20 observed at high heliolatitude by Solar Orbiter and compare them with near-ecliptic observations by the Wind spacecraft. Solar Orbiter, located at 0.38 au, a Carrington longitude of 139.7◦ and a latitude of −16.6◦, detected two ion events at ∼0.1-6 MeV and two electron events at ∼40-200 keV. These events exhibit clear velocity dispersion and strong field-aligned anisotropy. Velocity dispersion analysis of both ion and electron events yields path lengths consistent with the nominal Parker spiral length. Furthermore, the first electron event exhibits a double-power-law spectrum with an index of 2.3 ± 0.3 below a break energy of 58 ± 4 keV and an index of 4.0±0.2 at energies above, while the second electron event exhibits a single-power-law spectrum with an index of 4.6 ± 0.2. In contrast, the Wind spacecraft, located at 1 au, a Carrington longitude of 120.0◦, and a latitude of −7.1◦, observed only one electron event, which shows insignificant velocity dispersion and arrives ∼20 min later than expected. The Parker spiral footpoints of the two spacecraft were separated by ∼15◦ in longitude and ∼10◦ in latitude, providing a lower limit on the angular extent of impulsive electron events. The delayed arrival at Wind may be attributed to the electron diffusion in the solar source region.

The recovery of the ozone layer is expected by the middle of the 21st century, after the significant depletion detected during the 1980s. However, confirming this recovery critically relies on high-quality and long-term measurements, which play a key role in monitoring changes in atmospheric constituents and in detecting global trends.Here we present the homogenised ozone time series of Arosa/Davos. This time series is based on measurements performed by Dobson and Brewer spectroradiometers covering the period from 1926 to the present and constitutes the world’s longest continuous ground-based ozone dataset. This study focuses on the 1990 to 2024 period and the eventual recovery of ozone after the international efforts to reduce the chlorofluorocarbons emissions. We use the merged ozone times series of Arosa/Davos composed of three Brewer and three automated Dobson spectroradiometers data records. To reconcile the seasonal discrepancies between the Brewer and the Dobson dataset, we followed the methodology explained in previous studies. The improvements employed includes replacing the operational ozone absorption cross-section (Bass and Paur, 1985) with the measured by the University of Bremen (Serdyuchenko et al., 2014) and correcting the ozone effective temperature using the ozone sondes measurements from Payerne. Furthermore, the measurement uncertainty was derived for each of the six instruments to produce homogenised merged ozone dataset.The relocation of the instruments from Arosa to Davos during the period 2011-2021 was carefully analysed and allowed the determination of a constant transfer factor to ensure homogeneity of total column ozone between both sites. This factor was found to be equal to the climatological tropospheric ozone column differences. Finally, the datasets from all instruments were merged to combine one consistent single record.The robustness of this merged ozone time series should enable the detection of an ozone recovery signal, by reducing the possibility of misinterpretation due to instrumental artefacts. As a future work, we will aim to assess long-term ozone changes and evaluated the attribution of the stratospheric and tropospheric ozone from the potential recovery detection of this homogenised time series.

Dry periods can negatively impact river ecosystems through deficits in streamflow (hydrological droughts) and deteriorations in water quality. Yet, literature on water quality extremes is in its infancy and the co-occurrence of hydrological droughts and water quality extremes remains poorly understood, especially for different hydro-climatic regimes. With hydrological models often struggling to capture extreme conditions, observation-based studies from multiple catchments are crucial to gain insights on regional-scale patterns. High-frequency observations - at least daily - are needed to properly characterize often short-lived extreme events, but such observations are rarely available in current large-sample water quality datasets, which mostly have a relatively low temporal resolution (e.g., monthly). Therefore, data fragmentation, both in space and time, has hampered our understanding of the co-occurrence of hydrological droughts and water quality extremes to date. Specifically, here we ask the questions: Where and when do ecologically-relevant water quality extremes occur during hydrological droughts? and What are the hydro-climatic factors influencing these events? To address these questions, we rely on a newly-assembled dataset of water quantity and quality observations (i.e., streamflow, water temperature, electrical conductivity, and dissolved oxygen) at a daily resolution for 43 catchments across Europe and the USA and the period 2005−2024. For this case study, we derive hydrological droughts and water quality extremes (namely, sustained periods of abnormally low flows, abnormally high water temperature and electrical conductivity, and abnormally low dissolved oxygen) using a percentile-based approach with seasonally and catchment varying thresholds. We then focus specifically on concurrent hydrological droughts and water quality extremes, and in particular on those events exceeding thresholds that can lead to stress for aquatic animals. We finally characterize these events using metrics describing their magnitude and timing, and perform regression analyses to shed light on our research questions. Preliminary results indicate that hydrological droughts co-occurring with water quality extremes exhibit notable spatio-temporal variability. They further suggest that ecologically-relevant thresholds are exceeded in terms of water temperature in particular, and especially in catchments with human influence and minimal snow contribution. This study will enhance our mechanistic understanding on the potential impacts of hydrological droughts and water quality extremes on river ecosystems, by providing relevant information for river management under drying and warming.