Copernicus Ocean State Report, issue 6

The 6th issue of the Copernicus OSR incorporates a large range of topics for the blue, white and green ocean for all European regional seas, and the global ocean over 1993–2020 with a special focus on 2020

Calculation of the vertical net energy flux using an energy budget equation for the land surface

In dieser Arbeit wird auf unterschiedliche Berechnungsmethoden für den vertikalenNettoenergiefluss durch die Erdoberfläche eingegangen. Dafür werden drei unterschiedliche Methoden verwendet, zum einen die direkte Berechnung aus der Nettostrahlung und den turbulenten Flüssen wie man sie in Reanalysen wie ERA5 herunterladen kann. Eine weitere Berechnungsmöglichkeit stellt die Berechnung über eine atmosphärische Säule dar. Die dritte Methode ist eine diagnostische Auswertung einer Gleichung für die Beschreibung des Bodenwärmestromes über der Landoberfläche. Zuerst werden die dafür benötigten theoretischen Grundlagen erläutert. ImAnschluss daran werden die verwendeten Daten und Methoden beschrieben. Dadurch ist die Basis für den Vergleich der unterschiedlichen Berechnungsmethoden für den vertikalen Nettoenergiefluss geschaffen. Ein Vergleich der Berechnungsmethoden und eineDiskussion derUnterschiede ist von großem Interesse. Dafür wird als Untersuchungsgebiet die Landoberfläche von 40 ±N bis 90 ±N herangezogen. Da dies einen sehr großen Bereich darstellt wird auch auf einzelne Teilbereiche eingegangen. Über Landoberflächen ist die Konsistenz zwischen den vertikalen Nettoenergieflüssen auf der jahreszeitlichen Zeitskala vernünftig. Inkonsistenzen treten in höher gelegenen Gebieten nahe steiler Orographie auf. Die Gleichung für die Landoberfläche, wie sie hier vorgestellt wird, scheint über Eisschilden nicht zu funktionieren, wahrscheinlich wegen der unterschiedlichen Datenrepräsentation dort.In this thesis different calculation methods for the vertical net energy flux through the Earth’s surface are discussed. Three different methods are used for this purpose, firstly the direct calculation from the net radiation and the turbulent fluxes as they can be downloaded from reanalyses like ERA5. Another calculation possibility is the calculation over an atmospheric column. The third method is the diagnostic evaluation of an equation for the description of the soil heat flux over the land surface. At first the necessary theoretical foundations are explained. Afterwards the data and methods used are described. This provides the basis for comparing the different methods of calculating the vertical net energy flow. A comparison of the calculation methods and a discussion of the differences is of great interest. For this purpose, the land surface from40 ±Nto 90 ±Nis used as a study area. Since this is a very large area, individual sub-areas are also dealt with. Over land surfaces the consistency between the net vertical energy fluxes is reasonable on the seasonal time scale. Inconsistencies emerge in high surface regions and near steep orography. The land surface equation as it is presented here does not seem to work over ice sheets, likely because of different data representation there

Diagnostic evaluation of river discharge into the Arctic Ocean and its impact on oceanic volume transports

Abstract. This study analyses river discharge into the Arctic Ocean using state-of-the-art reanalyses such as the fifth-generation European Reanalysis (ERA5) and the reanalysis component from the Global Flood Awareness System (GloFAS). GloFAS, in it’s operational version 2.1, combines the land surface model Hydrology Tiled ECMWF Scheme for Surface Exchanges over Land, HTESSEL) from ECMWF’s ERA5 with a hydrological and channel routing model (LISFLOOD). Further we analyse GloFAS most recent version 3.1, which is not coupled to HTESSEL but uses the full configuration of LISFLOOD. Seasonal cycles, as well as annual runoff trends are analysed for the major Arctic watersheds – Yenisei, Ob, Lena and Mackenzie – where reanalysis-based runoff can be compared to available observed river discharge records. Further we calculate river discharge over the whole Pan-Arctic region and, by combination with atmospheric inputs, storage changes from the Gravity Recovery and Climate Experiment (GRACE) and oceanic volume transports from ocean reanalyses, try to close the non-steric water volume budget. Finally we provide best estimates for every budget equation term using a variational adjustment scheme. Seasonal river discharge peaks are underestimated in ERA5 and GloFAS v2.1 by up to 50 %, caused by pronounced declining trends due to spurious signals in ERA5s data assimilation system. The new GloFAS v3.1 product exhibits distinct improvements and performs best in terms of seasonality and long term means, however opposing to gauge observations it also features declining trends. Calculating runoff indirectly through the divergence of moisture flux is the only reanalyses based estimate that is able to reproduce the river discharge increases measured by gauge observations (Pan-Arctic increase of 2 % per decade). In addition we look into Greenlandic discharge, which makes out about 10 % of of the total Pan-Arctic discharge and features strong increases mainly due to glacial melting. The variational adjustment brought reliable estimates of the volume budget terms on an annual scale, requiring only moderate adjustments of less than 1 % for each individual term. Approximately 6584 ± 84 km3 freshwater leave the Arctic Ocean per year through it’s boundaries. About two thirds of this are recovered through runoff from the surrounding land areas to the Arctic Ocean (4379 ± 25 km3 per year) and about one third is supplied by the atmosphere. On a seasonal scale however the variational approach demonstrated that there are systematical errors present in the data-sets, that are not considered in their uncertainty estimation. Hence the budget residuals of some month were too large to be eliminated within the a priori spreads of the individual terms. </jats:p

Diagnostic evaluation of river discharge into the Arctic Ocean and its impact on oceanic volume transports

Abstract. This study analyses river discharge into the Arctic Ocean using state-of-the-art reanalyses such as the fifth-generation European Reanalysis (ERA5) and the reanalysis component from the Global Flood Awareness System (GloFAS). GloFAS, in its operational version 2.1, combines the land surface model (Hydrology Tiled European Centre for Medium-Range Weather Forecasts – ECMWF – Scheme for Surface Exchanges over Land, HTESSEL) from ECMWF’s ERA5 with a hydrological and channel routing model (LISFLOOD). Furthermore, we analyse GloFAS' most recent version 3.1, which is not coupled to HTESSEL but uses the full configuration of LISFLOOD. Seasonal cycles as well as annual runoff trends are analysed for the major Arctic watersheds – Yenisei, Ob, Lena, and Mackenzie – where reanalysis-based runoff can be compared to available observed river discharge records. Furthermore, we calculate river discharge over the whole pan-Arctic region and, by combination with atmospheric inputs, storage changes from the Gravity Recovery and Climate Experiment (GRACE) and oceanic volume transports from ocean reanalyses, we assess closure of the non-steric water volume budget. Finally, we provide best estimates for every budget equation term using a variational adjustment scheme. Runoff from ERA5 and GloFAS v2.1 features pronounced declining trends induced by two temporal inhomogeneities in ERA5's data assimilation system, and seasonal river discharge peaks are underestimated by up to 50 % compared to observations. The new GloFAS v3.1 product exhibits distinct improvements and performs best in terms of seasonality and long-term means; however, in contrast to gauge observations, it also features declining runoff trends. Calculating runoff indirectly through the divergence of moisture flux is the only reanalysis-based estimate that is able to reproduce the river discharge increases measured by gauge observations (pan-Arctic increase of 2 % per decade). In addition, we examine Greenlandic discharge, which contributes about 10 % of the total pan-Arctic discharge and features strong increases mainly due to glacial melting. The variational adjustment yields reliable estimates of the volume budget terms on an annual scale, requiring only moderate adjustments of less than 3 % for each individual term. Approximately 6583±84 km3 of freshwater leaves the Arctic Ocean per year through its boundaries. About two-thirds of this is contributed by runoff from the surrounding land areas to the Arctic Ocean (4379±25 km3 yr−1), and about one-third is supplied by the atmosphere. However, on a seasonal scale budget residuals of some calendar months were too large to be eliminated within the a priori spreads of the individual terms. This suggests that systematical errors are present in the reanalyses and ocean reanalysis data sets, which are not considered in our uncertainty estimation. </jats:p

Future perspectives of natural and technical snow in Austria

&amp;lt;p&amp;gt;Despite the large socio-economic and ecologic relevance of snow in Austria, no comprehensive assessment of the impact of climate change on snow in Austria existed until recently. Within the project &amp;amp;#8222;Future Snow Cover Evolution in Austria&amp;amp;#8221; (FuSE-AT, https://fuse-at.ccca.ac.at/) gridded observational datasets and the national climate scenarios (&amp;amp;#214;KS15) have been extended by basic variables and user oriented indicators around the topic snow. This has been realized by developing a gridded snow model for climatological time-scales, based on the operational snow model of ZAMG (SNOWGRID-CL) and driving it with gridded meteorological datasets for the past (1961 &amp;amp;#8211; 2019) and with the full ensemble of &amp;amp;#214;KS15 (including the emission pathways RCP2.6, RCP4.5 and RCP8.5) into the future (1961 &amp;amp;#8211; 2100) &amp;amp;#160;to generate daily snow variables on a 1 km x 1 km grid. The results are available for users via the Data Centre of the Climate Change Centre Austria (https://fuse-at.ccca.ac.at/).&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;This new dataset includes snow water equivalent, snow depth, new snow, run-off from snow melt and the number of hours with suitable meteorological conditions for technical snow generation (using different wet-bulb-temperatures as threshold criteria). In addition, numerous user-oriented indicators have been analyzed. In close cooperation with stakeholders from the sectors winter tourism, hydropower generation and water supply, case studies to demonstrate socio-economically relevant &amp;amp;#160;applications of this new dataset have been conducted.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The results show that the natural snow season length has significantly decreased already in the past in virtually all areas and altitude levels of Austria. Future scenarios of snow heavily depend on the emission pathway. The snow season length is expected to decrease by about three weeks (corresponds to -20% to -30% around 1500 m a.s.l.) until the mid-21st century in all scenarios, but it stabilizes on this level in RCP2.6, while it drastically further decreases in RCP8.5 to losses around -80% to -90% below 1500 m a.s.l. Further, we could demonstrate that the meteorological potential for generation of technical snow responds less sensitive to climate change than natural snow, but strongly depends on&amp;amp;#160; altitude, exposition, time horizon and emission pathway. More detailed results will be given in the presentation.&amp;lt;/p&amp;gt;</jats:p

Diagnostic evaluation of river discharge into the Arctic Ocean and its impact on oceanic volume transports

This study analyses river discharge into the Arctic Ocean using state-of-the-art reanalyses such as the fifth-generation European Reanalysis (ERA5) and the reanalysis component from the Global Flood Awareness System (GloFAS). GloFAS, in its operational version 2.1, combines the land surface model (Hydrology Tiled European Centre for Medium-Range Weather Forecasts – ECMWF – Scheme for Surface Exchanges over Land, HTESSEL) from ECMWF’s ERA5 with a hydrological and channel routing model (LISFLOOD). Furthermore, we analyse GloFAS' most recent version 3.1, which is not coupled to HTESSEL but uses the full configuration of LISFLOOD. Seasonal cycles as well as annual runoff trends are analysed for the major Arctic watersheds – Yenisei, Ob, Lena, and Mackenzie – where reanalysis-based runoff can be compared to available observed river discharge records. Furthermore, we calculate river discharge over the whole pan-Arctic region and, by combination with atmospheric inputs, storage changes from the Gravity Recovery and Climate Experiment (GRACE) and oceanic volume transports from ocean reanalyses, we assess closure of the non-steric water volume budget. Finally, we provide best estimates for every budget equation term using a variational adjustment scheme. Runoff from ERA5 and GloFAS v2.1 features pronounced declining trends induced by two temporal inhomogeneities in ERA5's data assimilation system, and seasonal river discharge peaks are underestimated by up to 50 % compared to observations. The new GloFAS v3.1 product exhibits distinct improvements and performs best in terms of seasonality and long-term means; however, in contrast to gauge observations, it also features declining runoff trends. Calculating runoff indirectly through the divergence of moisture flux is the only reanalysis-based estimate that is able to reproduce the river discharge increases measured by gauge observations (pan-Arctic increase of 2 % per decade). In addition, we examine Greenlandic discharge, which contributes about 10 % of the total pan-Arctic discharge and features strong increases mainly due to glacial melting. The variational adjustment yields reliable estimates of the volume budget terms on an annual scale, requiring only moderate adjustments of less than 3 % for each individual term. Approximately 6583±84 km3 of freshwater leaves the Arctic Ocean per year through its boundaries. About two-thirds of this is contributed by runoff from the surrounding land areas to the Arctic Ocean (4379±25 km3 yr−1), and about one-third is supplied by the atmosphere. However, on a seasonal scale budget residuals of some calendar months were too large to be eliminated within the a priori spreads of the individual terms. This suggests that systematical errors are present in the reanalyses and ocean reanalysis data sets, which are not considered in our uncertainty estimation

Die Zukunft des nat&amp;#252;rlichen und technischen Schnees in &amp;#214;sterreich

&amp;lt;p&amp;gt;Trotz der gro&amp;amp;#223;en sozio-&amp;amp;#246;konomischen und &amp;amp;#246;kologischen Bedeutung von Schnee in &amp;amp;#214;sterreich und trotz der zu erwartenden hohen Sensitivit&amp;amp;#228;t von Schnee auf die globale Erw&amp;amp;#228;rmung gab es bislang keine umfassende Einsch&amp;amp;#228;tzung der zuk&amp;amp;#252;nftigen Entwicklung von Schnee in &amp;amp;#214;sterreich in einem sich sehr schnell ver&amp;amp;#228;ndernden Klima. Das ACRP Projekt &amp;amp;#8222;Future Snow Cover Evolution in Austria&amp;amp;#8221; (FuSE-AT) hat diese L&amp;amp;#252;cke geschlossen, indem nicht nur Schneetrends der Vergangenheit fl&amp;amp;#228;chendeckend analysiert wurden, sondern auch die nationalen &amp;amp;#246;sterreichischen Klimaszenarien (&amp;amp;#214;KS15) um meteorologische und nutzerorientierte Indikatoren rund um das Thema &amp;quot;Schnee&amp;quot; erweitert und f&amp;amp;#252;r Anwender_innen zur Verf&amp;amp;#252;gung gestellt wurden. Im Zuge des Projekts wurden die t&amp;amp;#228;glichen Schneeh&amp;amp;#246;hen (Naturschnee) und das Potenzial zur Erzeugung von technischem Schnee f&amp;amp;#252;r die Vergangenheit (1961 bis 2019) sowie f&amp;amp;#252;r die Zukunft (bis 2100) mithilfe des Schneedeckenmodells SNOWGRID-CL berechnet. Grundlage f&amp;amp;#252;r diese Berechnungen waren Beobachtungsdatens&amp;amp;#228;tze f&amp;amp;#252;r die Vergangenheit und das gesamte Ensemble der &amp;amp;#246;sterreichischen Klimaszenarien &amp;amp;#214;KS15 f&amp;amp;#252;r die Zukunft. Es wurden die Variablen Schneeh&amp;amp;#246;he, Schneewasser&amp;amp;#228;quivalent und Abfluss aus Schneeschmelze auf Tagesbasis sowie saisonale Indikatoren (z. B. Schneedeckendauer, Anzahl der Stunden, in denen technische Beschneiung m&amp;amp;#246;glich ist) und eine Vielzahl weiterer nutzergerechter Indikatoren ausgewertet. Ein wesentlicher Bestandteil des Projekts war die starke Interaktion mit Stakeholdern von Beginn des Projekts an. Fallstudien in unterschiedlichen wirtschaftlichen Sektoren (Tourismus, Wasserkraft, Wasserversorgung) wurden mit Stakeholdern gemeinsam entworfen und durchgef&amp;amp;#252;hrt. &amp;amp;#160;&amp;lt;/p&amp;gt; &amp;lt;p&amp;gt;Die Ergebnisse zeigen, dass die Anzahl der Tage mit Schnee in &amp;amp;#214;sterreich bereits in der Vergangenheit in so gut wie allen Lagen signifikant abgenommen hat. Die Szenarien f&amp;amp;#252;r die Zukunft sind stark vom eingeschlagenen Emissionspfad abh&amp;amp;#228;ngig. W&amp;amp;#228;hrend sich die Schneesaison im RCP2.6 (&amp;amp;#8222;Paris-Ziel&amp;amp;#8220;) bis Mitte des 21. Jahrhunderts in mittleren Lagen um etwa 3 Wochen verk&amp;amp;#252;rzt (entspricht etwa -20% bis -30%), danach aber stabil bleibt, ist unter Annahme des &amp;amp;#8222;worst case Szenario&amp;amp;#8220; RCP8.5 unterhalb von 1500 m mit einem fast vollst&amp;amp;#228;ndigen Verlust der nat&amp;amp;#252;rlichen Schneedecke zu rechnen (-80% bis -90%). Weiters konnte gezeigt werden, dass die Bedingungen f&amp;amp;#252;r technische Schneeproduktion weniger sensitiv auf den Klimawandel reagiert als Naturschnee, aber je nach H&amp;amp;#246;henlage, Exposition, Zeithorizont und angenommenen Emissionspfad ganz unterschiedliche Auswirkungen entstehen.&amp;lt;/p&amp;gt;</jats:p