Chemical loss of Arctic ozone due to anthropogenic halogens is driven by temperature, with more loss occurring during cold winters favourable for formation of polar stratospheric clouds (PSCs). We ...show that a positive, statistically significant rise in the local maxima of PSC formation potential (PFP
) for cold winters is apparent in meteorological data collected over the past half century. Output from numerous General Circulation Models (GCMs) also exhibits positive trends in PFP
over 1950 to 2100, with highest values occurring at end of century, for simulations driven by a large rise in the radiative forcing of climate from greenhouse gases (GHGs). We combine projections of stratospheric halogen loading and humidity with GCM-based forecasts of temperature to suggest that conditions favourable for large, seasonal loss of Arctic column O
could persist or even worsen until the end of this century, if future abundances of GHGs continue to steeply rise.
The TROPOspheric Monitoring Instrument (TROPOMI) on board the Sentinel 5 Precursor (S5-P) satellite provides methane (CH4) measurements with high accuracy and exceptional temporal and spatial ...resolution and sampling. TROPOMI CH4 measurements are highly valuable to constrain emissions inventories and for trend analysis, with strict requirements on the data quality. This study describes the improvements that we have implemented to retrieve CH4 from TROPOMI using the RemoTeC full-physics algorithm. The updated retrieval algorithm features a constant regularization scheme of the inversion that stabilizes the retrieval and yields less scatter in the data and includes a higher resolution surface altitude database. We have tested the impact of three state-of-the-art molecular spectroscopic databases (HITRAN 2008, HITRAN 2016 and Scientific Exploitation of Operational Missions – Improved Atmospheric Spectroscopy Databases SEOM-IAS) and found that SEOM-IAS provides the best fitting results. The most relevant update in the TROPOMI XCH4 data product is the implementation of an a posteriori correction fully independent of any reference data that is more accurate and corrects for the underestimation at low surface albedo scenes and the overestimation at high surface albedo scenes. After applying the correction, the albedo dependence is removed to a large extent in the TROPOMI versus satellite (Greenhouse gases Observing SATellite – GOSAT) and TROPOMI versus ground-based observations (Total Carbon Column Observing Network – TCCON) comparison, which is an independent verification of the correction scheme. We validate 2 years of TROPOMI CH4 data that show the good agreement of the updated TROPOMI CH4 with TCCON (−3.4 ± 5.6 ppb) and GOSAT (−10.3 ± 16.8 ppb) (mean bias and standard deviation). Low- and high-albedo scenes as well as snow-covered scenes are the most challenging for the CH4 retrieval algorithm, and although the a posteriori correction accounts for most of the bias, there is a need to further investigate the underlying cause.
Carbon monoxide (CO) is an important atmospheric constituent affecting air quality, and
methane (CH4) is the second most important greenhouse gas contributing to human-induced
climate change. ...Detailed and continuous observations of these gases are necessary to better assess
their impact on climate and atmospheric pollution. While surface and airborne measurements are able
to accurately determine atmospheric abundances on local scales, global coverage can only be
achieved using satellite instruments. The TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5 Precursor satellite, which
was successfully launched in October 2017, is a spaceborne nadir-viewing imaging spectrometer
measuring solar radiation reflected by the Earth in a push-broom configuration. It has a wide swath
on the terrestrial surface and covers wavelength bands between the ultraviolet (UV) and the
shortwave infrared (SWIR), combining a high spatial resolution with daily global coverage. These
characteristics enable the determination of both gases with an unprecedented level of detail on a
global scale, introducing new areas of application. Abundances of the atmospheric column-averaged dry air mole fractions XCO and XCH4
are simultaneously retrieved from TROPOMI's radiance measurements in the 2.3 µm
spectral range of the SWIR part of the solar spectrum using the scientific retrieval algorithm
Weighting Function Modified Differential Optical Absorption Spectroscopy (WFM-DOAS). This algorithm
is intended to be used with the operational algorithms for mutual verification and to provide new
geophysical insights. We introduce the algorithm in detail, including expected error characteristics
based on synthetic data, a machine-learning-based quality filter, and a shallow learning calibration
procedure applied in the post-processing of the XCH4 data. The quality of the results based
on real TROPOMI data is assessed by validation with ground-based Fourier transform spectrometer
(FTS) measurements providing realistic error estimates of the satellite data: the XCO data
set is characterised by a random error of 5.1 ppb (5.8 %) and a systematic error of
1.9 ppb (2.1 %); the XCH4 data set exhibits a random error of 14.0 ppb
(0.8 %) and a systematic error of 4.3 ppb (0.2 %). The natural XCO and
XCH4 variations are well-captured by the satellite retrievals, which is demonstrated by a
high correlation with the validation data (R=0.97 for XCO and R=0.91 for XCH4 based
on daily averages). We also present selected results from the mission start until the end of 2018, including a first comparison
to the operational products and examples of the detection of emission sources in a single satellite
overpass, such as CO emissions from the steel industry and CH4 emissions from the
energy sector, which potentially allows for the advance of emission monitoring and air quality assessments
to an entirely new level.
Predicting radiative forcing due to Antarctic stratospheric ozone recovery requires detecting changes in the ozone vertical distribution. In this endeavor, the Limb Profiler of the Ozone Mapping and ...Profiler Suite (OMPS-LP), aboard the Suomi NPP satellite, has played a key role providing ozone profiles over Antarctica since 2011. Here, we compare ozone profiles derived from OMPS-LP data (version 2.5 algorithm) with balloon-borne ozonesondes launched from 8 Antarctic stations over the period 2012-2020. Comparisons focus on the layer from 12.5 to 27.5 km and include ozone profiles retrieved during the Sudden Stratospheric Warming (SSW) event registered in Spring 2019. We found that, over the period December-January-February-March, the root mean square error (RMSE) tends to be larger (about 20%) in the lower stratosphere (12.5-17.5 km) and smaller (about 10%) within higher layers (17.5-27.5 km). During the ozone hole season (September-October-November), RMSE values rise up to 40% within the layer from 12.5 to 22 km. Nevertheless, relative to balloon-borne measurements, the mean bias error of OMPS-derived Antarctic ozone profiles is generally lower than 0.3 ppmv, regardless of the season.
Recent advances in satellite observations of methane provide increased opportunities for inverse modeling. However, challenges exist in the satellite observation optimization and retrievals for high ...latitudes. In this study, we examine possibilities and challenges in the use of the total column averaged dry-air mole fractions of methane (XCH4) data over land from the TROPOspheric Monitoring Instrument (TROPOMI) on board the Sentinel 5 Precursor satellite in the estimation of CH4 fluxes using the CarbonTracker Europe-CH4 (CTE-CH4) atmospheric inverse model. We carry out simulations assimilating two retrieval products: Netherlands Institute for Space Research’s (SRON) operational and University of Bremen’s Weighting Function Modified Differential Optical Absorption Spectroscopy (WFM-DOAS). For comparison, we also carry out a simulation assimilating the ground-based surface data. Our results show smaller regional emissions in the TROPOMI inversions compared to the prior and surface inversion, although they are roughly within the range of the previous studies. The wetland emissions in summer and anthropogenic emissions in spring are lesser. The inversion results based on the two satellite datasets show many similarities in terms of spatial distribution and time series but also clear differences, especially in Canada, where CH4 emission maximum is later, when the SRON’s operational data are assimilated. The TROPOMI inversions show higher CH4 emissions from oil and gas production and coal mining from Russia and Kazakhstan. The location of hotspots in the TROPOMI inversions did not change compared to the prior, but all inversions indicated spatially more homogeneous high wetland emissions in northern Fennoscandia. In addition, we find that the regional monthly wetland emissions in the TROPOMI inversions do not correlate with the anthropogenic emissions as strongly as those in the surface inversion. The uncertainty estimates in the TROPOMI inversions are more homogeneous in space, and the regional uncertainties are comparable to the surface inversion. This indicates the potential of the TROPOMI data to better separately estimate wetland and anthropogenic emissions, as well as constrain spatial distributions. This study emphasizes the importance of quantifying and taking into account the model and retrieval uncertainties in regional levels in order to improve and derive more robust emission estimates.
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. The current TROPOMI CO
processing uses the HITRAN 2008 spectroscopic data with
updated water vapor spectroscopy and produces a CO data
product 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 6.2 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 fit quality (root-mean-square, rms,
differences between the simulated and measured spectrum)
of 1.5×10-10 mol s−1 m−2 nm−1 sr−1 and reduces the bias between TROPOMI and TCCON to
3.4 ppb, while HITRAN 2012 and HITRAN 2016 decrease the
bias even further below 1 ppb. HITRAN 2012 shows the
worst fit quality (rms = 2.5×10-10 mol s−1 m−2 nm−1 sr−1) of the tested cross sections
and furthermore introduces an artificial bias of about
-1.5×1017 molec cm−2 between TROPOMI CO and
the CAMS-IFS model in the Tropics caused by the H2O
spectroscopic data. Moreover, analyzing 1 year of
TROPOMI CO observations, we identified increased striping
patterns by about 16 % percent from November 2017 to
November 2018. For that, we defined a measure γ,
quantifying the relative pixel-to-pixel variation in CO in the
cross-track and along-track directions.
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
reduced the stripe pattern from γ=2.1 to γ=1.6.
However,
the destriping can be further improved, achieving γ=1.2 by
deploying a Fourier analysis and filtering of the
data, which not only corrects for stripe patterns in the
cross-track direction but also accounts for the
variability of stripes along the flight path.
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