Small island ecosystems are threatened by climate change in several ways. This includes the Galapagos archipelago in the Tropical Eastern Pacific, where limited freshwater makes the islands dependent on atmospheric supply through precipitation. However, precipitation distribution remains highly uncertain due to the lack of operational observation systems, and it is unclear how climate change will affect availability. Given its location, climate impacts are closely tied to changes in El Nino Southern Oscillation extremes during El Nino and La Nina years. Using a new measurement network incorporating a vertical rain radar profiler, we investigated seasonal rainfall changes (hot and cool) by analyzing El Nino/La Nina-like years, serving as surrogate for a locally warmer and locally cooler world. Our analysis demonstrates that in a locally warmer world, precipitation is increasing in both seasons. Rainfall characteristics and satellite-retrievals convective cloud frequency indicate more convective activity, intensifying heavy rainfall, especially during the hot season. In the cool season, drizzle is replaced by light rain throughout the vertical profile. In a locally cooler world, the hot season's typical midday rainfall maximum is replaced by oscillating, short-wavelength patterns and lower totals. Interestingly, the cool season in a La Nina-dominated world exhibits slightly higher rainfall than a neutral year, likely due to better condensation conditions of the advected moist air at lower air temperatures. Overall, results suggest improved total rainfall supply in a locally warmer world, but torrential rain could endanger the islands in the hot season. Furthermore, cool seasons shift from drizzle to light rain, though totals remain low overall.

Climate change with increasing air temperatures results in amplified hazards for human health by excessive heat. Compared to their non-urban surroundings, cities typically show elevated air temperatures, causing urban dwellers to be even more threatened by heat in warmer conditions. Up until now, studies could not conclusively clarify how climate change and urban effects on air temperature interact with each other over time scales covering decades since multi-decadal measurements from urban climate observation networks are generally scarce. Here, we present robust air-temperature trends for the Climate Normal 1991-2020 using quality-controlled data from eleven urban and 14 non-urban weather stations in the Berlin region, Germany, covering a wide range of urban and non-urban settings. We analyse trends for four daily variables as annual and seasonal means, as well as during heatwaves. Our findings highlight that climate change and the city interact linearly on the analysed time scales. This results in similar air-temperature trends in urban and non-urban areas, yet at different absolute levels. An exception is the daily minimum air temperature in spring, which shows different trends for urban and non-urban stations. Investigation of the built-up area around the stations and in the study region shows no significant change during the study period. This highlights that the observed warming is due to regional climate change and not related to urbanisation processes. By comparing trends for the last 30 years (1991-2020) with observational data since the end of the 19th century, we show that the recent rise in air temperature is unprecedented in the study region, indicating accelerated regional climate change. Our study, a first presenting 30 years of data from an urban climate observation network, offers a blueprint for investigating climate change in other cities with sufficient data.

The Central European Refined analysis (CER) was developed in 2016 as a high-resolution, reanalysis-based, gridded dataset for Central Europe. The second version (CER v2) aims to further improve the performance of the CER with a particular focus on precipitation data for the metropolitan region Berlin-Brandenburg. The simulation setup consists of two-way nested, cascaded domains for Germany (10 km grid spacing) and the region Berlin-Brandenburg (2 km grid spacing) and employs a daily re-initialization approach. Major changes from the precursor version include the use of ECMWF-ERA5 reanalysis forcing data and a newer WRF version, allowing for the production of longer time series. To further improve the precipitation performance for the CER v2 we performed sensitivity experiments with five cumulus and five microphysics schemes. The results of these test simulations were evaluated using one year of daily precipitation data at 244 stations of the German Weather Service (DWD) in the 2 km domain of the model. The best average performance was achieved for a combination of the conventional Kain-Fritsch cumulus and the Thompson microphysics scheme. Using this setup, we simulated the precipitation conditions for 30 years (1991-2020) and evaluated monthly and annual precipitation averages against station and radar data by the DWD. Here, the CER v2 showed a significant reduction in deviations and mean bias compared to the previous version. Based on the spatial resolution of the ERA5 data, we resampled the CER v2 and observational data to compare the performance of both datasets. We observed a wet bias in the ERA5 precipitation data for this region, which was significantly reduced in the CER v2. Results of monthly averages indicated a comparable performance to ERA5 data throughout most of the year. Deviations from the observational data were typically higher during the summer months. However, due to the significant bias reduction and the high spatial resolution, the CER v2 could provide important insights about the local- to mesoscale precipitation dynamic of this region.

Like many small oceanic islands, the Galapagos archipelago, renowned for its unique geographic location and exceptional endemic biodiversity, faces significant challenges under climate change. In particular, the atmospheric water supply for the ecosystem and the local population is under threat, with clouds and rain playing an important role in ensuring freshwater availability under climate change. Better planning of adaptation measures would require climate data on clouds as a prerequisite for precipitation and rainfall at high spatio-temporal resolution, which are not available in this area. Operational products such as satellite derived cloud and precipitation products or reanalysis data are widely used to compensate for the lack of local data availability but are often poorly suited for regional applications. In the current study, we aim to generate high quality area-wide cloud information to distinguish ecoclimatic cloud zones that may require different adaptation measures to climate change. To address this issue, we have developed a new physical rule-based cloud mask retrieval specifically tailored for the Galapagos Archipelago, based on data from the third generation GOES-16 Advanced Baseline Imager (ABI) geostationary satellite. The new Galapagos Rainfall Retrieval (GRR) cloudmask was tested against independent observational data and compared to both the operational GOES-16 ACM (ABI Clear sky Mask) and the MODIS cloudmask benchmark cloud mask. Our test results confirm that the GRR-cloudmask (Probability of Detection POD = 0.94, Critical Success Index CSI = 0.92-0.93) clearly outperforms the operational ACM-cloudmask (POD = 0.56-0.68, CSI = 0.55-0.67). Area-wide tests against the MODIS cloud mask showed a CSI of 0.72 and a POD of 0.74 for the ACM, which is superior to the GOES-16 ACM-cloudmask. We produced cloud frequency maps for all months and day slots and analysed cloud frequency using ancillary meteorological data. In general, the cool season (Jun-Dec) / night shows much higher cloud frequencies than the warm season (Jan-May) / daytime. However, regional cloud patterns differ along a west-to-east and south-tonorth gradient, depending on complex interactions of forcing parameters such as exposure to the main circulation, sea surface temperature zones, altitude and land cover. A k-mean cluster analysis resulted in nine ecoclimatic cloud zones over land, which are much more differentiated than the widely used four-zone classification. The results will help to develop more site-specific climate change adaptation planning for the iconic Galapagos National Park.

The Gal & aacute;pagos Archipelago exhibits a unique and high endemic biodiversity that is strongly affected by climate variability, mainly caused by the El Ni & ntilde;o-Southern Oscillation phenomenon. However, there exist few climate datasets for the islands and a long-term climate dataset at the meso-scale is not available. We present the Gal & aacute;pagos Archipelago Refined analysis data-set (GAR), a dynamically downscaled dataset of 2 h temporal resolution and 2 km horizontal grid spacing for the Gal & aacute;pagos Archipelago, that is based on ERA5 reanalysis data. The GAR is produced by the Weather Research and Forecasting Model (WRF V.4.3.3). Sensitivity experiments focused on precipitation and air temperature led to the selection of a suitable model setup for the region, which was developed using observational data from the Darwin Measurement Network (DMN) and the Charles Darwin Research Station (CDRS). We evaluated the performance of the model by reproducing the measured daily mean values at the Cerro Crocker (CC) and Puerto Ayora (PA) stations for the period from 01 April 2022 to 31 March 2023. The results show very strong correlations (rho(T,CC) = 0.94 and rho(T,PA) = 0.94) for air temperatures. For daily precipitation rates, measured by rain gauges, the GAR yields medium to strong correlation (p(Pg,CC )= 0.66 and p(Pg,PA) = 0.44). Specific humidity very strongly correlates with the measurements (p(SH,CC) = 0.88 and p(SH,PA) = 0.97). Analysis of the spatial patterns of precipitation, specific humidity, and temperature on the meso-scale indicated a strong dependency on altitude. Precipitation for the dry season is triggered mainly by orographic lifting, while wet season precipitation is driven by thermally induced convection. The GAR fulfils the need for high spatio-temporal resolution data on the Gal & aacute;pagos climate and serves as a valuable source for scientific research in this area. The GAR data are publicly available, and together with the downscaling approach evaluated here, this dataset can easily be extended into the future.

Climate change is accompanied by increasing air temperatures, resulting in amplified hazards for human health by excessive heat. Cities typically show elevated air temperatures as compared to their non-urban surroundings such that urban dwellers are even more threatened by heat. So far, studies could not conclusively clarify how climate change and urban effects on air temperature interact with each other over time scales covering decades since multi-decadal atmospheric data from urban climate observation networks are generally scarce. Here, we present robust air-temperature trends for the climate normal period 1991-2020 using quality-controlled data from eleven urban and 14 non-urban weather stations in Berlin, Germany, and the surrounding region, covering a wide range of urban and non-urban settings. We analysed trends for four daily variables as annual and seasonal mean values, as well as during heatwaves. The results show that climate change and the city interact linearly on the analysed time scales, also during heatwaves. This results in similar air-temperature trends in urban and non-urban areas but at different absolute levels. Investigation of the built-up area around the stations and in the study region shows no significant change in the study period, highlighting that the observed warming is due to regional climate change and not related to urbanisation processes. By comparing trends for the last 30 years with those at two stations with observational data for longer time periods, we show that the recent rise in air temperature is unprecedented in the study region, indicating accelerated regional climate change. Our study, the first one presenting 30 years of data from an urban climate observation network, offers a blueprint for investigating climate change in other cities with sufficient data and supports the design of solutions for adapting cities to climate change.

The Urban Climate Observatory (UCO) Berlin is an open and long-term infrastructure for integrative research on urban weather, climate, and air quality. Quality-controlled observations are carried out in order to study the interaction between atmospheric processes and urban structures, as well as climate variability and climate change in urban environments. It enables multi-scale, three-dimensional atmospheric studies integrating observational and numerical modelling methods. The UCO Berlin includes the following components: The Urban Climate Observation Network (UCON) Berlin provides long-term observations of atmospheric variables (air temperature, relative humidity, air pressure, global radiation, wind, precipitation) in the Urban Canopy Layer (UCL) at various locations since the 1990s. Since 2015 freely available data from Netatmo weather stations in Berlin and surrounding have been systematically collected (Crowdsourcing). The meteorological towers are located in the garden of the Institute of Ecology at Rothenburgstraße (ROTH) in Berlin-Steglitz since 2018 and on the roof of the main building of the TU Berlin at Campus Charlottenburg (TUCC) since 2014. Turbulent fluxes of sensible and latent heat as well as carbon dioxide are derived from eddy covariance (EC) systems, which combines an open-path gas analyzer and a three dimensional sonic anemometer-thermometer (IRGASON, Campbell Scientific). The EC-systems at ROTH are installed at 40 m, 30 m, 20 m, 10 m and 2 m above ground and at TUCC at 10 m above roof (56 m above ground). The down- and upwelling radiation is measured separately for short-wave and long-wave radiation (CNR4, Kipp & Zonen) at the same heights as the EC-systems. The seasonal development of vegetation is observed at both tower locations using phenocams part of the international PhenoCam (phenocam.nau.edu) network. The ROTH tower is an associate site of the European research infrastructure Integrated Carbon Observation System (ICOS) and part of the national ICOS-D network (ID: DE-BeR). Ground-based remote sensing is used to study the urban boundary layer since 2017. The UCO Berlin operates two Doppler LiDAR systems (Streamline XR, Halo Photonics) and provide profiles of the horizontal wind speed and wind direction as well as information on atmospheric turbulence. Cloud height, cloud cover and aerosol layers are recorded with ceilometers (CHM 15k, Lufft) at sites Grunewald and TUCC, which is part of the E-Profile Network of the European meteorological services EUMETNET. The ceilometer range is 15 km, the vertical resolution is 15 m and the temporal resolution is 15 s. A microwave radiometer (HATPRO-G5, RPG Radiometer Physics GmbH) provides vertical profiles of air temperature and absolute humidity up to an altitude of 10 km. Integrated liquid water path (LWP) and the integrated water vapor (IWV) are derived from measurements of the brightness temperature in 14 channels. An X-band Doppler weather radar with dual polarization (GMWR-25-DP, GAMIC) for precipitation research is in operation since autumn 2022 and has a range of 100 km. The website of the UCO Berlin provides a data portal for search of meta data and download of open climate data in Berlin and surrounding: https://uco.berlin

Lakes are directly exposed to climate variations as their recharge processes are driven by precipitation and evapotranspiration, and they are also affected by groundwater trends, changing ecosystems and changing water use.In this study, we present a downward model development approach that uses models of increasing complexity to identify and quantify the dependence of lake level variations on climatic and other factors. The presented methodology uses high-resolution gridded weather data inputs that were obtained from dynamically downscaled ERA5 reanalysis data. Previously missing fluxes and previously unknown turning points in the system behavior are identified via a water balance model. The detailed lake level response to weather events is analyzed by calibrating data-driven models over different segments of the data time series. Changes in lake level dynamics are then inferred from the parameters and simulations of these models.The methodology is developed and presented for the example of Gro ss Glienicker Lake, a groundwater-fed lake in eastern Germany that has been experiencing increasing water loss in the last half-century. We show that lake dynamics were mainly controlled by climatic variations in this period, with two systematically different phases in behavior. The increasing water loss during the last decade, however, cannot be accounted for by climate change. Our analysis suggests that this alteration is caused by the combination of regional groundwater decline and vegetation growth in the catchment area, with some additional impact from changes in the local rainwater infrastructure.

For next-generation weather and climate numerical models to resolve cities, both higher spatial resolution and subgrid parameterizations of urban canopy-atmosphere processes are required. The key is to better understand intraurban variability and urban-rural differences in atmospheric boundary layer (ABL) dynamics. This includes upwind-downwind effects due to cities' influences on the atmosphere beyond their boundaries. To address these aspects, a network of >25 ground-based remote sensing sites was designed for the Berlin region (Germany), considering city form, function, and typical weather conditions. This allows investigation of how different urban densities and human activities impact ABL dynamics. As part of the interdisciplinary European Research Council Grant urbisphere, the network was operated from autumn 2021 to autumn 2022. Here, we provide an overview of the scientific aims, campaign setup, and results from 2 days, highlighting multiscale urban impacts on the atmosphere in combination with high-resolution numerical modeling at 100-m grid spacing. During a spring day, the analyses show systematic upwind-city-downwind effects in ABL heights, largely driven by urban-rural differences in surface heat fluxes. During a heatwave day, ABL height is remarkably deep, yet spatial differences in ABL heights are less pronounced due to regionally dry soil conditions, resulting in similar observed surface heat fluxes. Our modeling results provide further insights into ABL characteristics not resolved by the observation network, highlighting synergies between both approaches. Our data and findings will support modeling to help deliver services to a wider community from citizens to those managing health, energy, transport, land use, and other city infrastructure and operations. SIGNIFICANCE STATEMENT: A yearlong field campaign with a dense and systematic network of sites provides comprehensive measurements of the atmospheric boundary layer to gain deep knowledge of urban-rural and intraurban variability of surface-atmosphere exchanges. Understanding these is of high relevance for developing next-generation numerical weather prediction and climate models. We showcase the campaign and highlight synergies between ground-based and satellite observations and high-resolution numerical weather prediction modeling on two example days. Our findings show multiscale interactions between city and atmosphere, including urban-induced effects beyond the city's boundaries ("urban plume") and urban impacts under heatwave conditions. These results are important for developing dynamic modeling frameworks, which will help in delivering services to make cities more resilient.

The Central Europe Refined Analysis (CER) was developed in 2016 as a high-resolution, reanalysis-based, gridded data set for Central Europe and the Berlin-Brandenburg metropolitan region of Germany in particular. The data set was successfully used for investigations of near-surface air temperatures, but showed inaccuracies in the simulated precipitation compared to station measurements. In this study we characterize the development of the second version of this data set (CER v2), which focused primarily on improving the performance of precipitation products. This new version uses an updated version of the WRF model and new ERA5 forcing data. Comprehensive sensitivity studies were carried out to optimize the physical parameterization of daily precipitation results. The combination of the Kain-Fritsch cumulus and the Thompson microphysics scheme was selected for the CER v2 due to the reduction of the domain average Mean Deviation (MD) by 77% and the Root Mean Squared Deviation (RMSD) by 18% when compared to the original CER setup. The validation of 30 years (1991-2020) of the CER v2 precipitation data against station data by the German Weather Service (DWD) revealed that the domain median RMSD was the lowest during the winter with seasonal median RMSD of 0.24 mm d-1 and the highest during the summer with 0.71 mm d-1 . The comparison against 20 years of radar data (2001-2020) identified the highest seasonal RMSD during the summer along the western and southern border of the model domain and in the northeast of Berlin with values above 1 mm d-1 . CER v2 data was compared to the CER v1 and ERA5 data for the time period of 2001-2018 on a resampled 0.25 degrees grid. In terms of the domain median RMSD and MD, the CER v2 outperformed the CER v1 and the ERA5 during the winter, spring and autumn. However during summer, the domain median CER v2 RMSD was 46% higher than for ERA5. One of the biggest advantages of the data set is the substantial reduction in the domain median annual MD, which was about 94% lower than for the ERA5 forcing data. Due to its longer available time series and increased performance compared to the previous version, the CER v2 could provide important insights about the local- to mesoscale precipitation dynamics of the study region and serve as a foundation for data-driven hydrological models.

Clouds play an important role in the climate system; nonetheless, the relationship between climate change in general and regional cloud occurrence is not yet well understood. This particularly holds for remote areas such as the iconic Galapagos archipelago in Ecuador. As a first step towards a better understanding, we analyzed the spatio-temporal patterns of cloud cover over Galapagos. We found that cloud frequency and distribution exhibit large inter- and intra-annual variability due to the changing influence of climatic drivers (trade winds, sea surface temperature, El Niño/La Niña events) and spatial variations due to terrain characteristics and location within the archipelago. The highest cloud frequencies occur in mid-elevations on the slopes exposed to the southerly trade winds (south-east slopes). Towards the highlands ( >900 m a.s.l), cloud frequency decreases, with a sharp leap towards high-level crater areas mainly on Isabela Island that frequently immerse into the trade inversion layer. With respect to the diurnal cycle, we found a lower cloud frequency over the islands in the evening than in the morning. Seasonally, cloud frequency is higher during the hot season (January–May) than in the cool season (June–December). However, spatial differences in cloudiness were more pronounced during the cool season months. We further analyzed two periods beyond average atmospheric forcing. During El Niño 2015, the cloud frequency was higher than usual, and differences between altitudes and aspects were less pronounced. La Niña 2007 led to negative anomalies in cloud frequency over the islands, with intensified differences between altitude and aspect.

<p>Process-based models are the standard tools today when trying to understand how physical systems work. There are situations however, when system understanding is not a primary focus and it is worth substituting existing process-based models with computationally more efficient meta-models (or emulators), i.e. proxies designed for specific applications. In our research we have explored potential data-driven meta-modeling approaches for applications in hydrology, designed to solve specific research questions.</p> <p>In order to find a suitable meta-modeling approach, we have experimented with a set of different data-driven methods. We have employed a multi-fidelity modeling approach, where we gradually increased the complexity of our models. In total five different approaches were investigated: linear model with ordinary least squares regression, linear model with two different Bayesian methods (Hamiltonian Monte Carlo and transdimensional Monte Carlo) and two machine learning approaches (dense artificial neural network and long short-term memory (LSTM) neural network).</p> <p>For method development the project case study of the Groß Glienicker Lake was used. This is a glacial lake near Berlin, with a strong negative trend in water levels in the last decades. Supported by the observation model from the Central European Refined analysis, we had a daily, high resolution meteorological dataset (precipitation and actual evapotranspiration) and lake level observations for 16 years.</p> <p>All of the used models are designed similarly: they predict lake level changes one day ahead using precipitation and evapotranspiration data from the previous 70 days. This interval was selected after an extensive parameter test with the linear model. By predicting the change in stored water, we linearize the problem, and by using a longer time interval we allow the methods to automatically compensate for any lag or memory effects inside the catchment. The different methods are evaluated by comparing the fits between the observed and the reconstructed lake levels.</p> <p>As expected, increasing the model and inversion complexity improves the quality of the reconstruction. Especially the use of nonlinear models was advantageous, the artificial neural network outperformed every other method. However, in the used example these improvements were relatively small – meaning that in practice the simplest linear method was advantageous due to its computational efficiency and robustness, and ease of use and interpretation.</p> <p>In this presentation we discuss the challenges of data preparation and optimal model design (especially the memory of the hydrological system), while finding the hyperparameters of the specific methods themselves was relatively straight forward. Our results suggest that problem linearization should be a preferred first step in any meta-modeling application, as it helps the training of nonlinear models as well. We also discuss data requirements, because we found that the size of our dataset was too small for the most complex LSTM method, which yielded unstable results and learned spurious background trends.</p>

<p>During heat waves, urban dwellers are exposed to elevated temperatures, especially during night-time when urban heat island (UHI) effects are most intense. Climate change is expected to further increase heat-stress hazards. There are only few studies that have investigated how UHI effects interfere with heat waves. Here, we present results from a sensitivity study in which we analyse non-linear effects of elevated meso-scale temperature forcing on micro-scale atmospheric processes. The study employs the large eddy simulation model PALM-4U. The ‘Tempelhofer Feld’ in Berlin, Germany, the largest park within the city, was used as study area. Starting point was a 24 h (plus 6 h spin-up) control simulation followed by a scenario simulation in which all temperature variables, not only air temperature, were increased by 1 K. The control simulation was configured to represent a real weather situation in an idealized form. Grid spacing was set to 10 m horizontally and 2 m vertically to resolve buildings and trees. A residential area to the east of the airport was simulated with a higher horizontal grid resolution of 2 m to investigate micro-scale atmospheric processes in more detail. The results show that the micro-scale response of near-surface air temperature to elevated meso-scale temperature forcing is not constant throughout the day with lower values during day-time and higher values during night-time, particularly in the early evening. In both simulations, the night-time inversion over the park continues into the settlement above the roof level. The study shows that there are weak non-linear effects leading to an amplification of the UHI during night-time. However, as linear effects dominate, adaptation measures with regard to heat stress may be planned on the basis of current weather and climate conditions, additionally documented by observational data, and subsequently evaluated by urban climate monitoring.</p>

<p>The standard approach of modeling lake level dynamics today is via process-based modeling. The development of such models requires an extensive knowledge about the investigated system, especially the different hydrological flow processes. When some of this information is missing, these models could provide distorted results and could miss important system characteristics.</p> <p>In this study, we show how data-driven modeling can help the identification of the key drivers of lake level changes. We are using the example of the Groß Glienicker Lake, a glacial, groundwater fed lake near Berlin. This lake has been experiencing a drastic loss of water in recent decades, whose trend became even faster in the last few years. There is a local controversy whether these changes are mainly weather driven, or caused by water use; and what mitigation measures could be used to counteract them. Due to the strong anthropogenic influence from multiple water-related facilities near the lake, and the lack of geological information from the catchment, there are many unknows about the properties of the hydrological processes, hence the development of a process-based model in the area is challenging. To understand the system better we combine data-driven models with water balance approaches and use this methodology as an alternative to classic hydrological modeling.</p> <p>The climatic model input (catchment-average precipitation and actual evapotranspiration) is generated by the Central European Refinement dataset (CER), which is a meteorological dataset generated by dynamically downscaling the Weather Research and Forecasting model (Jänicke et al., 2017). First, a data-driven model is constructed to predict the changes in lake levels one day ahead by using precipitation and evapotranspiration values from the last two months, a time interval that was selected after an extensive parameter analysis. This model is then further extended by additional inputs, such as water abstraction rates, river and groundwater levels. The fits of the different simulated lake levels are evaluated to identify the effects of the relevant drivers of the lake level dynamics. For a more mechanistic interpretation, a monthly water balance model was created using the same dataset. By calculating the different fluxes within the system, we were able to estimate the magnitudes of unobserved hydrological components.</p> <p>With the help of our modeling approach, we could rule out the influence of one of the nearby waterworks and a river. We have also found that the lake level dynamics over the last two decades was mainly weather-driven, and the lake level fluctuations could be explained with changes in precipitation and evapotranspiration. With the water balance modeling, we have shown that the long-term net outflux from the lake catchment has increased in the last few years. These findings are used to support the development of a local high-resolution hydrogeological model, which could be used to further analyze these processes.</p> <p>References</p> <p>Jänicke, B., Meier, F., Fenner, D., Fehrenbach, U., Holtmann, A., Scherer, D. (2017): Urban-rural differences in near-surface air temperature as resolved by the Central Europe Refined analysis (CER): sensitivity to planetary boundary layer schemes and urban canopy models. Int. J. Climatol. 37 (4), 2063-2079. DOI: 10.1002/joc.4835</p>

In recent years, Berlin and its surrounding area has experienced multiple intensive precipitation events, which caused significant damage and severely impaired the local infrastructure. An extreme value analysis of the available station data could improve the understanding of such events and provide valuable information for risk assessments in the region. Additionally, results gathered from this analysis will serve as a reference for similar evaluations with the Central European Refined analysis (CER), a gridded dataset generated via dynamical downscaling of ERA5 data using the Weather Research and Forecasting model. In this study, we assess the spatiotemporal dynamics of extreme precipitation event days in Berlin and Brandenburg using a generalized Pareto distribution (GPD). Daily precipitation data of the last 30 years was extracted at 137 stations of the German Meteorological Service. For all stations with a sufficient amount of data a seasonal time-dependent threshold was defined and independent exceedances were extracted using an automatic declustering scheme. The resulting threshold series was used to fit a GPD at each location with a time-dependent scale parameter to investigate temporal changes in extreme event days. After evaluating the goodness of fit the distribution model was used to calculate seasonal 2-, 10-, and 20-year return levels. The results indicate a high regional variability especially for the 20-year precipitation extremes with slightly higher values around Berlin reaching up to 130 mm d-1 in the summer. Return levels also tended to be higher in the northern parts of Berlin and Brandenburg during winter and in the south during spring.

In the Intergovernmental Panel on Climate Change report (IPCC), “Climate Change 2022: Impacts, Adaptation and Vulnerability” it is stated that more frequent and intense extreme events due to climate change have a significant impact on the loss and damage of nature and people, which particularly holds for precipitation. In the Galápagos archipelago, the primary source of water supply is rainfall, hence rainfall plays an important role for biodiversity and people in this iconic but remote region. The main assumption for Galapagos is that water supply is dominated by the cool season’s light Garúa rainfall originating from the Pacific stratus, which will significantly decrease under global warming conditions. At the same time, rainfall in the warm season shows large variability, particularly during extreme ENSO (El Niño-Southern Oscillation) events. While in the current decade, a decrease of strong El Niño rainfall events was observed in the eastern tropical Pacific, most (but not all) climate model projections of the CMIP6 ensemble reveal stronger El Niño rainfall under future warming. To date, short and long-term rainfall dynamics in the Galápagos are not well understood, largely due to a lack of consistent spatially-time series of meteorological in-situ observations. The research project DARWIN ("Dynamics of precipitation in transition: The water source for the Galápagos Archipelago under climate change") has recently established 11 Automatic weather stations (AWS) covering a W-E and luff-lee transects over three islands (Isabela, S. Cruz, S. Cristóbal). The location of the stations is to consider different local and regional precipitation formation mechanisms. We seek to resolve influences of the Equatorial Counter Current and the Humboldt Current, as well as the topographic exposition towards the main airstream. Furthermore, the altitudinal gradients concerning vertical dynamics of the trade inversion are considered. One main goal of the DARWIN project is to produce area-wide rainfall information by satellite retrievals and WRF dynamical downscaling. While warm-season rainfall is mainly driven by intense convection events, cool-season Garúa is assumed to be more in the drizzle intensity range. The area-wide techniques must thus properly model the very different types of occurring rain intensities in the cool and warm seasons. Hence, the observations from the AWS used as test and training data must be as accurate as possible. Beyond standard meteorology, we focus on different advanced observation principles (light, optical, radar, gauge) and their intercomparison, and warrant high-resolution measurements (up to one minute) including a vertical profiling of rainfall. The AWS stations in the Garúa zone are additionally equipped by a harp-type fog collector. The poster will present the overall structure of the project and some first results of the AWS network, with a focus on temporally high-resolution rainfall dynamics during different weather situations and precipitation types along the transects.

Theworldwide restrictions of social contacts that were implemented in spring 2020 to slowdown infection rates of the SARS-CoV-2 virus resulted in significant modifications in mobility behaviour of urban residents. We used three-year eddy covariance measurements of size-resolved particle number fluxes from an urban site in Berlin to estimate the effects of reduced traffic intensity on particle fluxes. Similar observations of urban surface-atmosphere exchange of sizeresolved particles that focus on COVID-19 lockdown-related effects are not available, yet. Although the site remained a net emission source for ultrafine particles (UFP, Dp < 100 nm), the median upward flux of ultrafine particles (FUFP) decreased from 8.78 x 10(7) m(-2) s(-1) in the reference period to 5.44 x 10(7) m(-2) s(-1) during the lockdown. This was equivalent to a relative reduction of-38 % for median FUFP, which was similar to-35 % decrease of road traffic intensity in the flux source area during that period. The size-resolved analysis demonstrated that, on average, net deposition of UFP occurred only during night when particle emission source strength by trafficwas at itsminimum, whereas accumulation mode particles (100 nm< Dp< 200 nm) showed net deposition also during daytime. The results indicate the benefits of traffic reductions as a mitigation strategy to reduce UFP emissions to the urban atmosphere.

The hydroclimate of the Tibetan Plateau (TP) and Central Asia (CA) plays a crucial role in sustaining surface water reservoirs and thus water resources in the respective regions. In this study, we investigate the changes in Asian hydroclimate and its driving forces during specific time intervals in the last 3 Ma. We conduct high‐resolution (∼0.75° per grid cell) general circulation model ECHAM‐5 experiments with boundary conditions for the mid‐Pliocene (∼3 Ma), the Last Glacial Maximum (LGM; ∼21 ka), the mid‐Holocene (∼6 ka), and the pre‐industrial. Results suggest that seasonally relatively high precipitation rates (>1 mm day −1 ) were longer in the mid‐Pliocene and shorter in the LGM, relative to the pre‐industrial. We calculate different monsoon indices to detect changes in the intensity, strength and duration of the East Asian summer monsoon (EASM), South Asian summer monsoon (SASM), and the Indian summer monsoon (ISM), and construct climatologies of mid‐latitude high‐level westerly jet (WJ) stream occurrences based on the ECHAM5 wind fields. Our results suggest that in warm periods (e.g., mid‐Pliocene or interglacial), the WJ migrates northward earlier in the year (April) and reaches higher latitudes than in the pre‐industrial, resulting in a wetter TP and CA. During cooler periods (e.g., LGM or glacial), the WJ migrates northward later in the year (June) and remains over lower latitudes, resulting in a drier TP and CA. Increased/decreased local precipitation in TP and CA for the mid‐Pliocene/LGM experiments correlates strongly with (a) intensity, strength and duration of the EASM, SASM, and the ISM and (b) WJ latitudinal position.

With the effects of the advancing climate change the intensity and frequency of extreme rainfall events in many regions of the world is likely to increase. Heavy rainfall has a significant impact on the propagation of electromagnetic waves during wireless data transmission, especially for higher frequency ranges above 10 GHz. For the development of future wireless communication technologies like Terahertz (THz) links, precise information about the development and characteristics of extreme rainfall events is essential, especially in urban regions where these new technologies are likely to be implemented. In this study we investigate the dynamic of heavy rainfall events in the city of Berlin and surrounding areas for the timeframe between 2011 and 2020. Stationary measurements and crowdsourcing data of the urban climate observatory network as well as the measurement network of the German Meteorological Service (Deutscher Wetterdienst, DWD) is statistically evaluated to characterize the intensity, duration, and spatiotemporal variability of extreme rainfall events. Furthermore, possible connections between rainfall and factors like land cover and topography are analyzed. The influence of the distance between individual measurement stations on these results is examined using auto correlations. The rainfall data will also be used for attenuation models based on recommendations of the International Telecommunications Union to evaluate the rain specific impact on wireless signal propagation. For this investigation several common frequency ranges for the 5th wireless communication standard (5G), as well as frequencies in the THz ranges (100 GHz to 10 THz), are used to allow for an assessment of the vulnerability of wireless communication networks in the area of Berlin. Furthermore, these results are used as a reference for similar evaluations with spatially and temporally resolved meteorological datasets including the Central Europe refined analysis generated with the weather research and forecasting model and the radar-based precipitation climatology data set by the DWD.

&lt;p&gt;In the Intergovernmental Panel on Climate Change report (IPCC), &amp;#8220;Climate Change 2022: Impacts, Adaptation and Vulnerability&amp;#8221; it is stated that more frequent and intense extreme events due to climate change have a significant impact on the loss and damage of nature and people, which particularly holds for precipitation.&lt;/p&gt;&lt;p&gt;In the Gal&amp;#225;pagos archipelago, the primary source of water supply is rainfall, hence rainfall plays an important role for biodiversity and people in this iconic but remote region. The main assumption for Galapagos is that water supply is dominated by the cool season&amp;#8217;s light Gar&amp;#250;a rainfall originating from the Pacific stratus, which will significantly decrease under global warming conditions. At the same time, rainfall in the warm season shows large variability, particularly during extreme ENSO (El Ni&amp;#241;o-Southern Oscillation) events. While in the current decade, a decrease of strong El Ni&amp;#241;o rainfall events was observed in the eastern tropical Pacific, most (but not all) climate model projections of the CMIP6 ensemble reveal stronger El Ni&amp;#241;o rainfall under future warming.&lt;/p&gt;&lt;p&gt;To date, short and long-term rainfall dynamics in the Gal&amp;#225;pagos are not well understood, largely due to a lack of consistent spatially-time series of meteorological in-situ observations. The research project DARWIN (&quot;Dynamics of precipitation in transition: The water source for the Gal&amp;#225;pagos Archipelago under climate change&quot;) has recently established 11 Automatic weather stations (AWS) covering a W-E and luff-lee transects over three islands (Isabela, S. Cruz, S. Crist&amp;#243;bal). The location of the stations is to consider different local and regional precipitation formation mechanisms. We seek to resolve influences of&amp;#160; the Equatorial Counter Current and the Humboldt Current, as well as the topographic exposition towards the main airstream. Furthermore, the altitudinal gradients concerning vertical dynamics of the trade inversion are considered. One main goal of the DARWIN project is to produce area-wide rainfall information by satellite retrievals and WRF dynamical downscaling.&amp;#160;&lt;/p&gt;&lt;p&gt;While warm-season rainfall is mainly driven by intense convection events, cool-season Gar&amp;#250;a is assumed to be more in the drizzle intensity range. The area-wide techniques must thus properly model the very different types of occurring rain intensities in the cool and warm seasons. Hence, the observations from the AWS used as test and training data must be as accurate as possible. Beyond standard meteorology, we focus on different advanced observation principles (light, optical, radar, gauge) and their intercomparison, and warrant high-resolution measurements (up to one minute) including a vertical profiling of rainfall. The AWS stations in the Gar&amp;#250;a zone are additionally equipped by a harp-type fog collector.&amp;#160; &amp;#160; &amp;#160;&amp;#160;&amp;#160; &amp;#160;&lt;/p&gt;&lt;p&gt;The poster will present the overall structure of the project and some first results of the AWS network, with a focus&amp;#160; on temporally high-resolution rainfall dynamics during&amp;#160; different weather situations and precipitation types along the transects.&lt;/p&gt;