application in climatology 2: long-term trends in persistence
DESCRIPTION
APPLICATION IN CLIMATOLOGY 2: LONG-TERM TRENDS IN PERSISTENCE. Radan HUTH , Monika CAHYNOVÁ, Jan KYSELÝ. Hess &Brezowsky groups of types dashed: lifetime (persistence) smoothed DJF. Hess &Brezowsky groups of types dashed: lifetime (persistence) smoothed JJA. - PowerPoint PPT PresentationTRANSCRIPT
APPLICATIONIN CLIMATOLOGY 2:
LONG-TERM TRENDS IN PERSISTENCE
APPLICATIONIN CLIMATOLOGY 2:
LONG-TERM TRENDS IN PERSISTENCE
Radan HUTH,
Monika CAHYNOVÁ,
Jan KYSELÝ
Radan HUTH,
Monika CAHYNOVÁ,
Jan KYSELÝ
Hess&Brezowsky groups of types
dashed: lifetime (persistence)
smoothed
DJF
Hess&Brezowsky groups of types
dashed: lifetime (persistence)
smoothed
JJA
Hess&Brezowsky: groups of types with cyclonic / anticyclonic character over
central Europe
dashed: lifetime (persistence)
smoothed
Hess&Brezowsky: all types
lifetime (persistence)
Application in climatology 3: Links between
circulation changes and climatic trends
in Europe
Application in climatology 3: Links between
circulation changes and climatic trends
in Europe
OutlineOutline
we want to assess the magnitude of climatic trends over Europe in 1961-2000 that can be linked to changing frequency of circulation types (as opposed to changing climatic properties of circulation types)
data– 29 stations from the ECA&D project, daily Tmax, Tmin, precipitation
– 8 objective catalogues from cat.1.2 (CKMEANS, GWT, Litynski, LUND, P27, PETISCO, SANDRA, TPCA), each in 3 variants with 9, 18, 27 CTs
– all COST733 domains except for D03 – lack of stations
methods– seasonal climatic trends from station data
– proportion of climatic trends linked to circulation changes
we want to assess the magnitude of climatic trends over Europe in 1961-2000 that can be linked to changing frequency of circulation types (as opposed to changing climatic properties of circulation types)
data– 29 stations from the ECA&D project, daily Tmax, Tmin, precipitation
– 8 objective catalogues from cat.1.2 (CKMEANS, GWT, Litynski, LUND, P27, PETISCO, SANDRA, TPCA), each in 3 variants with 9, 18, 27 CTs
– all COST733 domains except for D03 – lack of stations
methods– seasonal climatic trends from station data
– proportion of climatic trends linked to circulation changes
StationsStations
Trends in the frequency of CTsTrends in the frequency of CTs
0
20
40
60
80
100
%
D00 D01 D02 D03 D04 D05 D06 D07 D08 D09 D10 D11
+ MAM + JJA + SON + DJF
Percentage of days occupied by CTs with trends in the seasonal frequency significant at the 95% level in 1961-2000
Trends in the frequency of CTsTrends in the frequency of CTs
Magnitude of significant trends in frequency of CTs in GWTC10 (days per season in 1961-2000)
W SW NW C A N NE E SE SD00 -5D01 -8D02 6 -3D03 -5D04D05D06D07D08D09 -8 5 3D10D11D00D01 -4D02D03D04D05D06 2D07 -2D08D09 -7D10 -2D11
spri
ngsu
mm
er
W SW NW C A N NE E SE SD00 9D01D02D03 3 4D04D05D06D07 3D08 3D09D10D11D00 16 -8 -3D01 6D02 7D03D04 13 13 -7 -7D05D06 9 3 -8D07 20 -6 -7D08 16 -3 -10 -8D09D10 5 -4 2 -6D11 14 -7
autu
mn
win
ter
Results – seasonal climatic trendsResults – seasonal climatic trendstrend significant at the 95% level
Ratio of “hypothetical” (circulation-induced) and observed long-term seasonal trends.
The “hypothetical” trend is calculated from a daily series, constructed by assigning the long-term monthly mean of the given variable under the specific circulation type to each day.
See e.g. Huth (2001).
Method to attribute climatic trends to changes in frequency of circulation
types
Method to attribute climatic trends to changes in frequency of circulation
types
-8
-6
-4
-2
0
2
4
6
8
2 2 2 2 1 2 1 1 1 2 8 5 1 5 5 8 8 9 10 10 10 10 9 8 8 8 2 2 1 1 1
de
gre
es
Ce
lsiu
s
observed Tmax
January average Tmax under each CT
Ratio of circulation-induced (“hypothetical”) and observed trends 1961-2000 at stations where the observed trend is significant at
the 95% levelResults of 24 classifications on D00 and small domains
Averages of individual stations where observed trends are significant at the 95% level
Ratio of circulation-induced (“hypothetical”) and observed trends 1961-2000
Comparison of individual classifications
TX
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
0.4
0.5
0.6
CK
ME
AN
SC
09C
KM
EA
NS
C18
CK
ME
AN
SC
27G
WT
C10
GW
TC
18G
WT
C26
LITA
DV
ELI
TC
18LI
TT
CLU
ND
C09
LUN
DC
18LU
ND
C27
P27
C08
P27
C16
P27
C27
PE
TIS
CO
C09
PE
TIS
CO
C18
PE
TIS
CO
C27
SA
ND
RA
C09
SA
ND
RA
C18
SA
ND
RA
C27
TP
CA
C09
TP
CA
C18
TP
CA
C27
rati
o
MAM-Dxx JJA-Dxx SON-Dxx DJF-Dxx
MAM-D00 JJA-D00 SON-D00 DJF-D00TN
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
0.4
0.5
0.6
CK
ME
AN
SC
09C
KM
EA
NS
C18
CK
ME
AN
SC
27G
WT
C10
GW
TC
18G
WT
C26
LITA
DV
ELI
TC
18LI
TT
CLU
ND
C09
LUN
DC
18LU
ND
C27
P27
C08
P27
C16
P27
C27
PE
TIS
CO
C09
PE
TIS
CO
C18
PE
TIS
CO
C27
SA
ND
RA
C09
SA
ND
RA
C18
SA
ND
RA
C27
TP
CA
C09
TP
CA
C18
TP
CA
C27
rati
o
ConclusionsConclusions
Significant trends in the frequency of CTs occur mostly in winter in domains 00 and 04 through 11, and also in summer in the Mediterranean.
Climatic trends can be only partly explained by the changing frequency of CTs, the link being the strongest in winter. In the other seasons, within-type climatic trends are responsible for a major part of the observed trends.
Classifications in the small domains are usually more tightly connected with climatic trends than those in D00, except for the northernmost stations.
There are large differences between results obtained with individual classifications – therefore all studies using just a limited number of them should be taken with a grain of salt.
Application in climatology 4: Analysis of climate
model outputs
Application in climatology 4: Analysis of climate
model outputs
How to compare circulation types between two climates?How to compare circulation
types between two climates?
Isn’t it nonsense? We have just one climate...
Comparisons between– real climate and simulated present climate
model validation– simulated present and perturbed (typically
future) climate climate change response– real climate in two distinct periods (e.g.,
current vs. little ice age)
Isn’t it nonsense? We have just one climate...
Comparisons between– real climate and simulated present climate
model validation– simulated present and perturbed (typically
future) climate climate change response– real climate in two distinct periods (e.g.,
current vs. little ice age)
OBS CTR
“INSTRINSIC” TYPES
OBSERVED TYPES
projected onto CONTROL
OBS OBS CTR
BUT: isn’t it an artefact of the
projection?
projection in the opposite “direction”:
CONTROL TYPES
projected onto OBSERVED
CTRCTR OBS
How to compare circulation types between two climates?How to compare circulation
types between two climates?
(at least) four possible approaches
1. Find circulation types in each climate separately+ you may get truly dominant types in both
datasets (if you are lucky...)– no clear structure in data types are to a
certain extent random comparison may be misleading
(at least) four possible approaches
1. Find circulation types in each climate separately+ you may get truly dominant types in both
datasets (if you are lucky...)– no clear structure in data types are to a
certain extent random comparison may be misleading
How to compare circulation types between two climates?How to compare circulation
types between two climates?
2. Use types defined a priori, independently of the datasets
objectivized catalogues
types defined on a short(er) period
+ easy and fair comparison– may not reflect real structure in either
dataset
2. Use types defined a priori, independently of the datasets
objectivized catalogues
types defined on a short(er) period
+ easy and fair comparison– may not reflect real structure in either
dataset
How to compare circulation types between two climates?How to compare circulation
types between two climates?
3. Concatenation of two datasets, “joint” classification performed simultaneously for the two climates
(typically used with SOMs)+ good compromise: types are likely to be
close to ‘real’ types in both datasets
3. Concatenation of two datasets, “joint” classification performed simultaneously for the two climates
(typically used with SOMs)+ good compromise: types are likely to be
close to ‘real’ types in both datasets
How to compare circulation types between two climates?How to compare circulation
types between two climates?
4. Projection from one climate to the other and vice versa
+ wrong conclusions are eliminated
4. Projection from one climate to the other and vice versa
+ wrong conclusions are eliminated
Where to find it? ReferencesWhere to find it? References
Huth R. et al., 2008: Classifications of atmospheric circulation patterns: Recent advances and applications. Ann. N. Y. Acad. Sci., 1146, 105-152.
ad 1) (heat waves)Kyselý J., Huth R., 2008: Adv. Geosci., 14, 243-249.
ad 2) (trends in persistence)Kyselý J., Huth R., 2006: Theor. Appl. Climatol., 85, 19-36. Cahynová M., Huth R., 2009: Tellus A, 61, 407-416.
ad 3) (climate change vs. circulation)Huth R., 2001: Int. J. Climatol., 21, 135-153. Cahynová M., Huth R., 2009: Theor. Appl. Climatol., 96, 57-68.
ad 4) (analysis of GCM outputs)Huth R., 1997: J. Climate, 10, 1545-1561. Huth R., 2000: Theor. Appl. Climatol., 67, 1-18.
Huth R. et al., 2008: Classifications of atmospheric circulation patterns: Recent advances and applications. Ann. N. Y. Acad. Sci., 1146, 105-152.
ad 1) (heat waves)Kyselý J., Huth R., 2008: Adv. Geosci., 14, 243-249.
ad 2) (trends in persistence)Kyselý J., Huth R., 2006: Theor. Appl. Climatol., 85, 19-36. Cahynová M., Huth R., 2009: Tellus A, 61, 407-416.
ad 3) (climate change vs. circulation)Huth R., 2001: Int. J. Climatol., 21, 135-153. Cahynová M., Huth R., 2009: Theor. Appl. Climatol., 96, 57-68.
ad 4) (analysis of GCM outputs)Huth R., 1997: J. Climate, 10, 1545-1561. Huth R., 2000: Theor. Appl. Climatol., 67, 1-18.