dataesr/scientific-paragraphs-categorization huggingface dataset:
L. has analyzed the inundation on basis of optical data. T. B. has taken lead in writing the manuscript, supported by N. N. The continued (long-term) operation of the GPS stations in Central Sulawesi has been co-facilitated by the EU-ASEAN  SEAMERGES (2004 SEAMERGES ( -2006) )  and GEO2TECDI-1/2 projects  (2009) (2010) (2011) (2012) (2013). The GPS data acquisition and research were also partly funded by grants from the Dutch NWO User Support Programme Space Research  (2007) (2008) (2009) (2010) (2011) (2012) (2013) (2014) (2015) (2016) (2017) (2018) (2019) (2020) (2021) (2022) (2023). A special thanks to our local survey staff Rahman Umar and Urip for their continued support, including the inspection of the GPS stations directly after the earthquake. ALOS-2 data were provided under a cooperative research contract between GSI and JAXA.
The ownership of ALOS-2 data belongs to JAXA. Taco Broerse is funded by NWO grant ALWGO. 2018. 038. Olga Kleptsova was initially funded by a 2 months PD grant awarded to J. Pietrzak by the Department of Hydraulic Engineering, TU Delft which is gratefully acknowledged. Matthew Herman was funded by The Netherlands Research Centre For Integrated Solid Earth Science, grant ISES 2017-UU-22. COMET is the UK Natural Environment Research Council's Centre for the Observation and Modelling of Earthquakes, Volcanoes and Tectonics. We would like to dedicate our research work in memory of all casualties of the 2018 Palu earthquake. This includes Maya Pasau, who helped us with setting up one of the GNSS stations, her husband Yudi and their children Fiki and Al who tragically lost their lives when the tsunami struck Talise beach.
Data Availability Statement The data underlying this manuscript and model results are available at the 4TU Research data repository (https:// data. 4tu. nl) (https://doi. org/10. 4121/19168442). These data and results include: the GPS kinematic time series, GPS co-seismic displacement tables, raw GPS RINEX files, inundation contours, the data reduced SAR displacement fields, 3D displacement field estimate and corresponding displacement inversion input files, finite fault solution (map solution for each fault scenario), combined bathymetry-topography, and the vertical displacement fields for the finite fault solution of scenario IV. The satellite imagery that we use to estimate tsunami inundation is available at: https://www. maxar. com/open-data/indonesia-earthquake-tsunami. We have made use of colormaps by Colorbrewer and Fabio Crameri  (Crameri, 2018).
The software for this study is partly open access, as specified below: • QGIS as used for the analysis of tsunami inundation is open access software (https://www. qgis. org). • A research license for the GPS processing software GIPSY-OASIS II can be obtained at https://gipsy-oasis. jpl. nasa. gov. •  The Geodetic Bayesian Inversion Software GBIS is written in Matlab and is freely accessible at https://comet. nerc. ac. uk/downloadgbis. • H2Ocean is open source software and is freely accessible at https://github. com/OKleptsova/H2Ocean. git. • GSISAR used for SAR processing is not accessible to the public or research community under the policy of GSI.
To do so, we objectively extract repeating transient inflation signals at three volcanoes-Akutan, Okmok and Piton de la Fournaise-using a data-adaptive statistical methodology, namely Multichannel Singular Spectrum Analysis (M-SSA), applied to continuous GPS time series  (Walwer et al. , 2016). These three volcanoes, which display such repetitive inflation events, have the advantage that their plumbing systems are well constrained by independent geochemical, petrological, seismic and geodetic datasets. We illustrate the resemblance of their deformation patterns using phase portrait reconstructions  (Guckenheimer and Holmes, 1983; Ghil and Childress, 1987; Jordan and Smith, 2007) , which indicate a striking dynamical similarity amongst them. To extract the dynamical features that lead to the observed phase portraits, we use a simple nonlinear oscillator that shares qualitative behavior with the inflation cycles observed at the volcanoes studied herein.
This analogy, in turn, provides guidelines that we then use to build a physical model based on the plumbing system of the volcanoes being investigated. The present paper aims in particular at presenting the connection between statistical analysis of GPS time series and dynamical modeling of volcanoes. The data used and the methods applied are presented in Section 2. In Section 3, we apply M-SSA to the GPS time series. The similarities in the phase portraits obtained for the three volcanoes are discussed in Section 4. The model for pressure build-up through magma flow between connected reservoirs is formulated in Section 5. Concluding remarks follow in Section 6 and we emphasize the consistency between our dynamical modeling approach and the amount of information contained in the geodetic data extracted and analyzed using our M-SSA methodology.
Three Volcanoes and Their GPS Data Sets We used position time series from continuous GPS data at stations located on three volcanoes: Akutan (8 stations) and Okmok (5 stations) in Alaska, and Piton de la Fournaise (10 FIGURE 1 | Schematic inflation regimes observed at volcanoes and mentioned within the text. (A) Inflation with a monotonically decaying rate often explained by the linear dynamics of pressure build-up within a storage zone through magma supply (e. g. ,  Pinel et al. , 2010). (B) Regime of successive transient inflation episodes superimposed onto an inflation with constant rate; this regime is referred to as "episodic inflation events" by  Ji et al. (2017)  and it is observed for example at Akutan volcano. (C) Episodic inflation events superimposed onto a longer period inflation with decaying rate (e.
Applying M-SSA to the GPS Data Sets We performed the M-SSA analysis on GPS time series recorded at the three volcanoes under study, for time intervals that contain the same number of cycles for each volcano, i. e. , three episodes of inflation Having the same number of episodic inflation events for the three volcanoes allows us to consistently compare and discuss the statistical information obtained, as well as the dynamics inferred therefrom. Before applying M-SSA we removed the linear part of the time series that accounts for a large portion of the data variance. Removing the linear trend before applying M-SSA is commonly done to focus the analysis on the nonlinear part of the volcanic signal; see  Walwer et al. (2016). Linear trends represent, in all likelihood, longer-term inflation upon which the nonlinear volcanic signal we are interested in is superimposed.
ACKNOWLEDGMENTS We are grateful to  Claude Jaupart  for insightful discussions. It is a pleasure to thank  Andreas Groth  for sharing his M-SSA codes and for helping us with certain aspects of practically implementing the M-SSA methodology. DW thanks  Pascal Richet  for his thoughtful comments and corrections on the first version of the manuscript. It is a pleasure to acknowledge two reviewers as well as  Valerio Acocella  for constructive and insightful comments and suggestions on the paper's originally submitted manuscript. EC acknowledges support from the  Institut Universitaire de France. This work uses data products provided by the  Plate Boundary Observatory  operated by  UNAVCO for EarthScope  (www. earthscope. org)and by the  Nevada Geodetic Laboratory  (geodesy. unr. edu).
FUNDING This research was supported by a doctoral grant from  PSL Research University  (DW), and by research Grant No. 9AD01274  from the  INSU/CNRS program Programme National de Télédétection Spatiale (PNTS) (EC). The present paper is TiPES contribution # 128 ; this project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under Grant Agreement No. 820970.
Data availability. The BERTHA lidar data can be obtained upon request. The Arielle data are available in the PollyNET data base (https: //polly. tropos. de/). (PollyNET, 2021). The AERONET data are available via the AERONET web page (https://aeronet. gsfc. nasa. gov/) under the site 'Leipzig' (AERONET, 2021).
Acknowledgment We would like to thank the shipboard scientific party, captain, and crew of the R/V  Helmer Hanssen  for the 2016 Expedition. We also gratefully acknowledge the comments from anonymous reviewers, which improved this manuscript. This work was supported by the  Korea Ministry of Science and ICT  ( GP2020-038 ), the  Korea Ministry of Oceans and Fisheries  ( 1525011795 ), and the  Korea Ministry of Trade, Industry and Energy  (Project No. 20212010200010 ). W. -L. H. acknowledges the support from the project  ArcticSGD  through the  Norway Grants  and the  EEA Grants  ( UMO-2019/34/H/ST10/00645 ) and the project,  Cyrosphere-driven submarine groundwater in the Arctic , funded by  Swedish Research Council  (Project No:  2021-04962 ).
Growth rates Cell counts were conducted using a Guava easyCyte HT flow cytometer (software guavaSoft 3. 1. 1-detected via chlorophyll fluorescence) at the same time each day (after cell counts were estimated to be above 4000 cells mL À1 ). Cells were suspended in the Nalgene bottles to ensure cell concentrations were homogenous by inverting the bottle 5-10 times. Cells counts were conducted in duplicate (200 μL). The Nalgene bottle lids were covered in parafilm between each sampling to minimize gas exchange. Cell growth rates were determined using the equation growth rate = [Ln(D f ) À Ln(D 0 )]/t, where D f is the final concentration, D 0 is the initial concentration, and t is the time in days. At the end of the experiment, all samples were taken within 3 h of conducting cell counts.
Morphology Malformations in coccolith formation might lead to unstable coccospheres, which in turn may lead to impaired protection  (Monteiro et al. 2016; Kottmeier et al. 2022 ). To investigate potential malformation, 1 mL samples for scanning electron microscopy were filtered onto 0. 8-μm pore size polycarbonate filters (Millipore) and dried in a drying cabinet at 55 C for 24-48 h. A desktop Phenom G2 pro scanning electron microscope was used to take images for subsequent analysis (using ImageJ software) of coccosphere diameter (along the longest axis) and coccolith morphological features (distal shield length, distal shield width, inner circle diameter, tube width; Supporting Information Fig. S1 ). To determine whether the integrity of the coccolithophore cell was maintained, cell counts of collapsed (four or more interlocked coccoliths; Fig.
For the covariance matrix, the robust estimate was used which provides a more conservative model. A Type III analysis was chosen as it holds all the variables constant relative to each other. Data from each sample were averaged to create a single data point for each treatment. Assumptions of normality were satisfied for each dependent variable (Shapiro-Wilk test). Outliers were defined as any data point outside of the following ranges-3 rd quartile + 1. 5 * interquartile range or 1 st quartile-1. 5 * interquartile range, and were removed from the dataset for the statistical analysis. The reference category is included for comparison against the other predictors. The models were run using IBM SPSS v22. The GLM results are reported in detail in the Supporting Information (Table  S2 ).
Acknowledgments This work was funded by the  ASSEMBLE Plus  grant program (Project  730984_A+ ), the  Spanish Ministry of Science and Innovation, BIOCAL Project  ( PID2020-113526RB-I00 ) and  CALMED  project ( CTM2016-79547-R). Roberta Johnson  received a fellowship from  MINECO  ( FPI/BES-2017-080469 ). This work is contributing to the  ICTA "Unit of Excellence" (MINECO ,  MDM2015-0552 ;  CEX2019-000940-M ), and  The Marine  and  Environmental Biogeosciences research group of the Generalitat de Catalunya  ( 2017 SGR-1588 ). The authors would like to thank the specialized team at  Station Biologique De Roscoff  who helped us conduct the experiment. The authors would also like to thank the anonymous reviewers for their comments that helped to greatly enhance and refine the manuscript.
Code Availability The script used for the operational daily training of the ACT surrogate is available at https://github. com/acolette/ACT_v1. 0. The underlying CHIMERE Chemistry Transport Model is available at https://www. lmd. polytechnique. fr/chimere/chimere. php. https://doi. org/10. 5194/gmd-2020-433 Preprint. Discussion started: 23 April 2021 c Author(s) 2021. CC BY 4. 0 License.
Equations An overview of Pathfinder is presented in Figure  1. The model is composed of a climate module, of three separate modules for the carbon cycle (ocean, land without land use and land permafrost), and of two additional modules describing global impacts: sea level rise (SLR), and surface ocean acidification. We do not emulate cycles of other non-CO 2 gases. Mathematically, the model is driven by prescribing time series of any combination of two of four variables: global mean surface temperature (GMST) anomaly (noted T ), global atmospheric CO2 concentration (C), global non-CO2 effective radiative forcing (R x ), and global anthropogenic emissions of CO2 (E CO2 ). The model can therefore be run in the traditional emission-driven and concentration-driven modes, but also in a temperature-driven mode.
Sea level rise Global SLR has been implemented in Pathfinder as a variable of interest to model climate change impacts. In this version, it is firstly a proof of concept, modelled in a simple yet sensible manner. The total sea level rise (H tot ) is the sum of contributions from thermal expansion (H thx ), Greenland ice sheet (GIS; H gis ), Antarctica ice sheet (AIS; H ais ), and glaciers (H gla ): H tot = H thx + H gis + H ais + H gla (7) The thermal expansion contribution scales linearly with the OHC  (Goodwin et al. , 2017; Fox-Kemper et al. , 2021) : H thx = Λ thx U ohc (8) where Λ thx is the scaling factor of the thermosteric contribution to SLR. Note, however, that the thermal capacity of the climate module does not match that of the real-world ocean  (Geoffroy et al. , 2013b) , and so this equation cannot describe equilibrium SLR over millennial timescales.
Land carbon The land carbon module of Pathfinder is a simplified version of the one in OSCAR  (Gasser et al. , 2017 (Gasser et al. , , 2020)). It is shrunk down to four global carbon pools: vegetation, litter, active and passive soil (see Figure  3 ). All terrestrial biomes are lumped together, and there is therefore no accounting of the impact of land use change on the land carbon cycle in this version of Pathfinder. This is an extreme assumption -although very common in SCMs -motivated by simplicity, and it implies that CO2 emissions from fossil fuel burning and land use change are assumed to behave in the exact same way, in spite of their not doing so in reality  (Gitz and Ciais, 2003; Gasser and Ciais, 2013). The vegetation carbon pool (C v ) results from the balance between net primary productivity (NPP; F npp ), emission from wildfires (E fire ), emission from harvest and grazing (E harv ), and loss of carbon from biomass mortality (F mort ): dC v dt = F npp -E fire -E harv -F mort (22) NPP is expressed as its own preindustrial value multiplied by a function of CO2 and of GMST (r npp ).
e. structural parameters), σX is the relative standard deviation of the time series (without noise), ϵ X is the noise intensity, and AR1 is an autoregressive process of order 1 and autocorrelation parameter ρ X. This assumption leads to the final expression of the ξ k vector: X(t) = X µ (t) + σX X σ (t) + ϵ X AR1(t; ρ X ) (50 ξ k = {ξ j } n ξ j=1 , σC , ϵ C , ρ C , σT , ϵ T , ρ T , k (51) The Bayesian procedure is implemented using the Python computer language, specifically the PyMC3 package  (Salvatier et al. , 2016). The distribution sampling and parameter distributions estimation are done through a full-rank Automatic Differentiation Variational Inference (ADVI) algorithm  (Kucukelbir et al. , 2017) , with 100,000 iterations (and default algorithm options). A strength of full-rank ADVI is the algorithm's speed, and its ability to generate correlated (i.
Permafrost carbon The permafrost module's prior and structural parameters are recalibrated using the same algorithm as used by  Gasser et al. (2018) , but adapted to the global formulation of Pathfinder. First, the algorithm is run once to obtain a set of parameters reproducing the behavior of the multi-model global average of five permafrost models (with data from UVic (MacDougall, 2021) added to the four original models). This gives the values of the structural parameters (i. e. α lst , γ rt1 , γ rt2 , κ rt , a min , all α th,j , all τ th,j , ν thaw and ν froz ). Second, the algorithm is run five additional times, for each of the five permafrost models separately, with the structural parameters established in the first step, to obtain prior distributions for the remaining parameters (i. e.
Sea level rise To constrain the separate SLR contributions from thermal expansion, GIS, AIS and glaciers, we use the model-based SLR speed estimates over the recent past  (averaged over 2006-2018)  reported in the AR6 (Fox-Kemper et al. , 2021, Table  9. 5). To constrain the total contribution, we also use the historical  sea level rise inferred from tide gauges from the same source, although the value is corrected for the missed impact of uncharted glaciers  (Parkes and Marzeion, 2018). Contrarily to all other modules, the SLR module is not assumed to start at steady-state in 1750, which is represented through the λ ice (ice ∈ [gla, gis, ais]) parameters. We assume this is entirely due to the so-called little ice age (LIA) relaxation, which we assume can be simply modeled in Pathfinder through exponential decay of our three ice-related contributions since t 0 = 1750.
, 2021, Table 4. 5). The only significant difference is a smaller uncertainty range in our projections for the longer-term periods. Although this is the result of the efficiency of the Bayesian calibration, one might wonder whether the climate module is over-constrained (or equivalently, too limited in its number of parameters). The ocean carbon storage appears overestimated by 5% to 20% by Pathfinder across SSP scenarios. This is consistent with the upward adjustment of the ocean carbon sink stemming from our Bayesian calibration. To compare the land carbon storage with CMIP6 models, because our land carbon module does not include land use change processes, we correct the value reported by complex models by the cumulative land use change emissions of each scenario  (Riahi et al. , 2017; Gidden et al.
Concluding remarks In this paper, we have presented the Pathfinder model: a simple global carbon-climate model with selected impact variables, carefully designed to balance accuracy of representation and simplicity of formulation, and calibrated through Bayesian inference on the latest data from Earth system models and observations. Pathfinder has been shown to perform very well in comparison to complex models, although there remains room for further improvement of the model and its calibration setup. We identify four main avenues to improve the model. First, the ocean carbon module appears to be limited by its structure inherited from a 25-year-old (yet seminal) article  (Joos et al. , 1996). Although it is undoubtedly a significant undertaking, developing alternative formulations of the ocean carbon dynamic, calibrated on state-of-the-art ocean models and properly interacting with the ocean of the climate module, would benefit more than just the SCM community.
Acknowledgements. Development of Pathfinder was supported by the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement  820829  ( CONSTRAIN  project), and by the  Austrian Science Fund (FWF)  under grant agreement  P31796-N29  (ERM project). We thank  A. H. MacDougall  for sharing permafrost simulations made with UVic. 25 https://doi. org/10. 5194/egusphere-2022-802 Preprint. Discussion started: 25 August 2022 c Author(s) 2022. CC BY 4. 0 License.
global data set, Remote sensing of Environment, 95, 164-176, 2005. Code and data availability. The source code of Pathfinder is openly available at: https://github. com/tgasser/Pathfinder Author contributions. TG developed the model, with contributions from TB on sea level rise and PC on land carbon cycle. TG coded the model and its calibration. TB ran the diagnostic simulations and made the final figures. TB and TG wrote the manuscript with input from PC.
Experimental Data To study the relationship between PM10 and RR, this work uses data from Guadeloupe archipelago (16. 25 • N-61. 58 • W). This French overseas region located in the central Caribbean Basin experiences a tropical rainforest climate  [38, 39]. Due to its insular context, there are many microclimates in this small territory (∼1800 km 2 ; 390,250 inhabitants)  [40]. PM10 data were performed by Gwad'Air Agency (http://www. gwadair. fr/, accessed on 1 December 2021), which manages the Guadeloupe air quality network while RR data were provided by Météo France (https://meteofrance. gp/fr, accessed on 1 December 2021). Both measurements are collected simultaneously and continuously in the insular continental regime of the island, i. e. , under the same atmospheric conditions  [41]. To carry out this study, eleven years of PM10/RR data, respectively from January 2005 to December 2012 then from January 2015 to December 2017, are available at an hourly basis.
Coherence Function Analysis To assess an eventual relationship between PM10 and RR data in a dynamic way, the coherence function is firstly used. Figure  2  shows the coherence function Λ PM10/RR ( f ) for a frequency range 10 -9 f 10 -5 Hz. Overall, one can notice there is a correlation between PM10 and RR in the frequency domain. However, this correlation decreases as the frequency increases. Many peaks exceed the significant correlation threshold (Λ PM10/RR ( f ) ≥ 0. 70) introduced by Calif and Schmitt  [47]  at 9. 02 × 10 -9 f 6. 92 × 10 -8 Hz corresponding to 7. 0 T 53. 5 months (2. 2 years). At 9. 77 × 10 -8 f 3. 55 × 10 -7 Hz corresponding to 1. 4 T 4. 9 months, Λ PM10/RR ( f ) is between 50% and 70%. Λ PM10/RR ( f ) goes below 40% at 3. 98 × 10 -7 f 5. 57 × 10 -6 Hz corresponding to 2.
Finally, signal inversion is performed using the Klett-Fernald formalism  (Klett, 1981 (Klett, , 1985;; Fernald, 1984)  to derive individual R T profiles. The lidar inversion at stratospheric altitudes is highly sensitive to the molecular density and the clear-air reference altitude. ERA5 daily meteorological data are used for data processing and clear-air altitude is set between 28 and 32 km according to the signal dynamics. The total uncertainty on the R T is estimated to be around 7 % on the parallel channel R up to 28 km, with details available in  Tencé et al. (2022). On the perpendicular channel and associated R ⊥ backscatter ratio, the altitude-dependent uncertainty ranges from 10 % to 30 %. As for the depolarization ratio δ(z), assuming larger uncertainties on the aerosol depolarization ratio δ(z) rather than on the linear volume depolarization ratio at relevant altitudes in this paper we estimate the error of around 30 %  (Tencé et al.
NAT + mix category occupies a wider domain, from 15 to 25 km. Since this category includes mixtures, it is not surprising that its range is actually wider. As a reminder B05 defines the MIX category as any measurement not belonging to any of the other classes. Finally, temperature values associated with PSC types gathered in Fig. 5  are derived from ERA5 reanalyses and are relevant for the intercomparison of the classification schemes, but they may not resolve small-scale variations or features that are important for PSC formations pathways. Also, the time of lidar PSC measurement does not exactly match ERA5 data and MLS H 2 O and HNO 3 measurements, which necessarily generates uncertainty. The relevancy of ERA5 reanalyses in the study of PSC at DDU is reviewed in Sect. 4. 3.