A long-term climatology of North Atlantic Polar Lows - Institute of ...

Development of a Long-term
Climatology of North Atlantic
Polar Lows
Matthias Zahn 1,2 , Hans v. Storch 1,2 , Stephan Bakan 3
(1) University of Hamburg, Meteorological Institute, Germany
(2) GKSS Research Centre, Institute for Coastal Research, Germany
(3) Max Planck Institut für Meteorologie, Hamburg, Germany

Polar Lows
● Particular kind of low pressure
system
● mesoscale sized (typically
several hundred km in diameter)
● Intense/ strong winds (>15m/s),
● severe weather
● occurring poleward the Polar
Front in both hemispheres
● Typically induced by
disturbances in the air flow
● Typically driven by convective
processes
Spitzbergen
~300 km
© Dundee Satellite Receiving Station
2
Scandinavia

Further examples of Polar Lows
20.12.2002, 2:00
04.03.2008, 11:35 11.03.08, 15:25
Further reading:
Rasmussen and Turner, 2003:
Polar Lows: Mesoscale Weather Systems
in the Polar Regions
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)
• Polar Lows need to be automatically
detectable in such data!
LAM (~50 km)
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Setup of my PhD work
● Part 1: Can LAMs reproduce polar lows ?
● Part 2: How to detect polar lows
automatically in LAM output data
● Part 3: Compilation of Climatology
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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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Spitzbergen
Oct. 1993 case
Scandinavia
15.Oct.93, 05:24
© Dundee Satellite Receiving Station
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Mean Sea level pressure (hPa) and
10m wind speed
15. Oct. 1993,
6:00
NCEP
Dundee
15.10.93, 05:24
DWD
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Mean Sea level pressure (hPa) and
10m wind speed
15. Oct. 1993,
6:00
NCEP
DWD
Dundee
15.10.93, 05:24
CLM01nn CLM02nn CLM03nn CLM04nn
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Mean Sea level pressure (hPa) and
10m wind speed
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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0600 UTC
15 Oct 1993
Band-pass filtered mslp (hPa)
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:
Response
function: wave
lengths
between appr.
200 - 600 km
are retained
NERC Dundee Satellite Receiving Station
CLM22-sn, band pass filtered
0000 UTC 9 Dec 1993
weatherchart, DWD, 0000 UTC 9 Dec 1993
CLM22-sn, full field
0000 UTC 9 Dec 1993
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Jan 1998
case:
Response
function: wave
lengths
between appr.
200 - 600 km
are retained
NERC Dundee Satellite Receiving Station
CLM01-sn, band pass filtered
0000 UTC 18 Jan 1998
Weather chart, 0100 UTC 18 Jan 1998
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CLM01-sn, full mslp field
0000 UTC 18 Jan 1998

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
1 st : detection of all locations with a minimum in
the filtered mslp field < -1hPa
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Setup of the detection algorithm
1 st : detection of all locations with a minimum in the
filtered mslp field < -1hPa
2 nd : combine detected positions to individual
tracks, distance to next (3h) pos < ~200 km
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Setup of the detection algorithm
1 st : detection of all locations with a minimum in the
filtered mslp field < -1hPa
2 nd : combine detected positions to individual tracks,
distance to next (3h) pos < ~200 km
3 rd : 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 − T 500hPa ≥ 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
Tracks reproduced
and detected even
after a simulation time
of several decades
Oct 1993 Dec 1993
Jan 1998
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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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To varied
ws criteria
Algorithm's sensitivity
C > 0.9
To varied
dtz criteria
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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=0,72
C=0,58
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Monthly comparison of our
data (in black) with
observed data (in red)
u.r.: Norwegian Met.
Service
l.l.: Blechschmidt (2008)

Spatial density distribution
Bracegirdle, T. J. and S. L. Gray, 2006
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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
● No long-term trend detectable
● Strong interannual variability
● 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
● 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)
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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
Homepage: http://coast.gkss.de/staff/zahn/
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