dataesr/scientific-paragraphs-categorization huggingface dataset:
Benthic light requirements As an exercise to demonstrate the usability of the new dataset, an analysis of the light requirements of benthic macroalgae (kelps) was performed. This required a literature review of the light requirements and depth extensions of these organisms. It was found that the minimum light requirements (Emin) of Arctic kelps are typically between 40 and 50 mol photons m -2 y -1 , often equivalent to about 1% of surface irradiance (Table  2 ). The depth ranges of these organisms vary, but within fjords most are found between 0 and 20 m depth. clathratum and kelp juveniles, with the low-canopy assemblage often forming the depth limit.
, 2021). The monthly climatology of the shallow zone KPAR (Fig. 2B ) shows two different patterns. The first pattern, found in all Svalbard and Eastern Greenland sites, is a stable or decreasing KPAR from March to June, and then an increase until June to September, before decreasing again until the end of the illuminated part of the year. The second pattern, which is found in Western Greenland and Northern Norway, is a stable or fluctuating KPAR until August, followed by a rapid increase up until the end of the illuminated part of the year. While it is beyond the scope of the description of this dataset to investigate these patterns in detail, it is hypothesised here that the first pattern is representative of a system that is still dominated by a marine terminating glacier, and that the bulk of the turbidity in the water (i.
Bottom values: PARB As PARB is available at a monthly resolution (March to October) within this dataset, we can look at how this value has changed across all sites for each individual month (Table  3 , Fig. 4 ). In this way we are able to track changes to the phenology of PARB. Looking at the median values of PARB for all shallow pixels (depth ≤ 50 m), the most notable result is the large change in magnitude between months. June to August generally have much higher PARB than March/October, as would be expected. Less expected is the large interannual variance. This variance may mask significant changes over time. For example, although there is an apparent decrease in PARB for the month of June in Kongsfjorden, the change is not statistically significant (simple linear model; p = 0. 4). The same can be said for the apparent increase in PARB for Storfjorden in June (p = 0.
Code and data availability The code written for the analysis of these data, and the creation of the figures, may be found on GitHub at: https://github. com/FACE-IT-project/fjord_PAR (last access: 13 November 2023). The PAR dataset may most easily be accessed via the R package 'FjordLight', which can be installed via CRAN  (Gentili et al. , 2023b)   All data were generated from a base of daily gridded remotely sensed observations (see Sections 2. 2 and 2. 3). The primary variables created are: PAR(0 -), KPAR, and PARB (Table  5 ). These three variables are available at ~ 50m resolution across all sites. P-functions, which are a summary value and therefore not gridded, were computed from PARB and the surface area of all shallow (depth ≤ 50 m) and coastal (depth ≤ 200 m) pixels. For these four primary variables, four different levels were created and are available in this dataset:  Monthly: The average of all available daily data within a given month.
Expressed as units of mol photons m-2 day-1 (except KPAR [m-1]). For example, the monthly value for June 2006 is the average of all available days of data from 2006-06-01 to 2006-06-30. NB: A given pixel during a given month was required to have at least 20 days of available data to be included in the dataset.  Climatology: The average of all of the same months of data across the available years of data. For example, the July climatological value is the average of all July monthly values from 2003 to 2022.
NB: Due to latitudinal differences, some sites do not receive 20 days of light in October, and so are missing October climatologies (e. g. Kongsfjorden).  Yearly: The average of all available monthly values during a given year.  Global: The average of all yearly values. Note that because the primary utility of this dataset is identified to be PARB, only these data are available at the monthly temporal resolution. This technical choice was made because the inclusion of all monthly data for all variables would make the NetCDF files too large to load into memory for anything other than servers or very powerful desktop computers. It was determined that this would severely limit the usability of these data, and therefore it was preferable to remove the monthly data for PAR(0 -), KPAR, and the P-functions.
Conclusion The data product summarised in this report was designed to provide a number of variables throughout a range of EU Arctic fjords. Monthly PARB is the primary variable of interest, but the dataset also provides global mean values, annual values, and monthly climatologies for: PAR(0 -), KPAR, and PARB. The data are currently available from 2003 to 2022, but could potentially be updated annually because they are created from algorithms that utilise operational data streams applicable to MODIS, but also to VIIRS and future PACE missions. With the exception of Kongsfjorden (Svalbard), the available PARB in the EU Arctic fjords shows no significant signs of long-term change. However, although PAR(0 -) and KPAR are not changing much at an annual rate, PARB values of certain months (e. g.
The darkening effect has an overall larger impact on the annual PARB, because its timing coincides with the peak in PAR(0 -). These results are consistent with those of  Singh et al. (2022)  at Pan-Arctic scale, but emphasise the need to consider the local processes that control light attenuation. Because benthic algae rely on photosynthesis to survive throughout the year, any reduction in PARB at water depths where the algae are light limited is likely to have a negative effect. However, the changes observed in the PAR dataset are complex, and clearly non-linear. Therefore, trends presented here should not be extrapolated into the future. It is also known that at some point in the future the peak rates of terrestrial/glacial.
( https://dybdedata. kartverket. no/DybdedataInnsyn/). Gaps within the bathymetry of the Svalbard sites from this source were filled with IBCAO data, and interpolated down to 50 m. For Western Greenland (doi. org/10. 5194/essd-2023-462 Preprint. Discussion started: 8 December 2023 c Author(s) 2023. CC BY 4. 0 License. Tunua and Nuup Kangerlua), 150 m data were utilised from v5. 0 of the IceBridge BedMachine Greenland product (IDBMG4;Morlighem et al. , 2017), which is on the NSIDC Sea Ice Polar Stereographic North projection (WGS 84; EPSG:3413). For Eastern Greenland (Young Sound), a site-specific dataset created by Rysgaard et al. (2003)  and subsequently improved with additional data was used. This has a spatial resolution of 100 m on a WGS 84 datum with EPSG:4326 projection.
in the dataset expressed as integers(i. e. 2003 -2022). irradianceLevelThe values (mol m -2 day -1 ) used to define the steps (x-axis) in the P-functions. depth Depth expressed as negative values [m] elevation Elevation above sea level expressed as positive values [m] area Surface area of pixel [km 2 ] AreaOfCoastalZone Sum of the surface area (km 2 ) of the pixels within the study site with a depth of 200 m or shallower. AreaOfShallowZone Sum of the surface area (km 2 ) of the pixels within the study site with a depth of 50 m or shallower.
doi. org/10. 5194/essd-2023-462 Preprint. Discussion started: 8 December 2023 c Author(s) 2023. CC BY 4. 0 License. runoff will be reached (this may have already occurred in parts of Svalbard), after which fjord waters are expected to lighten again as terrestrial/glacial runoff reduces. One must also consider that PAR thresholds for important benthic species are to some extent driven by ambient seawater temperature (e. g. eelgrass - Staehr & Borum, 2011;  kelps -Niedzwiedz & Bischof, 2023). Therefore, as the Arctic warms, PAR thresholds (and thus historical depth ranges) will change regardless of how turbid the water may or may not be in the future. As shown in the inhabitable area example (Section 3. 4), this dataset can be useful for a suite of research questions (e. g. , Fig1). The high spatial resolution PAR(0 -), KPAR, and PARB values can also be integrated into a workflow that uses any number of other datasets.
For example, species distribution modelling (SDM) within fjords must be done at very high resolution, but tends to use only global values. Whereas the life cycle of an organism within the water column could be better understood by utilising the monthly climatologies of KPAR. Or the use of annual PAR(0 -) to understand changes in irradiance received by fjord surface waters over time. Other examples include the potential benefits of using this dataset for forcing or initial conditions in state-of-the-art ocean-biogeochemical models and/or forcing for ecosystem box models. The purpose here is to demonstrate some of the many potential applications of this dataset, which fills a gap in the physical understanding of EU Arctic fjord systems. doi. org/10. 5194/essd-2023-462 Preprint. Discussion started: 8 December 2023 c Author(s) 2023.
or GitHub at: https://face-it-project. github. io/FjordLight (last access: 13 November 2023). https://doi. org/10. 5194/essd-2023-462 Preprint. Discussion started: 8 December 2023 c Author(s) 2023. CC BY 4. 0 License. 380 385 390 395 400 The data are also available for download at the World Data Center PANGAEA as a series of NetCDF files, one for each fjord: https://doi. pangaea. de/10. 1594/PANGAEA. 962895 18.
Static data 2. 2. 1 Hydrological modeling The setup of Flood-PROOFS East Africa required the collection of several static and dynamic data, which are described in the following. The Digital Elevation Model (DEM) is taken from the Hydrologic Derivatives for Modeling and Applications (HDMA) database  (Verdin, 2017) , with spatial resolution of 3 arc-second (~90 m at the equator). HDMA comes with a pre-computed and corrected set of hydrological derivatives, including channel network and basin partitioning. Ancillary data including flow accumulation and drainage direction were extracted from the DEM with GRASS GIS (https://grass. osgeo. org/). The DEM was upscaled at the chosen domain resolution and carved using the 90 m stream network. Land use and land cover information at 300 m resolution are taken from the ESA-CCI Land Cover map v2  (ESA, 2017) , which was used to estimate the soil characteristics and the vegetation cover.
Weather forecasts are taken from the Global Forecast System (GFS) of the United States National Centers for Environmental Prediction (NCEP), together with the corresponding 30-member ensemble product GEFS. Both products have 0. 25° resolution and are acquired up to 120 hour forecast range at hourly resolution for GFS and 3-hourly for GEFS. Daily discharge data was collected at ~200 gauging stations in the GHA region. Data sources are the Global Runoff Data Centre (GRDC), the African Database of Hydrometric Indices (ADHI,  Tramblay et al. , 2020) , and the national hydrometeorological services of Burundi, Sudan, South Sudan, Tanzania and Uganda. After a screening for data quality, period of record and minimum record length of 3 years, we selected 56 stations to use for calibration and 78 for model validation.
Parameter regionalization was performed on those domains with no calibration stations, according to criteria of proximity and climatic conditions. Calibrated parameters were then used in a set of long-term hydrological simulations over 2001-2022, having three key functions. First, to estimate suitable initial conditions to initialize the operational forecasts. This is particularly useful for large river basins (e. g. , Nile, Juba-Shabelle) with long memory (i. e. , more than 1 year), thus requiring long initialization periods to adequately characterize their water balance conditions. Second, to serve as an evaluation dataset to be compared with observed discharges. Third, long term simulations are analyzed statistically to extract discharge annual maxima and estimate extreme value distributions at each pixel of the river network.
Maximum forecast population affected was 3. 9 millions in Sudan (Table  1 ), of which 1. 9 in the country capital. The peak of impacts of the floods in South Sudan was forecast on 10 September 2020, with 0. 9 million people affected. Forecasts compare well in magnitude with recorded impacts in Sudan, South Sudan and Uganda, from the EMDAT database (EM-DAT, 2023). For comparison, estimates by GloFAS are one order of magnitude lower, with about 224,000 people affected in the three countries and only 40,000 in Sudan. In FPEA, people affected in Khartoum and in the states along the Nile and Blue Nile River are generally above the figures reported by UN-OCHA (https://www. unocha. org/sudan, see comparison in Figure  7 ). This is partly attributed to a considerable overestimation of the inundated area for all flood scenarios in the JRC flood hazard maps  (Dottori et al.
hydrosheds. org/products/hydrolakes and https://www. globaldamwatch. org/directory. Observed discharges from the GRDC database are freely available for download at https://www. bafg. de/GRDC. Global flood hazard maps from  Dottori et al. (2016)  can be downloaded from https://data. jrc. ec. europa. eu/collection/id-0054. Lack of coping capacity values were taken from the INFORM Risk Index at https://drmkc. jrc. ec. europa. eu/inform-index/INFORM-Risk.
/doi. org/10. 5194/egusphere-2023-804 Preprint. Discussion started: 2 May 2023 c Author(s) 2023. CC BY 4. 0 License. depending on the domain, with D01 and D02 (including the Nile River Basin) at 0. 03° (~3. 3 km), D15 (including the Juba-Shabelle river basin) at 0. 02° (~2. 2 km), D13a, D13b and D13c (the three main Tanzanian islands) at 250 m, and all the other domains at 0. 01° (~1. 1 km) resolution. Point features implemented include the largest 19 reservoirs and 20 lakes, extracted from the Global Dam Watch (Mulligan et al. , 2021), the FAO-AQUASTAT-Dams (https://www. fao. org/aquastat/en/databases/dams) and the HydroLAKES (Messager et al. , 2016) datasets. Both lakes and reservoirs were selected among those having total storage larger than 300 Mm 3. An additional lake was inserted to model the Sudd swamps in South Sudan.
Acknowledgements. The authors thank the financial support for the  AMIGOS  project from the  Région Bretagne ( ARED fellowship  for PLB) and from the  ISblue project (Interdisciplinary graduate school  for the blue planet,  ANR-17-EURE-0015 ) co-funded by a grant from the  French government  under the program  Investissements d'Avenir. The authors also want to thank  Maxime Beauchamp  and  Noémie Le Carrer  for their helpful comments on the study.
Code and data availability. Python codes are available in the following GitHub open repository: https://github. com/pilebras/AnDA_weight_ idealAMOC/tree/main under the GNU license. They include the code for generating the data (using an idealized AMOC model developped in  Sévellec and Fedorov 2014) , performing the experiments using AnDA (adapted from the code available at https://github. com/ptandeo/AnDA) and obtaining the figures of the paper.
While comparable seasonal information is available for aerosol samples collected at the nearby Zeppelin observatory (ebas. nilu. no), giving a detailed description of the yearly behavior of PAHs in the atmosphere, contemporary records of their distribution in the snow are not available. In this work we aim to better elucidate the sources and processes that control PAH concentrations in snow, in terms of both long-range and local scales. Therefore, this work aims to identify: -how the atmospheric PAH signal is actually recorded in the snow deposition; -how post-depositional processes and local conditions may affect this record. The final goal is to fill a knowledge gap between information from atmospheric studies and the data retrievable from snow-pits and cores to support the reconstruction of environmental and paleoclimatic trends of organic compounds archived in the cryosphere.
5 L of ultrapure water for organic analyses (Purelab Flex; ELGA LabWater) and then following the procedure described above. Internal standard (50 μl of 13 C PHE at 1 ng Ll -1 ) was added respectively at the beginning of the procedure to perform trueness tests (n • = 3; Table  SI1 ) or after the final concentration to calculate the recovery rates (n • = 3; Table  SI1 ). Results were mean-blank corrected, and the method detection limits (MDLs) were calculated as three times the standard deviation of the blank signals (Table  SI1 ). To study the source region of the air masses reaching the Gruvebadet Aerosol Laboratory, back-trajectories were computed using the NOAA-HYSPLIT model 5. 0. 0  (Stein et al. , 2015). The trajectories were calculated for 120 h, starting every 6 h at 10 m above ground level (AGL) with backward direction using vertical velocity and NCEP/NCAR reanalysis database (horizontal resolution of 278 km), according to previous studies on atmospheric circulation in the same site  (Feltracco et al.
Acknowledgements This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement no. 689443  via  ERA_PLANET Strand 4  project  iCUPE (Integrative and Comprehensive Understanding on Polar Environments ). We are grateful to the  Arctic Station Dirigibile Italia  from the  Italian National Research Council -Institute of Polar Science (CNR-ISP)  for logistical support. We acknowledge the help of  ELGA LabWater  in providing the  PURELAB Pulse  and PURELAB Flex, which produced the ultrapure water used in these experiments.
Acknowledgements Many thanks to  Nathalie Poulet-Crovisier  and  Florent Goussard  ( MNHN CR2P ) for help with 3D segmentation and visualisation. Also thanks to  Marta Bellato  and  Patricia Wils  at  AST-RX , platform for Scientific Access to X-ray Tomography, UMS 2700 2AD Data Acquisition and Analysis for Natural History  CNRS-MNHN, Paris.
Funding This work was funded by a  Paris Île-de-France Region  grant ( Domaine d'intérêt majeur [DIM] "Matériaux anciens et patrimoniaux" ) awarded for the  DIM PHARE (Pharyngeal Evolution: illuminating its function in early jawed vertebrates)  project. R. P. D. is now supported by the  Marie Skłodowska-Curie Action DEADSharks. A. P. is supported by the  Agence nationale de la recherche  (Grant CE02)  Terre vivante ,  jeunes chercheurs ou des jeunes chercheuses ,  MACHER ( Mechanical Adaptation to Crushing in the Holocephalan Evolutionary Radiation).
Multi-temporal Synthetic Aperture Radar Interferometry (MT-InSAR) is the only geodetic technique allowing to measure ground deformation down to mm/yr over continuous areas. Vegetation cover in equatorial regions favors the use of L-band SAR data to improve interferometric coherence. However, the electron content of ionosphere, affecting the propagation of the SAR signal, shows particularly strong spatio-temporal variations near the equator, while the dispersive nature of the ionosphere makes its effect stronger on low-frequencies, such as L-band signals. To tackle this problem, range split-spectrum method can be implemented to compensate the ionospheric phase contribution. Here, we apply this technique for time-series of ALOS-PALSAR data, and propose optimizations for low-coherence areas. To evaluate the efficiency of this method to retrieve subtle deformation rates in equatorial regions, we compute time-series using four ALOS-PALSAR datasets in contexts of low to medium coherence, showing slow deformation rates (mm/yr to cm/yr).
, 2018 (Liang et al. , , 2019)) , also with examples in high coherence regions. In these studies, the comparison between InSAR derived velocity maps after ionospheric correction and GNSS velocities shows an agreement reaching 2-5 mm/yr. Liao et al. (2018)  adapted the split-spectrum method for InSAR measurement of ice velocity in polar regions, also prone to ionospheric disturbances, and with poor coherence conditions. However, this study only uses single interferograms (no time-series inversion), and the motions observed are rather large (several meters per year). In this paper, we propose to evaluate the performance of the splitspectrum technique for correcting time-series of L-band InSAR data in equatorial regions. The latter are particularly challenging because (1) ionospheric perturbations are common and have strong amplitude and gradients; and (2) coherence is often low due to the dense vegetation cover, justifying the use of L-band data.
The split-spectrum method Several approaches have been investigated in order to compensate the effect of ionosphere in InSAR phase. Among the methods developed to extract the ionospheric phase from the SAR signal itself, without using any external TEC data (e. g. from GNSS), one can differentiate approaches using Faraday rotation, azimuth shift, and range splitspectrum. Methods using Faraday rotation (e. g. Pi et al. , 2011; Li et al. , 2020)  can retrieve absolute TEC for each acquisition, but they require full polarimetric SAR images, which are not widespread in current satellite missions. Azimuth shift based methods (e. g. Raucoules and de Michele, 2010; Jung et al. , 2013)  have proven to be efficient in retrieving the relative ionospheric phase screen, however they require a spatial integration, amplifying noise, and they are limited by the difficulty to separate between azimuth shifts due to ionosphere and shifts due to ground motion (for instance when studying earthquakes).
Measurement of the time-series' accuracy To evaluate the performances of the ionospheric correction on the final time-series of interferometric phase, we mainly use the statistical spatial dispersion of the phase at each time step. As the ionospheric noise is dominating in the datasets presented hereafter, we assume that the dispersion reflects mostly this contribution, and is thus a good indicator of the quality of the correction. The output of the time-series inversion is obtained relative to the first period, which residual atmospheric contribution contaminates all successive maps. To avoid this, we subsequently compute the average of the 3 to 6 acquisitions containing the least ionospheric noise, i. e. the images with the lower variances before ionospheric correction and use this map to reference de time-series.
Among them, Tungurahua (Fig. 1a ) is an andesitic stratovolcano with intermittent activity since 1999, which last erupted in 2006. Large-scale inflation was observed on this volcano using InSAR time-series of Envisat  (Champenois et al. , 2014)  and ALOS-PALSAR  (Morales Rivera et al. , 2016)  data. However, these studies do not include a procedure for compensating ionosphere noise. For instance, Morales Rivera et al. (  2016 ) indicate that they eliminated the ALOS acquisitions contaminated by important ionospheric signals, i. e. 17% of their dataset. We process two ascending tracks of ALOS-PALSAR data over southern Ecuador, using five frames for each track, resulting in the coverage of ~150 × 400 km region (Fig. 1a ). This region encompasses the CCPP and the Tungurahua volcano. Both tracks include 18 complete acquisitions between late-2006 and early-2011 (Table  1 ).
Sumatra Sumatra is located along a subduction zone where the Indo-Australia plate plunges under the Sunda microplate. There, oblique subduction leads to right-lateral motion on the Sumatran Fault (Fig. 1c ) located in the forearc region. Although the Sumatran fault is almost 2000 km long, we focus on its northernmost part, in the Aceh segment, where fault creep at a rate of 1. 6-2. 0 cm/yr was first observed using GNSS between 2005 and 2010  (Ito et al. , 2012). Tong et al. (2018)  found consistent results using time-series of ALOS-PALSAR data. Their study used no ionospheric compensation procedure, and rejected the contaminated interferograms. We processed one frame of an ALOS-PALSAR ascending track crossing the northernmost part of the Sumatran fault (Fig. 1c ). The dataset includes 15 acquisitions between early-2007 and late-2010 (Table  1 ).