Derivation of tropospheric methane from TCCON CH₄ and HF total column observations

The Total Carbon Column Observing Network (TCCON) is a global ground-based network of Fourier transform spectrometers that produce precise measurements of column-averaged dry-air mole fractions of atmospheric methane (CH₄). Temporal variability in the total column of CH₄ due to stratospheric dynamics obscures fluctuations and trends driven by tropospheric transport and local surface fluxes that are critical for understanding CH₄ sources and sinks. We reduce the contribution of stratospheric variability from the total column average by subtracting an estimate of the stratospheric CH₄ derived from simultaneous measurements of hydrogen fluoride (HF). HF provides a proxy for stratospheric CH₄ because it is strongly correlated to CH₄ in the stratosphere, has an accurately known tropospheric abundance (of zero), and is measured at most TCCON stations. The stratospheric partial column of CH₄ is calculated as a function of the zonal and annual trends in the relationship between CH₄ and HF in the stratosphere, which we determine from ACE-FTS satellite data. We also explicitly take into account the CH₄ column averaging kernel to estimate the contribution of stratospheric CH₄ to the total column. The resulting tropospheric CH₄ columns are consistent with in situ aircraft measurements and augment existing observations in the troposphere

First data set of H<sub>2</sub>O/HDO columns from the Tropospheric Monitoring Instrument (TROPOMI)

Global measurements of atmospheric water vapour isotopologues aid to better understand the hydrological cycle and improve global circulation models. This paper presents a new data set of vertical column densities of H2O and HDO retrieved from short-wave infrared (2.3 µm) reflectance measurements by the Tropospheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5 Precursor satellite. TROPOMI features daily global coverage with a spatial resolution of up to 7 km×7 km. The retrieval utilises a profile-scaling approach. The forward model neglects scattering, and strict cloud filtering is therefore necessary. For validation, recent ground-based water vapour isotopologue measurements by the Total Carbon Column Observing Network (TCCON) are employed. A comparison of TCCON δD with ground-based measurements by the Multi-platform remote Sensing of Isotopologues for investigating the Cycle of Atmospheric water (MUSICA) project for data prior to 2014 (where MUSICA data are available) shows a bias in TCCON δD estimates. As TCCON HDO is currently not validated, an overall correction of recent TCCON HDO data is derived based on this finding. The agreement between the corrected TCCON measurements and co-located TROPOMI observations is good with an average bias of (−0.2±3)×10$^{21}$ molec cm$^{-2}$ ((1.1±7.2) %) in H$_{2}$O and (−2±7)×10$^{17}$ molec cm$^{-2}$ ((−1.1±7.3) %) in HDO, which corresponds to a mean bias of (−14±17) ‰ in a posteriori δD. The bias is lower at low- and mid-latitude stations and higher at high-latitude stations. The use of the data set is demonstrated with a case study of a blocking anticyclone in northwestern Europe in July 2018 using single-overpass data

Arctic stratospheric dehydration – Part 2: Microphysical modeling

Large areas of synoptic-scale ice PSCs (polar stratospheric clouds) distinguished the Arctic winter 2009/2010 from other years and revealed unprecedented evidence of water redistribution in the stratosphere. A unique snapshot of water vapor repartitioning into ice particles was obtained under extremely cold Arctic conditions with temperatures around 183 K. Balloon-borne, aircraft and satellite-based measurements suggest that synoptic-scale ice PSCs and concurrent reductions and enhancements in water vapor are tightly linked with the observed de- and rehydration signatures, respectively. In a companion paper (Part 1), water vapor and aerosol backscatter measurements from the RECONCILE (Reconciliation of essential process parameters for an enhanced predictability of Arctic stratospheric ozone loss and its climate interactions) and LAPBIAT-II (Lapland Atmosphere–Biosphere Facility) field campaigns have been analyzed in detail. This paper uses a column version of the Zurich Optical and Microphysical box Model (ZOMM) including newly developed NAT (nitric acid trihydrate) and ice nucleation parameterizations. Particle sedimentation is calculated in order to simulate the vertical redistribution of chemical species such as water and nitric acid. Despite limitations given by wind shear and uncertainties in the initial water vapor profile, the column modeling unequivocally shows that (1) accounting for small-scale temperature fluctuations along the trajectories is essential in order to reach agreement between simulated optical cloud properties and observations, and (2) the use of recently developed heterogeneous ice nucleation parameterizations allows the reproduction of the observed signatures of de- and rehydration. Conversely, the vertical redistribution of water measured cannot be explained in terms of homogeneous nucleation of ice clouds, whose particle radii remain too small to cause significant dehydration

Nitric acid trihydrate nucleation and denitrification in the Arctic stratosphere

Nitric acid trihydrate (NAT) particles in the polar stratosphere have been shown to be responsible for vertical redistribution of reactive nitrogen (NO_y). Recent observations by Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) aboard the CALIPSO satellite have been explained in terms of heterogeneous nucleation of NAT on foreign nuclei, revealing this to be an important formation pathway for the NAT particles. In state of the art global- or regional-scale models, heterogeneous NAT nucleation is currently simulated in a very coarse manner using a constant, saturation-independent nucleation rate. Here we present first simulations for the Arctic winter 2009/2010 applying a new saturation-dependent parametrisation of heterogeneous NAT nucleation rates within the Chemical Lagrangian Model of the Stratosphere (CLaMS). The simulation shows good agreement of chemical trace species with in situ and remote sensing observations. The simulated polar stratospheric cloud (PSC) optical properties agree much better with CALIOP observations than those simulated with a constant nucleation rate model. A comparison of the simulated particle size distributions with observations made using the Forward Scattering Spectrometer Probe (FSSP) aboard the high altitude research aircraft Geophysica, shows that the model reproduces the observed size distribution, except for the very largest particles above 15 μm diameter. The vertical NO_y redistribution caused by the sedimentation of the NAT particles, in particular the denitrification and nitrification signals observed by the ACE-FTS satellite instrument and the in situ SIOUX instrument aboard the Geophysica, are reproduced by the improved model, and a small improvement with respect to the constant nucleation rate model is found

Improving the TROPOMI CO data product: update of the spectroscopic database and destriping of single orbits

On 13 October 2017, the Tropospheric Monitoring Instrument (TROPOMI) was launched on the Copernicus Sentinel-5 Precursor satellite in a sun-synchronous orbit. One of the mission's operational data products is the total column concentration of carbon monoxide (CO), which was released to the public in July 2018. Using HITRAN 2008 spectroscopic data with an updated water vapor spectroscopy, the CO data product is compliant with the mission requirement of 10 % precision and 15 % accuracy for single soundings. Comparison with ground-based CO observations of the Total Carbon Column Observing Network (TCCON) show systematic differences of about 6.4 ppb and single orbit observations are superimposed by a significant striping pattern along the flight path exceeding 5 ppb. In this study, we discuss possible improvements of the CO data product. We found that the molecular spectroscopic data used in the retrieval plays a key role for the data quality where the use of the Scientific Exploitation of Operational Missions – Improved Atmospheric Spectroscopy Databases (SEOM-IAS) and the HITRAN 2012 and 2016 releases reduce the bias between TROPOMI and TCCON due to improved CH4 spectroscopy. SEOM-IAS achieves the best spectral fitting quality and reduces the bias between TROPOMI and TCCON to 3.3 ppb while HITRAN 2012 and HITRAN 2016 decrease the bias even further below 1.1 ppb. Here, HITRAN 2012 worsens the fitting quality and furthermore introduces an artificial bias to the TROPOMI CO data product in the tropics caused by the H2O spectroscopic data. Moreover, analyzing one year of TROPOMI CO observations, we identified increased striping patterns by about 16 % percent from November 2017 to November 2018. To mitigate this effect, we discuss two destriping methods applied to the CO data a posteriori. A destriping mask calculated per orbit by median filtering of the data in the cross-track direction significantly improves the data quality. However, still better quality is achieved by a Fourier analysis and filtering of the data, which corrects not only for stripe patterns in cross-track direction but also accounts for the variability of stripes along the flight path

Derivation of tropospheric methane from TCCON CH₄ and HF total column observations

The Total Carbon Column Observing Network (TCCON) is a global ground-based network of Fourier transform spectrometers that produce precise measurements of column-averaged dry-air mole fractions of atmospheric methane (CH₄). Temporal variability in the total column of CH₄ due to stratospheric dynamics obscures fluctuations and trends driven by tropospheric transport and local surface fluxes that are critical for understanding CH₄ sources and sinks. We reduce the contribution of stratospheric variability from the total column average by subtracting an estimate of the stratospheric CH₄ derived from simultaneous measurements of hydrogen fluoride (HF). HF provides a proxy for stratospheric CH₄ because it is strongly correlated to CH₄ in the stratosphere, has an accurately known tropospheric abundance (of zero), and is measured at most TCCON stations. The stratospheric partial column of CH₄ is calculated as a function of the zonal and annual trends in the relationship between CH₄ and HF in the stratosphere, which we determine from ACE-FTS satellite data. We also explicitly take into account the CH₄ column averaging kernel to estimate the contribution of stratospheric CH₄ to the total column. The resulting tropospheric CH₄ columns are consistent with in situ aircraft measurements and augment existing observations in the troposphere

-WAVVAP) campaign

[1] We present a validation study for the ground-based Middle Atmospheric Water Vapour Radiometer (MIAWARA) operating at 22 GHz. MIAWARA measures the water vapor profile in the range of 20-80 km. The validation was conducted in two phases at different geographical locations. During the first operational period the radiometer was operated at middle latitudes in Bern, Switzerland, and the measured water vapor profiles were compared with the HALOE satellite instrument. The agreement between HALOE and MIAWARA was for most altitudes better than 10%. The agreement between the balloon instruments and MIAWARA was better than 2% for a total number of 10 comparable flights. This showed the potential of MIAWARA in water vapor retrieval down to 20 km. In addition, the northern Finland MIAWARA profiles were compared with POAM III water vapor profiles. This comparison confirmed the good agreement with the other instruments, and the difference between MIAWARA and POAM was generally less than 8%. Finally, the tipping curve calibration was validated with tipping curve measurements of the All-Sky Multi Wavelength Radiometer (ASMUWARA) which was operated 10 months side by side with MIAWARA. The agreement of the tropospheric opacity derived from these tipping curves agree within 1%

Forecasting global atmospheric CO_2

A new global atmospheric carbon dioxide (CO_2) real-time forecast is now available as part of the pre-operational Monitoring of Atmospheric Composition and Climate – Interim Implementation (MACC-II) service using the infrastructure of the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS). One of the strengths of the CO_2 forecasting system is that the land surface, including vegetation CO_2 fluxes, is modelled online within the IFS. Other CO_2 fluxes are prescribed from inventories and from off-line statistical and physical models. The CO_2 forecast also benefits from the transport modelling from a state-of-the-art numerical weather prediction (NWP) system initialized daily with a wealth of meteorological observations. This paper describes the capability of the forecast in modelling the variability of CO_2 on different temporal and spatial scales compared to observations. The modulation of the amplitude of the CO_2 diurnal cycle by near-surface winds and boundary layer height is generally well represented in the forecast. The CO_2 forecast also has high skill in simulating day-to-day synoptic variability. In the atmospheric boundary layer, this skill is significantly enhanced by modelling the day-to-day variability of the CO_2 fluxes from vegetation compared to using equivalent monthly mean fluxes with a diurnal cycle. However, biases in the modelled CO_2 fluxes also lead to accumulating errors in the CO_2 forecast. These biases vary with season with an underestimation of the amplitude of the seasonal cycle both for the CO_2 fluxes compared to total optimized fluxes and the atmospheric CO_2 compared to observations. The largest biases in the atmospheric CO_2 forecast are found in spring, corresponding to the onset of the growing season in the Northern Hemisphere. In the future, the forecast will be re-initialized regularly with atmospheric CO_2 analyses based on the assimilation of CO_2 products retrieved from satellite measurements and CO_2 in situ observations, as they become available in near-real time. In this way, the accumulation of errors in the atmospheric CO_2 forecast will be reduced. Improvements in the CO_2 forecast are also expected with the continuous developments in the operational IFS

Trajectory studies of Polar Statospheric Cloud Lidar Observations at Sodankyla (Finland) during SESAME: comparison with box model results of particle evolution

Polar statospheric clouds (PSC) were observed with the milti-wavelengh lidar of the MOANA project during SESAME. The physical state, liquid or solid, of the cloud particles can be inferred from the lidar data. Using isentropic back-trajectories to obtain the thermal history of the sampled air masses, it is possible to reconcile most of the observations with current ideas on PSC formation and evolution. When the cloud particles were identified as liquid, changes in the size distributionof the droplets along the trajectory ewre calculated using micro-physical box model. Backscatter ratios ......Published165-1811.8. Osservazioni di geofisica ambientaleJCR Journalreserve