dataesr/scientific-paragraphs-categorization huggingface dataset:
This shows the efficiency of the split-spectrum compensation approach and moreover suggests that residual ramps are better estimated on interferograms once they are corrected of the IPS. The residual LOS velocity fluctuations, higher for track 111 than for track 110, likely stem from residual atmospheric delays and DEM errors, as part of them seem to correlate with the topography. We analyze quantitatively the consistency of the profiles from Fig. 7b  and c  in Fig. 8. Two subsets, separated by a 1. 2 rad threshold on RMS misclosure, are created in order to differentiate low and high coherence areas (corresponding to high and low RMS misclosure, respectively). Regarding the low RMS misclosure dataset, the LOS velocity with ramp removal only (Fig. 8a ) seems linearly correlated between the two tracks.
9 , for the different correction strategies shown throughout this study. The dispersion of raw time-series (grey bars) allows to compare the level of ionospheric perturbation of the different datasets, and confirms the more regular occurrence of strong perturbations in Ecuador (T110 and T111) than in Trinidad (T119) and Sumatra (T499). The simple ramp removal strategy (blue bars) appears to reduce, sometimes importantly, the dispersion of acquisitions. However, as noticed earlier, this improvement is very limited for acquisitions showing a large initial dispersion (i. e. mostly acquisitions with non-linear patterns). In contrast, the combination of split-spectrum correction and ramp removal (red bars) allows systematically to reduce the dispersion by at least a factor two with respect to the raw time-series.
Conclusion Ionospheric perturbations can be a major source of noise in InSAR, especially using L-band data in equatorial regions. When trying to detect small ground motion using time-series analysis of InSAR data in such conditions, compensating this source of error becomes mandatory. In this study, we use the now widespread range split-spectrum technique to correct time-series of ALOS-PALSAR data in different equatorial contexts. As we deal with low to medium coherence datasets, we adapt the implementation of the technique in order to optimize signal-to-noise ratio, especially during multilooking, unwrapping, filtering, and timeseries inversion steps. We use the time-series corrected using this procedure to demonstrate the efficiency of the ionospheric compensation, and to evaluate empirically the reached accuracy.
From a geostatistical analysis, we derive an empirical accuracy for the LOS velocity measurement using such datasets, highlighting its dependence on spatial distance. From the typical ALOS-PALSAR archive, we show that deformation rates down to ~6 mm/yr at large distances (>50 km, such as interseismic strain accumulation) can be detected. Looking at small wavelength deformation, such as fault creep (<10 km), the detection threshold is around 3 mm/yr. Similar accuracy estimates are derived from the analysis of the overlapping data between two adjacent tracks in Ecuador. These values depend on interferometric coherence, as the ionospheric phase screen computation is very sensitive to phase noise. Other noise sources, such as DEM errors or residual tropospheric delay, contribute to this error, but to a lesser extent.
The impacts of the accelerated glacier retreat in recent decades on runoff changes are still unknown in most Andean catchments, thereby increasing uncertainties in estimating and managing water availability. Here, we used a monthly time step to simulate glacier evolution and related runoff changes for 36% of the glacierized 15 surface area of the Andes (11,282 km 2 in 786 catchments, 11°N-55°S) using the Open Global Glacier Model (OGGM) and a corrected and evaluated version of the TerraClimate dataset between 2000 and 2019. The glacier mass balance and volume were calibrated glacier-by-glacier. The simulation results were evaluated with in situ data in three documented catchments and 15 glaciers. Our results show that the glacier volume (-8. 3%) and surface area (-2. 2%) are reduced in 93% of the catchments between the periods 2000-2009 and 2010-2019.
Historical climate data We used two climate datasets: the TerraClimate reanalysis and in situ measurements from meteorological stations. TerraClimate is based on reanalysis data since 1958, with a 4 km grid size at a monthly time scale, and was 80 validated with the Global Historical Climatology Network (temperature, r = 0. 95, MAE = 0. 32°C; precipitation, r = 0. 9, MEA = 9. 1%)  (Abatzoglou et al. , 2018). The mean temperature was estimated from the maximum and minimum temperature whereas precipitation data is accumulated on a monthly basis. The meteorological records were compiled from Andean organizations and scientific reports  (Rabatel et al. , 2011; MacDonnell et al. , 2013; Schaefer et al. , 2017; CECs, 2018; Shaw et al. , 2020; Hernández et al. , 2021; CEAZA, 2022; DGA, 2022; 85 GLACIOCLIM, 2022; IANIGLA, 2022; Mateo et al.
Glacier mass balance 130 The mass balance datasets were comprised of the global glacier surface elevation change product of  Hugonnet et al. (2021)  and in situ measurements of the glacier surface mass balance since 2000 from different institutions (e. g. ,  Marangunic et al. , 2021; WGMS, 2021). Hugonnet et al. 's (2021)  product was quantified for each glacier using the OGGM toolbox. Then, the geodetic mass balance estimates were obtained for every glacier of the RGI v6. 0. In situ measurements of the glacier surface mass balance available for all glaciological regions (Tropical Andes,.
495 (from stakes to a catchment scale) and temporally (from hourly to monthly), as well as different in situ and fixed (literature) melt factor values for the snow and ice temperature index. Taking these differences into account, we found a regional pattern for the melt factor using the same methodology at a monthly time step. The mean calibrated melt factor values decrease from the Tropical Andes toward the Wet Andes (TA = 0. 5 ± 0. 3 mm h -1 ºC - 1 , DA = 0. 6 ± 0. 2 mm h -1 ºC -1 , WA = 0. 2 ± 0. 1 mm h -1 ºC -1 ) (see Table  1  and Figure  3 ). This geographical distribution aligns with our evaluation of the TerraClimate dataset. The lowest mean temperatures estimated in the.
520 Furthermore, we applied different precipitation factor values in the Tropical Andes (1), Dry Andes (1. 9 to 4) and Wet Andes (2. 3 to 4), in order to increase the annual amplitude of the simulated mass balance. These values are in agreement with former studies, for example, similar values were used in the Dry Andes  (Masiokas et al. , 2016; Burger et al. , 2019; Farías-Barahona et al. , 2020). Values that are too high could lead to an overestimation of precipitation on some glaciers. However, to confirm that the precipitation factor produces realistic precipitation 525 values, we adjusted the standard deviation of the simulated mass balance to the observed mass balance, a method similar to that proposed in  Marzeion et al. (2012)  and  Maussion et al. (2019). On the other hand, the uncertainty of the calibrated melt factors come from the climate and geodetic mass balance datasets used to run and calibrate the model, respectively.
Acknowledgments We acknowledge LabEx OSUG@2020 ( Investissement d'Avenir ,  ANR10 LABX56 ). The first author would like to thank  Dr. Shelley MacDonell  ( University of Canterbury -CEAZA ),  Ashley Apey  ( Geoestudios ),  Dr. Marius 575 Schaefer  ( U. Austral de Chile ), and  Claudio Bravo  and  Sebastián Cisterna  ( CECs, Centro de Estudios Científicos de Valdivia ) for the data provided. In addition, the first author thanks the OGGM support team, especially  Patrick Schmitt ,  Lilian Schuster ,  Larissa van der Laan ,  Anouk Vlug ,  Rodrigo Aguayo  and  Fabien Maussion. Lastly , the first author greatly appreciates discussing the results for specific glaciers or catchments with  Dr. Ezequiel Toum  ( IANIGLA, Argentina ),  Dr. Álvaro Ayala  ( CEAZA, Chile ),  Dr. Lucas Ruiz  ( IANIGLA, Argentina ),  Dr.
, 2019). The analysis of organic acids was conducted using a HPLC-MS (GP40 Dionex), with negative mode electrospray ionization  (Borlaza et al. , 2021). The diversity of bacteria in PM10 samples was investigated by high-throughput sequencing (Illumina) of metabarcoded ribosomal gene amplicons, from whole genomic DNA extracted using the commercial DNeasy PowerWater kit (Qiagen). Polymerase chain reaction (PCR) amplification was performed using the primers 515F and 806R, as recommended by the Earth Microbiome Project  (Caporaso et al. , 2012). The sequence data obtained from Illumina MiSeq (2x250bp) were analyzed through the FROGS pipeline  (Escudié et al. , 2018)  using Silva 132 as the reference database  (Quast et al. , 2013) , as in  Péguilhan et al. (2021). In addition, a ceilometer was deployed to characterize the boundary layer evolution and the cloud cycle.
by using Lagrangian trajectory tools (FLEXPART, CAT). Their results concerned the origin of air masses from the atmospheric transport at the synoptic scale and showed that the dominant large-scale air masses are easterly under the influence of trade winds and that the strongest biogenic contributions coincided with air masses passing over the northeastern part of La Réunion. For the BIO-MÄIDO campaign, to determine the origin of the air masses arriving at PF-1 and MO at the scale of the entire island, high spatio-temporal back-trajectories have been calculated using Meso-CAT. The used Meso-NH simulations cover the whole campaign and use three embedded domains at 2 km, 500 m and 100 m of horizontal resolution. Rocco et al. (2022) first exploited these back-trajectories by combining them with soil data from the Corine 2018 land-cover database (Geoportail, https://www.
03 (operational taxonomy units clustered at 97% sequence similarity, i. e. ~ prokaryotic species level see  Amato et al. (2017)  for details) in total, of which 6,935 were affiliated at >95% identity to know sequences in databases. The vast majority of these (99. 6%) were bacteria, the remaining being attributed to Archaea. As for culturable bacteria from clouds, the phylum Proteobacteria dominated (29. 3% of the sequences), before Firmicutes, Actinobacteria and Bacteroidetes represented each by ~16% of the sequences. Among others, Cyanobacteria, Planctomycetes and Acidobacteria notably contributed each 3% to 4% of the sequences. This composition is consistent with airborne bacteria at other places of the planet using similar methods (e. g. Amato et al. , 2017; Péguilhan et al. , 2021).
, 2022b). The database from the BIO-MAÏDO campaign is a unique opportunity to better understand the contribution of multiphase atmospheric chemistry on SOA formation. However, deep analysis of the database does not allow to quantify this contribution even if it shows clear evidence of cloud processing on the OM composition of particles sampled at MO-5. Simulations with the explicit cloud chemistry model CLEPS is ongoing to investigate organic matter processing by cloud. A case study has been selected for this simulation, the 28 th of March 2019 was chosen for several reasons: the amount of cloud water sampling was sufficient for allowing deep chemical composition analysis  (Dominutti et al. , 2022a) , the observed cloud that day is typical of a cloud forming on the slope at the end of the morning and evaporating before arriving at the MO-5 (see section 3.
The database from the campaign is original, combining dynamical, microphysical, chemical, biological, and particles' size parameters (droplets and aerosols). The study of the mixing boundary layer air masses advected by thermal breezes at MO-5 during the daytime shows two preferred trajectories routes both corresponding to the return branches of the trade winds associated with the up-slopes thermal breezes. The first preferred route for airmass trajectories passed through the south allowing air masses to pass over the forests located between 1000 m and 1500 m asl in the south-west of the island. The route of the second set of trajectories is going up the western flank of the island, also indicating a passage in the marine boundary layer. A detailed analysis based on a highresolution Meso-NH simulation is made for the 28 th of March.
Both results indicate the presence of photochemical aged aerosols at MO-5, potentially impacted by cloud processing depending on the day and the trajectories of air masses arriving at MO-5. The analysis of cloud chemical composition allows a thorough identification of organic compounds in cloud water. Despite this, around 80% in average of dissolved organic compounds in cloud water are undefined highlighting the complexity of the cloud organic matter. These results, obtained from analysis of the BIO-MAÏDO campaign database, must be completed by numerical modeling to answer the three main objectives of the BIO-MAÏDO project i. e. understand which are the main formation pathways of SOA in humid tropical atmosphere (gaseous phase versus aqueous phase); improve multiphase processes leading to SOA formation in a 3D model; examine whether the presence of bacteria in aqueous phase could contribute to SOA formation.
In detail, we constructed the three precipitation scenarios as follows: Scenario 1 (an earlier and longer wet season) was obtained by replacing the 2011/12 October and November data by the data from the same month of another year chosen among the two wettest months in the dataset (Figure  3b ). Scenario 2 (a more intense wet season) was obtained by replacing the months of December to March by those in the dataset that had the highest number of events ≥2 mm•d -1 (Figure  3c ). Finally, scenario 3 (wetter September, October, November, April and May), was obtained by selecting the months in the transition seasons that had above average precipitation compared to the long term onglacier average (Figure  3d ). For all the scenarios, we used a trial-and-error procedure to determine which year's monthly data to use to replace corresponding data in the 2011/2012 dataset, to achieve the targeted ∼20% increase in annual precipitation amounts.
e. for all scenarios) which, via the precipitation gradient applied in the simulations (+10%/100 m up to 5200 m a. s. l. ), implies that over 2. 56 m w. e. reaches the upper part of the glacier. At the glacier terminus, the cumulative mass loss, which ranges between 9. 0 and 10. 1 m w. e. depending on the scenario (Figure  5 ), is significantly higher than that observed in recent times, when the glacier wide mass balance has been close to zero (for example, measurements over the year 2000/2001 show B Glacier-wide = 0. 49 m w. e. and B Glacier terminus = -6. 24 m w. e. ). This difference can be explained by the larger accumulation zone in the LIA configuration (2. 50 km 2 versus 1. 55 km 2 in 2000/2001) and similar annual precipitation amounts (2000/2001 is one of the wettest year in the current climate dataset) leading to a larger accumulation volume (an extra 1.
Acknowledgements This study was conducted in the framework of the  International Joint Laboratory LMI GREAT-ICE , the  International Research Network Andes-C2H  (IRD and Andean partners) and the  French Service National d'Observation GLACIOCLIM  (www. glacioclim. osug. fr, UGA-OSUG, CNRS-INSU, IRD, IPEV, INRAE ). GLACIOCLIM is coordinated in La Paz by  Dr. Alvaro Soruco  ( UMSA/IGEMA ) who organizes the follow-up of the glaciological measurements on Zongo Glacier. The authors are grateful to everyone involved in the long-term in-situ monitoring of  Zongo Glacier  and acknowledge the support of  LabEx OSUG@2020  ( Investissements d'Avenir- ANR-10-LABX-0056 ).
When using DYNAMICO, spatial resolutions ranging from 200 to 25 km were used. For the intermediate resolutions, we compared the results of these simulations with those obtained with the native dynamical core of the LMDZ. We also confronted the resulting TCs structure and statistical properties with the robust results of the literature described above, focusing on the global and regional scales, but also on the scale of the TCs themselves. The outline of the paper is as follows. In section 2, we present IBTracs, our reference database for the observations (2. 1), the IPSL model (2. 2), the simulations (2. 3), the TCs tracking methodology (2. 4), and finally the diagnostics we used (2. 5). The results are presented in section 3 which is divided into two parts. First, section 3. 1 details how resolution affects different aspects of tropical cyclones' climatology.
Observations: IBTrACS In this study, we use data from the International Best Track Archive for Climate Stewardship (IBTrACS,  Knapp et al. 2010 ) version 4 as a reference. It is the most comprehensive database of observed TCs. However, because of its collective nature, it contains inhomogeneities that must be dealt with. In the present paper, we use the "since 1980" subset  (Knapp et al. , 2018)  retrieved on January 8th, 2023. The data processing of the database is the same as in  Bourdin et al. (2022) , detailed in their figure  B1. We briefly summarize it below for completeness. Seasons 1981 to 2021 are used. Tracks labeled as "spur" are removed. For consistency with our simulation data, only 6-hourly time steps are kept, and we prefer to use 10-minutes averaged winds. For this reason, WMO winds are prioritized when available.
Simulations The HighResMIP framework All the simulations were run according to the protocol for Tier 1 historical  atmosphere-only simulations of the HighResMIP framework described in  Haarsma et al. (2016). Forcings for solar radiation, ozone, and greenhouse gases concentration are the same as in CMIP6. Specific HighResMIP forcings are a daily 1/4°HadISST2based dataset for the sea-surface temperatures (SST) and a simplified aerosol optical properties formulation incorporating volcanic and anthropogenic changes.
Data availability statement The HighResMIP simulations are available through ESGF at https://esgf-node. ipsl. upmc. fr/projects/cmip6-ipsl/. ERA5 data is available on the Climate Data Store https://cds. climate. copernicus. eu/. IBTrACS is available online at https://www. ncei. noaa. gov/products/international-best-track-archive. ONI time series was retrieved from https://www. cpc. ncep. noaa. gov/data/indices/oni. ascii. txt. Additional data was stored on Zenodo (https://doi. org/10. 5281/zenodo. 8202411): • TC tracks in all the simulations before and after the STJ filtering; • Composite NetCDFs; • Large scale variables used in the GPI, and the GPI itself, in ICO-VHR and ERA5.
20 0. 29 0. 13 0. 15 WNP 0. 12 -0. 27 0. 13 0. 25 ENP 0. 31 0. 48* 0. 36* 0. 40* NATL 0. 12 0. 60* 0. 43* 0. 56* NI -0. 13 0. 16 0. 32 0. 12 SI -0. 11 -0. 03 0. 04 -0. 12 SP 0. 33 0. 27 -0. 25 0. 23 AUS 0. 10 0. 09 -0. 14 0. 11 CP 0. 50* 0. 18 0. 38* 0. 33 Table A2 Pearson correlation coefficient for the seasonal ACE in each basin compared to the observation. * and bold denote significant correlations. Overall, our results clearly demonstrate that the IPSL model is a suitable tool to study the properties and evolution of tropical cyclones on climate scales and to make progress on the open issues highlighted above. Acknowledgments. The research is supported by public funding. S. Bourdin and S. Fromang were financially supported by the CEA, and by the EUR IPSL-Climate Graduate School through the ICOCYCLONES and ICOCYCLONES2 projects, man- aged by the ANR under the "Investissements d'avenir" programme with the reference ANR-11-IDEX-0004 -17-EURE-0006 The TGCC and the IPSL Data and Computing Center ESPRI provided simulation and data processing infrastructure.
The objective of this study is to represent the spatio-temporal dynamics of flow intermittence at the reach level in meso-scaled river networks (between 120 km 2 and 350 km 2 ). The daily and spatially distributed flow condition (flowing or dry) is predicted using the J2000 distributed hydrological model coupled with a Random Forest classification model. Observed flow condition data from different sources (water level measurements, photo traps, water temperature measurements, citizen science applications) are used to build the predictive model. This study aims to evaluate the impact of the observed flow condition dataset (sample size, spatial and temporal representativity) on the performance of the predictive model. Results show that the hybrid modelling approach developed in this study allows to accurately predict the spatio-temporal patterns of drying in the 3 catchments.
Spatial data Topography, soil types, landuse and hydrogeology information is needed for the spatially distributed hydrological modelling. The following data sources were used: -Topography: EU-DEM v1. 1  (Copernicus, 2016)  with a 25 m resolution for the Albarine DRN, the Andalucía DEM 85 (Portal Ambiental de Andalucía. 2010) with a 10 m resolution for the Genal DRN, and the 10 m DEM Finland (National Land Survey of Finland) for the Lepsämänjoki DRN. -Soil: Soil classes were used from the European Soil Database v2. 0 (European Commission;  Panagos et al. (2012) ). Physical parameters were also used from the European Soil Database v2. 0 (field capacity, saturated water content, depth to rock). In the Genal DRN (Spain) texture and bulk density data was used from soil profiles  (Llorente et al.
Observed flow state data analysis Table  5  shows general statistics on the distribution of the observed flow state between the different datasets and the coverage of the river networks. The gauging stations are the main source of observed data in term of number of observations. They give information on long time periods with regular time step, but the number of station in the DRN is limited (1 station in the Genal and Lepsämänjoki DRNs and 5 stations in the Albarine DRN), which means that stations cannot bring useful information about the spatial patterns of drying in the DRNs. The field campaigns (with expertise) is the second source of observed data. They cover a shorter time period than the stations (3 months in Lepsämänjoki and 3. 5 years in the Albarine), but have a better spatial coverage of the river network than the stations.
Sensitivity to the type of flow state observed data Figures  12  and 13  show the sensitivity of the model to the type of observed data used to train the RF. The first result is that the prediction of flow intermittence is very sensitive to the expertise data. Indeed, when this dataset is removed from the training sample, the FAR increases (43% for Albarine, 40% for Genal, and 7% for Lepsämänjoki), the drying is more intense during the summer in Lepsämänjoki (maximum annual of dry fraction of the river network between 6 and 8% without expertise data versus 8 to 11% with expertise), and the drying is twice more intense and last much longer in the Albarine DRN. Field campaign data also have a large impact on the prediction of flow intermittence, especially in the Albarine DRN where the model is only able to predict 50% of the dry days without the field data.
The drying is much reduced during the summer and there is no drying simulated from November to June. On the opposite, in the Lepsämänjoki DRN, the FAR is increased without the field campaign data and the drying is very over-estimated. Expertise and field campaign data are the two most impactful datasets in Albarine and Lepsämänjoki. In the Genal DRN, the results show that the simulated seasonal pattern of drying is very different without the Google Earth data, with a lot of drying predicted during the winter season, which can reach unrealistic values (up to 70% of dry river network in January) (Fig. 13 ). In a DRN characterised by high intermittence of flows, and with little field observations, flow intermittence observation from remote sensing dataset can be very useful to better constrain the RF model.