Harmonized time series of column-averaged mole fractions of atmospheric methane and ethane over the period 1999–2014 are derived from solar Fourier transform infrared (FTIR) measurements at the ...Zugspitze summit (47° N, 11° E; 2964 m a.s.l.) and at Lauder (45° S, 170° E; 370 m a.s.l.). Long-term trend analysis reveals a consistent renewed methane increase since 2007 of 6.2 5.6, 6.9 ppb yr−1 (parts-per-billion per year) at the Zugspitze and 6.0 5.3, 6.7 ppb yr−1 at Lauder (95 % confidence intervals). Several recent studies provide pieces of evidence that the renewed methane increase is most likely driven by two main factors: (i) increased methane emissions from tropical wetlands, followed by (ii) increased thermogenic methane emissions due to growing oil and natural gas production. Here, we quantify the magnitude of the second class of sources, using long-term measurements of atmospheric ethane as a tracer for thermogenic methane emissions. In 2007, after years of weak decline, the Zugspitze ethane time series shows the sudden onset of a significant positive trend (2.3 1.8, 2.8 × 10−2 ppb yr−1 for 2007–2014), while a negative trend persists at Lauder after 2007 (−0.4 −0.6, −0.1 × 10−2 ppb yr−1). Zugspitze methane and ethane time series are significantly correlated for the period 2007–2014 and can be assigned to thermogenic methane emissions with an ethane-to-methane ratio (EMR) of 12–19 %. We present optimized emission scenarios for 2007–2014 derived from an atmospheric two-box model. From our trend observations we infer a total ethane emission increase over the period 2007–2014 from oil and natural gas sources of 1–11 Tg yr−1 along with an overall methane emission increase of 24–45 Tg yr−1. Based on these results, the oil and natural gas emission contribution (C) to the renewed methane increase is deduced using three different emission scenarios with dedicated EMR ranges. Reference scenario 1 assumes an oil and gas emission combination with EMR = 7.0–16.2 %, which results in a minimum contribution C > 39 % (given as lower bound of 95 % confidence interval). Beside this most plausible scenario 1, we consider two less realistic limiting cases of pure oil-related emissions (scenario 2 with EMR = 16.2–31.4 %) and pure natural gas sources (scenario 3 with EMR = 4.4–7.0 %), which result in C > 18 % and C > 73 %, respectively. Our results suggest that long-term observations of column-averaged ethane provide a valuable constraint on the source attribution of methane emission changes and provide basic knowledge for developing effective climate change mitigation strategies.
The COVID-19 pandemic is causing projected annual CO2 emission reductions up to −8% for 2020. This approximately matches the reductions required year on year to fulfill the Paris agreement. We pursue ...the question whether related atmospheric concentration changes may be detected by the Total Carbon Column Observing Network (TCCON), and brought into agreement with bottom-up emission-reduction estimates. We present a mathematical framework to derive annual growth rates from observed column-averaged carbon dioxide (XCO2) including uncertainties. The min–max range of TCCON growth rates for 2012–2019 was 2.00, 3.27 ppm/yr with a largest one-year increase of 1.07 ppm/yr for 2015/16 caused by El Niño. Uncertainties are 0.38 0.28, 0.44 ppm/yr limited by synoptic variability, including a 0.05 ppm/yr contribution from single-measurement precision. TCCON growth rates are linked to a UK Met Office forecast of a COVID-19-related reduction of −0.32 ppm yr−2 in 2020 for Mauna Loa. The separation of TCCON-measured growth rates vs. the reference forecast (without COVID-19) is discussed in terms of detection delay. A 0.6 0.4, 0.7-yr delay is caused by the impact of synoptic variability on XCO2, including a ≈1-month contribution from single-measurement precision. A hindrance for the detection of the COVID-19-related growth rate reduction in 2020 is the ±0.57 ppm/yr uncertainty for the forecasted reference case (without COVID-19). Only assuming the ongoing growth rate reductions increasing year-on-year by −0.32 ppm yr−2 would allow a discrimination of TCCON measurements vs. the unperturbed forecast and its uncertainty—with a 2.4 2.2, 2.5-yr delay. Using no forecast but the max–min range of the TCCON-observed growth rates for discrimination only leads to a factor ≈2 longer delay. Therefore, the forecast uncertainties for annual growth rates must be reduced. This requires improved terrestrial ecosystem models and ocean observations to better quantify the land and ocean sinks dominating interannual variability.
Throughout spring and summer 2020, ozone stations in the northern extratropics recorded unusually low ozone in the free troposphere. From April to August, and from 1 to 8 kilometers altitude, ozone ...was on average 7% (≈4 nmol/mol) below the 2000–2020 climatological mean. Such low ozone, over several months, and at so many stations, has not been observed in any previous year since at least 2000. Atmospheric composition analyses from the Copernicus Atmosphere Monitoring Service and simulations from the NASA GMI model indicate that the large 2020 springtime ozone depletion in the Arctic stratosphere contributed less than one-quarter of the observed tropospheric anomaly. The observed anomaly is consistent with recent chemistry-climate model simulations, which assume emissions reductions similar to those caused by the COVID-19 crisis. COVID-19 related emissions reductions appear to be the major cause for the observed reduced free tropospheric ozone in 2020.
TROPOMI (the TROPOspheric Monitoring Instrument), on board the Sentinel-5 Precursor (S5P) satellite, has been monitoring the Earth's atmosphere since October 2017 with an unprecedented horizontal ...resolution (initially 7 km.sup.2 x3.5 km.sup.2, upgraded to 5.5 km.sup.2 x3.5 km.sup.2 in August 2019). Monitoring air quality is one of the main objectives of TROPOMI; it obtains measurements of important pollutants such as nitrogen dioxide, carbon monoxide, and formaldehyde (HCHO). In this paper we assess the quality of the latest HCHO TROPOMI products versions 1.1.(5-7), using ground-based solar-absorption FTIR (Fourier-transform infrared) measurements of HCHO from 25 stations around the world, including high-, mid-, and low-latitude sites. Most of these stations are part of the Network for the Detection of Atmospheric Composition Change (NDACC), and they provide a wide range of observation conditions, from very clean remote sites to those with high HCHO levels from anthropogenic or biogenic emissions. The ground-based HCHO retrieval settings have been optimized and harmonized at all the stations, ensuring a consistent validation among the sites.
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.
Consistent validation of satellite CO2 estimates is a prerequisite for using multiple satellite CO2 measurements for joint flux inversion, and for establishing an accurate long-term atmospheric CO2 ...data record. Harmonizing satellite CO2 measurements is particularly important since the differences in instruments, observing geometries, sampling strategies, etc. imbue different measurement characteristics in the various satellite CO2 data products. We focus on validating model and satellite observation attributes that impact flux estimates and CO2 assimilation, including accurate error estimates, correlated and random errors, overall biases, biases by season and latitude, the impact of coincidence criteria, validation of seasonal cycle phase and amplitude, yearly growth, and daily variability. We evaluate dry-air mole fraction (X(sub CO2)) for Greenhouse gases Observing SATellite (GOSAT) (Atmospheric CO2 Observations from Space, ACOS b3.5) and SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) (Bremen Optimal Estimation DOAS, BESD v2.00.08) as well as the CarbonTracker (CT2013b) simulated CO2 mole fraction fields and the Monitoring Atmospheric Composition and Climate (MACC) CO2 inversion system (v13.1) and compare these to Total Carbon Column Observing Network (TCCON) observations (GGG2012/2014). We find standard deviations of 0.9, 0.9, 1.7, and 2.1 parts per million vs. TCCON for CT2013b, MACC, GOSAT, and SCIAMACHY, respectively, with the single observation errors 1.9 and 0.9 times the predicted errors for GOSAT and SCIAMACHY, respectively. We quantify how satellite error drops with data averaging by interpreting according to (error(sup 2) equals a(sup 2) plus b(sup 2) divided by n (with n being the number of observations averaged, a the systematic (correlated) errors, and b the random (uncorrelated) errors). a and b are estimated by satellites, coincidence criteria, and hemisphere. Biases at individual stations have year-to-year variability of 0.3 parts per million, with biases larger than the TCCON predicted bias uncertainty of 0.4 parts per million at many stations. We find that GOSAT and CT2013b under-predict the seasonal cycle amplitude in the Northern Hemisphere (NH) between 46 and 53 degrees North latitude, MACC over-predicts between 26 and 37 degrees North latitude, and CT2013b under-predicts the seasonal cycle amplitude in the Southern Hemisphere (SH). The seasonal cycle phase indicates whether a data set or model lags another data set in time. We find that the GOSAT measurements improve the seasonal cycle phase substantially over the prior while SCIAMACHY measurements improve the phase significantly for just two of seven sites. The models reproduce the measured seasonal cycle phase well except for at Lauder_125HR (CT2013b) and Darwin (MACC). We compare the variability within 1 day between TCCON and models in June-July-August; there is correlation between 0.2 and 0.8 in the NH, with models showing 10-50 percent the variability of TCCON at different stations and CT2013b showing more variability than MACC. This paper highlights findings that provide inputs to estimate flux errors in model assimilations, and places where models and satellites need further investigation, e.g., the SH for models and 45-67 degrees North latitude for GOSAT and CT2013b.
Among the more than 20 ground-based FTIR (Fourier transform infrared) stations currently operating around the globe, only a few have provided formaldehyde (HCHO) total column time series until now. ...Although several independent studies have shown that the FTIR measurements can provide formaldehyde total columns with good precision, the spatial coverage has not been optimal for providing good diagnostics for satellite or model validation. Furthermore, these past studies used different retrieval settings, and biases as large as 50 % can be observed in the HCHO total columns depending on these retrieval choices, which is also a weakness for validation studies combining data from different ground-based stations.
The impact of SEOM–IAS (Scientific Exploitation of Operational Missions–Improved Atmospheric Spectroscopy) spectroscopic information on CO columns from TROPOMI (Tropospheric Monitoring Instrument) ...shortwave infrared (SWIR) observations was examined. HITRAN 2016 (High Resolution Transmission) and GEISA 2015 (Gestion et Etude des Informations Spectroscopiques Atmosphériques 2015) were used as a reference upon which the spectral fitting residuals, retrieval errors and inferred quantities were assessed. It was found that SEOM–IAS significantly improves the quality of the CO retrieval by reducing the residuals to TROPOMI observations. The magnitude of the impact is dependent on the climatological region and spectroscopic reference used. The difference in the CO columns was found to be rather small, although discrepancies reveal, for selected scenes, in particular, for observations with elevated molecular concentrations. A brief comparison to Total Column Carbon Observing Network (TCCON) and Network for the Detection of Atmospheric Composition Change (NDACC) also demonstrated that both spectroscopies cause similar columns; however, the smaller retrieval errors in the SEOM with Speed-Dependent Rautian and line-Mixing (SDRM) inferred CO turned out to be beneficial in the comparison of post-processed mole fractions with ground-based references.
Methane ( CH 4) is a potent greenhouse gas with a large temporal variability. To increase the spatial coverage, methane observations are increasingly made from satellites that retrieve the ...column-averaged dry air mole fraction of methane (XCH 4). To understand and quantify the spatial differences of the seasonal cycle and trend of XCH 4 in more detail, and to ultimately help reduce uncertainties in methane emissions and sinks, we evaluated and analyzed the average XCH 4 seasonal cycle and trend from three Greenhouse Gases Observing Satellite (GOSAT) retrieval algorithms: National Institute for Environmental Studies algorithm version 02.75, RemoTeC CH 4 Proxy algorithm version 2.3.8 and RemoTeC CH 4 Full Physics algorithm version 2.3.8. Evaluations were made against the Total Carbon Column Observing Network (TCCON) retrievals at 15 TCCON sites for 2009–2015, and the analysis was performed, in addition to the TCCON sites, at 31 latitude bands between latitudes 44.43°S and 53.13°N. At latitude bands, we also compared the trend of GOSAT XCH 4 retrievals to the NOAA’s Marine Boundary Layer reference data. The average seasonal cycle and the non-linear trend were, for the first time for methane, modeled with a dynamic regression method called Dynamic Linear Model that quantifies the trend and the seasonal cycle, and provides reliable uncertainties for the parameters. Our results show that, if the number of co-located soundings is sufficiently large throughout the year, the seasonal cycle and trend of the three GOSAT retrievals agree well, mostly within the uncertainty ranges, with the TCCON retrievals. Especially estimates of the maximum day of XCH 4 agree well, both between the GOSAT and TCCON retrievals, and between the three GOSAT retrievals at the latitude bands. In our analysis, we showed that there are large spatial differences in the trend and seasonal cycle of XCH 4. These differences are linked to the regional CH 4 sources and sinks, and call for further research.
Satellite measurements of the spatiotemporal distributions of atmospheric CO2 concentrations are a key component for better understanding global carbon cycle characteristics. Currently, several ...satellite instruments such as the Greenhouse gases Observing SATellite (GOSAT), SCanning Imaging Absorption Spectrometer for Atmospheric CHartographY (SCIAMACHY), and Orbiting Carbon Observatory-2 can be used to measure CO2 column-averaged dry air mole fractions. However, because of cloud effects, a single satellite can only provide limited CO2 data, resulting in significant uncertainty in the characterization of the spatiotemporal distribution of atmospheric CO2 concentrations. In this study, a new physical data fusion technique is proposed to combine the GOSAT and SCIAMACHY measurements. On the basis of the fused dataset, a gap-filling method developed by modeling the spatial correlation structures of CO2 concentrations is presented with the goal of generating global land CO2 distribution maps with high spatiotemporal resolution. The results show that, compared with the single satellite dataset (i.e., GOSAT or SCIAMACHY), the global spatial coverage of the fused dataset is significantly increased (reaching up to approximately 20%), and the temporal resolution is improved by two or three times. The spatial coverage and monthly variations of the generated global CO2 distributions are also investigated. Comparisons with ground-based Total Carbon Column Observing Network (TCCON) measurements reveal that CO2 distributions based on the gap-filling method show good agreement with TCCON records despite some biases. These results demonstrate that the fused dataset as well as the gap-filling method are rather effective to generate global CO2 distribution with high accuracies and high spatiotemporal resolution.
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