Variability and trends of the tropical tropopause derived from a 1980–2021 multi-reanalysis assessment

As the tropopause plays a key role in regulating the entry of air from the troposphere into the stratosphere and in controlling stratosphere-troposphere exchange, variation of the tropopause impacts the atmospheric dynamics, circulation patterns, and the distribution of greenhouse gases in the upper troposphere and lower stratosphere (UTLS). Therefore, it is of particular interest to investigate the climatological characteristics and trends of the tropopause. Previous studies have investigated the tropopause characteristics using reanalyses and multi-source observations. This study extends the analysis of long-term variability and trends of tropical tropopause characteristics in earlier studies from 1980 up to 2021 using the modern ERA5 reanalysis and compares the results with those of other reanalyses, including ERA-Interim, MERRA-2, and NCEP1/2. Our analysis reveals a general rise and cooling of the tropical tropopause between 1980 and 2021. The geopotential height has increased by approximately 0.06 ± 0.01 km/decade (at a 95% confidence level), while the temperature has decreased by −0.09 ± 0.03 K/decade (at a 95% confidence level) for both the lapse rate tropopause and the cold point tropopause in ERA5. However, from 2006 to 2021, ERA5 shows a warming tropical tropopause (0.10 ± 0.11 K/decade) along with a slower rise in tropopause height (0.05 ± 0.02 km/decade) (at a 95% confidence level). Furthermore, our analysis demonstrates a decline in the rise and cooling of the tropical tropopause since the late 1990s, based on moving 20-year window trends in ERA5. Similar trends are observed in other investigated reanalyses. In addition, this study evaluated the variability of the width of the tropical belt based on tropopause height data from the reanalyses. The ERA5 data show a narrowing tropical belt (−0.16 ± 0.11°/decade) for the time period 1980–2021 according to the relative threshold method. It reveals a tropical widening (0.05 ± 0.22°/decade) for the period between 1980 and 2005, followed by a tropical narrowing (−0.17 ± 0.42°/decade) after 2006. However, the large uncertainties pose a challenge in drawing definitive conclusions on the change of tropical belt width. Despite the many challenges involved in deriving the characteristics and trends of the tropopause from reanalysis data, this study and the open reanalysistropopause data sets provided to the community will help to better inform future assessments of stratosphere-troposphere exchange and studies of chemistry and dynamics of the upper troposphere and lower stratosphere region

A decadal satellite record of gravity wave activity in the lower stratosphere to study polar stratospheric cloud formation

Atmospheric gravity waves yield substantial small-scale temperature fluctuations that can trigger the formation of polar stratospheric clouds (PSCs). This paper introduces a new satellite record of gravity wave activity in the polar lower stratosphere to investigate this process. The record is comprised of observations of the Atmospheric Infrared Sounder (AIRS) aboard NASA's Aqua satellite from January 2003 to December 2012. Gravity wave activity is measured in terms of detrended and noise-corrected 15 µm brightness temperature variances, which are calculated from AIRS channels that are the most sensitive to temperature fluctuations at about 17–32 km of altitude. The analysis of temporal patterns in the data set revealed a strong seasonal cycle in wave activity with wintertime maxima at mid- and high latitudes. The analysis of spatial patterns indicated that orography as well as jet and storm sources are the main causes of the observed waves. Wave activity is closely correlated with 30 hPa zonal winds, which is attributed to the AIRS observational filter. We used the new data set to evaluate explicitly resolved temperature fluctuations due to gravity waves in the European Centre for Medium-Range Weather Forecasts (ECMWF) operational analysis. It was found that the analysis reproduces orographic and non-orographic wave patterns in the right places, but that wave amplitudes are typically underestimated by a factor of 2–3. Furthermore, in a first survey of joint AIRS and Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) satellite observations, nearly 50 gravity-wave-induced PSC formation events were identified. The survey shows that the new AIRS data set can help to better identify such events and more generally highlights the importance of the process for polar ozone chemistry

The MIPAS/Envisat climatology (2002–2012) of polar stratospheric cloud volume density profiles

A global data set of vertical profiles of polar stratospheric cloud (PSC) volume density has been derived from Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) space-borne infrared limb measurements between 2002 and 2012. To develop a well characterized and efficient retrieval scheme, systematic tests based on limbradiance simulations for PSCs from in situ balloon observations have been performed. The finally selected wavenumber range was 831–832.5 cm$^{-1}$. Optical constants of nitric acid trihydrate (NAT) have been used to derive maximum and minimum profiles of volume density which are compatible with MIPAS observations under the assumption of small, non-scattering and larger, scattering PSC particles. These max/min profiles deviate from their mean value at each altitude by about 40 %–45 %, which is attributed as the maximum systematic error of the retrieval. Further, the retrieved volume density profiles are characterized by a random error due to instrumental noise of 0.02–0.05 μm$^{3}$ cm$^{-3}$, a detection limit of about 0.1–0.2 μm$^{3}$ cm$^{-3}$ and a vertical resolution of around 3 km. Comparisons with coincident observations by the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) on the CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations) satellite showed good agreement regarding the vertical profile shape. Quantitatively, in the case of supercooled ternary solution (STS) PSCs, the CALIOP dataset fits to the MIPAS retrievals obtained under the assumptions of small particles. Unlike for STS and NAT, in the case of ice PSCs the MIPAS retrievals are limited by the clouds becoming optically thick in the limb-direction. In these cases, the MIPAS volume densities represent lower limits. Among other interesting features, this climatology helps to study quantitatively the on-set of PSC formation very near to the South Pole and the large variability of the PSC volume densities between different Arctic stratospheric winters

Observation of cirrus clouds with GLORIA during the WISE campaign: detection methods and cirrus characterization

Cirrus clouds contribute to the general radiation budget of the Earth and play an important role in climate projections. Of special interest are optically thin cirrus clouds close to the tropopause due to the fact that their impact is not yet well understood. Measuring these clouds is challenging as both high spatial resolution as well as a very high detection sensitivity are needed. These criteria are fulfilled by the infrared limb sounder GLORIA (Gimballed Limb Observer for Radiance Imaging of the Atmosphere). This study presents a characterization of observed cirrus clouds using the data obtained by GLORIA aboard the German research aircraft HALO during the WISE (Wave-driven ISentropic Exchange) campaign in September and October 2017. We developed an optimized cloud detection method based on the cloud index and the extinction coefficient retrieved at the microwindow 832.4–834.4 cm$^{-1}$. We derived macro-physical characteristics of the detected cirrus clouds such as cloud top height, cloud bottom height, vertical extent and cloud top position with respect to the tropopause. The fraction of cirrus clouds detected above the tropopause is on the order of 13 % to 27 %. In general, good agreement with the clouds predicted by the ERA5 reanalysis dataset is obtained. However, cloud occurrence is ≈ 50 % higher in the observations for the region close to and above the tropopause. Cloud bottom heights are also detected above the tropopause. However, considering the uncertainties, we cannot confirm the formation of unattached cirrus layers above the tropopause

Cirrus cloud shape detection by tomographic extinction retrievals from infrared limb emission sounder measurements

An improved cloud-index-based method for the detection of clouds in limb sounder data is presented that exploits the spatial overlap of measurements to more precisely detect the location of (optically thin) clouds. A second method based on a tomographic extinction retrieval is also presented. Using CALIPSO data and a generic advanced infrared limb imaging instrument as examples for a synthetic study, the new cloud index method has a better horizontal resolution in comparison to the traditional cloud index and has a reduction of false positive cloud detection events by about 30 %. The results for the extinction retrieval even show an improvement of 60 %. In a second step, the extinction retrieval is applied to real 3-D measurements of the airborne Gimballed Limb Observer for Radiance Imaging in the Atmosphere (GLORIA) taken during the Wave-driven ISentropic Exchange (WISE) campaign to retrieve small-scale cirrus clouds with high spatial accuracy

Exploration of machine learning methods for the classification of infrared limb spectra of polar stratospheric clouds

Polar stratospheric clouds (PSCs) play a key role in polar ozone depletion in the stratosphere. Improved observations and continuous monitoring of PSCs can help to validate and improve chemistry–climate models that are used to predict the evolution of the polar ozone hole. In this paper, we explore the potential of applying machine learning (ML) methods to classify PSC observations of infrared limb sounders. Two datasets were considered in this study. The first dataset is a collection of infrared spectra captured in Northern Hemisphere winter 2006/2007 and Southern Hemisphere winter 2009 by the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) instrument on board the European Space Agency's (ESA) Envisat satellite. The second dataset is the cloud scenario database (CSDB) of simulated MIPAS spectra. We first performed an initial analysis to assess the basic characteristics of the CSDB and to decide which features to extract from it. Here, we focused on an approach using brightness temperature differences (BTDs). From both the measured and the simulated infrared spectra, more than 10 000 BTD features were generated. Next, we assessed the use of ML methods for the reduction of the dimensionality of this large feature space using principal component analysis (PCA) and kernel principal component analysis (KPCA) followed by a classification with the support vector machine (SVM). The random forest (RF) technique, which embeds the feature selection step, has also been used as a classifier. All methods were found to be suitable to retrieve information on the composition of PSCs. Of these, RF seems to be the most promising method, being less prone to overfitting and producing results that agree well with established results based on conventional classification methods

A multi-wavelength classification method for polar stratospheric cloud types using infrared limb spectra

The Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) instrument on board the ESA Envisat satellite operated from July 2002 until April 2012. The infrared limb emission measurements represent a unique dataset of daytime and night-time observations of polar stratospheric clouds (PSCs) up to both poles. Cloud detection sensitivity is comparable to space-borne lidars, and it is possible to classify different cloud types from the spectral measurements in different atmospheric windows regions. Here we present a new infrared PSC classification scheme based on the combination of a well-established two-colour ratio method and multiple 2-D brightness temperature difference probability density functions. The method is a simple probabilistic classifier based on Bayes' theorem with a strong independence assumption. The method has been tested in conjunction with a database of radiative transfer model calculations of realistic PSC particle size distributions, geometries, and composition. The Bayesian classifier distinguishes between solid particles of ice and nitric acid trihydrate (NAT), as well as liquid droplets of super-cooled ternary solution (STS). The classification results are compared to coincident measurements from the space-borne lidar Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument over the temporal overlap of both satellite missions (June 2006–March 2012). Both datasets show a good agreement for the specific PSC classes, although the viewing geometries and the vertical and horizontal resolution are quite different. Discrepancies are observed between the CALIOP and the MIPAS ice class. The Bayesian classifier for MIPAS identifies substantially more ice clouds in the Southern Hemisphere polar vortex than CALIOP. This disagreement is attributed in part to the difference in the sensitivity on mixed-type clouds. Ice seems to dominate the spectral behaviour in the limb infrared spectra and may cause an overestimation in ice occurrence compared to the real fraction of ice within the PSC area in the polar vortex. The entire MIPAS measurement period was processed with the new classification approach. Examples like the detection of the Antarctic NAT belt during early winter, and its possible link to mountain wave events over the Antarctic Peninsula, which are observed by the Atmospheric Infrared Sounder (AIRS) instrument, highlight the importance of a climatology of 9 Southern Hemisphere and 10 Northern Hemisphere winters in total. The new dataset is valuable both for detailed process studies, and for comparisons with and improvements of the PSC parameterizations used in chemistry transport and climate models

A climatology of polar stratospheric cloud composition between 2002 and 2012 based on MIPAS/Envisat observations

The Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) instrument aboard the European Space Agency (ESA) Envisat satellite operated from July 2002 to April 2012. The infrared limb emission measurements provide a unique dataset of day and night observations of polar stratospheric clouds (PSCs) up to both poles. A recent classification method for PSC types in infrared (IR) limb spectra using spectral measurements in different atmospheric window regions has been applied to the complete mission period of MIPAS. The method uses a simple probabilistic classifier based on Bayes' theorem with a strong independence assumption on a combination of a well-established two-colour ratio method and multiple 2-D probability density functions of brightness temperature differences. The Bayesian classifier distinguishes between solid particles of ice, nitric acid trihydrate (NAT), and liquid droplets of supercooled ternary solution (STS), as well as mixed types. A climatology of MIPAS PSC occurrence and specific PSC classes has been compiled. Comparisons with results from the classification scheme of the spaceborne lidar Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) on the Cloud-Aerosol-Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite show excellent correspondence in the spatial and temporal evolution for the area of PSC coverage (APSC) even for each PSC class. Probability density functions of the PSC temperature, retrieved for each class with respect to equilibrium temperature of ice and based on coincident temperatures from meteorological reanalyses, are in accordance with the microphysical knowledge of the formation processes with respect to temperature for all three PSC types. This paper represents unprecedented pole-covering day- and nighttime climatology of the PSC distributions and their composition of different particle types. The dataset allows analyses on the temporal and spatial development of the PSC formation process over multiple winters. At first view, a more general comparison of APSC and AICE retrieved from the observations and from the existence temperature for NAT and ice particles based on the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis temperature data shows the high potential of the climatology for the validation and improvement of PSC schemes in chemical transport and chemistry–climate models

Infrared limb emission measurements of aerosol in the troposphere and stratosphere

Altitude-resolved aerosol detection in the upper troposphere and lower stratosphere (UTLS) is a challenging task for remote sensing instruments. Infrared limb emission measurements provide vertically resolved global measurements at day- and nighttime in the UTLS. For high-spectral-resolution infrared limb instruments we present here a new method to detect aerosol and separate between ice and non-ice particles. The method is based on an improved aerosol–cloud index that identifies infrared limb emission spectra affected by non-ice aerosol or ice clouds. For the discrimination between non-ice aerosol and ice clouds we employed brightness temperature difference correlations. The discrimination thresholds for this method were derived from radiative transfer simulations (including scattering) and Michelson Interferometer for Passive Atmospheric Sounding (MIPAS)/Envisat measurements obtained in 2011. We demonstrate the value of this approach for observations of volcanic ash and sulfate aerosol originating from the Grímsvötn (Iceland, 64° N), Puyehue–Cordón Caulle (Chile, 40° S), and Nabro (Eritrea, 13° N) eruptions in May and June 2011 by comparing the MIPAS volcanic aerosol detections with Atmospheric Infrared Sounder (AIRS) volcanic ash and SO2 measurements