Inversión del co2 y de parámetros colisionales de los espectros de mipas en la atmósfera terresetre

• Autores: Ángel Aythami Jurado Navarro
• Directores de la Tesis: Bernd Funke (codir. tes.) , Manuel López Puertas (codir. tes.)
• Lectura: En la Universidad de Granada ( España ) en 2015
• Idioma: español
• Tribunal Calificador de la Tesis: Lucas Alados-Arboledas (presid.) , Francisco José Olmo Reyes (secret.) , Martin Kauffmann (voc.) , Rolando R. Garcia (voc.) , Natalia Calvo Fernández (voc.)
• Enlaces
  • Tesis en acceso abierto en: DIGIBUG
• Resumen

  • Introduction CO2 is an important greenhouse gas and is is one of the master pieces in the energy balance of the Earth and, therefore, in its thermal structure. There are a large number of studies performed from the last century to the present day and they show how the CO2 concentration is being continually increased. The impact of this increase in the cooling and heating of the different atmospheric layers is clear. For instance, the trend of the temperature in the troposphere has been positive meanwhile, in the mesosphere, has been negative. Since the beginning of industrialization, mankind has been emitting increasing amounts of harmful volatile substances into the atmosphere changing its composition but we still have to answer what are the effects of these emissions on the different observed atmospheric changes.

    Regarding the energy balance, the most important cooling mechanism of the stratosphere, mesosphere and lower thermosphere is the CO2 15 um emission. The daytime heating rate is mainly induced by the near-infrared CO2 bands (2.7 and 4.3 um) and it is also important. It is worth to highlight that these infrared emissions come from ro-vibrational transitions of the molecule and they are in a non local thermodynamic equilibrium (non-LTE) in the middle and upper atmosphere.

    From the first in situ rocket measurements in the seventies until the most recent satellite measurements from solar occultation instruments like ACE or limb emission instruments like SABER, we have significantly gained knowledge about the CO2 abundance. It is worth to mention the great difficulty of analyzing the infrared emissions from the different CO2 bands and to derive its abundance from these emissions, primary coming from the complex non-LTE processes.

    Motivation and Objectives In the last 10 years there had been several interesting studies regarding CO2 and its impact on the Earth radiative balance. Emmert et al. (2010) suggested that during the 2008 solar minimum, an increase of CO2 may partially provoke a low thermospheric density peak induced by by a faster CO2 cooling of the mesosphere and lower thermosphere. Emmert et al. (2012) found an increase of COx (CO+CO2) notably larger than the growth rate expected from the CO2 increase by human activities in the troposphere. This has been very recently confirmed by the independent measurements taken by the SABER/TIMED instrument (Yue et al., 2015). Beagley et al. (2010) proposed a new CO2 loss process (sequestration in dust) around the mesopause as a possible explanation of the overestimation of CMAM CO2 simulations compared to ACE profiles. Garcia et al. (2014) have shown more recently that the ACE CO2 distribution can be explained by the WACCM model and there is no such need for invoking a new CO2 loss mechanism. DeSouza-Machado et al. (2007) retrieved the tropospheric and stratospheric temperature from nadir 4.3 um spectral measurements and found that non-LTE can induce errors of up to 10 K and therefore, they have to be corrected for.

    All these studies indicate that a more precise knowledge of the spatial-temporal distribution of CO2 in the mesosphere and thermosphere is crucial for a better understanding of the energy balance of this important region, of its dynamical coupling to the regions below, and for understanding its response to anthropogenic changes.

    The accurate knowledge of the non-LTE populations of the CO2 states emitting near 4.3 um has limited the accuracy of the CO2 abundances derived from limb emission measurements. The MIPAS spectra, with its unprecedented high spectral resolution, allow to make significant advances in the knowledge of the different CO2 non-LTE process.

    Thus, the major aim of this work is to retrieve global distributions of the CO2 abundance (volume mixing ratio or vmr) from the MIPAS high resolution infrared spectra in the mesosphere and thermosphere. At these altitudes, the 4.3 um emissions are in non-LTE conditions (Lopez-Puertas and Funke, 2015). Thus is necessary to know very accurately the non-LTE populations of the emitting levels to retrive an accurate CO2 concentration MIPAS spectra, because of its extraordinary spectral resolution and high sensitivity offers an unique opportunity to improve our knowledge of those non-LTE processes. This then constitutes the first major objective of this thesis: to retrieve the main non-LTE parameters (including their temperature dependence) affecting the population of the CO2 levels from the MIPAS spectra in the 4.3 and 10 um regions. Once the non-LTE model is improved with the retrieved rates, we perform the retrieval of the CO2 vmr, that constitutes the second major aim of this work. Once the CO2 abundances are retrieved, we analyze the quality of the data and perform a thorough error analysis. In addition, we perform a validation study by comparing the MIPAS CO2 data with the previous independent measurements of ACE and SABER. Furthermore the main physical features of the retrieved global distribution of CO2 are analyzed with the help of the 3D Whole Atmosphere Community Climate Model (WACCM).

    Theoretical Basis Energy transfer in Earth's atmosphere occurs mainly in form of electromagnetic radiation. This physical phenomenon is known as the radiative transfer. Accordingly, to know the basics of the interaction between radiation and matter is essential to perform an appropriate analysis in this area. The main processes that characterize the interaction of the radiation with a given medium like the terrestrial atmosphere are: absorption, emission and scattering.

    The equation which describes the radiance, Lv(s,sv), at a given atmospheric point s, in the direction sve, along an optical path from so to s in a given medium is known as the radiative transport equation (RTE): Lv(s,sve) = Lv(s0,sve) exp(t(s0,sve)) + int(kv(s') na(s') Jv(s',sve) exp(t(s',sve)) dsv), where kv(s') y na(s') are the absorption coefficient and the absorbing molecules density, Jv(s',sv) is the source function and, exp(t(s',sve)) express the attenuation of the radiation through a given medium of optical thickness t(s',sv).

    Due to the nature of the emissions to be studied is necessary to describe the local thermodynamic equilibrium (LTE) conditions. It is possible to consider the thermodynamic equilibrium not in the whole atmosphere but in gas parcels as a close approximation to reality. Under these conditions, the energy emitted by the components of this gas parcel in thermodynamic equilibrium at a certain frequency (v) and temperature (T) can be described by the Planck function. The molecular and atomic velocities distribution is given by the Maxwellian distribution at that temperature. In these cases, the source function Jv(T) can be approximated by Bv(Tk). It is important to note, however that the radiance of the radiative field, Lv, is not necessarily be given by the Planck function Bv(Tk). In addition, the population distribution of the excited levels is given by the Boltzmann's law: ni/nj = gi/gj exp(Ei-Ej/kTk) where i and j indicates the two states involved in the transition; ni and nj their respective populations; gi and gj their degeneracies and Ei and Ej their energies.

    In a planetary atmosphere, where kinetic energy transfer prevails, the approximation of LTE provides an accurate representation of the realistic case.

    These approximation can not be used when: thermal collisions are not fast enough to provide the energy lost by radiation (spontaneous emission); or when the energy levels are over-populated by an external (e.g. the solar) radiative field; or when the vibration-vibration, electronic-vibration, chemical recombination, photochemical reactions, dissociative recombination and collisions with charged particles become important.

    In these non-LTE conditions is essential to know all the microscopic processes populating the different energy levels involved. The Statistical Equilibrium Equation (SEE) expresses the balance between all these processes and provides the level populations: n2/n1 = B12 Lv(m) + pt + pnt / n1 A21 +B21 Lv(m) +lt +lnt where n2 y n1 are the population of the 1 and 2 levels, B12, B21 y A21 are the Einstein coefficients of induced absorption, induced emission, and spontaneous emission respectively, pt y pnt are the the specific loss rate of n2 in thermal and non-thermal processes, Lv(m) is the mean radiance over all solid angles.

    The main problem of study the non-LTE radiance lies in calculating the source function, Jv, that no longer coincide with the Planck function. To obtain the source function is necessary to resolve the RTE+SEE system of equations using a numerical method. A commonly used approach is the Curtis matrix method (Curtis, 1956; Lopez-Puertas and Taylor, 2001) which algebraically solves a combination of SEE and linearised RTE for two levels systems connected by particular radiative transitions, sequentially for all the considered levels. Another method is the Lambda iteration which alternates SEE calculations involving all the energy levels with RTE calculations involving all atmospheric layers.

    To obtain the different atmospheric parameters desired from radiance measurements, is necessary to solve the inverse problem. There are two main difficulties regarding the inverse problem: i) to go from discrete measurements to the retrieval of continuous profiles; and ii) the non-linearities of the retrieval equations. A theory of the inversion problem applied to atmospheric measurements is presented by Rodgers (2000).

    The forward problem is expressed by y = F(x) + e, where y a set of observed spectra, F(x) is a non-linear function representing the radiative transfer equation depending on the retrieval parameter x y e expresses the random errors due to the instrumentation. In order to perform the linearization of the equation with respect to a reference state, x0, it is usually introduced the Jacobian, K, of the F(x) function with respect to the atmospheric parameter, x, y = F(x0) + K (x - x0 ) + e.

    This expresses the sensitivity of the measurement with respect to a given parameter.

    The measurement errors are represented by the covariance matrix, Sy and the regularization matrix is R. With all these elements, the iterative solution to the inverse problem takes the following form: xi+1 =xi +[K^T Sy^-1 K+R]^-1 × [K^T Sy^-1(y - F(x)) - R(xi - xa)] The selection of a certain regularization depends on how accurate the a priori information is. Rodgers (2000) propose the "optimal estimation" or "maximum a posteriori" methods when a certain knowledge on the a priori information is given. Furthermore, Tikhonov (1963) proposed a regularization which represents a smoothing operator.

    In this work, the KOPRA code is used to resolve the RTE from the limb observations of MIPAS, the GRANADA code is used to compute the non-LTE populations essential to resolve the RTE, and the RCP code is used to, from the combinations of these codes, obtain the retrieved parameter following an iterative process.

    The Measurements In this work we analysed the MIPAS upper atmosphere (UA) observation mode measurements. MIPAS is a mid-infrared emission limb emission spectrometer designed and operated for measurement of atmospheric trace species from space (Fischer et al., 2008). It was part of the payload of Envisat launched on 1 March 2002 with a sun-synchronous polar orbit of 98.55ºN inclination and an altitude of 800 km. MIPAS had a global coverage from pole to pole passing the equator from north to south at 10:00 a.m. local time 14.3 times a day and taking daytime and nighttime profiles of spectra. The instrument's field-of-view is 30 km in horizontal and approximately 3 km in vertical direction. From January 2005 until the end of Envisat's operations on 8 April 2012, MIPAS measured at a reduced spectral resolution of 0.0625 cm-1. The MIPAS observations analyzed here from the upper atmosphere (UA) mode have an along-tracking horizontal sampling of about 515 km recording a rear viewing sequence of 35 spectra every 63s. This mode has the most extended altitude coverage (42 to 172 km) allowing to retrieve CO2 up to about 150 km.

    Conclusions and Future Work Regarding the retrieval of the main collisional parameters, a non-LTE retrieval scheme, used in previous analysis of the MIPAS non-LTE emission, has been applied to the MIPAS 10 and 4.3 um spectra (Jurado-Navarro et al., 2012, 2013). The wide altitude and latitude ranges have allowed to retrieve also the temperature dependence of the rates in the range of 130 to 250 K. All of them were retrieved with a much better accuracy that known before. The most salient results (see Jurado-Navarro et al., 2015c) are: The kvv,2 derived here has a stronger temperature dependence than that used before. Also, it is significantly larger than previous values at temperatures below 300 K, ranging from a factor of 1 to 1.5 from 300 K to 130 K. This is a result of major importance since this rate controls the populations of the CO2(10011) and (10012) levels, and therefore the atmospheric radiation near 4.3 um and 2.7 um at upper mesospheric and lower thermospheric limb paths.

    The collisional rates for the V-V transfer from high energy levels, kvv,3 and kvv,4, have been retrieved here for the first time. We have retrieved a temperature dependence of 1/T^0.6, slightly stronger than T^(1/2), with values larger than previous rates by about 20% at mesospheric temperatures. We have not found any significant difference between kvv,3 and kvv,4.

    We have retrieved values for the kF1 and kF2 rates very different from those previously used. kF1 is about 43 times smaller than the rate derived by Sharma and Wintersteiner (1985) and about 4.7 times larger than that used by Shved et al. (1998). kF2 is about a factor of 9 to 5 (4.5 to 2.5 for kF2) larger than those used by Sharma and Wintersteiner (1985) and Shved et al. (1998) for temperatures of 150 and 250 K, respectively; and a factor of 2 to 3.5 (4 to 7 for kF2) smaller for the same temperature interval than those included in Lopez-Puertas and Taylor (1989) and Lopez-Puertas et al. (1998). This leads to significant changes in the modeled atmospheric radiation of the two stronger 4.3 um second hot bands, decreasing them by 20-30% for mid-latitude conditions.

    The derived kvt collisional rate is smaller (factor of 0.7) than the values used recently by Funke et al. (2012). It is also smaller by factors of 0.38 and 0.57 at 150 and 250 K than those used in the analysis of SPIRE, SAMS and ISAMS measurements; and by factors of 0.71 and 0.65 than those used by Shved et al. (1998) and incorporated in the analysis of CRISTA and SABER measurements. The kO(1D) collisional rate derived here jointly with kvt, is very similar to that used by Funke et al. (2012). This means that it is factors of 1.5 and 1.1 (at 150 and 250 K, respectively) larger than those used in the analysis of SPIRE, SAMS and ISAMS measurements; and a factor of 1.21 larger than those used by Shved et al. (1998) and incorporated in the analysis of CRISTA and SABER measurements.

    The new rates have very important effects on the atmospheric limb spectral radiances in the 10, 4.3 and 2.7 um spectral regions when compared to typical collisional rates used in previous works.

    The CO2 vmrs have been retrieved using MIPAS daytime limb emission spectra from the 4.3 um region in its upper atmosphere (UA) mode (data version CO2_620.0) (Jurado-Navarro et al., 2015a,b). The retrieved CO2 covers from 70 km up to about 140 km. The inversion has been performed by using a non-LTE retrieval scheme developed at IAA/IMK. It takes the advantage of other (simultaneous) MIPAS measurements of atmospheric parameters, as the kinetic temperature (up to ~100 km) from the CO2 15 um region Garcia-Comas et al. (2014), the thermospheric temperature from the NO 5.3 um, the O3 measurements (up to ~100 km), which allows a strong constrain of the O(1D) concentration below ~100 km, and an accurate calculation of O(1D) above ~100 km. The non-LTE model incorporates the accurate vibrational-vibrational and vibrational-translational collisional rates derived from the MIPAS spectra and described above.

    The precision of the retrieved CO2 vmr profiles varies with altitude ranging from ~1% below 90 km, to 5% around 120 km and larger than 10% above 130 km. The larger values at higher altitudes are due to the lower signal-to-noise ratio. There are very little latitudinal and seasonal variations, which are driven by the solar illumination conditions.

    The retrieved CO2 profiles have a vertical resolution of about 5-7 km below 120 km, with an increase of up to 12 km at ~100 km, and between 10 and 20 km at 120-140 km.

    The most important features observed on the retrieved CO2 can be summarized in: The retrieved CO2 shows the major general features expected and predicted by models: the abrupt decline of the CO2 vmr above 80-90 km, caused by the predominance of the molecular diffusion, and the seasonal change of the latitudinal distribution. The latter is reflected by higher CO2 abundances in polar summer from 70 km up to ~95 km, and lower CO2 vmr in the polar winter, both induced by the ascending and descending branches of the meridional circulation, respectively. Above ~95 km, CO2 is more abundant in the polar winter than at mid-latitudes and polar summer regions, caused by the reversal of the meridional circulation in that altitude region.

    The typical solstice and equinox latitudinal distributions are found in the data periods of about 2.5 months in each hemisphere, and the solstice/equinox seasonal transition usually takes place rather quickly, in about one month.

    We have found a small decrease in MIPAS CO2 vmr at ~80 km at polar summer latitudes. We are not sure if this is real, e.g., remnant CO2 air from previous season, or a retrieval artifact caused by a combination of high solar zenith angles and low temperatures.

    With the aim of validating the retrieved MIPAS CO2 vmr, a detailed comparison has been performed with the most recent satellite measurements taken by ACE and SABER. The major results can be summarized in: In equinox, MIPAS shows an overall good agreement with ACE. MIPAS CO2 measurements are slightly larger than those of ACE at 80-100 km. At higher altitudes, the differences varies with latitude, being comprised always within ±20%.

    Also in equinox, the agreement of MIPAS with SABER is even better than with ACE, being within 5% below 100 km. In the 100-120 km region, MIPAS tends to be a ~20% larger in polar regions and a ~10-20% smaller near the tropics.

    During solstice there is, in general, a very good agreement between the CO2 measurements of the three instruments. The agreement of MIPAS with ACE is similar as for equinox conditions, but there is a clear distinction in the polar summer, where the decay of the CO2 vmr with altitude above ~100 km is steeper in MIPAS than in ACE. This behavior is also present in SABER data (in excellent agreement with MIPAS) and is it is even more prominent (probably too steep) in WACCM simulations (see below).

    The agreement of MIPAS with SABER is in general very good. MIPAS CO2 is, however, smaller (~5%, and probably too low) around 80 km in the polar summer. SABER, on the other hand, seems to be too low at 60-80 km in the polar winter in January, and too high at 65-95 km in the polar winter in February.

    WACCM generally overestimates the CO2 of the three instruments above ~90 km at mid- and tropical latitudes during equinox. However, the opposite occurs during solstice at mid and high latitudes of the summer hemisphere, when WACCM CO2 decreases more pronouncedly than the CO2 of the three instruments at those altitudes.

    In order to draw more general conclusions we have also compared zonal mean cross-section and latitudinal band profiles for the annual mean of 2010. The results showed that: MIPAS measurements are generally larger (~5%) than ACE ones at 80-100 km and 20-30% smaller at 100-120 km.

    MIPAS and SABER agree very well up to 100 km. At 100-120 km, they also agree very well when averaging over all latitudes, but MIPAS is generally larger than SABER except near the equator where MIPAS is lower. That is, in the 100-120 km region, globally, ACE is the largest, SABER the smallest and MIPAS is in between.

    SABER CO2 measurements between 100 and 120 km are globally smaller than MIPAS, ACE and WACCM.

    From the detailed comparison of WACCM simulations with MIPAS observations we draw the following conclusions: Overall, WACCM reproduces the major CO2 vertical distribution patterns and the seasonal and latitudinal variations observed in MIPAS data.

    Below about 100 km the agreement is very good, with differences smaller than ±10% (for Pr = 2) except for solstice conditions near 90-100 km where they can be as large as 20%, being WACCM smaller. In the polar summer, near 80 km, MIPAS is smaller by up to 5%, which might point to a problem in the MIPAS CO2 retrieval.

    At 100-140 km altitudes, WACCM CO2 is generally larger than MIPAS at equinox at almost all latitudes with differences of 20-100%. In the summer hemispheres, however, WACCM CO2 is generally lower in 20-70%. In the winter hemispheres WACCM CO2 is also generally larger than MIPAS although not as large as in equinox.

    In general, MIPAS favors more a value of Pr = 2 (larger eddy diffusion), principally below around 100 km. In the 100-120 km region and above, however, WACCM simulations with Pr=4 somehow mitigates the WACCM Pr=2 overestimations and give an overall better agreement with MIPAS.

    The WACCM/MIPAS comparison give rise to new questions. For example, can the different MIPAS/WACCM CO2 seasonal/latitudinal distributions be explained by a seasonal/latitudinal dependence of the eddy diffusion?; or is the lower thermosphere residual circulation in WACCM too strong?; or are they caused by an underestimation of the photochemical losses of CO2 in WACCM? Finally, the MIPAS global (60ºS-60ºN) CO2 vmr above ~100 km showed a decrease in 2011 with respect to 2010, which is consistent with the larger solar activity of 2011. This result is also consistent with SABER measurements and WACCM predictions. ACE measurements show also a decrease from 95 km up to 110 km but not between 110 and 120 km.

    In a near future, the most urgent work will be to complete the CO2 retrieval from the whole MIPAS optimized resolution upper atmosphere (UA) mode data set, from 2005 to the end of the Envisat mission in 2012. This CO2 database will then cover nearly a complete solar cycle and, therefore, could be used to perform an inter-annual variability study. Of particular importance is to check if it shows also a trend of CO2 in the middle and upper atmosphere larger than that expected from the CO2 anthropogenic increase in the lower thermosphere, as has been recently reported from the ACE (Emmert et al., 2012) and SABER (Yue et al., 2015) measurements.

    The MIPAS CO2 measurements have the advantages of providing simultaneously and globally, besides CO2, the CO abundance, and covering higher altitudes, up to 140 km. These would allow to study in detail the COx chemistry and the dynamics of the middle thermosphere, practically an unexplored region.

    Other potential work to be carried out in the future is the study of the upper atmosphere infrared cooling. Comparison of the CO2 and NO cooling rates calculated with the MIPAS products and those measured by SABER would be very valuable. Also, comparison with WACCM simulations would be very useful, since MIPAS offers important constraints as the temperature, and the CO, CO2 and NO relative concentrations.

    On the algorithm development side, it would be very useful to develop the "setups" for the retrievals of CO2 from the middle atmosphere (MA) and Noctilucent Cloud (NLC) MIPAS observations modes.

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