Preprints
https://doi.org/10.5194/hess-2021-318
https://doi.org/10.5194/hess-2021-318

  29 Jun 2021

29 Jun 2021

Review status: this preprint is currently under review for the journal HESS.

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

Susanna Winkelbauer1, Michael Mayer1,2, Vanessa Seitner1, Ervin Zsoter2, Hao Zuo2, and Leopold Haimberger1 Susanna Winkelbauer et al.
  • 1Department of Meteorology and Geophysics, University of Vienna, Vienna Austria
  • 2European Centre for Medium-Range Weather Forecasts, Reading, United Kingdom

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.

Susanna Winkelbauer et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2021-318', Anonymous Referee #1, 23 Jul 2021
    • AC1: 'Reply on RC1', Susanna Winkelbauer, 24 Sep 2021
  • RC2: 'Comment on hess-2021-318', Anonymous Referee #2, 30 Aug 2021
    • AC2: 'Reply on RC2', Susanna Winkelbauer, 24 Sep 2021

Susanna Winkelbauer et al.

Susanna Winkelbauer et al.

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Short summary
We evaluate Arctic river discharge using in-situ observations and state-of-the art reanalyses, inter alia the most recent Global Flood Awareness System (GloFAS) river discharge reanalysis version 3.1. Further we combine reanalyses data, in-situ observations, ocean reanalyses and satellite data and use a lagrangian optimization scheme to close the Arctics volume budget on an annual and seasonal scale, resulting in one reliable and up-to-date estimate of every volume budget term.