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Fractional abundances of branched glycerol dialkyl glycerol tetraethers (brGDGT) from the Arid Central Asian Data Base (ACADB) for surface brGDGT samples

Tropische Klima-Gletscher-Interaktion auf langer Zeitskala

Gletscher erfuhren während des 20. Jahrhunderts weltweit einen Rückgang, welcher meist der globalen Erwärmung zugeschrieben wird. Wechselwirkungen zwischen Klima und Gletschern in den hohen und mittleren Breiten sind dabei relativ gut verstanden. Tropische Gletscher hingegen sind meist schwer zugänglich und befinden sich in einer Region der Atmosphäre, aus der es extrem wenige Beobachtungen gibt. Folglich ist viel weniger über sie bekannt. Jüngste Studien zeigten, dass tropische Gletscher auf Veränderungen der Feuchte stärker reagieren als auf Veränderungen der Temperatur, und Verbindungen zwischen Gletscher-Massenbilanz und großräumiger Klimavariabilität existieren: Auf dem Kilimanjaro in Tanzania z. B. hängt die Massenbilanz von der Variabilität des Dipol-Modus' im Indischen Ozean ab, und die Gletscher der Cordillera Blanca in Peru reagieren auf niedrig-frequente Änderungen im El Nino-System. Dabei ist aber immer noch unklar, welche Rolle anthropogene (z. B. Emission von Treibhausgasen) oder natürliche (z. B. Vulkanismus) Änderungen im Antrieb des Klimasystems, interne Variabilität, oder eine Kombination dieser Möglichkeiten spielen. Um den beobachteten Rückgang der tropischen Gletscher zu verstehen und ihr zukünftiges Verhalten im nächsten Jahrhundert abschätzen zu können, muss man die Klimadynamik verstehen, die die atmosphärischen Bedingungen über den Gletschern kontrolliert. Das vorgeschlagene Projekt vereint die besten verfügbaren glaziologischen Messungen von drei Kontinenten mit Rekonstruktionen der Gletscherausdehnung und jahrhundertelangen Ensemble-Klimarekonstruktionen aus vier verschiedenen, gekoppelten Klimamodellen , um das Wissen über die tropische Klimadynamik auf der Zeitskala der Gletscher zu erweitern. Zentral dafür sind die Identifikation der Klimavariabilitäts-Mechanismen auf Zeitskalen zwischen Jahrzehnten und einem Jahrhundert, sowie die Rekonstruktion der Variabilität in der Vergangenheit und ihre Auswirkungen auf die Gletscher. Diese Rekonstruktionen werden mit Hilfe von direkten Beobachtungen und Proxy-Rekonstruktionen des Zustandes der Atmosphäre und der Gletscher validiert. Die Methodik erlaubt es, den Einfluss von interner Variabilität und anthropogenem Einwirken während des 20. Jahrhunderts zu separieren. Dies ermöglicht Aufschlüsse zur langfristigen Vorhersagbarkeit und zu Abschätzungen der Bedingungen für die Gletscher im 21. Jahrhundert.

Kosmogenes Al-26 in Atmosphären- und Klimaforschung

Eine der wissenschaftlichen Herausforderungen unserer Zeit ist ein besseres Verständnis des Klimas auf der Erde. Zum Beispiel sind der Einfluß einer 'variablen' Sonne und die Wechselwirkungen in großen Bereichen unserer Erde (Atmosphäre, Biosphäre, Ozean, Land) noch weitgehend offene Fragestellungen. Zum besseren Verständnis dieser Prozesse, welches für zuverläßliche Klimamodelle unumgänglich ist, bedarf es vor allem einer umfassenden Datenerhebung. Hierbei gewinnen in jüngster Zeit die Messungen von kosmogenen Radionukliden mit Hilfe der ultra-sensitiven Nachweismethode der Beschleunigermassenspektrometrie eine immer größere Bedeutung. Wir beabsichtigen, die Liste der für die Atmosphären- und Klimaforschung bereits verwendeten kosmogenen Radionuklide (Be-10, C-14, Cl-36), um das bisher kaum erforschte Isotop Al-26 (Halbwertszeit = 0.72 Millionen Jahre) zu erweitern. Wir erwarten dadurch einen Beitrag zum besseres Verständnis von Klimaprozessen. Insbesondere bietet sich eine Kombination von Al-26 mit Be-10 (Halbwertszeit = 1.5 Millionen Jahre) zur Datierung alter Klimaarchive (z.B. tiefer Eisbohrkerne) an. Bevor jedoch Al-26 als ein sogenannter 'Proxy' für Klimaprozesse verwendet werden kann, muss Grundlagenarbeit geleistet werden. Im speziellen müssen die Quellen und Transportvorgänge von Al-26 in der Atmosphäre studiert werden. Damit verbunden ist die Entwicklung geeigneter Verfahren für die Messung von Luft- und Eisproben. Ein wichtige Voraussetzung für dieses Projekt ist die Existenz einer modernen Anlage für Beschleunigermassenspektrometrie. Der Vienna Environmental Research Accelerator (VERA) am Institut für Isotopenforschung und Kernphysik der Universität Wien bietet ideale Bedingungen für Messungen von Al-26. Mit dieser Anlage wurde kürzlich das weltweit niedrigste Isotopenverhältnis von Al-26/Al-27 (ca5e-16) gemessen. Das vorgeschlagene Projekt soll in enger Zusammenarbeit mit der 'Eisgruppe' des Instituts für Umweltphysik (IUP) der Universität Heidelberg durchgeführt werden. Das IUP wird nicht nur Proben von Luftfiltern und Eis aus der Atarktis und anderen Bereichen der Erde zur Verfügung stellen, sondern auch seine langjährige Erfahrung in Atmosphären- und Klimaforschung einbringen. Wir sind der Überzeugung, daß sich die verschiedenen Erfahrungsbereiche der zwei Gruppen für dieses multidisziplinäre Projekt optimal ergänzen.

Homogenized mean monthly temperature time series (HClim), station Hom_HCLIM_Meiningen, Germany

Instrumental meteorological observations are essential for analysing past climate and reconstructing climate variability. However, many of the long instrumental climate series, some extending back to 1658, have been affected by inhomogeneities (artificial shifts) caused by changes in measurement conditions such as station relocations, instrumentation changes, and environmental modifications. To address this problem, homogenization procedures have been developed to detect and adjust such inhomogeneities. In this work, the records undergo homogenization analysis, during which these inhomogeneities are identified and corrected. The Standard Normal Homogeneity Test (SNHT), developed by Hans Alexandersson, is applied as the statistical method, comparing candidate series with neighbouring reference stations to assess relative homogeneity. The article presents homogenization analyses using three different tools (CLIMATOL, BART, and PHA) applied to the published global multivariable monthly instrumental climate database HCLIM (doi:10.1594/PANGAEA.940724). The resulting database includes the best-performing homogenized series - those produced by BART - comprising 2,892 homogenized temperature time series covering the period 1757–2020.

Homogenized mean monthly temperature time series (HClim), station Hom_HCLIM_Bremervoerde, Germany

Instrumental meteorological observations are essential for analysing past climate and reconstructing climate variability. However, many of the long instrumental climate series, some extending back to 1658, have been affected by inhomogeneities (artificial shifts) caused by changes in measurement conditions such as station relocations, instrumentation changes, and environmental modifications. To address this problem, homogenization procedures have been developed to detect and adjust such inhomogeneities. In this work, the records undergo homogenization analysis, during which these inhomogeneities are identified and corrected. The Standard Normal Homogeneity Test (SNHT), developed by Hans Alexandersson, is applied as the statistical method, comparing candidate series with neighbouring reference stations to assess relative homogeneity. The article presents homogenization analyses using three different tools (CLIMATOL, BART, and PHA) applied to the published global multivariable monthly instrumental climate database HCLIM (doi:10.1594/PANGAEA.940724). The resulting database includes the best-performing homogenized series - those produced by BART - comprising 2,892 homogenized temperature time series covering the period 1757–2020.

Homogenized mean monthly temperature time series (HClim), station Hom_HCLIM_Aue, Germany

Instrumental meteorological observations are essential for analysing past climate and reconstructing climate variability. However, many of the long instrumental climate series, some extending back to 1658, have been affected by inhomogeneities (artificial shifts) caused by changes in measurement conditions such as station relocations, instrumentation changes, and environmental modifications. To address this problem, homogenization procedures have been developed to detect and adjust such inhomogeneities. In this work, the records undergo homogenization analysis, during which these inhomogeneities are identified and corrected. The Standard Normal Homogeneity Test (SNHT), developed by Hans Alexandersson, is applied as the statistical method, comparing candidate series with neighbouring reference stations to assess relative homogeneity. The article presents homogenization analyses using three different tools (CLIMATOL, BART, and PHA) applied to the published global multivariable monthly instrumental climate database HCLIM (doi:10.1594/PANGAEA.940724). The resulting database includes the best-performing homogenized series - those produced by BART - comprising 2,892 homogenized temperature time series covering the period 1757–2020.

Homogenized mean monthly temperature time series (HClim), station Hom_HCLIM_Emden-Nesserland, Germany

Instrumental meteorological observations are essential for analysing past climate and reconstructing climate variability. However, many of the long instrumental climate series, some extending back to 1658, have been affected by inhomogeneities (artificial shifts) caused by changes in measurement conditions such as station relocations, instrumentation changes, and environmental modifications. To address this problem, homogenization procedures have been developed to detect and adjust such inhomogeneities. In this work, the records undergo homogenization analysis, during which these inhomogeneities are identified and corrected. The Standard Normal Homogeneity Test (SNHT), developed by Hans Alexandersson, is applied as the statistical method, comparing candidate series with neighbouring reference stations to assess relative homogeneity. The article presents homogenization analyses using three different tools (CLIMATOL, BART, and PHA) applied to the published global multivariable monthly instrumental climate database HCLIM (doi:10.1594/PANGAEA.940724). The resulting database includes the best-performing homogenized series - those produced by BART - comprising 2,892 homogenized temperature time series covering the period 1757–2020.

Homogenized mean monthly temperature time series (HClim), station Hom_HCLIM_MergentheimBad-Neunkirchen, Germany

Instrumental meteorological observations are essential for analysing past climate and reconstructing climate variability. However, many of the long instrumental climate series, some extending back to 1658, have been affected by inhomogeneities (artificial shifts) caused by changes in measurement conditions such as station relocations, instrumentation changes, and environmental modifications. To address this problem, homogenization procedures have been developed to detect and adjust such inhomogeneities. In this work, the records undergo homogenization analysis, during which these inhomogeneities are identified and corrected. The Standard Normal Homogeneity Test (SNHT), developed by Hans Alexandersson, is applied as the statistical method, comparing candidate series with neighbouring reference stations to assess relative homogeneity. The article presents homogenization analyses using three different tools (CLIMATOL, BART, and PHA) applied to the published global multivariable monthly instrumental climate database HCLIM (doi:10.1594/PANGAEA.940724). The resulting database includes the best-performing homogenized series - those produced by BART - comprising 2,892 homogenized temperature time series covering the period 1757–2020.

Homogenized mean monthly temperature time series (HClim), station Hom_HCLIM_Bamberg, Germany

Instrumental meteorological observations are essential for analysing past climate and reconstructing climate variability. However, many of the long instrumental climate series, some extending back to 1658, have been affected by inhomogeneities (artificial shifts) caused by changes in measurement conditions such as station relocations, instrumentation changes, and environmental modifications. To address this problem, homogenization procedures have been developed to detect and adjust such inhomogeneities. In this work, the records undergo homogenization analysis, during which these inhomogeneities are identified and corrected. The Standard Normal Homogeneity Test (SNHT), developed by Hans Alexandersson, is applied as the statistical method, comparing candidate series with neighbouring reference stations to assess relative homogeneity. The article presents homogenization analyses using three different tools (CLIMATOL, BART, and PHA) applied to the published global multivariable monthly instrumental climate database HCLIM (doi:10.1594/PANGAEA.940724). The resulting database includes the best-performing homogenized series - those produced by BART - comprising 2,892 homogenized temperature time series covering the period 1757–2020.

Homogenized mean monthly temperature time series (HClim), station Hom_HCLIM_Bremen, Germany

Instrumental meteorological observations are essential for analysing past climate and reconstructing climate variability. However, many of the long instrumental climate series, some extending back to 1658, have been affected by inhomogeneities (artificial shifts) caused by changes in measurement conditions such as station relocations, instrumentation changes, and environmental modifications. To address this problem, homogenization procedures have been developed to detect and adjust such inhomogeneities. In this work, the records undergo homogenization analysis, during which these inhomogeneities are identified and corrected. The Standard Normal Homogeneity Test (SNHT), developed by Hans Alexandersson, is applied as the statistical method, comparing candidate series with neighbouring reference stations to assess relative homogeneity. The article presents homogenization analyses using three different tools (CLIMATOL, BART, and PHA) applied to the published global multivariable monthly instrumental climate database HCLIM (doi:10.1594/PANGAEA.940724). The resulting database includes the best-performing homogenized series - those produced by BART - comprising 2,892 homogenized temperature time series covering the period 1757–2020.

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