Conference Agenda

Earth’s climate is influenced profoundly by anthropogenic greenhouse gas (GHG) emissions. The lack of available global CO₂ and CH₄ measurements makes it difficult to estimate their emissions accurately. Satellite measurements would be very helpful for understanding the global CO₂ and CH₄ flux distribution if CO₂ and CH₄ column-averaged dry air mole fractions (XCO₂ and XCH₄) could be measured with a precision of better than 2 ppm. To this point, one of our main objectives of this research is to use a combination of ground-based measurements of CO₂ and CH₄ and data from current satellite observations (TanSat, GOSAT/-2, OCO-2/-3 and TROPOMI) to validate and evaluate satellite retrievals with retrieval inter-comparisons, to assess them against model calculations and to ingest them into inverse methods to assess surface flux estimates of CO₂ and CH₄.

The aim of Global Stocktakes requires more accurate and larger coverage measurement in future missions. The next generation of TanSat mission, known as TanSat-2 mission kicked-off two years ago. To improve the measurement coverage and repeat frequency, TanSat-2 satellite will sample atmosphere at 1500 km wide across-track swaths, with a footprint size of 3 km ×3 km and hence the measurement covers the global in daily flight. The NIR/SWIR hyperspectral measurement on backscattered sunlight by the spectrometers onboard TanSat-2 includes 0.4 um (ultraviolet), 0.76 um (O₂ A), 1.61 um (CO₂) and 2.0 um (CO₂) bands. There will also be an aerosol optical property to improve the XCO₂ and XCH₄ accuracy. The TanSat-2 XCO₂ measurement precision is designed to be better than 1 ppm for each pixel.

The preliminary OSSE on global and regional scale indicates high efficiency for the TanSat 2 emission in error reduction for top-down flux estimation. We also develop a new method to separate the ecosystem and anthropogenic emission which will be helpful for atmospheric inversion method toward the Global Stocktake. The TanSat mission has been used in city carbon emission signature investigation, which show the capability of TanSat-2 to detect the anthropogenic emission signals.

To evaluate TanSat-2 potential for estimating surface CH₄ fluxes at a weekly scale, we designed a series of observing system simulation experiments (OSSEs) using an existing Ensemble Kalman Filter framework. These experiments focused on the sensitivity of flux estimates to systematic errors (μ) and random errors (σ) in XCH₄ measurements, as well as the impact of satellite swath width. Our findings indicate that with a target precision of 8 ppb, the globally inverted CH₄ flux exhibits an accuracy of 5.1 ± 1.7%, with a reduction of 86.6 ± 11.2% in the a priori uncertainties. The accuracy in the Northern Hemisphere mid-latitudes reaches at 1.9 ± 1.6%. Increasing temporal resolution from 1-month to 1-week reduces flux discrepancies in most regions, except Africa, which is also highly sensitive to XCH₄ bias. In particular, the accuracy of inverted CH₄ fluxes in North Africa declines by 20.4% for each ppb increase in global μ levels. It leads to a 2.3% rise (~11 Tg/yr) in the global total discrepancy. The σ value affects both the accuracy and precision of optimized fluxes, especially in North American Boreal and Tropical Asia, but these effects can be mitigated by expanding the satellite swath width from 1,000 to 3,000 pixels. CH₄ fluxes in Eurasia Temperate and North America Temperate show higher reliability and resilience to variations in swath width and measurement uncertainties. For μ-sensitive regions like Africa, expanding swath width alone offers limited improvements, highlighting the need for complementary observations from other platforms.

To understand the hot-spot emission measurement capability of TanSat-2 mission, we developed a parametric model and a database to swift assess the quantification capability of satellites and configure satellite technique characteristic for certain detection goal. we get the detection thresholds of several satellites and find that the high spatial resolution is much more important to monitor CH₄ emissions than measurement precision. For quantifying emissions, improving measurement precision is a more effective way. A more accurate simulation model and estimation method are needed for the assessment of the quantified capacity of satellites for low-intensity emissions.

The Climate Change Act (Northern Ireland) 2022 (Act) sets a target of an at least 100% reduction in net zero greenhouse gas (GHG) emissions by 2050 (i.e., net zero emissions by 2050) for Northern Ireland compared to baseline, along with interim targets including an at least 48% reduction in net emissions by 2030. The greenhouse gases include carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), sulphur hexafluoride (SF6) and nitrogen trifluoride (NF3). There is greater difference around emissions in Northern Ireland / Ireland compared to other parts of the United Kingdom due to the relative importance of methane and nitrous oxide emissions in the agriculture sector (Figure 1). In the Northern Ireland, the GHG emissions are mainly from agriculture, buildings, transport and power generation (Figure 2). The fossil fuel has been replaced by renewable energy eventually for the carbon emission reduction.

Figure 1. Composition of emissions between Northern Ireland and UK in 2022.

Figure 2. Greenhouse gas emissions by sector in Northern Ireland, 2022

Figure 3. Change in emissions by sector from base year -2022 in Northern Ireland

Comparing with the other sectors the GHG emissions from the agricuture has increased 15% with the baseline in 1990 (Figure 3). The GHG emissions from the agricultural takes 29.1% compared to the other sector and methane (26%) is mainly produced by livestock (Figure 3). It needs to find out the GHG emissions in agricultural sector in more detailed.

Globally, methane is the second most important greenhouse gas (GHG). Its contribution to global warming is estimated at 27 times that of carbon dioxide, over a 100 year period. Biogenic methane (methane produced by animals and plants) is one of the predominant GHG emissions emitted from global agricultural, the majority of which is originates from ruminant livestock as enteric or manure methane. The agricultural sector has a target to reduce its greenhouse gas emissions by 25% by the year 2030, relative to 2018 levels, developing strategies to reduce enteric methane will be crucial to meeting Ireland’s agricultural climate targets. A 500 kg beef animal on a high concentrate diet produces 230 g methane per day and a 550 kg dairy cow grazing on pasture emits about 320-330 grams of methane per day. Methane associated with ruminant livestock production accounts for three-quarters (74%) of Irish agricultural GHG emissions. As a result, reducing the volume of methane produced by ruminant livestock, will be critical to achieving the agricultural sectors 2030 GHG emissions reduction target.
Figure 4. Methane emission through the year from 2009 to 2022 in Ireland

Emissions of methane are more difficult to estimate than carbon dioxide, and the agriculture sector makes up a larger share of Northern Ireland’s emissions than in other parts of the UK. This greenhouse gas emission estimates are based on a wide range of data sources and sources of uncertainty include statistical differences, assumptions, proxy datasets and expert judgement. In addition, the natural variability in the processes that are being modelled introduce uncertainty. The uncertainties presented are a close approximation of the 95% confidence interval. For the percentage reduction between the base year and 2022, the uncertainty interval ranges from 20% to 37%. Therefore, data from satellite will be used to support the analysis with verified accuracy. The GHG production monitored by the Satellites are retrieved and validated by the project partner from China from the year 1990 to 2022 on the geolocation of Ireland, N. Ireland and Island (Figure 4). From the data analysis, it has found that the differential between them is less than 2%. Detailed emission on CO2 and CH4 will be analysed along with the emission through the agriculture applications.