ANGEOAnnales GeophysicaeANGEOAnn. Geophys.1432-0576Copernicus GmbHGöttingen, Germany10.5194/angeo-33-363-2015Long-term midlatitude mesopause region temperature trend deduced from
quarter century (1990–2014) Na lidar observationsSheC.-Y.joeshe@lamar.colostate.eduKruegerD. A.YuanT.Physics Department, Colorado State University, Fort Collins, CO
80523, USACenter for Atmospheric and Space Sciences, Utah State University,
Logan, UT 84322, USAC.-Y. She (joeshe@lamar.colostate.edu)19March201533336336930January2015–2March2015This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://angeo.copernicus.org/articles/33/363/2015/angeo-33-363-2015.htmlThe full text article is available as a PDF file from https://angeo.copernicus.org/articles/33/363/2015/angeo-33-363-2015.pdf
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 ± 0.99 K decade-1 at 85 km,
increasing to a maximum of 2.8 ± 0.58 K decade-1 between 91 and
93 km, and then decreasing to a warming trend above 103 km. The geographic
altitude dependence of the trend is in general agreement with model
predictions. To shed light on the nature of the warming episode, we show that
the recently reported prolonged global surface temperature cooling after the
Mt Pinatubo eruption can also be very well represented by the same response
function.
Atmospheric composition and structure (middle atmosphere
– composition and chemistry; pressuredensityand temperature; volcanic
effects)Introduction
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
∼ 10 K decade-1, suggesting that after 2 decades, the observed
trend remains uncertain (Beig, 2006). These observational temperature trend
results were referenced in Table I of She et al. (2009).
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 ± 1.32 K decade-1 at 87 km, reaching a maximum value of
1.55 ± 1.15 K decade-1 at 91 km, and turning into a warming
trend above 102 km. The magnitude and altitude dependences are consistent
with the prediction of the Spectral Mesosphere/Lower Thermosphere Model
(SMLTM) (Akmaev et al., 2006) and of the Hamburg Model of the Neutral and
Ionized Atmosphere (HAMMONIA) (Schmidt et al., 2006). Subsequent substantial
reviews on thermospheric trends, Lašttovička et al. (2012) and
Cnossen (2012), included some studies on the mesopause region neutral
temperatures. Recent observational reports on mesopause region temperature
trends at specific altitudes include Offermann et al. (2010) and Hall et
al. (2012), based on ∼ 10-year data sets. The former utilized the
annual mean OH imager temperatures between 1988 and 2008 over Wippertal
(51∘ N, 7∘ E) and reported a long-term trend at 87 km of
-2.3 ± 0.6 K decade-1 in temperatures; the latter utilized
meteor wind radar between October 2001 and October 2012 at Svalbard
(78∘ N, 16∘ E) calibrated by satellite measurements and
reported a temperature trend at 90 km of -4 ± 2 K decade-1.
Na Lidar data sets and long-term regression analysis
The Colorado State University (CSU) Na lidar performed nocturnal mesopause
region temperature observations between March 1990 and March 2010 at Fort
Collins, CO (41∘ N, 105∘ W). It employed a vertical beam
between 1990 and 2001. Since 2002, the lidar has operated in 2- or 3-beam
geometry for simultaneous temperature and wind measurements, leading to two
or three mean temperatures at a given altitude each night. The lidar was
relocated to Utah State University (USU) and has continued its regular
observation at Logan, Utah (42∘ N, 112∘ W) since
September 2010. Because of similar geographical coordinates, we combine the
data from both locations to form a data set from March 1990 to December 2014,
denoted as (90-14_Avg). The extension _Avg indicates that, unlike the
data set employed in previous publications such as (90-07), which utilized
temperatures from the beam with the largest signal at 3.7 km vertical
resolution, here we use the average of temperatures acquired from two or
three beams at 2.0 km vertical resolution.
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),
tMPE= 1.45 years, which peaked in 1993 and mostly died away
near the beginning of 1999 for altitudes between 88 and 102 km (Fig. 3a).
Since the warming episode is in our data, we must account for it in the
analysis, whether its causes are fully understood or not. As a result, a
nonlinear least-square regression analysis is required for long-term study.
Time series of nocturnal mean temperature recorded by a Na lidar at
86 km (a) and at 99 km (b). Included in (b) is
also 81-day F10.7 solar flux in red with the times for Mount Pinatubo
eruption (MPE), tMPE, and solar minima (Solar Min) marked. Data
(black circles) between March 1990 and March 2010 were acquired at CSU
(41∘ N, 105∘ W), and those (blue circles) between
September 2010 and December 2014 were acquired at USU (42∘ N,
112∘ W).
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, T(z,t), of (90-14_Avg), a time series of
1200 points, as
T(z,t)=Tfit(z,t)+TRes(z,t),
where,
Tfit(z,t)=α(z)+A1(z)cos(2πt)+B1(z)sin(2πt)+A2(z)cos(4πt)+B2(z)sin(4πt)+β(z)t+γ(z)P(z,t)+δ(z)Q81(t);Pz,t=2/expt0-t/t1+expt-t0/t2,
where t is time in years from 1 January 1990, α(z) is independent
of time, and the 4 A–B terms represent annual and semiannual variations.
The three long-term effects have the amplitudes β(z), γ(z),
and δ(z), in this model. The strong warming episode in our data,
initially attributed to the Mt Pinatubo eruption in June 1991 (She et al.,
1998), is represented by an amplitude γ(z) times P(z,t)=2/exp(t0-t)/t1+exp(t-t0)/t2, with parameters t0(z),t1(z), and t2(z), for the delay, rise, and decay time,
respectively. The delay time here is relative to 1 January 1990, with the Mt
Pinatubo eruption at 1.45 years (see Fig. 1b). Other long-term responses
include δ(z), the solar response in K/SFU with Q81(t) being the
81-day averaged F10.7 solar flux, and β(z), the linear trend in
K years-1. The residual from the best fit is TRes(z,t).
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.
Temperature trend deduced from quarter century lidar data
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 γ(z)=0. Also shown are
the published results from F-11P(90-07) and F-7P(90-07) from the shorter data set
from March 1990 to 2007. As expected, the uncertainty from the 25-year data
set is smaller than that from the 18-year data set. The cooling trend in
F-11P(90-14_Avg) starts from an insignificant value of 0.64 ± 0.99 K decade-1 at 85 km, increases to a maximum of
2.8 ± 0.58 K decade-1 between 91 and 93 km, and then gradually decreases to a warming trend above 103 km. Turning from a cooling to a warming trend
above 100 to 120 km in geographic altitudes is the result of the cooling and
contraction of underlying atmosphere, the lower thermosphere, the mesosphere,
and the stratosphere; it is predicted by models (Akmaev, 2012; Qian et al.,
2013). This does not occur in the seven-parameter analysis (see
F-7P(90-14_Avg) in Fig. 2). A similar difference between F-11P(90-07) and
F-7P(90-07) is also evident. To our knowledge, metal resonance lidar is the
only ground-based instrument that covers the entire mesopause region, and
ours is the only Na lidar with a long enough data set to see this trend
reversal.
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-1, respectively, for F-11P(90-14_Avg) and the previously
published F-11P(90-07). However, below we investigate the discrepancy between
the two 11-parameter analyses, i.e., the 25-year data set has a larger
cooling trend by ∼ 1 K decade-1.
Since the three long-term effects with magnitudes β(z),γ(z),
and δ(z) are not independent in our analysis, we can understand their
mutual influences by realizing that, in addition to the annual and semiannual
variations, the observed temperature at a given time is the sum of three
contributions β(z)t, γ(z)P(z,t), and δ(z)Q81(t).
Because the solar flux, Q81(t), is a quasi-periodic function with a
period of ∼ 11 years, for data sets longer than 11 years, the
dominating competition is between the warming episode and trend. There is
then a trade-off between the two best-fit values, which depend upon the
observed values in the entire time series, i.e., data length. To see how this
interdependence or correlation affects the ∼ 1 K decade-1
discrepancy more explicitly, we recall that the proxy of the warming episode
is the function γ(z)P(z,t), which is shown in Fig. 3a for selected
altitudes. This function rises to a peak temperature (max temperature),
Tp, at the time tp=t0+ℓn(t2/t1)/[(1/t1)+(1/t2)]. Comparing tp and Tp in
the 25- and 18-year-long data sets reveals the difference of their warming
episode affecting the temperature trend. We plot these quantities as a
function of altitude in Fig. 3b for F-11P(90-14_Avg) and for F-11P(90-07).
Note that the altitude dependences for the two data sets are similar. Between
88 and 102 km, where the lidar signal is strong, we see little difference in
tp but a systematic difference in Tp between the two
data sets. Above 93 km, tp is about constant, but Tp
increases continuously. Note that the peak warming (or maximum temperature
response), Tp, from the shorter data set is consistently higher
by 0.5 to 2.5 K, depending on altitude, implying that a larger share of
observed temperatures in the 1990s are attributed to the warming episode;
this leads to a lower share for the trend assessment and thus a smaller
cooling trend. With the longer data set, the reverse is true. Since the
longer data set assesses the lingering warming episode more fully, it can
render better judgment on the sharing between the two competitors; of course,
more data leads to statistical accuracy and thus to a smaller uncertainty
than the corresponding trend deduced from the shorter data set as shown in
Fig. 2. We thus accept F-11P(90-14_Avg) from 25 years of Na lidar
observations, marked in solid black circles, as the deduced midlatitude
mesopause region temperature trend.
(a) Episodic warming in the 1990s at selected altitudes. By
the beginning of 1999, the warming episode is basically over between 88 and
102 km. (b) The peak temperature (maximum temperature response),
Tp, and the time at which it occurs (or the time of max response),
tp, of warming episode deduced from 25-year data set
(90-14_Avg) is shown in black solid circles and open circles, respectively. Results
shown in red solid squares and open squares were deduced from the 18-year
data set (90-07).
Discussions
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.
Mesopause warming and global surface cooling in the 1990s
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 ∼ 7 years at altitudes between 88 and 102 km as shown in
Fig. 3a. There is, however, a comprehensive study of global mean surface
temperature change in response to volcanic eruption and ENSO (El
Niño–Southern Oscillation) events by Thompson et al. (2009). In response
to the Mt Pinatubo eruption, they found a peak cooling of ∼ 0.3 K
about 1.5 years after the Pinatubo eruption, which, remarkably, also lingered
for ∼ 7 years. Changing the sign of their deduced global surface
temperature response and multiplying it by a factor of 50, we obtain an
episodic response very similar to our mesopause region temperatures response.
Better yet, we find that the scaled global surface temperature change,
STR ⋅ (-50), deduced by Thompson et al. (2009) can be fitted to
γ(z)P(z,t) that was used to represent the warming episode deduced
from Na lidar observation. Next, we compare the peak delay time and the
response time constant, tpd=tp-tMPE=tp-1.45, and τ=t1+t2, respectively, for the two
observed episodes that occurred in the same time frame but ∼ 100 km
apart in height.
(a) A comparison between 100 ⋅ stratospheric
aerosol (black) and the scaled global surface temperature response,
STR ⋅ (-50), in blue dots, from Thompson et al. (2009), along with
its best fit (blue curve) and episodic warming response (EWR) in temperatures
at 100 km (red curve). Both STR and EWR are over at the beginning of 1999,
∼ 7.5 years after the Mt Pinatubo eruption. (b) Deduced
altitude-dependent time constant of the response, τ, peak delay time,
tpd, and mean age, tMA, for the warming episodic
function, respectively in open blue circles, open red circles, and solid
black circles. Marked at the bottom of the figure are the times for the
surface temperature response, respectively by blue and red crosses and the
letter M. Data, STR ⋅ (-50) in (a) is derived from
http://www.atmos.colostate.edu/~davet/ThompsonWallaceJonesKennedy/.
Thompson et al. (2009) analyzed the surface temperature response to a volcano
T(t) by using the forcing function F(t) in terms of a simple model system
of exponential decaying memory with time constant τ0=Cβ,
where C is the effective heat capacity of the global atmospheric–oceanic
mixed layer per unit area and β is the damping coefficient, a measure
of the climate sensitivity. They deduced C=4.8×107 J m-2 K-1, equivalent to the effect of the global
atmosphere plus 9 m of the global oceanic mixed layer, and set β to
be 2/3 K (W m-2)-1 leading to a time constant for the model system
of τ0=Cβ=3.2×107 s = 1.01 years. Though the
system response to an impulse is a simple exponential decaying function with
a memory of ∼ 1 year, the forcing function, with the Northern
Hemisphere aerosol index as a proxy lasted several years through the end of
1994. The Aero Index (NH) ⋅ 100 is shown in Fig. 4a. Thompson et
al. (2009) produced a cumulative response with a memory much longer than
1 year; the scaled response, STR ⋅ (-50), is also shown. Since
STR ⋅ (-50) has a shape similar to the mesopause region warming
episode, we fit it to the function γ(0)P(0,t)+ background,
giving γ=13.0 K, t0=2.42 years, t1=0.35 years, t2=1.70 years, and tp=2.88 years, along with a background of
0.39 K, shown in Fig. 4a as the blue curve, which is seen to match the
scaled surface temperature response very well. The time constant and peak
delay time for the global surface temperature response are, respectively,
τ=2.05 years and tpd=1.43 years. Also in Fig. 4a is the
warming response at 100 km in altitude (with the same background of 0.39 K
added), the best-fit parameters of which are γ=11.4 K, t0=2.81, t1=0.20 years, t2=1.60 years and tp=3.17 years, or τ=1.80 years and tpd=1.65 years. It is
evident that both blue and red curves have a similar shape, both spanning
about 7 years, and thus can be represented by the same function.
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 τ and peak
delay time tpd are plotted as a function of altitude along with
these values for the surface temperature response at the bottom of the
figure. Averaged over the warming episode between 88 and 102 km, we find
τ=1.81± 0.42 years and tpd=1.82± 0.26 years, compared with the surface temperature anomaly of
τ=2.05 years and tpd=1.43 years. In addition, since the
functional shape of both anomalies represents the distribution of the transit
times of respective events, the concept of the “age of air” (AOA) (Waugh
and Hall, 2002) is useful. Though the AOA concept was mostly applied to the
transport of species from the tropical troposphere to the stratosphere, we
use it here to describe the temporal history of an episodic response to a
strong impulsive forcing. In fact, when area is normalized, the response of
both surface temperature and mesopause temperatures after the Mt Pinatubo
eruption is what is called the “age spectrum” in the AOA literature. Though
all information on the transport process in question is contained in the age
spectrum, the mean age (or the first moment of the spectrum in reference to
the time of the forcing impulse, i.e., tMPE here) of air,
tMA, may be used as a rough measure of the life of the process.
With the “age spectrum” at each altitude, we can compute the associated
mean age, tMA, also plotted in the figure, along with an
upper-case M for the surface temperature response, tMA=2.51 years. Averaged between 88 and 102 km, we have tMA=2.92± 0.33 years for the warming response.
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 tMA
is the time that the direct plus indirect (dynamic and feedback) effect of
Pinatubo aerosol reaches the midlatitude mesopause region from the tropic
lower stratosphere. For global surface cooling, the time tMA
starts as the perturbation moves from the tropical lower stratosphere down
through the global troposphere to the global atmospheric–oceanic mixed layer
and the subsurface ocean (Thompson et al., 2009). We propose no detailed
mechanism, which may be complicated, that leads to the mesopause warming
after the Mt Pinatubo eruption. We hope that similarities in the observed and
deduced response times between the volcanic eruption and the observed
episodes, along with treating the surface temperature cooling with the same
response function, will spur scientists and modelers to ascertain the causal
effects of these strong episodic responses that occurred at the same time but
∼ 100 km apart in height.
A single linear trend or piecewise linear trends
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
∼ 30 years or more for analysis (Cnossen, 2012), and the practice of
modelers, typically using 20 or ∼ 50 years of simulation for trend
studies (Akmaev et al., 2006; Garcia et al., 2007; Berger and Lübken,
2011). It is nonetheless an assumption (Laštovička et al., 2012).
Since a primary cause of a long-term temperature change is the anthropogenic
emission of greenhouse gases, a single linear trend over a 25-year span may
not reflect the reality of the rate of greenhouse emission changes. The
emission of CO2 and CH4 into the atmosphere continues to increase.
Indeed, Emmert et al. (2012) recently reported the observed increase of
thermospheric CO2 concentration. However, the loss rates (between 50 and
30 hPa) of the dramatic Antarctic ozone decrease (ozone hole, appeared in
the late 1970s) have remained stable since 2000 (Hassler et al., 2011). In
the midlatitudes, the trend began to reverse in the late 1990s (Akmaev, 2012; Qian
et al., 2013), and it has been stabilized in recent years at a level below that
in the 1960s. This recent O3 rate change should slow down the cooling
rate in the mesosphere and justify the use of a nonlinear (Keckhut et al.,
2011) or piecewise linear trend for the regression analysis
(Laštovička et al., 2012). Indeed, in analyzing the long-term
variation in the reflection heights of radio waves from 1961 to 2009 (Bremer and
Berger, 2002) and investigating temperature trends in the summer mesopause,
Berger and Lübken (2011) and Lübken et al. (2013) found it
appropriate to use three different linear trends with breaking points in 1979 and
1997. One could then reanalyze our 25-year data using piecewise trends in
the future. In this case, we would replace the term β(z)t in Eq. (1)
by two different linear trends joined at a breaking point. Again, because the
influences of solar flux, warming episode, and trends on temperatures are not
independent, all these terms, along with the break point, if it is to be
statistically determined, must be included in Eq. (1) to compete for the same
nightly mean temperatures for regression analysis.
Conclusions
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 ± 0.99 K decade-1 at 85 km, increases to a maximum of
2.8 ± 0.58 K decade-1 between 91 and 93 km, and then gradually
decreases to a warming trend above 103 km. Compared to the trend from the
shorter data set, (90-07), which has a marginally significant cooling maximum
of 1.55 ± 1.15 K decade-1 at 91 km (She et al., 2009), the
quarter century data set gives a statistically quite significant cooling
trend, larger by ∼ 1 K decade-1. The discrepancy is due to the
competition between warming episode and temperature trend. Since the longer
data set assesses the long-lasting warming episode more fully, it leads to
more significant results. The mean uncertainty between 88 and 102 km are 0.6
and 1.3 K decade-1, respectively, for the long and shorter data sets.
The altitude dependence from the two data sets is quite similar, and their
magnitudes are in the general range predicted by models. The trends reported
here are -1.0 ± 1.0 K decade-1 at 87 km and
-2.5 ± 0.5 K decade-1 at 90 km; they are consistent with the
recently reported trends: respectively, -2.3 ± 0.6 K decade-1
(Offermann et al., 2010) and -4 ± 2 K decade-1 (Hall et al.,
2012).
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, tpd, and mean age, tMA, to be, respectively,
1.43 and 2.51 years for surface temperature and 1.82 and 2.92 years for
mesopause region warming. These similarities between the global surface
temperature anomaly and the mesopause warming episode should hopefully spur
community scientists and modelers to figure out the causal effects of these
interesting phenomena.
Acknowledgements
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.
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