Theoretical models and observations have suggested that the increasing greenhouse gas concentration in the troposphere causes the upper atmosphere to cool and contract. However, our understanding of the long‐term trends in the upper atmosphere is still quite incomplete, due to a limited amount of available and well‐calibrated data. The European Incoherent Scatter radar has gathered data in the polar ionosphere above Tromsø for over 33 years. Using this long‐term data set, we have estimated the first significant trends of ion temperature at altitudes between 200 and 450 km. The estimated trends indicate a cooling of 10–15 K/decade near the F region peak (220–380 km altitude), whereas above 400 km the trend is nearly zero or even warming. The height profiles of the observed trends are close to those predicted by recent atmospheric general circulation models. Our results are the first quantitative confirmation of the simulations and of the qualitative expectations.

1 Introduction Anthropogenic increases of greenhouse gases have a cooling effect in the middle and upper atmosphere with the neutral and ionized gases, i.e., thermosphere and ionosphere. Computer modeling performed by Roble and Dickinson [1989] predicted that global cooling would occur in the upper atmosphere in conjunction with global warming in the troposphere due to the long‐term increase of greenhouse gas concentrations. This effect of greenhouse gases has been referred to as “greenhouse cooling.” If greenhouse gas concentrations are doubled, then the neutral temperature is predicted to decrease by 50 K in the thermosphere and by 10 K in the mesosphere. Also, the neutral density is expected to decrease by 40–50%. In the past two decades efforts have been made to identify such trends in the upper atmosphere. Long‐term changes of the mesospheric temperature, inferred from Rayleigh lidar and sodium lidar and also airglow observations, were reviewed [e.g., Beig et al., 2003]. A negative trend in the lower and middle mesosphere with an amplitude of a few degrees (2–3 K) per decade was found, but no significant trend near the mesopause. From the orbital decay of near‐Earth space objects, long‐term trends in thermospheric total mass density were inferred [e.g., Keating et al., 2000; Emmert et al., 2004, 2008]. An overall decrease of a few percent/decade was found. The height of the ionospheric F region peak and its electron density measured by ionosondes showed long‐term trends consistent with a cooling of the thermosphere [e.g., Bremer, 1992; Ulich and Turunen, 1997; Ulich et al., 2003; Qian et al., 2014]. Also from incoherent scatter (IS) radar data, long‐term trends have recently been estimated. Advantages of these data are that (1) IS radars directly measure electron density (Ne) and temperature (Te), and ion temperature (Ti) in the ionosphere, and that (2) they provide altitude profiles of ionospheric and thermospheric parameters. Trends in the ionosphere over time intervals that are considerably longer than the 11 year long solar cycle are expected to mainly originate from trends in the neutral thermospheric temperature, composition, and winds [Qian et al., 2008, 2009]. Ti is expected to be a proxy for the neutral temperature Tn under these conditions: (1) data obtained during moderate‐high geomagnetic activity periods are removed, and (2) data affected by variable heating by solar extreme ultraviolet (EUV) radiation are corrected. Holt and Zhang [2008] analyzed the trend of Ti measured with the Millstone Hill IS radar at geographic 46.2° north and 71.5° west. The data were from the years 1978 to 2007. They fitted a trend of −4.7 K/yr in Ti at 375 km altitude. In a later investigation, Zhang et al. [2011] and Zhang and Holt [2013] estimated a Ti trend of −2 K/yr at 350 km altitude at local noon using again Millstone Hill IS radar data, this time over 1968–2006, which is a 10 year longer data set. The trend shows an altitude dependence: Between 200 and 550 km a cooling rate increased with increasing height, and at 450 km the cooling rate was about −3 K/yr. Donaldson et al. [2010] showed estimates of the Ti trend with the Saint Santin IS radar at geographic 44.6° north and 2.2° east. The data between 1966 and 1988 were used. The result was a trend of −3 K/yr at 350 km altitude, and also the cooling became stronger with increasing height above 200 km. Qian et al. [2011] modeled the change of Ti due to a doubling of CO 2 using a general circulation model (GCM). The outcome was a negative Ti trend (−50 K) below ~350 km altitude in the F region ionosphere, while positive Ti trends were predicted above 350 km altitude and between 100 and 120 km altitude. Thus, there are disagreements between this model and estimates from IS radar data: The Ti trend predicted with the National Center for Atmospheric Research (NCAR) thermosphere‐ionosphere‐mesosphere electrodynamics general circulation model (TIME‐GCM) is only −0.5 K/yr, whereas −2–3 K/yr was estimated from the observations at middle latitudes [Laštovička et al., 2006, 2012]. Also, the altitude dependence of the Ti trend is very different: The model produced a positive Ti trend above 350 km at all latitudes [Qian et al., 2011], but from the incoherent scatter radar data a strongly negative trend is derived. In recent studies of long‐term upper atmospheric trends the discrepancies between model predictions and observations could be reduced: Ti cooling at middle latitudes was found to be smaller (~1 K/yr at 325 km altitude) under low geomagnetic activity [Zhang and Holt, 2013], and the rate of thermospheric density decrease in solar minimum, based on recent model predictions, is significantly higher than in the previous works and in better agreement with the trend derived from satellite drag observations [Qian et al., 2014]. For this study we studied long‐term Ti changes and estimated trends using a database of European incoherent scatter (EISCAT) UHF radar observations at Tromsø (Location: 69.6° geographic North, 19.2° geographic East). The data span about three solar cycles from 1981 to 2013. Figure 1 shows Ti variations above Tromsø at local noon from October 1981 to May 2013. Of particular interest is a comparison of the Ti trends in the auroral zone (Tromsø) and at middle latitudes (Millstone Hill and Saint Santin). The air temperature both over land and the ocean surface shows rapid warming in the Arctic for the 50 years from 1958 to 2008, about 2° per 100 years [Hansen et al., 2010; Smith et al., 2008], but averaged globally only about 0.6° in the twentieth century [e.g., Intergovernmental Panel on Climate Change, 2007]. By using a data set from a high‐latitude location (Tromsø), we can investigate whether the Arctic warms more rapidly also in the thermosphere. Figure 1 Open in figure viewer PowerPoint Color‐coded ion temperature at local noon from 1 h preintegrated EISCAT Tromsø UHF radar data over altitude (230–470 km) and time (years 1981–2013).

2 Method We analyzed the raw data from the EISCAT Tromsø UHF radar between October 1981 and May 2013. The radar is operated roughly 2000 h/yr with generally more operations toward the later years. An operation, also called experiment, lasts typically 36–48 h, but also a few long operations, in one case up to 26 days are included. Only measurements pointing along the geomagnetic field (elevation angle of about 77.5° and azimuth angle of about 186°) and in the vertical direction, which is most of all data, were used. The correlated IS signals need to be integrated over many radar pulses in order to obtain a sufficiently high signal‐to‐noise ratio. Normal integration intervals are about 1–5 min, but here we reanalyzed all the data and integrated for 1 h to get lower statistical errors per data point. To integrate over such a long time, we implemented a filter to remove signals from hard targets such as satellites and space debris in the raw data [Ogawa et al., 2009]. The long integration is acceptable and beneficial when the ionospheric densities, temperatures, and drifts are relatively constant [e.g., Forme et al., 1998]. Because only data from the day time (8–16 LT) were used, variations of ionospheric parameters within the integration of 1 h were probably small. Also, geomagnetic active times were excluded, thus filtering out the variable effects of frictional heating due to energy flow from the near‐Earth space into the upper atmosphere. Nonlinear fits of theoretical IS spectra to the 1 h integrated raw data yielded the electron density and temperature, and the O+ temperature Ti. Two sets of fit results were kept, one with a fit error <15 K for Ti, and the second set with an error <50 K. The two sets were further filtered using the Kp index. For the first set the condition was Kp < 2, and for the second Kp < 3. Thus, our filtered temperature has a low statistical error. Ti due to solar activity, Ti_sol, in three versions from regression equations using different proxies for solar activity and directly the solar zenith angle (θ): (1) (2) (3) Then we fitted variations ofdue to solar activity,_sol, in three versions from regression equations using different proxies for solar activity and directly the solar zenith angle (): F 10.7 is the radio flux at 10.7 cm wavelength, which itself is known to monitor well the solar flux at (E)UV wavelengths which get absorbed mostly in the upper atmosphere and cause variable heating there. The average F 10.7ave is calculated as (4) F 10.7 81day_ave ). This F 10.7ave can be more suitable for describing solar EUV variation [Richards et al., 1994 MgII index, based on the emission core of the Mg II doublet (280 nm), has also been used as an alternative for modeling EUV, UV, and total solar irradiance [Viereck et al., 2004 Ti versus F 10.7 (F 10.7ave or MgII) showed a clear saturation effect at high values of these indexes, as shown by Holt and Zhang [ 2008 is the radio flux at 10.7 cm wavelength, which itself is known to monitor well the solar flux at (E)UV wavelengths which get absorbed mostly in the upper atmosphere and cause variable heating there. The averageis calculated aswhere the averaging is over 81 days (shown as the). Thiscan be more suitable for describing solar EUV variation [.,]. Theindex, based on the emission core of thedoublet (280 nm), has also been used as an alternative for modeling EUV, UV, and total solar irradiance [.,]. Scatterplots of theversusor) showed a clear saturation effect at high values of these indexes, as shown by]. Therefore, we included also quadratic terms in equations 1 3 , and the fits gave as expected negative coefficients for these terms. Ti_obs) to obtain the residual temperature ΔTi (= Ti_obs − Ti_sol); and finally, we estimated linear trends of ΔTi from linear by fitting to (5) b indicates the linear trend in K/yr. The estimated variations due to solar activity and elevation were removed from the averaged and filtered observations (_obs) to obtain the residual temperature Δ(=_obs −_sol); and finally, we estimated linear trends of Δfrom linear by fitting towhereindicates the linear trend in K/yr.

3 Results and Discussion Figure 2 shows the ion temperature at 310–340 km altitude and fitted variations. Red dots in Figures 2a and 2b denote the observed temperature from the EISCAT Tromsø UHF radar. Blue lines in Figures 2a and 2b show Ti_sol. In Figure 2a this was estimated using F 10.7ave , equation 2, and in Figure 2b using MgII, equation 3. In both fits also the solar zenith angle was included. After subtracting Ti_sol from the observed data, Ti_obs, we obtained ∆Ti shown as red circles in Figures 2c and 2d. Then, we estimated linear trends of ∆Ti from linear fits. They showed slightly different dependence on the solar activity indexes: −1.0 K/yr for F 10.7ave in Figure 2c and −1.4 K/yr for MgII in Figure 2d. Superposed on the linear trends are perhaps variations with time scale of longer than 11 years (~16 years) which are not removed by our subtraction of the solar activity effects. We are not able to find explanation for these variations, but can say that there is no correlation with major volcanic eruptions or El Niño events [e.g., NOAA National Climatic Data Center, 2012]. Figure 2 Open in figure viewer PowerPoint Ti after removal of the solar effects and (in blue) a linear fit to it. In Figures F 10.7ave ), and in Figures MgII line. (a, b) The ion temperature at 310–340 km altitude (red dots) and the fitted variations due to solar radiation and activity (blue line). (c, d) The residual (in red)after removal of the solar effects and (in blue) a linear fit to it. In Figures 2 a and 2 c the fit is based on average 10.7 cm radio flux (), and in Figures 2 b and 2 d the fit is based on theline. Figure 3 shows how the Ti trend and also the corresponding correlation coefficients vary with altitude. Using the three different solar activity indicators, the trend is always between −0.5 and −1.5 K/yr at 230–380 km height. The cooling rate is maximal at 330 km altitude, and above it decreases again. The trend at 400–470 km altitude becomes null or even warming, ~ 1 K/yr. Note that error bars indicate the 95% confidence interval. The trends derived using the F 10.7 and MgII indicators are very similar, using F 10.7ave we get about 0.5 K/yr less cooling at all altitudes (F 10.7 and MgII). Figure 3 Open in figure viewer PowerPoint Height profiles of (a) ion temperature trends and corresponding (b) correlation coefficients. Three different solar activity indexes were tried and are shown in different colors. The Ti trends of F 10.7ave are in relatively good agreement with the TIME‐GCM prediction. Moreover, the height profiles of the Ti trends derived from both the MgII data and the F 10.7ave index are close to the TIME‐GCM results if we use only data when Kp < 2 (under quieter geomagnetic conditions). The cooling trends were about −0.5 to −0.8 K/yr at 330 km altitude (not shown here). The correlation coefficients of the linear fits to the residual ion temperatures are 0.85 below 200 km and 0.9–0.95 above 200 km up to 450 km altitude (see Figure 3b). Above 250 km altitude, the correlation coefficients of the fits to F 10.7ave or MgII residuals are about 2% higher than that to normal F 10.7 . Figure 4 shows how the fitted Ti trend varies with local time between 8 and 16 LT. The trends are not significantly different over the 8 h interval, always about −1.5 to −0.5 K/yr at 300–330 km altitude and −0.5–1.0 K/yr above 350 km altitude. Warming trends above 400 km around noon are slightly weaker than those at prenoon/postnoon. This does not apply for data in the evening or at night (not shown), when correlation coefficients became rather low (<0.7) and no clear Ti trends were seen. Also at middle latitudes, the Ti trends were different between day and night [Zhang and Holt, 2013]. Moreover, recent GCM results show that secular changes of the F region ionosphere peak density (N m F 2 ) and altitude (h m F 2 ) exhibit large variations with local time in the low geomagnetic latitude region, but not at high latitudes [Qian et al., 2014]. Detailed investigations of upper atmospheric trends, which focus on the day‐night difference at high latitudes, are desirable in the near future. Figure 4 Open in figure viewer PowerPoint MgII index, equation Color‐coded ion temperature trends shown over altitude and local time between 8 and 16 LT, derived by using theindex, equation 3 The increasingly positive/warming Ti trend above 350 km altitude is in contrast with that found at midlatitudes [Donaldson et al., 2010; Zhang et al., 2011; Zhang and Holt, 2013], where cooling up to the maximum observed altitude at about 500 km is seen. A downward shifting pressure level can lead to apparent warming at fixed height [Zhang and Holt, 2013], but mainly where the temperature gradient is positive upward, in the lower thermosphere. Here we think that heat input to the ionospheric plasma combined with a decreasing ion cooling rate (because of the negative neutral density trend) [Qian et al., 2011, 2014] might cause the apparently positive Ti trend above 350 km height. Since this is not seen at midlatitudes, probably heat input in the vicinity of the dayside cusp, nightside aurora, ionospheric troughs, etc. is significantly large only at high latitudes and altitudes, and only there Ti becomes not a good proxy of Tn anymore. Further studies, perhaps using also the electron temperature, will need to assess this issue more quantitatively.

4 Conclusions In this study, we present estimates of ionospheric Ti trends at high latitude using a long‐term database of EISCAT UHF radar observations at Tromsø spanning about three solar cycles during 1981–2013. The trends at Tromsø of the residual Ti, after removal of solar activity influence, are −0.5 to −1.5 K/yr in the F region (200–380 km) under geomagnetically quiet conditions. This is less than those estimated from Millstone Hill [Zhang et al., 2011] and Saint Santin IS radar data at middle latitudes, and it is comparable to the GCM predictions by Qian et al. [2011] but still somewhat larger. CO 2 concentration is presumably increasing also in the upper atmosphere, and the rate of CO 2 increase in the upper atmosphere is expected to be even faster than that in the lower atmosphere [Emmert et al., 2012]. We suggest that this might explain why our observed cooling is slightly stronger than we expected from the published model predictions. In the topside ionosphere (400–470 km) the trends were −0.5–1 K/yr, which is similar as seen in the GCM predictions. Thus, the height profiles of our observed trends are relatively close to those obtained from the GCM [see Qian et al., 2011, Figure 5b]. Our results, based on one of the longest time series of reliable temperature measurement from the ionosphere, are the first quantitative confirmation of the simulations and of the so far rather qualitative expectations. The method described in this paper can be used to estimate long‐term trends also at other IS radars and so lead to globally more comparable estimates of temperature trends in the upper atmosphere. They may have an impact on the modeling of upper atmospheric density which is important for planning space flight and for predicting orbits of satellites, debris, etc.

Acknowledgments We are indebted to the director and staff of EISCAT for operating the facility and supplying the raw radar data. EISCAT is an international association supported by research organizations in China (CRIRP), Finland (SA), France (CNRS, till end 2006), Germany (DFG, till end 2011), Japan (NIPR and STEL), Norway (NFR), Sweden (VR), and the United Kingdom (NERC). This work is supported by the NIPR Project KP‐9 and also partly supported by Grants‐in‐Aid for Scientific Research B (22403010, 23340144, and 25287126) by the MEXT, Japan. The temperature data used in this study are a part of the EISCAT database in NIPR which is available at http://polaris.nipr.ac.jp/~eiscat/eiscatdata/. The F 10.7 and MgII indexes were obtained from NOAA and the Institute of Environmental Physics, University of Bremen. These data are available at ftp://ftp.ngdc.noaa.gov/STP/GEOMAGNETIC_DATA/INDICES/KP_AP/ and http://www.iup.uni‐bremen.de/gome/gomemgii.html. The Editor thanks Jan Laštovička and an anonymous reviewer for their assistance in evaluating this paper.