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

30

40

50

60

70

80

A

l

t

i

t

u

d

e

(

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

1

1

1

1

1

1

1

1

1

1

1

.5

1

.

51

.

5

1 . 5

1 . 5

1. 5

1 . 5

1

.

5

1.5

2

2

2

2

2

2

2

.

5

2

.

5

2

.

5

2

.

5

3

3

3

3

.

5

3

.5

4

4

4

.

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

1

1

1

1

1

1

.5

1 . 5

1

.

5

1 . 5

1. 5

1 . 5

1. 5

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.