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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.
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.
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.
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.
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.
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.
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.
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.
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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