dataesr/scientific-paragraphs-categorization huggingface dataset:
Foraminiferal communities were studied in two depth intervals (0-0. 5 cm and 0. 5-1 cm,  Richirt et al. , in prep. ). Live foraminifera were discriminated from dead specimens using the CTG staining method  (Bernhard et al. 2006). In all the samples where living foraminifera were found, the morphospecies Ammonia tepida and Elphidium excavatum were strongly dominant. Here we measured the elemental composition of calcite tests of A. tepida (size fraction 150-315 µm, 0-0. 5 cm interval) sampled in July 2012 at ST1, ST2 and ST3. The test size was measured on SEM images as the maximum diameter using the image processing software ImageJ  (Schneider et al. , 2012). The cleaning of all specimens, in order to remove sediment adherences  (Barker et al. , 2003) , included rinsing three times with ultra-pure water, followed by one rinse in methanol and three final rinses in ultra-pure water.
During each rinse, samples were slightly agitated with a vortex machine  (Petersen et al. , 2018). Guo et al. (2019)  showed that this type of physical cleaning in combination with LA-ICP-MS yields Mn/Ca ratios of live benthic foraminifera comparable to solution analysis (by ICP-MS) of specimens which have undergone a protocol involving oxidative and reductive cleaning. In fact, the integration of contamination from the surface of the test into the calcite Mn/Ca signal is avoided by the data treatment of LA-ICP-MS analysis involving the screening of indicator elements such as Al (e. g. ,  Koho et al. , 2015; Barras et. , 2018; Petersen et al. , 2018; Guo et al. , 2019). We measured the elemental composition of single chambers of the calcite tests using LA-ICP-MS, i. e. , an ArF excimer laser (193 nm, Analyte G2, Teledyne Photon Machines) coupled to a quadrupole ICP-MS (Varian Bruker 820-MS).
Statistical analyses We performed statistical analyses under R (R Core Team, 2016) and we used the package ggplot2 for graphical representation  (Wickham, 2009). We used the Shapiro-Wilk test to verify if data are normally distributed. For normally distributed data, we performed ANOVA and t-tests with the Bonferroni adjustment as post-hoc test to compare datasets. For data likely not to follow a normal distribution we performed a Kruskal-Wallis test and Wilcoxon-Mann-Whitney as post-hoc test. In all cases, a p value below 0. 05 was considered as significant.
In fact, for this dataset which is not likely to follow a normal distribution p values for Kruskal-Wallis and Wilcoxon-Mann-Whitney as post-hoc test confirm that there is a significant difference for ST1 compared to ST2 and ST3 (not the case fo ST2 and ST3, Tab. 2). Also the mean Mn/Ca per specimen is significantly higher in ST1 compared to the other two stations (0. 36 ±0. 12 mmol/mol, mean ±SD, for ST1 versus 0. 18 ±0. 08 mmol/mol and 0. 20 ±0. 15 mmol/mol for ST2 and ST3, respectively, Fig. 4A , p values in Tab. 3). There is no significant difference between the three stations considering the Mn/Ca ratio of the penultimate chamber, or of the combined n-1 and n-2 chambers (Fig. 4B /C, ANOVA p value = 0. 404 and 0. 138, respectively). The measurements made in the central part of the test (Fig.
Sediment geochemistry During the same monthly sampling campaigns in 2012 the pore water and solid-phase sediment geochemistry was also analysed. This large data set has been presented previously by  Seitaj et al. (2015 Seitaj et al. ( , 2017) )  and  Sulu-Gambari et al. (2016a ,b, 2017a). Despite the strong seasonal oxygen depletion in Lake Grevelingen, the occurrence of free sulfide in the bottom water (euxinia) is likely prevented or at least delayed, due to the presence of cable bacteria  (Seitaj et al. , 2015). The metabolic activity of these filamentous bacteria involves an electrical coupling between oxygen reduction at the sediment surface and sulfide oxidation in the deeper suboxic sediment via long-distance electron transport  (Malkin et al. , 2014; Meysman et al. , 2015; Nielsen et al.
4. 5 Paleoecological consequences of the observations of Mn/Ca ratios in benthic foraminifera from Lake Grevelingen The present data show that the registration of the pore water Mn dynamics in the coastal foraminifer A. tepida is not straightforward, which is to a large degree due to the high complexity of the Mn cycling itself. The two phenomena leading to strong increases of Mn 2+ concentration in the upper sediment layers, cable bacteria activity and summer hypoxia, are registered for our specimens sampled in summer in the central part and in the last chambers of the test, respectively. However, since at least at ST2 and ST3, where A. tepida is present year-round, specimens would have continued to add chambers after the summer hypoxia, in fossil specimens the response to summer hypoxia would not systematically be found in the last added chambers.
Gambari, Dorina Seitaj and Mathilde Hagens are thanked sincerely for providing the water column and pore water data. This study was financially supported by the Darwin Center for Biogeosciences and the European Research Council under the European Union's Seventh Framework Programme (FP/2007-2013) through ERC Grants 306933 to FJRM and 278364 to CPS. We are thankful to the Region Pays de la Loire for financing the MADONA project, including the PhD allocation of JP.
The cloud information comes from SEVIRI on board Meteosat: it provides a cloud mask as well as a cloud classification. Rain is not detected separately from the cloud per se: some clouds are likely to precipitate and the detection of these clouds will obviously include the detection of rain. We first describe the data sets relevant for this study (Sect. 2). In Sect. 3, we will elaborate on the methodology. Results will be presented over land surfaces as well as over ocean (to illustrate the difference in behaviour over these two surface types), focusing on the detection of the cloud contamination on the MW observations over land (Sect. 4). Section 5 concludes this study.
Data sources The different data sources are described here, namely the SEVIRI cloud classification and the GMI brightness temperatures (T bs ). The steps to create a consistent data set are described, along with a preliminary analysis of the observations. Using ancillary data to help characterize the atmospheric and surface conditions related to the cloud occurrence (such as land surface emissivity atlases) could help the cloud detection but at the cost of increasing the complexity to apply it. For flexibility and convenience, the detection of the cloud contamination will be exclusively built from passive MW observations.
Data set preparation and preliminary analysis The SEVIRI and GMI data have very different spatial and temporal resolutions. We need to find the closest matching observations and relocate them on a common grid for further processing. Grid cells with a low-quality flag are avoided for both GMI and SEVIRI. Each GMI observation has a time stamp that is used to find the closest SEVIRI scan. With SE-VIRI data every 15 min, there is a maximum of 7. 5 min difference between GMI measurements and the corresponding SEVIRI classification. Given the spatial resolution, several SEVIRI cells will obviously fall in one GMI grid cell. In the training data set, only GMI observations associated with a unique target SEVIRI class are kept. There may be some mismatch between observed radiance and the SEVIRI cloud type due to inhomogeneous clouds at a scale lower than the footprint, especially for the lowest-frequency channels.
The training and testing data sets The training and testing data sets are constructed using the collocated GMI observations and SEVIRI cloud information. To cover the full diversity of cloud situations, a full year of data have been sampled with 72 days (Sect. 2). The SEVIRI acquisition disk excludes the high-latitude regions and does not cover the full snow-and ice-free continents either. How-  ever, it was shown in  Aires et al. (2011)  that the calibration of a cloud classification on the SEVIRI disk with MW observations can be extrapolated to the other continents and we are confident that the methodology will be applicable outside the SEVIRI disk, excluding the snow and ice regions. In the database, we ensure that every cloud type is equally represented. This process ensures that the obtained classification will not be biased towards the most frequent cloud situations, disregarding the less frequent ones.
We therefore sample the same number of clear and cloudy situations, with each cloud type equally represented in the cloudy part. This resulted in 1 million samples for each of the 10 cloud types, and 10 million cloud-free samples. For a cloud classification model, with 11 different possible output classes, the database is built with a similar repartition of classes, giving around 11 million observations. The resulting databases are then randomly divided into the training (80 %) and the testing (20 %) data sets.
Detecting clouds over ocean As described in Sect. 3. 1, the database is created with an equal distribution of cloudy and clear conditions and a balanced repartition between the different cloud types. The cloud detection is evaluated for the three MW frequency ranges (all channels, below 100 GHz only, below 40 GHz only), and the results are presented in Table  3  for the test data set. The cloud detection performs well over ocean, reaching at least 80 % accuracy, even with a reduced number of channels. The low emissivity of the ocean (∼ 0. 5) and its relative homogeneity makes it possible to correctly detect the cloud presence, even at low MW frequencies. These cloud detection results are very encouraging and the natural next step is to investigate a cloud classification over ocean, with the same MW frequency ranges.
Detecting clouds over land A similar cloud detection method is applied over land. The NN classifiers are built using the three different MW frequency ranges as inputs and with one output indicating the clear vs. cloudy probability. The specifics of the model and database are described in Sect. 3. 1 and 3. 2. Similar to Table  3 over ocean, Table 4  (top part) presents the accuracies reached over land by the three frequency ranges. The classification performance deteriorates compared to the ocean case, as expected. Nevertheless, even for the worst case (with only five low frequency channels available), true positive and negative detections are close to 70 %. 2. The result of the detection has been analysed further, as a function of the cloud type (lower part of Table  4 ). Note that these are only a detail of the previous results (top part of Table  4 ) separated by each original cloud type.
To focus on the clouds that do impact the MWs, we rebuild a training data set, suppressing the three ambiguous classes previously mentioned (namely classes 7, 8, and 11). The idea behind this new training database is that removing ambiguities at the learning stage will improve the classification. In other words, removing the ambiguous SEVIRI cloud types from the training database allows the model to ignore these phenomena, which are mostly detected in vis-IR. The lower sensitivity to clouds in MW is thus accounted for in the new training data set. The results of this new classification are provided in Table 5, separately for the clear grid cells (class 1), for the cloudy grid cells with clouds that do contaminate the MW (the MW cloud-contaminated grid cells, i. e. classes 2, 3, 4, 5, 6, 9, and 10), and for the cloudy grid cells corresponding to the three cloud types that are difficult to detect with MW (the ambiguous grid cells, ignored in the training data set, i.
In Fig. 6 , models are applied to the data over land to create the three maps. For each map a different threshold is applied: 0. 1 with the lowest channels (a), 0. 05 with channels up to 100 GHz (b) and 0. 01 with all channels available (c). The fourth subplot (d) is the precipitating observations according to Ferraro's 89 GHz algorithm. The fifth subplot (e) shows the SEVIRI cloud type. We can analyse the output of this map: -The agreement between models and the increased number of flagged points with more channels is clearly visible (a, b, c). -In some areas, the cloudy grid cells do not appear to be detected (i. e. red area). When looking at the detail of the SEVIRI cloud types (subplot e) in that area we find out they are mostly fractional/semi-transparent or low clouds, which explains the low contamination rate, according to our definition.
Cloud classification from SEVIRI(Derrien and Le Gléau,  2005). Six full days each month in 2015 provide 72 different daily situations that represent a large variation in the possible cloud types and surface conditions, covering the full diurnal and annual cycles. The cloud classes are described in Table2. High semi-transparent clouds are mostly cirrus of varying thickness, possibly over lower clouds. The fractional cloud class corresponds to cells that are only partly cloudy and to heterogeneous cloud cover. The other cloud types represent the continuum of possible cloud states, with varying opacity and height. Some of these clouds are likely to precipitate, and rain cases are naturally included in the database.
Acknowledgements. This study was partly funded by the  Centre National Centre National d'Études Spatiales (CNES, projects GPM-R and ISMAR)  and the  European Space Agency (ESA)  project  LST-CCI  (contract no. 4000123553/18/I-NB ). We acknowledge  Hervé Le Gléau  and  Gaelle Kerdraon  ( Centre de Météorologie Spatiale de Météo-France ) for providing guidance to interpret the vis-IR cloud classification,  Martin Stengel  ( Deutscher Wetterdienst ) for providing expertise on using the SEVIRI data, and  Die Wang  ( Brookhaven National Laboratory ) and  Victorial Galligani  ( Centro de Investigaciones del Mar y la Atmósfera ) for providing valuable comments to improve the manuscript. We also extend our thanks to the referees for the constructive insights that were given during the review process.
Data availability. The CLAAS-2 Cloud property data set using SEVIRI -Edition 2 (CLAAS-2, https://doi. org/10. 5676/EUM_SAF_CM/CLAAS/V002; Finkensieper et al. , 2016) is publicly available from the Satellite Application Facility on Climate Monitoring (CM SAF). The GPM GMI_R Common Calibrated Brightness Temperatures Collocated L1C 1. 5 h 13 km V05 (GPM_1CGPMGMI_R, https://doi. org/10. 5067/GPM/GMI/R/1C/05; Berg, 2016) is provided by NASA.
As a result we are unable to reliably predict how the Arctic climate will evolve in the future  (Screen et al. 2018). Even worse, we cannot evaluate the substantial consequences a warming and thawing Arctic might have for midlatitude weather  (Cohen et al. 2014; Walsh 2014; Cohen et al. 2018). Therefore, several international efforts are underway to improve model projections of the Arctic climate, such as the Polar Prediction Project and the Year of Polar Prediction  (Jung et al. 2016 ). However, models alone will not resolve the Arctic climate issue. They often use simple parameterizations, which need to be verified, tested, and improved by measurements. The required observations are still sparsely distributed across the Arctic. As a consequence further data should be collected in well-planned and dedicated campaigns to document and understand the Arctic climate changes.
The jointly planned and organized observations took place around Svalbard, Norway, in May and June 2017. ACLOUD consisted of airborne observations by two research aircraft, Polar 5 and Polar 6  (Wesche et al. 2016). PASCAL involved measurements from the Research Vessel (R/V) Polarstern  (Knust 2017 ) and an ice floe camp [including the Balloon-bornE moduLar Utility for profilinG the lower Atmosphere (BELUGA) tethered balloon]. A detailed summary of the measurements performed during PASCAL can be found in  Macke and Flores (2018). Additionally, measurements from the permanent joint research base operated by Alfred Wegener Institute (AWI) and the French Polar Institute Paul-Émile Victor (IPEV; AWIPEV) at Ny-Ålesund (Svalbard) were involved  (Neuber 2006). The ACLOUD and PASCAL campaigns are unique in that both were tightly coordinated under the collaborative (AC) 3 program  (Wendisch et al.
These measurements investigated aerosol-cloud-radiation interactions. ACCACIA was conducted to measure aerosol and cloud effects on the Arctic surface energy balance and climate. STABLE mainly investigated the impact of leads on the ABL and cold-air outbreaks with a focus on the flow between the inner Arctic and the marginal sea ice zone. NETCARE focused on carbonaceous aerosol, ice cloud formation and impact, and ocean-atmosphere interactions. ARISE collected airborne data on clouds, atmospheric radiation, and sea ice properties between the sea ice minimum in September and the beginning of refreezing in late autumn. ACSE looked at Arctic clouds in summer. N-ICE studied how the rapid shift to a younger and thinner sea ice regime in the Arctic affects energy fluxes and sea ice dynamics. FRAMZY 1999 FRAMZY , 2002 FRAMZY , 2007 FRAMZY , 2008 FRAMZY , 2009  Fram The general strategy of the measurements as well as the meteorological, sea ice, and cloud conditions during ACLOUD/PASCAL are introduced in the following section.
The measured and retrieved quantities collected during the campaigns are briefly explained in the "Measured quantities and major instrumentation" section, while most of the details, in particular with respect to the instruments, are given in a separate online supplement (https://doi. org/10. 1175/BAMS -D-18-0072. 2). Then two illustrative examples of collocated measurements are presented to demonstrate the potential of the combined datasets. The major part of the paper ("Four pieces of the Arctic cloud puzzle") discusses selected measurement cases to investigate four pieces of the Arctic cloud puzzle. Finally, in the "Summary and open questions" section, a summary of the paper is given, including questions to be answered in the forthcoming data analyses of the ACLOUD and PASCAL campaigns and future research activities.
cloud Macrophysical properties-local differences. Figure  7  shows data derived from surface-based profile measurements with lidar, cloud radar, and a microwave radiometer. The measurements were collected at the AWIPEV site in Ny-Ålesund and at the R/V Polarstern. They were utilized for daily cloud classification with the Cloudnet categorization algorithm  (Illingworth et al. 2007)  for the period of the ACLOUD and PASCAL campaigns (23 May-26 June 2017). Each observed profile was checked for cloud phase between cloud bottom and cloud top. When only liquid water was detected within a single cloud layer, it was considered a single-layer liquid water cloud (single liquid). The same procedure was applied to single-layer ice clouds (single ice). If both cloud liquid water and ice phases were detected in one layer, then the cloud was counted as a single-layer mixed-phase cloud (single mixed phase).
11f ). The fraction of sulfate and metal containing particles increased with increasing altitude. NaCl, OC, and levoglucosan stayed rather constant and only TMA decreased with increasing height. Puzzle piece 3: Atmospheric radiation. Quantifying the Arctic radiative energy budget is key for an improved 858 MAY 2019 | understanding of factors and processes leading to Arctic amplification. Therefore, important quantities determining the radiative energy budget and therefore inf luencing Arctic amplification, were measured (surface albedo, near-surface net radiation, cloud radiative forcing, heating/cooling rates). The observed data were partly compared with the output of a dynamical numerical weather prediction (NWP) model. The respective results are reported in this subsection. The measurements presented here were assembled during 15 low-level aircraft flights (average altitude about 80 m; roughly 16 flight hours; coverage of a horizontal distance of roughly 3,800 km; spatial resolution of about 3 m, that is, 20-Hz time resolution) and two tethered balloon observations.
Additionally, ICON shows a tendency toward a stronger, enhanced cloud mode for optically thicker clouds. As a result, the net radiative balance simulated by ICON is too high as compared to the measurements. These deficiencies can be attributed to the underestimation of sea ice surface albedo and the ensuing errors in the surface energy budget. Generally, the surface albedo measurements from the ACLOUD/ PASCAL campaigns might be used to verify and further improve the surface albedo parameterization. surface cloud radiatiVe forcing-WarMing or cooling By clouds. Cloud radiative forcing is calculated as the difference between airborne measurements of net solar and terrestrial irradiance and equivalent cloud-free values simulated with the radiative transfer model package libRadtran  (Mayer and Kylling 2005).
The almost identical turbulence instrumentation aboard the two aircraft allowed combining the datasets and therefore doubled the number of locations where ABL profiles were obtained. aircraft MeasureMents. An example of measurements, obtained with the Polar 6 aircraft, is shown in Fig. 18. This case was characterized by the presence of an optically thick stratocumulus layer in the upper part of the ABL. There were no higher-level clouds over the measurement area and, thus,.
3 Through this research, it is anticipated that substantial advances will be made in understanding Arctic cloud processes that will ultimately help to clarify the role of clouds in Arctic amplification. Data availability: The data collected during ACLOUD will be made freely available within the World Data Center PANGAEA, and will be published in  Ehrlich et al. (2019) : "A comprehensive in situ and remote sensing data set from the Arctic CLoud Observations Using airborne measurements during polar Day (ACLOUD) campaign" to be submitted to Earth System Science Data. Access journal articles, monograph titles, and BAMS content using your iOS, Android, or Blackberry phone, or tablet.
Experimental protocol The DYAMOND protocol specified a simulation of 40 days and 40 nights, beginning at 0Z, 1 August 2016, using global models with a grid spacing of 5 km or less. The global (9 km) meteorological analysis taken from the European Centre for Medium Range Weather Forecasts (ECMWF) was used to initialize the atmospheric state. ECMWF operational daily sea-surface temperatures and sea-ice concentrations were also used as initial and boundary data. These input fields are archived and made available as part of the DYAMOND dataset. Models were left free to formulate their own initialization of the land surface and adopted a variety of strategies for this purpose. ICON, for instance, took soil moisture from ECMWF and converted to a soil moisture index (SMI) which was then vertically interpolated to the ICON soil level SMI and soil temperature which it then converted back to soil moisture using ICON's soil characteristics.
Ways of dealing with different naming conventions, grids, units, etc. were things the project aimed to explore in post-processing, in part to guide development of domain-specific analysis languages, like the Climate Data Operators (CDO). From this perspective, having each group's native format as output was actually advantageous. Expediency was also the reason why no attempt was made to coordinate and homogenize the initialization of the land surface. This expedient approach achieved its aims. The idea of DYAMOND arose in October 2017, during a workshop of the cloud-feedback intercomparison project in Tokyo, the outline of the experimental specifications was drafted at the end of November of the same year, and eight of the nine participating groups had finished their runs and (mostly) uploaded the data before a year had passed.