Peter Hoffmann on 29 October 2020 and Chris Hall on 9 August 2021

Specular meteor radars (SMRs) and partial reflection radars (PRRs) have been observing mesospheric winds for more than a solar cycle over Germany (

As Earth orbits around the Sun, the duration of the four seasons is well defined at ground level at middle latitudes. Higher up between 50 and 100 km in the mesosphere and lower thermosphere (MLT), the separation is not well defined.
Earth's atmosphere is a complex system governed by several processes that are continuously evolving (e.g., radiative heating, coupling, mixing processes). The dynamics of the MLT are forced mainly by solar radiation and the wave activities arising in the lower atmosphere, such as planetary waves, gravity waves, and tides

The above-mentioned summer characteristics exhibit interannual variabilities and interactions with adjacent layers, highlighting the importance of studying this season as well as its long-term behavior. Previous works, like

For several years, long-term studies aimed to investigate the anthropogenic influence in the atmosphere.
Having in mind the atmosphere as a whole and considering that the temperature changes affect life at ground level, we can compare the summer length with the vegetation growing season. The normalized difference vegetation index (NDVI) is obtained from CO

The middle-atmosphere studies, mentioned above, were made before 2010. Since the beginning of the 21st century, new radar systems have been deployed in Germany and Norway. After more than one solar cycle of system operations, we are able to look into the long-term trends.
In this study, we aim to analyze the long-term variability of the mean zonal wind reversal, which occurs around March and in September by implementing two different definitions of summer length.
Both definitions applied to radar wind measurements are related to different processes and regions in MLT and incorporate altitude and latitude features. With these definitions we search for possible correlations with known forcing events from above or below the MLT, like, e.g., solar activity measured by the Lyman

To study the MLT summer length, we combine MLT winds from specular meteor radars (SMRs) and mesospheric winds from partial reflection radars

The paper is organized as follows. Section

Considering the difficulties in obtaining a homogeneous data set, which are well known in long-term studies

SMRs detect meteor trails between mostly 75 and 110 km altitude, measuring their position in space and radial velocity to derive the mean background winds

Given the inherent variability within the radar measurements, the wind data set of 1 h resolution was first smoothed by a 16 d-width sliding window. The smoothing suppresses short-term fluctuations, which are caused by, e.g., gravity waves and tides as well as instrumental effects, which are not within the focus of this study. For this long-term study dealing with a length of up to 31 years, the principal component analysis

PRRs use the mechanism of partial reflection through the ionized component in the atmosphere as a tracer for the neutral motions in the MLT between 50 and 100 km altitude, depending on the instrument configuration and by means of the solar
and geomagnetic conditions

Equivalently to the descriptions given for the SMR data, we implemented a 16 d sliding window and the principal component analysis capturing 98.2 %–99.3 % of the total variance with the first two principal components. The time window implemented in the principal component analysis is DOY 50–280 and 70–95 km.

On board the Aura EOS satellite is the MLS instrument, sensing atmospheric temperatures from the troposphere up to 90 km

A mean zonal wind climatology for both latitudes and combinations of stations is shown in Fig.

Combined mean zonal wind climatologies at

Both climatologies depict a reversal of the wind (grey line) around March–April, when the wind reverses from eastward (red) to westward (blue) at all altitudes. Between April and May at high altitudes, the wind changes from westward to eastward (grey line), and the temporal evolution of this reversal occurs rapidly from 100 km down to around 90 km (86 km for middle latitudes). From early June, the mean zonal wind reverses slowly with decreasing altitude until 85 km (78 km at middle latitudes) until middle September. Later on, the wind direction reverses rapidly from westward to eastward from these altitudes downwards, indicating the end of the summer in the MLT in middle September, around 1 week before the autumnal equinox. The dynamics of the mean zonal wind displays a clear dependence of altitude with respect to latitude

Figure

Mean zonal wind reversal comparison and summer length definitions (MLT-SL and M-SL) at high and middle latitudes. The mean zonal wind reversal (0 m s

The MLT-SL definition is established by the mean zonal wind reversal from westward to eastward at both the upper and lower altitudes.
The altitudes depend primarily on the temporal evolution of the mean zonal wind reversal and in consequence on the latitude. These altitudes are chosen where the mean zonal wind reversal occurs rapidly and simultaneously for several kilometers.
Considering these characteristics, at high latitudes the MLT summer beginning (SB) is chosen at 96 km height and the MLT summer end (SE) at 82 km (Fig.

The M-SL is selected at the same altitude, varying only by latitude. The summer beginning and summer ending are considered when the final mean zonal wind reversal occurs from eastward to westward and later from westward to eastward, for high latitudes at 82 km and for middle latitudes at 74 km (see Fig.

Here we briefly describe how the radar data have been processed and the results are obtained. We first calculate the daily mean of hourly winds for each altitude and site. Then the mean is smoothed by a 16 d running window, shifted by 1 d. In order to compress the data and to further reduce its variability and to be able to focus on the long-term changes, we implemented a principal component analysis (see details in Sect.

Summer length at high latitudes: on the left is shown MLT-SL and M-SL on the right: panel

Figure

To explore the long-term behavior, we fit a linear function and apply the Student's

Summary of the mean values and their standard deviations for each time series with the corresponding values of the slope from the linear fit and confidence values (cv) divided into three categories: less than 80 % without a star, greater than 90 % with one star and greater than 95 % with two stars.

Confidence value:

The M-SL (Fig.

The last row for both definitions (Fig.

Summer length at middle latitudes: similar to Fig.

The middle-latitude results are shown in Fig.

The mean values, with the standard deviations from Figs.

As mentioned previously, in the case of middle latitudes one can extend the study of the M-SL using the Juliusruh PRR to 31 years by combining zonal winds obtained at the same place but with different partial reflection radar systems and measuring techniques. Figure

In this section we discuss the obtained result. Noteworthy, for both latitudes and definitions, is that the variability of the summer lengths is dominated by the summer beginning and, thus, by the winter conditions. Since our results display a latitudinal dependency, we also divided our discussion by latitude. In addition, we discuss the results of our summer definitions with respect to other definitions used in earlier studies. The long-term behavior of our results, including the 31-year analysis, is discussed separately.

As both definitions represent different processes from the different altitudes (in the summer beginning) and therefore times in the year, there is also a significant difference observed in the variability.
The observed variability is higher in M-SB due to the proximity to the winter conditions, which is modulated by the planetary wave activity, final warmings, etc.

The MLT-SL shows peculiar values for the years 2004, 2012 and 2013, with an earlier MLT-SB. The 2013 winter-to-summer transition was reported by

A similar period was recently studied at the MLT Northern Hemisphere high latitudes by

In the case of M-SL the years 2012 and 2016 strongly deviate from the mean behavior of the M-SL.
In 2012 the satellite data show an early M-SB (DOY 74), while the radar (DOY 89) depicts a value not far from the mean (DOY 92). At high latitudes, in the Northern Hemisphere the winter is dominated by the behavior of the polar vortex and its temporal dependence on the position. Considering the satellite zonal mean geostrophic zonal winds are an average in longitude and the radar only shows the mean zonal wind at a fixed longitude, it is reasonable to find differences between these individual observations.
Thus, here we can see the complexity of understanding the winter time in a localized position (radar site) compared with the average in longitude (obtained from satellite).
In contrast to this example, 2016 has an earlier M-SB for both instruments (DOY 61 PRR and DOY 57 MLS) as a consequence of the event categorized as a final warming

In the time series we were not able to find a relation to Lyman

As we move far away from the polar vortex and approach the middle latitudes, the summer beginning displays less variability than at high latitudes, and there is a clear time–latitude difference in the time series (also indicated in Fig.

Looking into the unusual years seen at high latitudes, the reversal during 2012 (Fig.

Once more, similarly to high latitudes, the mean zonal wind reversal dates do not depict a connection to the solar activity or the other events (i.e., ENSO, QBO, strong polar-night jet oscillation or MSSW).

Comparing the definitions proposed in this work with the one made by

Inspired by the comparison between the summer duration in the MLT and that at ground level made by

A linear regression was implemented for all the time series and the result proven with a Student's

The slope of the 31-year linear regression (negative) shows an opposite direction (positive) to the shorter version (17-year time series, Fig.

Evidence of breakpoints in the long-term studies has been reported by several authors.

Smoothed mean zonal winds between 2004/2005 and 2020 from different radars located at high and middle latitudes (Andenes SMR–Tromsø SMR, Saura PRR, Juliusruh SMR–Collm SMR and Juliusruh PRR), as well as MLS measurements, are used to study two different summer length definitions (see Sect.

With the obtained time series, we analyzed the summer length and studied the variability and the linear tendency. We looked into the dates and the different events occurring in the upper and lower atmosphere to understand the events modifying the summer length. Furthermore, we compared the summer length to the growing season length.
The results are summarized as follows.

The summer length is determined by the mean zonal wind reversal, which depends on the actual latitude and altitude. High latitudes showed more variability than middle latitudes for both definitions. The summer beginning presents most of the variability that is transferred to the summer length. The summer end occurs for all latitudes in the same week before the autumn equinox and presents no significant linear trend.

MLT-SL definition: the summer starts around 7 May at high latitudes (SL

M-SL definition: this is more variable than the MLT-SL due to the higher variability in the summer beginning, which is more prone to the winter conditions. The summer starts between the end of March and the beginning of April for high latitudes and 1 week later at middle latitudes (see Table

At middle latitudes, the length of the growing season at ground level is similar or has around 10 d difference (depending on the author) to the M-SL.

After analyzing the time series and trying to relate it to other events (solar activity, QBO, ENSO, strong polar-night jet oscillations, and MSSW), we were not able to find a direct influence on the summer length or summer beginning. Only for the M-SL did we find 1 year (with a strong MSSW and 2016 final warming) being directly affected. The 17-year time series are short for studying the period related to QBO or ENSO. On the other hand, with the 31-year time series (see Fig.

The data to produce the figures are available in HDF5 format at

JJ, TR, JLC and PH developed the idea and helped in the interpretation of results. MH assisted in the implementation and interpretation of PCA. VM and YY provided the wind analysis used for the Microwave Limb Sounder values. CH and MT ensured the operation of the Tromsø specular meteor radar and CJ of the Collm specular meteor radar. Furthermore, JJ wrote the manuscript with input from all the coauthors.

Christoph Jacobi is editor-in-chief and topical editor of

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We acknowledge use of NASA/GSFC's Space Physics Data Facility's OMNIWeb service, and OMNI data. We thank the Free University of Berlin for the provision of the QBO data. We thank the NOAA/National Weather Service, Climate Prediction Center for the ENSO index.

This research has been supported by the Deutsche Forschungsgemeinschaft (VACILT, grant nos. PO 2341/2-1 and JA 836/47-1) and the Bundesministerium für Bildung und Forschung (TIMA, grant no. 01 LG 1902A).The publication of this article was funded by the Open Access Fund of the Leibniz Association.

This paper was edited by Andrew J. Kavanagh and reviewed by two anonymous referees.