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.
River outgassing has proven to be an integral part of the carbon cycle. In Southeast Asia, river outgassing quantities are uncertain due to lack of measured data. Here we investigate six rivers in ...Indonesia and Malaysia, during five expeditions. CO2 fluxes from Southeast Asian rivers amount to 66.9 ± 15.7 Tg C per year, of which Indonesia releases 53.9 ± 12.4 Tg C per year. Malaysian rivers emit 6.2 ± 1.6 Tg C per year. These moderate values show that Southeast Asia is not the river outgassing hotspot as would be expected from the carbon-enriched peat soils. This is due to the relatively short residence time of dissolved organic carbon (DOC) in the river, as the peatlands, being the primary source of DOC, are located near the coast. Limitation of bacterial production, due to low pH, oxygen depletion or the refractory nature of DOC, potentially also contributes to moderate CO2 fluxes as this decelerates decomposition.
Carbon monoxide (CO) is an important atmospheric constituent affecting air quality, and methane (CH.sub.4) 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 hydrological cycle and its response to environmental variability such as temperature changes is of prime importance for climate reconstruction and prediction. We retrieved deuterated water/water ...(HDO/H2O) abundances using spaceborne absorption spectroscopy, providing an almost global perspective on the near-surface distribution of water vapor isotopologs. We observed an unexpectedly high HDO/H2O seasonality in the inner Sahel region, pointing to a strong isotopic depletion in the subsiding branch of the Hadley circulation and its misrepresentation in general circulation models. An extension of the analysis at high latitudes using ground-based observations of deltaD and a model study shows that dynamic processes can entirely compensate for temperature effects on the isotopic composition of precipitation.
The goal of this study is to determine how H2O and HDO measurements in water vapor can be used to detect and diagnose biases in the representation of processes controlling tropospheric humidity in ...atmospheric general circulation models (GCMs). We analyze a large number of isotopic data sets (four satellite, sixteen ground‐based remote‐sensing, five surface in situ and three aircraft data sets) that are sensitive to different altitudes throughout the free troposphere. Despite significant differences between data sets, we identify some observed HDO/H2O characteristics that are robust across data sets and that can be used to evaluate models. We evaluate the isotopic GCM LMDZ, accounting for the effects of spatiotemporal sampling and instrument sensitivity. We find that LMDZ reproduces the spatial patterns in the lower and mid troposphere remarkably well. However, it underestimates the amplitude of seasonal variations in isotopic composition at all levels in the subtropics and in midlatitudes, and this bias is consistent across all data sets. LMDZ also underestimates the observed meridional isotopic gradient and the contrast between dry and convective tropical regions compared to satellite data sets. Comparison with six other isotope‐enabled GCMs from the SWING2 project shows that biases exhibited by LMDZ are common to all models. The SWING2 GCMs show a very large spread in isotopic behavior that is not obviously related to that of humidity, suggesting water vapor isotopic measurements could be used to expose model shortcomings. In a companion paper, the isotopic differences between models are interpreted in terms of biases in the representation of processes controlling humidity.
Key Points
Isotopic evaluation with in situ, satellite, ground‐based remote‐sensing data
Consistent features and model‐data differences across data sets
Isotopic GCMs share common biases
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.
We present a new remote sensing based method to estimate dissolved organic carbon (DOC) flux discharged from rivers into coastal waters off the Sarawak region in Borneo. This method comprises three ...steps. In the first step, we developed an algorithm for estimating DOC concentrations using the ratio of Landsat-8 Red to Green bands B4/B3 (DOC (μM C) = 89.86 ·e0.27·(B4/B3)), which showed good correlation (R = 0.88) and low mean relative error (+5.71%) between measured and predicted DOC. In the second step, we used TRMM Multisatellite Precipitation Analysis (TMPA) precipitation data to estimate river discharge for the river basins. In the final step, DOC flux for each river catchment was then estimated by combining Landsat-8 derived DOC concentrations and TMPA derived river discharge. The analysis of remote sensing derived DOC flux (April 2013 to December 2018) shows that Sarawak coastal waters off the Rajang river basin, received the highest DOC flux (72% of total) with an average of 168 Gg C per year in our study area, has seasonal variability. The whole of Sarawak represents about 0.1% of the global annual riverine and estuarine DOC flux. The results presented in this study demonstrate the ability to estimate DOC flux using satellite remotely sensed observations.
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.
Methane retrievals from near‐infrared spectra recorded by the SCIAMACHY instrument onboard ENVISAT hitherto suggested unexpectedly large tropical emissions. Even though recent studies confirm ...substantial tropical emissions, there were indications for an unresolved error in the satellite retrievals. Here we identify a retrieval error related to inaccuracies in water vapor spectroscopic parameters, causing a substantial overestimation of methane correlated with high water vapor abundances. We report on the overall implications of an update in water spectroscopy on methane retrievals with special focus on the tropics where the impact is largest. The new retrievals are applied in a four‐dimensional variational (4D‐VAR) data assimilation system to derive a first estimate of the impact on tropical CH4 sources. Compared to inversions based on previous SCIAMACHY retrievals, annual tropical emission estimates are reduced from 260 to about 201 Tg CH4 but still remain higher than previously anticipated.
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