Methane (CH4) is a potent greenhouse gas with high radiative forcing and a relatively short atmospheric lifetime of around a decade. We used a decade-long dataset (2011-2022) from the Fourier transform spectrometer at the California Laboratory for Atmospheric Remote Sensing (CLARS-FTS) to quantify a dramatic increase in methane observed in 2020. We report an increase of 1.13 ppb/month starting in 2020 until the end of 2021, compared to a growth rate of 0.345 ppb/month from 2016 to 2019. The observed increase in methane concentrations in 2020 is of significant concern due to its potential contribution to global warming. The Total Carbon Column Observing Network (TCCON) is then used to examine the global geospatial variability of the increase in methane. The results suggest an approximately uniform rise in methane globally. Finally, results from a two-box model used to simulate atmospheric chemical processes of methane production and loss indicate that changes in OH alone are insufficient to explain the rise in atmospheric methane. Encouragingly, recent data from 2022 suggest a deceleration in the methane growth rate, indicating a potential slowdown in the methane increase observed in 2020.

Top-down estimates of CO2 fluxes are typically constrained by either surface-based or space-based CO2 observations. Both of these measurement types have spatial and temporal gaps in observational coverage that can lead to biases in inferred fluxes. Assimilating both surface-based and space-based measurements concurrently in a flux inversion framework improves observational coverage and reduces sampling biases. This study examines the consistency of flux constraints provided by these different observations and the potential to combine them by performing a series of six-year (2010–2015) CO2 flux inversions. Flux inversions are performed assimilating surface-based measurements from the in situ and flask network, measurements from the Total Carbon Column Observing Network (TCCON), and space-based measurements from the Greenhouse Gases Observing Satellite (GOSAT), or all three datasets combined. Combining the datasets results in more precise flux estimates for sub-continental regions relative to any of the datasets alone. Combining the datasets also improves the accuracy of the posterior fluxes, based on reduced root-mean-square differences between posterior-flux-simulated CO2 and aircraft-based CO2 over midlatitude regions (0.35–0.50~ppm) in comparison to GOSAT (0.39–0.57~ppm), TCCON (0.52–0.63~ppm), or in situ and flask measurements (0.45–0.53~ppm) alone. These results suggest that surface-based and GOSAT measurements give complementary constraints on CO2 fluxes in the northern extratropics and can be combined in flux inversions to improve observational coverage. This stands in contrast with many earlier attempts to combine these datasets and suggests that improvements in the NASA Atmospheric CO2 Observations from Space (ACOS) retrieval algorithm have significantly improved the consistency of space-based and surface-based flux constraints.

The Greater Toronto Area (GTA, pop. 6.4 million) is the populous city in Canada, thus accurately quantifying GHG emissions from the GTA is an important step towards meeting Canada’s commitments to reduce its Greenhouse gas emissions. Mitigation of methane (CH4) emissions is of particular importance when setting the country’s policy measures to meet the GHG reduction goal since it can be economically advantageous. In this study, a methane emission inventory with a high spatial resolution was prepared using individual facility reports gathered for each municipality in the GTA. Measurements using portable Fourier Transform Spectrometers (FTS), are then used to monitor CH4 levels in the GTA continuously. Four FTS instruments were installed in different locations in the city in line with the most frequent wind directions based on historical observations. High peak events were investigated and with measurements of tracer concentrations and wind data, the emissions from the city were estimated. At the end, we investigated how the results could be used to improve the existing emission inventory.

In this study, we examine the ability of the data assimilation of global satellite-based carbon monoxide (CO) observations to constrain high-latitude boreal wildfire emissions. We compare the optimized emissions from inversions using CO measurements from the Measurement of Pollution in the Troposphere (MOPITT) and Infrared Atmospheric Sounding Interferometer (IASI). We found that both inversions yield generally consistent posterior CO emissions globally; however, distinct differences are observed for the episodic 2017 Canadian wildfires. The 3-day global coverage of MOPITT limits its ability to accurately optimize emissions, while the daily global coverage of IASI provides a moderate improvement despite its lower surface sensitivity. Through a series of observing system simulation experiments (OSSEs), we show that the temporal coverage of IASI most strongly influenced the posterior estimates, while the differences in vertical sensitivities of MOPITT and IASI have a minor contribution.

Terrestrial vegetation is known to be an important sink for carbon dioxide (CO2). However, fluxes to and from vegetation are often not accounted for when studying anthropogenic CO2 emissions in urban areas. This project seeks to quantify urban biogenic fluxes in the Greater Toronto and Hamilton Area located in Southern Ontario, Canada. Toronto is Canada’s most populated city but also has a large amount of green-space, covering approximately 13 % of the city. In addition, vegetation is not evenly distributed throughout the region. We therefore expect biogenic fluxes to play an important role in the spatial patterns of CO2 concentrations and the overall local carbon budget. In order to fully understand biogenic fluxes they can be partitioned into the amount of CO2 sequestered via photosynthesis, gross primary productivity (GPP), and the amount respired by vegetation, ecosystem respiration (Reco). Solar induced chlorophyll fluorescence (SIF) measured from space has been shown to be a valuable proxy for photosynthesis and thus can be used to estimate GPP. Vegetation models, including the Urban Vegetation Photosynthesis and Respiration Model (UrbanVPRM) and the SIF for Modelling Urban biogenic Fluxes (SMUrF) model, have also been used to estimate both GPP and Reco In this study we compare modelled and SIF-derived biogenic CO2 fluxes at a 500 m by 500 m resolution, to ground-based flux tower measurements in Southern Ontario to determine how well these methods estimate biogenic CO2 fluxes. This study works towards determining the importance of biogenic fluxes in the Greater Toronto and Hamilton Area. Furthermore, the results of this work may inform policy makers and city planners on how urban vegetation affects CO2 concentrations and patterns within cities.

TanSat is the 1st Chinese carbon dioxide (CO) measurement satellite, launched in 2016. In this study, the University of Leicester Full Physics (UoL-FP) algorithm is implemented for TanSat nadir mode XCO retrievals. We develop a spectrum correction method to reduce the retrieval errors by the online fitting of an 8 order Fourier series. The model and a priori is developed by analyzing the solar calibration measurement. This correction provides a significant improvement to the O A band retrieval. Accordingly, we extend the previous TanSat single CO weak band retrieval to a combined O A and CO weak band retrieval. A Genetic Algorithm (GA) has been applied to determine the threshold values of post-screening filters. In total, 18.3% of the retrieved data is identified as high quality compared to the original measurements. The same quality control parameters have been used in a footprint independent multiple linear regression bias correction due to the stronger correlation with the XCO retrieval error. Twenty sites of the Total Column Carbon Observing Network (TCCON) have been selected to validate our new approach for the TanSat XCO retrieval. We show that our new approach produces a significant improvement on the XCO retrieval accuracy and precision when compared to TCCON with an average bias and RMSE of -0.08 ppm and 1.47 ppm, respectively. The methods used in this study can help to improve the XCO retrieval from TanSat and subsequently the Level-2 data production, and hence will be applied in the TanSat operational XCO processing.