http://dx.doi.org/10.1016/j.jms.2015.12.007. Reference: ...... [63] Mario J. Molina and F.S. Rowland, Stratospheric Sink for Chlorofluoromethanes: Chlorine ...
Accepted Manuscript Ground-based spectroscopic measurements of atmospheric gas composition near Saint Petersburg (Russia) Yury Timofeyev, Yana Virolainen, Maria Makarova, Anatoly Poberovsky, Alexander Polyakov, Dmitry Ionov, Sergey Osipov, Hamud Imhasin PII: DOI: Reference:
S0022-2852(15)30025-4 http://dx.doi.org/10.1016/j.jms.2015.12.007 YJMSP 10654
To appear in:
Journal of Molecular Spectroscopy
Received Date: Revised Date: Accepted Date:
30 September 2015 13 December 2015 17 December 2015
Please cite this article as: Y. Timofeyev, Y. Virolainen, M. Makarova, A. Poberovsky, A. Polyakov, D. Ionov, S. Osipov, H. Imhasin, Ground-based spectroscopic measurements of atmospheric gas composition near Saint Petersburg (Russia), Journal of Molecular Spectroscopy (2015), doi: http://dx.doi.org/10.1016/j.jms.2015.12.007
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Ground-based spectroscopic measurements of atmospheric gas composition near Saint Petersburg (Russia) Yury Timofeyev, Yana Virolainen, Maria Makarova, Anatoly Poberovsky, Alexander Polyakov, Dmitry Ionov, Sergey Osipov, Hamud Imhasin Department of Physics, Saint Petersburg State University, 1 Ulyanovskaya str., 198504 SaintPetersburg, Russia
Keywords: ground-based spectroscopic measurements, FTIR, atmospheric gas composition, modeling, comparisons
ABSTRACT Since early 2009, high-resolution solar absorption spectra have been recorded at the Peterhof station (59.82 N, 29.88 E) of Saint Petersburg State University located in the suburbs of St. Petersburg. Measurements are made with the Fourier Transform Infrared (FTIR) system, which consists of Bruker IFS 125HR instrument (with maximum spectral resolution of 0.005 cm‒1) and self-designed solar tracker. We derived total column (TC) of a dozen of atmospheric gases from recorded spectra and performed the error analysis of these retrievals. Furthermore, we analysed the temporal variability of the important climatically active gases, such as H2O, CH4, O3, CO, NO2, etc. near St. Petersburg and compared our retrievals with independent ground-based and satellite data, as well as with the results of EMAC model numerical simulations. Currently, the results of our measurements and the measuring system are under validation for entering the international Network for the Detection of Atmospheric Composition Change (NDACC). 1. Introduction The observed changes in the Earth's climate and ozonosphere stimulated the intense studies of the spatial and temporal variability of the content of various climatically active gases [1]. A significant amount of information about atmospheric gas composition is derived from ground-based measurements of direct solar radiation spectra. In 1912, for the first time, Fowle [2] had determined the total column amount of water vapour in the Earth’s atmosphere using measurements of direct solar IR radiation. At about the same time, Fabry and Busson had measured the ozone total columns using solar radiation measurements in the UV region [3]. In the end of the 1940s, scientists of the Institute of Astrophysics of Liège University had started IR spectroscopic measurements of solar radiation at high altitude research station Jungfraujoch (Switzerland), studying Earth's atmosphere and determining CH4, HF, HCl, and other gas
1
components [4]. Since that time, solar radiation measurements have been widely used for the monitoring of the Earth’s gaseous composition. Nowadays, there are two international ground-based spectroscopic networks for observations of atmospheric gas composition ‒ Network for the Detection of Atmospheric Composition Change ‒ NDACC [5] and Total Carbon Column Observing Network ‒ TCCON [6]. Both of them are equipped with the Fourier Spectrometers (FS) of high spectral resolution, which enable measuring the content of a large number of atmospheric gases. Such measurements allow investigating the spatial and temporal variations of gas composition of the atmosphere, including long-term trends; examine various numerical models of the atmosphere; validate different satellite measurements of various gases content. It is also possible to use the ground-based spectroscopic measurements of solar radiation for correcting the characteristics of atmospheric molecules’ spectroscopic parameters (see, e.g., [7]) and estimating the quality of different remote sensing methods. In Russia, first IR spectroscopic measurements of solar radiation had been used for water vapour retrieval in 1960s [8‒10]. In 1970s, Dianov-Klokov with his colleagues from the Institute of Atmospheric Physics of Russian Academy of Science (IFA RAS) had started the first regular ground-based IR spectroscopic measurements of atmospheric gas composition [11]. Later, regular ground-based spectroscopic measurements were conducted at the Institute of Experimental Meteorology (Obninsk), Saint Petersburg State University (SPbU), and the Institute of Atmospheric Optics (Tomsk) [12‒14]. For a long time, such measurements were carried out in the USSR and Russia using instrumentation with the medium spectral resolution, which allowed determining the total column content of a limited list of gases, such as methane, carbon monoxide, carbon dioxide, and water vapour. In 2009, Atmospheric Physics Department of SPbU (at the Peterhof station) and the Ural Federal University (at the Kaurovka station) have started ground-based IR spectra measurements of solar radiation with high spectral resolution using spectral systems based on the FS Bruker IFS-125HR and Bruker IFS-125M, respectively, which allow monitoring the wide range of atmospheric 2
gases [15‒34]. High spectral resolution measurements of the solar radiation were used for water vapour retrieving at the Institute of Atmospheric Optics in Tomsk [35]. This paper is an overview of the results obtained at SPbU in recent years with FTIR (FS Bruker) measurements. In the second section, we briefly describe the Peterhof site for atmospheric sounding and the used instrumentation. The third section gives the information about the methodology of measurements and the retrieval errors for total column (TC) of different gases. The fourth section summarizes the scientific results: investigation of temporal behaviour of TC amounts of the retrieved atmospheric gases, comparison of our measurements with independent ground-based and satellite data. In conclusion, we present the most significant findings. 2. Measurements overview 2.1. Geographic location of the Peterhof site Remote Sensing Laboratory of SPbU has started the FTIR observations at the Peterhof station in January 2009. Location of the measurement site is indicated on the map (see Fig. 1) by red circle, geographic coordinates are as follows: 59.88° N, 29.82° E (20 m above sea level). FTIR system together with other equipment for atmospheric monitoring [36] are located at the University campus, in a suburb of greater Saint Petersburg (~35 km from the city centre). It is worth mentioning that Saint Petersburg is the fourth largest city in Europe with the population of 5.1 million people.
3
Fig.1. Location of the Peterhof FTIR site.
Atmospheric FTIR measurements using the Sun as a light source are performed under cloudless conditions or when breaks in cloud cover allow registering spectra. The Saint-Petersburg region suffers from cyclone activity for about 140 days in a year; therefore, the number of overcast days is relatively high for the Peterhof station (~165 days per year). The distribution of measurement days over a year is strongly non-uniform due to climate/weather specifics and the day length changes. Statistics of measurement days obtained over the period of 2009‒2014 shows that for the Peterhof site typically we have about 100 of measurement days per year. The number of spectra, which can be registered during a sunny day, is mainly determined by the day length, which varies from 18h 55min in June to 5h 50min in December. Short daylight hours and consequently high values of Sun zenith angle (minimal value of SZA on December 22 reaches 83.50°) significantly limit the number of spectra registered during wintertime. For comparison of FTIR gas measurements with independent source of information, we used the data from different sources: ground-based (DOAS, M124, CIMEL, MW devices) and space borne (OMI, MLS, ACE-FTS, MIPAS, IASI, GOME devices). 4
2.2. FTIR system 2.2.1. Configuration details FTIR system at the Peterhof site consists of Bruker IFS 125HR instrument placed in an air-conditioned room and self-designed solar tracker. Fig. 2 depicts the SPbU FTIR system. Solar tracking system is located on the roof of the laboratory building at a distance of about 12 m above the FTIR device. It is controlled and managed by means of a shadow type photoelectric sensor. The sensor is placed on the rim of the spherical mirror (marked as “3” in the Fig.2) of the luminous flux entry system towards Fourier spectrometer. The tracking accuracy is about 0.8 arc minutes.
Fig.2. FTIR system at the Peterhof site: Bruker IFS 125HR and self-designed solar tracker. Lines show the path of light beam (by dashed line we indicate light path in tube from roof to the room where interferometer is placed).
The
spectrometer
operates
in
forward/backward
mode
when each
interferogram is obtained from both forward and backward scans. It has a maximum optical path difference (OPD) of 180 cm, yielding a spectral resolution of up to 0.007 cm‒1 after Norton-Beer medium apodization. Two detectors, MCT (Mercury-Cadmium-Telluride) and InSb (Indium-Antimonide), cover the spectral range between 650 cm‒1 and 5400 cm‒1 (see Table 1). In order to improve the 5
signal-to-noise ratio, we co-add measured spectra for up to 12-min measurement interval. The averaged signal-to-noise ratio in the line-free continuum amounts from hundred to thousand (depending on the considered band). Table 1 presents the typical configuration of IFS 125HR for atmospheric observations. Table 1 Configuration details for Bruker IFS 125HR Unapodized Spectral
spectral
Aperture,
band, cm‒1
resolution,
mm
cm‒1
Single scan time, s
Number of scans
Filter
Beam splitter
Detector
5400‒2500
0.005
0.8‒1.0
72
6‒10
F1
KBr
InSb
3400‒1700
0.005
0.8‒1.0
72
6‒10
F2
KBr
InSb
1400‒650
0.005
2.0‒2.5
72
6‒10
F3
KBr
MCT
2.2.2. Alignment Control In order to monitor the alignment of interferometer, most of the FTIR sites of the NDACC network usually use 2 cm length cells, which are filled by pure HBr under low pressure (where the Doppler FWHM of HBr spectral line is reached). Cell transmittance spectra, recorded using the interferometer’s MIR internal light source, allow the retrieving of instrumental line shape by means of LINEFIT v.14 software [37]. Our HBr cell (#61 among cells issued by NDACC network) with wedged sapphire windows contains HBr under the pressure of ~ 2.3 mbar. Since the first measurement of HBr spectrum in April 2012, the alignment of FTIR spectrometer IFS 125 HR is being routinely controlled. The example of the instrument’s modulation efficiency and phase error retrieved from the HBr cell spectrum acquired on the 20th of May 2013 is given in Fig. 3. Both plots in Fig. 3 show that IFS 125 HR is aligned well enough: the loss of modulation efficiency over the optical path difference (OPD=180 cm) does not exceed 3% and phase error has small values (without significant changes) all along the OPD.
6
a)
b)
Fig. 3. Instrument’s modulation efficiency (a) and phase error (b) as functions of optical path difference (OPD).
7
3. Methodology 3.1. Retrieval strategy High-resolution spectra measured by Bruker IFS 125HR include many distinct and overlapping absorption lines of various atmospheric gases. For retrieving of target species TC, we used the microwindows, in which radiances are more sensitive to changes in target gas rather than in interfering gases. As a rule, we used spectral windows recommended by NDACC community or in the papers with special numerical investigation (see, e.g. [38, 39]). Table 2 presents the main characteristics of inversion strategy: versions of retrieval software and regularization type; spectroscopic databases; spectral intervals used for retrievals of target gas and interfering atmospheric species taken into account during spectra processing; and references for detailed information on retrieval. We routinely use two inversion codes for deriving TC of atmospheric gases from recorded spectra: SFIT2 and PROFFIT. Hase et al. [40] showed that with the similar constraints the results of PROFFIT and SFIT retrievals were in excellent agreement. We use two type of regularization method in the gases retrieval processing: Optimal Estimation Method (see, e.g., [41, 42]) and Phillips-Tikhonov approach [43, 44]. As a rule, for the retrieval of target gases, we use the following input and a priori data: - NCEP pressure and temperature profiles interpolated to FTIR measurement time; - WACCM v.5 simulation results [45] as a priori profiles of gases; - HITRAN 2008 data [46] (excluding methane, which is retrieved with HITRAN 2000 data).
8
Table 2 Retrieval software, regularization type and spectral intervals used for regular atmospheric gases retrieval at the Peterhof site (OE – optimal estimation method, TP – Tikhonov-Philips method) Gas
Retrieval code/ regularization
HCl
SFIT v.3.93/OE
O3
PROFFIT 9.6/TP
NO2
SFIT v.3.93/OE
H2O
PROFFIT 9.6/TP
CH4
SFIT v.3.92/OE
CO
SFIT v.3.92/OE
CCl3F CCl2F2
SFIT v.3.93/OE SFIT v.3.93/OE
HF
SFIT v.3.93/OE
CO2 N2O
SFIT v.3.92/OE SFIT v.3.93/OE
ClONO2
PROFFIT 9.6/TP
HNO3
SFIT v.3.92/OE
Microwindows, cm1
2727.73–2727.83 2775.70–2775.80 2925.80–2926.00 991.25–993.80 1001.47–1003.04 1005.00–1006.90 1007.35–1009.00 1011.15–1013.55 2914.590–2914.707 1110.00–1113.00 1117.30–1117.90 1120.10–1122.00 1196.00–1200.40 1220.50–1221.50 1251.75–1253.00 2613.70–2615.40 2835.50–2835.80 2921.00–2921.60 2057.70–2058.00 2069.56–2069.76 2157.50–2159.15 810–880 916–924 4000.86–4001.10 4038.81–4039.07 4109.77–4110.07 2626.3–2627.0 2551.435–2552.400 779.0–779.8 780.0–780.3 780.3–781.3 867.5–870.1
Interfering species
Reference
O3, N2O, NO2, CH4, H2O
[21]
H2O, CO2, C2H4, O3 (isotopes)
[24]
CH4, HDO
[22]
H2O (isotopes), N2O, CO2, CH4, O3
[33]
H2O, CO2, NO2, HDO
[25]
O3,CO2,OCS, N2O, H2O
[16]
H2O, CO2, O3 CO2, N2O, HNO3, H2O, NH3
[18]
O3, СH4, H2O
[28]
СH4, H2O CH4, HDO, O3
[26] [19]
H2O, CO2, O3, HNO3, C2H2
[31]
H2O, СО2, OCS
[23]
The quality of the retrieval (the determining of target gases from measured radiances) is usually tested by the comparison of measured and calculated spectra (spectral fitting). Fig. 4 demonstrates the example of the spectral fitting in three microwindows, which we use for the methane retrieval: the residual of two spectra totals thousandths of the atmospheric transmission function.
9
Fig. 4. Example of spectral fitting for microwindows used for CH4 total column retrievals (FTIR spectrum was acquired on the 24.05.2014, 12:59, local time).
3.2. Error budget Error analysis is an important part of any experimental study. Without knowledge of real errors, we cannot estimate the significance of the obtained results. Traditionally, errors are divided into systematic and random parts. Systematic errors are constant in a series of measurements, random – change randomly with the time. The measurement noise is a typical random error; the uncertainty of spectroscopic parameters is a typical systematic error, which can be negative or positive depending on the considered molecule and/or spectral band. Estimation of contribution of systematic and random components to the total measurement error of the retrieved gases is given in Table 3. The retrieval errors change significantly from gas to gas. For example for methane, random errors near 0.5–1%, for ClONO2 – 19%. The results of our error estimation are in good agreement with independent error analysis for FTIR devices of the same type [47– 50].
10
Table 3 Estimated errors of FTIR total columns retrievals of different gases (dash means that the error has not been estimated) Errors, %
O3
HCl
NO2
H2O
CH4
CO
Random
1-2
4-5
8-18
1–2
0.5–1 2–3
Systematic
2-3
3-4
10-30
2–3
3–4
Total
2-5
5-6
15-30
3–4
3–4
CO2 N2O CCl3F
HF
HNO3 ClONO2
~1
1–3
~13
2–3
1–2
~19
5–6
‒
5–7
~10
4–5
~7
~18
6–7
‒
6–9
15–18 5–7
7–8
~26
We have studied the errors from different sources: instrumental (zero baseline offset,
measurement
noise,
etc.),
measurements
conditions
(temperature
uncertainty, solar zenith angle), spectroscopic (the uncertainty of line intensity and halfwidths). The detailed analysis of the error budget for Peterhof FTIR measurements can be found in references given in Table 2. Usually, we use two methods for error assessment: the analysis of error matrices obtained in retrieval procedure with standard algorithm (PROFFIT or SFIT [40]) or closed-loop numerical modelling, estimating manually the influence of changing the initial parameters on the retrieval state (see, e.g. [21, 51]). Tables 4 and 5 demonstrate the examples of the detailed assessment of the error budget using these two approaches: error matrices analysis for water vapour retrieval (Table 4) and closedloop modelling of considered error sources for HCl retrieval (Table 5) [21]. Table 4 Estimated errors of integrated water vapor retrieval by FTIR method
Error source
Uncertainty
Zero baseline offset
0.5%
Temperature profile
1 K (35 km)
Line intensity
2%
Pressure broadening coefficient
5%
Measurement noise
From residuals
ψsyst ψrand
Systematic error (%)
Random error (%)
0.5
0.5
0.2
0.1
0.3
0.7
0.6
0.3
1.0
0.0
2.7
1.0
0.0
0.9
Total (3.6±0.6)% (3.3±0.6)% (1.2±0.2)% ψsyst and ψrand are the weights of systematic and random uncertainties, respectively. 11
Table 5 Estimated error of HCl TC retrieval by FTIR method Error source
Systematic error, % Random error, %
Calculated airmass
0.0
1.0
A priori profile
0.7
1.9
A priori covariance matrix
0.6
3.4
Spectral lines intensity
2.0
0
Spectral lines halfwidth
0.5
2.0
0
0.5
3.8
4.5
Spectral measurements noise Total
For the water vapour retrieval (see Table 4), spectroscopic line parameters uncertainty is the major source of systematic errors, whereas random errors are mostly due to the measurement noise. The more precise laboratory measurements of spectral lines characteristics will increase the accuracy of atmospheric gas composition measurements. It is worth mentioning that for systematic errors of HCl retrieval (see Table 5), the presented total value is the “upper bound” estimation, which means that in practice this value is usually lower due to the compensation of negative and positive separate systematic contributions. Thus, in stable atmospheric and measurement conditions, daily variations of HCl do not exceed 1%, which gives the “high bound” of the random measurement error. 4. Results and discussion 4.1. Different examples of ground based measurements Before 2009, our laboratory had in operation the self-designed IR grating spectrometer of low spectral resolution (0.4‒0.6 cm‒1), which allowed registering spectra in CH4 and CO bands [52, 53]. Spectra processing was performed by selfdesigned algorithm, which was based on the optimal estimation technique [13]. CH4 TC data are available from 1991 to 2011 (CO – from 1995). Despite of the low resolution of this spectrometer and relatively high uncertainty of CH4 daily means (1‒4%), twenty years of measurements allow us revealing statistically 12
significant trend of (0.2±0.1) %/yr for 1991‒2011 [20]. These data (Fig. 5) also reflect the period of 2005‒2011, when both total columns and concentrations of CH4 have significantly increased in the atmosphere [54, 55] and the period of 1991‒2005, without any trend. The comparison of trends obtained with the grating spectrometer and FTIR system [56] demonstrated their good agreement.
Fig. 5. CH4 TC daily means and trend estimations (IR grating spectrometer of low spectral resolution) over Peterhof site [20].
We used our ground-based spectroscopic measurements for investigation of temporal behaviour of gaseous composition: their daily, seasonal variations, and long-term trends. Moreover, we compared them with independent measurements (ground-based and satellite) and with the results of numerical modelling. Fig. 6 depicts the overview of the time series of a number of atmospheric gases (see Table 2) that we obtained by the beginning of 2013.
13
Fig. 6. Time series of atmospheric gases total columns derived from FTIR measurements near Saint Petersburg. 14
CH4 Time series of CH4 TC daily means retrieved from FTIR measurements are plotted in Fig. 7, where error bars correspond to CH4 TC daily variations.
Fig. 7. CH4 TC daily means (FTIR measurements).
Analysis of CH4 TC for 2009‒2014 shows that growth rates for the Peterhof site vary from year to year: (0.18±0.18) %/yr for 2009‒2012, (0.13±0.11) %/yr for 2009‒2013 and (0.3±0.2)%/yr for 2009‒2014. In 2014, the mean level of CH4 TC grew up in comparison with CH4 TCs observed for the previous years. CO As we have mentioned in previous section, besides FTIR measurements we had time series of CO TCs, which were retrieved from spectra obtained using IR grating spectrometer of low spectral resolution (Fig. 14) [53]. Data from both spectrometers have been harmonized using the period of simultaneous observation during 2009‒2010. Seventeen years (1998‒2015) of CO measurements did not reveal a significant trend (it equals to (‒0.02±0.1) %/yr) of CO TC for the Peterhof station. The previous paper on the analysis of CO TC observations performed by low resolution grating spectrometer [57] also reported that for the period of 1996‒2009, no significant trends were obtained ((0.0±2.0) % per year). The CO 15
TC trends estimated for the period of 1996–2006 from the observations at the four European FTIR stations (Jungfraujoch, Zugspitze, Harestua and Kiruna) varied from (‒0.45±0.16)%/yr for Jungfraujoch to (‒1.00±0.24)%/yr for Zugspitze [58].
Fig. 8. CO TC daily means and trend estimation (measurements for both, FTIR and IR grating spectrometer of low spectral resolution, are plotted).
The longest time series (from 1974) of CO TCs are available for the Zvenigorod station, which is operated by Institute of Atmospheric Physics, Russian Academy of Sciences [59, 60]. It was demonstrated in [61] that for the period between 1974 and 2008, Zvenigorod site’s observations also showed negative CO TC trend of ~0.4.%/yr. CO long-term tendencies for all sites mentioned above are negative but the trend’s values differ from each other. Detailed discussion of the possible reasons that may lead to such differences is beyond the scope of this paper; nevertheless, we mention some of them: CO dataset lengths, trend estimation techniques, and specific of observational sites location. H2O Much attention at our laboratory is paid to the measurements of the integrated water vapour (IWV) – the most important greenhouse gas. We compared FTIR measurements at Peterhof site with radiosonde launches at the Voeykovo station, 50 km northeast from Peterhof site [33]. As the result, the differences between two types of measurements increased with the increase of IWV values. Moreover, for larger IWV values, FTIR measurements tended to overestimate radiosondes data, 16
for smaller IWV values – vice versa, FTIR underestimated radiosondes. The mean values of IWV for the whole period of measurements were close for the both datasets. However, in some days the relative difference between pairs of two datasets reached 50% and more. These days corresponded to different air masses probed in Voeykovo and Peterhof. As we have mentioned earlier, in addition to FTIR, the Peterhof site has a number of devices for atmospheric gas composition measurements. Intercomparison of the FTIR retrieval with data, obtained from other nearby devices, can help to estimate the “real” accuracy of different remote sensing methods. Since March 2013, SPbU Peterhof observing station has been operating MW radiometer RPG-HATPRO (Radiometer Physics GmbH ‒ Humidity And Temperature PROfiler) manufactured by the German company Radiometer Physics GmbH (http://www.radiometer-physics.de/rpg/html/Home.html). Fig. 9 shows time series of considered IWV measurements. For the detailed analysis of two datasets, we formed four subsets of pairs depending on IWV values ranges [62]. In the dry atmospheric conditions, MW measurements overestimated the FTIR data.
Fig. 9. IWV daily means over Peterhof site [62]. 17
Fig. 10 depicts the scattering plot of FTIR IWV measurements vs. MW, CIMEL and radiosondes at the Voeykovo station. MW measurements slightly overestimate FTIR values. The relative mean differences are larger for the small and medium IWV values that are usual for Saint Petersburg. Thus, for the subset with IWV values less than 4 mm the mean difference is equal to (‒8±7)%; subset with IWV values from 4 to 10 mm gives the mean relative difference of (‒4±6)%; for subsets with larger values of IWV, mean differences do not exceed 1% in absolute values. We proposed that systematic difference between MW and FTIR IWV measurements might be caused by the errors in the spectroscopic parameters in 1.35 cm water vapour line (see, [46]).
Fig. 10. Scattering plot for IWV retrievals comparison at the Peterhof site.
CIMEL photometers are measuring IWV at hundreds of ground-based stations of AERONET network (http://aeronet.gsfc.nasa.gov/). We have shown that
CIMEL
photometer,
calibrated
by
the
manufacturer,
significantly
underestimates the IWV obtained by other devices in Petergof. We may conclude from the inter-comparison of different devices that it is necessary to perform an additional calibration of CIMEL photometer as well as the possible correction of interpretation technique of CIMEL measurements for Peterhof site. HCl HCl together with ClONO2 are reservoir gases for active chlorine in the stratosphere, playing an important role in the stratospheric ozone chemistry [63]. At the same time, HCl is a common material at the Earth’s surface, which 18
participates in vital processes of almost all animals, including human beings. Therefore, it can be observed in noticeable concentrations near surface. In [30], we proposed the method, which allows the separation of tropospheric and stratospheric contribution of HCl to its total column. Thus, we can remove from consideration variable HCl concentrations in the boundary layer and compare its stratospheric columns with satellite measurements, which are usually available only from a level of 150 mbar and higher. The comparison of stratospheric HCl column (above 100 mbar level), obtained by FTIR and MLS devices is shown in Fig. 11. We averaged MLS measurements within 500 km radius from the Peterhof site (daytime). FTIR and MLS retrievals are in good agreement showing the same temporal variations through the whole period of comparisons (with mean difference of 4.4% and standard deviation from the means of 5.7%). The largest variability of HCl columns is observed during the events of the polar vortex intrusion (the most significant changes are related to January‒February, 2010). The longest late winter – early spring polar vortex was determined (using ERA Interim reanalysis data) in 2014. This period requires the additional simultaneous analysis of other ozonerelated stratospheric gases behaviour.
Fig. 11. Measurements of HCl by two methods: ground-based FTIR and satellite measurements by MLS. A polar vortex being near Peterhof is shown as short vertical lines on the abscissa.
19
O3 We extended the time series of ozone TC measurements by FTIR to the end of 2014 (see [24] for earlier results). Here, we present FTIR measurements of ozone TC near Saint Petersburg for the period between April 2009 and November 2014. We compare them with OMI satellite measurements and ground-based measurements of ozone site in Voeykovo (filter ozonometer M124 and Dobson spectrometer), 50 km northeast from the Peterhof site. We use daily averaged ground-based measurements and averaged OMI measurements in 200 km area from
St.
Petersburg
(overpass
data
‒
http://avdc.gsfc.nasa.gov/index.php?site=1593048672&id=28). Fig. 12 depicts the scattering plot of this comparison, Table 6 – its statistical characteristics.
Fig. 12. Scattering plot for ozone TC retrievals comparison at the Peterhof site. Table 6 Statistical characteristics of the comparison: FTIR vs. other devices Device
Number of days
Bias
Correlations
OMI
318
3.8 ± 2.6% 0.986 ± 0.002
M124
217
1.7 ± 2.5% 0.984 ± 0.002
Dobson
130
1.7 ± 2.5% 0.983 ± 0.003 20
FTIR overestimates independent ground-based measurements (1.7%) and satellite measurements (3.8%). Standard variations from means in pairs’ comparison are smaller than the total errors of individual measurements. Besides the standard retrieval in the 1000 cm‒1 spectral region (see Table 2), we have tried to examine the possibilities of ozone TC retrieval in the 3040 cm‒1 spectral region. It is worth mentioning that the higher wavenumbers interval has weaker ozone signatures. However, the signal-to-noise ratio in measured spectra of the 3040 cm‒1 spectral region is 5 times higher comparing to the 1000 cm‒1 spectral intervals (InSb vs. MCT detectors – see Table 1), so small signatures are compensated by low noise. Fig. 13 demonstrates the ozone TC time series, measured by FTIR system in two above-mentioned spectral regions. Garcia et al. [64] studied the ozone TC retrieval in similar spectral regions and FTIR system at the subtropical site Izaña (Tenerife, Spain) and showed the 7% systematic differences between the retrieval in these intervals. We obtained a smaller bias (3.8%) for six years of measurements. Nevertheless, we will further extend this analysis for better understanding of the FTIR retrieval features.
Fig. 13. Time series of ozone TC obtained by FTIR measurements in two different spectral regions. 21
4.2. Comparison with modelling We have also compared our retrievals with the numerical modelling. For this purpose, we used EMAC model, which is a numerical chemistry and climate simulation system that includes sub-models describing troposphere and middle atmosphere processes and their interaction with oceans, land and human influences. The simulation included a comprehensive atmospheric chemistry setup for the troposphere, the stratosphere and the lower mesosphere [65].
Fig. 14. Scattering plot of measurements and modelling comparison for several stratospheric gases total columns at the Peterhof site [66].
In [66], we compared the EMAC simulations and FTIR retrievals for O 3, HNO3, HCl, and NO2 total columns. Mean diurnal and monthly means for 20092012 were analysed in details. The analysis had shown that seasonal variations of considered trace gases were well simulated by the EMAC model. At the same time, we observed some discrepancies, in particular, biases between simulated and 22
measured results (see, Fig. 14). Earlier, we have compared ClONO2 TC in [31], where we showed that EMAC model underestimated ClONO 2 remote sensing observations with means of (6.6±32)% and (5.8±21)% for ground-based and satellite (MIPAS) measurements, respectively. In [29], we demonstrated the systematic differences between measured and calculated data of 1.3% and 0.3% for the values of TC and column-averaged mole fraction of methane, respectively. For better understanding of disagreements between model and measurements, we plan to compare and analyse the partial columns of the considered gases as the next step of the research. 4.3. Comparison with satellite measurements Satellite measurements allow monitoring the atmospheric content through the whole globe. At the same time, these measurements need to be validated by the network of various ground-based sites and devices. The Peterhof site is located on the edge of moderate and high latitudes. This feature allow estimating the satellite measurements under different atmospheric conditions and air masses. We have already compared our FTIR measurements with a number of satellite data. Table 7 presents the overview of satellite devices, with which we compared our FTIR retrievals: their remote sensing methods, spectral resolution and ranges. Fig. 15 depicts the seasonal cycle of NO2 stratospheric content near St. Petersburg obtained by FTIR and satellite measurements (OMI, SCIAMACHY, GOME, GOME-2) [22]. The best coincident between ground-based and satellite measurements is observed for OMI device.
23
Table 7 Comparison of Peterhof FTIR measurements with satellite data Instrument ACE-FTS MIPAS
MLS IASI OMI
GOME
GOME-2
SCIAMACHY
GOSAT
Method
Spectral range
Spectral resolution
Sun occultation Thermal limb emission
2.2‒13.3 µm
0.02 cm
4.15‒14.6 µm
0.035 cm
ClONO2 [31] Ozone [24] HCl [21] Ozone [32] Ozone [27] NO2 [22]
‒1
‒1
Compared gases HCl [21] HF [28]
Thermal emission
Lines near 118, 190, 240, 640, 2500 GHz
Thermal emission Scattered solar radiation Scattered solar radiation Scattered solar radiation Scattered solar radiation Scattered solar radiation
2.2‒13.3 µm
0.5 cm‒1
270‒500 nm
~0.5 nm
240‒790 nm
0.17‒0.33 nm
Ozone, NO2 [27]
250‒790 nm
0.2‒0.4 nm
Ozone [27] NO2 [22]
240‒1700 nm, selected regions between 2000 and 2400 nm
0.2‒0.5 nm
NO2 [22]
2.2‒13.3µm
0.02 cm‒1
CO2 [26] CH4 [25]
Fig. 15. Seasonal cycle of stratospheric NO2 over Peterhof site [22]. 24
An example of ground-based–satellite comparison of seasonal cycle is presented in Fig. 16 for another stratospheric species – ClONO2 [31]. The same seasonal cycle of ClONO2 is produced by FTIR and MIPAS measurements. However, we observe a systematic difference between both instruments, which vary from month to month. It is worth mentioning that due to the difficulties of FTIR ClONO2 measurements in winter time, the size of the compared dataset is quite small – for three years of the comparison we have coincided measurements only for 43 days.
Fig. 16. Seasonal cycle of ClONO2 column over Peterhof site [31].
Table 7 shows that one of the most significant atmospheric species – ozone – is measured by a great number of satellite devices. Usually, satellites (due to the measuring geometry) are sensitive to ozone content in stratosphere, where it plays a role of a shield from the UV solar radiation. At the same time, ozone is a strong pollutant in troposphere, where it affects the environment and human health. Our ground-based FTIR system allows the retrieving of tropospheric ozone. Researchers from LISA (Paris) determine tropospheric ozone using IASI satellite measurements. In [32], we compared the ground-based FTIR measurements pf tropospheric ozone with IASI-LISA retrievals near Peterhof. Fig. 17 depicts the scattering plot of the considered comparison. At low values of tropospheric ozone 25
columns, ground-based measurements overestimated satellite data, at high values, vice versa, satellite data exceeded ground-based. In general, we observed a good agreement between the both datasets: 1.6±7.8 DU, significantly better at the warm period of measurements [32].
Fig. 17. Scattering plot of ozone tropospheric column comparison (FTIR–IASI) over Peterhof site [32].
5. Conclusions In 2009, Atmospheric Physics Department of Saint Petersburg State University has started ground-based IR spectra measurements of the solar radiation with high spectral resolution using spectral system based on the FTIR instrument Bruker IFS-125HR, which allows monitoring of the wide range of atmospheric gases. FTIR system is located at the University campus, in a suburb of greater Saint Petersburg (~35 km from the city centre). In order to monitor the alignment of interferometer, we use 2 cm length cells, which are filled by pure HBr under low pressure. Since the first measurement of HBr spectrum in April 2012, the alignment of FTIR spectrometer IFS 125 HR is being routinely controlled. We use 26
two inversion codes for deriving total columns (TC) of atmospheric gases from recorded spectra: SFIT2 and PROFFIT. We have studied the errors of the FTIR TC gas retrievals from different sources:
instrumental
measurements
(zero
conditions
baseline
(temperature
offset,
measurement
uncertainty,
solar
noise, zenith
etc.), angle),
spectroscopic (the uncertainty of line intensity and halfwidths). The retrieval errors change significantly from gas to gas. For example, for methane random errors are near 0.5‒1%, for ClONO2 ‒ 19%. The most important source of systematic errors is the uncertainty of the spectral lines parameters, which can achieve 10 and more per cents. For comparison of FTIR gas measurements with independent source of information, we have used the data from different sources: ground-based (DOAS, Dobson, M-124, CIMEL, MW devices, radiosondes) and space borne (OMI, MLS, ACE-FTS, MIPAS, IASI, GOME, AMSU devices). We have used our ground-based spectroscopic measurements for investigation of temporal behaviour of gaseous composition: their daily, seasonal variations, and long-term trends. In addition, we have compared our retrievals with independent measurements (ground-based and satellite) and with the results of numerical modelling. Mainly, we deal with the total or stratospheric column content. However, we can retrieve columns of some species in different atmospheric layers. For example, we have compared our ozone tropospheric columns retrievals with LISA IASI data. In [32], we have shown the good agreement between both datasets: 1.6±7.8 DU. Our estimation of CH4 trends for twenty years of measurements (low spectral resolution) has allowed us revealing statistically significant trend of (0.2±0.1) %/year for 1991‒2011 [20]. These data also reflect the period of 2005‒2011 when both, total columns and concentrations of CH4, have significantly increased in the atmosphere. The analysis of CH4 TC for 2009‒2014 (high spectral resolution) has shown that growth rates for the Peterhof site vary from year to year: (0.18±0.18) 27
%/year
for
2009‒2012,
(0.13±0.11)
%/year
for
2009-2013
[65]
and
(0.3±0.2)%/year for 2009‒2014. We have analysed the time series of CO TCs that are retrieved from spectra obtained using IR grating spectrometer of low spectral resolution and Bruker IFS 125HR. Data from both spectrometers are harmonized using the period of simultaneous
observation
during
2009‒2010.
Seventeen
years
of
CO
measurements do not reveal significant trend (it equals to (‒0.02±0.1)%/year) of CO TC for the Peterhof station. Much attention is paid to the comparison of different remote sensing methods for IWP retrieval: FTIR, MW RPG-HATPRO, CIMEL, radiosondes and for ozone columns: FTIR, ozonometer M-124, Dobson spectrometer, satellite devices. As the results of this comparison, we have observed some systematic differences between various pairs of dataset. For example, MW measurements slightly overestimate FTIR IWV values. The relative mean differences are larger for the small and medium IWV values that are usual for Saint Petersburg. We have shown that CIMEL photometer calibrated by the manufacturer significantly underestimates the IWV obtained by other devices. In addition, FTIR overestimates independent ground-based measurements (1.7%) and satellite measurements (3.8%) ozone total column. We have obtained a bias (3.8%) for six years of measurements between ozone total column measurements used for standard retrieval in the 1000 cm‒1 spectral region and TC retrieval in the 3040 cm‒1 spectral region. We have also compared our retrievals with the numerical modelling. For this purpose, we use EMAC model, which is a numerical chemistry and climate simulation system that includes sub-models describing troposphere and middle atmosphere processes and their interaction with oceans, land and human influences. We have compared the EMAC simulations and FTIR retrievals for O 3, HNO3, HCl, and NO2 total columns. The analysis shows that seasonal variations of these gases are well simulated by the EMAC model. At the same time, there are some discrepancies, in particular, biases between simulated and measured results. 28
Currently, the results of our measurements and the measuring device are under validation for entering the international Network for the Detection of Atmospheric Composition Change (NDACC). Acknowledgement Measurement facilities were provided by Geo Environmental Research Centre “Geomodel” of Saint Petersburg State University. Funding The experimental part of the study was supported by the Russian Foundation for Basic Research [grant numbers 15-05-07524, 14-05-00897]. The processing and analysis of the data were performed with financial support of the Russian Science Foundation [grant number 14-17-00096].
29
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Y. González, Quality assessment of ozone total column amounts as monitored by ground-based solar absorption spectrometry in the near infrared (> 3000 cm−1), Atmos. Meas. Tech. 7 (2014) 3071–3084, doi:10.5194/amt-7-3071-2014. [65] P. Jöckel, H. Tost, A. Pozzer, C. Brühl, J. Buchholz, L. Ganzeveld, P. Hoor, A. Kerkweg, M. G. Lawrence, R. Sander, B. Steil, G. Stiller, M. Tanarhte, D. Taraborrelli, J. van Aardenne, and J. Lelieveld, The atmospheric chemistry general circulation model ECHAM5/MESSy1: consistent simulation of ozone from the surface to the mesosphere, Atmos. Chem. Phys. 6 (2006) 5067–5104. doi: 10.5194/acp-6-5067-2006. [66] Ya.A. Virolainen, Yu.M. Timofeyev, A.V. Polyakov, D.V. Ionov, O. Kirner, A.V. Poberovskii, H. Imhasin, Comparison of ground-based measurements of O3, HNO3, HCl and NO2 total contents with data of numerical modeling, Izvestiya, Atmospheric and Oceanic Physics 52 (2016) №1 (Forthcoming), (Engl. transl.)
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Highlights
The multi-year monitoring of a dozen of atmospheric gases near St. Petersburg (Russia) is carried using the spectral system based on the FTIR instrument Bruker IFS-125HR. FTIR gas measurements are compared with independent sources of information: groundbased (DOAS, Dobson, M-124, CIMEL, MW devices, radiosondes) and space borne (OMI, MLS, ACE-FTS, MIPAS, IASI, GOME, AMSU). Ground-based spectroscopic measurements are used for studying the temporal variability of the important climatically active gases, such as H2O, CH4, O3, CO, NO2, etc.
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The monitoring of the atmospheric gases composition by FTIR spectroscopy near Saint-Petersburg (Russia)
Daily means of the CH4 total content and trend estimations over Peterhof site 38
