dataesr/scientific-paragraphs-categorization huggingface dataset:
CMIP5 data are used in the 5th assessment report of the Intergovernmental Panel on Climate Change (IPCC). The latest synthesis Report in progress in 2021 is IPCC AR6 3 , which uses CMIP6 datasets but in the present paper, we use CMIP5 data because of broader availability of climate variables. We use models from the following climate centers: NASA, Goddard Institute for Space Studies (GISS-E2-H, USA), Institut Pierre Simon Laplace (IPSL-CM5A-NR, France), Bureau of Meteorology -Commonwealth Scientific and Industrial Research Organisation (ACCESS1-0, BoM-CSIRO, Australia), Beijin Climate Center (bcc-csm1-1-m, China), Institute of Numerical Mathematics (inmcm4, Russia), Norwegian Climate Centre (NorESM1-ME, Norway), Canadian Centre for Climate Modelling and Analysis (CanESM2, Canada). The spatial resolution goes from 0.
75°to 2. 5°depending on the model (See Table  1 ). Each climate model produces a potentially biased estimate of multiple climate variables at the spatial resolution given in Table  1  and on a monthly basis. 1 In particular, we need the climate variables produced by climate models on a time period covered by reanalysis (cf. section 2. 2) to define the bias correction functions (cf. section 5. 1). 2 Climate data is available on the Copernicus Climate data store: https://cds. climate. copernicus. eu/. 3 https://www. ipcc. ch/assessment-report/ar6/. 1 x 1 bcc-csm1-1-m (BCC, China) 1 x 0. 7402597 IPSL-CM5A-MR (IPSL, France) 1 x 1 INMCM4 (INM, Russia) 2 x1. 5 To reduce the influence of these biases, we use a large number of models and consider the distribution of results provided by all the models.
ERA-5 Reanalysis Climate reanalyses describe the historical climate conditions, obtained by assimilating all available observations into the models. They provide numerical estimates of atmospheric parameters (e. g. air temperature, pressure and wind) at different altitudes / pressure levels, and surface parameters (such as rainfall, soil moisture content, ocean-wave height and sea-surface temperature) on a single level. We use reanalyses to calibrate the cyclone generation algorithm based on the most realistic available estimates of climate variables. We use ERA-5 reanalysis from the European Centre for Medium-Range Weather Forecasts (ECMWF) to fit CATHERINA model 4. This dataset covers the Earth on a 30 km grid (∼ 0. 25 °) and resolves the atmosphere using 137 levels from the surface up to a height of 80 km.
Historical cyclone tracks (IBTrACS) We use the International Best Track Archive for Climate Stewardship (IBTrACS) database  (Knapp et al. , 2010)  5. This database provides information on past cyclone tracks at 3-hour frequency. We remove the events classified as disturbance or extra tropical, and do not consider the South Atlantic basin 6. Climate reanalysis availability requires to focus on cyclones since 1980, which reduces the database to 4 411 cyclones. In the context of an integrated damage assessment, to focus on the events that have a potentially substantial impact on assets, we select only tropical cyclones with maximum wind speed exceeding 35 m. s -1 obtaining 2 245 cyclones. In Figure  2 , we plot the central pressure along cyclone life. This graph suggests that the cyclone phases are fully represented in the database i.
e. from the genesis to dissipation. The trajectories of central pressure in the North Indian basin appear more erratic, which means that we can expect to obtain less significant relationships in this basin. Specific examples of the evolution of central pressure and wind are given in Figure  3  for some famous named cyclones in the North Atlantic basin. We extract the maximum wind speed, cyclone eye pressure and coordinate variations of the eye from this database. et al. (2019)  present a methodology to downscale physical asset values on a high-resolution grid using a combination of nightlight intensity, population data, and global country indicators and make their dataset fully available  (Eberenz et al. , 2020b). These estimates of physical asset value are based on the light intensity L i -from Nighttime lights of the Black Marble 2016 annual composite of the VIIRS day-night band (DNB)  (Román et al.
Global disaster database (EM-DAT) The fitting of the damage functions was performed by  Eberenz et al. (2020a)  on reported damages in the EM-DAT database  (Guha-Sapir et al. , 2018)   10. Filtering the database by sub-type 'tropial cyclone' allows to extract 1 855 tropical cyclones in the period between 1980 and 2021, among which 1 101 events have a reported total damage cost in USD. In terms of damage, tropical cyclones are, using the full set of observations from 1980 to 2021, the most damaging events reported (see Figure  4 ). This database includes a start date field (day, month and year) allowing us to map 455 events with the events reported in IBTrACS using start year and month and country 11. We use this database to validate our simplified estimation process. 3 Cyclone generation algorithm 145 Our model structure follows  Bloemendaal et al.
Wind-pressure relationship (WPR) We describe the cyclone intensity through its central pressure P c t , which is linked with the maximum wind through an empirical relationship. Let V t be the maximum 10-min sustained wind speed (in m. s -1 ) 14 of the cyclone at time t. This maximum wind is observed around 50 km away from the storm center on average 15 and reported in IBTrACS dataset for historically observed cyclones. The wind-pressure relationship (WPR) is calibrated on the whole cyclone database as follows: V t = a (P env (x t , y t , t) -P c t ) b , (3) where P env (x, y, t) is the mean sea level pressure (MSLP) extracted 500 km away from the eye location at this time in ERA-5 and P c t is the central pressure extracted from IBTrACS. This relationship is illustrated in Figure  9  and the parameters a and b are fitted on ERA-5 and IBTrACS data using nonlinear least squares.
13 To reduce this effect and better encompass the dependency of the cyclone displacement on the location of the eye, and following Bloemendaal et al. (2020), we fitted these relationships locally using an additional grouping by 5°longitude and latitude sections. 14 For the data from the World Meteorological Organisation (WMO) and the agencies reporting 1 or 3-minutes sustained wind speed, we performed the conversion to 10 minutes sustained wind speed using the coefficients suggested by  Knapp et al. (2010). See Figure  A1  in the Appendix for more details about the agencies and reporting bias. 15 Radii of maximum wind are also reported in IBTrACS but this information is not central for national level assessment.
08  P c t- where (x t , y t , t) are the coordinates of the eye defined in Equations (  1 ) and (2), SST(x t , yt, t) and T tropo (x t , y t , t) are respectively the sea-surface and tropopause temperatures, R d = 287. 058 J • kg -1 • K -1 is the specific gas constant for dry air, MSLP(x t , y t , t) is the mean local sea level pressure, RH(x t , y t , t) is the near surface relative humidity at 2 meters extracted from the monthly dataset of ERA-5 climate reanalysis or CMIP climate models. f (y t ) = 2ω sin(y t ) is a Coriolis parameter depending on the latitude, r env is the distance between the eye and the area under regular conditions (fixed at 500 km), q env and q * c respectively are the specific humidity at environmental conditions and at saturation, i. e. for RH = 100%, in the eye. ∆S m is the difference of moist entropy between the environment and the storm center and L υ is the latent heat of vaporization.
to a regional damage calibration Using the reported damage estimates from the International Disaster Database (EM-DAT) crossed with cyclone tracks (IB-TrACS), and geographical and socio-economic information along these tracks, Lüthi (2019) refined the damage function approach using machine learning techniques introducing region-specific damage functions. We recall the main steps of the methodology presented in  (Lüthi, 2019; Eberenz et al. , 2020a)  to define the regional damage functions. The authors first defined the event damage ratio (EDR) as a ratio of simulated damage (SED) to normalized reported damage (NRD) for each cyclone: EDR(i, j) = SED(i, v h (j)) NRD(i). The total damage ratio (TDR) is then defined in each region summing over events: TDR(j) = i SED(i, v h (j)) i NRD(i). For each event, there is a value for v h allowing to optimally calibrate the explicit damage function given in Equation (  14 ).
We apply the CDF-t correction technique along our historical synthetic tracks and compute the maximum potential intensity following section 3. 3. 3. The pressure follows the dynamic process introduced in section 3. 3. 4 and the corresponding wind is derived from the WPR (see section 3. 3. 1). We define the model error as the relative error ( χCMIP-χERA5 χERA5 ) between the value produced by the model and the one produced by the reanalysis ERA-5. Figure  18  displays the average relative errors and shows that a 4% relative error in the description of the maximum potential intensity can lead to a 35% error in the description of the implied wind compared to the result obtained with ERA-5. On average the CDF-t correction technique clearly reduces the error between the MPI estimated with climate reanalysis and the one computed from modeled climate data as well as (more importantly) the error in the description of the maximum wind speed.
Conclusions This paper proposes a relatively simple structural framework to generate synthetic storms based on large-scale climate data. We show that when used with reanalysis data and CMIP5 models, our method produces tracks consistent with historical observations. The synthetic tracks generated with our model have several applications. The first one is in natural disaster risk management, to calibrate adaptation measures. For this purpose, the track generation algorithm may be enhanced, for instance, by including dependency in the latitudinal and longitudinal incremental displacement, coupling with a meteorological forecasting model, or including ground topography to model the cyclone displacement over land. Another major field of application is to climate financial risk management, where this scenario-based events database can be used to evaluate physical risks and compute portfolio exposures.
Code and data availability. Code is available from the E4C datahub at https://www. e4c. ip-paris. fr/#/fr/datahub/projects, and Zenodo at 10. 5281/zenodo. 5645516. The deposit includes R scripts with functions, a fitting script, the parameters of the fitting of all the relationships on ERA-5, historical and future synthetic tracks, and a sample of simulated annual damage per country and scenario. We included a user guide for data exploration and a quick start guide.
The different agencies worldwide report central pressure and maximum wind speed but use sometimes different standards. In particular, the reporting can vary in terms of sustained wind speed. According to the dataset documentation the North Atlantic -U. S. Miami (NOAA NHC) bureau (hurdat/atcf)gives the 1-minute winds speed while Tokyo i. e. RSMC Tokyo (JMA) provides dirrectly the 10-minute sustained wind speed (Similarly, newdelhi corresponding to RSMC New Delhi (IMD) gives the 3-minute wind speed; reunion -RSMC La Reunion (MFLR), the Australian TCWCs (TCWC Perth, Darwin, Brisbane) (BOM), the RSMC Nadi (FMS); TCWC Wellington (NZMS) provide the 10 minute sustained wind speed and (CMA) 2-minute sustained wind). The lack of reporting standards between agencies is a source of uncertainty in the input data.
Because of the time slicing of the CMIP5 climate data available in the climate data store, we launch the models on 25 consecutive years over this period: 2070-2095 for IPSL, BCC, NCC, CCCMA, 2075-2100 for GISS, 2085-2095 for INM (only a 10 year slice of climate data is available on the CDS for this model). We repeat this process changing the seed 12 times to obtain 300 representative years in each model (except INM for which we only have 120 years).
, 2014). Considering the limitations of temporal interpolation schemes, techniques of multiple data source fusion are attractive as they take advantage of information provided by several datasets  (Cammalleri et al. , 2013; Gao et al. , 2006; Li et al. , 2017). The use of multi-resolution ET products is interesting to fill gaps in moderately or highly spatiallyresolved products with temporal information contained in coarser ones. For instance,  Cammalleri et al. (2013)  adapted the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), which was originally developed for generating "a synthetic daily surface reflectance product at Landsat Enhanced Thematic Mapper Plus (ETM+) spatial resolution" by  Gao et al. (2006) , to produce continuous "Landsat-like" daily ET images using temporal series of MODIS images.
1. a (Martens et al. , 2017). It is forced with radiation fluxes and atmospheric temperature from ERA-Interim  (Dee et al. , 2011) , precipitations from the Multi-Source Weighted Ensemble Precipitation dataset (MSWEP)  (Beck et al. , 2017)  and microwave surface soil moisture from the ESA Climate Change Initiative soil moisture (ESA CCI SM v2. 3) data set  (Liu et al. , 2012; Wagner et al. , 2012). The version 3. 1. a of GLEAM has been recently validated against measurements from 91 eddy-covariance towers and 2325 soil moisture sensors  (Martens et al. , 2017).
API is computed according the following formulation: API d = API d-1 e -1 ω 1 + ω 2 R d (8) with the subscript d for the considered day, ω 1 et ω 2 parameters calibrated on field measurements of the 0-10 cm soil water content, and R (mm) daily rainfall. Finally PAMEAS computes ET from the sum of soil evaporation (E) and canopy transpiration (T). Appendix C describes the calibration of the PAMEAS model against SiSPAT simulations at field scale over the 2005-2012 period. SiSPAT model was used to develop and calibrate PAMEAS at field scale because it allows to separate soil evaporation from canopy transpiration.
Eddy-covariance measurements The two reference local plots of the AMMA CATCH NOS were equipped for continuous monitoring of meteorological variables (rainfall, air pressure, temperature and humidity, wind speed and direction, four-component radiations) and soil variables (shallow ground heat flux, temperature and moisture profiles down to a depth of 2. 5 m). Eddy Covariance (EC) systems were also implemented, measuring high frequency (20 Hz) wind speed (3 components), air temperature and water vapor, to derive sensible and latent heat flux estimates (see Table  1 ). Turbulent fluxes were derived as described by  Ramier et al. (2009) , using EdiRe software (R. Clement, University of Edinburgh) and following CarboEurope recommendations  (Mauder and Foken, 2004). Full details of the setup and the sensors can be found in  Ramier et al.
, 2011). Most of the models used the ECOCLIMAP2 Africa database  (Kaptué Tchuenté et al. , 2011)  to derive soil and vegetation characteristics including LAI. Nevertheless some of them had their own formulations or specific data for some variables (see  Grippa et al. , 2017  for more details). In this study, we used the ensemble average and standard deviation of LSMs simulations in term of spatial mean over our study site as a basis of comparison at mesoscale.
3: Eq. (  14 ) is applied at 1-km resolution with E3S (ET), disaggregated GLEAM (B 1 = G 1km ) and calibrated PAMEAS (B 2 = P 1km ) leading to the fusion between E3S, GLEAM and PAMEAS "Fusion-EGP" as follow: ET(t) = E3S(t -1)+w G (t)[G 1km (t) -G 1km (t -1) ]+w P (t)[P 1km (t) -P 1km (t -1) ] (20) 2. 4. 2. 2. Evaluation of the fusion method. Three sets of data are used for the evaluation of the fusion approach: • ground measurements of ET at one millet field and one fallow site (see section 2. 3. 1) and simulations of the Simple Soil-Plant-Atmosphere Transfers (SiSPAT) model thoroughly calibrated and validated against field observations which both permitted an evaluation at local scale (see Section 2. 3. 2); • spatially distributed simulations by an ensemble of 20 LSMs that were performed in the framework of the AMMA Land surface Model Intercomparaison Project Phase 2 (ALMIP2) program (see Section 2.
When considering analysis of the interpolation of ET on in-situ datasets, RMSE ranging between 0. 2 and 0. 9 mm. d -1 were obtained for a large range of ecosystems and climates over Contiguous United States and the Mediterranean  (Delogu et al. , 2021; Guillevic et al. , 2019). The lowest values were obtained under semi-arid conditions, over wheat crops in Morocco  (Delogu et al. , 2021)  and woody savannahs in Southwestern USA  (Guillevic et al. , 2019)  which were characterized by low daily ET values. Larger RMSE values, above 0. 4 mm. d -1 , were obtained in wetter conditions, in particular for industrial crops, grasslands and forests. In our study, the values of RMSE int , which quantify the impact of the interpolation method alone (Table  3 ), were falling in the middle of that range of performances (0.
Nonetheless, in the case of a higher number of images, the performance gain compared to a simpler interpolation method would probably become too low given the higher complexity of the fusion method developed in the present study. One of the main advantages of the method relies on the integration of precipitation data that provide essential information on the variations of ET during long period. This is in line with the results obtained by  Delogu et al. (2021)  over a large number of in-situ datasets. However, an analysis of the interest of such method over very long period, up to several weeks, without ET data, as it can happen when using Landsat data, still has to be investigated. One can anticipate that in such situation the performance of the method would greatly depends on the quality of the calibration of an interpolation support such as PAMEAS.
Quite similar results are obtained at mesoscale compared to the mesoscale mean of ALMIP2 models simulations showing the spatial consistency of the here developed fusion algorithm. We also show that fusion-based ET estimates are in better agreement with LSMs references at both local and mesoscale than those based on either GLEAM or PAMEAS estimates alone, showing the benefit of the multi-source fusion approach. The local calibration of PAMEAS API parameters substantially improves performance of the fusion approach at local scale without modifying its performance at mesoscale. This last point highlights the interest of a multi-point local calibration of the model against surface soil moisture measurements for future improvements of the fusion approach. It is believed that such a fusion approach is particularly appealing to take advantage of multisource and multi-resolution datasets to produce consistent continuous ET estimates.
Acknowledgements The ground data set was acquired and disseminated by the  AMMA-CATCH NOS  (www. amma-catch. org) in the framework of the  AMMA (African Monsoon Multidisciplinary Analysis) Program. Special thanks are thus due for the AMMA-CATCH team members. Authors thanks  Monique Oï ,  Jean-Philippe Chazarin ,  Ibrahim Maïnassara ,  Florent Arpin-Pont ,  Hélène Barral  and all members of the local IRD team in Niger for having provided us long series and good quality ground measurements. We also thank the  Land Processes Distributed Active Archive Centre (LP DAAC)  for the availability of MODIS data and the  Global Land Evaporation Amsterdam Model team  for the availability of GLEAM evapotranspiration product. Lastly, authors are grateful to the members of the  ALMIP2  project for providing the models simulations and for their very useful analysis of models behaviors and performances.
1 ). IBASAM is coded in C++ language, and an R package named MetaIBASAM has been developed (https://github. com/Ibasam/MetaIBASAM). Each IBASAM can be parametrized by users to represent a unique population with its own demographic (area, distance), phenotypic, genetic (initial distribution), environmental (water temperature, discharge), and anthropogenic (exploitation rate) characteristics. One simulation of MetaIBASAM returns all information on individuals (e. g. , unique ID, population of origin, current population, body size, genetic values) and nests (e. g. , ID of parents, number of eggs) two times per year for each population.
We ran the simulation using R version 3. 6. 3 and the package MetaIbasam version 0. 0. 6. Code and R scripts are freely available at https://github. com/Ibasam/Portfolio. 3. 2. Simulations outcomes analysis 3. 2. 1. Network spatial structure In our application set up, each population was independently "stable", i. e. , was not growing or declining. Thus, while source-sink populations are commonly determined based on demography (growth rate) and immigrants rates, we classified each population as sink, neutral, or source based on the ratio of incoming individuals (immigrants) to outgoing individuals (emigrants) in the sea-adult returns. We considered populations with a ratio above one as sinks, those with a ratio below one as sources, whereas the neutral populations have a ratio near one. Regardless of the fixed dispersal rate, source-sink dynamics within a network of fifteen populations emerged from the model due to asymmetric dispersal that was driven by differences in population size and spatial structure (Fig.
Demographic consequences of dispersal: population network stability, synchrony, and persistence For each dispersal scenario and simulation, we measured the stability and diversity of the network using the portfolio effect and synchrony metrics. The portfolio effect (hereafter PE) is a metric measuring the stabilizing effect of population diversity on metapopulation dynamics  (Schindler et al. 2010) , by comparing the measured metapopulation variance over the time series to the theoretical variance expected if the metapopulation was considered as a unique population. To do so, we used the mean-variance method from the R package ecofolio  (Anderson et al. 2013). Using this approach, if this ratio equals 1, it means that the network is as stable as expected if it was a single population. If the ratio exceeds 1, it suggests evidence of a portfolio effect.
We want to thank the Bretagne Grands Migrateurs association and angling clubs for allowing us to access to Atlantic salmon datasets from Brittany. We also wish to thank  Guillaume Thirel  for providing us with the air temperature and river flow data, as well as  Marie Nevoux ,  Emanuel Fronhofer ,  Charlotte Récapet , and  Philip Crowley  for helpful discussions on this work. Finally, we thank  Jacques Labonne  for reviewing an earlier version of this manuscript and two anonymous reviewers for their thoughtful feedback and suggestions that greatly improved this manuscript. obtained via the  ORE-DiaPFC observatory  and can be accessible on https://github. com/ORE-DiaPFC/Abondance/tree/master/Scorff,and on demand, respectively.
, 2008). This expectation appears to be met in severely overexploited populations including multiple genera of marine fish, where long-term genomic datasets have documented ongoing declines in allelic richness and heterozygosity  (Pinsky & Palumbi, 2014). However, studies of less intensively hunted populations have uncovered mixed results. For example, in red deer (Cervus elaphus), hunting-estate populations show lower genetic diversity and more inbreeding than protected populations  (Martinez et al. , 2002) , whereas in Eurasian red foxes (Vulpes vulpes), hunted populations show higher genetic variability than unhunted ones  (Frati et al. , 2000). A mechanism by which hunting is expected to influence the strength and pattern of population genetic structure is via its effects on dispersal.