Conference Agenda

The concentration of carbon dioxide (CO₂) in the atmosphere has been rapidly increasing since the 1750s, and CO₂ has been recognized as one of the most significant greenhouse gases responsible for global climate warming. To understand and mitigate anthropogenic CO₂ emissions, regional carbon flux estimation is required for identifying CO₂ sources and sinks. The first scientific experimental CO₂ satellite of China - Chinese carbon dioxide observation satellite (TanSat) was launched in 22 Dec, 2016. After on-broad test and calibration, TanSat has been measuring the backscattered sunlight in scientific earth observation mode and produces XCO₂ data for more than two years.

The inter-comparison study between UoL-FP and IAPCAS retrieval algorithm provide a valuable experiment and help to improvement on TanSat data retrieval and results accuracy. The TCCON validation indicate a well-agreed result that need to be further investigate the in future studies. The solar induced chlorophyll fluorescence (SIF) can be approached from clear solar lines from TanSat. The TanSat SIF product indicate the seasonal variations of vegetation growth. Aerosols significantly impact CO₂ retrieval precision by modifying the light path in hyperspectral measurements in the NIR/SWIR. After investigate the information contain in measurement, a new approach is proposed to optimize the aerosol model used in the TanSat CO₂ retrieval algorithm to reduce CO₂ uncertainties associated with aerosols. The TanSat preliminary results has been compared with GEOS-Chem model, and show a consistent picture. We also find a stronger North-South gradient in the satellite dataset compared to the model. The model also shows a shallower seasonal amplitude by as much as 2 ppm when compared to the satellite observation. The validation campaign in Beijing, inner Mongolia with multiple instrument coordinate measurement, incl. EM27/SUN, AirCore and POPS shows a preliminary result in Greenhouse satellite validations.

The Chinese scientist has been visiting the Sodankyla station that has been significant contribute to greenhouse gas measurement relative issues communities, join the AirCore experiment and visiting for a joint research on TanSat data retrieval that greenhouse gas data applications in the cooperation framework of Dragon programme among U.K., Finland and China.

The atmospheric carbon dioxide (CO₂) concentration has increased to more than 405 parts per million (ppm) in 2017 due to human activities such as deforestation, land-use change and burning of fossil fuels. Although there is broad scientific consensus on the damaging consequences of the change in climate associated with increasing concentrations of greenhouse gases, fossil CO₂ emissions have continued to increase in recent years mainly from rapidly developing economies and China is now the largest emitter of CO₂ generating about 30% of all emissions globally. To allow more reliable forecast of the future state of the carbon cycle and to support the efforts for mitigation greenhouse gas emissions, a better understanding of the global and regional carbon budget is needed. Space-based measurements of CO₂ can provide the necessary observations with dense coverage and sampling to provide improved constrains on of carbon fluxes and emissions.

The Chinese Global Carbon Dioxide Monitoring Scientific Experimental Satellite (TanSat) was established by the National High Technology Research and Development Program of China with the main objective of monitoring atmospheric CO₂ and CO₂ fluxes at the regional and global scale. TanSat has been successfully launched in December 2016 to continue and extend space-based observations from the Japanese GOSAT and the NASA OCO-2 missions. In this presentation, we will focus on an evaluation of satellite observations of CO2 and CH4 from GOSAT over China and the wider Eastern Asia region. We will contrast GOSAT observations against state-of-the-art model calculations and ground-based validation data and we discuss how well we can observe signals from anthropogenic emissions.

In this presentation we give an overview of the recent recearch activities that have taken place at the Finnish Meteorological Institute related to remote sensing of greenhouse gases with links to the DRAGON project “Monitoring greenhouse gases from space: Cal/Val and applications with focus in China and high latitudes“.

The satellite remote sensing of greenhouse gases using SWIR wavelengths is becoming more and more important method to understand the global distribution of methane and carbon dioxide concentrations. This is highlighted by the growing number of satellite missions targeted for greenhouse gases including GOSAT, OCO-2, TanSat, Sentinel 5P and most recently GOSAT-2 (2018) and OCO-3 with launch in April 2019 as well as ambitious plans like the proposed Copernicus high priority anthropogenic CO2 Monitoring mission. The satellite observations rely heavily on ground-based validation and bias correction and therefore, the stringent requirements on satellite observation are reflected also on the requirements for ground-based validation.

The FTIR instrument in Finnish Meteorological Institute’s premise in Sodankylä is one of the core high latitude sites for satellite validation as part of the Total Carbon Column Observing Network (TCCON) with regular observations from March till October since 2009. The AirCore balloon launches have been performed since 2013 to obtain accurate in-situ profiles of methane, carbon dioxide and carbon monoxide from troposphere to lower stratosphere. During summer 2018 a UAV version of AirCore system was tested. Sodankylä is also an ICOS site providing high quality in-situ concentration observations and eddy-covariance flux estimates at different altitudes in the new 25 m tower. These different GHG measurements together with additional measurements e.g. solar induced fluorescence provide valuable information for satellite validation. We present recent and on-going validation activities of OCO-2, TROPOMI and GOSAT and validation campaigns that have taken place in Sodankylä.

The improved data quality of the satellite observations facilitates further analysis of the global distribution of greenhouse gases and its variability. We have studied the seasonal variability and spatiotemporal distribution of greenhouse gases by analysing spatially/temporally OCO-2 and GOSAT data. The developed methods are applicable to TanSat data as well.

Carbon Dioxide (CO₂) is a main anthropogenic greenhouse gas whose concentration increase leads to heating of the troposphere and subsequently to global warming. The well-developed ground-based networks either using remote sensing or in-situ technology provide highly accurate reference measurement but their coverage is too coarse to inform reliable on regional carbon fluxes. Satellite measurement of the total column CO₂ from shortwave infrared hyperspectral measurement can provide highly accurate and precision measurements from space with sufficient coverage and resolution to improve the situation and help to advance our understanding of CO₂ and carbon fluxes.

The Chinese carbon dioxide observation satellite (TanSat), which is the first Chinese greenhouse gas monitoring satellite and a ESA third party mission and has been supported by the Ministry of Science and Technology of China, the Chinese Academy of Sciences, and the China Meteorological Administration, was launched on 22 Dec 2016, Following the European Space Agency (ESA) SCanning Imaging Absorption SpectroMeter for Atmospheric CHartographY (SCIAMACHY) on board the ENVIronmental SATellite (ENVISAT), which is the first space-based instrument to provide SWIR CO₂ band hyperspectral detection, the next generation satellites GOSAT and OCO-2 launched in 2009 and 2014, respectively.

A hyperspectral grating spectrometer onboard the TanSat is monitoring the column-averaged CO₂ dry-air mixing ratio (XCO₂) over the globe. In-orbit calibration tests were completed in the summer of 2017, and the performance of the instrument has since been evaluated in test sessions. Subsequent to on-board testing and calibration, TanSat has been operationally measuring backscattered sunlight in its scientific earth observation mode and produces XCO₂ data for more than two years now.

In this study, we use two retrieval algorithms to approach the XCO₂ from TanSat hyperspectral measurements, (1) IAPCAS, Institute of Atmospheric Physics Carbon dioxide retrieval Algorithm for Satellite remote sensing (IAPCAS), is TanSat retrieval algorithm that has also been used for GOSAT (ATANGO) and OCO-2 retrieval studies. (2) UoL-FP, The University of Leicester ‘full physical’ algorithm, has been used for GOSAT retrieval and provide XCO₂ product to the ESA Climate Change Initiative (CCI) and the Copernicus Climate Change Service. The retrieval accuracy and precision of both, the IAPCAS and UoL algorithm, has been well investigated by verifying them against TCCON measurement. The fitting residual has been analyzed, and PCA based analysis method and retrieval show an improvement on residual. The approaches for aerosol and cirrus treatment have been inter compared and indicate that the methods adopted by both algorithms are reasonable to deal with the particle scattering. Validation against TCCON measurement show a well agreement on both algorithms. The method used in this study and results can help to improve the XCO₂ retrieval from TanSat and subsequently the level-2 products.

Top-down flux inversions have been used to infer surface CO2 fluxes from the observed variations of atmospheric CO2 concentrations, which has led to substantial improvements in our understanding of the global carbon cycle. Most of top-down inversions rely on the in-situ observation network with sparse and unevenly distributed spatial coverage. As a result, the inferred surface fluxes have limited temporal and spatial resolutions, with large uncertainty over many regions critical to global carbon cycle.

Recently, space-based instruments such as the JAXA GOSAT, the NASA OCO-2 satellite and the Chinese TanSat,have been developed to measure column dry-air mole fraction of the targeted greenhouse gases (such as XCO₂ or XCH₄) with unprecedented precision. However top-down inversions based on space-borne observations can be comprised by varied observation coverage, and by small uncharacterized biases, reflecting the complexity in accurately modelling the radiative transfer in the atmosphere, particularly in the presence of cloud and aerosol scattering.

To examine observation constraints on CO₂ flux over China by the three different satellite measurements, we experimentally assimilated recent versions of the GOSAT, OCO-2 and TanSat XCO₂ retrievals over the same time periods. We compared the resulting fluxes with the prior estimates as well as with the fluxes inferred from the in-situ atmospheric CO₂ observations. We further validated our results by comparing the posterior model CO₂ simulations with independent in-situ observations.

The extensive burning of fossil fuels and cement production has increased the global mean carbon dioxide (CO₂) concentration from 280 ppm before the Industrial Revolution to 410 ppm today. More specifically, urban areas are responsible for 70% of global anthropogenic emissions, playing a key role in climate change. Of particular interest is China’s recent economic growth, which has resulted in the country becoming the largest emitter of CO₂,generating about 30% of all anthropogenic emissions. In this work, we aim to use satellite data to more precisely quantify urban CO₂ emissions.

NASA’s OCO-2 satellite launched in 2014 and TanSat from China Aerospace Science launched in 2016. These instruments have the capability to resolve CO₂ concentrations at a high spatial resolution over polluted areas, allowing emission sources to be observed for the first time from space. Using data from OCO-2, an estimation of regional enhancement of ΔXCO₂ over the city of Los Angeles has been quantified compared to the background area. However, from these measurements it is not possible to distinguish the origin of the measured CO₂, since satellite observations occur at a specific time and location. Because of this, column footprints of the air particles using the high-resolution Numerical Atmospheric-dispersion Modelling Environment (NAME) have been calculated in order to evaluate which part of an urban area contributed to the satellite observations. To ultimately estimate how much CO₂ is emitted from cities, a combination of these footprints along with fluxes from different emission inventories such as EDGAR are needed to quantify the enhancement of CO₂.

An estimation of the background CO₂ concentration also needs to be performed to understand its contribution to the satellite measurements. In this work, the background concentration of CO₂ will be estimated from the global chemistry transport model CarbonTracker and the NAME calculation of the air mass history, at the same resolution as the model.

The methodology presented in this work for Los Angeles serves as a test case for future analyses over Chinese cities in order to better quantify their contribution to global emissions.

Methane is the second most important greenhouse gas in the atmosphere. Globally, the largest natural source of methane is almost equal to the largest anthropogenic source of methane: a little over 30% of the total methane emissions are from agriculture and waste (largest anthropogenic source) and approximately 30% are from wetlands (largest natural source). Currently, most of the wetland methane emissions originate from the tropics but the amount and evolution of Arctic and subarctic methane emissions are highly uncertain and involve several open questions. These questions need to be answered in order to understand the role of the Arctic and subarctic regions in the changing climate.

The common characteristics for the Arctic and subarctic regions are high seasonal temperature variations and snow cover over frozen ground during winter. These are important properties for the wetland methane emissions, as the amount of emitted methane from a specific wetland depends on, for instance, soil moisture and the temperature of the ground. Frost and snow have both direct and indirect effects on how the wetlands acts as methane source or sinks, for example, during spring, after the snowmelt, methane flux from the ground increases when the soil temperature increases. These correlations between the seasonality of frost, snow and methane have been previously studied mainly based on in situ measurements. In situ measurements have spatial limitations, especially in the Arctic and subarctic areas: due to the remote locations and infrastructures, it is almost impossible to create a spatially comprehensive measurement network. To increase the spatial distribution of methane observations, the observations are increasingly made from satellites.

Here we investigate the seasonal variability of column-averaged methane and its correlation with the seasonality of soil frost and snow, using different Earth Observation data. We combine several different data sets and show the large-scale seasonal dependencies between methane and frost or snow, noting also special features in the different parts of the Arctic and subarctic regions.

To study the seasonal variability of methane, we use space-based column-averaged methane observations from the Greenhouse Gases Observing Satellite (GOSAT), from the Tropospheric Monitoring Instrument (TROPOMI) on board Sentinel-5 Precursor satellite and ground-based column-averaged methane retrievals from Fourier Transform Infrared Spectrometer (FTIR) measurements made at Sodankylä, Finland. The Sodankylä FTIR is part of the Total Carbon Column Observing Network (TCCON) that is a global network of ground-based observations of column-averaged greenhouse gases. The TCCON observations are the main validation source for space-based greenhouse gas observations. To detect the state of soil freezing, we use the Soil Freeze/Thaw product, which applies the observations from European Space Agency’s (ESA) Soil Moisture and Ocean Salinity (SMOS) satellite. The Soil Freeze/Thaw product is developed at Finnish Meteorological Institute (FMI). The seasonality of snow cover is studied with GlobSnow snow extent (SE) and snow water equivalent (SWE) products that are also developed at FMI. In addition, we show inverse model results for methane concentrations and fluxes from CarbonTracker Europe - CH4 data assimilation system, and compare the fluxes to the freezing periods estimated from space-based Soil Freeze/Thaw product.