Development of a Long-term
Climatology of North Atlantic
Polar Lows
Matthias Zahn1,2, Hans v. Storch1,2, Stephan Bakan3
(1) University of Hamburg, Meteorological Institute, Germany
(2) GKSS Research Centre, Institute for Coastal Research, Germany
(3) Max Planck Institut für Meteorologie, Hamburg, Germany
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occurring poleward the Polar
Front in both hemispheres
Typically induced by
disturbances in the air flow
Typically driven by convective
processes
ia
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~300 km
nav
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ndi
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Particular kind of low pressure
system
mesoscale sized (typically
several hundred km in diameter)
Intense/ strong winds (>15m/s),
severe weather
Sca
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Spi
t
ber zgen
Polar Lows
© Dundee Satellite Receiving Station
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Further examples of Polar Lows
Further reading:
Rasmussen and Turner, 2003:
Polar Lows: Mesoscale Weather Systems
in the Polar Regions
20.12.2002, 2:00
04.03.2008, 11:35
11.03.08, 15:25
16.1.1995, 9:00
IPY-Thorpex field campaign:http://www.ipy-thorpex.com/ , images from http://www.sat.dundee.ac.uk/
Kolstad, E. W. & T. J. Bracegirdle & I. A. Seierstad: Marine cold-air outbreaks in the North Atlantic: temporal distribution and associations with
large-scale atmospheric circulation. Climate Dynamics, published online 19 June, 2008. DOI:10.1007/s00382-008-0431-5
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Longterm climatology
• Dataset of Polar Low cases
• Comprehensive measurements are
required to compile such a dataset
• long in time
• high in spatial detail
• Homogeneous
• Problem: Such measurements do
usually no exist
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Solution
• Use of numerical models in
combination with existing
measurements to reconstruct the
past state of the atmosphere:
=> dynamical downscaling: NCEP (~200 km)
LAM (~50 km)
• Polar Lows need to be automatically
detectable in such data!
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Setup of my PhD work
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Part 1: Can LAMs reproduce polar lows ?
Part 2: How to detect polar lows
automatically in LAM output data
Part 3: Compilation of Climatology
Ensemble simulations for three
polar low cases in climate mode
NCEP (~200 km)
CLM (~50 km)
Oct. 1993 (Dec. 1993, Jan. 1998)
Initialised: approx. 2 week prior to PL formation
With spectral Nudging (4x) and without (4x) (v. Storch et
al. 2000)
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Sca
ndi
n av
ia
Spi
tzber
gen
Oct. 1993 case
15.Oct.93, 05:24
© Dundee Satellite Receiving Station
Mean Sea level pressure (hPa) and
10m wind speed
15. Oct. 1993,
6:00
Dundee
15.10.93, 05:24
NCEP
DWD
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Mean Sea level pressure (hPa) and
10m wind speed
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15. Oct. 1993,
6:00
NCEP
CLM01nn
CLM02nn
DWD
CLM03nn
Dundee
15.10.93, 05:24
CLM04nn
Mean Sea level pressure (hPa) and
10m wind speed
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15. Oct. 1993,
6:00
NCEP
DWD
Dundee
15.10.93, 05:24
CLM01nn
CLM02nn
CLM03nn
CLM04nn
CLM01sn
CLM02sn
CLM03sn
CLM04sn
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Two dimensional bandpass filter
isotropic filters are
able to separate
large, medium and
small spatial scales
in a limited
(regional) gridded
field.
Feser, F., and H. von Storch, 2005: Spatial two-dimensional discrete filters for limited area model evaluation purposes. Mon. Wea Rev. 133,
1774-1786
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Band-pass filtered mslp (hPa)
0600 UTC
15 Oct 1993
NCEP
DWD
(Response
function: wave
lengths between
appr. 200 and 600
km are retained)
CLM01nn
CLM02nn
CLM03nn
CLM04nn
CLM01sn
CLM02sn
CLM03sn
CLM04sn
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Dec 1993
case:
NERC Dundee Satellite Receiving Station
weatherchart, DWD, 0000 UTC 9 Dec 1993
Response
function: wave
lengths
between appr.
200 - 600 km
are retained
CLM22-sn, band pass filtered
0000 UTC 9 Dec 1993
CLM22-sn, full field
0000 UTC 9 Dec 1993
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Jan 1998
case:
NERC Dundee Satellite Receiving Station
Weather chart, 0100 UTC 18 Jan 1998
Response
function: wave
lengths
between appr.
200 - 600 km
are retained
CLM01-sn, band pass filtered
0000 UTC 18 Jan 1998
CLM01-sn, full mslp field
0000 UTC 18 Jan 1998
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Intermediate results
• In principle, Polar Lows can be reproduced
with CLM run in climate mode
• Though, there may be deviations in location
and amount of pressure minima
• Without nudging the large scale, the formation
of Polar Lows is subject to considerable
ensemble variability
• A digital filter could be useful for an automatic
detection
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Setup of the detection algorithm
st
1 : detection of all locations with a minimum in
the filtered mslp field < -1hPa
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Setup of the detection algorithm
st
1 : detection of all locations with a minimum in the
filtered mslp field < -1hPa
2nd : combine detected positions to individual
tracks, distance to next (3h) pos < ~200 km
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Setup of the detection algorithm
st
1 : detection of all locations with a minimum in the
filtered mslp field < -1hPa
2nd : combine detected positions to individual tracks,
distance to next (3h) pos < ~200 km
3rd : checking further constraints along the tracks:
• strength of the minimum ( ≤ −2hPa once along the track)
• wind speed ( ≥ 13.9 m/s once along the track)
• air-sea temperature difference ( SST − T500hPa ≥ 43K)
• north south direction of the track
• limits to allowable adjacent grid boxes
OR: strength of the minimum in the bandpass filtered
mslp field decreases below −6hPa once
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Setup of long-term simulation:
CLM 2.4.6
initialised: 1.1.1948
finished : 28.2.2006
driven by NCEP/NCAR reanalysis 1
spectral nudging of scales > 700 km
together with the algorithm enables a long-term
climatology of Polar Lows
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Tracks of three cases
Oct
Oct 1993
1993
Dec
Dec 1993
1993
Jan
Jan 1998
1998
Tracks reproduced
and detected even
after a simulation time
of several decades
Time series of the number of
polar lows per winter
• Mean number of
polar lows: 56
• Most active winter
was PLS 1981
• Fewest polar lows
were detected in
PLS 1964
• Strong inter annual
variability,
σ = ± 13
• No longterm trend
visible
Number of detected polar lows per polar low season. One
polar low season is defined as the period starting 1 Jul
and ending 30 Jun the following year.
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Algorithm's sensitivity
To varied
dtz criteria
To varied
ws criteria
C > 0.9
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Climatological comparison
Number of detected polar lows per polar low season. Our data (black)
and observations (red) by Wilhelmsen (1985)
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Climatological comparison
Number of detected polar lows per polar low season. Our data (black)
and observed (red) by MetNo, Noer, (pers. comm.)
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C=0,58
C=0,72
Monthly comparison of our
data (in black) with
observed data (in red)
u.r.: Norwegian Met.
Service
l.l.: Blechschmidt (2008)
Spatial density distribution
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Bracegirdle, T. J. and S. L. Gray, 2006
Number of Polar Lows in various
subregions
Subregions, for which the number of detected polar lows were counted (R1-R14).
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Canonical Correlation Analysis
(CCA)
Method to study the correlation bewteen two (or
more) random vectors, e.g. X and Y
we used:
X: number of Polar Lows per PLS and subregion
Y: gridded mean MSLP fields per PLS
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Links to large scale mean pattern
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Links to large scale mean pattern
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Final results
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No long-term trend detectable
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Strong interannual variability
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No one to one similarity to other studies, but
qualitative similarity
Large scale link: more southward mean flow
=> more Polar Lows
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Ideas for future work
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Investigate atmospheric conditions during
Polar Low events
Do the same work for future szenarios
Statistics on different parameters (e.g.
LHFLs)
Use the CCA results to assess Polar Low
behavior on timescales beyond NCEP/NCAR
(MSLP fields of Trenberth)
Thank you very much
for your attention
Zahn, M., and H. von Storch (2008), A long-term climatology of
North Atlantic polar lows, Geophys. Res. Lett., 35, L22702,
doi:10.1029/2008GL035769
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Homepage: http://coast.gkss.de/staff/zahn/