dataesr/scientific-paragraphs-categorization huggingface dataset:
Weber-CH4 The diffusive sea-air CH4 flux reconstruction by  (Weber et al. , 2019)  is based on a compilation of 120,000 individual concentration and partial pressure measurements from the MEMENTO database  (Kock & Bange, 2015)  and additional cruise datasets  (Weber et al. , 2019). These measurements were converted to CH4 disequilibrium using atmospheric partial pressure from the NOAA Global Monitoring Division archive, which has collected flask samples from a global network of monitoring stations since 1980 (www. esrl. noaa. gov/gmd/ccgg/) and extrapolated to a 0. 25-degree monthly climatology using 10,000 artificial neural network and random regression forest models, each trained with 70% of the data. Air-sea fluxes were computed by combining each climatology with one of four piston velocity relationships  (Wanninkhof, 1992 (Wanninkhof, , 2014;; Nightingale et al.
tion to the biogenic CH 4 production pathway, CH 4 emissions can also be driven by geologically sourced methane, originating from shallow seafloor seeps fed by hydrocarbon reservoirs or high-latitude hydrates (Ruppel & Kessler, 2017;Puglini et al. , 2020). Overall, the distribution of coastal CH 4 emissions (Figure 11) can largely be understood in terms of water depth, organic matter production and delivery to sediments, and freshwater inputs. -24-manuscript submitted to Global Biogeochemical Cycles Acknowledgments The authors have no conflicts of interest. A. K. H. acknowledges support from the  National Science Foundation (NSF) Graduate Research Fellowship Program  under Grant  DGE-2039656. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author and do not necessarily reflect the views of the NSF.
L. R. and E. L. were partly funded by  National Oceanic and Atmospheric Administration  award  NA21OAR4310119. L. R. and M. M. thank  NASA  for financial support via the grant  NASA   OCO -2   Science Team  Grant  80NSSC18K0893. L. R. additionally thank the  Princeton Institute for Computational Science and Engineering (PICSciE)  for High Performance Computing (HPC) provision, storage and support. D. B. acknowledges support from  NSF  grant  OCE-1847687 , and computational resources from the Expanse system at the  San Diego Supercomputer Center  through allocation TG-OCE170017 from the  Advanced Cyber infrastructure Coordination Ecosystem: Services and   Support (ACCESS) program , which is supported by  NSF  grants  2138259 ,  2138286 ,  2138307 ,  2137603 , and  2138296. J. D. M. and N. G.
acknowledge support from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement no. 821003  (project 4C) and no. 820989  (project COMFORT). N. G. additionally thanks for the support of the  Swiss National Science Foundation  through grant agreement no  175787  (Project X -EBUS ). J. S. was supported by the  Research Council of Norway  (grant no  270061 ) and acknowledges provision of HPC and storage resources by UNINET/sigma2 (nn/ns9560k). C. L. Q. was funded by the  UK Royal Society  (grant no. RP\R1\191063 ). Simulations of the NEMO-PlankTOM model were carried out by  D. Willis  on the HPC Cluster supported by the  Research and Specialist Computing Support  service at the  University of East Anglia. G. G. L. is a research associate of the  FRS-FNRS  at the  Université Libre de Bruxelles.
Finally, we thank the  RECCAP2  organisers and scientific steering committee for coordinating and supporting this effort. tion to the biogenic CH 4 production pathway, CH 4 emissions can also be driven by geologically sourced methane, originating from shallow seafloor seeps fed by hydrocarbon reservoirs or high-latitude hydrates (Ruppel & Kessler, 2017;Puglini et al. , 2020). Overall, the distribution of coastal CH 4 emissions (Figure 11) can largely be understood in terms of water depth, organic matter production and delivery to sediments, and freshwater inputs. -24-manuscript submitted to Global Biogeochemical Cycles Acknowledgments The authors have no conflicts of interest. A. K. H. acknowledges support from the  National Science Foundation (NSF) Graduate Research Fellowship Program  under Grant  DGE-2039656.
Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author and do not necessarily reflect the views of the NSF. L. R. and E. L. were partly funded by  National Oceanic and Atmospheric Administration  award  NA21OAR4310119. L. R. and M. M. thank  NASA  for financial support via the grant  NASA   OCO -2   Science Team  Grant  80NSSC18K0893. L. R. additionally thank the  Princeton Institute for Computational Science and Engineering (PICSciE)  for High Performance Computing (HPC) provision, storage and support. D. B. acknowledges support from  NSF  grant  OCE-1847687 , and computational resources from the Expanse system at the  San Diego Supercomputer Center  through allocation TG-OCE170017 from the  Advanced Cyber infrastructure Coordination Ecosystem: Services and   Support (ACCESS) program , which is supported by  NSF  grants  2138259 ,  2138286 ,  2138307 ,  2137603 , and  2138296.
J. D. M. and N. G. acknowledge support from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement no. 821003  (project 4C) and no. 820989  (project COMFORT). N. G. additionally thanks for the support of the  Swiss National Science Foundation  through grant agreement no  175787  (Project X -EBUS ). J. S. was supported by the  Research Council of Norway  (grant no  270061 ) and acknowledges provision of HPC and storage resources by UNINET/sigma2 (nn/ns9560k). C. L. Q. was funded by the  UK Royal Society  (grant no. RP\R1\191063 ). Simulations of the NEMO-PlankTOM model were carried out by  D. Willis  on the HPC Cluster supported by the  Research and Specialist Computing Support  service at the  University of East Anglia. G. G. L. is a research associate of the  FRS-FNRS  at the  Université Libre de Bruxelles.
2. 2. 2 22 Observation-based N 2 O and CH 4 flux products We used two observation-based estimates of the N 2 O and CH 4 fluxes. In each case, we use an estimate based on a simple extrapolation of the MEMENTO (MarinE MethanE and NiTrous Oxide) database to the 45 MARgins and CATchments Segmentation (MAR-CATS, Figure S2) coastal regions (referred to as MARCATS-N2O and MARCATS-CH4.
Coastal-SOM-FFN-k w is only shown in the wide coastal ocean for discussion and is not used to compute the pCO 2 -product median. 2. 2. 2 Observation-based N 2 O and CH 4 flux products We used two observation-based estimates of the N 2 O and CH 4 fluxes. In each case, we use an estimate based on a simple extrapolation of the MEMENTO (MarinE MethanE and NiTrous Oxide) database to the 45 MARgins and CATchments Segmentation (MAR-CATS, Figure S2) coastal regions (referred to as MARCATS-N2O and MARCATS-CH4.
These northern hemisphere regions are relatively well sampled by the SOCAT pCO 2 database (FigS1), suggesting that the model-product difference might be largely attributable to model biases. Models and pCO 2 -products also differ on Antarctic shelves, in particular around the Antarctic Peninsula (around 60 • S which is also relatively well sampled compared to the rest of the coastal ocean) where models simulate a weak sink (about -1 mol m 2 yr -1 ) but pCO 2 -products show a weak source (about +1 mol m 2 yr -1 , Figure5and Figure6a). On the Antarctic shelves, however, the model-product mismatch in CO 2 flux density is confined to a relatively small surface area and the impact on the net global flux is smaller compared to the mismatch found in the northern extratropics. We note that the model median yields less negative or more positive flux densities (i.
the product is available at https://figshare. com/articles/dataset/ocean ch4 nc/9034451. S1. c Global ocean biogeochemical models CCSM-WHOI The Community Earth System Model (CESM) is the global ocean component of a coupled climate/earth system model. The ocean component, the Biogeochemical Elemen- tal Cycle (BEC) model, consists of an upper-ocean ecologi- cal module and a full-depth ocean biogeochemistry module both embedded in a three-dimensional (3-D) global phys- ical ocean general circulation model. The physical model is the Parallel Ocean Program (POP) z-level, hydrostatic, primitive equation model. The specific CESM-LR version used here has coarse, non-eddy resolution and is described in detail in.
Code and data availability. The code used to run the FEM simulations will be provided upon direct request to the corresponding author. The RHOSSA data are available at https://www. envidat. ch/dataset/wfj_rhossa (doi:10. 16904/envidat. 151). Author contributions. The research subject was designed by HL with inputs from KF. HL received the funding. The research was performed by KF with the help of HL. MM contributed to the implementation of the FEM Solver used in this work, and its extension to anisotropic materials. Firn core data were obtained by JF. The manuscript was written by KF and HL with inputs from JF and MM.
Results Here we examine the reactivity of α-pinene with varying levels of NO 3 radicals and the in uence of different HOM composition on NPF. We draw on three datasets: experiments conducted in the CLOUD chamber (Cosmics Leaving OUtdoor Droplets) at CERN (European Organization for Nuclear Research); laboratory oxidation ow reactor (OFR) experiments; and eld measurements in a wetland surrounded by boreal forests (Siikaneva, Finland) (see Methods for experimental details). Controlled laboratory experiments were performed using α-pinene, as it is one of the most abundant biogenic VOCs in the atmosphere  33  and one of the key precursors for the production of HOMs  4. We oxidized α-pinene in the presence of O 3 and NO 3 radicals produced from the reaction of NO 2 with O 3. Various mass spectrometers equipped with chemical ionization inlets using different reagent ions (i.
Acknowledgements. The authors thank the  MASSALYA instrumental platform (Aix Marseille Université , lce. univ-amu. fr) for the technical support and the instrumentation calibration. The authors would like to thank  Patrick Tassel  and  Pascal Perret  for the technical support on the chassis dynamometer experiments at  AME-EASE ,  Univ Gustave Eiffel, Lyon. The authors thank  Amélie Bertrand  for the development of PeTeR v3. 5 toolkit. https://doi. org/10. 5194/egusphere-2023-1894 Preprint. Discussion started: 13 September 2023 c Author(s) 2023. CC BY 4. 0 License.
Strong ground motion in Northern Chile Seismicity in the Northern Chile subduction zone results from the convergence between the Nazca and South American plates at a rate of ∼7 cm yr -1  (Angermann et al. , 1999). Our study area is near the city of Iquique, where the Centro Sismológico Nacional (CSN) and the Integrated Plate Boundary Observatory Chile (IPOC) networks capture one of the richest pictures of the Chilean seismicity. All the ground motion records used in this study were compiled from data obtained by the strong-motion instruments in the CSN and IPOC seismic arrays. Our main database consists of earthquakes identified in the CSN earthquake catalog (Figs. 1  and 2 ), which are mainly aftershocks of the 𝑀 𝑤 8. 1 2014 Iquique  (Ruiz et al. , 2014; Schurr et al. , 2014; Twardzik et al.
, 2022)  and the 𝑀 𝑤 7. 6 2005 Tarapacá  (Peyrat et al. , 2006; Delouis and Legrand, 2007)  earthquakes. The records in our main database contain events with local magnitude Ml > 4 and horizontal PGA > 0. 005 g. In addition to our main database, we generate a secondary ground motion database from mining continuous seismic ground motion for events with Ml > 2 recorded at station PSGCX during the years 2007-2014 (Fig. 3 ). We exclude this secondary database from estimations of the parameter 𝜅, given that events with Ml < 4 most likely overlap the corner frequency with the frequency range where we estimate the anelastic attenuation (2-40 Hz). Despite the presence of confounding effects, this secondary database allows us to qualitatively assess the effect of anelastic attenuation over a larger number of events spanning magnitudes Ml 2-5.
5 (Fig. 3 ). We chose PSGCX station to compile this secondary database because anelastic attenuation localized near the station (or 𝜅 0 ) is expected to be small given the surrounding fast seismic wave speeds (𝑉 𝑆30 = 1639 m/s  Leyton et al. , 2018a). We chose the years 2007-2014 because this time window includes enough aftershocks from the largest earthquakes recorded in the area. We classify the regional seismicity in three groups: up-dip events, down-dip events, and intermediate depth events. Up-dip events consist of earthquakes with hypocenters located as far as 80 km from the trench. Down-dip events consist of earthquakes with hypocenters located farther than 80 km from the trench and depths shallower than 60 km. Intermediate depth events consist of earthquakes with depths ranging from 60 to 350 km (Fig.
High-frequency ground motion down dip the plate boundary We first qualitatively assess the ground motion spectra (i. e. , FAS) of records in our secondary database consisting of events recorded at station PSGCX (𝑉 𝑆30 = 1639 m/s  (Leyton et al. , 2018a) ). We observe that the ground motion spectrum decays with a different incline for each group of events (Fig. 3 ). Up-dip events show the steepest decay in high frequency amplitude, whereas down-dip events better preserve highfrequency amplitude out of the three event groups. Given that PSGCX locates directly on top of down-dip events, the path for down-dip events is the shortest of the three groups (Fig. 1 ). Such relatively short path naturally produces the lowest anelastic attenuation, represented by the path-integrated 𝜅 (Eq. (  3 )), and roughly explains the flatter shape and elevated high-frequency content of down-dip event spectra.
Two trends in the parameter 𝜅 versus hypocentral distance We now calculate the parameter 𝜅 for all events in our main database. The magnitude (𝑀 𝑙 > 4) and PGA (PGA > 0. 005 g) thresholds in this database allow us to mostly avoid corner frequency effects and obtain a high signal-to-noise ratio. In addition to PSGCX, we concentrate in stations T01A (𝑉 𝑆30 = 339 m∕s  Leyton et al. , 2018a)  and T07A (𝑉 𝑆30 = 326 m∕s  Leyton et al. , 2018a) , which are among the stations with the largest number of recordings in the area for the considered time window. We observe two strikingly distinct trends in the parameter 𝜅 versus hypocentral distance (i. e. , 𝜅 𝑟 ) when comparing up-dip and intermediate depth events (Fig. 5 ). For simplicity, we split the 𝜅 results between events located to the east and west directions from stations PSGCX, T01A, and T07A.
Acknowledgments We are thankful to the  Centro Sismológico Nacional (CSN)  and the  Integrated Plate Boundary Observatory Chile (IPOC)  for acquiring and storing the strong ground motion data used in this study. This work was partially supported by the  Programa de Riesgo Sísmico (PRS)  of the  Universidad de Chile. S. R. acknowledges funding by  FONDECYT  grant  N • 1200779.
Data and resources The CSN strong-motion database is available at evtdb. csn. uchile. cl (last access: September 2016). The continuous acceleration data for station PSGCX can be requested on the German Research Center for Geosciences' website at geofon. gfz-potsdam. de. The CSN earthquake catalog is available at the CSN's webpage www. sismologia. cl. Some figures were made using the Generic Mapping Tools v. 5. 2. 1  (Wessel and Smith, 1998).
Section 2 introduces the dataset from which we learn the data-driven model and its structure. Section 3 presents the neural network framework, the choices we made about its architecture, and its optimization. Section 4 introduces the metrics for evaluating the results on the surrogate model. Section 5 delivers and discusses the results of the neural networks training, forecast skill abilities and advection capabilities of the surrogate model, as well as an analysis of the diffusion phenomenon introduced above. The discussions and conclusions are given in Section 6 and Section 7. Appendices provide technical information and further illustrations of the results.
Warmer atmospheric temperatures accelerate sea ice melting, leading to reductions in ice extent and thickness, while colder temperatures can promote ice growth and expansion. Based on these considerations, we supplemented to the sea-ice thickness the 2-meter temperature (T2M), and the atmospheric u-and v-velocities in 10 meters height (U10 and V10). Those forcings come from the ERA5 reanalysis dataset  (Hersbach et al. , 2020)  of the ECMWF. ERA5 forcings are interpolated on neXtSIM Lagrangian grid using a nearest neighbors scheme. Furthermore, to guide the temporal development of the sea ice, forcings at time t + 6h and t + 12h are added as predictors to the neural network, as commonly done in sea-ice forecasting  (Grigoryev et al. , 2022). Because these forcings are also to guide the neXtSIM simulations, we assume a perfect knowledge of the forcing, albeit operational sea-ice forecasts use atmospheric forecasts as forcings.
Preparation of the dataset for supervised learning Let us represent the sea-ice thickness at time t by x t ∈ R 512×512. The land-masked grid-cells are systematically assigned a value of zero thickness. For small signal levels, the noise induced by the imperfections of the neural network can overshadow the signal contained in the data. Consequently, to increase the signal in the target and decrease the auto-correlation, we chose a lead time of 12 hours, rather than the lead time of 6 hours as possible by the dataset. The neural network is trained to predict the increment in SIT instead of the absolute SIT. The increments of the SIT y t+∆t for ∆t = 12 hours are given by the difference to a persistence forecast, y t+∆t ≜ x t+∆t -x t. (1) Based on the current SIT x t and given forcings F, our objective is to construct a neural network f θ (x t , F) with its parameters θ, which predicts the SIT increment y f t+∆t , y f t+∆t = f θ (x t , F).
2018 is used as year for testing: the results were evaluated once and only once on this year at the end of the study, after the hyperparameters were chosen for the neural network. For longer forecasts, to evaluate seasonal forecasts, another test dataset was selected between 2006 and 2008. The input and target data are normalized by a global per-variable mean and standard deviation. These statistics are estimated over the entire training dataset and applied to all datasets. Table  1. Inputs and targets for the neural networks. The table shows the predictors, including sea-ice thickness (SIT) at different time steps, and atmospheric variables: 2-meter temperature (T2M), 10-meter wind components (U10 and V10). The target is the increment of the SIT 12 hours later (∆t = 12 h). The evaluated neural networks use either the last 3 columns as input, learning with a single timestep for SIT (xt), or all columns, learning with both xt-∆t and xt.
Neural network architecture Convolutional neural networks (CNN) are largely used in computer vision and have shown to be scalable to high-dimensional datasets  (Pinckaers et al. , 2022). These networks are based on convolutional layers, designed to recognize translation-invariant patterns. In the case of sea-ice thickness, the neural networks need to detect, e. g. , leads, as well as the marginal ice zone, irrespective of their actual locations. The UNet  (Ronneberger et al. , 2015)  is a convolutional neural network architecture with an encoder-decoder structure. In the encoding part, convolutional layers and max-pooling layers are stacked in order to extract spatially more and more compressed features. As convolutional layers are localized by definition, the spatial compression helps the network to extract more globalized features.
From Eq. (  3 ), we can see that the results of the partial convolution only depend on the valid input values (as X ⊙ M). The scaling factor sum(1)/sum(M) adjusts the results as the number of valid input values for each convolution varies. It has been used previously in order to recover missing regions from observational datasets  (Kadow et al. , 2020). In this study, the goal is not to recover data, but to avoid artifacts near land caused by the underestimation in normal convolutions. The algorithm for partial convolution is further described in Appendix C1.
Neural network training The neural networks are trained on a single NVIDIA A100 GPU with a batch size of eight samples. As optimizer, AdamW  (Loshchilov and Hutter, 2017)  is used with a learning rate of γ = 5 × 10 -5 and a weight decay, scheduled with a 3 steps piecewise constant decay, starting at w = 1 × 10 -6. If the loss in the independent validation dataset plateaus for 20 epochs, the training is stopped early. We trained four different neural networks, as described in Table  2. By setting λ to either 0 or 100, we switch the additional loss function constrain on or off, checking its influence on the performance. Additionally, we test if additional temporal guidance by giving an additional time step as input helps the neural network to predict the increment in the sea-ice thickness. 4 Surrogate modeling and evaluation methods.
Our baseline for the model comparison is constantly predicting the initial conditions without any increment, a so-called persistence forecast, i. e. the sea-ice thickness is unchanged over time. It is a commonly used baseline in sea-ice forecasting, as the auto-correlation of the sea-ice thickness in time is high up to a 1 month-lead time  (Lemke et al. , 1980; Blanchard-Wrigglesworth et al. , 2011). We also compare the surrogate model to the daily climatology, computed on a day-of-year basis over the complete training dataset.
Short-term forecasting In this paragraph, we will assess the performance of the surrogate model on a short-term timescale, specifically up to a onemonth lead time. The metrics mentioned in Sec 4. 2 are computed using the 2018 test dataset. They are reported in Table  3. We believe that the global RMSE serves as a proxy for the consistency of the surrogate model, which we define as the averaged sea-ice thickness in the domain. Based on this idea, we anticipate that the globally-constrained neural network will demonstrate improved performance for forecast lead times exceeding 12 hours. The introduction of a global penalization term in the constrained neural network reduces the global RMSE by one order of magnitude within 12 hours compared to the absence of penalization. However, the impact of the global loss term (as defined in Eq.
https://doi. org/10. 5194/egusphere-2023-1384 Preprint. Discussion started: 23 August 2023 c Author(s) 2023. CC BY 4. 0 License. Code and data availability. The authors will provide access to the data and weights of the neural networks upon request. The source code for the experiments and the neural networks is publicly available under https://github. com/cerea-daml/nextsim-surrogate. The outputs of neXtSIM model will be made available upon requests. Forcings data are publicly available in the Copernicus Data Store https://cds. climate. copernicus. eu Video supplement. A video of the seasonal forecast for the year 2017 is available at https://doi. org/10. 5446/62131. Author contributions. EO provided the data and the insigths about neXtSIM. CD, TSF, AF, and MB refined the scientific questions and prepared an analysis strategy.
Equilibrium thermodynamics led also, during the second half of the twentieth century, to the development of geo-thermometers and geo-barometers rooted in the thermodynamics of solid solutions and cationic exchanges and/or mutual solubility between coexisting minerals [see Krogh  Ravna and Paquin, 2003, Spear, 1993, for reviews]. Applications of metallurgical concepts were at the origin of thermo-barometers based on disorder/order phase transitions  [Carpenter, 1981]  or discontinuous precipitations  [Joanny et al. , 1991]. Decisive progress in thermodynamic analysis has been made since the late 1980s with (i) the production of internally consistent thermodynamic databases  [Berman, 1988 , Holland and Powell, 2004 , 2011] , (ii) the formulations of mixing properties and activity-composition relationships for mineral solid-solutions and melts  [Green et al.