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Spatial distribution of aerosol and meteorological parameters measured during flight SourceFFR_ALADINA_20241017_15 with the UAS ALADINA near Frankfurt airport in October 2024

Exposure to ultrafine aerosol particles (UFPs) can cause adverse effects on human health, local environment and climate. Air traffic is associated with the emission of high numbers of UFPs, which results in increased UFP number concentrations close to airports. So far, the spatial distribution and variability of UFPs is poorly understood in the atmospheric boundary layer. The uncrewed aerial system (UAS) ALADINA (Application of Lightweight Aircraft for Detecting In-situ Aerosols, e.g. Altstädter et al., 2015) was operated close to the largest airport in Germany at Frankfurt airport (FRA) between 11 and 19 October 2024. The dataset provides airborne in-situ observations of the spatial distribution of aerosol particle number concentration with different sizes and meteorological parameters of temperature, humidity, wind, surface temperature and short-wave irradiance, as well as accurate position and orientation of ALADINA. Data are available from 26 measurement flights, comprising a number of 122 vertical profiles between ground and a maximum altitude of 750 m above mean sea level (ASL) and about 70 horizontal legs at different but constant altitude, e.g. in 100 m altitude intervals. Details about the ALADINA measurements will be provided in a publication (Harm-Altstädter et al., in prep.) soon.

Spatial distribution of aerosol and meteorological parameters measured during flight SourceFFR_ALADINA_20241016_13 with the UAS ALADINA near Frankfurt airport in October 2024

Exposure to ultrafine aerosol particles (UFPs) can cause adverse effects on human health, local environment and climate. Air traffic is associated with the emission of high numbers of UFPs, which results in increased UFP number concentrations close to airports. So far, the spatial distribution and variability of UFPs is poorly understood in the atmospheric boundary layer. The uncrewed aerial system (UAS) ALADINA (Application of Lightweight Aircraft for Detecting In-situ Aerosols, e.g. Altstädter et al., 2015) was operated close to the largest airport in Germany at Frankfurt airport (FRA) between 11 and 19 October 2024. The dataset provides airborne in-situ observations of the spatial distribution of aerosol particle number concentration with different sizes and meteorological parameters of temperature, humidity, wind, surface temperature and short-wave irradiance, as well as accurate position and orientation of ALADINA. Data are available from 26 measurement flights, comprising a number of 122 vertical profiles between ground and a maximum altitude of 750 m above mean sea level (ASL) and about 70 horizontal legs at different but constant altitude, e.g. in 100 m altitude intervals. Details about the ALADINA measurements will be provided in a publication (Harm-Altstädter et al., in prep.) soon.

Schwerpunktprogramm (SPP) 1889: Regional Sea Level Change and Society (SeaLevel), Ursachen regionaler Meeresspiegelvariationen auf dekadischen und längeren Zeitskalen (DECVAR)

Die Studie zielt ab auf die Untersuchung von momentanen und vergangenen Meeresspiegeländerungen auf regionaler Skala. Hierbei sollen Ursachen und Mechanismen identifiziert werden, die diese Variationen erklären können; dabei werden freie Klima-Moden ebenso berücksichtigt wie externe Antriebe, einschließlich anthropogene Einflüsse. Dadurch werden Methoden und Wissen entwickelt, mit denen in regionale Meeresspiegelbeobachtungen jeweils natürliche Variationen und anthropogenen Effekten identifiziert werden können. Erzielte Erkenntnisse werden verwendet werden, um Ursachen und Mechanismen von Meeresspiegelvariationen um die zweiten Untersuchungsgebiete des SPP herum (dem westlichen tropischen Pazifik und Indonesischem Archipel ebenso wie der Nordsee) zu analysieren.

Analyse langfristiger Änderungen in der Tidedynamik der Nordsee

Langjährige Pegelaufzeichnungen aus dem Gebiet der südöstlichen Nordsee zeigen seit Mitte des 20. Jahrhunderts signifikante Veränderungen im lokalen Tideregime. Während der mittlere Meeresspiegel (englisch: Mean Sea Level, MSL) über die vergangenen 150 Jahre generell dem globalen Mittel gefolgt ist, deuten Auswertungen der mittleren Tidehoch- und Tideniedrigwasser auf signifikant abweichende Trends hin. So sind die Tidehochwasser signifikant schneller als der MSL angestiegen, während die Tideniedrigwasser deutlich geringere oder teils negative Trends aufzeigen. Daraus resultierte eine gleichzeitige Zunahme des Tidehubs (die Differenz aus Tidehoch- und Tideniedrigwasser) von ca. 10 % seit 1955. Derartige Veränderungen haben direkte Auswirkungen auf den Küstenschutz. So ergeben sich bei einem Anstieg der mittleren Tidehochwasser größere Wassertiefen, wodurch das Wellenklima insbesondere im Bereich der Wattflächen und Außensände in der Deutschen Bucht beeinflusst wird. Größere Wellenhöhen und damit höhere Orbitalgeschwindigkeiten und Brandungsenergien sind die unmittelbare Folge, die zu großflächigen Erosionen führen kann. Gleichzeitig beeinflussen geringere Tideniedrigwasser die Schiffbarkeit der flachen Küstengewässer. Durch den vergrößerten Tidehub treten größere Tidestromgeschwindigkeiten auf, die z.B. Ausräumungen der Tiderinnen, verstärkte Erosionen an Inselsockeln, Strandräumungen und im Zusammenhang mit Sturmfluten Dünen- und Kliffabbrüchen verursachen können. Dies verdeutlicht, dass neben den global wirkenden übergeordneten Veränderungen im MSL (Massenänderungen, thermale Expansion) auch regionale Phänomene und Prozesse eine wichtige Rolle für die Ausprägung der Wasserstände spielen. Eine Berücksichtigung solcher Faktoren in den Projektionen zukünftiger Wasserstände setzt voraus, dass vergangene Entwicklungen und zugrunde liegende Prozesse ausreichend verstanden sind. Das übergeordnete Ziel von TIDEDYN besteht daher in der Analyse der in der Vergangenheit bereits aufgetreten Veränderungen im lokalen Tideregime der Nordsee. Die beobachtete Zunahme des Tidehubs ist in ihrer starken Ausprägung ein weltweit einzigartiges Phänomen, welches bis heute nicht erklärt werden kann. Als mögliche (aber bisher unerforschte) Ursachen kommen z.B. langfristige Änderungen im MSL, morphologische Änderungen im Küstenvorfeld (natürlich oder anthropogen, z.B. Ausbaggerungen oder Baumaßnahmen wie Eindeichungen) oder saisonale Änderungen in der thermohalinen Schichtung des Ozeans in Frage. Durch die integrierte Analyse von hochauflösenden numerischen Modellen (barotrop und baroklin) und Beobachtungsdaten mit robusten Methoden der Zeitreihenanalyse, sollen die Änderungen im Tideregime der Nordsee über die vergangen 60-70 Jahre beschrieben, modelliert und systematisch erforscht werden sowie einzelne Prozesse mittels Sensitivitätsstudien voneinander abgegrenzt werden.

Multibeam bathymetry processed data (Atlas Hydrosweep DS 2 echo sounder entire dataset) of RV POLARSTERN during cruise ANT-XIX/1 (PS61), Atlantic Ocean

Multibeam data were collected during RV Polarstern cruise ANT-XIX/1 (2001-11-08 to 2001-11-30). Multibeam sonar system was Atlas Hydrographic Hydrosweep DS 2 multibeam echo sounder. Data are processed with Caris HIPS, including sound velocity correction by cross fan calibration, tidal correction with TPXO9_atlas_v5 (https://www.tpxo.net), and manual cleaning. The soundings are combined in daily files, the format is XYZ ASCII (<Lon> <Lat> <Depth in meters, positive up, relative to mean sea level>). Additional grids have been computed with depth dependent cell size to visualize the data. These grids are not meant for scientific analysis or navigation, but for overview purposes only.

Multibeam bathymetry processed data (Atlas Hydrosweep DS 3 echo sounder entire dataset) of RV POLARSTERN during cruise PS132, Transit, Atlantic Ocean

Multibeam data were collected during RV Polarstern cruise PS132 (2022-08-31 to 2022-09-29). Multibeam sonar system was Atlas Hydrographic Hydrosweep DS 3 multibeam echo sounder. Data are processed with Caris HIPS, including sound velocity correction with SV data from CTDs, XBTs and World Ocean Atlas 18 (https://www.ncei.noaa.gov/archive/accession/NCEI-WOA18), tidal correction with TPXO9_atlas_v5 (https://www.tpxo.net), and manual cleaning. The soundings are combined in daily files, the format is XYZ ASCII (<Lon> <Lat> <Depth in meters, positive up, relative to mean sea level>). Additional blockmedian grids have been computed with depth dependent cell size to visualize the data. These grids are not meant for scientific analysis or navigation, but for overview purposes only.

Meteogramm bis H+78 EDDB Berlin (Brandenburg) - Meteogram up to H+78 EDDB Berlin (Brandenburg)

3 Tage Vorhersage. Wind, Temperatur, Bodendruck, Bedeckung, Konvektionswolken und Niederschlag. - 3 days forecast. Wind, temperature, pressure mean sea level, cloud cover, convective clouds and precipitation.

Meteogramm bis H+78 11030 Tulln Langenlebarn - Meteogram up to H+78 11030 Tulln Langenlebarn

3 Tage Vorhersage. Wind, Temperatur, Bodendruck, Bedeckung, Konvektionswolken und Niederschlag. - 3 days forecast. Wind, temperature, pressure mean sea level, cloud cover, convective clouds and precipitation.

Meteogramm bis H+78 08306 Palma de Mallorca - Meteogram up to H+78 08306 Palma

3 Tage Vorhersage. Wind, Temperatur, Bodendruck, Bedeckung, Konvektionswolken und Niederschlag. - 3 days forecast. Wind, temperature, pressure mean sea level, cloud cover, convective clouds and precipitation.

A spatially explicit Global Reef Island Database (GRID) that captures distribution, diversity and relative vulnerability of the world's low-lying reef islands

Low-lying coral reef islands harbour a distinct, yet highly threatened biological and cultural diversity that is increasingly exposed to climate change impacts. The combination of low elevation, small size, sensitivity to changes in boundary conditions (sea level, waves and currents, locally generated sediment supply) and at some locations high population densities, is why low-lying reef islands (LRIs) are considered among the most vulnerable environments on Earth to climate change. To date, their global distribution and influence of climatic, oceanographic, and geologic setting are only poorly documented or restricted to smaller scales. Here, I present the first detailed global analysis of LRIs utilising freely available global datasets to produce a global reef island database (GRID) and associated intrinsic and extrinsic characteristics that can be used within a coastal vulnerability index (CVI). All datasets used to create the GRID were released between 30 November 2015 and 3 August 2023, while the current version of the GRID database was completed in November 2024. When developing the GRID, LRIs are defined as landmasses <30 km² located on or within 1 km of coral reef and with an elevation of <16 m. Development of the GRID required: 1) the creation of a global shoreline vector file containing the geographic distribution of LRIs and 2) the development of a comprehensive global database of LRIs including eight intrinsic and ten extrinsic variables extracted from global datasets. Intrinsic variables include: 1) human populations, 2) island area, 3) island perimeter, 4) mean elevation, 5) island circularity/shape, 6) underlying reef type, 7) geographic isolation and 8) distance to the nearest neighbouring reef island. Extrinsic variables include: 1) mean water depth, 2) standard deviation of mean water depth, 3) mean annual significant wave height, 4) mean annual wave period, 5) mean spring tidal range, 6) relative tidal range, 7) wave-tide regime, 8) relative wave exposure, 9) relative tropical storm exposure and 10) year-2100 projected median sea level rise rate. The GRID was initially derived from version 2.1 of the UNEP-WCMC Global Island Database, a global shoreline vector file based on geometry data from Open Street Map® (OSM) and released in November 2015. The initial vector file was projected using the Mollweide projection, an equal-area pseudo cylindrical map projection chosen for its accurate derivation of area, especially in regions close to the equator, where most LRIs are located. The final GRID contains 34,404 individual LRIs distributed throughout tropical regions of the world's oceans, amassing a total land area of nearly 11,000 km² with approximately 60,740 km of shoreline and housing around 2.6 million people. While intrinsic variables are typically spatially homogenous, LRIs are generally highly spatially clustered throughout the GRID with respect to extrinsic variables. The spatial distribution of LRIs within the GRID was validated using: 1) published data and 2) quantitative accuracy assessments using satellite imagery. Spatial distributions of LRIs captured in the GRID are extremely consistent with those published in the literature (r² = 0.96) and those derived from independent analysis of satellite imagery (r² = 0.94). Finally, the GRID was used to develop an island vulnerability index (IVI) for each LRI on a scale of 0-1 with 0 representing no vulnerability and 1 representing maximum vulnerability. The GRID database is provided as a tab-delimited text file as well as ESRI shapefiles (points and polygons in WGS84 and Mollweide projection) and a comma-separated value file.

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