Research ArticleCLIMATOLOGY

A new dynamical systems perspective on atmospheric predictability: Eastern Mediterranean weather regimes as a case study

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Science Advances  05 Jun 2019:
Vol. 5, no. 6, eaau0936
DOI: 10.1126/sciadv.aau0936
  • Fig. 1 CDFs of inverse persistence (θ) and local dimension (d) for EM synoptic systems.

    The NCEP/NCAR reanalysis 1948–2017 θ (A) and d (B) CDFs for the different synoptic systems: Red Sea Troughs (RSTs), Persian Troughs (PTs), Highs (H), Cyprus Lows (CLs), and Sharav Lows (SLs). CDFs for θ (C) and d (D) of deep and shallow CLs as defined by Alpert et al. (7). Arrows indicate the snow events in Jerusalem for the available TIGGE data (27, 28). The 12 December 2013 storm named Alexa with values of d = 13.71 and θ = 0.71 is also shown.

  • Fig. 2 Transition probability matrices for EM synoptic systems.

    (A) NCEP/NCAR reanalysis synoptic systems probability transition matrix for 1948–2017. (B) CMIP5 ensemble mean probability transition matrix for 1986–2005. Rows sum up to 100%. The synoptic systems are RSTs, PTs, Highs (H), CLs, and SLs.

  • Fig. 3 SLP mean composites for eastern Mediterranean synoptic systems.

    SLP composites for the top and bottom five percentiles of the d and θ distributions (A to D) and the CL, High, PT, and RST synoptic systems (E to H). The values are provided as deviations in Pa from the mean SLP value of each composite. The corresponding absolute SLP patterns for the synoptic systems are shown in fig. S3.

  • Fig. 4 CDFs of inverse persistence (θ) for CMIP5 models and NCEP/NCAR reanalysis.

    Inverse persistence (θ) CDFs for the synoptic systems in eight CMIP5 models (table S2) and the NCEP/NCAR reanalysis over 1986–2005: (A) CANESM, (B) CCSM4, (C) HadGEM2CC, (D) HadGEM2ES, (E) NCEP/NCAR reanalysis, (F) IPSL, (G) MPI, (H) MRI, and (I) NORESM.

  • Fig. 5 CDFs of local dimension (d) for CMIP5 models and NCEP/NCAR reanalysis.

    Local dimension (d) CDFs for the synoptic systems in eight CMIP5 models (table S2) and the NCEP/NCAR reanalysis over 1986–2005: (A) CANESM, (B) CCSM4, (C) HadGEM2CC, (D) HadGEM2ES, (E) NCEP/NCAR reanalysis, (F) IPSL, (G) MPI, (H) MRI, and (I) NORESM.

  • Fig. 6 Transition probability matrices for CMIP5 models and the NCEP/NCAR reanalysis.

    Rows sum up to a 100%. The synoptic systems are RSTs, PTs, Highs (H), CLs, and SLs. The numerical values of the matrices are shown in table S4. (A) CANESM, (B) CCSM4, (C) HadGEM2CC, (D) HadGEM2ES, (E) NCEP/NCAR reanalysis, (F) IPSL, (G) MPI, (H) MRI, and (I) NORESM.

Supplementary Materials

  • Supplementary material for this article is available at http://advances.sciencemag.org/cgi/content/full/5/6/eaau0936/DC1

    Table S1. Snow events in Jerusalem from the available TIGGE database (27, 28).

    Table S2. The eight CMIP5 models used in the present study with the following information: Modeling center (or group), institute ID, model name, and horizontal resolution (°) following Taylor et al. (29).

    Table S3. Model rank scores for each synoptic group with respect to the NCEP/NCAR reanalysis.

    Table S4. CMIP5 models and NCEP/NCAR reanalysis transition probabilities for the different synoptic groups.

    Table S5. Model mean absolute differences for the transition probabilities of each synoptic group with respect to the NCEP/NCAR reanalysis (%).

    Fig. S1. The study region following Alpert et al. (7).

    Fig. S2. Meteogram from ECMWF’s EPS (Ensemble Prediction System) forecast system initialized on Wednesday, 11 December 2013, 00:00 UTC at the location of Jerusalem (31.9°N 35.2°E; 815 m).

    Fig. S3. Mean SLP composite maps from NCEP/NCAR reanalysis as classified by the Alpert et al. (7) synoptic classification algorithm for 1986–2005.

  • Supplementary Materials

    This PDF file includes:

    • Table S1. Snow events in Jerusalem from the available TIGGE database (27, 28).
    • Table S2. The eight CMIP5 models used in the present study with the following information: Modeling center (or group), institute ID, model name, and horizontal resolution (°) following Taylor et al. (29).
    • Table S3. Model rank scores for each synoptic group with respect to the NCEP/NCAR reanalysis.
    • Table S4. CMIP5 models and NCEP/NCAR reanalysis transition probabilities for the different synoptic groups.
    • Table S5. Model mean absolute differences for the transition probabilities of each synoptic group with respect to the NCEP/NCAR reanalysis (%).
    • Fig. S1. The study region following Alpert et al. (7).
    • Fig. S2. Meteogram from ECMWF’s EPS (Ensemble Prediction System) forecast system initialized on Wednesday, 11 December 2013, 00:00 UTC at the location of Jerusalem (31.9°N 35.2°E; 815 m).
    • Fig. S3. Mean SLP composite maps from NCEP/NCAR reanalysis as classified by the Alpert et al. (7) synoptic classification algorithm for 1986–2005.

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    Correction (5 April 2020): In a previous version, Figure 3 and Figure S3 were plotted incorrectly. The main text and supplementary material has been updated with corrected figures.

    The original version is accessible here.

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