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Species level size-normalised weight data for at depth analysis

This dataset contains a compilation of published and new SNW data with corresponding environmental data extracted from CMIP6 that are used in the at depth species level Bayesian regression modelling. Environmental data for G. truncatulinoides comes from 200m depth, all other environmental data is from the sea surface (≤ 20 m).

(Table A1) Raw data for both AMT20 Atlantic Ocean transect samples and western Pacific Ocean transect (SO228 and SO256) samples

Sea surface salinity (SSS) is the least constrained major variable of the past (paleo) ocean but is fundamental in controlling the density of seawater and thus large-scale ocean circulation. The hydrogen isotopic composition (δD) of non-exchangeable hydrogen of algal lipids, specifically alkenones, has been proposed as a promising new proxy for paleo SSS. The δD of surface seawater is correlated with SSS, and laboratory culture studies have shown the δD of algal growth water to be reflected in the δD of alkenones. However, a large-scale field study testing the validity of this proxy is still lacking. Here we present the δD of open-ocean Atlantic and Pacific surface waters and coincident δD of alkenones sampled by underway filtration. Two transects of approximately 100° latitude in the Atlantic Ocean and more than 50° latitude in the Western Pacific sample much of the range of open ocean salinities and seawater δD, and thus allow probing the relationship between δD of seawater and alkenones. Overall, the open ocean δD alkenone data correlate significantly with SSS, and also agree remarkably well with δD water vs δD alkenone regressions developed from culture studies. Subtle deviations from these regressions are discussed in the context of physiological factors as recorded in the carbon isotopic composition of alkenones. In a best-case scenario, the data presented here suggest that SSS variations as low as 1.2 can be reconstructed from alkenone δD.

Group level size-normalised weight data

This dataset contains a compilation of published and new SNW data with corresponding sea surface (≤ 20 m) environmental data extracted from CMIP6 that are used in the group level Bayesian regression modelling.

Species level size-normalised weight data

This dataset contains a compilation of published and new SNW data with corresponding sea surface (≤ 20 m) environmental data extracted from CMIP6 that are used in the species level Bayesian regression modelling.

Timeline - Land Surface Temperature (Mean) Level 3 - Europe, Monthly

This dataset provides monthly maximum Land Surface Temperature (LST) values over Europe, derived from 1-km AVHRR observations. The data is generated by DLR and provided in the framework of the TIMELINE project. LST values are retrieved using physically-based split- and mono-window algorithms and corrected for atmospheric influences and surface emissivity. Only cloud-free observations with sensor view angles below 50 degrees are used. Due to reliance on infrared observations, data may be limited under persistent cloud cover. To ensure temporal consistency across sensors and overpass times, an orbit drift correction method was applied. This method harmonizes LST values to a fixed reference time of 13:00 local solar time, approximating the daily maximum temperature. The dataset is gridded in a 1-km LAEA ETRS89 projection. The product is provided in four tiles, covering the extent of the European Environmental Agency (EEA) reference grid, which includes the area from 900 000 m East and 900 000m North to 7 400 000m East and 5 500 000m North. The TIMELINE (TIMe Series Processing of Medium Resolution Earth Observation Data assessing Long-Term Dynamics In our Natural Environment) project, led by the German Remote Sensing Data Center (DFD) of the German Aerospace Center (DLR), focuses on generating a consistent, multi-decadal time series derived from NOAA and Metop AVHRR data. Spanning more than 40 years from the early 1980s to the present this dataset covers Europe and North Africa. TIMELINE establishes an operational environment for the systematic reprocessing of AVHRR raw data into Level 1b, Level 2, and Level 3 geoinformation products at 1.1 km spatial resolution. These products maintain uniform standards in format, projection, and spatial coverage. The dataset includes a comprehensive suite of land and atmospheric parameters such as atmospherically corrected surface reflectance, NDVI, snow cover, fire hotspots, burnt area, land and sea surface temperatures, and various cloud physical properties (e.g., cloud top temperature). By combining traditional and innovative remote sensing products with robust processing algorithms and state-of-the-art validation techniques, TIMELINE provides a unique, high-quality dataset for global change research.

The hindcast data for sea surface temperature [K] from GPC_Offenbach (DWD).

This resource contains the monthly mean sea surface temperature [K] for 6 months. The period of hindcast data is January, 1993 - December, 2019. The format of resource is GRIB2. It is provided through the web site of WMO Lead Centre for LRF MME (Long Range Forecast Multi-Model Ensemble). The web site requests a user account. The Grade A(GPCs) and Grade B(NMHSs, RCCs) users can download the data USAGE: Menu: Data and Plot > Data Exchange > Search/Download. This hindcast data is made by GPC_Offenbach (DWD) using an operational seasonal prediction system. For more detailed information about the seasonal forecasts of GPC_Offenbach (DWD) visit the web site http://www.dwd.de/EN/ourservices/seasonals_forecasts/start.html.

The forecast data for sea surface temperature [K] from GPC_Offenbach (DWD).

This resource contains the monthly mean sea surface temperature [K] for 6 months. The format of resource is GRIB2. It is provided through the web site of WMO Lead Centre for LRF MME (Long Range Forecast Multi-Model Ensemble) on about the 15th of each month. The web site requests a user account. The Grade A(GPCs) and Grade B(NMHSs, RCCs) users can download the data USAGE: Menu: Data and Plot > Data Exchange > Search/Download. This forecast data is made by GPC_Offenbach (DWD) using an operational seasonal prediction system. For more detailed information about the seasonal forecasts of GPC_Offenbach (DWD) visit the web site http://www.dwd.de/EN/ourservices/seasonals_forecasts/start.html.

AVHRR - Sea Surface Temperature (SST) - Europe

The AVHRR Mulitchannel Sea Surface Temperature Map (MCSST) was the first result of DLR's AVHRR pathfinder activities. The goal of the product is to provide the user with actual Sea Surface Temperature (SST) maps in a defined format easy to access with the highest possible reliability on the thematic quality. After a phase of definition, the operational production chain was launched in March 1993 covering the entire Mediterranean Sea and the Black Sea. Since then, daily, weekly, and monthly data sets have been available until September 13, 1994, when the AVHRR on board the NOAA-11 spacecraft failed. The production of daily, weekly and monthly SST maps was resumed in February, 1995, based on NOAA-14 AVHRR data. The NOAA-14 AVHRR sensor became some technical difficulties, so the generation was stopped on October 3, 2001. Since March 2002, NOAA-16 AVHRR SST maps are available again. With the beginning of January 2004, the data of AVHRR on board of NOAA-16 exhibited some anormal features showing strips in the scenes. Facing the “bar coded” images of NOAA16-AVHRR which occurred first in September 2003, continued in January 2004 for the second time and appeared in April 2004 again, DFD has decided to stop the reception of NOAA16 data on April 6th, 2004, and to start the reception of NOAA-17 data on this day. On April 7th, 2004, the production of all former NOAA16-AVHRR products as e.g. the SST composites was successully established. NOAA-17 is an AM sensor which passes central Europe about 2 hours earlier than NOAA-16 (about 10:00 UTC instead of 12:00 UTC for NOAA-16). In spring 2007, the communication system of NOAA-17 has degraded or is operating with limitations. Therefore, DFD has decided to shift the production of higher level products (NDVI, LST and SST) from NOAA-17 to NOAA-18 in April 2007. In order to test the performance of our processing chains, we processed simultaneously all NOAA-17 and NOAA-18 data from January 1st, 2007 till March 29th, 2007. All products are be available via EOWEB. Please remember that NOAA-18 is a PM sensor which passes central Europe about 1.5 hours later than NOAA-17 (about 11:30 UTC instead of 10:00 UTC for NOAA17). The SST product is intended for climate modelers, oceanographers, and all geo science-related disciplines dealing with ocean surface parameters. In addition, SST maps covering the North Atlantic, the Baltic Sea, the North Sea and the Western Atlantic equivalent to the Mediterranean MCSST maps are available since August 1994. The most important aspects of the MCSST maps are a) correct image registration and b) reasonable cloud screening to ensure that only cloud free pixels are taken for the later processing and compositing c) for deriving MCSST, only channel 4 and 5 are used.. The SST product consists of one 8 bit channel. For additional information, please see: https://wdc.dlr.de/sensors/avhrr/

Der Einfluss von Modellfehlern auf ENSO Projektionen für das 21. Jahrhundert

El Niño/Southern Oscillation (ENSO) ist die dominate Mode der Klimavariabilität des gekoppelten Ozean-Atmosphäre-Systems im tropischen Pazifik und ergibt sich aus einem komplexen Zusammenspiel zwischen verstärkenden und dämpfenden Feedbacks. Angesichts seiner großen sozioökonomischen Auswirkungen ist es sehr wichtig genau vorherzusagen, wie sich ENSO unter der globalen Erwärmung verändern wird. Obwohl in den letzten Jahrzehnten Verbesserungen bei der Simulation von ENSO erreicht wurden, bleibt eine realistische Darstellung von ENSO und seiner Projektion unter der globalen Erwärmung eine Herausforderung. Die Projektionen von ENSO unterscheiden sich stark zwischen den Klimamodellen, die an den Phasen 3 und 5 des Coupled Model Intercomparison Project (CMIP3 und CMIP5) teilnehmen. Obwohl diese Modelle ENSO simulieren, der in einfachen Indizes mit Beobachtungen übereinstimmt, unterscheidet sich die zugrunde liegende Dynamik stark von der beobachteten. In Beobachtungen wächst eine anfängliche SST-Anomalie während ENSO-Ereignissen durch windinduzierte Änderungen der Ozeandynamik. Dieser Tendenz wirkt ein dämpfendes Feedback der atmosphärischen Wärmeflüsse entgegen, insbesondere durch die Sonneneinstrahlung (SW) und latenten Wärmeflüsse. In den meisten Klimamodellen ist jedoch das Wind-SST-Feedback zu schwach und das SW-SST-Feedback fehlerhaft positiv, so dass ENSO ein Hybrid aus Wind-getriebener und SW-getriebener Dynamik ist. In den Modellen mit dem größten Fehler trägt der SW-SST-Feedback zum Wachstum der SST-Anomalie in ähnlichem Maße wie das Wind-SST-Feedback bei. In den Klimamodellen existiert ein breites Spektrum an ENSO-Dynamiken, das die große Streuung der ENSO-Projektionen für das 21. Jahrhunderts erklären könnte.Im IMBE21C-Projekt untersuchen wir die Auswirkungen der Modellfehler auf die ENSO-Projektionen. Mit einer neuen Methode, der „Offline Slab Ocean SST“, können wir die Rolle der verstärkenden und dämpfenden Feedbacks quantifizieren. Dafür separieren wir die SST-Änderungen der Wind-getriebenen Meeresdynamik von der durch atmosphärische Wärmeflüsse verursacht werden. In diesem Projekt werden wir diese Methode verwenden, um den Antrieb und die Dämpfung in der beobachteten ENSO-Dynamik zu quantifizieren und mit dem in Klimamodellen simulierten ENSO zu vergleichen, um die Fehler in der simulierten ENSO-Dynamik zu identifizieren und zu quantifizieren. Des Weiteren werden wir den Einfluss der fehlerhaften ENSO-Dynamik auf die Projektionen von ENSO im Klimawandel analysieren, indem wir die Modelle in Gruppen mit realistischer und fehlerhafter ENSO-Dynamik unterteilen. Darüber hinaus werden wir die Gesamtunsicherheit der projizierten ENSO-Amplitudenänderung in Modellunsicherheit, Szenariounsicherheit und Unsicherheit aufgrund interner Variabilität aufteilen. Insgesamt zielt das IMBE21C Projekt darauf ab, durch innovative Methoden die Quellen von Unsicherheiten in ENSO-Projektionen zu identifizieren und diese zu reduzieren.

WP1.3 Der Aufbau von Eisschilden - Simulation und Untersuchung des Beginns der letzten Eiszeit mit MPI-ESM

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