dataesr/scientific-paragraphs-categorization huggingface dataset:
For each of the individual NH 3 emission sources, a subregion is defined that spans 4 • × 6 • using the complete 10-year dataset of the IASI-A instrument. Each set is then analyzed for NH 3 and CO anomalies, defined as the mean of all observations ±5 and ±3 standard deviations. The corresponding days with unusual NH 3 and CO concentrations and ±1 d are filtered from the dataset. Figure  2  shows an example of the data selection for the location of Togliatti in Russia. The total column time series clearly shows the anomalously high total column concentrations during the Russian fires in 2010  (R'Honi et al. , 2013) , which are omitted from our dataset using the previously explained steps. Finally, the list of omitted days found in for IASI-A is used to filter the same observation days from both the IASI-B and CrIS datasets to create consistency between the datasets.
3  and 4 , using CrIS 2013-2017 data. The weighted means of the original dataset and rotated dataset are shown in Fig. 3a  and b , respectively. The fitted datasets in the longitude-latitude domain and in the downwind-crosswind domain are shown in Fig. 3c  and d , with the location of the Kirovo-Chepetsk point source highlighted as a cyan dot. The dashed black box prescribes the area used to fit the plume. This is an example of a weaker source with a well-known source location for which the rotation still shows a roughly defined plume shape. Figure  4a  shows the corresponding mean cross section of the binned observations in the downwind direction. Each of the lines corresponds to the mean of a different wind speed interval. It shows that the peak of the plume moves downwind and decreases in amplitude with increased wind speeds as expected.
In three of these four cases, the satellite emission estimates do not agree with the HTAPv2 inventory, with the inventory showing much lower total emissions. Most of the industrial emissions are computed using empirical estimates and are not measured (E-PRTR database); therefore, large differences can be expected. The fits for the NH 3 production plants near Shiraz are a good example of the limited applicability of this method in regions with large elevation changes nearby and only a single outflow direction. While the fits may seem reasonable (see Figs. D1, D2 and D3 in the Appendix), the overall wind speed is very low and the plume shape is not well defined. This limitation is more visible for IASI-A than for IASI-B and CrIS, which can be due to small deviations in the concentration field.
Lastly, while there is a bias between the CrIS and IASI-A and -B total columns, an offset between the products does not have too much of an impact on emissions, as this should be caught by the background parameter. Any ratio between the products, however, can create a ratio in the emission estimates. Dammers et al. (2017b)  validated the CrIS and IASI(-Table  3. Mean lifetime and σ for all successful fits with a correlation > 0. 5 and a SNR > 2 for the 5-year IASI-A, IASI-B and CrIS and 10-year IASI-A datasets. A) NH 3 products. Compared to the FTIR-NH 3 product, the IASI product was biased low for lower total columns, while CrIS biases high. For increasing total column densities, the bias in the CrIS columns disappears, while IASI shows a smaller and smaller negative bias. Coarsely transformed to an IASI-to-CrIS ratio, it can be expected that the CrIS estimates will be higher than IASI, as IASI likely underestimates the total columns and thus the emission total.
As noted earlier, the validation by  Dammers et al. (2017b)  was for the previous versions of both the CrIS and IASI retrievals. The IASI retrieval has evolved, with the new retrieval showing much improved performance  (Van Damme et al. , 2017). Figure  8  shows a comparison of emissions between (fixed) fits to the satellite data and the point emission databases of the United States (NEI), Canada (NPI), Europe (E-PRTR) and Australia (NPI) for point sources in the emission databases. Overall, the point-source inventories compare poorly to the fitted emissions. Only the E-PRTR inventory has a majority of locations for which the fitted totals are within an order of magnitude of the totals from the in-ventory. While an uncertainty of 100 %-300 % is to be expected following  Kuenen et al. (2014) , some estimates are multiple orders larger than reported emissions.
The satellite emission fits compare slightly better to the HTAPv2 database (Figs. 9 and 10); here, we summed the satellite emission fits to all HTAPv2 emissions in a 1. 00 • × 0. 50 • box surrounding the source location. However, here we also find that in 55 (IASI-A:33, IASI-B:39) cases the inventories emissions are either missing or underestimated by more than an order of magnitude. A total of 72 (IASI-A:71, IASI-B:64) locations are within a factor of 2 of the CrIS emission estimates and in most cases represent agricultural regions (illustrated by the triangles). These results, using a more sophisticated method to estimate the satellite-based emissions, confirm the underestimation of the current emission inventory reported for EDGAR v4. 3. 1 by  Van Damme et al. (2018). A comparison between the results of this study and those obtained using a box model approach is provided in Fig.
All the sources successfully estimated with CrIS combined have a total emission of 5622 kt yr -1 , which is about ∼ 2. 5 times more than the current amount in the HTAPv2 inventory (2086 kt yr -1 ) over the same locations. Applying the same method to the longer 10year IASI-A dataset for yearly estimates, we are able to observe short-and long-term variations in emissions from both large and small sources, with emissions matching changes to   2018 ). The regional trends in the emissions are similar to earlier findings based on the AIRS satellite  (Warner et al. , 2017). The results, however, may not necessarily be reflective of the entire region, as only the larger more isolated sources are included in these studies satellite estimates. The sum of the HTAPv2 emissions from all point sources with successful fits makes up about 5 % of the global total emissions in the HTAPv2 inventory.
B5). Equation (B2), f (x, y), describes the diffusion of our species perpendicular to the downwind direction. Similar to  Fioletov et al. (2015) , we adjusted the shape of the plume downwind to account for the size of the pixel shape and ensure convergence for a larger number of fits. Using a non-linear fitting algorithm, we can fit the equation to the total column distribution and estimate a, σ and λ. The fits are performed in Python using the non-linear curvefit package from the SciPy module  (Jones et al. , 2001)  using the Levenberg-Marquardt algorithm, which minimizes the difference between the given distribution and the fitted values. The fit is performed on a subset of the rotated grid, spanning 25 km in each crosswind direction, and 36 and 90 km in the upwind and downwind directions.
(2016) and de Foy et al. (2014)  and has previously been successfully used to more accurately estimate SO 2 emissions  (Fioletov et al. , 2015). Figure  E1  shows the comparison of emission estimates using a box model approach  (Van Damme et al. , 2018)  vs. the results of this study. As Van Damme et al. (  2018 ) used a mean lifetime of 12 for their reported results, we scale the estimates by a factor (12/2. 5) to adjust the lifetime to the lifetime found in this study. In addition, they used only the 9 years of IASI-A observations available at that time, from a previous dataset (ANNI-NH 3 v2. 1), and the exact varying radius of the pixel footprints for the pixel averaging. Despite these differences, we find a correlation of r = 0. 62, with our study overall showing higher emissions with a relative difference of about 31 %.
Acknowledgements. The CrIS input Level1 and Level2 data were obtained from the  NOAA Comprehensive Large Array-Data Stewardship System (CLASS)  (Liu et al. , 2014), with special thanks to  Axel Graumann  (NOAA). The authors acknowledge the Aeris data infrastructure (https://iasi. aeris-data. fr/nh3/,last access:  13 June 2018 ) for providing access to the IASI-NH 3 and IASI-CO data used in this study. We thank  ECMWF  for allowing free use of their meteorological fields. We acknowledge the use of the HTAPv2 dataset http://edgar. jrc. ec. europa. eu/htap_v2/(last access: 10 December 2017). We thank  Jacob Hedelius  and  Mohit Manocha  for the useful discussions. Review statement. This paper was edited by  Qiang Zhang  and reviewed by two anonymous referees.
Code and data availability. The near-real-time IASI NH 3 (ANNI-NH 3 -v2. 1) and CO (FORLI) data used in this study are freely available through the Aeris database http://iasi. aeris-data. fr/nh3/ (last access: 13 June 2018) and http://iasi. aeris-data. fr/CO/ (last access: 17 April 2018). The CrIS data are currently available upon request and will be publicly released at the end of 2019. All Python code used to create any of the figures and/or to create the underlying data is available on request.
This yields 50 regional steric sea-level trend fields, corrected for the model drift and relative to their global average. Manometric sea-level trends are computed from each ensemble member as the difference between sea-level trends and full-depth steric sea-level trends. Note that manometric sea-level trends refer to the ocean bottom pressure term in regional sea-level changes (see  Gregory et al. 2019  for more details). The observed regional sea-level trend field is calculated over the same 1993-2015 period from the Climate Change Initiative (CCI,  Legeais et al. 2018 ) dataset derived from several altimeter missions. The observed regional sea-level trend field is finally obtained by removing the global average from the latter field and used to assess the realism of the ensemble simulation.
Using the outputs of a large ensemble of 1/4° global oceanic hindcasts, we showed that, in certain coastal areas, this "noise" may blur the detection of regional sea-level trends. Our results do not concern (and do not question the anthropic origin of) the globally averaged 1993-2015 sea-level trend, which was removed from our datasets: they concern the regional contrasts of sea-level trends near the coasts, specifically their external or internal origin. Our main results may be summarized as follows: • Coastal sea-level trends are not significantly impacted by the ocean-driven multi-decadal LFCIV over most (80-83%) of the global ocean area located less than 25 km away from the coasts. They may be attributed to non-oceanic (atmospheric or anthropic) causes with 95% confidence level. • However, coastal sea-level trends cannot be unambiguously attributed to atmospheric drivers over the remaining 17-20% of the global ocean coastal area.
Acknowledgements This work is a contribution to the  OCCIPUT  and PIRATE projects. PIRATE (https :// seale vel. jpl. nasa. gov/science/ostsc ience team/scien tistl inks/scien tific inves tigat ions2 017/pendu ff/) is funded by  CNES  through the  Ocean Surface Topography Science Team (OST-ST). OCCIPUT (https ://meom-group. github. io/proje cts/occip ut/) was funded by  ANR  through contract  ANR-13-BS06-0007-01. This work was also supported by the  French national program LEFE/INSU  and has received funding from the  European Union   Horizon 2020 research and innovation program  under grant agreement No  633211. It is also part of the  Copernicus Marine Environment Monitoring Service (CMEMS) GLO-HR  project; CMEMS is implemented by Mercator Ocean International in the framework of a delegation agreement with the  European Union.
Specimen imaging and reconstruction The results were obtained by comparison of the selected morphological structures between fossil and recent taxa using stereo microscopy. The specimens were observed under Zeiss Discovery V20 and Nikon SMZ1500 stereomicroscopes in a dry state and rarely under a film layer of ethyl alcohol. Photographs were taken with a Canon D550 digital camera, with MP-E 65 mm and EF 50 mm lenses. The original photographs were processed using Adobe Photoshop CS6, and for some images the focus-stacking software Helicon Focus Pro and Zerene Stacker were used. A few samples preserved with high three-dimensional relief were additionally examined with a Keyence VHX VH-Z20UR digital microscope. Abbreviations used for morphological structures are: bc, banded pattern of coloration, with alternating light and dark stripes; ds, dorsal spines; ma, precursor of convex vein MA; oe, broad outer edge of sheath (a developing wing); ps, pronotal spines; pw, prothoracic winglets; sp, spiracles; tg, tracheal gills.
Acknowledgements. We are indebted to the scientific illustrator  Zuzana C ̌adová (ZC)  for the magnificent line drawing of palaeodictyopteran larval exuvia found in Sosnowiec (Poland). The authors are grateful to  Susan Butts  and  Jessica Utrup  (both  Peabody Museum of Natural History, Yale University, New Haven, USA ),  Neil Clark  ( Hunterian Museum, Glasgow, UK ),  Angelika Leipner  ( Museum am Schölerberg, Osnabrück, Germany ),  Claire Mellish  (The  Natural History Museum, London, UK ),  Nina Sinisthenkova  and  Alexander Rasnitsyn  (both  Paleontological Institute, Russian Academy of Sciences, Moscow, Russia ),  Peter Fenton  and  David Rudkin  (both  Royal Ontario Museum, Toronto, Canada ),  David Gelsthorpe  ( Manchester Museum , Manchester, UK), and  Paul Mayer  (The  Field Museum, Chicago, USA ), who kindly provided access to the collections under their care.
vations, and making use of a set ICOsahedral Non-hydrostatic Large Eddy Model (ICON-LEM) simulations over Germany. The key idea is to assess to which extent the model-simulated aerosol-cloud interaction effects might be detected and attributed in comparison to various observational datasets. This is done in terms of quantification of the impact on cloud macro-and microphysics, precipitation, and radiation, as a response of the modification of CCN concentrations, the small aerosol particles necessary for water vapor to condensate and form cloud droplets. Two CCN input configurations were used in the high-resolved ICON-LEM simulations (156 m horizontal resolution), which were performed over Germany on a selected date (2 May 2013). For the control simulation, CCN concentrations as estimated for 2 May 2013 from a detailed aerosol model were used.
, 2016). In the offline calculation based on COSMO-MUSCAT, the CCN number concentration of the multi-modal size distribution at a fixed supersaturation is calculated according to Abdul-Razzak and Ghan  (2000). Details on the used hygroscopicity parameters as well as the derivation of number size distributions from the simulated speciated aerosol mass can be found in  Genz et al. (2019). as regridded to the MODIS data resolution of 1 km. Only cloudy satellite pixels with assigned liquid cloud phase as well as good quality and solar zenith angles below 50 • were considered, to exclude uncertain/problematic cloudy retrievals. 2 For liquid clouds, cloud droplet number concentration (N d ) is derived from cloud effective radius (r e ) and cloud optical thickness (τ c ) as in  Quaas et al. (2006) ; where α = 1.
The absolute (relative) differences between perturbed and control simulations are for the mean CBH -4 m (-0. 37 %) and for the CC 0. 4 % (0. 70 %). The same tendency is found in the all-domain simulation means (Table  7 ) where the perturbed ICON-LEM shows on average higher cloud tops and bases in comparison to the control simulations. The difference between mean cloud top pressures is -263 Pa (0. 35 %) and between mean cloud base pressures, -140 Pa (0. 17 %). The perturbed simulation also shows higher total cloud cover by 0. 16% (relative difference: 0. 20 %) in the domain average (Table  7 ). Cloud fraction can also be assessed from MODIS satellite data and compared to the COSP-processed ICON-LEM output (as in Section 3. 3 for LWP and N d ). The domain-average cloud fraction for the four MODIS overpasses is 0.
Satellite data and network ground-based remote sensing were used as observational reference data. For all of these quantities, the ICON model simulated systematic changes between the perturbed (P2R) and control (C2R) aerosol runs. However, in each case, the difference between either model simulation and the observations was larger than the difference between the simulations in response to the different aerosol conditions. In addition, the natural cloud variability was large compared to the signal. A possible exception is that at large LWP (> 200 g m -2 ), the control simulation was consistent with the satellite retrievals, while the perturbed simulation showed too large LWP. Small changes between perturbed and control simulations are found in the surface rain rate domain average mean (-2. 6 %).
Acknowledgements. We thank  Christine Knist  from the  Lindenberg Meteorological Observatory -Richard Assmann Observatory  for providing us with data from the microwave radiometers; and  Björn Stevens  for his advice in the "Radiative implications" section. This work is funded by the  German Federal Ministry of Education and Research (BMBF)  within the  framework programme "Research for Sustainable Development (FONA) ", www. fona. de,through the  research programme "HD(CP)² -High Definition Clouds and Precipitation for Cli-.
, 2016). Like all supervised machine learning models, this approach relies on a training dataset to determine the nonlinear statistical relationships. In this case, in situ turbulent flux and GPP estimates from eddy covariance towers are used  (Beer et al. , 2010; Jung et al. , 2011). Such an approach relies implicitly on an assumption that a long temporal record of these variables at a small number of sites captures the full range of behavior and sensitivities of terrestrial ecosystems around the globe. In addition, extreme and therefore rare events may be difficult to capture based on the limited data availability. Alternatively, one can use a machine learning approach, such as an artificial neural network (ANN), trained on globally representative but imperfect estimates of the fluxes (such as those from models) to parameterize the nonlinear statistical relationships between remote sensing observations and surface fluxes.
This approach has been successfully used for global soil moisture retrieval  (Aires et al. , 2012; Kolassa et al. , 2013 Kolassa et al. , , 2016;; Rodriìguez-Fernández et al. , 2015)  and surface heat flux retrieval  (Jiménez et al. , 2009). Such ANNs require a target dataset for training. Climate model simulations of the relevant geophysical variable are usually used as the training dataset to facilitate subsequent data assimilation efforts  (Aires et al. , 2012; Kolassa et al. , 2013 Kolassa et al. , , 2016)). However, the downside of this approach is that the resulting fluxes estimated by the ANN often exhibit some of the same biases as the simulations used to train the network  (Rodriìguez-Fernández et al. , 2015) , even if improvements can be achieved such as a more realistic seasonal cycle as it is informed by the seasonal cycle of the remote sensing data  (Jiménez et al.
, 2014; Zhang et al. , 2016a). SIF retrievals are also integrated with photosynthesis estimates from the National Center for Atmospheric Research Community Land Model version 4 (NCAR CLM4), which result in significant improvement of the photosynthesis simulation  (Lee et al. , 2015). As GPP relates to plant transpiration through stomata regulation  (Damour et al. , 2010; DeLucia and Heckathorn, 1989; Dewar, 2002) , and transpiration water fluxes dominate continental ET  (Jasechko et al. , 2013) , the use of remotely sensed SIF has the potential to also better constrain estimates of the continental water (LE) and energy (H ) cycles, in addition to the carbon (GPP) cycle. In this study, we develop an ANN approach to retrieve monthly estimates of LE, H , and GPP on a global scale. The network uses remotely sensed SIF estimates in addition to other data including precipitation, temperature, soil moisture, snow cover, and net radiation as inputs (predictor).
To our knowledge, this is the first study that uses remotely sensed SIF estimates on a global scale to retrieve LE and H surface turbulent fluxes along with GPP. Moreover, to reduce any errors, we introduce a Bayesian perspective to generate the target dataset for the ANN. Multiple estimates of LE, H , and GPP are selected according to an a priori probability that reflects the quality and information content of the dataset at the particular pixel of interest (details are provided in Sect. 3. 2). This approach enables us to generate a robust target dataset for remote sensing observations along with a statistical algorithm for the retrieval, while bypassing the need for a land surface model and radiative transfer scheme. We use the triplet of GLEAM, ECMWF, and FLUXNET-MTE (Multi-Tree Ensemble) for training of LE and H and the triplet of MODIS-GPP, ECMWF, and FLUXNET-MTE for GPP training.
This new global product is named WECANN (Water, Energy, and Carbon Cycle with Artificial Neural Networks). WECANN monthly estimates for the period 2007-2015 are provided on a 1 • × 1 • resolution grid and with units of W m -2 for LE and H and gC m -2 day -1 for GPP. The spatial coverage of WECANN is presented in Fig. S1  in the Supplement. It includes all the land areas, except for Greenland, Antarctica, and any 1 • × 1 • pixel that permanently has more than 75 % water, snow, or ice. To estimate the fraction of water, snow, and ice in each pixel, we used the 0. 05   1. All the data presented here are projected and gridded on a 1 • × 1 • geographic grid and averaged at monthly temporal resolution. Finally, independent datasets used for evaluation of the ANN retrievals are presented in Sect.
Training datasets Four products are introduced in this section, and a triplet of them is used for training of each of the LE, H , and GPP (Sect. 3. 2). For LE and H , training is performed based on GLEAM, FLUXNET-MTE, and ECMWF ERA HTESSEL (Hydrology Tiled ECMWF Scheme for Surface Exchanges over Land). For GPP, training is performed on FLUXNET-MTE, ECMWF ERA HTESSEL, and MODIS-GPP. Table  1  summarizes the characteristics of the training datasets used here.
GLEAM The Global Land Evaporation Amsterdam Model (GLEAM) is a set of algorithms to estimate terrestrial evapotranspiration using satellite observations  (Martens et al. , 2017; Miralles et al. , 2011a). GLEAM is a physically based model composed of (1) a rainfall interception scheme, driven by rainfall and vegetation cover observations; (2) a potential evaporation scheme, calculated from the Priestley and Taylor (1972) equation and driven by satellite observations; and (3) a stress factor attenuating potential evaporation, based on a semiempirical relationship between microwave vegetation optical depth (VOD) observations and root zone soil moisture estimates (based on a running water balance for rainfall and assimilating satellite soil moisture). The data are provided at a 0. 25 • × 0. 25 • spatial resolution and daily temporal resolution and start in 1980.
FLUXNET-MTE The FLUXNET-MTE provides global surface fluxes at 0. 5 • × 0. 5 • spatial resolution derived from empirical upscaling of eddy covariance measurements from the FLUXNET global network  (Baldocchi et al. , 2001). The MTE method used is an ensemble learning algorithm that enables the learning of a diverse sequence of different model trees by perturbing the base learning algorithm  (Jung et al. , 2009 (Jung et al. , , 2010 (Jung et al. , , 2011)). The data cover the period from January 1982 to December 2012 and can be used for benchmarking land surface models and assessment of biosphere gas exchange. We use LE, H , and GPP estimates from FLUXNET-MTE.