Past and projected future changes
of North Atlantic
polar low frequency
Matthias Zahn
Institute of Coastal Research
Helmholtz-Zentrum Geesthacht
Matthias.Zahn@hzg.de
July 25, 2014
Visiting AORI, 26 Jul 2014, University of Tokyo, Japan
Polar lows, characteristics
◮
◮
◮
◮
◮
◮
diameter < 1000km
strong winds > 13.9 ms
heavy precipitation
poleward Polar Fronts in
winter
spiral cloud and cloud free
core
comma shape cloud pattern
c
Dundee
Satellite Receiving Station
Development
initial disturbance
◮ lots form along sea ice edge
◮ instable conditions
◮ driven by convective
processes
◮ develop in cold air outbreaks
◮ rapid development
◮ T can drop sharply
◮ short lasting (12 to 36 hrs)
Example: 7 Jan 2009
SST − T500hPa >52K
◮
http://polarlow.met.no/STARS-DAT/
Long-term: climatology
Research questions:
◮
◮
◮
how does frequency change?
do areas of occurrence shift?
(how do intensities change?)
Requirements:
◮
◮
◮
long in time
high in spatial detail
homogeneous
Problem: DO NOT EXIST
Solution: dynamical downscaling
Model grid
◮
◮
◮
global data (re-analysis or GCM) long in time
and homogeneous
downscaled by means of Regional Climate
Model (RCM) COSMO-CLM-2.4.6.
run with and without spectral nudging
Example of a reproduced PL case
15 Oct 1993, 6:00, The Swan
Example of a reproduced PL case
15 Oct 1993, 6:00, The Swan
Example of a reproduced PL case
15 Oct 1993, 6:00, The Swan
Next Problem: detection
Decades of data
◮
◮
◮
eye detection
infeasible for
long-term data
humans decide
subjectively
automated
procedure needed
Solution:
◮
◮
counted cases of 2 yrs, Blechschmidt (2008), GRL
spatial band-pass filter
separates information at different scales
Example: spatial filter
(a) full field
(b) low pass
(c) band pass
(d) high pass
Example of band-pass filtered MSLP
200-600km retained, PLs emerge as distinct minima
Dec 1993 case
2
1
&
A
6
5 ;;
A
+*#$*+%. #0 //
5 ;;
6 &
Jan 1998 case
&
&
A
#3*#*+3. ( //
5 )-
5 )-
6 &
Detection and tracking scheme
1. record all locations at which filt. MSLP minimum ≤ −1hPa
2. combine detected positions to individual tracks, distance to
next (3h) pos ≤ 200km
3. checking further constraints along tracks:
◮
◮
◮
◮
◮
filt. minimum ( ≤ −2hPa once along track)
wind speed (≥ 13.9 ms once along track)
air-sea temperature difference (SST − T500hPa ≥ 43K )
no northward direction of track
limits to allowable adjacent grid boxes
OR: minimum in band-pass filtered MSLP ≤
6hPa once
Tracks of 3 (amoung >3300) polar lows
Reproduced and detected after more than 40 years simulation time
Spatial density of PLs (1949-2005)
80˚
280˚
70
˚
˚
290
60
˚
0˚
350˚
340
˚
0˚
33
0˚
˚
32
40
31
0˚
˚
30˚
20˚
10˚
Number of PLs per area unit
50
0˚
30
5.5
5.0
4.5
4.0
3.5
3.0
2.5
2.0
1.5
1.0
0.5
0.0
Annual numbers of PLs (1949-2005)
Number of PLs per Polar Low Season (PLS), one PLS from July until June next year
Comparison with observed cases
bias, but qualitative
similarity to observation
data
black: our data (Zahn and v.Storch, 2008)
adjusted to observation data
red: MetNo (pers. comm.)
Wilhelmsen (1985)
Blechschmidt (2008)
Link to large scale flow 1949 - 2005
−
−−−
→
Canonical Correlation Analysis, CCA 1, statistically linking vectors of mean MSLP and
−−−−→
No PL per PLS
Future projections
90˚
280˚
80˚
70
˚
0˚
29
60
˚
0˚
30
˚
50
0˚
350˚
˚
340
0˚
33
0˚
˚
40
30˚
20˚
Model grid
10˚
32
◮
0˚
◮
driven by ECHAM5/MPI-OM
C20: control with GHG 1960-1990
B1,A1B,A2: GHG for 2070-2100 (AR4)
31
◮
Projected cumulative frequency of PLs in
IPCC-scenarios and annual cycle
significant decrease in the
number of PLs per winter
14
Blechschmidt
Wilhelmsen
REA
C20
B1
A1B
A2
average number of polar lows per month
12
10
8
6
4
2
0
Jan
Mar
May
Jul
month
Sep
Number of PLs per month
Nov
Spatial density distribution, differences
northward shift of genesis region
Polar Lows and projected vertical stability
◮
◮
atmosphere warms stronger
than ocean surface
proxy for frequency of
favourable PL conditions
decreases
Evolution of SST and T500hPa
Polar Lows and projected vertical stability
Zahn and v.Storch (2010), Nature
◮
◮
Area and time-averaged icefree SST − T500hPa
over maritime northern North Atlantic
proxy for frequency of
favourable PL conditions
decreases
large inter model bias, but same
direction of change
Evolution of SST and T500hPa
Linkage of vertical stability changes to
ocean circulation
Spatial response vertical
stability
Regression with ocean
circulation
Multi-model mean response (A1B - C20) in
vertical stability S = T500hPa − SST (= −vdT )
over ice free ocean (Black iso-lines: SST
response)
S regressed on the AMOC response across
CMIP3 model (A1B - C20) space over ice free
ocean (Black iso-lines: regression SST-AMOC)
Frequency changes of Polar Lows
No change in recent past, but high
interannual variability
Significant decrease of annual number
in response to global warming
Decrease linked to more stable mean
conditions
Currently: global downscaling
low-res global-domain
❄
CliSAP Research Topic B-4: Regional
Storms and their Marine Impacts
◮ apply a global model - ECHAM6
◮ dynamical downscaling to global
domain (M. Schubert-Frisius)
◮ spectral nudging
◮
◮
high-res global-domain
which nudging configuration?
how well are storms simulated?
Several experiments differing by ...
VARIABLES USED FOR NUDGING:
◮
dv: divergence and vorticity
NCEP WAVES USED FOR NUDGING:
◮
◮
◮
N1: zonal and meridional T42
N1 T30: zonal and meridional T30
n1: zonal and meridional T21
STRENGTH OF NUDGING:
◮
alpha: strength of maximum nudging
... and by
VERTICAL PROFILE (strength of alpha):
◮
◮
◮
◮
av: as in RCM, alpha slowly increases above 750hPa
dvw: plateau, strong increase above 750hPa until
500hPa, constant until abrupt decrease above 3hPa
ev: strong increase above 750hPa until 500hPa, then
slowly decreases
echam6: alpha constant from surface to TOA
Which configuration is most suitable
for representing tropical storms?
Comparison to observed typhoons in 2004:
Identify storm tracks each of the EH6 experiments
◮
◮
◮
apply spectral filter to vorticity, T13-T65 (1500-200km)
detect locations of th1 >= 0.00006 1s and merge to
tracks
dismiss tracks with:
◮ wind speed below 13.9 m/s
◮ shorter then 4 time steps
◮ land along more than 50% of track
Compared with observation data of storms
◮
BT: typhoons, RSMC Tokyo - Typhoon Center
http://www.jma.go.jp/jma/jma-eng/jma-center/rsmc-hp-pub-eg/besttrack.html
◮
HUR: hurricanes, HURDAT, NOAA
http://www.nhc.noaa.gov/data/hurdat/hurdat2-atlantic-1851-2012-060513.txt
TOKAGE, October 2004
110˚
60˚
120˚
130˚
140˚
150˚
160˚
170˚
180˚
190˚
200˚
BEST Track Storm
NCEP values
50˚
EH6_no_nudging L: 47
EH6_N1_echam6 L: 34
EH6_N1_av_05dv L: 40
EH6_N1_dvw_005dv L: 36
EH6_N1_T30_dvw_01dv L: 37
EH6_N1_T30_dvw_05dv L: 34
EH6_N1_T30_av_05dv L: 40
EH6_n1_dvw_005dv L: 32
EH6_n1_dvw_01dv L: 40
EH6_n1_av_05dv L: 54
distance to Best Track
400
200
[km]
40˚
500
30˚
Wind speed
30
20
20˚
[m/s]
40
10
0
1025
MSLP
1000
975
950
0˚
simulations initialised Jan 2014
12
13
14
15
16
17 18
time
19
20
21
22
925
[hPa]
10˚
NOCK-TEN, October 2004
110˚
60˚
120˚
130˚
140˚
150˚
160˚
170˚
180˚
190˚
200˚
BEST Track Storm
NCEP values
50˚
EH6_no_nudging L: 0
EH6_N1_echam6 L: 41
EH6_N1_av_05dv L: 38
EH6_N1_dvw_005dv L: 28
EH6_N1_T30_dvw_01dv L: 28
EH6_N1_T30_dvw_05dv L: 22
EH6_N1_T30_av_05dv L: 19
EH6_n1_dvw_005dv L: 21
EH6_n1_dvw_01dv L: 48
EH6_n1_av_05dv L: 52
distance to Best Track
400
200
[km]
40˚
500
Wind speed
40
30
20
20˚
[m/s]
30˚
10
0
1025
MSLP
1000
975
950
0˚
simulations initialised Jan 2014
14 15 16 17 18 19 20 21 22 23 24 25 26
time
925
[hPa]
10˚
MSLP fields 17 Oct 00:00 (TOKAGE)
PS_2004-10-17,00, NCEP
MSLP_2004-10-17,00,EH6_N1_T30_dvw_01dv
40˚
NCEP
30˚
20˚
10˚
110˚
120˚
990
130˚
140˚
150˚
160˚
1010
no nudging
20˚
10˚
110˚
120˚
990
130˚
140˚
30˚
30˚
30˚
30˚
20˚
20˚
20˚
20˚
20˚
10˚
10˚
10˚
10˚
170˚
150˚
160˚
1000
1010
1020
20˚
10˚
120˚
130˚
1000
1010
140˚
1020
150˚
hPa
1030
160˚
120˚
130˚
140˚
150˚
160˚
170˚
40˚
1010
1020
30˚
T30 dvw 05dv
30˚
30˚
30˚
20˚
20˚
20˚
20˚
10˚
10˚
10˚
10˚
120˚
130˚
140˚
150˚
160˚
170˚
30˚
30˚
20˚
20˚
10˚
10˚
170˚
1000
1010
1020
T30 av 05dv
110˚
120˚
990
130˚
1000
1010
140˚
1020
150˚
hPa
1030
160˚
140˚
150˚
160˚
170˚
hPa
1030
30˚
20˚
10˚
120˚
130˚
140˚
150˚
160˚
30˚
20˚
20˚
10˚
10˚
170˚
hPa
1030
MSLP_2004-10-17,00,EH6_n1_av_05dv
1000
1010
1020
40˚
30˚
170˚
1020
40˚
990
40˚
1010
n1 dvw 01dv
110˚
MSLP_2004-10-17,00,EH6_N1_T30_av_05dv
990
1000
40˚
hPa
1030
40˚
130˚
MSLP_2004-10-17,00,EH6_n1_dvw_01dv
40˚
110˚
120˚
990
40˚
170˚
10˚
110˚
hPa
1030
MSLP_2004-10-17,00,EH6_N1_T30_dvw_05dv
1000
40˚
n1 dvw 005dv
40˚
MSLP_2004-10-17,00,EH6_N1_echam6
N1 echam6
990
110˚
990
40˚
110˚
40˚
hPa
1030
40˚
30˚
30˚
T30 dvw 01dv
hPa
1030
1020
40˚
30˚
40˚
MSLP_2004-10-17,00,EH6_no_nudging
1000
MSLP_2004-10-17,00,EH6_n1_dvw_005dv
40˚
40˚
n1 av 05dv
30˚
20˚
10˚
110˚
120˚
990
130˚
1000
typhoons are deepended, black: BT, green:
1010
140˚
1020
150˚
hPa
1030
160˚
170˚
MSLP fields 24 Oct 00:00 (NOCK-TEN)
PS_2004-10-24,00, NCEP
MSLP_2004-10-24,00,EH6_N1_T30_dvw_01dv
40˚
NCEP
30˚
20˚
10˚
110˚
120˚
990
130˚
140˚
150˚
160˚
1010
no nudging
20˚
10˚
110˚
120˚
990
130˚
140˚
30˚
30˚
30˚
30˚
20˚
20˚
20˚
20˚
20˚
10˚
10˚
10˚
10˚
170˚
150˚
160˚
1000
1010
1020
20˚
10˚
120˚
130˚
1000
1010
140˚
1020
150˚
hPa
1030
160˚
120˚
130˚
140˚
150˚
160˚
170˚
40˚
1010
1020
30˚
T30 dvw 05dv
30˚
30˚
30˚
20˚
20˚
20˚
20˚
10˚
10˚
10˚
10˚
120˚
130˚
140˚
150˚
160˚
170˚
30˚
30˚
20˚
20˚
10˚
10˚
170˚
1000
1010
1020
T30 av 05dv
110˚
120˚
990
130˚
1000
1010
140˚
1020
150˚
hPa
1030
160˚
140˚
150˚
160˚
170˚
hPa
1030
30˚
20˚
10˚
120˚
130˚
140˚
150˚
160˚
30˚
20˚
20˚
10˚
10˚
170˚
hPa
1030
MSLP_2004-10-24,00,EH6_n1_av_05dv
1000
1010
1020
40˚
30˚
170˚
1020
40˚
990
40˚
1010
n1 dvw 01dv
110˚
MSLP_2004-10-24,00,EH6_N1_T30_av_05dv
990
1000
40˚
hPa
1030
40˚
130˚
MSLP_2004-10-24,00,EH6_n1_dvw_01dv
40˚
110˚
120˚
990
40˚
170˚
10˚
110˚
hPa
1030
MSLP_2004-10-24,00,EH6_N1_T30_dvw_05dv
1000
40˚
n1 dvw 005dv
40˚
MSLP_2004-10-24,00,EH6_N1_echam6
N1 echam6
990
110˚
990
40˚
110˚
40˚
hPa
1030
40˚
30˚
30˚
T30 dvw 01dv
hPa
1030
1020
40˚
30˚
40˚
MSLP_2004-10-24,00,EH6_no_nudging
1000
MSLP_2004-10-24,00,EH6_n1_dvw_005dv
40˚
40˚
n1 av 05dv
30˚
20˚
10˚
110˚
120˚
990
130˚
1000
typhoons are deepended, black: BT, green:
1010
140˚
1020
150˚
hPa
1030
160˚
170˚
MSLP vs tracks for 2004 typhoons
50
CPs:11
CPs:16
CPs:15
CPs:16
CPs:13
CPs:15
CPs:16
CPs:13
CPs:15
CPs:15
no_nudging
N1_echam6
N1_av_05dv
N1_dvw_005dv
N1_T30_dvw_01dv
N1_T30_dvw_05dv
N1_T30_av_05dv
n1_dvw_005dv
n1_dvw_01dv
n1_av_05dv
mean difference to MSLP of BT [hPa]
40
TYPHOONS (ws_gt_30ms):
AERE
CHABA
CONSON
DIANMU
MA-ON
MEARI
MEGI
MINDULLE
NAMTHEUN
NIDA
NOCK-TEN
RANANIM
SONGDA
SUDAL
TINGTING
TOKAGE
30
20
10
0
0
500
1000
1500
mean distance to BT [km]
difference of tracks vs. difference of MSLP
MSLP vs tracks for 2004 hurricanes
50
CPs:7
CPs:6
CPs:7
CPs:7
CPs:5
CPs:6
CPs:6
CPs:4
CPs:7
CPs:4
no_nudging
N1_echam6
N1_av_05dv
N1_dvw_005dv
N1_T30_dvw_01dv
N1_T30_dvw_05dv
N1_T30_av_05dv
n1_dvw_005dv
n1_dvw_01dv
n1_av_05dv
mean difference to MSLP of BT [hPa]
40
TYPHOONS (ws_gt_30ms):
ALEX
CHARLEY
DANIELLE
FRANCES
GASTON
IVAN
JEANNE
KARL
LISA
30
20
10
0
0
500
1000
1500
mean distance to BT [km]
difference of tracks vs. difference of MSLP
Currently underway
◮
◮
◮
longterm (1948-today) simulations
applying T30 dvw 01dv nudging
will take at least until Jan 2015 to finish
detection of storms will need to be adapted
High potential:
◮
◮
◮
homogeneously investigate storms over
different ocean basins
homogeneously investigate their interactions
and linkages to large scale driving
to be used driving further models down the
chain (RCM, ocean models, transport models)
Thank you very much for your attention
ご清聴ありがとうございました
http://coast.hzg.de/staff/zahn/
Sensitivity of alorithm, c >0.8
6
K
Perhaps: Link to large scale flow 1949 2005
CCA 2 between mean ~MSLP and No~ PL per PLS
✒