EGU General Assembly 2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Underestimation of Arctic warming trends in sub-seasonal forecasts

Steffen Tietsche1, Frederic Vitart2, Michael Mayer1,3, Antje Weisheimer2,4, and Magdalena Balmaseda2
Steffen Tietsche et al.
  • 1European Centre for Medium-Range Weather Forecasts, Bonn, Germany
  • 2European Centre for Medium-Range Weather Forecasts, Reading, UK
  • 3University of Vienna, Vienna, Austria
  • 4University of Oxford, Oxford, UK

The Arctic has warmed substantially over the last decades and will continue to do so owing to global warming in conjunction with polar amplification. The changing mean state poses many challenges to the construction, evaluation and calibration of subseasonal-to-seasonal forecasting systems, because it puts into question the representativeness of the system's retrospective forecasts (reforecasts). Furthermore, any inconsistencies with observed trends degrade the forecast skill and point to deficiencies in either the physical modelling or the initialization methods. Here, we assess the consistency of boreal winter trends of surface air temperature (SAT) in the Eurasian Arctic between the ERA5 reanalysis and ECMWF sub-seasonal reforecasts initialised from ERA5, for the 35-year period 1986-2021. We present methods to quantify robustness and importance of the observed trends, and to quantify the consistency of reforecast trends with these observed trends. We find that, in large parts of the marine Arctic, the reforecasts clearly underestimate the reanalsyis warming trend of about 1 K per decade at lead times beyond two weeks. For longer lead times, the reforecast trend is less than half of the reanalysis trend, with very high statistical significance. We present a series of numerical experiments to investigate potential reasons for the trend underestimation. These concern the sea-ice thermodynamic coupling to the atmosphere, impact of sea surface temperatures, and possible remote atmospheric influences from the North Atlantic and the Tropics. The outcome of these experiments provides guidance for future improvements in the physical forecast model and data assimilation methods needed to faithfully represent and predict Arctic climate variability and change.

How to cite: Tietsche, S., Vitart, F., Mayer, M., Weisheimer, A., and Balmaseda, M.: Underestimation of Arctic warming trends in sub-seasonal forecasts, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-14141,, 2023.