The long-term midlatitude temperature trend between 85 and 105 km is deduced
from 25 years (March 1990–December 2014) of Na Lidar observations. With a
strong warming episode in the 1990s, the time series was least-square fitted
to an 11-parameter nonlinear function. This yields a cooling trend starting
from an insignificant value of 0.64

Roble and Dickinson (1989) estimated the effects of hypothetical future
increases in greenhouse gas concentrations on the global mean structure and
predicted considerable cooling in the mesosphere and thermosphere. About this
time, a number of long-term temperature observations in the mesopause region
(80–110 km) were initiated or reinitiated at locations in the Northern
Hemisphere with passive OH emissions and/or active probes, such as Na lidar
and falling spheres. These observations and those in the Southern Hemisphere
via OH emission, as well as the long series of Russian rocket measurements
and OH emissions between about 1960 and 1995 over a wide range of latitudes,
measured cooling trends in the mesopause region ranging from 0 to

Based on the nocturnal lidar temperatures acquired between March 1990 and
December 2007 (data set (90-07)), the same paper reported a linear long-term
trend, starting from an insignificant cooling trend of
0.28

The Colorado State University (CSU) Na lidar performed nocturnal mesopause
region temperature observations between March 1990 and March 2010 at Fort
Collins, CO (41

As an overview, we plot the 25 years of nightly mean temperatures at 86 km,
which shows large annual and semiannual variation, and at 99 km, an altitude
with minimal annual and small semiannual variation (She and von Zahn, 1998),
respectively, in Fig. 1a and b. The data acquired at CSU
(March 1990–March 2010) is in black and that acquired at USU
(September 2010–December 2014) is in blue; apart from a small data gap in
2010, the two sets of data blend nicely. From Fig. 1a summer is about
60–80 K cooler than winter at 86 km. At 99 km one can see long-term
temperature variation. The 81-day averaged daily F10.7 solar flux also
plotted in the figure, in the red curve, shows that the nightly mean
temperatures track the variation in solar flux after 1993. Note that there
exists a warming episode after the Mt Pinatubo Eruption (MPE),

Time series of nocturnal mean temperature recorded by a Na lidar at
86 km

Following She et al. (2009), who performed regression analysis on a shorter
data set (90-07), a time series with 894 points, we express the nocturnal
temperature at each altitude,

Since all effects of comparable strengths must be included in the time series for the nonlinear regression analysis (Akmaev, 2012) and the warming episode, solar activity and linear trend are not independent, the best fit of one term will affect that of the other and they will depend upon the length of the data set.

The long-term linear trend of the 11-parameter fit to the long data set,
F-11P(90-14_Avg), is shown in Fig. 2 along with F-7P(90-14_Avg),
deduced from the 7-parameter fit by setting

Linear temperature trend from the quarter century data set with 11- and 7-parameter analyses, respectively denoted as F-11P(90-14_Avg) in black solid circles and F-7P(90-14_Avg) in black open circles. Shown for comparison are those data published based on an 18-year data set denoted as F-11P(90-07) in red solid squares and F-7P(90-07) in open red squares.

Compared to the trends deduced from the shorter data set, (90-07), we note
that the difference between F-7P(90-07) and F-11P(90-07) is bigger than the
difference between F-7P(90-14_Avg) and F-11P(90-14_Avg) because the
influence of the warming episode on the temperature trend is reduced in a longer
data set. Statistically, the results from the longer data set are more
accurate; the mean uncertainty between 88 and 102 km is 0.6 and
1.3 K decade

Since the three long-term effects with magnitudes

The warming episode in our data plays a critical role in the temperature trend analysis based on our data sets. Furthermore, we assume a single temperature trend over 25 years in analysis. We thus shall discuss these issues before the final conclusion.

A significant 6 K warming in 1992 and 1993 between 60 and 80 km was
reported by Rayleigh lidar observations in southern France and attributed to
the Mt Pinatubo eruption (Keckhut et al., 1995). Our suggestion (She et al.,
1998) that the observed warming episode is one of the consequences of the Mt
Pinatubo eruption does not yet have a clear geophysical causal relationship.
To our knowledge, there has been no succinct explanation or model simulation
published that relates the direct radiative and/or indirect dynamical effects
of the Pinatubo eruption to the observed response in the mesosphere which
lingered for

Thompson et al. (2009) analyzed the surface temperature response to a volcano

A more complete comparison between the warming episode in the mesopause
region temperatures and the global surface temperature anomaly is shown in
Fig. 4b, where the warming episode response time constant

All these time constants are deduced from observational data. Assuming the
Pinatubo aerosol reached the tropical lower stratosphere in negligible time
as Mt Pinatubo erupted, for the warming episode, the mean age

The use of a single linear trend for a long data set is consistent with the
classic recommendation of the World Meteorological Organization (WMO), using

We have performed a regression analysis for the deduction of the mesopause
region temperature trend based on an unprecedented Na lidar data set between
March 1990 and December 2014. The 81-day averaged F10.7 solar is used as a
proxy for solar activity, and a linear trend is assumed. Owing to a strong
warming episode in the 1990s, the quarter century data set (90-14_Avg) is
least-square fitted to an 11-parameter nonlinear model. The temperature trend
shown in Fig. 2 starts from an insignificant value of
0.64

With regard to an interesting connection, we analyzed the surface temperature
response after the Mt Pinatubo eruption reported by Thompson et al. (2009)
with the same functional dependence as that used for the observed warming
episode in the mesopause region. We determined the respective peak delay
time,

The lead author expresses his appreciation to Rashi Akmaev, Rolando Garcia, Gary Thomas, Susan Solomon, Liying Qian, Uwe Berger, and Ingrid Cnossen for helpful discussion and offprints and to Dave Thompson for the use of surface temperature data. This study was performed as part of a collaborative research program supported under the Consortium of Resonance and Rayleigh Lidars (CRRL), National Science Foundation grants AGS-1041571, AGS-1135882, and AGS-1136082. Topical Editor C. Jacobi thanks one anonymous referee for his/her help in evaluating this paper.