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

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