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New remote sensing satellites for Earth Observation provide information to enhance situational awareness during exceptional socio-economic and natural events. Specifically, satellite-based observations of air pollutants, when combined with auxiliary (spaceborne and not) data, can provide new insights on societal and economic changes taking place around the world. Here we present a few applications of space-based atmospheric observations to support Finnish authorities. A recent history of such episodes includes: monitoring air quality changes in Helsinki during the COVID-19 pandemic; assessing methane emissions from the NordStream pipeline leakage over the Baltic Sea; assessing the environmental and social effects of the war in Ukraine as part of Finnish international cooperation projects. We integrate multiple data sources acquired by different spaceborne sensors including, e.g., the nitrogen dioxide (NO2) retrievals from the Copernicus Sentinel-5 Precursor/TROPOMI (TROPOspheric Monitoring Instrument), Sentinel-2 false color imagery, NASA VIIRS fire products as well as models and other environmental data.

We characterize major anthropogenic point sources in the South African Highveld region using Orbiting Carbon Observatory-3 (OCO-3) Snapshot Area Map (SAM) carbon dioxide (CO2) and Sentinel-5 Precursor (S5P) TROPOspheric Monitoring Instrument (TROPOMI) nitrogen dioxide (NO2) observations. Altogether we analyze six OCO-3 SAMs. We estimate the emissions of six power stations (Kendal, Kriel, Matla, Majuba, Tutuka and Grootvlei) and the largest single emitter of greenhouse gas in the world, Secunda CTL synthetic fuel plant. We apply the cross-sectional flux method for the emission estimation, and we extend the method to fit several plumes at the same time. Overall, the satellite-based emission estimates are in good agreement (within the uncertainties) as compared to emission inventories, even for the cases where several plumes are mixed. We also discuss the advantages and challenges of the current measurement systems for greenhouse gas emission monitoring and reporting, and the applicability of different emission estimation approaches to future satellite missions such as the Copernicus CO2 Monitoring Mission (CO2M) and the Global Observing SATellite for Greenhouse gases and Water cycle (GOSAT-GW), including the joint analysis of CO2 and NO2 observations.

The atmospheric carbon dioxide (CO2) concentration has increased to more than 405 parts per million (ppm. 1ppm=10-6m/s2) 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 and as part of the Dragon programme of ESA and the Ministry of Science and Technology (MOST), a team of researchers from Europe (UK and Finland) and China has evaluated early TanSat data and contrast it against data from the GOSAT mission and models. In this manuscript, we report on retrieval intercomparisons of TanSat data using two different retrieval algorithms, on validation efforts for the Eastern Asia region using GOSAT CO2 data and first assessments of TanSat and GOSAT CO2 data against model calculations using the GEOS-Chem model.

The increase in atmospheric greenhouse gas concentrations of CO2 and CH4, due to human activities, is the main driver of the observed increase in surface temperature by more than 1 °C since the pre-industrial era. At the 2015 United Nations Climate Change Conference held in Paris, most nations agreed to reduce greenhouse gas emissions to limit the increase in global surface temperature to 1.5 °C. Satellite remote sensing of CO2 and CH4 is now well established thanks to missions such as NASA’s OCO-2 and the Japanese GOSAT missions, which have allowed us to build a long-term record of atmospheric GHG concentrations from space. They also give us a first glimpse into CO2 and CH4 enhancements related to anthropogenic emission, which helps to pave the way towards the future missions aimed at a Monitoring & Verification Support (MVS) capacity for the global stock take of the Paris agreement. China plays an important role for the global carbon budget as the largest source of anthropogenic carbon emissions but also as a region of increased carbon sequestration as a result of several reforestation projects. Over the last 10 years, a series of projects on mitigation of carbon emission has been started in China, including the development of the first Chinese greenhouse gas monitoring satellite mission, TanSat, which was successfully launched on 22 December 2016. Here, we summarise the results of a collaborative project between European and Chinese teams under the framework of the Dragon-4 programme of ESA and the Ministry of Science and Technology (MOST) to characterize and evaluate the datasets from the TanSat mission by retrieval intercomparisons and ground-based validation and to apply model comparisons and surface flux inversion methods to TanSat and other CO2 missions, with a focus on China.

Hydroxysteroid (17-beta) dehydrogenase type 1 (HSD17B1) converts low-active estrogen estrone to highly active estradiol. Estradiol is necessary for normal postpubertal mammary gland development; however, elevated estradiol levels increase mammary tumorigenesis. To investigate the significance of the human HSD17B1 enzyme in the mammary gland, transgenic mice universally overexpressing human HSD17B1 were used (HSD17B1TG mice). Mammary glands obtained from HSD17B1TG females at different ages were investigated for morphology and histology, and HSD17B1 activity and estrogen receptor activation in mammary gland tissue were assessed. To study the significance of HSD17B1 enzyme expression locally in mammary gland tissue, HSD17B1-expressing mammary epithelium was transplanted into cleared mammary fat pads of wild-type females, and the effects on mammary gland estradiol production, epithelial cells and the myoepithelium were investigated. HSD17B1TG females showed increased estrone to estradiol conversion and estrogen-response element-driven estrogen receptor signaling in mammary gland tissue, and they showed extensive lobuloalveolar development that was further enhanced by age along with an increase in serum prolactin concentrations. At old age, HSD17B1TG females developed mammary cancers. Mammary-restricted HSD17B1 expression induced lesions at the sites of ducts and alveoli, accompanied by peri- and intraductal inflammation and disruption of the myoepithelial cell layer. The lesions were shown to be estrogen dependent, as treatment with an antiestrogen, ICI 182,780, starting when lesions were already established reversed the phenotype. These data elucidate the ability of human HSD17B1 to enhance estrogen action in the mammary glandin vivoand indicate that HSD17B1 is a factor inducing phenotypic alterations associated with mammary tumorigenesis.

We present a global distribution of surface methane (CH4) emission estimates for 2000–2012 derived using the CarbonTracker Europe-CH4 (CTE-CH4) data assimilation system. In CTE-CH4, anthropogenic and biospheric CH4 emissions are simultaneously estimated based on constraints of global atmospheric in situ CH4 observations. The system was configured to either estimate only anthropogenic or biospheric sources per region, or to estimate both categories simultaneously. The latter increased the number of optimizable parameters from 62 to 78. In addition, the differences between two numerical schemes available to perform turbulent vertical mixing in the atmospheric transport model TM5 were examined. Together, the system configurations encompass important axes of uncertainty in inversions and allow us to examine the robustness of the flux estimates. The posterior emission estimates are further evaluated by comparing simulated atmospheric CH4 to surface in situ observations, vertical profiles of CH4 made by aircraft, remotely sensed dry-air total column-averaged mole fraction (XCH4) from the Total Carbon Column Observing Network (TCCON), and XCH4 from the Greenhouse gases Observing Satellite (GOSAT). The evaluation with non-assimilated observations shows that posterior XCH4 is better matched with the retrievals when the vertical mixing scheme with faster interhemispheric exchange is used. Estimated posterior mean total global emissions during 2000–2012 are 516 ± 51 Tg CH4 yr−1, with an increase of 18 Tg CH4 yr−1 from 2000–2006 to 2007–2012. The increase is mainly driven by an increase in emissions from South American temperate, Asian temperate and Asian tropical TransCom regions. In addition, the increase is hardly sensitive to different model configurations (

International audience; Global Ozone Monitoring by Occultation of Stars (GOMOS) on board Envisat has performed about 440 000 night-time occultations during 2002–2012. Self-calibrating measurement principle, good vertical resolution, excellent pointing accuracy and the wide vertical range from the troposphere up to the lower thermosphere make GOMOS profiles interesting for different analyses. The GOMOS ozone data are of high quality in the stratosphere and the mesosphere, but the current operational retrieval algorithm (IPF v.6) is not optimized for retrievals in the upper troposphere–lower stratosphere (UTLS). In particular, validation of GOMOS profiles against ozonesonde data has revealed a substantial positive bias (up to 100 %) in the UTLS region. The retrievals in the UTLS are challenging because of low signal-to-noise ratio and the presence of clouds. In this work, we discuss the reasons for the systematic uncertainties in the UTLS with the IPF v.6 algorithm or its modifications based on simultaneous retrievals of several constituents using the full visible wavelength range. The main reason is high sensitivity of the UTLS retrieval algorithms to an assumed aerosol extinction model. We have developed a new aerosol–insensitive ozone profile inversion algorithm for GOMOS data in the UTLS using a DOAS-type method at visible wavelengths. The method uses minimal assumptions about the atmospheric profiles. The ozone retrievals in the whole altitude range from the troposphere to the lower thermosphere are performed in two steps, as in the operational algorithm: spectral inversion followed by the vertical inversion. The horizontal column ozone densities retrieved in the spectral inversion follow V6 profiles in the middle atmosphere and follow the triplet ozone profiles in the UTLS. The vertical inversion is performed as in IPF v6 with the Tikhonov-type regularization according to the target resolution. The validation of new retrieved ozone profiles with ozonesondes show dramatic reduction of GOMOS ozone biases in the UTLS. The new GOMOS ozone profiles are also in a very good agreement with measurements by MIPAS, ACE-FTS and OSIRIS satellite instruments in the UTLS. It is also shown that the known geophysical phenomena in the UTLS ozone are well reproduced with the new GOMOS data.

Hydroxysteroid (17β) dehydrogenase type 3 (HSD17B3) deficiency causes a disorder of sex development in humans, where affected males are born with female-appearing external genitalia, but are virilized during puberty. The hormonal disturbances observed in the Hsd17b3 knockout mice (HSD17B3KO), generated in the present study, mimic those found in patients with HSD17B3 mutations. Identical to affected humans, serum T in the adult HSD17B3KO mice was within the normal range, while a striking increase was detected in serum A-dione concentration. This resulted in a marked reduction of the serum T/A-dione ratio, a diagnostic hallmark for the patients with HSD17B3 deficiency. However, unlike humans, male HSD17B3KO mice were born with normally virilized phenotype, but presenting with delayed puberty. In contrast to the current belief, data from HSD17B3KO mice show that the circulating T largely originates from the testes, indicating a strong compensatory mechanism in the absence of HSD17B3. The lack of testicular malignancies in HSD17B3KO mice supports the view that testis tumors in human patients are due to associated cryptorchidism. The HSD17B3KO mice presented also with impaired Leydig cell maturation and signs of undermasculinization in adulthood. The identical hormonal disturbances between HSD17B3 deficient knockout mice and human patients make the current mouse model valuable for understanding the mechanism of the patient phenotypes, as well as endocrinopathies and compensatory steroidogenic mechanisms in HSD17B3 deficiency.

Methane ( CH 4) is a potent greenhouse gas with a large temporal variability. To increase the spatial coverage, methane observations are increasingly made from satellites that retrieve the column-averaged dry air mole fraction of methane (XCH 4). To understand and quantify the spatial differences of the seasonal cycle and trend of XCH 4 in more detail, and to ultimately help reduce uncertainties in methane emissions and sinks, we evaluated and analyzed the average XCH 4 seasonal cycle and trend from three Greenhouse Gases Observing Satellite (GOSAT) retrieval algorithms: National Institute for Environmental Studies algorithm version 02.75, RemoTeC CH 4 Proxy algorithm version 2.3.8 and RemoTeC CH 4 Full Physics algorithm version 2.3.8. Evaluations were made against the Total Carbon Column Observing Network (TCCON) retrievals at 15 TCCON sites for 2009–2015, and the analysis was performed, in addition to the TCCON sites, at 31 latitude bands between latitudes 44.43°S and 53.13°N. At latitude bands, we also compared the trend of GOSAT XCH 4 retrievals to the NOAA’s Marine Boundary Layer reference data. The average seasonal cycle and the non-linear trend were, for the first time for methane, modeled with a dynamic regression method called Dynamic Linear Model that quantifies the trend and the seasonal cycle, and provides reliable uncertainties for the parameters. Our results show that, if the number of co-located soundings is sufficiently large throughout the year, the seasonal cycle and trend of the three GOSAT retrievals agree well, mostly within the uncertainty ranges, with the TCCON retrievals. Especially estimates of the maximum day of XCH 4 agree well, both between the GOSAT and TCCON retrievals, and between the three GOSAT retrievals at the latitude bands. In our analysis, we showed that there are large spatial differences in the trend and seasonal cycle of XCH 4. These differences are linked to the regional CH 4 sources and sinks, and call for further research.

A new approach was recently introduced for the task of estimation of parameters of chaotic dynamical systems. Here we apply the method for stochastic differential equation (SDE) systems. It turns out that the basic version of the approach does not identify such systems. However, a modification is presented that enables efficient parameter estimation of SDE models. We test the approach with basic SDE examples, compare the results to those obtained by usual state-space filtering methods, and apply it to more complex cases where the more traditional methods are no more available.

We describe a new methodology for deriving source-specific emission ratios of nitrogen oxides (NOx) to carbon dioxide (CO2) from space-based TROPOspheric Monitoring Instrument (TROPOMI) and Orbiting Carbon Observatory-2 (OCO-2) observations. The approach is based on scaling the observed ratio along the OCO-2 track with simulated data, in order to obtain the NOx-to-CO2 emission ratio at the source. We analyze fourteen TROPOMI/OCO-2 collocations from near the Matimba coal-fired power station in South Africa. We obtain a mean NOx-to-CO2 emission ratio of 2 . 6 × 10 − 3 and standard deviation of 0 . 6 × 10 − 3 (or 23%). When applied to NOx emission estimates derived from TROPOMI data, we obtain annual CO2 emissions of 62, 60 and 58 kt/d for the years 2018, 2019 and 2020, respectively, with standard deviation of 20 kt/d (or 33%). These values are consistent with existing inventories such as the Open-source Data Inventory for Anthropogenic CO2 (ODIAC). The proposed method will also work ideally for new and upcoming satellite observations systems such as OCO-3, CO2M and GOSAT-GW.

NASA's carbon dioxide mission, Orbiting Carbon Observatory-2, has been operating for three full years (2015–2017). Here, we provide a global (60° S–60° N) view of the XCO2 anomalies along with their annual variations and seasonal patterns. We show that the XCO2 anomaly patterns are robust and consistent from year-to-year. We compare these anomalies to fluxes from anthropogenic, biospheric and biomass burning and to model-simulated local concentration enhancements. We find that, despite the simplicity of the method, the anomalies describe the spatio-temporal variability of XCO2 (including anthropogenic emissions and seasonal variability related to vegetation and biomass burning) consistently with more complex model-based approaches. We see, for example, that positive anomalies correspond to fossil fuel combustion over the major industrial areas (e.g., China, eastern USA, central Europe, India, and the Highveld region in South Africa), shown as large positive XCO2 enhancements in the model simulations. Also, we find corresponding positive anomalies and fluxes over biomass burning areas during different fire seasons. On the other hand, the largest negative anomalies correspond to the growing season in the northern middle latitudes, characterized by negative XCO2 enhancements from simulations and high SIF values (indicating the occurrence of photosynthesis). Finally, we show how XCO2 anomalies facilitate the detection of anthropogenic signatures for several local scale case studies, both in the Northern and Southern Hemisphere. The results demonstrate the potential of satellite-based XCO2 observations for understanding the role of man-made and natural contributions to the atmospheric CO2 levels.

The pituitary gonadotrophins and testosterone are the main hormonal regulators of spermatogenesis, but estradiol is also known to play a role in the process. The hormonal responses in the testis are partially mediated by somatic Sertoli cells that provide nutritional and physical support for differentiating male germ cells. Hydroxysteroid (17β) dehydrogenase 1 (HSD17B1) is a steroidogenic enzyme that especially catalyzes the conversion of low potent 17keto-steroids to highly potent 17β-hydroxysteroids. In this study, we show that Hsd17b1 is highly expressed in Sertoli cells of fetal and newborn mice, and HSD17B1 knockout males present with disrupted spermatogenesis with major defects, particularly in the head shape of elongating spermatids. The cell-cell junctions between Sertoli cells and germ cells were disrupted in the HSD17B1 knockout mice. This resulted in complications in the orientation of elongating spermatids in the seminiferous epithelium, reduced sperm production, and morphologically abnormal spermatozoa. We also showed that the Sertoli cell-expressed HSD17B1 participates in testicular steroid synthesis, evidenced by a compensatory up-regulation of HSD17B3 in Leydig cells. These results revealed a novel role for HSD17B1 in the control of spermatogenesis and male fertility, and that Sertoli cells significantly contribute to steroid synthesis in the testis.-Hakkarainen, J., Zhang, F.-P., Jokela, H., Mayerhofer, A., Behr, R., Cisneros-Montalvo, S., Nurmio, M., Toppari, J., Ohlsson, C., Kotaja, N., Sipilä, P., Poutanen, M. Hydroxysteroid (17β) dehydrogenase 1 expressed by Sertoli cells contributes to steroid synthesis and is required for male fertility.

Many dynamical models, such as numerical weather prediction and climate models, contain so called closure parameters. These parameters usually appear in physical parameterizations of sub-grid scale processes, and they act as "tuning handles" of the models. Currently, the values of these parameters are specified mostly manually, but the increasing complexity of the models calls for more algorithmic ways to perform the tuning. Traditionally, parameters of dynamical systems are estimated by directly comparing the model simulations to observed data using, for instance, a least squares approach. However, if the models are chaotic, the classical approach can be ineffective, since small errors in the initial conditions can lead to large, unpredictable deviations from the observations. In this paper, we study numerical methods available for estimating closure parameters in chaotic models. We discuss three techniques: off-line likelihood calculations using filtering methods, the state augmentation method, and the approach that utilizes summary statistics from long model simulations. The properties of the methods are studied using a modified version of the Lorenz 95 system, where the effect of fast variables are described using a simple parameterization.

Hydroxysteroid (17β)-dehydrogenase type 1 (HSD17B1) catalyzes the conversion of low active 17-ketosteroids, androstenedione (A-dione) and estrone (E1) to highly active 17-hydroxysteroids, testosterone (T) and E2, respectively. In this study, the importance of HSD17B1 in ovarian estrogen production was determined using Hsd17b1 knockout (HSD17B1KO) mice. In these mice, the ovarian HSD17B enzyme activity was markedly reduced, indicating a central role of HSD17B1 in ovarian physiology. The lack of Hsd17b activity resulted in increased ovarian E1:E2 and A-dione:T ratios, but we also observed reduced progesterone concentration in HSD17B1KO ovaries. Accordingly with the altered steroid production, altered expression of Star, Cyp11a1, Lhcgr, Hsd17b7, and especially Cyp17a1 was observed. The ovaries of HSD17B1KO mice presented with all stages of folliculogenesis, while the corpus luteum structure was less defined and number reduced. Surprisingly, bundles of large granular cells of unknown origin appeared in the stroma of the KO ovaries. The HSD17B1KO mice presented with severe subfertility and failed to initiate pseudopregnancy. However, the HSD17B1KO females presented with normal estrous cycle defined by vaginal smears and normal puberty appearance. This study indicates that HSD17B1 is a key enzyme in ovarian steroidogenesis and has a novel function in initiation and stabilization of pregnancy.—Hakkarainen, J., Jokela, H., Pakarinen, P., Heikelä, H., Kätkänaho, L., Vandenput, L., Ohlsson, C., Zhang, F.-P., Poutanen, M. Hydroxysteroid (17β)-dehydrogenase 1–deficient female mice present with normal puberty onset but are severely subfertile due to a defect in luteinization and progesterone production.

The paper discusses the main uncertainties of wild-land fire emission estimates used in the AQMEII-II case study. The wild-land fire emission of particulate matter for the summer fire season of 2010 in Eurasia was generated by the Integrated System for wild-land Fires (IS4FIRES). The emission calculation procedure included two steps: bottom-up emission compilation from radiative energy of individual fires observed by MODIS instrument on-board of Terra and Aqua satellites; and top-down calibration of emission factors based on the comparison between observations and modelled results. The approach inherits various uncertainties originating from imperfect information on fires, inaccuracies of the inverse problem solution, and simplifications in the fire description. These are analysed in regard to the Eurasian fires in 2010. It is concluded that the total emission is likely to be over-estimated by up to 50% with individual-fire emission accuracy likely to vary in a wide range. The first results of the new IS4FIRESv2 products and fire-resolving modelling are discussed in application to the 2010 events. It is shown that the new emission estimates have similar patterns but are lower than the IS4FIRESv1 values.

This paper shows the results of the comparison of satellite SO2 observations from OMI (Ozone Monitoring Instrument) and OMPS (Ozone Mapping Profiler Suite) with ground-based measurements during the Icelandic Holuhraun fissure eruption in September 2014. The volcanic plume reached Finland on several days during the month of September. The SO2 total columns from the Brewer direct sun (DS) measurements in Sodankylä (67.42° N, 26.59° E), northern Finland, are compared to the satellite data. The operational satellite SO2 products are evaluated for high latitude conditions (e.g. large solar zenith angle, SZA). The results show that the best agreement can be found for lowest SZAs, close-to-nadir satellite pixels, cloud fraction below 0.3 and small distance between the station and the centre of the pixel. Under good retrieval conditions, the difference between satellite data and Brewer measurements remains mostly below the uncertainty on the satellite SO2 retrievals (up to about 2 DU at high latitudes). The satellite products assuming a priori profile with SO2 predominantly in the planetary boundary layer give total column values with the best agreement with the ground-based data. The analysis of the SO2 surface concentrations at four air quality stations in northern Finland shows that the volcanic plume coming from Iceland was located very close to the surface. This is connected to the fact that this was a fissure eruption and most of the SO2 was emitted into the troposphere. This is an exceptional case because the SO2 volcanic emissions directly affect the air quality levels at surface in an otherwise pristine environment like northern Finland. The time evolution of the SO2 concentrations peaks during the same days when large SO2 total column values are measured by the Brewer in Sodankylä and enhanced SO2 signal is visible over northern Finland from the satellite maps. Thus, the satellite retrievals were able to detect the spatiotemporal evolution of the volcanic plume as compared to the surface observations. Furthermore, direct-broadcast SO2 satellite data (from both OMI and OMPS instruments) are compared for the first time against ground-based observations.

INTRODUCTION Earth’s carbon cycle involves large fluxes of carbon dioxide (CO 2 ) between the atmosphere, land biosphere, and oceans. Over the past several decades, net loss of CO 2 from the atmosphere to the land and oceans has varied considerably from year to year, equaling 20 to 80% of CO 2 emissions from fossil fuel combustion and land use change. On average, the uptake is about 50%. The imbalance between CO 2 emissions and removal is seen in increasing atmospheric CO 2 concentrations. In recent years, an increase of 2 to 3 parts per million (ppm) per year in the atmospheric mole fraction, which is currently about 400 ppm, has been observed. Almost a quarter of the CO 2 emitted by human activities is being absorbed by the ocean, and another quarter is absorbed by processes on land. The identity and location of the terrestrial sinks are poorly understood. This absorption has been attributed by some to tropical or Eurasian temperate forests, whereas others argue that these regions may be net sources of CO 2 . The efficiency of these land sinks appears to vary dramatically from year to year. Because the identity, location, and processes controlling these natural sinks are not well constrained, substantial additional uncertainty is added to projections of future CO 2 levels. RATIONALE The NASA satellite, the Orbiting Carbon Observatory-2 (OCO-2), which was launched on 2 July 2014, is designed to collect global measurements with sufficient precision, coverage, and resolution to aid in resolving sources and sinks of CO 2 on regional scales. Since 6 September 2014, the OCO-2 mission has been producing about 2 million estimates of the column-averaged CO 2 dry-air mole fraction ( X CO 2 ) each month after quality screening, with spatial resolution of 2 per sounding. Solar-induced chlorophyll fluorescence (SIF), a small amount of light emitted during photosynthesis, is detected in remote sensing measurements of radiance within solar Fraunhofer lines and is another data product from OCO-2. RESULTS The measurements from OCO-2 provide a global view of the seasonal cycles and spatial patterns of atmospheric CO 2 , with the anticipated year-over-year growth rate. The buildup of CO 2 in the Northern Hemisphere during winter and its rapid decrease in concentration as spring arrives (and the SIF increases) is seen in unprecedented detail. The enhanced CO 2 in urban areas relative to nearby background areas is observed with a single overpass of OCO-2. Increases in CO 2 due to the biomass burning in Africa are also clearly observed. The dense, global, X CO 2 and SIF data sets from OCO-2 are combined with other remote sensing data sets and used to disentangle the processes driving the carbon cycle on regional scales during the recent 2015–2016 El Niño event. This analysis shows more carbon release in 2015 relative to 2011 over Africa, South America, and Southeast Asia. Now, the fundamental driver for the change in carbon release can be assessed continent by continent, rather than treating the tropics as a single, integrated region. Small changes in X CO 2 were also observed early in the El Niño over the equatorial eastern Pacific, due to less upwelling of cold, carbon-rich water than is typical. CONCLUSION NASA’s OCO-2 mission is collecting a dense, global set of high-spectral resolution measurements that are used to estimate X CO 2 and SIF. The OCO-2 mission data set can now be used to assess regional-scale sources and sinks of CO 2 around the globe. The papers in this collection present early scientific findings from this new data set. El Niño impact on carbon flux in 2015 relative to 2011, detected from Greenhouse Gases Observing Satellite (GOSAT) and OCO-2 data. OCO-2 uses reflected sunlight to derive X CO 2 and SIF. This shows OCO-2 X CO 2 data over North America from 12 August 2015 to 26 August 2015.

This overview paper highlights the successes of the Ozone Monitoring Instrument (OMI) spanning more than 12 years of the OMI data record. Data from OMI has been used in a wide range of applications. Due to its unprecedented spatial resolution, in combination with daily global coverage, OMI plays a unique role in measuring trace gases important for the ozone layer, air quality and climate change, including new research findings using these satellite data. Due to the operational Very Fast Delivery (VFD) (direct readout) and Near Real Time (NRT) availability of the data, OMI also plays an important role in the development of operational services in the atmospheric chemistry domain.

The extended Kalman filter (EKF) is a popular state estimation method for nonlinear dynamical models. The model error covariance matrix is often seen as a tuning parameter in EKF, which is often simply postulated by the user. In this paper, we study the filter likelihood technique for estimating the parameters of the model error covariance matrix. The approach is based on computing the likelihood of the covariance matrix parameters using the filtering output. We show that (a) the importance of the model error covariance matrix calibration depends on the quality of the observations, and that (b) the estimation approach yields a well-tuned EKF in terms of the accuracy of the state estimates and model predictions. For our numerical experiments, we use the two-layer quasi-geostrophic model that is often used as a benchmark model for numerical weather prediction.

Satellite-based data are very important for air-quality applications in the Baltic Sea region, because they provide information on air pollution over the sea and where ground-based and aircraft measurements are not available. Both the emissions from urban sites over land and ships over sea, contribute to tropospheric NO2 levels. Tropospheric NO2 monitoring at high latitudes using satellite data is challenging because of the reduced light hours in winter and the weak signal due to the low Sun, which make the retrieval complex. This work presents a characterization of tropospheric NO2 columns based on case-study analysis in the Baltic Sea region, using the Ozone Monitoring Instrument (OMI) tropospheric NO2 standard product. Previous works have focused on larger seas and lower latitudes. The results of this paper showed that, despite the regional area of interest, it is possible to distinguish the signal from the main coastal cities and from the ships by averaging the data over a~seasonal time range. The summertime NO2 emission and lifetime values (E' = (1.5 ± 0.6) mol s−1 and τ = (3 ± 1) h, respectively) in Helsinki were estimated from the decay of the signal with distance from the city center. These results agree within the uncertainties with the emissions from the existing database. For comparison, the results for the cities of Saint Petersburg and Stockholm are also shown. The method developed for megacities was successfully applied to smaller-scale sources, in both size and intensity, which are located at high latitudes (~60° N). The same methodology could be applied to similar-scale cities elsewhere, as long as they are relatively isolated from other sources. Transport by the wind plays an important role in the Baltic Sea region. The NO2 spatial distribution is mainly determined by the contribution of westerly winds, which dominate the wind patterns during summer. The comparison between the ship emissions from model calculations and OMI NO2 tropospheric columns supports the applicability of satellite data for ship emission monitoring. In particular, both the ship emission data and the OMI observations showed similar year-to-year variability, with a drop in the year 2009, corresponding to the effect of the financial crisis.

The problem of characteristic vertical profile of smoke released from wildland fires is considered. A methodology for bottom-up evaluation of this profile is suggested and a corresponding global dataset is calculated. The profile estimation is based on: (i) a semi-empirical formula for plume-top height recently suggested by the authors, (ii) satellite observations of active wildland fires, and (iii) meteorological conditions evaluated for each fire using output of the numerical weather prediction model. Injection profiles of the plumes from all fires recorded globally from March 2000 till November 2012 are estimated with a time step of 1 h. The resulting 4-dimensional dataset is split into daytime and nighttime subsets. The subsets are projected onto a global grid with a resolution of 1° × 1° × 500 m, aggregated to a monthly level, and normalised by total emissions in each vertical column. Evaluation of the obtained dataset was performed in several ways. Firstly, the quality of the semi-empirical formula for plume-top computations was evaluated using updated MISR fire Plume Height Project data. Secondly, the upper percentiles of the profiles are compared with an independent dataset of space lidar CALIOP. Thirdly, the results are compared with the distribution suggested for AEROCOM modelling community. Finally, the inter-annual variations of the calculated profiles are estimated.

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.

Global Ozone Monitoring by Occultation of Stars (GOMOS) on board Envisat has performed about 440 000 night-time occultations during 2002–2012. Self-calibrating measurement principle, good vertical resolution, excellent pointing accuracy and the wide vertical range from the troposphere up to the lower thermosphere make GOMOS profiles interesting for different analyses. The GOMOS ozone data are of high quality in the stratosphere and the mesosphere, but the current operational retrieval algorithm (IPF v.6) is not optimized for retrievals in the upper troposphere–lower stratosphere (UTLS). In particular, validation of GOMOS profiles against ozonesonde data has revealed a substantial positive bias (up to 100 %) in the UTLS region. The retrievals in the UTLS are challenging because of low signal-to-noise ratio and the presence of clouds. In this work, we discuss the reasons for the systematic uncertainties in the UTLS with the IPF v.6 algorithm or its modifications based on simultaneous retrievals of several constituents using the full visible wavelength range. The main reason is high sensitivity of the UTLS retrieval algorithms to an assumed aerosol extinction model. We have developed a new aerosol–insensitive ozone profile inversion algorithm for GOMOS data in the UTLS using a DOAS-type method at visible wavelengths. The method uses minimal assumptions about the atmospheric profiles. The ozone retrievals in the whole altitude range from the troposphere to the lower thermosphere are performed in two steps, as in the operational algorithm: spectral inversion followed by the vertical inversion. The horizontal column ozone densities retrieved in the spectral inversion follow V6 profiles in the middle atmosphere and follow the triplet ozone profiles in the UTLS. The vertical inversion is performed as in IPF v6 with the Tikhonov-type regularization according to the target resolution. The validation of new retrieved ozone profiles with ozonesondes show dramatic reduction of GOMOS ozone biases in the UTLS. The new GOMOS ozone profiles are also in a very good agreement with measurements by MIPAS, ACE-FTS and OSIRIS satellite instruments in the UTLS. It is also shown that the known geophysical phenomena in the UTLS ozone are well reproduced with the new GOMOS data.

Gobal methane emissions were estimated for 2000–2012 using the CarbonTracker Europe-CH4 (CTE-CH4) data assimilation system. In CTE-CH4, the anthropogenic and biosphere emissions of CH4 are simultaneously constrained by global atmospheric in-situ methane mole fraction observations. We use three configurations developed in Tsuruta et al. (2016) to assess the sensitivity of the CH4 flux estimates to (a) the number of unknown flux scaling factors to be optimized which in turn depends on the choice of underlying land-ecosystem map, and (b) on the parametrization of vertical mixing in the 30 atmospheric transport model TM5. The posterior emission estimates were evaluated by comparing simulations to surface in-situ observation sites, to profile observations made by aircraft, to dry air total column-averaged mole fractions (XCH4) observations from the Total Carbon Column Observing Network (TCCON), and to XCH4 retrievals from the Greenhouse gases Observing SATellite (GOSAT). Our estimated posterior mean global total emissions during 2000–2012 are 516 ± 51 Tg CH4 yr−1, and emission estimates during 2007–2012 are 18 Tg CH4 yr−1 greater than those from 2001–2006, mainly driven by an 35 increase in emissions from the south America temperate region, the Asia temperate region and Asia tropics. The sensitivity of the flux estimates to the underlying ecosystem map was large for the Asia temperate region and Australia, but not significant in the northern latitude regions, i.e. the north American boreal region, the north American temperate region and Europe. Instead, the posterior estimates for the northern latitude regions show larger sensitivity to the choice of convection scheme in TM5. The Gregory et al. (2000) mixing scheme with faster interhemispheric exchange leads to higher estimated CH4 emissions at northern latitudes, and lower emissions in southern latitudes, compared to the estimates using Tiedtke (1989) convection scheme. Our evaluation with non-assimilated observations showed that posterior mole fractions were better matched with the 5 observations when Gregory et al. (2000) convection scheme was used.

CarbonTracker Europe-CH4 (CTE-CH4) inverse model versions 1.0 and 1.1 are presented. The model optimizes global surface methane emissions from biosphere and anthropogenic sources using an ensemble Kalman filter (EnKF) based optimization method, using the TM5 chemistry transport model as an observation operator, and assimilating global in-situ atmospheric methane mole fraction observations. In this study, we examine sensitivity of our CH4 emission estimates on the ensemble size, covariance matrix, prior estimates, observations to be assimilated, assimilation window length, convection scheme in TM5, and model structure in the emission estimates by performing CTE-CH4 with several set-ups. The analyses show that the model is sensitive to most of the parameters and inputs that were examined. Firstly, using a large enough ensemble size stabilises the results. Secondly, using an informative covariance matrix reduces uncertainty estimates. Thirdly, agreement with discrete observations became better when assimilating continuous observations. Finally, the posterior emissions were found sensitive to the choice of prior estimates, convection scheme and model structure, particularly to their spatial distribution. The distribution of posterior mole fractions derived from posterior emissions is consistent with the observations to the extent prescribed in the various covariance estimates, indicating a satisfactory performance of our system.

In this paper, we present the first global comparisons between the two unique satellite-borne data sets of nitrogen trioxide (NO3) vertical profiles retrieved from the GOMOS (Global Ozone Monitoring by the Occultation of Stars) stellar occultations and the SAGE III (Stratospheric Aerosol and Gas Experiment) lunar occultations. The comparison results indicate that between the altitudes 25 km and 45 km the median difference between these two data sets is within ± 25%. The study of zonal median profiles shows that the climatologies calculated from GOMOS and SAGE III profiles are comparable and represent the same features in all latitude bands. No clear systematic differences are observed. The median profiles are closest in the tropics and slightly deviating at high latitudes.