on the long-term trends in noctilucent clouds as observed...

On the long-term trends in noctilucent clouds as observed from the ground (Moscow and Lithuania)

and on the trends in the OH summer temperature as

measured in Moscow

P. Dalin1, S. Kirkwood1, N. Pertsev2, V. Perminov2, A. Dubietis3, R. Balčiunas4, K. Černis5, V. Romejko6

1. Swedish Institute of Space Physics, Kiruna, Sweden2. A.M. Obukhov

Institute of Atmospheric Physics, RAS, Moscow, Russia

3. Department of Quantum Electronics, Vilnius University, Vilnius, Lithuania4. Melioratoriu

6-34, LT-30235 Vidiskes, Lithuania

5.

Institute of Theoretical Physics and Astronomy, Vilnius University, Vilnius,Lithuania

6. The Moscow Association for NLC research, Moscow, Russia

The PMC Trends workshop, LASP -

University of Colorado, Boulder, May 3-4, 2012

1

1. Temperature trends in the atmosphere at midlatitudes2. Measurements of the OH temperature in Moscow3. OH temperature trend in the summer mesopause at ~57°N4. Moscow NLC trend analysis for 1962-20115. Lithuanian NLC trend analysis for 1991-2011 and 1973-2011 6. How our data is relevant for long-term PMC modeling7. Conclusions on long-term trend analysis around the

summer mesopause

OUTLINE2

Fig. 1. The long-term variations in the mean monthly temperatures for winter (December) and summer (June) at different heights according to rocket measurements in Volgograd at 48.7°N (30-80 km), airglow (87 km OH-layer, 92 km Sodium layer, 97 km O557.7

layer) and radiophysical measurements (105-110 km).

The hydroxyl temperatures at around 87 km were measured in Zvenigorod, Wuppertal, Yakutsk, Maynooth, Quebec and Delaware.

There is a general decrease in winter temperature between 30 and 92 km, but the winter temperature increases in the upper atmosphere at 97 and 108 km.

For the summer temperature the situation is different, and it is

of importance for us. The trend in summer temperature equals to zero at 30, 40, 50 and 60 km, but it is negative at 70 and 80 km, but it is zero again around the mesopause at 87 and 92 km, and the summer temperature increases in the upper atmosphere at 108 km.

It is evident that there is ZERO SUMMER temperature trend at midlatitudes at ~87 km from 1960 to 1996, and NEGATIVE SUMMER trend at ~80 km from 1969 to 1996!

Temperature trends in the atmosphere at midlatitudes3

(Fig. 1 from Semenov et al., 2002)

The hydroxyl (OH) airglow intensity (OH-layer located in the range of 82–92 km with the maximum intensity at 87 km) provides information on temperature around the mesopause. Nowadays, temperature is measured with a modern spectrograph equipped with a CCD at Zvenigorod

Observatory near Moscow (55.7°N, 36.8°E). The measurements are conducted on a year-round operation basis on clear and semi-clear nights from 2000 to present time. The measurements are centered at zenith angle of 53°

and azimuth of 22.6°

counted from North to West. It allows us to measure the OH rotational temperature above latitudes of 56–57°N.

OH temperature as measured in Moscow

Fig. 2. A typical OH and O2 spectrum observed on May 19, 2001. The rotational temperature analysis is based on the OH intensity at the band 6-2.

Fig. 3. An example of seasonal variations in the OH temperature in 2011. Minimum temperatures are observed in the summer time.

2011

4

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012158

159

160

161

162

163

164

165

166

Years

Tem

pera

ture

[ K

]

Model:Y= − 0.351±0.760⋅ t − 0.541±4.186 ⋅ Lyα + 165.5

OH Temperature data

All the analyzed parameters (temperature or NLC quantities) are fitted with a two-dimensional regression model which includes dependence on time and solar cycle, which is represented by the Lyman-alpha flux (Lyα

):

Y= b1

·(t–t0

) + b2

·Lyα

(t–lag) + b3

where are b1, b2 are fit parameters for the time trend and for the Lyα

flux, respectively, b3 is the fit constant, t0 is the start year of the time series, lag is the phase lag between the solar cycle and NLC or T data sets.

OH temperature trend in the summer mesopause at 56-57°N

Fig. 4. The OH minimum temperatures for summer seasons from 2000 to 2011 (blue curve) and its two-dimensional regression model (red curve). Lag=0 year.

5

3.4 3.6 3.8 4 4.2 4.4 4.6 4.8 5 5.2 5.4160

161

162

163

164

165

166

167

Tem

pera

ture

[ K

]

Moscow OH temperature vs. solar cycle. Time dependece has been removed

Lyα flux [1011 ph /cm2 /s]

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012158

160

162

164

166

168

170

Years

Tem

pera

ture

[ K

]

Moscow OH temperature vs. time. Solar cycle has been removed

Y= −0.351 ± 0.381⋅ t

Y= −0.541 ± 2.095 ⋅Lyα

OH temperature trend in the summer mesopause at 56-57°N

Fig. 5. The upper panel: OH temperature vs. the Lyα

flux, with the time dependence being subtracted. The lower panel: OH temperature vs. time, with the solar cycle being subtracted.

It is seen that the summer temperature dependence on solar flux is close to zero, and the linear dependence on time is slightly negative (–0.35 K/yr) and it lacks statistical significance.

6

1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 20100

5

10

15

20

25

30

NLC

cas

es

Years

Moscow NLC observations

Model: Y= 0.227 ± 0.110⋅ t −3.706 ± 2.152⋅ Lyα + 23.5

Moscow NLC trend analysis for 1962-2011

Fig. 7 shows

the Moscow NLC observations, starting from 1962 (black curve), and its regression model (blue curve). The found regression coefficients are highly significant. However, it is evident there are two different statistics: before and after 2005. The 2005 year is the start of the digital epoch

in NLC observations in Moscow. After establishing automated digital cameras we are able to monitor the twilight sky longer period, from the end of May to the middle of August, compared to the period of visual observations conducted from the end of May to the middle of July. Also, digital images allow us to better identify NLC under complex weather conditions. That is why the number of NLC events starting from 2005 is greater than those before the digital epoch. To take into

account these different statistics, we apply a procedure of normalization by the number of clear and semi-clear weather nights

during an NLC season.

7

1. Port Glasgow (Scotland). 2. Athabasca (Canada)3. Kamchatka (Russia). 4. Novosibirsk (Russia). 5. Moscow (Russia). 6. Vilnius (Lithuania). 7. Århus

(Denmark).

Fig. 6.

1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010−0.4

−0.3

−0.2

−0.1

0

0.1

0.2

0.3

0.4

rela

tive

NLC

num

ber

Years

Moscow NLC relative number, normalized by the number of clear nights. Solar cycle has been removed

Y= 0.0002 ± 0.0031⋅ t


Fig. 8 shows the Moscow NLC relative number, normalized by the number of clear and semi-clear weather nights. The solar cycle has been subtracted with correlation lag equal to 1 year. The long-term trend in relative NLC occurrence frequency equals to

zero.

8

1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010

0

100

200

300

400

500

600

700

Inte

gral

NLC

brig

htne

ss [m

arks

]

Moscow NLC brightness observations

1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010−6

−4

−2

0

2

4

6

8

10

Years

Rel

ativ

e br

ight

ness

of N

LC [r

el. m

arks

] Relative NLC brightness, normalized by the number of clear nights. Solar cycle has been removed

Model: Y= 4.813±2.096⋅ t −76.101±41.000⋅ Lα + 415.3

Y= 0.057 ± 0.072 ⋅ t


Fig. 9 shows the Moscow NLC brightness which is traditionally visually estimated on a 5-point scale. This is so called the integral NLC brightness that is

a total brightness for a whole NLC season. There is a significant positive trend as seen in the upper panel. Again, there are two different

statistics before and after 2005. However, after normalizing by the number of clear and semi-clear weather nights we arrive at a slight positive trend (0.057 Mark/yr) which lacks statistical significance (lower panel).

9

1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 201212

16

20

24

28

32

36

40

NLC

cas

es

Years

Lithuanian NLC observations, all brightness

Model: Y= 0.109 ± 0.345 ⋅t −0.762 ± 2.953 ⋅Lyα +57.5

Lithuanian NLC trend analysis

Fig. 10 illustrates the number of all NLC displays for 20 years from 1991 to 2011

(black curve), NLC data include a weather corrected factor. The blue line is its regression model which includes dependence on time and solar cycle. The black line is a linear dependence on time, which shows a rather strong positive trend.

The Lithuanian data base consists of two data sets:1. Observations of NLCs of all brightnesses, i.e. all observed NLCs from 1991 to 2011.2. Observations of VERY BRIGHT NLCs from

1973 to 2011.

10

1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012−6

−4

−2

0

2

4

6

8

10

12

NLC

cas

es

Years

Lithuanian NLC observations of all brightness. Solar cycle has been removed.

Y= 0.109 ± 0.291⋅ t


Fig. 11 illustrates the long-term trend in the Lithuanian observations of NLCs of all brightnessesfrom 1991 to 2011. The black line is the regression model after subtracting the solar cycle.

One can see that there is a small positive trend (0.11 N/yr) which lacks statistical significance.

11

1970 1975 1980 1985 1990 1995 2000 2005 2010−2

0

2

4

6

8

10

NLC

cas

es

very bright Lithuanian NLC

Year

Model: Y= 0.039 ± 0.058⋅t −2.143 ± 0.902 ⋅Lyα + 11.3


Fig. 12 illustrates time series of the Lithuanian observations of VERY BRIGHT NLCs from 1973 to 2011 (black curve). The blue line is the 2D regression model which includes the dependence on time and solar cycle. The black line is a linear dependence on time, which shows a rather strong positive trend.

12

1970 1975 1980 1985 1990 1995 2000 2005 2010−4

−3

−2

−1

0

1

2

3

4

5

6

NLC

cas

es

Years

Very bright Lithuanian NLC. Solar cycle has been removed.

Y= 0.039 ± 0.056 ⋅t


Fig. 13 shows the long-term trend in the Lithuanian observations of VERY BRIGHT NLCs. The black line is the regression model after subtracting the solar component.

It is evident that

there is a slight positive trend (0.04 N/yr), however, it has no statistical significance.

13

How our data is relevant for long-term PMC modeling

Modeling of PMC trends is a function of the temperature trend and water vapor trend around the summer mesopause, that is:

PMCtrend

= f (Ttrend

, H2Otrend

)

If a PMC model is flexible to atmospheric conditions at different latitudes then:

► with OH summer temperature measurements

we can contribute to the PMC/NLC modeling at midlatitudes

of 56-57°N

during several decades;

► it would be worth running such a PMC model for latitudes of 57-60°N

(where NLCs are commonly observed) for which we have reliable long-term NLC data. Our NLC data sets could be used as a reference base

for comparison with the output of a PMC model.

14

Conclusions on long-term trend analysis around the summer mesopause

1. There exists a slight negative trend (-0.35 K/yr) in minimum temperatures in the summer time at ~87 km

as measured by the OH spectrometer in Moscow from 2000 to 2011. However, the trend lacks statistical significance. The long-term trend

in the summer mesopause temperature is about zero

at around 87 km from 1960 to 1996

, and the T-trend is slightly negative at around 80 km from 1969 to 1996. The last one has been derived from rocket measurements in Volgograd (Russia) by Golitsyn et al. (1996), Semenov et al. (2002). All these trends are valid for midlatitudes

only.

2. There is no statistically significant long-term trend in the Moscow NLC observations from 1962 to 2011. This is valid both for time series of the NLC number and NLC brightness estimations. Please note that the Moscow ground-based NLC observations are longer than any satellite time series

of PMCs. It is possible to combine time series of NLC observed visually (the old epoch) and NLC registered with digital cameras (the digital epoch). The correct procedure is to normalize NLC quantities by the number of clear and semi-clear weather nights, which does not provide more scattering of the data points.

3. The statistical analysis of the Lithuanian data sets demonstrates that there is a slight long-term positive trend (0.11 N/yr) in NLC observations of all brightnesses

from 1991 to 2011

as well as in very bright NLCs (0.04 N/yr) from 1973 to 2011. However, both trends lack statistical significance.

4. Zero OH temperature trend in the summer mesopause at ~87 km

probably explains zero trends in the NLC occurrence frequency. At the same time, a slight negative temperature trend at 80 km

might explain positive trends (0.04 N/yr) in very bright NLCs, by the Lithuanian data,

and in the NLC brightness (0.057 Mark/yr), by the Moscow data. The reason is that the brightest NLCs are composed of largest particles, which build at the lowermost level (at 80-81 km). The positive long-term trend in water vapor around the summer mesopause (if exists) does not provide a plausible explanation on it. In case of existence of long-term trend in water vapor we should see the increase both in the NLC occurrence frequency and

in the NLC brightness.

5. Our OH summer temperature measurements

can be used as the input for PMC/NLC modeling at midlatitudes

of 56-57°N. It would be worth running such a PMC model for latitudes of 57-60°N

for which we have reliable long-

term NLC data. Our NLC data could be used as a reference base for comparison with the output of a PMC model.

15

THANKS!

The PMC/NLC and T-trend analysis …

to be continued

16

−10 −8 −6 −4 −2 0 2 4 6 8 10−0.6

−0.5

−0.4

−0.3

−0.2

−0.1

0

0.1

0.2

0.3

0.4

Lag [ years ]

Cor

r. c

oeff.

Cross−correlation between the NLC number and solar cycle

NLC numberrelative NLC number

Additional slides in case of available time or for questions

Cross-correlation dependence between the Moscow NLC number and solar cycle (solid line), and between the relative NLC number and solar cycle (dashed line).

17


Fig. 4. Time series of zonal mean July (1–31) temperatures (red) at 83 km and seasonal mean (6 June to 21 July) centroid

NLC heights (blue) at 69°N. The data are fitted with straight lines to temperatures and altitudes from 1961–1994 and find slopes of 0.080 ±

0.016 K/yr, and 0.0166 ±

0.0048 km/yr.

Fig. 6. Time series of H2O at 83 km at 30–31 May and at 1–10 July at 69N. The data have been smoothed by running mean over 2 years. HALOE observations of H2O from Hervig

and Siskind

[2006] for 80 km and 65N–70N are also shown, again smoothed by running mean over 2 years (black line). A straight line fit shows an average increase of H2O(July) of 0.053 ±

0.01 ppmv/yr.

From Lübken, Berger, and Baumgarten

(2009). Model results of mesospheric ice layers and background conditions at 69°N from 1961 to 2008 are analyzed:

The OH temperature trend at ~87 km at 56-57°N is significantly different from that seen in Fig.4

18


Dalin, P., Kirkwood, S., Andersen, H., Hansen, O., Pertsev, N., Romejko, V.: Comparison of long-term Moscow and Danish NLC observations: statistical results, Annales Geophysicae, 24, 2841-2849, 2006.

Dubietis, A., Dalin, P., Balciunas, R., Cernis, K.: Observations of noctilucent clouds from Lithuania, J. Atmos. Sol-Terr. Phys., 72, 14-15, 1090-1099, doi:10.1016/j.jastp.2010.07.004, 2010.

Golitsyn, G.S., Semenov, A.I. ,Shefov, N.N., Fishkova, L.M., Lysenko, E.V., and Perov, S.P.: Long-term temperature trends in the middle and upper atmosphere. Geophys. Res. Lett., 23, 14, 1741-1744, 1996.

Kirkwood, S., Dalin, P., Réchou, A.: Noctilucent clouds observed from the UK and Denmark –

trends and variations over 43 years, Annales Geophysicae, 26, 1243-1254, 2008.

Lübken, F.-J., Berger, U., and Baumgarten, G.:

Stratospheric and solar cycle effects on long-term variability ofmesospheric ice clouds, J. Geophys. Res., 114, D00I06, doi:10.1029/2009JD012377, 2009.

Perminov, V. and Pertsev, N.: Seasonal features of the response of temperature and emission intensities in the mesopause on solar activity variations. Geomagnetism and Aeronomy, 49, 1, 91-99, 2009.

Pertsev, N. and Perminov, V.: Response of the mesopause airglow to solar activity inferred from measurements at Zvenigorod, Russia. Annales Geophysicae, 26, 1049–1056, 2008.

Romejko, V.A., Dalin, P.A., Pertsev, N.N.: Forty years of noctilucent cloud observations near Moscow: database and simple statistics, J. Geophys. Res., 108, D8, 8443, 2003.

Semenov, A.I.: Long-term temperature trends for different seasons by hydroxyl emission. Physics and Chemistry of the Earth (B), 25, 5-6, 525-529, 2000.

Semenov, A.I., Shefov, N.N., Lysenko, E.V., Givishvili, G.V., Tikhonov, A.V.: The season peculiarities of behaviour

of the long-term temperature trends in the middle atmosphere on the mid-latitudes. Physics and Chemistry of the Earth, 27, 529–534, 2002.

Shettle, E.P., DeLand, M.T., Thomas, G.E., and Olivero, J.J.: Long term variations in the frequency of polar mesospheric clouds in the Northern Hemisphere from SBUV, Geophys. Res. Lett., 36, 2803–2806, doi:10.1029/2008GL036048, 2009.

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