Conference Agenda

The accuracy requirements of satellite remote sensing of atmospheric composition and, in particular, greenhouse gases are challenging. The validation of the measurements is highly important in the development of satellite remote sensing systems.

The Chinese team has developed XCO₂ retrieval algorithm for TanSat Level 2 data processing and has updated it after the successful launch of TanSat in December 22, 2016. The FMI team has carried out validation studies of GOSAT and OCO-2 satellite retrieval results and they plan to contribute to the validation of TanSat observations. To support the satellite validation using FTS observations, AirCore CO₂ profile observations at Sodankylä will be used. Both FMI and IAP teams will work together to verify and investigate the precision of the retrieval results.

It is well established that the increase in atmospheric concentrations of CO₂ and CH₄ due to anthropogenic activities is a major driver for climate change. However, our understanding of the role of natural and anthropogenic contributions to the carbon cycle within a dynamic Earth system is still insufficient which leaves predictions of our future climate uncertain.

We have now access to dedicated satellite observations of atmospheric CO₂ and CH₄ concentrations which provide us with densely sampled data over regions poorly sampled by surface networks. This allows us to critically test and evaluate models of the carbon exchange for key regions such as China.

Currently, three CO₂ satellite sensors are in orbit, JAXA’s GOSAT, NASA’s OCO-2 and the Chinese TanSat mission, which gives us an unique opportunity to intercompare observations from multiple space-based CO₂ datasets over China and to use them jointly to assess model calculations.

Due to the anthropogenic greenhouse gas emissions our climate is changing. Climate forecasts are needed so that we can prepare, mitigate and adapt to the changing climate. The high northern latitudes are especially sensitive to climate change. The quantification and monitoring of the carbon cycle processes are crucial for the understanding of climate system feedbacks. Recently launched satellite instruments mesuring greenhouse gases provide important information related to the carbon cycle processes that can jointly be analysed with ground based observations and compared/combined with modeling. In this presentation we discuss recent recearch activities that have taken place at the the Finnish Meteorological Instiute related to satellite observation of greenhouse gases. Moreover, we present our plans to participate in the validation of Chinese TanSat satellite’s carbon dioxide observations using ground based instruments at Sodankylä as well as our plans on using the TanSat data for studying spatial and teporal variability of carbon dioxide and emisison regions.

Atmospheric Carbon dioxide (CO₂)is one of the major anthropogenic greenhouse gas that remains significant uncertainties in global carbon cycle and climate change studies. Hyper spectral near infrared and shortwave infrared (NIR/SWIR) measurement from space could provide global column-average CO₂ dry-air mixing ratio (XCO₂) in advanced accuracy and precisions to reduce the uncertain of climate prediction. After the Greenhouse gas monitoing satellite including GOSAT and OCO–2 from USA-NASA and Japan-JAXA, China’s carbon dioxide observation satellite (TanSat) has been successful launched in 2016. TanSat is a carbon dioxide observation satellite funded and supported by the Ministry of Science and Technology of the People’s Republic of China and the Chinese Academy of Sciences. The TanSat retreival algorithm has been developed to approeach the XCO₂ in a highly accuracy and precision reqiurment. The TanSat algortihm has been applied on GOSAT (ATANGO) and OCO-2 measumrent and well optimaized before it applied in TanSat operational data processing. The retrieval algorithm and its perfomance over China has been studied. The ATANGO data product has been used in carbon flux inversion in China.

Anthropogenic CO₂ emissions from fossil fuel combustion have large impacts on climate. In order to monitor the increasing CO₂ concentrations in the atmosphere, accurate spaceborne observations—as available from the Orbiting Carbon Observatory-2 (OCO-2)—are needed. This work provides the first direct observation of anthropogenic CO₂ from OCO-2 over the main pollution regions: eastern USA, central Europe, and East Asia. This is achieved by deseasonalizing and detrending OCO-2 CO₂ observations to derive CO₂ anomalies. Several small isolated emission areas (such as large cities) are detectable from the anomaly maps. The spatial distribution of the CO₂ anomaly matches the features observed in the maps of the Ozone Monitoring Instrument NO₂ tropospheric columns, used as an indicator of atmospheric pollution. The results of a cluster analysis confirm the spatial correlation between CO₂ and NO₂ data over areas with different amounts of pollution. We found positive correlation between CO₂ anomalies and emission inventories. The results demonstrate the power of spaceborne data for monitoring anthropogenic CO₂ emissions.

China represents a significant contribution to global observed variations of atmospheric carbon dioxide (CO₂) due to its large landmass, and its high fossil fuel emissions associated with unprecedented economic growth. We report CO₂ fluxes in 2014 and 2015 inferred, using an ensemble Kalman Filter [Feng et al, 2009], from data collected from five new regional background ground-based sites over China (together with available NOAA sites), and fluxes inferred from OCO-2 (version 7) and GOSAT (UoL v7) XCO₂ data. We find that the resulting posterior CO₂ concentrations are generally consistent with independent validation data downwind of the Chinese mainland. To better understand the response of the Chinese biosphere to the 2015 El Niño we compare the magnitude and distribution of CO₂ fluxes inferred from different data sets. We find that the net CO₂ emissions over China inferred from GOSAT and OCO-2 XCO₂ retrievals are higher than those from the in-situ data. Our results highlight the importance of space-based observations for top-down flux inversions. Chinese TanSat project, together with existing and planned missions, will significantly improve flux estimates over China.

CO₂ is a long-lived trace gas which acts as the most important greenhouse to the global climate. For its short-wavelength transparent and long-wavelength absorbed characteristic, the increase of CO₂ caused by human activities induces global warming and a series of climate changes since the industrial revolution. Although it is almost well-mixed in the atmosphere, CO₂ mixing ratio varies with time, decreases with altitude, and spaces between sources and sinks, the temporary emissions such as fires and burning occurring near the surface on regional scale changing CO₂ mixing ratio sharply and damped by atmospheric transport and diffusion in a short time. It is necessary to infer the spatial distribution of carbon sources and sinks distribution with repeated CO₂ measurements on a large scale, and this requires very demanding measurement accuracy to the relative small variation of CO₂ in the atmosphere. A stringent precision of space-borne CO₂ data, for example 1 ppm or better, is required to address the largest number of carbon cycle science questions. A high measurement sensitivity and global covered observation is expected by space-borne IPDA (Integrated Path Differential Absorption) lidar which has been designed as the next generation measurement. By selecting the absorption features of the lidar operating wavelength appropriately, IPDA lidar could obtain the dry air total column CO₂ mixing ratio named XCO₂ by compared two echo pulse signals reflected from the Earth with a high weight to the boundary layer. In this paper an assessment is made to describe the various error sources limiting the accuracy and precision of the measurement. An overview is presented of the relative contribution of each error source including the inadequate knowledge of the atmosphere pressure and temperature, surface reflectivity, reflected surface elevation, and errors from lidar system such as shot noise, dark noise and spectral purity. A global simulation is used to investigate the sources of errors associated with the configurations of lidar system and the environment parameters. The simulation is carried out over global scale with atmosphere pressure and temperature from NCEP, surface elevation model and surface reflectivity from MODIS, and the CO₂ distribution is from OCO-2 dataset. The results identifies that surface albedo plays an important role in the process of satellite remote sensing. It may be inferred that errors impacted by reflectance of the Earth vary with seasons. This would need an improved acknowledge on seasonal variations of surface reflectivity and provide a guidance for improving the accuracy of space-borne IPDA detection.