Understand the counter-intuitive seasonal variations of upper atmospheric temperature and wind, and the

associated long-term temperature changes over Fort Collins, CO (41N, 105W)

You undoubtedly know that the weather system (or wind) in the troposphere where we live always moves from West to East, and that winter is always colder than summer. Then, you may be surprised if I tell you that in the mesopause region (80 – 100 km), the summer is colder than winter, and the wind blows towards East in summer and switches from Eastward to Westward at ~ 95 km in winter. Two questions could immediately arise: Are these counter-intuitive upper atmospheric behaviors true (experimentally verifiable)? Can we understand them?In this talk, I hope to achieve four objectives: (1) to explain these bizarre behaviors from simple (freshman) physics, (2) to verify these behaviors by the unique lidar observations (using laser induced fluorescence from naturally occurring atmospheric Na atoms) over Fort Collins, CO, (3) to compare the observed results with the leading atmospheric models, thereby demonstrating the difficulties the modelers face, and (4) based on 17 years regular observations of nocturnal mesopause region temperature, we deduce and attempt to understand the long-term temperature changes (global warming in the troposphere and global cooling in the upper atmosphere).

Outline and Conclusions• Introduction: Why necessary? How to measure?• Two unique midlatitude lidar data sets

- Four years full-diurnal-cycle TUV observations- Seventeen years nocturnal temperatures

• Observed seasonal variations in mesopause region (80 – 110 km) TUV consistent with our qualitative understanding; model-observation difference smaller than differences between models.

• Observed cooling trend about 7 times too big if episodic warming in the 1990’s is ignored; it agrees with model prediction if included.

• Conclusions

Radar Gap:25 – 60 km

Observing atmospheric layers with active probes

XUV

UV

IR/VIS

3 P

3 S

23/2

21/2

(16)

F = 1 (3)

F = 2 (5)0

0.1

0.2

0.3

0.4

0.5

0.6

-4 -3 -2 -1 0 1 2 3 4

FREQUENCY SHIFT (GHz)

450 K

175 K 190 K

210 K

NO

RM

ALI

ZED

FLU

OR

ESC

ENC

E

•R2-f = Ic / Ia

•RT = (I+ + I- ) / 2Ia; RW = (I+ - I- ) / Ia

Laser induced fluorescence to measure temperature & windT (2-freq) or T-W (3-freq) measurement

1nm = 35 cm-1; 1 cm-1 = 30 GHz1MHz = 0.6m/s; 2MHz ~ 0.3K

a

+–c

-0.4 -0.2 0.0 0.2 0.4 0.6

0.2

0.3

0.4

0.5

0.6

0.7Con_5(97.0)WTS

Wind Ratio[(I+-I

-)/I

a]

Tem

pe

ratu

re R

ati

o [

(I++

I - )/

2I a]

280K

200K

120K-1

00

m/s

0m

/s

10

0m

/s

T-W Calibration Curves

0.0

0.5

1.0

1.5

2.0

2.5

-1.5 -1 -0.5 0 0.5 1 1.5

Flu

ore

scen

ce (

Arb

. U

nit

)

Frequency (GHz)

a

b

-: - 1,281.4 MHza: - 651.4 MHz+: - 21.4 MHzc: +187.8 MHz

+-

NaD

2 Doppler-Free Spectrum

c

0.0

0.1

0.2

0.3

0.4

0.5

100 150 200 250 300

CalibCurves.Con.5(97.0)A

Tem

pera

ture

Rati

o

Temperature(K)

R2-f = Ic / Ia

RT = (I+ + I- ) / 2Ia; RW = (I+ - I- ) / Ia

R2-

f2-f and 3-f methods

Eight-year Fort Collins climatology of 3.7 km and 1 month smoothed nocturnal temperature (left) and Na density (right) – She et al. GRL 2000

Climatology – Two-level Mesopause

Month

T (K)A

ltit

ud

e (k

m)

#m-3

Global Zonal Wind Patterns at Solstice – CIRA ‘86

Can we understand this zonal wind structure simply? Yes and may be!

MLTMesopause Region

At rest Rotate c.c.wCoriolis “force” make flow rightturn on NH, and left turn on SH

How do we understand wind and its structure

Why wind blow along isobars?

(warm)

(cold)

┴ to press.

Motion viewed from turntable in t, as if acc. by due to a pseudo(Coriolis) force ┴ v to the right: right (left) turn in NH (SH)

v 2

North pole out ; South pole in

Solar heating and general circulation

Forcing: Coriolis “force” and absorption by ground and O3

layer produces zonal winds in troposphere, stratosphere and lower mesosphere:

Midlatitude Westly in troposphere: Equator warmer than Poles (summer and winter) → wind towards East

Midlatitude Westly in winter stratosphere: Equator warmer than Poles; Ozone → wind towards East

Midlatitude Eastly in summer stratosphere: Equator cooler than Poles → wind towards West

Big question: Why zonal wind change direction in the MLT, while there is no major heating source in that region?

Adapted from Fig. 3 of Lindzen, JGR (1981).

Gravity wave (GW or buoyancy wave) propagation: filtering and breaking

Body force:Summer, East

Body force:Winter, West

If moving in the same direction, and 0<cW < u, propagation forbidden

21v '

2

v '?as

Waves break, deposit momentum, induce meridional flow

• GW propagates upward growing in amplitude, breaks and deposit momentum that applies body force and changes the direction of flow.

• The body force (BF) is balanced by the Coriolis force (CF), inducing circulation (meridional drift from SP to WP, and and vertical) cooling summer and warming winter MLT.

East in, West out; Red BF, Green CF

NASA satellite image. Amsterdam Island in the Indian Ocean. December 19, 2005.

v:sin

vw

s

ShokeCone

No wave can propagate (in medium at rest) outside the shock cone. Wave cannot propagate to the right because 0 < Vw < Vs or 0<cw<us.

In the frame of wind, u > 0, the situation may be depicted as:

What if the medium moves with a speed u = us? The source will look stationary on, earth frame, like in this real example

us=-uw wc c u

Moving with u

u>0cw

Earth frame

0 w sc u

Gravity wave observed with Yucca Ridge all sky imager 5/11/2004. Yue et al.

Gravity Waves also called buoyancy waves, are waves in the atmosphere propagating against a stratified background state. The restoring force for gravity wave oscillations is the buoyancy force resulting from the displacements of these air parcels from equilibrium.

NEXRAD echo top height chart, at 0305 UT

Gravity wave generation by discontinuities:Topography (above example)Wind shearConvection

0400UT

Mean-state climatology of mesopause region temperature, zonal and meridional winds

• General seasonal variations of mesopause region temperature and horizontal wind (though counter-intuitive as discussed) has been anticipated, yet different GCMs disagree in predictions.

• True mean-state observations with the removal of tidal perturbations are rare or not in existence. To do so require full-diurnal-cycle observations.

• With 4-years of 24-hour continuous (full-diurnal-cycle) observations, Yuan et al. (JGR, 2008) have presented results and compared to three leading GCMs (WCCAM3, HAMMONIA and TIME-GCM).

Full-diurnal-cycle data set used in this study (10 sets each month on the average)

The observed results are in qualitative agreement with our current understanding of the mesopause region thermal and dynamical structure, with two-level mesopause and sharp winter-summer transition, zonal wind switching directions in both summer and winter, as well as “residual” meridional flow from summer pole to winter pole.

The tuning of gravity wave spectra giving rise to different wind filtering could lead to a difference between the different simulations in WACCM3 as large as that between different models. The difference between observation and one model is smaller than those between different models suggests the value of this type of observation.

Temperature Trend: Observed cooling trends from different locations in the mesopause region ranging from 0 to ~10 K/decade. After two decades, the observed trend remains inconclusive.

160

180

200

220

240

260

0 3 6 9 12 15 18

Te

mp

era

ture

(K

)

Time (Year starting Jan 01, 1990)

Nightly Mean Temperature at 85.5km (90-07)

(a)

-40

-20

0

20

40

0 3 6 9 12 15 18

Residual Temperature (90-07_Res_85.5km)

Res

idu

al T

emp

era

ture

(K

)

Time (Year starting Jan 01, 1990)

(c)

-40

-20

0

20

40

0 3 6 9 12 15 18

Residual Temperature (90-07_Res_98.5km)

Res

idu

al T

emp

era

ture

(K

)

Time (Year starting Jan 01, 1990)

(d)

Long-term variation of nocturnal temperaturesTime series at the two-level mesopause (86 km; 99 km)

160

180

200

220

240

0 3 6 9 12 15 18

Te

mp

era

ture

(K

)

Time (Year starting Jan 01, 1990)

Nightly Mean Temperature at 98.5km (90-07)

Mt. PinatuboEruption Solar Min

Solar Max

(b)

1 1

2 2 (

T( , ) ( ) ( ) cos(2 ) ( )sin(2 )

( ) cos(4 ) ( )sin(4 ) ) ( ,

( )

( ) ( ))

z t z A z t B z t

A z Pz t B

z t

z Q tz z tt

Pinatubo Response: P(t) = 2/{exp(t0-t)/t1 + exp(t - t0)/t2} with the response amplitude (z). [She et. al., 1998]

Solar response: (z), in K/SFU [0.05 K/SFU ~ 8 K/SC (11 Y)] with Q(t) being the 81-day smoothed F10.7 solar flux

Linear temperature trend, (z), in K/years

What variations contain in our data?Annual and semiannual variations, z-dependentLong-term variations: Trend, SC, and episodic response?

Three different analyses with the same data set (1990-2007):7 P, 11P, and 7PR: Pinatubo response ignored, included and removed.

-0.02 0 0.02 0.04 0.06 0.0885

90

95

100

105

F-7P (90-01_F81)F-7P (90-07_F81)F-11P (90-07_F81)F-7P (97-07-F81)F-7P (98-07-F81)

Solar Response (K/SFU)

Alt

itu

de

(km

) 0.05 K/SFU ~ 8 K/SC

(a)

-9.0 -6.0 -3.0 0.0 3.0 6.085

90

95

100

105

F-7P (90-01_F81)F-7P(90-07_F81)F-11P(90-07_F81)F-7P (97-07-F81)F-7P (98-07-F81)

Change (K/decade)

Alt

itu

de

(km

)

(b)

The solar cycle response in K/SFU (a), and temperature trends in K/decade (b), between 85 and 105km for data sets in different periods: 1990–2001 (open black circles), 1990–2007 (red open/solid squares), 1997–2007 (open blue diamonds), and 1998–2007 (solid blue diamonds).

-0.02 0 0.02 0.04 0.06 0.0885

90

95

100

105

Lidar(90-07)F-7PR

Lidar(90-07)F-7P

Solar Response (K/SFU)

Alt

itu

de

(km

)

0.05 K/SFU ~ 8 K/SC

(a ) Solar Cycle Effect

-8 -6 -4 -2 0 2 485

90

95

100

105

Lidar_F-7PRLidar_F-7P

Change (K/decade)

Alt

itu

de

(km

)

(b) Temperature Trend

Same nocturnal mean temperature data (1990 – 1997) used. Comparison between the analyses with “Pinatubo effect” removed (Red) and ignored (Black).

-3 -2 -1 0 1 2 385

90

95

100

105

Lidar_F-11P

Lidar_F-7PRSMLTM

HAMMONIA

Change (K/decade)

Alt

itu

de

(km

)

Temperature Trend Comparisons

The temperature trend using lidar data (1990 – 2007) and ignore the volcanic response yields cooling trend of as much as 6.8 K/decade.

With a Pinatubo term included, the observed negative linear trend between 85 and 100 km peaks at 91 km with a value of ~1.5 K/decade, and it turns positive at ~ 102 km.

We observed an insignificant cooling trend of 0.28 K/decade at 87 km.

This trend result compares favorably with the SMLTM and HAMMONIA model predictions.

Lidar Trend compares with model predictions

F-11P and F-7PR in agreement with different error bars

Response to Mt. Pinatubo Eruption: mesopause vs. global-mean surface temperatures

0

5

10

15

20

0 5 10 15

87 km92 km97 km102 km_TWJK*(- 50)

Te

mp

era

ture

Ch

an

ge

(K)

Year (from Jan 01,1990)

Beginningof 1998MPE

Response to MPE - Mesopause and GS Temperatures

A recent analysis of long-term climate variability of global-mean surface temperatures by Thompson et al. (J. Climatr, 2009), revealed an episodic cooling after Mt. Pinatubo eruption also with ~1.5 year delayed peak response, which is deduced to have a recovery time of ~7 years (blure dots, *-50)

Can we believethe Pinatubo response thatdelayed 1.5 Y and lingered7 years?

Conclusion• The CSU Na lidar has begun regular mesopause region

observations of nocturnal temperature and Na density since 1990; of full-diurnal cycle DNa, T, U, V since 2002; and, in addition, of zonal momentum flux since September 2006.

• The 17 years (1990-2007) nocturnal temperatures are used to deduce an episodic response in the 1990’s following Mt. Pinatubo eruption, solar cycle effect and temperature trends, with the deduced trends in agreement with model predictions.

• The 4 years full diurnal cycle T, U, V observations have been used to deduce tidal-removed climatologies. The observed TUV climatology is in agreement with our current understanding of the mesopause region; when compared with three leading GCMs, the observation-model differences appear to be smaller than the differences between models.

Krueger and She (2009): Seasonal variation of T, trend, solar:Based on1990-2007 “Pinatubo removed” nocturnal temperatures

Comparison between means with night- and day-only data, all 80 hours, and harmonic fit reveal the aliasing in the determination of mean-state from data less than 24-hour long and with data gap.

First 80 hours of continuous observation; She et al. (GRL, 2003)

2

22

1. 2009 :

' 4n

NYuanet al on semidiurnal tides m

gh H