dataesr/scientific-paragraphs-categorization huggingface dataset:
On average, three to four DPR pixels fall into the 5 km-radius. We will hereafter refer to training and validation database as the data used to adjust the weights and hyperparameters of the network and the test database as the one used for generalization and assessment of performances. Because natural rain occurrence is low, a scene selection is made as follow: images with at least 100 pixels with rain > 0. 1 mm. hr -1 or at least 10 pixels > 100 mm. hr -1 are kept to build the training/validation database. Each image is composed of 4 channels: the 37 and 89 in both H and V polarization while the surface rain is used as target.
IV. COMPARISON WI T H DPR Two example cases, excluded from the learning and validation dataset, are given on Fig. 2  and 3. The first case is super typhoon Nanmadol observed on September 16 th , 2022, while it is close to its peak intensity over the Philippine Sea, South of Japan. The second case is a frontal band over France observed on August 18 th , 2018. The first case is a good illustration of oceanic retrieval while the second case is a mixed continental and coastal case. In addition to comparisons with the DPR in the common part of the swath, comparisons with the GPROF algorithm  [4]  are also presented. GPROF is the GPM operational product, based on a per-pixel-Bayesian approach. GPROF is considered as a reference in the GPM community and uses auxiliary data to constrain the solutions (temperature, surface type, humidity, cloud cover…).
4 mm/hr than both DRAIN and DPR. DRAIN on this aspect is very consistent with the DPR as one would expect since DRAIN was developed solely from DPR data without any other assumptions concerning the relation brightness temperatures -rain intensities. The reasons behind this light rain enhancement in GPROF is described in  [17]. This light rain difference is more likely to be visible over ocean where, in addition to the higher frequency channels, GPROF uses 10 and 19 GHz channels which are directly linked to liquid water emission. The second comparison is made at global level over the whole year 2019. A few granules with bad TBs at either 37 or 89 GHz are excluded. A mask is used to check systematic differences between land and ocean. Unlike for GPROF, this mask is not used as an input in DRAIN but only once the retrieval is performed to assess the performances over the two different surface types.
This mask is the python "regionmask" package which is based on Natural Earth, a free vector and raster map data (naturalearthdata. com) at 1:110m resolution (1 cm = 1100 km). The BD for both DRAIN and GPROF show an average at 0. 33 mm/hr and 0. 50 mm/hr respectively, meaning it is mainly made of the detection of some light rain by the DPR. Table  3  shows the Probability Of Detection (POD) and the False Alarm Ratio (FAR) which are often used to assess the performances of rain retrieval algorithms  [18]. Perfect POD is 1 while perfect FAR is 0. Both algorithms offer similar PODs with a small advantage for DRAIN, specially over land. The low score of GPROF's FAR is logically due to the light rain overdetection with respect to DPR as mentioned above. Table  4  shows the bias and Root Mean Square Error (RMSE) for the two algorithms, compared to DPR for their respective true positive only.
The mosaic data are then co-located and averaged at the DRAIN resolution and pixels position. Between January 1st 2019 and December 31st 2019, 1565 overpasses are kept, with the condition that more than 50 DRAIN pixels fall into the mosaic domain: 8° West to 12° East and 39° North to 54° North. First, a pixel-by-pixel performance is evaluated for the three rain estimators: DRAIN, DPR and GPROF. Contingency table and F1-scores are computed and presented Table  6 , 7 and 8 respectively. The total number of pixels taken into account differs for each estimator because of the swath difference. The three estimators show performances that are close with a few differences. GPROF shows a better POD than both DRAIN and DPR but its FAR is degraded by the excess of light rain detected. On the other hand, DPR and DRAIN miss some of the rain which degrades their respective POD but their precision remains high.
Ref   4  but for GPROF. The total number of co-located pixels is 5611805. The DRAIN, DPR and GPROF data are also averaged on 0. 2°x0. 2° grid to minimize the possible impact of localization errors between the ground and satellitebased estimates. Figure  8  shows the scatter plot for each of the estimators against the Météo-France mosaic. Only the grid-boxes where the mosaic >= 0 mm. hr -1 are accounted for. For each scatter plot, the associated linear regression (blue-dotted line) is shown. For DRAIN the corresponding R 2 =0. 29 while it is 0. 10 for DPR and 0. 20 for GPROF. Below 1 mm/hr, all estimators are mostly centered on the x=y line and then the points spread out with a general underestimation. DPR appears more spread than both DRAIN and GPROF but the data sample is about four times smaller and the narrow swath might induce more important edges artifacts.
VI. CONCLUSIONS From a set of 103,000 images of co-located data between GMI brightness temperatures and DPR surface rain, a U-net was trained to retrieve the latter over the whole swath of the radiometer. To minimize the impact of surface emissivity, work with the highest spatial resolution possible and at the same time remaining easily transposable to other instruments, only 37 and 89 GHz horizontal and vertical polarization brightness temperatures are used as an input. The strength of Unets is their ability to process brightness temperature scenes as an image, as opposed to most existing algorithms that proceed pixel by pixel. Evaluation of the developed algorithm is performed two ways. First, a comparison with the DPR surface rain itself on a set of images that was not in the training database (whole of 2019) is presented.
DRAIN shows a good agreement in terms of structure and intensities which ensures the good quality of the generalization. When compared on the same dataset, DRAIN is generally on par with GPROF or slightly better. This is most noticeable over land where GPROF was not trained on DPR surface rain. Second, a comparison is performed with Météo-France 1 km 2 -resolution mosaic over the same period as a fully independent dataset. The trends are similar. DRAIN performances are close to GPROF if not slightly better. The most noticeable difference is observed in the coastal regions where GPROF tends to overestimate the rain intensities when compared to the mosaic. These four channels used here are present on most passive microwave radiometers of the GPM constellation which will facilitate transposition of the developed U-net to other available sensors of the constellation as proposed in  [21].
Despite their specific biodiversity and the ecosystem services they furnish, peat bogs remain poorly understood, especially in terms of their responses to changes at multiple spatial scales. Many subarctic peat systems face acute risks, due to current global change. In this study, using a multi-metric approach, we assessed the relative value of plants and spiders as ecological indicators in contrasted peat bogs of a French archipelago (St. Pierre-et-Miquelon), located in the North Atlantic. In 2021, pitfall trapping and suction sampling collected spiders while phytosociological relevés were used to document vascular plants. A total of 69 species of spiders and 38 species of vascular plants were identified, and both taxonomic (TD) and functional (FD, based on traits related to dispersal, size and ecosystem functioning) diversity were then estimated for three levels of species relative abundance (Hill numbers q = 0, 1 and 2) using the iNEXT 3D package for alpha diversity as well as iNEXT beta3D for beta diversity.
Sampling with a suction sampler was performed every two weeks, resulting in four capture sessions, using an inverted leaf blower (15 cm tube diameter, 720 m3/h suction capacity). Sampling consisted of 50 times two seconds randomly distributed aspirations across a 20 m side square corresponding to the area sampled by each group of 4 pitfall traps. This resulted in 72 aspiration samples. For the analysis, spiders from the four pitfall traps and the associated suction samples were pooled together as well as the vascular plants data from the four associated relevés.
Environmental variables Environmental variables were assessed at two scales, that of local and landscape. Local variables were air temperature and hygrometry and were measured every 10 min via field data logger (©Tinytag Plus 2 -TGP-4017) during the 45-day sampling season, with one data logger for each sub-site. Minimum, maximum, mean and standard deviations were computed. Microtopography of bogs is a known proxy of soil hygrometry  (Rydin et al. , 2013)  and was measured through drone photography (model: ©FC6310S 8. 8 mm; flight altitude 55 m; Resolution of 1. 4 cm/ px and altitude precision to the level of 1 cm). A five meter buffer around each sampling point was assessed and variations in the microtopography were calculated along with the rugosity index (based on the ruggedness index of topographic heterogeneity by  Riley et al.
For this, the RLQ function of ade4 package  (Dray and Dufour, 2007)  was used. In addition, following the method in  Borcard et al. (2018) , a Fourth corner analysis was done to assess the pairwise relationships between traits and environmental variables as a complementary method to the RLQ as proposed by  Dray et al. , (2012 Dray et al. , ( , 2014) )  as it allows to link the traits and environment matrices that were both tied to the assemblage matrix within the RQL. To assess the relative importance of local and landscape variables, alpha diversity, and multiple regressions were done with a stepwise model selection procedure by AIC  (Akaike, 1974) , implemented in the MASS R package  (Venables and Ripley, 2002)  on rarefied taxonomic and functional diversity based on Chaos estimator with the BAT package  (Cardoso et al.
, 2015). Relative contributions of explicative variables to the model were estimated using the relaimpo package  (Grömping, 2006)  with the R 2 contribution averaged over orderings among regressors  (Lindeman et al. , 1980; Chevan and Sutherland, 1991). To assess the relative importance of local and landscape impacts on spiders and vascular plants communities as well as on beta diversity (both taxonomic and functional) a variance partitioning was done following Borcard et al. (  2018 ) and  Lafage et al. (2015). Taxonomic beta diversity was based on Bray Curtis distance while functional distance matrixes were based on kernel density n-dimensional hypervolume methods (Mammola and Cardoso, 2020) implanted in the BAT package  (Cardoso et al. , 2015). All data analyses were conducted using the free software R-3.
Acknowledgements The study was undertaken within the framework of the  MobBiodiv '2020 project of the  French Office for Biodiversity (OFB)  initiated and carried out by the association " France Nature Environnement St. Pierre et Miquelon " (FNE SPM). We thank  Paul Scoffoni  and the members of FNE SPM as well as the DTAM members, including  Franck Urtizberea ,  Daniel Koelsch  and  Alexandre Hublart  for materials and field support and  Roger Etcheberry  for his botanical expertise. We thank the  laboratory of Géoarchitecture  for hosting the laboratory analysis and desk work as well as that of the samples. Finally, we want to thank the following trainees:  Emma Salvo ,  Chloé Akyazi ,  Tristan Brian  and  Julien Tchilinguirian  for their help in both the field and lab work.
, 2018). These scenarios were important in IPCC SR15 (Masson-Delmotte et al. ), but less so in IPCC AR6 WG III  (Shukla et al. , 2022)  as the scenarios started to become dated, did not explore policy-relevant alternative mitigation strategies and were superseded by more recent literature. While the original SSP modelling exercise covered all the SSPs more or less evenly  (Rogelj et al. , 2018; Riahi et al. , 2017) , the SSP2 'middle of the road' scenario has since been used by most modelling groups as a default socioeconomic pathway and represents more than 90% of the 1202 scenarios with a climate assessment in IPCC AR6 WG III scenarios database  (Riahi et al. , 2022). The SSP-RCP framework was also focussed around particular challenges (mitigation and adaptation), and had limited scope to address other contemporary questions such as temperature overshoot  (Riahi et al.
Non-exhaustive list of policy-relevant questions. We postulate a non-exhaustive list of policy-relevant questions. The degree to which the framing pathway design can address those questions or not will influence to what degree the framing climate pathway dataset can help the forthcoming seventh assessment cycle of IPCC (AR7) and the wider scientific literature based on CMIP7 to be policy relevant. a. What are the potential climate outcomes of current climate policy targets? The climate ambition reflected in the current set of National Determined Contributions (NDCs) is insufficient to meet the Paris Agreement temperature goal  (Riahi et al. , 2022; Meinshausen et al. , 2022). However, beyond the near-term, different interpretations of the stringency and credibility of expressed net zero targets provide for a broad range of different emission trajectories and subsequent warming outcomes ranging from projected warming between 2.
To a lesser degree that same issue existed in the previous generations of RCP and SSP climate pathways, as ESMs did not share the same internal radiative forcing for the same input dataset of concentrations, emissions and land use patterns, yet were all labelled 1. 9W/m 2 or 2. 6W/m 2 pathways. We propose two ways to address this issue: Firstly, the headline names of the scenarios do not include the temperature level, but rather a qualitative label on the emission pathway. Secondly, we suggest continuing the practice of using the previous IPCC assessment cycle's findings (here, AR6) to design the scenarios for the next assessment cycle. For example, the SSP scenarios for the CMIP6 and the AR6 report, including the Special Report on Global Warming on 1. 5°C, were selected to match 1. 9W/m 2 or 2.
Lastly, sometimes the argument is made that emerging pattern scaling or regional emulator approaches could fill in and extrapolate ESM results. While that might be increasingly possible in the future (especially if proposed ESM experiments provide good training datasets), the current set of regional emulators is however not yet able to fill that niche. That is particularly true for peak and decline (overshoot) scenarios that have the potential to exhibit hysteresis in terms of large scale and regional warming and precipitation patterns  (Pfleiderer et al. , 2023). For example, approaches that rely on global-mean temperatures as one of their input parameters, are not yet adequately capable of distinguishing warming, stable and cooling worlds, before, at or after a peak in global-mean temperatures (see point 5.
Acknowledgements The authors would like to thank  Matthew Gidden ,  Alaa Al Khourdajie ,  Hideo Shiogama ,  Janna Sillmann ,  Anna Zehrung  and other valued colleagues for very helpful comments on an earlier draft. CFS, BMS, AT, SS, JR, and JS acknowledge funding from the  European Union 's  Horizon 2020 research and innovation programmes  under grant agreement No  101003687  (PROVIDE). GPP acknowledges funding from the  European Union 's  Horizon 2020 research and innovation programmes  under grant agreement No  821003  (4C) and the  European Union 's  Horizon Europe Research and Innovation Programme  under grant agreement No  101056306  (IAM COMPACT). RS and ZN acknowledge funding from the  European Union 's  Horizon 2020 research and innovation programmes  under grant agreement No  101003536  ( ESM2025 ).
, 2015). The bottom turbulent variables are fixed at their molecular values and are modified according to the mixing induced by the internal tides not resolved by the model following the parameterisation of  Lavergne et al. (2016). The lateral friction condition is a partial slip condition. As for GLO12, the bathymetry used in CAR36 is a combination of ETOPO1  (Amante and Eakins, 2009)  and GEBCO8  (Becker et al. , 2009)  databases. These very high-resolution data (respectively: 1 arc minute and 30 arc seconds) are directly interpolated on the 1/36° ORCA grid. Ten grid points around the CAR36 domain ensure the transition between the bathymetry of the regional model and that of the forcing global model. CAR36 is forced by hourly atmospheric fields (10 m wind, surface pressure, 2 m temperature, relative humidity, precipitation, short-and longwave radiative fluxes) provided by ECMWF empirical IFS formulae  (ECMWF, 2014; Brodeau et al.
Observations databases for validation In order to evaluate the CAR36 and GLO12 simulations over the year 2019, five observations databases were used and extracted over the region and period of interest. in the missing values between the tracks, thus obtaining a homogeneous field. This relatively low-resolution product resolves structures down to the mesoscale. As a preamble to their intercomparison, the effective resolution of AVISO SSH L4 maps, CAR36 and GLO12, which defines the scales that the analyses actually resolve, is illustrated in Figure  3  with a spectral analysis performed over the year 2019 and within the CAR36 domain. As expected, CAR36 is more energetic than the 2 other products. The tail of the spectrum expands with the resolution as the dissipation zone is pushed further towards the fine scales.
Note that the vertical lines correspond to the concentration of EKE, probably due to the shear stress between the islands and the resulting stronger current speed perturbations. The westward propagation of EKE is more marked in the case of CAR36. For a better analysis of this energy transfer, the relative vorticity field is diagnosed over the period of evolution of the studied NBC eddy from its entry into the domain until its dissipation, which allows distinguishing the fine-scale structures emerging on the Caribbean Sea side and the way they are resolved by the GLO12 and CAR36 models. Conventional observational data used so far do not cover the sub-mesoscale range, thus other datasets are needed to validate the small scales reproduced by CAR36. For this purpose, the 5 km resolution Sargassum detection data is used with Sargassum aggrgations superimposed to CAR36 simulated vorticity field to verify the correct positioning of fine structures (Figure  12 ).
https://doi. org/10. 5194/gmd-2023-183 Preprint. Discussion started: 5 October 2023 c Author(s) 2023. CC BY 4. 0 License. better represents hurricanes in this area. The hindcasts of CAR36 forced by ECMWF data described in this paper will be compare by the ones of CAR36 forced by AROME-ANTILLES for the same period 2019. 8 Data availability All the native CAR36 simulation data used in this paper are available following this link: https://doi. org/10. 5281/zenodo. 8386855 9 Author contribution Sylvain Cailleau led the study presented in this paper, he processed the different observation and model data, developed and applied validation diagnostics. He wrote the manuscript with contributions from all co-authors. Laurent Bessières set up the CAR36 regional forecasting system with the help of Guillaume Reffray and Jean-Michel Lellouche and he carried the CAR36 simulations out.
We aimed to include all sources of fossil CO 2 emissions. The global inventory dataset CDIAC-FF (Gilfillan & Marland, 2021), which excludes emissions from lime production, the Emissions Database for Global Atmospheric Research (EDGARv7. 0) from the European Commission Joint Research Centre  (Crippa et al. , 2022) , and the PRIMAPhist version 2. 3. 1  (Gütschow et al. , 2021; Gütschow et al. , 2016)  were used in this study. We also used historical emissions from the CEDS (v2021_04_21) inventory  (Hoesly et al. , 2017; McDuffie et al. , 2020) , which was widely used in climate modelling. Regional specific datasets such as the Caron Emission Accounts and Datasets (CEADs)  (Long et al. , 2020; Shan et al. , 2018; Shan et al. , 2020).
Forests (△Cfor) Forests cover ~14% of East Asia, contribute the main part of the carbon sink in terrestrial ecosystems. Our annual estimate of carbon stock change of forests (△Cfor) in East Asia is provided as the total amount from each East Asian countries. There were four regional datasets with detailed country-level information about △Cfor we included: three inventory-based estimates, FAO (Food and Agriculture Organization) Statistical reviews (FAO, 2021), the regional estimate published by  Pan et al. (2011) , an remote sensing estimate of aboveground live biomass (AGB) from L. Xu et al. (2021)  and a model-based estimate from the OSCAR  (Gasser et al. , 2020). (  1 ) FAO offered the country-level forests biomass carbon flux based on the activity data from FAO Forest Resources Assessments (FRA) in five-year cycles.
(2)  Pan et al. (2011)  used the biomass expansion factors applied to convert volume estimates from inventory data of China  (Fang et al. , 2001) , Japan  (Fang et al. , 2005) , and Korea  (Choi & Chang, 2004)   the estimate was based on area-based expansion. A total of 6 estimates for Japan were available since 2000, the mean result was calculated in sector 3. For the Republic of Korea, an estimate using the national inventory data for different land types was added. For Mongolia, the extra estimate from L-VOD was also available. For the Democratic People's Republic of Korea, the mean result from the regional datasets was used.
, oxidize, emitting to CO 2 into the atmosphere. For China, we calculated the average △Cpro based on an inventory-based dataset and two literature estimates. (1) The NCCC offered an estimate for △Cpro in 2010 using the IPCC Tier 2 method. They applied the △Cpro in Europe  (Janssens et al. , 2003)   (Houghton, 2007; Shilong Piao et al. , 2009). Six estimates, including three bookkeeping approaches, the updated estimates from BLUE  (Hansis et al. , 2015) , OSCAR  (Gasser et al. , 2020) , and H&N2017.
The latest versions which go extend until to 2019 were used in this study. The signal from fossil fuel emissions was removed at the post-processing stage from the inversions mentioned above. We additionally removed the emissions from waste and landfills. The average result (including emissions from natural ecosystems and agricultural ecosystems) from the above five estimates since 2000 has been calculated for East Asia. For the CH 4 and N 2 O emissions from fossil fuel and industry, the latest versions of three global datasets: EDGAR, CEDS and PRIMAP-HIST were used to estimate emissions related to fossil fuel and industry. National inventories the NCCC for China and the NIES for Japan were also included. Non-CO 2 emissions from waste and landfills includes emissions from managed and non-managed landfills (solid waste disposal on land), and wastewater handling, where all kinds of waste are deposited  (Saunois et al.
China is the largest aquaculture producer globally, so errors from omitting in other East Asia countries are expected to be small. In this study, we focused on the emissions from aquaculture in China due to data limitations. The CH 4 fluxes from aquaculture was acquired from two latest comprehensive studies  (Dong et al. , 2023; Yifei Zhang et al. , 2022). Zhang et al. (2022)  presented a nationwide metadata analysis from 132 aquaculture sites in China based on 62 published papers. Four landbased aquaculture systems were taken into account, including the coastal wetland reclamation system (CWRS), inland pond system (IPS), lake/reservoir system (LRS) and rice-field system (RFS). Dong et al. (2023)  analyzed the CH 4 emissions from aquaculture ponds in China with a database of 55 field observations, which corresponds to the emissions from IPS ecosystems.