24 Aug 2022
24 Aug 2022
Status: a revised version of this preprint is currently under review for the journal WCD.

Validation of boreal summer tropical-extratropical causal links in seasonal forecasts

Giorgia Di Capua1,2, Dim Coumou2,3,4, Bart van der Hurk3,5, Antje Weissheimer6,7, Andrew G. Turner8,9, and Reik V. Donner1,2 Giorgia Di Capua et al.
  • 1Department of Water, Environment, Construction and Safety, Magdeburg-Stendal University of Applied Sciences, Magdeburg, 39114, Germany
  • 2Earth System analysis, Potsdam Institute for Climate Impact Research (PIK) – Member of the Leibniz Association, Potsdam, 14473, Germany
  • 3Water and Climate Risk Department, Institute for Environmental Studies (IVM), VU University of Amsterdam, Amsterdam, 1081 HV, Netherlands
  • 4Royal Netherlands Meteorological Institute (KNMI), De Bilt, 3730 AE, Netherlands
  • 5Department of Flood Risk Management, Deltares, Delft, 2629 HV, Netherlands
  • 6European Centre for Medium-Range Weather Forecasts, Reading, RG2 9AX, UK
  • 7National Centre for Atmospheric Science, University of Oxford, Oxford, OX1 4BH, United Kingdom
  • 8Department of Meteorology, University of Reading, Reading, RG6 6UR, United Kingdom
  • 9National Centre for Atmospheric Science, University of Reading, Reading, RG6 6UR, United Kingdom

Abstract. Much of the forecast skill in the mid-latitudes on seasonal timescales originates from deep convection in the tropical belt. For boreal summer, such tropical-extratropical teleconnections are less well understood as compared to winter. Here we validate the representation of boreal tropical – extratropical teleconnections in a general circulation model in comparison with observational data. To characterise variability between tropical convective activity and mid-latitude circulation, we identify the South Asian monsoon (SAM) – circumglobal teleconnection (CGT) pattern and the western North Pacific summer monsoon (WNPSM) – North Pacific high (NPH) pairs as the leading modes of tropical-extratropical coupled variability in both reanalysis (ERA5) and seasonal forecast (SEAS5) data. We calculate causal maps, an application of the PCMCI causal discovery algorithm which identifies causal links in a 2D field, to show the causal effect of each of these patterns on circulation and convection in the Northern Hemisphere. The spatial patterns and signs of the causal links in SEAS5 closely resemble those seen in ERA5, independent of the initialization date of SEAS5. However, the strength of causal links in SEAS5 is often too weak (about two thirds of those in ERA5). By performing a subsampling (over time) experiment, we identify those regions for which SEAS5 data well reproduce ERA5 values, e.g. South-eastern US, and highlight those where the bias is more prominent, e.g. North Africa. We demonstrate that different El Niño – Southern Oscillation phases have only a marginal effect on the strength of these links. Finally, we discuss the potential role of model mean-state biases in explaining differences between SEAS5 and ERA5 causal links.

Giorgia Di Capua 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 wcd-2022-48', Anonymous Referee #1, 29 Sep 2022
    • AC1: 'Reply on RC1', Giorgia Di Capua, 09 Nov 2022
  • RC2: 'Comment on wcd-2022-48', Anonymous Referee #2, 12 Oct 2022
    • AC2: 'Reply on RC2', Giorgia Di Capua, 09 Nov 2022
    • AC3: 'Reply on RC2', Giorgia Di Capua, 09 Nov 2022

Giorgia Di Capua et al.

Giorgia Di Capua et al.


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Short summary
Heavy rainfall in tropical regions interacts with mid-latitude circulation patterns and this interaction can explain weather patterns in the Northern Hemisphere during summer. . In this analysis we detect these tropical – extratropical interaction pattern both in observational datasets and data obtained by atmospheric models and assess how well can atmospheric model reproduce the observed patterns. We find a good agreement although these relationships are too weak in model data.