Analysis of a 4 year radiosonde data set at Dome C for characterizing temperature and moisture conditions of the Antarctic atmosphere

Transcription

1 JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 116,, doi: /2011jd015803, 2011 Analysis of a 4 year radiosonde data set at Dome C for characterizing temperature and moisture conditions of the Antarctic atmosphere Claudio Tomasi, 1 Boyan Petkov, 1,2 Elena Benedetti, 1 Luca Valenziano, 3 and Vito Vitale 1 Received 11 February 2011; revised 19 April 2011; accepted 25 April 2011; published 4 August [1] A 4 year set of vertical profiles of pressure, temperature and relative humidity derived from 1113 radiosounding measurements performed at Dome C (Antarctica) at 12:00 UTC of each day, from late March 2005 to March 2009, were analyzed using an updated procedure for removing the most important temperature and humidity errors and dry biases. The monthly mean vertical profiles of pressure, temperature, and moisture parameters were determined, providing evidence of the strong seasonal variations in temperature occurring within the ground layer and in the tropopause region. The results are presented for use in the analysis of the ground based measurements of both short and long wave radiation budget terms routinely performed at this site, and to better investigate the water vapor role in the Antarctic Plateau surface atmosphere system. The evaluation of atmospheric water vapor content W (precipitable water) is of great relevance for astronomical studies, to verify that high atmospheric transmission conditions exist, especially in the submillimeter and millimeter range, for the exceptionally dry air characteristics of the Dome C atmosphere. The monthly mean data sets of moisture parameters were analyzed to determine the monthly mean vertical profiles of absolute humidity, and to evaluate the daily values of W, varying from less than 0.30 mm (from June to October) to more than 0.60 mm in January, on average. The monthly mean percentiles of W indicate that this parameter is expected to be lower than 0.20 mm on at least 25% of days from May to October, indicating that very high atmospheric transparency conditions should occur in the infrared millimeter spectral range on at least 40 days during austral autumn and winter. Citation: Tomasi, C., B. Petkov, E. Benedetti, L. Valenziano, and V. Vitale (2011), Analysis of a 4 year radiosonde data set at Dome C for characterizing temperature and moisture conditions of the Antarctic atmosphere, J. Geophys. Res., 116,, doi: /2011jd Institute of Atmospheric Sciences and Climate, Consiglio Nazionale delle Ricerche, Bologna, Italy. 2 International Centre for Theoretical Physics, Trieste, Italy. 3 Institute of Space Astrophysics and Cosmic Physics, INAF, Bologna, Italy. Published in 2011 by the American Geophysical Union. 1. Introduction [2] The analysis of radiosounding data recorded at Antarctic sites can provide useful information to the scientific community involved in polar sciences, investigating the radiation exchange mechanisms between the surface atmosphere system and outer space that govern global change processes in Antarctica [Turner et al., 2009]. Dome C station (75 06 S, E, 3233 m a. m. s. l.) on the Antarctic Plateau presents exceptionally good atmospheric conditions for radiometric validation activities applied to infrared satellite sensors, such as the Atmospheric Infrared Sounder (AIRS) [Walden et al., 2006], and for investigating the local climate [Wendler and Kodama, 1984] and the dependence of ground level shortwave and long wave radiation measurements on atmospheric temperature and moisture conditions [Orsini et al., 2002; Town et al., 2005; Lanconelli et al., 2009]. In this context, local radiosonde measurements provide useful data for examining sets of in situ measurements taken with groundbased solar, optical and infrared radiometers [Walden et al., 1997, 1998, 2005; Orsini et al., 2000; Kenyon and Storey, 2006; Moore et al., 2008], as well as measurements of outgoing short and long wave radiance fluxes derived from satellite observations [Yamanouchi and Kawaguchi, 1984; Yamanouchi and Charlok, 1995; Gettelman et al., 2006; Town et al., 2007]. [3] High altitude Antarctic Plateau sites are also known to have the best atmospheric transparency conditions on Earth for submillimetre and millimeter astronomy [Bally, 1989; Chamberlin et al., 1997; Lane, 1998; Lawrence et al., 2004; Calisse et al., 2004; Minier et al., 2008]. Large installations have been built at South Pole, including the 10 m class telescope that started operation in 2007 [Padin et al.,2008],while other interesting sites are being studied, such as Dome A (81 40 S, W, 4093 m a. m. s. l.), the highest one, and Dome C, where the French Italian station currently operates 1of18

2 all year round [Walker et al., 2004; Vernin et al., 2007; Hagelin et al., 2008; Lawrence et al., 2008; Tothill et al., 2008]. A number of projects have recently been proposed to exploit the specific characteristics of the Antarctic Plateau, particularly those of Dome C [Minier et al., 2007; Griffin et al., 2008; Saunders et al., 2008]: the projects involving millimeter and submillimetre observations are considered among the most promising [Martin, 2007]. [4] Astronomical site testing measurements have been performed at Dome C over recent years to ascertain the main meteorological features occurring throughout the year [e.g., Valenziano and Dall Oglio, 1999; Aristidi et al., 2005]. The data recorded during these experiments confirm that meteorological conditions favorable to astronomical observations are frequently observed at this high altitude site, with low temperature, low wind speed near the surface, weak atmospheric turbulence and very small columnar atmospheric contents of water vapor (hereinafter referred to as precipitable water W) [Saunders et al., 2008]. Atmospheric conditions at Dome C have been monitored since 2005 using radiosondes launched on daily basis. Early data were reported and discussed by Tomasi et al. [2006] (T06 hereinafter), providing a first comparison with the results obtained at other astronomical sites, such as South Pole [Chamberlin et al., 1997], Mauna Kea (Hawaii Is.) [Hogg, 1992], and Atacama desert (Chile) [Lane, 1998; Giovanelli et al., 2001]. [5] The radiosounding activity of the meteorological group of the Antarctic Project (National Agency for new technologies, Energy and sustainable Economic development (ENEA), Centro Ricerche (C. R.), Casaccia, Rome, Italy) at Dome C started in late March 2005, to provide a continuous supply of data to the scientific community and satisfy the SCAR XXVII 12 recommendation for the provision of upper air data on the Antarctic Plateau, in order to both improve numerical atmospheric modeling and precipitable water estimates and test novel vertical profiling techniques. To comply with this recommendation, it was decided to integrate the results presented by T06 over a limited period of 5 months only (from December to May) with the analysis of a 4 year set of radiosonde measurements, performed from late March 2005 to late March 2009, which constitute a unique data set recorded at this high altitude site of the Eastern Antarctic Plateau region. Some similar results are also available for the inland Antarctic Plateau, as measured at the Dome Fuji (Dome F) station (77 30 S, E, 3810 m a. m. s. l.) [Hirasawa et al., 1999; Yamanouchi et al., 2003]. [6] The results are reported here to characterize the monthly variations of the thermodynamic conditions of the Dome C atmosphere throughout the year. These data were recorded not only during the austral summer and fall months but also during the coldest period of the year, when solar radiation does not illuminate this remote site and the Dome C atmosphere. Bearing in mind that significant errors may affect the radiosonde measurements for such extreme thermal and dry air conditions, in the present work it was decided to improve the T06 overall correction procedure. For this purpose, use was made of (1) the lag error corrections estimated by Rowe et al. [2008] (R08 hereinafter) in the presence of strong temperature inversions near the ground at Dome C; (2) the algorithm of Cady Pereira et al. [2008] (C08 hereinafter) for correcting the RS80 A solar heating dry biases affecting the relative humidity (RH) measurements, properly normalized to the dry bias estimates made by R08 at Dome C; and (3) the algorithms of Miloshevich et al. [2009] (M09 hereinafter) for correcting the RS92 instrumental errors affecting RH. Using these algorithms, more reliable evaluations of RH have been obtained than those achieved by T06 in April May [7] Considering that the present results could also be useful for both climatological studies and site testing applied to astronomical observational activities, they have been made available to the scientific community on the POLAR AOD website ( accessible on request of the password to the corresponding author. 2. Analysis of the 4 Year Radiosounding Data Set [8] The 4 year set of raw radiosonde data was recorded by examining an overall number of 1113 radiosounding measurements taken routinely at Dome C, at 12:00 UTC of each day, from 25 March 2005 to 31 March 2009, by the meteorological group of the Antarctic Project (ENEA, C. R., Casaccia, Rome, Italy). Examining the data set recorded from late March 2005 to March 2009 excepting August November 2007 (due to logistic problems resulting in a failure to supply radiosondes for that period), a variable number of radiosoundings was collected for each month, obtaining an overall 4 year number of 75 (in October) to 110 (in January) radiosoundings per month. The surface level values of pressure p, temperature T and relative humidity RH recorded at 12:00 UTC with the barograph, thermograph and hygrograph of the Concordia meteorological station were also collected to check the quality of the surface level radiosonde data before launch. [9] In general, each radiosonde measurement consisted of (1) values of p and T taken at more than 800 standard and additional levels in the altitude range from the surface to 10 km, and at stratospheric levels up to km altitude; and (2) values of RH measured in general at all the tropospheric levels mentioned above and often at supplementary levels from 10 to no more than 15 km. The data were obtained using Vaisala RS92 sondes for about 97% of the measurement days, and RS80 A models for 36 radiosonde launches only. At each radiosonde measurement level, a triplet of signals giving the measurements of p (in hpa), T (in C), and RH (in %) was sent by the transmitter onboard the radiosonde to the ground station. The data were recorded every 2 s and, hence, in height steps of m, since the balloon ascent rate was equal to 5 6 ms 1 at all levels. Geometrical altitude z was calculated for each triplet of radiosonde signals p, T and RH, using the following algorithm proposed by the manufacturer, Dz ¼ ðr=gþ * þ T i * 2 T iþ1 lnðp i =p iþ1 Þ; ð1þ based on the assumption that the air is in hydrostatic equilibrium. In equation (1), R is the universal gas constant, g is the gravitational acceleration, assumed to be equal to m s 2 at the 75 S latitude, T i * and T i+1 * are the virtual temperature values (measured in K), and p i and p i+1 are the 2of18

3 Table 1. Characteristics of the Barocap, Thermocap, and Humicap Sensors Mounted on the Two Models of Vaisala Radiosondes Used to Carry Out the Radiosounding Data at Dome C, as Given by the Manufacturer or Provided in the Literature Characteristics RS80 A a Vaisala Radiosonde Model Barocap Sensor Measurement range hpa hpa Declared resolution 0.1 hpa 0.1 hpa Total uncertainty in sounding (accuracy) ±0.5 hpa ±0.5 hpa Repeatability in calibration 0.5 hpa (from 1060 to 3 hpa) 0.4 hpa (from 1080 to 100 hpa) 0.3 hpa (from 100 to 3 hpa) Reproducibility in sounding 0.5 hpa (from 1060 to 3 hpa) 0.5 hpa (from 1080 to 100 hpa) 0.3 hpa (from 100 to 3 hpa) Thermocap Sensor Measurement range K K Declared resolution 0.1 K 0.1 K Total uncertainty in sounding (accuracy) ±0.2 K ±0.5 K Repeatability in calibration 0.2 K 0.15 K Reproducibility in sounding 0.2 K (from 1060 to 50 hpa) 0.3 K (from 50 to 15 hpa) 0.4 K (from 15 to 3 hpa) RS K (from 1080 to 100 hpa) 0.3 K (from 100 to 20 hpa) 0.5 K (from 20 to 3 hpa) Humicap Sensor Measurement range 2% 100% RH 0% 100% RH Declared resolution 1% RH 1% RH Total uncertainty in sounding (accuracy) <±3% RH ±5% RH Repeatability in calibration 2% RH 2% RH Reproducibility in sounding <3% RH 2% RH a As given by the manufacturer; see air pressure values, measured for both parameters at the ith and the (i + 1)th radiosonde levels, respectively. At each ith level, T i * was calculated according to the following relationship [Dubin et al., 1966]: T i T i * ¼ 1 0:379ðe i =p i Þ ; ð2þ where partial pressure e i is given by the product RH i E(T i ), where the saturation water vapor pressure in the pure phase over a plane surface of pure liquid water, E(T i ), is determined as a function of T using the Bolton [1980] formula. This formula approximates very well the values of the Smithsonian tables [List, 1966] within the K temperature range and those of the Murphy and Koop [2005] formula at temperatures lower than 220 K. [10] The values of Dz were calculated for each layer between two consecutive radiosonde levels in terms of equation (1), starting from the surface level, where the surface pressure p o was measured with good precision by the barograph of the Concordia meteorological station (3233 m a. m. s. l.). [11] The RS92 sondes were used to measure parameters p, T and RH with a new model of Barocap silicon sensor, the F Thermocap sensor and a new model of heated twin Humicap sensors, respectively. The RS80 A model radiosondes were used on 36 days only, equipped with Barocap capacitive aneroid, traditional Thermocap sensor and A Humicap sensor. The main characteristics of the two sensors are listed in Table 1, as given by the manufacturer. Bearing in mind that the measurements are all affected by experimental errors of various origins, accurate procedures were adopted in the present analysis to reduce the systematic errors of the various sensors, as described in the following subsections Evaluations of the Barocap Errors [12] On the basis of the Barocap characteristics given in Table 1, the Barocap sensors mounted on the two Vaisala radiosonde models were estimated to be affected at Dome C by instrumental errors smaller than 1 hpa over the usual operational range from 630 to 3 hpa. Taking into account that errors as large as 10 hpa at surface level were found at South Pole by Hudson et al. [2004] on inspecting the RS80 Barocap measurements, a rigorous check of the RS80 A Barocap performances was made by us at Dome C by comparing the surface values of pressure p o measured by the radiosondes a few minutes prior to launch and the simultaneous values of p o recorded at 12:00 UTC by the barograph of the Concordia meteorological station. The discrepancies between the ground level measurements of p o given by the Barocap and Concordia barograph were found to be on average equal to (0.06 ± 0.50) hpa, and, hence, within the Barocap accuracy in most cases. In view of the very close agreement, the ground level RS80 A measurements of p o were considered to be all fully reliable and not affected by errors like those indicated by Hudson et al. [2004] at South Pole. The good performances of the RS80 A Barocap were presumably due to the choice of the ENEA Antarctic Project team to leave the radiosondes outdoor for at least 5 min before launching Evaluations of the Thermocap Errors and Their Corrections [13] The Thermocap sensor mounted on the RS80 A sondes was estimated to reach its equilibrium with the ambient air within 30 s at the low temperatures usually observed at South 3of18

4 Figure 1. Scatterplot of the corrected values of temperature T(z) obtained from the 12:00 UTC radiosonde data at levels z = 4.6, 19.4, and 43.4 m versus the simultaneous values (read with an accuracy of 1 K), as measured by Genthon et al. [2010] on 19 days from 16 January to 3 February 2008, using the thermohygrographs placed at the same levels on the Dome C meteorological tower. The regression line (solid line) is drawn for intercept close to 0.79 K and slope coefficient equal to 0.997, giving a regression coefficient R = Dashed lines define the standard error of estimate SEE = ±0.90 K. Pole [Hudson et al., 2004]. This evaluation was assumed to be valid also for the radiosonde observations at Dome C, where temperature conditions comparable with those of South Pole are usually recorded during the year. Mahesh et al. [1997] compared radiosonde temperature measurements at South Pole with those made by thermistors onboard a tethered balloon, finding that marked thermal lag errors usually affect the radiosonde measurements up to an altitude of about 250 m above the surface, i.e., up to 40 s after launch. They suggested that allowing the radiosonde to equilibrate outdoors for 1 min prior to launch would eliminate such lag errors. At Dome C, all the radiosondes were routinely prepared in an appropriate room and then left in the external ambient air for at least 5 min before launch, thus avoiding errors such as those evidenced by Mahesh et al. [1997]. [14] The temperature data given by the Thermocap sensors can often be affected by errors caused by heating due to the incoming solar and/or infrared radiation, heat conduction from the other radiosonde components, and heat exchanges between the sensor and environment. Errors of this kind affecting the RS80 A Thermocap sensor were corrected following the Luers and Eskridge [1995] procedure, while those of the F Thermocap sensor onboard the RS92 sondes were neglected, as suggested by Luers [1997]. [15] The RS80 A and RS92 Thermocap data are also affected by lag errors depending mainly on air density and ventilation speed. The RS80 A lag errors were corrected over the whole altitude range, except within the temperature inversion ground layer (at all levels z < 4 km) usually observed at Dome C, using the algorithm derived for a radiosonde ascent rate of 6 m s 1 by Tomasi et al. [2004] on the basis of the numerous and statistically significant set of check measurements analyzed by Huovila and Tuominen [1991]. The algorithm was found to yield time lag estimates increasing on average from 3 s at the Dome C surface level to about 10 s at the 26 hpa level (i.e., at levels varying between 22 and 25 km throughout the year), which lead to lag errors <0.2 K at levels ranging between 4 and 25 km. According to the characteristics given by the manufacturer, the time constant of RS90/92 Thermocap sensor was estimated to increase from about 0.4 s at p = 630 hpa to 2.5 s at 10 hpa (z 46 km). However, Luers [1997] pointed out that the RS90 Thermocap sensor is smaller than the previous models and its response time is sufficiently rapid to respond to temperature gradients in the atmosphere without significant errors. Conversely, examining the radiosonde data recorded at Dome C, R08 found that the temperature corrections frequently associated with steep near surface temperature inversions cannot be neglected, while those relative to upper levels outside the thermal inversion ground layer were typically <0.2 K up to 4 km, and therefore almost negligible beyond 4 km. For such time lags (<2 s at the surface and varying between 0 and 4 s within the thermal inversion ground layer), R08 estimated that a shift of roughly 0 to 12 m in the temperature profile should occur at all the ground layer levels, leading to errors equal to (0.5 ± 0.7) K. Taking such evaluations into account, we decided to neglect the lag errors affecting the temperature data at altitudes higher than 4 km, and to correct the temperature data at the lower levels by assuming that the lag error increases linearly with vertical temperature gradient g i =dt/dz at the ith level, passing from null values for g i = 0 up to (1) 1.0 K for g i 1 K/m in the case of RS80 A data and (2) 1.2 K for g i 1.4 K/m in the case of RS92 data. The measurements of g i performed at the 4.6 m and 43.5 m levels of the Dome C meteorological tower by Genthon et al. [2010] during the summer period from 16 January to 4 February 2008, were found to provide daily values of g i ranging between 0.1 and +0.2 K/m, which are considerably lower than the upper limits of g i established by R08. On the other hand, examining the present set of temperature vertical profiles over the first 50 m above the ground, as measured on the austral winter days (from April to October), the daily average gradient g I was found to vary between 0.01 and 0.62 K/m, presenting an average value of 0.32 ± 0.14 K/m. Therefore, the lag errors affecting the present Thermocap measurements should not exceed ±0.1 K on the austral summer days and be considerably lower than the limit of 1.0 K estimated by R08 on the winter days. [16] A check test on the reliability of the present summer estimates of T(z) made within the ground layer through the above corrections is presented in Figure 1, plotting the present temperature values obtained at the three levels z = 4.6, 19.4 and 43.4 m above the surface from the present 12:00 UTC radiosoundings versus the simultaneous (20:00 LT) values measured at the same levels by Genthon et al. [2010] (read by us with an accuracy of 1 K) on 19 days from 16 January to 3 February The test provides evidence of the close agreement between radiosounding and meteorological tower 4of18

5 measurements, as proved by the regression coefficient of found for the best fit line (having intercept lower than 0.8 K and slope coefficient close to unity) and by the standard error of estimate SSE < 1 K, in spite of the 1 K accuracy with which the tower temperature data have been read by us Evaluations of the Humicap Errors and Their Corrections [17] The time constant of the A Humicap sensor was estimated by Miloshevich et al. [2004] (M04 hereinafter) to increase in a nearly exponential fashion from 0.2 s to about 200 s as air temperature T decreases from 298 K to 193 K. On the basis of such estimates, a correction algorithm was defined by us for the calculation of this time constant as a function of T, giving a correction term that decreases rapidly with tropospheric altitude. The H Humicap time constant of the RS92 sondes was instead estimated to assume appreciably smaller values, due to the use of a thinner polymer layer than the one employed in the A Humicap sensor, which assures a much faster response time at low temperatures [Miloshevich et al., 2006] (M06 hereinafter). Taking into account the characteristics of the two Humicap time constants, the RH data were corrected, following a procedure based on the combined use of the algorithms proposed by Wang et al. [2002] (W02 hereinafter) and M04, and the dry bias estimates of R08 and M09, the latter being related to atmospheric conditions similar to those observed in the Antarctic atmosphere. More precisely, the original RH data were analyzed following a procedure consisting of five steps: [18] 1. Before launch, each radiosonde was checked by comparing its ground level measurement of RH with that simultaneously recorded by the hygrograph of the Concordia meteorological station. Very small discrepancies were found in all the ground checks, confirming the evaluations of M09 that (1) Vaisala RS92 ground check corrections are in general limited to within ±0.5% RH and (2) small roundoff errors may affect the RS92 measurements, causing uncertainties comparable with the ground check ones, which mainly occur because the RH measurements in the standard RS92 processed data files are reported as integers. [19] 2. The construction of a skeleton profile as proposed by M04 was then applied within all the altitude intervals containing sequences of constant values of RH with altitude, to obtain more schematic and simplified vertical profiles. [20] 3. The RH data were subsequently corrected for (1) so called basic calibration model (BCM) errors, (2) chemical contamination (CC) dry biases, (3) temperature dependence (TD) dry biases, and (4) sensor aging (SA) dry biases: First, the A Humicap measurements of the RS80 sondes were corrected by following the T06 criteria, based on the use of the W02 algorithms for correcting the BCM errors, and the CC and SA dry biases, and on the evaluations of Miloshevich et al. [2001] for correcting the TD dry biases. Second, the H Humicap data of the RS92 sondes were (1) corrected for BCM errors in terms of the M06 evaluations of such calibration errors, which were estimated to increase in general from 0.6% to 2% RH in the Dome C atmosphere; (2) not corrected for CC dry biases, according to the M04 suggestions for the RS90 sondes, whose Humicap sensors were manufactured with improved polymer characteristics similar to those mounted on the RS92 sondes; (3) not corrected for TD dry biases, as suggested by M06, because all the radiosondes employed at Dome C were manufactured after 25 June 2001; and (4) not corrected for SA dry biases, according to the M06 evaluations for the RS92 sondes. [21] 4. All the RH vertical profiles obtained after step 3 were subsequently analyzed to remove the RH lag errors through the application of the M04 procedure, which consists of the following three substeps: (1) a first smoothing procedure, to minimize the tiny changes in slope; (2) a lag correction procedure utilizing the most suitable time constant values of the A and H Humicap sensors; and (3) a further smoothing procedure to remove any slight discontinuity in each RH vertical profile. The time lag errors in RH are caused by the slow Humicap sensor response at low temperatures. Sharp variations in temperature very frequently occur within the ground layer at Dome C, with T increasing by more than 20 K from surface to the inversion layer top level, i.e., over an altitude range of m on summer days, and of m on the coldest winter days. For such abrupt changes in temperature, spurious errors can be made using the M04 procedure, which are associated with Humicap sensor lag times, often estimated to exceed 5 10 s for T < 30 C. We verified that the rigorous application of the M04 lag correction procedure to the RH data measured within the ground layer, for extremely high values of the temperature vertical gradient, may often lead to very large and unrealistic spikes of RH, at least within the first m, presenting extreme temperature gradient conditions. To remove these spikes, we decided to correct further each vertical profile of RH by applying a moving average procedure of order 3 (i.e., over an altitude range of m) to the RH data obtained with the M04 procedure from surface level to the top level z* at which RH starts to assume more stable values with altitude. For an overall number of 945 cases (i.e., for 85% of the total number of the present radiosoundings), level z* was found to vary between 20 and 270 m, yielding an average value of (55 ± 33) m. The relative errors made in calculating the total precipitable water due to the adoption of the additional moving average technique within the thermal inversion ground layer, were estimated to be mostly smaller than 0.1%. [22] 5a. The daytime solar heating (SH) dry biases affecting the RS80 A data were corrected using the correction algorithm defined by C08. This was done by multiplying the daytime RH data by the SH dry bias correction factor F SH calculated by adjusting the C08 midlatitude algorithm to the Antarctic atmosphere conditions of Dome C. The original algorithm of C08 yields the difference F SH 1 as a function of solar zenith angle SZA over the range 54 SZA 86, in the following general form: F SH 1 ¼ exp eff secðszaþ ; ð3þ where coefficient a = was assumed to represent the effect of solar heating on the RS80 Humicap sensor, and the effective optical depth t eff of the atmosphere was kept equal to 0.2 in the midlatitude atmosphere, over the entire short wave spectrum. To adapt equation (3) to the Antarctic atmosphere conditions, a realistic value of t eff = 0.1 was assumed in place of 0.2, since it is given by the sum of 5of18

6 Figure 2. Vertical profiles of the correction factor G(p, RH) relative to the instrumental percentage error of the RS92 Humicap measurements evidenced by Miloshevich et al. [2009], separately for seven different RH classes during (left) nighttime and (right) daytime RS92 measurements of RH performed at tropospheric levels from 640 to 250 hpa. (1) the Dome C yearly average value of Rayleigh scattering optical depth equal to at 0.50 mm wavelength [Tomasi et al., 2010] (in place of the sea level value of relative to the midlatitude standard atmosphere [Tomasi et al., 2005]) and (2) the summer average aerosol optical depth at Dome C equal to 0.03 at visible wavelengths [Tomasi et al., 2007]. The A Humicap algorithm defined in equation (3) for t eff = 0.1 provides a value of the percentage SH dry bias equal to 7.5% RH for SZA = 62. Considering the higher estimates found by R08 for the solar radiation dry bias of the RS90 sondes, evaluated to vary between 12% and 15% at SZA = 62, it was assumed that their performances found for the harsh environmental conditions of Dome C should also affect the less stable RS 80 sondes. It was therefore decided to substitute the RS80 A value of a proposed by C08 at midlatitudes with a value of 0.12 to better fit the R08 evaluations of SH dry biases at Dome C. With this choice, we obtained a new dependence curve of the SH dry bias on SZA giving dry bias corrections of 10% RH at SZA = 54 (in place of about 5% RH for the midlatitude atmosphere) and values of 3% RH at SZA = 86 (in place of 0.4% RH). The RS80 A data were not corrected for further instrumental and empirical errors, because they were already corrected at step 3 for the CC, TD and SA dry biases using the W02 algorithms. [23] 5b. An alternative correction procedure was applied to the RS92 data of RH to correct both empirical/instrumental errors and the SH dry biases induced by the effects of incoming solar radiation. Such errors were evidenced by M09 through comparisons of RS92 data with simultaneous measurements of RH performed with some reference sensors (cryogenic frost point hygrometer, microwave radiometer, and ARM Surface Temperature/Humidity Reference system). The corrections were separately evaluated for the nighttime and daytime radiosounding measurements, only in the latter case including the SH dry biases. For this purpose, we used the pair of algorithms proposed by M09 to determine the vertical profiles of the nighttime and daytime correction factors G(p, RH) as a function of pressure p, as shown in Figure 2 for various RH classes. It can be seen that factors G(p, RH) relative to the various RH classes assume values appreciably lower than unity up to hpa levels and greater than unity at the upper levels, which clearly decrease at all levels as RH gradually decreases. Using a linear interpolation procedure in RH between the values of G(p, RH) obtained at each radiosonde level, the correction factors were calculated as a function of SZA for both nighttime and daytime conditions. [24] In order to give an idea of the corrections made to the raw RH data using the above multistep procedure, in Figure 3 the vertical profiles of raw RH data taken on 19 October 2005, with a RS80 A sonde, and on 30 December 2007, with a RS92 sonde, are compared with those obtained after steps 4 and 5a, respectively. The first example was limited to the corrections made up to step 4 of the present procedure, because the radiosounding data of 19 October 2005, were taken for SZA = 89 and, hence, for very weak incoming solar radiation, thus presenting in practice negligible SH dry biases. This example actually shows only the corrections made using the four W02 algorithms for instrumental errors and dry biases and the M04 procedure for lag errors. It can be seen that, compared to the raw data, appreciably higher values of RH were obtained, by (1) about 10% within the altitude range from surface to nearly 5 km, for temperatures varying between 225 and 238 K; and (2) by 10% 20% in the upper troposphere from 7.5 to 9.5 km, with temperatures ranging between 220 and 206 K. [25] Conversely, it can be noticed in Figure 3a that very small corrections of the dry biases and lag errors were made at steps 3 and 4, when applying the W02 algorithms and 6of18

7 Figure 3. Vertical profiles of temperature T (red curves) and RH (other colors), obtained at various steps of the present correction procedure applied to the radiosonde data taken on (a) 19 October 2005 (12:00 UTC) using a RS80 A radiosonde and (b) 30 December 2007 (12:00 UTC) using a RS92 radiosonde. Black curves refer to the raw RH data; green curves refer to the RH data obtained after step 4, i.e., after correction for the SH dry biases and lag errors of Miloshevich et al. [2004]; and blue curves (Figure 3b) refer to the RH data obtained after step 5b, i.e., after correction for the instrumental errors evidenced by Miloshevich et al. [2009], also including SH dry biases during the daytime radiosounding measurement. determined at some significant levels. For the RS80 A data, the present correction procedure was found to provide higher values of RH than the raw data, by (1) 20% 30% near the surface (with SD ±10%, on average), (2) less than 10% from 3.5 to 6 km (with increasing SD from ±5% to more than ±10%), and (3) 10% 20% at the upper altitudes (with SD no lower than ±10%). Considerably lower values of DRH were instead found for the RS92 data, with values (1) of 5% 15% within the ground layer (SD ±10%), (2) very close to null and sometimes negative from 3.5 to 5 km (SD within ±3%), and (3) of a few percent at the upper tropospheric levels (SD within ±5%). Therefore, Figure 5a provides evidence of the higher quality of the RS92 measurements of RH with respect to the RS80 A ones. Figure 5b presents a comparison between the present corrections of DRH made on 33 days of April and May 2005, and those made by T06, who used a correction procedure not including the C08 corrections for the SH dry biases and the M09 corrections for the instrumental errors of RS92 data. The comparison indicates that the present procedure provides only small corrections of RH at all the tropospheric levels, which are on average no higher than ±3%, and thus substantially comparable with those obtained following the T06 procedure. Such agreement arises from the fact that only RS92 radiosounding measurements were performed during April M04 procedure to the raw RS92 data, respectively. The M09 instrumental error corrections subsequently made at step 5b were found to be of a few percent only within the ground layer, for T(z) < 240 K, and greater (ranging between 5% and 15%) at the upper levels from 5 to 9 km, for T(z) varying between 237 and 217 K. [26] In Figure 4, the values of RH obtained at the levels z = 4.6, 19.4 and 43.4 m above the suface, applying the present correction procedure to the 12:00 UTC radiosounding measurements, are plotted versus the simultaneous (20:00 LT) measurements of RH with respect to liquid water, as derived from the Genthon et al. [2010] data of RH with respect to ice at the same three levels. The two data sets, recorded on 19 days from 16 January to 3 February 2008, show a satisfactory agreement, giving a best fit line with intercept lower than RH RS = 4%, slope coefficient close to unity, and regression coefficient R = The standard error of estimate (SEE) was found to be equal to ±3%, clearly indicating that the present radiosonde measurements of RH are reliable also within the lower part of the thermal inversion ground layer. This confirms the appropriateness of applying a further moving average procedure to the RH data, as made here at the end of step 4 to compensate for the over corrections caused by the M04 procedure. [27] More generally, to give a measure of such corrections, the difference DRH = RH cor RH raw was calculated between each corrected value and the corresponding raw value of RH, for all the RS80 A and RS92 vertical profiles of RH. The overall average vertical profiles of DRH obtained for the two radiosonde models are shown in the left part of Figure 5, together with the standard deviations (SD) Figure 4. Scatterplot of the values of RH obtained applying the present correction procedure to the 12:00 UTC raw radiosonde data at levels z = 4.6, 19.4, and 43.4 m versus the simultaneous values measured by Genthon et al. [2010] on 19 days from 16 January to 3 February 2008, with thermohygrographs placed at the same levels on the Dome C meteorological tower, after conversion of the RH data with respect to ice into RH data with respect to liquid water. The regression line is drawn for intercept equal to 3.7% and slope coefficient of 1.07, giving a regression coefficient R = Dashed lines define the standard error of estimate SEE = 3%. 7of18

8 Figure 5. (a) Comparison between the average vertical profiles of the difference DRH = RH cor RH raw between the corrected final and raw values of RH, as obtained for the two sets of RS 80A radiosonde (solid circles) and RS92 radiosonde (open circles) data recorded over the 4 year measurement period. (b) Comparison between the average vertical profiles of the difference DRH between final and raw data of RH, as obtained by Tomasi et al. [2006] (open squares) from the radiosoundings performed on 33 days in April and May 2005, and those obtained examining the same set of radiosounding data using the present procedure (solid squares). In both parts of the graph, the horizontal bars define the standard deviations SD calculated at some selected levels only. 3. Monthly Mean Vertical Profiles of Pressure, Temperature, and Moisture Parameters [28] The 1113 vertical profiles of parameters p, T and RH determined above by correcting the raw radiosonde data were examined to obtain the values of these meteorological parameters and of absolute humidity q(z) at common fixed levels, chosen in regular steps of 25 m from 3.25 to 4 km, 50 m from 4 to 5 km, 100 m from 5 to 12 km, and 250 m from 12 km to the radiosonde top level. At such fixed levels of each vertical profile, the values of p(z) were calculated through an exponential interpolation in altitude between the pressure data, whereas (1) the values of T(z) were calculated following a linear interpolation procedure in altitude and (2) the values of RH were determined through linear intepolation up to the 12 km level, including also the values lower than 2%, which were considered realistic according to M09 even if comparable with their accuracy (see Table 1). At upper levels, the profiles of p(z), T(z) and RH(z) are often lacking on the austral winter days, because of the frequent rubber balloon breaks due to the extremely cold temperatures. However, the lack of moisture data at stratospheric levels does not prevent us from obtaining realistic values of precipitable water, because the stratospheric content of water vapor is generally smaller than 1% of that present in the Antarctic troposphere below the 12 km level. This is clearly confirmed by the calculations made for the Subarctic Winter Standard Atmosphere model of Anderson et al. [1986] integrating the vertical profile of absolute humidity from the Dome C surface level (= km) to 120 km altitude, which indicate that nearly 99% of precipitable water is present below the 10 km level, with only about 1% present from 10 to 120 km. [29] On the basis of such calculations, we decided to consider only the RH data taken from surface level to 12 km, for defining the moisture conditions of the atmosphere and determining precipitable water. The fixed levels suitable for the calculations of the vertical profiles of RH(z) and absolute humidity q(z) were thus established in compliance with those of T(z) Analysis of Pressure Data [30] The 1113 daily pressure data sets were subdivided into 12 monthly subsets, from which the monthly mean vertical profiles of p(z) were calculated at all the fixed levels from surface to the highest radiosonde top level. The monthly top levels were found to vary appreciably with May 2005, for nighttime conditions only, and, hence, in the absence of solar heating effects. Therefore, the differences shown in the right part of Figure 5 are only due to the use of the M06 and M09 algorithms made for correcting the nighttime instrumental errorsbyt06andinthepresent work, respectively. Figure 6. Annual cycles of the monthly mean values of pressure p o at surface level z o (solid circles) and pressure p(z) atthez = 5 km (open circles) and z =10km(open squares) altitudes, obtained by examining the whole 4 year data sets of daily pressure values at these levels, from early April 2005 to late March Solid vertical bars give the corresponding standard deviations. 8of18

9 Figure 7. (a) Monthly mean vertical profiles of temperature T(z) obtained from the 4 year data sets recorded over the the altitude range from surface level to 5 km, for the 6 months from (left) March to August and (right) September to February. (b) As in Figure 7a, over the altitude range from 5 to 30 km. season, from less than 15 km in the austral winter to more than 25 km in summer, owing to the frequent radiosonde balloon explosions occurring at km levels due to the very cold temperature conditions characterizing the low stratosphere during the austral winter. Figure 6 shows the seasonal variations of the monthly mean values of air pressure p(z) at the surface level and at 5 and 10 km levels, together with their monthly SD, varying between 5 hpa in January and 11 hpa in August at the surface level. It can be seen that the monthly average values of surface level pressure p o describe an annual cycle with a maximum of ± 4.8 hpa in January and a minimum of ± 8.8 hpa in October, therefore presenting an overall variation that is considerably greater than the SD values. Similar annual cycles of p(z) can also be observed at the 5 km and 10 km altitudes in Figure 6, in both cases presenting the maxima in January and December, and minima from August to October. Similar patterns were also found at stratospheric levels, where pressure decreases in an exponential fashion as a function of altitude (depending closely on atmospheric air density and temperature) up to values varying between a minimum of 15.9 ± 0.7 hpa and a maximum of 26.0 ± 0.2 hpa at 25 km throughout the year. Also at these altitudes, the annual pressure variations were found to be appreciably greater than the corresponding SD values Analysis of Temperature Data [31] The homogeneous profiles of T(z), obtained by linear interpolation in altitude of the temperature values corrected in subsection 2.2 were then subdivided into monthly sets to determine the monthly mean vertical profiles of this quantity, which were then used to describe the seasonal variations of the thermal atmospheric conditions. Figure 7 presents the monthly mean vertical profiles of T(z) determined from surface level to 30 km altitude, with SD values that are (1) no greater than 10 K at all the tropospheric levels throughout the year, (2) not exceeding 10 K at all stratospheric levels from 10 to 30 km during the period from March to August, and (3) ranging between 10 and 15 K in the km altitude range from September to February. In the austral winter months, the vertical profiles of T(z) are limited in height, due to the lack of measurements on account of the frequent balloon breaks occurring at low stratospheric altitudes for extremely cold air conditions. Figure 7 shows that tropospheric temperature decreases from February to August (by 6 8 K on average) and subsequently increases from September to the austral summer. In the stratosphere, the monthly average temperature was foundtodecrease(byabout35kat20kmaltitude)inthe period from February to August, and subsequently increase from September to December to January (passing from 195 K to more than 235 K at the 20 km level). [32] Figure 7a clearly shows that a large variability characterizes the monthly mean vertical profiles of T(z) within the ground layer of the Dome C atmosphere. The seasonal variations in the thermal conditions of the high troposphere and low stratosphere also contribute to causing marked changes in the thermal characteristics of the tropopause region, which can be well represented in terms of the monthly mean values of temperature minimum T m and its altitude z t. For this reason, particular attention was paid here to the month to month variations of the above seven parameters, when defining the seasonal variations of the temperature vertical profile within the ground layer and the temperature changes in the tropopause region. The main results are presented in the following two paragraphs Seasonal Changes in the Thermal Structure of the Temperature Inversion Ground Layer [33] Extensive discussions on the thermal structure of the surface inversion layer of the atmosphere above the Antarctic Plateau were made in the past decades [Dalrymple, 1966; Kuhn et al., 1977]. Considering that the analysis of the present measurements can contribute to improve the knowledge of the seasonal variations in the temperature conditions of the surface inversion layer above the high altitude Antarctic sites, it was decided to pay particular attention to the analysis of the temperature data measured within the ground layer at Dome C. The vertical profiles of T(z) shown in Figure 7a reveal strong inversion features near the ground for most of the year. The seasonal variations in the thermal conditions of the ground layer can be described in terms of the monthly average values of the following five parameters: (1) temperature T o at the surface level, (2) temperature T g at the top level of the inversion ground layer, (3) difference DT = T g T o, (4) depth Dz of the temperature inversion ground layer, and (5) average vertical gradient g of temperature calculated over the depth Dz. Thus, parameters T o, 9of18

10 Figure 8. (top) Annual cycles of the monthly mean values of surface level temperature T o (solid diamonds), temperature T g at the top level of the inversion ground layer (open diamonds), and difference DT (solid triangles) between T g and T o. (middle) As in Figure 8, top, for the depth Dz (solid squares) of the temperature inversion ground layer, and the thermal gradient g (open squares) calculated over the depth Dz. (bottom) As in the upper and middle parts, for the altitude z t of the temperature minimum (solid circles) and the temperature minimum T m (open circles). The vertical bars represent the monthly standard deviations. T g and Dz are independent, while DT and g are derived from the previous ones. The series of monthly mean values of the five parameters are shown in Figure 8, together with their SD values determined for the monthly data sets recorded over the 4 years of radiosoundings. Examining them, it can be noticed that: [34] 1. The monthly mean values of T o describe an annual cycle with a maximum of 244 K in December January, and the lowest values ranging between 210 and 212 K from April to September. [35] 2. The annual cycle of T g is characterized by a maximumof246kinjanuaryandaminimum<235kin October, with monthly SD values ranging between 2.7 K (October) and 5.3 K (September). Therefore, the monthly mean values of T g are considerably more stable than those of T o throughout the year, slightly exceeding those of T o in January and December only. [36] 3. As a consequence of the month to month variations in T o and T g, the monthly mean values of DT also follow a well defined annual cycle, with values of 2 K in December and January, gradually increasing to over 24 K during the winter period from April to July and maintaining values of 23 K in the following 2 months, with SD values varying between less than 2 K in December and January and more than 5 K from February to September. [37] 4. The average inversion layer depth Dz assumes the rather low monthly mean values of 130 and 200 m in December and January, for relative SD values close to 90% in both months, and presents rather high values from February to October, ranging between 440 and 540 m. However, small month to month changes in this parameter were found throughout the year, considerably more limited than the relative SD, generally found to vary between 35% and 50%. [38] 5. Associated with the above month to month variations in DT and Dz, the monthly mean values of gradient g were evaluated to vary between 22 and 25 K/km from November to February, assuming higher values between 45 and 64 K/km from March to October. Considering that these monthly average values were obtained with SD > ±20 K/km in all cases, it is evident that the temperature inversion characteristics in the ground layer vary greatly during the year, with austral summer values of g that are about 3 times smaller than the winter ones. [39] The above results clearly indicate that temperature inversion features are always present within the Dome C ground layer, having marked positive gradients for most of the year and more uniform thermal conditions in the two central months of austral summer only. To provide evidence of the strong day to day variations in both T o and T g taking place over various months and throughout the year, Figure 9 shows a comparison between the monthly relative frequency histograms of the two parameters. Considering that DT was estimated to be close to 2 K in December and January, the relative frequency histograms were drawn in steps of 2 K, to obtain a sufficiently good resolution of the day to day changes of this parameter over the year. The sequence of monthly pairs of histograms clearly show that the two dispersion histograms are (1) very similar in January, both presenting peaked and symmetrical shapes, with a difference d T between the two median values of T g and T o found to be <2 K; (2) only in part overlapping in February, with the histogram of T g more peaked than that of T o and d T 10 K; (3) almost totally separated in March, with a well pronounced peak of T g and a more dispersed and positively skewed histogram of T o, giving d T > 20 K; (4) clearly separated in the following months from April to September, with more peaked and symmetrical dispersions of T g and more dispersed and in general positively skewed dispersions of T o, giving values of d T ranging between 23 and 27 K; (5) less dispersed but still separated in October, with d T close to 17 K; (6) partially overlapping and peaked in November, with d T 6 K; and (7) almost totally overlapping in December, with peaked and symmetrical features giving a value of d T <2K Seasonal Changes in the Thermal Characteristics of the Tropopause Region [40] The seasonal changes of the tropopause thermal conditions can be easily appreciated in Figure 7b, which shows the monthly mean vertical profiles of T(z) determined over the entire 4 year period. The sequence of monthly mean profiles from March to August describe the gradual cooling of the troposphere and low stratosphere, followed by a marked 10 of 18