CMMT composites

Below is the composite mean specific humidity and atmospheric circulation for each of the Antarctic Cloud Mass Meridional Transport (CMMT) regions over the entire study period (1 November 1992 – 31 October 2012).

For this analysis, 6-hourly, 500 hPa specific humidity, zonal wind and meridional wind data from the European Centre for Medium-Range Weather Forecasts Interim Reanalysis (ERA-Interim; Dee et al., 2011) was downloaded for the period 1979–2016. Daily mean fields were calculated from the 6-hourly data and the wind fields were used to calculate the 500 hPa streamfunction. In order to calculate the streamfunction and specific humidity anomaly, the mean value for each day over the period 1979–2016 was calculated to produce a daily climatology, and then the corresponding climatological mean value was subtracted at each data time to obtain the anomaly data.

When calculating the composite mean field for a given region, all days for which a CMMT event was recorded (i.e. at any time of the day) were used, regardless of whether the event was designated as a skirting event or not. While this is a rather coarse approach, I suspect a more detailed approach (e.g. it would be more correct to use the 6-hourly data and try to break down skirting events according to time spent in each region) would yield similar results.

First impressions on recognizable structures in the composites:

  • The composite mean circulation for Ellsworth Land (Figure \ref{fig:ellsworth}) resembles the Pacific-South American (PSA) pattern. The PSA pattern has traditionally been linked to convection in the tropical Pacific (and thus is routinely introduced as a mechanism by which ENSO can influence the high southern latitudes), however a couple of recent studies (Irving et al., 2016; O’Kane et al., 2017) have challenged this assumption (they find it is instead an intrinsic feature of the mid-to-high latitude circulation that is largely independent of tropical forcing). This might explain why there was no strong association between CMMT events in Ellsworth Land and ENSO indices.

  • There are a limited number of seasons/regions that show a fairly coordinated zonal wave number 4 pattern around the hemisphere (e.g. Queen Maud Land in MAM; Figure \ref{fig:queen_maud}). This wavenumber 4 variability is to be expected (Figure 4b of Irving et al. (2015) shows that zonal wavenumber 4 is dominant at daily timescales), but I’m actually not sure about the relationship between such coordinated daily timescale patterns and the well known monthly timescale features like the ZW3 pattern. (i.e. does a strong monthly mean ZW3 pattern consist of lots of daily mean fields that have a coordinated wavenumber 4 pattern? This might be true - I could possibly look into that if need be.)

  • In many seasons/regions (particularly for the annual plots) the streamfunction anomalies are strong in the vicinity of the region of interest but weak further afield (I’ve used an exaggerated number of contours in the plots to try and make sure we don’t miss any spatial structures, but if you use a more reasonable contour interval it becomes clearer that in many cases the far field anomalies are rather weak). This might be an interesting finding in itself, in that it suggests that there isn’t an obvious large-scale dynamical climate phenomena (e.g. ENSO, SAM, ZW3, PSA pattern, etc) associated with events in that season/region (i.e. perhaps local factors are much more important - future work could try and identify some of those local factors?)

  • It might be worth looking at the individual daily fields associated with some of the events to get a feel for if the composites are representative (i.e. if there is a lot of variability between events the composite mean might not be particularly representative)

\label{fig:queen_maud}Composite mean 500 hPa specific humidity anomaly for days where a CMMT event (skirting or otherwise) was identified for the Queen Maud Land region. Gray streamlines indicate the direction of the composite mean wind, while the black contours show the composite mean streamfunction anomaly (dashed contours indicate negative values and the contour interval is \(1.0\times 10^{6}\>m^{2}s^{-1}\)). There are 612 (annual), 129 (DJF), 124 (MAM), 151 (JJA) and 208 (SON) days included in each composite respectively.

\label{fig:enderby}As per Figure \ref{fig:queen_maud}, but for Enderby Land. There are 467 (annual), 143 (DJF), 99 (MAM), 99 (JJA) and 126 (SON) days included in each composite respectively.

\label{fig:queen_mary}As per Figure \ref{fig:queen_maud}, but for Queen Mary Coast. There are 453 (annual), 98 (DJF), 113 (MAM), 113 (JJA) and 129 (SON) days included in each composite respectively.

\label{fig:wilkes}As per Figure \ref{fig:queen_maud}, but for Wilkes Land. There are 507 (annual), 110 (DJF), 114 (MAM), 162 (JJA) and 121 (SON) days included in each composite respectively.

\label{fig:victoria}As per Figure \ref{fig:queen_maud}, but for Victoria Land. There are 281 (annual), 63 (DJF), 74 (MAM), 54 (JJA) and 90 (SON) days included in each composite respectively.

\label{fig:marie_byrd}As per Figure \ref{fig:queen_maud}, but for Marie Byrd Land. There are 899 (annual), 146 (DJF), 209 (MAM), 283 (JJA) and 261 (SON) days included in each composite respectively.

\label{fig:ellsworth}As per Figure \ref{fig:queen_maud}, but for Ellsworth Land. There are 1444 (annual), 242 (DJF), 405 (MAM), 426 (JJA) and 371 (SON) days included in each composite respectively.


  1. D. P. Dee, S. M. Uppala, A. J. Simmons, P. Berrisford, P. Poli, S. Kobayashi, U. Andrae, M. A. Balmaseda, G. Balsamo, P. Bauer, P. Bechtold, A. C. M. Beljaars, L. van de Berg, J. Bidlot, N. Bormann, C. Delsol, R. Dragani, M. Fuentes, A. J. Geer, L. Haimberger, S. B. Healy, H. Hersbach, E. V. Hólm, L. Isaksen, P. Kllberg, M. Köhler, M. Matricardi, A. P. McNally, B. M. Monge-Sanz, J.-J. Morcrette, B.-K. Park, C. Peubey, P. de Rosnay, C. Tavolato, J.-N. Thépaut, F. Vitart. The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Quart. J. Roy. Meteor. Soc. 137, 553–597 (2011). Link

  2. Damien Irving, Ian Simmonds. A New Method for Identifying the Pacific–South American Pattern and Its Influence on Regional Climate Variability. Journal of Climate 29, 6109–6125 (2016). Link

  3. Terence J. O’Kane, Didier P. Monselesan, James S. Risbey. A Multiscale Reexamination of the Pacific–South American Pattern. Monthly Weather Review 145, 379–402 (2017). Link

  4. Damien Irving, Ian Simmonds. A Novel Approach to Diagnosing Southern Hemisphere Planetary Wave Activity and Its Influence on Regional Climate Variability. Journal of Climate 28, 9041–9057 (2015). Link

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