Abstract. Precise quantification of climate change depends on long time series of meteorological variables. Such time series should be as homogeneous as possible but some changes of measurement conditions cannot be prevented. At German climate reference stations, parallel measurements are used to analyze the effects of changes in measurement systems for example for the transition from manual to automatic instruments. These parallel measurements aim to identify measurement uncertainties and to analyze the comparability of measurement systems to investigate the homogeneity. In this study, we investigate daily sunshine duration. Traditionally, manual measurements of daily sunshine duration are taken with Campbell-Stokes sunshine recorders. For automatic measurements the SONIe or SCAPP instrument is used. The different measurement principles (glass sphere and photodiode) cause systematic differences between the observations. During summer, values for manual observations are larger especially in case of frequent alternations between sunny and cloudy conditions. Furthermore, the standard deviation of the differences between the two measurement systems is larger during summer because of the greater day length. To adjust the automatic measurements a linear regression model is suggested based on parallel measurements from 13 climate reference stations in Germany. To validate the regression coefficients, a leave-one-out cross validation was performed (by leaving out data of individual stations). The regression coefficients (derived from different sets of stations) are similar, thereby indicating a robust data set for the estimation of the linear model. With this method we want to prevent breaks in long time series of daily sunshine duration caused by the transition from manual to automatic instruments.
High-quality time series of meteorological observations are required for reliable assessments of climate trends. To analyze inhomogeneities in time series, parallel measurements can be used. Germany's national meteorological service DWD (Deutscher Wetterdienst) operates a network of climate reference stations. At these stations, manual and automatic observations have been taken in parallel. These parallel measurements therefore allow analyzing the impact of the transition on the homogeneity of time series of several meteorological parameters. Here, we present results for temperature. The differences between automatic and manual measurements are tested on breakpoints caused by instrumental defects or changes in the measurement conditions. The time series are highly correlated such that small breaks can be identified. The detected breakpoints are verified against metadata if available. In the case of no available metadata information, a procedure is suggested to identify the inhomogeneous time series (manual or automatic time series). Afterwards, the time series are homogenized. The homogenized time series are used to analyze the impact of changing the observing system from manual to automatic measurements on daily mean temperature.
<p>Eine der Aufgaben des Deutschen Wetterdienstes ist die Klima&#252;berwachung f&#252;r Deutschland. Dazu verwendet der DWD die Daten der Wetterstationen in seinem Messnetz in Kombination mit den historischen Klimadaten, die auch durch Vorg&#228;ngerorganisationen des DWD erhoben wurden. F&#252;r den Zeitraum seit 1881 sind somit fl&#228;chendeckende und systematisch erhobene Messungen verf&#252;gbar, die f&#252;r eine Beschreibung des Klimawandels in Deutschland genutzt werden. F&#252;r den Zeitraum 1881 bis 2020 betr&#228;gt der lineare Trend der Temperatur +1,6&#176;C. Das zur&#252;ckliegende Jahrzehnt lag dabei allerdings deutlich oberhalb der Trendlinie, wodurch das aktuelle Jahrzehnt (2011-2020) bereits um 2 &#176;C w&#228;rmer war als der Beginn der Zeitreihe (1881-1910).</p> <p>F&#252;r eine verl&#228;ssliche Kommunikation des Klimazustands und eine einordnende Beschreibung von Klima&#228;nderungen ist es zu einen essentiell, kontinuierlich die Qualit&#228;t der zugrundeliegenden Daten zu analysieren und gegebenenfalls zu korrigieren, zum anderen die Darstellungs- und Kommunikationsformen an die &#214;ffentlichkeit weiterzuentwickeln.</p> <p>Um die Homogenit&#228;t der meteorologischen Zeitreihen zu gew&#228;hrleisten, betreibt der DWD mehrere Klimareferenzstationen, an denen Parallelmessungen von historischen und operationellen Messinstrumenten durchgef&#252;hrt werden. Mithilfe dieser Messungen werden die Vergleichbarkeit der Messungen untersucht, Messunsicherheiten abgesch&#228;tzt und gegebenenfalls Methoden zur Homogenisierung der Messreihen entwickelt.</p> <p>Zurzeit werden klimatologische Indizes sowohl innerhalb des DWD als auch mit nationalen und internationalen Partnern standardisiert. F&#252;r einige der gebr&#228;uchlichsten Indizes (z.B. Hei&#223;e Tage und Tropische N&#228;chte) existieren unterschiedliche Definitionen, die parallel verwendet werden. Um widerspr&#252;chliche Aussagen zu vermeiden, m&#252;ssen einheitliche Definitionen verwendet werden oder es sollte ausdr&#252;cklich auf die jeweils verwendete Definition hingewiesen werden.</p> <p>F&#252;r die Kommunikation des beobachteten Klimawandels werden unterschiedliche grafische Aufbereitungen der Daten f&#252;r verschiedene Medien und Plattformen eingesetzt. In diesem Vortrag wird auch ein &#220;berblick &#252;ber aktuelle Kommunikationskan&#228;le (z. B. Deutscher Klimaatlas, DWD-Klima-Twitterkanal) sowie die Zugangsm&#246;glichkeiten zu den zugrundeliegenden Klimadaten des DWD gegeben.</p> <p>&#160;</p> <p><strong>Literatur und weiterf&#252;hrende Links:</strong></p> <ul> <li>Zeitreihen der Klima&#228;nderung in Deutschland: https://www.dwd.de/zeitreihen</li> <li>Informationsportal 'Beobachteter Klimawandel in Deutschland':<br />https://www.dwd.de/klima-deutschland</li> <li>Deutscher Klimaatlas: https://www.deutscher-klimaatlas.de</li> <li>Twitterkanal 'DWD Klima und Umwelt': https://twitter.com/DWD_klima</li> <li>Offener Zugang zu den Klimadaten des DWD: https://opendata.dwd.de/climate_environment/CDC/ &#160;https://cdc.dwd.de/portal</li> <li>Kaspar, F., M&#252;ller-Westermeier, G., Penda, E., M&#228;chel, H., Zimmermann, K., Kaiser-Weiss, A., Deutschl&#228;nder, T.: Monitoring of climate change in Germany &#8211; data, products and services of Germany's National Climate Data Centre, Adv. Sci. Res., 10, 99&#8211;106, https://doi.org/10.5194/asr-10-99-2013, 2013.</li> </ul>
Abstract. The World Meteorological Organization (WMO) established Regional Climate Centres (RCCs) around the world to create science-based climate information on a regional scale within the Global Framework for Climate Services (GFCS). The paper introduces the satellite component of the WMO Regional Climate Centre on Climate Monitoring (RCC-CM) for Europe and the Middle East. The RCC-CM product portfolio is based on essential climate variables (ECVs) as defined by the Global Climate Observing System (GCOS), spanning the atmospheric (radiation, clouds, water vapour) and terrestrial domains (snow cover, soil moisture). In the first part, the input data sets are briefly described, which are provided by the EUMETSAT (European Organisation for the Exploitation of Meteorological Satellites) Satellite Application Facilities (SAF), in particular CM SAF, and by the ESA (European Space Agency) Climate Change Initiative (CCI). In the second part, the derived RCC-CM products are presented, which are divided into two groups: (i) operational monitoring products (e.g. monthly means and anomalies) based on near-real-time environmental data records (EDRs) and (ii) climate information records (e.g. climatologies, time series, trend maps) based on long-term thematic climate data records (TCDRs) with adequate stability, accuracy and homogeneity. The products are provided as maps, statistical plots and gridded data, which are made available through the RCC-CM website (www.dwd.de/rcc-cm).
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