dataesr/scientific-paragraphs-categorization huggingface dataset:
There are detailed eyewitness accounts about the tsunami by German colonists from various settlements on New Guinea and New Britain, which deliver highly valuable far-field information about the propagation of the tsunami (cf. Ward and Day 2003). These eyewitness accounts in combination with the 3D seismic data represent a unique dataset that allow the reconstruction of the emplacement of the Ritter Island sector collapse deposits and the genesis of the corresponding tsunami. Based on these constraints, the main aim of this paper is to perform coupled numerical landslide-tsunami simulations of the 1888 Ritter Island sector collapse and compare these model results with the historic tsunami observations to achieve a better understanding of tsunami genesis during the event. We use the numerical landslidetsunami simulation code VolcFlow to perform simulations of various collapse scenarios along two seismically defined slide planes.
3D seismic reconstruction of the 1888 Ritter Island sector collapse The combination of 2D and 3D seismic data covering the western slope of the failed volcanic cone of Ritter Island and the neighbouring seafloor revealed compressional deformation, which stretches from the volcanic edifice more than 10 km to the west, manifesting as a complex pattern of thrusting and folding (Fig. 2 ,  Karstens et al. 2019). The deformation affects sediments up to 500 m beneath the seafloor at the foot of Ritter Island and is limited by a shear zone rather than a sharp slide plane (Fig. 2 ). A toreva block, representing an intact remnant of the failed western flank of Ritter Island, also shows internal folding. The folding within the toreva block increases with depth, and thickened packages within the synforms of these folds indicate that the compressional deformation developed over a long period 1 3 of cone growth and likely occurred episodically (Fig.
2 ;  Karstens et al. 2019). This long-lasting gradual spreading along a deep shear surface defines the first phase of slope instability at Ritter. The second phase comprises the catastrophic collapse of the volcanic cone at March 13, 1888. This collapse occurred along a distinct slide plane, which defines most of the present-day morphology of the Ritter Island collapse scarp. The volcanic cone failed, disintegrated, and travelled west. It did not deposit any intact blocks within the area of the 3D seismic data (except the toreva block). This highlights the highly energetic character of the mobilized mass  (Karstens et al. 2019). Watt et al. (2019)  inferred that the failed mass must have disintegrated rapidly, and that this efficient fragmentation was likely facilitated by the bedded, clastic nature of the primary edifice.
Geophysical data The simulations presented here are based on the interpretation of high-resolution 2D and P-Cable 3D seismic data acquired during expedition SO252 on board of R/V SONNE in late 2016. The P-Cable 3D seismic system consisted of 16 eight-channel streamers. The 250-m-long 2D seismic streamer had 160 channels with a group spacing of 1. 56 m. We used two 105/105 cubic inch GI airguns in harmonic mode as the seismic source. The 3D seismic processing steps were source-receiver geometry corrections, bandpass frequency filtering, normal moveout correction, stacking, trace interpolation, and 3D time migration using a constant seismic velocity of 1500 m/s. The 60 km 2 P-Cable 3D seismic cube has a lateral resolution of 3. 25 m and vertical resolution of approximately 6 m at the seafloor.
The 2D seismic processing steps were bandpass filtering, normal move-out correction, stacking, and 2D stolt-migration using a constant seismic velocity of 1500 m/s. In addition, we acquired bathymetric data using Kongsberg EM710 and EM122 multibeam echosounder systems, which were integrated into a bathymetric grid with a lateral resolution of 5 m. This grid was combined with GEBCO bathymetry and SRTM elevation datasets to create the regional grid required for the tsunami simulations.
At the same time, the geophysical observations indicate that sliding mass was channelized in between the volcanic ridges west of Ritter rather than travelling above these, which already occurred during simulations with 10,000 Pa. Therefore, we did not lower the yield strength below this value in any simulation. Vol-cFlow uses two surfaces, the pre-slide topography and the slide plane, as input parameters for the simulations (Fig. 3a ). Based on previous interpretations  (Karstens et al. 2019) , we defined two slide scenarios using the present-day seafloor with the surficial exposed failure plane for our "shallow slide plane scenario" and the deep shear zone for our "deep slide plane scenario" (Fig. 3b, c ). We reconstructed the pre-slide topography based on a combination of geophysical data and historic accounts about the shape and height of the island (comparable to the approaches by  Johnson 1987; Ward and Day 2003; Day et al.
For the Ritter Island sector collapse, it is possible to constrain two scenarios for the tsunamigenic phase of the collapse, which differ by limiting the failure to the volcanic cone or by also including the deep-seated deformation of seafloor sediments that extends across the basin west of the volcano (and which comprises ~ 2/3 of the total slide volume). The seismic data from Ritter cannot reveal whether the catastrophic phase of collapse was strictly limited to the shallow slide plane (i. e. the volcanic cone) or included additional movement and deformation of the basin-filling sediments along the deeper plane as well  (Karstens et al. 2019). In either case, a single tsunamigenic phase of movement is indicated by the observations of one wave train associated with the 1888 Ritter Island collapse  (Day et al.
2016). In case of Ritter, the seismic data constrain these parameters and yield a maximum height of the crater-mimicking initial tsunami wave of ~ 107 m. VolcFlow cannot incorporate representations of submarine explosive eruptions and thus we superimposed the initial tsunami impulse onto the tsunami wave field from the landslide simulations. This approach is a simplification, because it ignores the complex coupling between landslide emplacement and the eruption, which may have amplifying or weakening effects on the tsunami and the slide dynamics, but is nevertheless useful to make a first-order assessment of the potential tsunamigenic contribution of a shallow explosive eruption. Most crucially, our approach assumes that the two processes were closely spaced in time, such that both contributed constructively to simultaneous tsunami generation.
Acknowledgements Open Access funding provided by Projekt DEAL. We would like to thank the  German Ministry of Science and Education (BMBF)  for funding this study through the  "Ritter Island project " ( 03G0252A ). We thank the master and the crew of the  RV Sonne  for their support during research cruise SO252. Furthermore, we thank the reviewers  Irene Manzella  and  Alessandro Fornaciai  for improving the quality of this study with their helpful comments. We would like to thank the  GEBCO and SRTM  initiatives for providing access to.
g. Davidson et al 2012; Ferreira et al 2014; Nobre et al 2016). Data and modeling tools are urgently required to anticipate possible consequences of the evolution of climate and anthropological forcing. The CAMREX project worked on the Amazon plain and estimated annual sediment flux based on the daily variation of water surface slope  (Meade et al 1985). Since 1995, the Environmental Research Observatory (SO-Hybam) and national institutes from the Amazon basin have been working together to build a discharge and suspended sediments dataset to understand the dynamics of water and sediment fluxes in different parts of the Amazon basin. the sediment flux was also estimated through indirect methods, such as turbidity and MODIS images  (Armijos et al 2017; Espinoza-Villar 2013 , 2018 , Dos Santos et al 2018) , or by using the spatial and temporal variation of gravitational fields from GRACE satellite data  (Mouyen et al 2018).
They noted that the temporal variability of precipitation might have a major effect on water discharge and sediment dynamics. However, the authors used a short database on sediment flux to validate the model. The use of empirical relationships requires a large and suitable dataset to catch significant statistical trends. Setting up such a dataset is one of the main goals of the SO-HYBAM because this method is not used for the Amazon basin. The relationship between inter-annual rainfall and the concentration of suspended sediments in the Amazon plain is currently mostly unknown. Several studies based on different databases propose various main control factors for the annual denudation rate in the Andes including climate variability, lithology and topographic slope  (Pepin et al 2013 , Aalto et al 2006).
We analyzed a database of more than 20 years of regular suspended sediment sampling, where direct empirical relationships can be established between rainfall, water discharge, [Cf] and  [Cc]  for the main tributaries in the Amazon basin. These relationships reflect the current hydrologic condition in Amazon basin and show the impacat of rainfall on the water and sediment flux at a monthly step where the transfer's processes are better reproducible in a basin of this size. Finally, this relationship is a the basis for determining the change in sediment production from natural to anthropogenic influence.
Data on discharge, rainfall and sedimentology This study used discharge and suspended sediment data provided by SO HYBAM available at http://www. ore-hybam. org/. Discharge was measured with a Rio Grande 600 kHz RDI Acoustic Doppler Current Profiler (ADCP) with a global positioning system (GPS). Field water discharge in each gauging station was performed regularly, usually three and four times in the year. For the Tamshiyacu, Caracarai, and Porto Velho gauging station, we used the rating curve between the level of the river and water discharge. , The Manning-Strickler equation was used to calculate daily discharge. in the Manacapuru, Fazenda Vista Alegre, and Óbidos gauging stations, where there is no direct relationship due to backwater  (Meade et al 1991 , Vauchel et al 2017). Rainfall data was gathered from the merged Climate Hazards Group Infrared Precipitation (CHIRPS) dataset.
CHIRPS uses the global cold cloud duration as a primary source to calculate global precipitation. This initial estimation is then calibrated with the precipitation product from TRMM-3B42 V7 and information from the global rain gauge network, resulting in a high spatial resolution rainfall data set (0. 25°×0. 25°). Satellite data provide daily and monthly precipitation data sets from January 1981 to December 2017  (Funk et al 2015). CHIRPS data haves been compared previously with data from observation-based rainfall products and from a meteorological station in the Amazon basin  (Espinoza et al 2019). HELIX project (1976 HELIX project ( -1977)) ; brown diamond = CAMREX project  (1982) (1983) (1984) , measurements used by  Meade at al (1985). White circles are surface suspended sediments and red squares are suspended sediments' discrete cross-section measurements = SO-HYBAM observatory , c) Mean monthly rainfall in the Amazon basin at the Óbidos gauging station  (CHIRPS dataset (1981 (CHIRPS dataset ( -2017)) , and mean monthly water discharge of Amazon basin at the Óbidos gauging station (1981-2016), d) Temporal suspended sediment concentration of [Cf] and [Cc] at Óbidos station.
(2) To evaluate the river bed shear stress, we used stage-discharge-rating curves to estimate hydraulic parameters with a and b fitting coefficients for section-averaged flow velocity and hydraulic radius  (Camenen et al 2014). We also explored the related hydro-sedimentary model to discuss the potential use of a sediment transport capacity  (Camenen and Larson 2008)  to predict the sand suspended load. The supplementary material summarizes the methodology and the data that have been used as input to the Camenen et al (2014)'s model including, particle sizes, crosssection at gauging stations and discharge datasets. Eventually, we also studied the monthly relationship between rainfall and fine suspended sediment flux (Qsf) with a and b fitting coefficients. (3).
Rainfall and discharge Relationship. The counter-clockwise hysteresis between rainfall and discharge shows the response time between the maximum peak rainfall in the basin and the maximum draining away to the main river at OBI. We found that the maximum peak rainfall is between January and March, and the maximum peak of discharge is between May and June. The counterclockwise hysteresis is reproducible every year, which implies that the same rising and falling events occur during the same periods each year. This produces a reliable lag time for the entire OBI dataset (Figure  2a ). The same trend is observed at the other gauging stations, with different lag times between rainfall in all basins and discharge peaks. The lag time is 102 days, with r 2 = 0. 98 at OBI for the period 1981 to 2016 (Figure  2b ).
Good agreement is observed between the model and data for all stations except for the OBI with a large overestimation is observed. The results indicates that the sand suspension capacity is reached a priori; thus, this result validates the use of an empirical formula for prediction. In the case of OBI, the difference may be explained by the singularity of this station located in a very constrained zone with high velocities. At OBI, the sand concentrations are indeed more regulated by the upstream reach that is larger and less dynamic. Bedload has been calculated using the  Camenen and Larson (2005)  formula (see Supplementary Material 3. 3 Relationship between rainfall and fine suspended sediment flux Qsf. To establish the relationship between Qsf and rainfall, we used a dataset of [Cf] derived from surface sample ([Cf] surface ) every 10 days via a bottle filled in the middle of the cross-section at each gauging station.
Figure1. a) The Amazon basin and location of gauging stations: Tamshiyacu (TAM) on the Amazon River, Manacapuru (MAN) on the Solimões River, Caracarai (CAR) on the Branco River, Porto Velho (PTV) and Fazenda Vista Alegre (FVA) on the Madeira River, and Óbidos (OBI) on the Amazon River. b) Temporal records of the measured suspended sediments load at the Óbidos gauging station: green diamond = ALPHA HELIX project(1976)(1977); brown diamond = CAMREX project(1982)(1983)(1984), measurements used by Meade at al (1985). White circles are surface suspended sediments and red squares are suspended sediments' discrete cross-section measurements = SO-HYBAM observatory(1995- 2015), c) Mean monthly rainfall in the Amazon basin at the Óbidos gauging station(CHIRPS dataset (1981(CHIRPS dataset ( -2017)), and mean monthly water discharge of Amazon basin at the Óbidos gauging station (1981-2016), d) Temporal suspended sediment concentration of [Cf] and [Cc] at Óbidos station.
Acknowledgements The authors thank  Thomas Dunne  and the anonymous reviewers for their comments and observations, which helped us to improved this work. We also thank the  Observation Service for the Geodynamical, Hydrological and Biogeochemical Control of Erosion/Alteration  and  Material Transport in the Amazon Basin (SO-HYBAM ) and the  National Water Agency (ANA) , the  Geological Survey of Brazil (CPRM) , and the  IHESA  project for support during field campaigns. We also thank  Bosco Alfenas  and  Chuck Goll. We thank the  National Institute of Amazonian Research (INPA) , the  University of the State of Amazons (UEA) , and the  Climate and Environmental Program (CLIAMB) , especially  Dr. Luis Cândido  and  Dr. Rita Valeria Andreoli. We also thank the  PNICP-Peru  through the ' N∘397-PNICP-PIAP-2014 ' contract.
10. 1029/2019GL086043 NOAA-20CR, we use an updated version (NOAA-20CRv2c) covering the period 1851-2014. This version corrects for sea ice using COBE-SST2 and enables more observations from the International Surface Pressure Databank Version 3. 2. 9, compared to the original version. ERA-20C covers the period 1900-2010, and its assimilation includes observations of surface pressure and surface marine winds only. CERA-20C is a 10-member ensemble of coupled climate reanalyses covering the period 1901-2010. It is based on the CERA assimilation system, which assimilates only surface pressure and marine wind observations as well as ocean temperature and salinity profiles  (Laloyaux et al. , 2018). In this study, we use one member of CERA-20C. In addition, we use the combined land and marine temperature anomalies of the HadCRUT Version 4.
In contrast to ERAf, the ocean heat content increases earlier by ~2 years (Figure  4f ). In addition to heat fluxes, the ocean circulation is modulated by wind stress. During strong NAO events (minus 1-2 years), the barotropic stream function is positively correlated with CEU_SAT at 30-50°N,. Shown are the correlation coefficients for (a-e) summer and (f-j) winter sea-level pressure leading the CEU_SAT by (a, f) 4 years, (b, g) 3 years, (c, h) 2 years, (d, i) 1 year, and (e, j) 0 years. Here we used the 5-to 20-year band-pass filtered data from the NOAA-20CR.
Figure3. Correlation of subdecadal CEU_SAT with sea-level pressure for the midtwentieth century. Shown are the correlation coefficients for (a-e) summer and (f-j) winter sea-level pressure leading the CEU_SAT by (a, f) 4 years, (b, g) 3 years, (c, h) 2 years, (d, i) 1 year, and (e, j) 0 years. Here we used the 5-to 20-year band-pass filtered data from the NOAA-20CR.
We also note that the profiles for the two eddy-permitting models (green and orange lines in Fig. 1b, d ) lie within the spread of the CMIP5 models, indicating that resolution (at least to eddy-permitting spatial scales) does not drastically change stratification in the region. The De-PreSys3 assimilation run closely matches the stratification in EN4. 2. 1, which supports the DePreSys assimilation run as a reasonable observation-constrained reference for the models. The comparison of both observation-based datasets with the rest of simulations suggests that, in the subsurface, all models are too warm and most of them are too salty; these two biases have a competing effect on the mean subsurface den-sity. Because of these cancelling effects, several models show a comparatively better representation of the subsurface densities when compared to EN4.
preindustrial), which might imply different mean states  (Thornalley et al. , 2018). Also, climatological values of the AMOC26 strength are notably weaker in DePreSys3 than in RAPID, a difference that is not explained by the different temporal periods covered by each dataset (not shown) and that implies that DePreSys3 might also be underestimating the real AMOC45 strength. This underestimation could be larger than shown in Fig. 8 , as evidence suggests that RAPID calculations from mooring arrays might be underestimating the AMOC strength by ∼ 1. 5 Sv  (Sinha et al. , 2018). A potentially important factor behind the intermodel spread in Fig. 4b  is the mean density stratification in the Labrador Sea. Figure  9  suggests that, indeed, the PC1-LSD vs. AMOC26 spread is partly influenced by the density stratification in this region.
Conclusions and discussion This article has explored, in a multi-model context, the linkages between subsurface density in the subpolar North Atlantic (SPNA) and the ocean circulation further south. In particular, it has explored the role of Labrador Sea density (LSD) in driving western boundary density anomalies (WBD) and the ocean circulation, as well as the impact on upper-ocean temperature changes in the SPNA. The analysis was based on two control simulations with eddy-permitting models (a preindustrial one with HadGEM3-GC2 and a present-day one with HiGEM) and on 20 CMIP5 preindustrial experiments. Furthermore, where possible these characteristic model features have been computed in observational datasets and in a simulation assimilating observations. The major findings are listed below.
e. r ∼ 0. 4) in the real world. However, there are many reasons why this number is still very uncertain, and further work is needed to assess its validity. A caveat of this study is that the simulations and observation-based datasets employed are not directly comparable, as they differ in the background radiative forcing levels, the length of the period used to compute the climatologies, and even the way some indices, like the AMOC, are computed. We also recognize that there is large uncertainty within the observationally derived metrics. For instance, the assimilation run in DePreSys3, which is used to constrain relationships, clearly underestimates the mean AMOC strength at 26 • N with respect to RAPID (see Fig. 8b ) and therefore might also be underestimating the AMOC at higher latitudes.
Acknowledgements. We thank the  UK Met Office  for providing the model data from GC2 used in this study and all the research centres that contributed to CMIP5 and made their data available. The HiGEM control was created using the  ARCHER UK National Supercomputing Service. This work used JASMIN, the UK collaborative data analysis facility, and was largely supported by the  NERC  projects " Dynamics and Predictability of the Atlantic Meridional Overturning and Climate Project " (DYNAMOC,  NE/ M005127/1 , and  NE/M005097/1 ) and " Wider Impacts of Subpolar North Atlantic Decadal Variability on the Ocean and Atmosphere " (WISHBONE,  NE/T013516/1 , and  NE/T013540/1 ). Pablo Ortega's work was additionally supported by the  Spanish Ministry of Economy, Industry and Competitiveness  through the  Ramon y Cajal grant  ( RYC-2017-22772 ).
Jon I. Robson  was additionally supported by the  NERC  ACSIS programme, and  Rowan T. Sutton  was supported by  NERC  via the  National Centre for Atmospheric Science (NCAS). Leon Hermanson  was supported by the  Met Office Hadley Centre Climate Programme  funded by  BEIS and Defra. Steve G. Yeager  acknowledges support from the  National Science Foundation (NSF ) research grant  OCE-2040020  and from the  International Laboratory for High-Resolution Earth System Prediction (iHESP). Adam Blaker ,  Joel Hirschi , and  Babblu Sinha  were additionally supported by the  NERC  project  ACSIS  ( NE/N018044/1 ). Financial support. This research has been supported by the  Natural Environment Research Council  (grant nos. M005127/1 ,  NE/M005097/1 ,  NE/T013516/1 ,  NE/T013540/1 , and  NE/N018044/1 ) and the  Ministerio de Economía, Industria y Competitividad, Gobierno de España  (grant no.
P. Ortega et al. :  Labrador Sea subsurface density and North Atlantic multidecadal variability mains to be tested if similar conclusions can be drawn from eddy-resolving simulations. Code availability. The main scripts used in the analysis and other supporting information that may be useful to reproduce the results of this article are archived at the Barcelona Supercomputing Center and will be shared upon request by the corresponding author. Data availability. Outputs from the CMIP5 simulations can be downloaded from the corresponding ESGF node: https://esgf-node. llnl. gov/projects/cmip5/ (last access: April 2021, ESGF, 2021). EN4 observations used in this study correspond to version 2. 1 of the dataset, available at https://www. metoffice. gov. uk/hadobs/en4/ download-en4-2-1. html (last access: April 2021, UK Met Office, 2021).