trends in the upper stratosphere - lower mesosphere
DESCRIPTION
Trends in the Upper Stratosphere - Lower Mesosphere. Philippe Keckhut, Chantal Claud, Bill Randel, NOAA/CPC. Temperature trends 1979-1994. STTA-SPARC Ramaswamy et a., Rev. Geoph., 2001 MTTA/IAGA-ICMA Beig et al, Rev. Geoph., 2003. Significant trends 1-3 K/decade - PowerPoint PPT PresentationTRANSCRIPT
Trends in the Upper Stratosphere - Lower Mesosphere
Philippe Keckhut, Chantal Claud,Bill Randel, NOAA/CPC
I P S L
Temperature trends 1979-1994
Rockets 8°S-34°N
Lidar OHP 44°N
• Significant trends
• 1-3 K/decade
• Homogeneous from 8°s to 44°N
Keckhut et al., J. Geophys. Res., p447, 1999
Beig et al, Rev. Geoph., 2003
STTA-SPARC Ramaswamy et a., Rev. Geoph., 2001MTTA/IAGA-ICMA Beig et al, Rev. Geoph., 2003
Data available
Site Lat. Period Cont. instrument Reference
La Réunion
Low latitude stations
Table Mountain, US
Wallops Island, US
Ryori, Japan
OHP, France
Hohenpeissenberg, Gr
Volgograd, Russia
Heiss Island
NCEP
SSU Nash
21°S
8°S-34°N
34.4°N
37.5°N
39°N
44°N
47.8°N
48.7°N
80.6°N
Global
Zonal
1994-2005
1969-1992
1988-2004
1970-1991
1969-1996
1979-2001
1987-2005
1969-1995
1969-1995
1979-1993
1979-2004
Yes
No
Yes
No
No
Yes
Yes
No
No
Yes
Yes
Lidar NDSC
Rocket US
Lidar NDSC
Rocket US
Rocket Japan
Lidar NDSC
Lidar NDSC
Rocket US
Rocket US
SSU
SSU
Fadhuile et al.
Keckhut et al., 1999, Baldwin et al., 2001
-
Schmidlin et al., 1998
Keckhut and Kodera, 1990
Ramaswamy et al., 2001
-
Kubicki et al., submitted-
Keckhut et al.,
Keckhut et al., 2001
Austin et al., Ramaswamy et al ….
Instrumental changes on soviet rocket
• Volgograd sensor changes
Estimated from the time serie analyses
Estimated from the aerothermic calculations
Raw data
Corrected data
Kubicki et al., submitted toJASTP, 2004.
Tidal interferences
They induce large interferences in data comparisons, trends and satellite validations
6K
Keckhut et al., J. Geophys. Res., p10299, 1996
Keckhut et al., J. Geophys. Res., p447, 1999
Solar heating or other biais related with Solar time +
Tidal interferences
• Volgograd
Time of launch Averaged temperature 45-55 km
2:00
10:0015:00
Kubicki et al., submitted toJASTP , 2004.
Global trend estimates• NCEP/NOAA analyses appear
to be biased by tides
• SSU-Nash is probably not biased, but provides only zonal means.
NCEP analyses at 1 hPa (≈50 km)
Keckhut et al., J. Geophys. Res., p546, 2001
Tidal variability
• The use of tidal models ? Tidal variability ?
• Data assimilation? Tidal representation
-80 -60 -40 -20 0 20 40 60 80
10
20
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40
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80
A
l
t
i
t
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e
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k
m
)
0
.5
0
.5
0 . 5
0 . 5
0 . 5
0. 5
0
.5
0
.5
0
.5
0 . 5
0. 5
11
1
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51
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1. 5
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2
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5
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2
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5
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3
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5
3
.5
4
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.
5
4 . 5
5
5
5. 56
6
.57
(a)
-80 -60 -40 -20 0 20 40 60 80
10
20
30
40
50
60
70
80
Latitude (°)
A
l
t
i
t
u
d
e
(
k
m
)0
.
5
0
.5
0. 5
0.5
0
.5
0 . 50
.5
0
.
5
1 1
1
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1 . 5
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5
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22
2
(b)
• Reproduce realistic tides• Tidal variability is around 20%• Tidal variability can be reproduced with realistic ozone and water vapor fields and planetary waves forcing
3D Rose/Reprobus model at SA
Diurnal
Semi-diurnal Morel et al., JASTP, p251, 2004
The multi-parameter regressions (AMOUNTS)
(Hauchecorne et al., 1991; Keckhut et al., 1995)
•To evaluate temperature trends and variability (for data and model outputs) and Spurious changes, it is necessary to parametrize the variability:
T(t) = m + St + A•Trend + B•Solar + C•QBO + D•ENSO + E•AO + F.Step(ti) + Nt
•The A, B, C, D, E, F terms represent the amplitude of trends / factors of variability and bias.The residuals (AR(1)) include all the variability not considered in the parametrization.The analysis of the residual terms :
model inadequacies the degree of confidence of the analysis
• Data are filtered according to time of the day
Heiss Island 81°N
0
50
100
150
200
250
300
350
400
450
0:00 1:00 2:00 3:00 4:00 5:00 6:00 7:00 8:00 9:0010:0011:0012:0013:0014:0015:0016:0017:0018:0019:0020:0021:0022:0023:00
Hour(UT)
Launched time distribution
Heiss Island Trends
Trends are similar with Volgograd
Trends as a function of latitude
Volgograd
OHP, _ _
Wallops, ---
Riory, ….
US tropical°°°° US tropical
WallopsOHP
Volgograd
RiorySummer Winter
Kubicki et al., submitted toJASTP, 2004.US tropical: 8°S-34°NWallops Island: 37,5°NRyori, Japan: 39°NOHP, France 44°NVolgograd 49°N
Updated stratospheric temps from SSU/MSU
* Thanks to John Nash (MetO), Jim Miller, Mel Gelman and Roger Lin (NOAA CPC)
note ‘flattening’of trends nearstratopause
small long-term cooling in
middle stratosphere
MSU4
SSU15x
SSU25
SSU35x
SSU47x
Upper stratosphere: SSU vs. HALOE
SSU 47x~43-57 km
HALOEintegrated toapproximate
SSU 47x
Stratospheric Sounding Unit (SSU)* operational measurements since 1979
* ~10-15 km thick layer temperaturessynthetic channels
SSU /OHP @ 35X, 36X, 47X (35, 42, 50 km)
Other NDSC lidar data setsTemperature @ Hohenpeissemberg
(Germany)
220
225
230
235
240
245
250
255
260
265
270
1987 1989 1991 1993 1995 1997 1999 2001 2003 2005
Time (Year)
Temperature (Kelvin)35X
36X
47X
La Réunion lidar station 21°S
• Warming of around 2 K/decade
• In agreement with SSU
• Only 10 years
Bill: How do we interpret stratopause variability?
?
~43-57 km
Bill: How do we interpret stratopause variability?
?
~43-57 km
Methodologic error
• The methodologic error is strongly related to the noise level
• After 20 years methodologic errors are negligeable
• Non linear trend estimates are possible – on domain 2 and depend on
data length and residual noise and
– on domain 3 only related to residual noise
Kerzenmacher et al., QJSRT, 2005
Conclusions• Three types of data sets
– Rocket 1970-1990– Lidar 1990-2005– SSU 1979-2005
• Discontinuities– Rocket : pb with time of measurements and sensor changes– Lidar : autocalibrated, darktime measurements, larger biais around 30 and 70-80
km– SSU/NCEP: Tides
• R and L in good agreement with SSU around 50 km larger trends in tropics around 25-35 km
• Flattering after 1995 in good agreement with NDSC Lidars. Need more investigations.
• How to take into account slope changes: use ozone data as forcing.