dataesr/scientific-paragraphs-categorization huggingface dataset:
490 For a detailed description of the GSWP3-W5E5 dataset and its constituents, see  Mengel et al. , 2021. Unfortunately, for some variables, GSWP3 shows discontinuities at every turn of the month. The monthby-month bias adjustment applied in its creation is responsible for this artefact  (Rust et al. , 2015). In order to overcome this issue, which also affects GSWP3-W5E5, we additionally provide 20CRv3-495 W5E5, a dataset where W5E5 v2. 0 is backward-extended using ensemble member 1 of the Twentieth Century Reanalysis version 3 (20CRv3;  Slivinski et al. , 2019 Slivinski et al. , , 2021)) , interpolated to 0. 5°and then biasadjusted to W5E5 v2. 0 using ISIMIP3BASD v2. 5. The 20CRv3-W5E5 data are continuous at every turn of the month thanks to the application of ISIMIP3BASD v2. 5 in running-window mode (see section 3.
1). Since GSWP3 is based on 20CRv2, the 20CRv3-W5E5 dataset can be considered an update of 1978. Finally, we also provide the 'raw' 20CRv3 data, i. e. , ensemble member 1 of 20CRv3, interpolated to 0. 5°but not bias-adjusted to any other dataset. This dataset is included since it was generated with only one method and did not need to be combined with another dataset to fully cover the 20th century. Default counterfactual data. To simulate the baseline 'no climate change' state of a human or natural 515 system that is required for impact attribution, we provide a detrended version of the observational factual forcing data using the ATTRICI approach (ATTRIbuting Climate Impacts,  Mengel et al. , 2021). (Karger et al. , 2017 (Karger et al. , , 2021 (Karger et al. , , 2022a)). This algorithm applies topographic adjustments based on surface altitude (orog) information from the Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010;  Danielson and Gesch, 2011).
Tropical cyclone (TC) data (factual) TC Tracks (position of storm centre, central pressure, environmental pressure, radius of 655 maximum wind speed and the outermost closed isobar). We provide processed track information of historical TCs from 1950 to 2021. The information is derived from IBTrACS, the most comprehensive global dataset of historical TC activity  (Knapp et al. , 2010)  that provides information about the location of the storm centre, the pressure at the centre and at the outermost closed isobar as well as the maximum 1-minute sustained wind speed as reported by the WMO Regional Specialised Meteorological  We provide two additional along-track variables that are taken from the European Reanalysis (ERA5;  Hersbach et al. , 2020) , and that are needed for the computation of precipitation (see below): The 695 temperature at the storm centre on the 600 hPa pressure level, and the wind speed on the 850 hPa pressure level, averaged over the 200-500 km annulus around the storm centre.
Precipitation. We are also planning to provide rainfall fields, following a physics-based model that 730 simulates convective TC rainfall by relating the precipitation rate to the total upward velocity within the TC vortex  (Zhu et al. , 2013). The approach has been successfully applied in rainfall risk assessments in the US  (Feldmann et al. , 2019; Gori et al. , 2022). The rain rate will be simulated for all events in the IBTrACS database at 2-hourly temporal and 300 arc-seconds (approximately 10 km) spatial resolution within the storm extent defined by the ROCI storm track variable. We will also aggregate the result to 735 the maximum 24-hourly rainfall during the entire storm duration since this variable is frequently used for rainfall risk assessment studies  (Fagnant et al. , 2020). Different TC wind profiles can be used as an input for the rain field model  (Lu et al.
g. , glacial isostatic adjustments  (Caron et al. , 2018; Whitehouse, 2018)   we provide a counterfactual sea-level dataset as forcing for coastal impact models  (Treu et al. , submitted 2023). To this end the long term trend in the HLT data  was identified by a simple quadratic model in time and subtracted from the factual HLT data. The quadratic model assumes a 800 constant acceleration of sea-level rise over time. Analysis of sea level rise acceleration shows variation throughout the last century with an acceleration phase in the early century followed by a deceleration and then again acceleration until today  (Dangendorf et al. , 2019). By design, this variation is not included in our quadratic trend estimate. In general, we expect our trend estimation to largely exclude natural variability from the trend due to the low dimensionality of the trend model and the long data 805 period.
Ocean data (factual) 810 Default factual data. For the fisheries and marine ecosystem models, we provide a number of physical and biogeochemical variables for the period 1961 to 2010 at different depth levels in the ocean (see Table  10 ). Since direct measurements of these variables are very scarce  (Sarmiento and Gruber, 2006; WOCE Atlas, 2023) , the only way to obtain a globally (or even regionally) complete and consistent 815 forcing dataset is to use numerical models. Global ocean models, which also serve as oceanic components of Earth System models, often simulate many or all of the required variables. To let observations at least indirectly enter the oceanic forcing data for ISIMIP3a, we provide outputs from an ocean model run that is forced by an observation-based reanalysis product of atmospheric forcing  (Liu et al.
, 2021). Compared to the oceanic forcing  (Stock et al. , 2014)  provided to generate the ISIMIP2a 820 simulations for the marine ecosystems and fisheries sector  (Tittensor et al. , 2018) , this new dataset is based on the latest GFDL-MOM6 and COBALTv2 physical and biogeochemical ocean models running on a tripolar 0. 25°grid and using the JRA-55 reanalysis  (Tsujino et al. , 2018)  as the surface forcing, in contrast to the inter-annual forcing dataset of  Large and Yeager, 2009 , which was previously used to drive GFDL-MOM4. The simulations also account for dynamic, time-varying river freshwater and 825 nitrogen inputs that were simulated based on GFDL's land-watershed model LM3-TAN (Land Model version 3 with Terrestrial and Aquatic Nitrogen;  Lee et al. , 2019) , adjusted using observations from the Global Nutrient Export from WaterSheds (NEWS) database  (Seitzinger et al.
, 2006). To create the default 'obsclim' climate-related forcings for the fisheries and marine ecosystem models these ocean We currently do not provide counterfactual versions of the ocean data forcing, though options are being 845 explored. Table  8 : ISIMIP3a oceanic climate-related forcing. Variables with suffixes -bot, -surf, and -vint were obtained from the seafloor, the top layer of the ocean, and vertical integration, respectively. Extractions for individual grid cells available in ASCII format for regional models (see Table  1 ). Extractions for individual grid cells available in ASCII format for regional models (see Table  1 ). available in ASCII format for regional models (see Table  1 ). Extractions for individual grid cells available in ASCII format for regional models (see Table  1 ).
945 The original HYDE 3. 2 data distinguishes four categories of land use: rainfed and irrigated cropland, managed pastures, and more extensively managed rangelands (see Table  11 ). The latter two categories are combined to grazing lands (ISIMIP variable 'pastures'). In LUH2 the crop land information is further downscaled to five crop types: C3 annual plants, C3 950 perennial plants, C3 nitrogen fixing plants, C4 annual plants and C4 perennial plants. In the same vein as the HYDE case, the LUH2 data set distinguishes between rainfed and irrigated croplands. For the purpose of driving the ISIMIP impact models, the LUH2 data was interpolated from the original 0. 25°× 0. 25°to the standard ISIMIP 0. 5°× 0. 5°global grid.
955 In a further downscaling step the 5 crops land use data has been downscaled even further to 15 crop types. For this purpose the Monfreda land use dataset  (Monfreda et al. , 2008)     The LUH2 data set also includes national application rates of industrial nitrogen fertiliser  (Hurtt et al. , 975 2020). This does not include manure. The fertiliser data is not based on HYDE but was derived from other sources. The data for the years 1915-1960 are based on  (Smil, 2001 (Smil, ), 1961 (Smil, -2011      If cultivars are fixed according to the method described above this simulation will be considered a '2015soc' simulation as long as other direct human drivers are also held constant at 2015 levels.
Fertiliser input However, if, e. g. , fertiliser inputs are varied over time according to provided forcing data (see section 1030 4. 4), the run will be considered a 'histsoc' run. GGCMI is currently working on a temporally resolved global crop calendar at the same spatial resolution based on various new data sources including agricultural ministries, census reports, phenological data bases, experimental sites, etc. This data set will be published separately and could then be used to inform 'histsoc' simulations. 1035   2 ). For the simulations 1050 described here, dams with (projected) construction dates after 2020 are not considered; these will become relevant in the ISIMIP3b group III simulations, with exception of the Grand Ethiopian.
Dams and reservoirs Renaissance Dam, which we decided to include since its reservoir reached a first stage of filling of 4. 9  Additionally, a visual validation and, where appropriate, manual relocation were applied with the aim to find the best fitting grid cell from a hydrological perspective. Due to the grid cell resolution, reservoirs might get assigned to a grid cell that include the main stream or already a confluence of one or more tributaries even though the dam is located in a particular tributary according to the database. In those for the latter are provided as static geographic information (see Table  1 ). (Sorø), France (Le Bray), Italy (Collelongo) and Finland (Hyytiälä)  (Reyer et al. , 2020b). For the historical period, observed stem numbers and forest thinning types are provided in the same ways as in ISIMIP2b from the PROFOUND Database  (Reyer et al.
Acknowledgements This article is based upon work from  COST Action   CA19139   PROCLIAS  ( PROcess -based models for 1195  CLimate Impact Attribution across Sectors ), supported by  COST  (European  Cooperation in Science and Technology ; https://www. cost. eu). Funding from the  EU Horizon 2020 research and innovation program  under grant agreement  821010  (CASCADES) supported the work of C. P. O. R. , J. V. and I. D. VdV. , and the provisioning of the high resolution climate data, and supported the work of ST under grant agreement No  820712  (RECEIPT). SL received funding from the  German Research Foundation   1200  (DFG, project number  427397136 ). The  German Federal Ministry Ministry of Education and Research (BMBF)  supported the work under the research projects  ISIAccess  ( 16QK05 ),  SLICE  ( 01LA1829A ), https://doi.
This data is based on ISIMIP2a 'varsoc' simulations for 1901-2005 and extended by RCP6. 0 simulations from the Water Futures and Solutions project up to 2021 (Wada et al. , 2016b). Years before 1901 have been filled with the value for year 1901. Lake and reservoir surface area optional Total lake and reservoir area fractions (percentage of grid cell) calculated from the HydroLAKES v1. 0 (Messager et al. , 2016) and GRanDv1. 3 databases (Lehner et al. , 2011b) mapped to 0. 5 degrees resolution. Areas increase with time because of the increasing number of reservoirs documented in GRanDv1. 3. Reservoirs from 2017 onwards are kept constant. This data set differs from the lake surface areas provided as static geographic forcing (see below) which describe the surface area of one representative lake per grid cell and does not change over time.
ID optional HydroLAKES reference to relate HydroLAKES and GLOBathy database fields to the representative lakes described above. This dataset contains IDs of the 41449 representative lakes used in ISIMIP, which are a subset of the about 1. 4 million lakes contained in the HydroLAKES and GLOBathy database. HydroLAKES IDs for big lakes optional This dataset is analogous to the one above, but only contains IDs of 93 large lakes. It can be used to produce global plots with conspicuous large lakes. To be used together with the file storing the big lakes mask. Big lakes mask optional This dataset indicates the 0. 5°grid cells actually occupied by each of the 93 large lakes, which can be larger than a single grid cell. It can be used to produce global plots with conspicuous large lakes. To be used together with the big lakes IDs in the dataset above.
In ISIMIP3a, we address attribution to changes in the climate-related systems itself, e. g. , trends in atmospheric temperature and precipitation, coastal water levels, and atmospheric CO₂ concentrations. The provided counterfactual forcing data comprises daily atmospheric climate derived from the ISIMIP observational climate datasets (see section 3. 1); daily counterfactual coastal water levels derived from the ISIMIP historical 375 coastal water level dataset (see section 3. 3); and constant 1901 atmospheric CO₂ and CH₄ concentrations (see Table.
https://doi. org/10. 5194/egusphere-2023-281 Preprint. Discussion started: 14 March 2023 c Author(s) 2023. CC BY 4. 0 License. from the data sets above for 72 lakes that have been identified within the lake sector as locations (grid cells of the ISIMIP 0. 5°grid) where models can be calibrated based on observed temperature profiles and hypsometry (depth and area). Tropical cyclone tracks and windfields mandatoryWe do not provide 'no climate change' TC tracks and windfields but the original tracks from the IBTrACS database(Knapp et al. , 2010; period   1841 -2021)  windfields calculated by Holland model (Holland, 2008 (Holland, , 1980) )  should be used in combination with the counterfactual water levels to estimate the impacts of sea level rise on TC induced damages, losses or replacement, see section 3.
daily GSWP3-W5E5 (factual and counterfactual, 1901-2019), 20CRv3-W5E5 (factual and counterfactual, 1901-2019), 20CRv3-ERA5 (factual and counterfactual, 1901-2021), 20CRv3 (factual and counterfactual, 1901-2015) Near-Surface Specific Humidity huss kg kg-1 0. 5°grid, daily GSWP3-W5E5 (factual and counterfactual, 1901-2019), 20CRv3-W5E5 (factual and counterfactual, 1901-2019), 20CRv3-ERA5 (factual and counterfactual, 1901-2021), 20CRv3 (factual and counterfactual, 1901-), 20CRv3-W5E5 (factual and counterfactual, 1901-2019), 20CRv3-ERA5 (factual and counterfactual, 1901-2021), 20CRv3 (factual and counterfactual, 1901-2015) https://doi. org/10. 5194/egusphere-2023-281 Preprint. Discussion started: 14 March 2023 c Author(s) 2023. CC BY 4. 0 License. (factual and counterfactual, 1901-2019), 20CRv3-W5E5 (factual and counterfactual, 1901-2019), 20CRv3-ERA5 (factual and counterfactual, 1901-2021), 20CRv3 (factual and counterfactual, 1901-We provide four observational datasets specifically generated for the evaluation experiments of ISIMIP3a: GSWP3-W5E5, 20CRv3-W5E5, 20CRv3-ERA5, and 20CRv3.
All four datasets have daily temporal and 0. 5°spatial resolution. Their temporal coverage varies, with GSWP3-W5E5 and 20CRv3-W5E5 covering 1901-2019, while 20CRv3-ERA5 covers 1901-2021 and 20CRv3 covers 1901-2015. Instead of excluding datasets that do not cover the most recent years, we focussed 475on including datasets that start in 1901, to allow for a common spin-up procedure (described in section 2. 1 for the 'obsclim + histsoc; default' experiment), in order to support models that need to spin up, e. g. , their carbon pools under stable climate-related and direct human forcings before they can do the actual experiments. 480The GSWP3-W5E5 dataset is based on W5E5 v2. 0 (Lange et al. , 2021) , which is also used as the observational reference dataset for the bias adjustment of climate input data for ISIMIP3b(Frieler et   al.
input datasets are provided in ISIMIP3a in order to facilitate climate input data-related quantifications of uncertainty in the associated impact assessments. Those datasets are not based on W5E5 to account for trend and variability artefacts in W5E5 that are related to the climatological infilling 505 procedures used to deal with gaps in the station observations employed for the bias adjustment of ERA5 for the production of WFDE5 (for a detailed description of this caveat see https://data. isimip. org/caveats/20/). The first of the additional ISIMIP3a climate input datasets is 20CRv3-ERA5, which was created in the same way as 20CRv3-W5E5, but using ERA5 instead of W5E5 for the time period 1979-2021, and also as the bias adjustment target for the time period 1901-510.
org/10. 5194/egusphere-2023-281 Preprint. Discussion started: 14 March 2023 c Author(s) 2023. CC BY 4. 0 License. 525 for quantifying the contribution of the observed climate change (no matter from where the trends originate) to observed long-term changes in impact indicators but also for quantifying the contribution of the observed trend in climate to the magnitude of individual impact events. High resolution atmospheric factual data (CHELSA-W5E5). This dataset is provided to facilitate the 530 high resolution sensitivity experiment described in section 2. 1. It covers the global land area at 30'' (~1 km) horizontal and daily temporal resolution from 1979 to 2016 for the variables precipitation (pr), surface downwelling shortwave radiation (rsds), and daily mean, minimum and maximum near-surface air temperature (tas, tasmin, tasmax).
include orographic effects into daily mean near-surface wind speed (sfcwind) we follow the approach of(Brun et al. , 2022b), and use an aggregation of the Global Wind Atlas 3. 0 data (Technical University https://doi. org/10. 5194/egusphere-2023-281 Preprint. Discussion started: 14 March 2023 c Author(s) 2023. CC BY 4. 0 License. of Denmark, 2023) in combination with daily 0. 5°sfcwind from W5E5. We first regrid both the Global 600 Wind Atlas data and the W5E5 sfcwind data to the target grid of 30'' using bilinear interpolation. The Global Wind Atlas data product (𝑠𝑓𝑐𝑊𝑖𝑛𝑑 𝐺𝑊𝐴 𝑐𝑙𝑖 ) represents average wind speeds for 2008 to 2017. We therefore average daily regridded W5E5 data over this time period (𝑠𝑓𝑐𝑊𝑖𝑛𝑑 𝑊5𝐸5 𝑐𝑙𝑖). We assume surface wind speeds follows a Weibull distribution and log-transform both datasets before computing the difference 𝛥𝑠𝑓𝑐𝑊𝑖𝑛𝑑 𝑐𝑙𝑖 , whereby a small positive constant (c) was added to all data points before 605 applying the transformation to avoid the problem that log(0) is undefined.
or human 760 interventions such as ground water abstraction(Wada et al. , 2016a). HLT encompasses factual and counterfactual coastal water levels along global coastlines from 1901 to 1978 on monthly resolution and from 1979 to 2015 on hourly resolution. The counterfactual is derived from the factual dataset by removing the trend in relative sea level since 1900. The detrending preserves the timing of historical extreme sea-level events similar to the counterfactual atmospheric climate forcing described in section 765 3. 1. Hence, the data can be used for an event-based attribution of, e. g. , observed flooding to observed relative sea-level rise with tuples of impact simulations driven with the factual and counterfactual dataset. It is important to highlight that 'attribution to observed changes in relative water levels' does not imply attribution to anthropogenic climate forcing because such observed changes may include trends that are not driven by human greenhouse gas emissions.
Important sources for such trends are 770 the ongoing adjustments of ice sheets, glaciers and the earth crust to climate conditions before industrialization(Slangen et al. 2016) and the land subsidence due to water, gas and oil extraction(Nicholls et al. 2021). In the following the derivation of the data is described in more detail. Defaultfactual data. To capture the impacts of extreme water levels we provide hourly observation-775 based coastal water levels as forcing data. To this end we combine the Coastal Dataset for the Evaluation of Climate Impact (CoDEC) dataset (Muis et al. , 2020) that describes high frequency variation of sea level along global coastlines with a recent reconstruction of observed long-term sealevel rise (Dangendorf et al. , 2019). The CoDEC hourly data builds on a shallow-water model with fixed ocean density driven by ERA5 wind and atmospheric pressure fields.
The CoDEC data thus starts only 780 in the year 1979 and does not include variations due to ocean density changes and multi-year trends from observed sea-level rise or vertical land movement. In contrast, the hybrid reconstructions (HR) dataset from Dangendorf et al. , 2019 represents sea-level change since 1900 on a monthly timescale, including density variations and multi-year trends. Long term sea-level change in HR is based on fitting theoretically known and modelled spatial-temporal fields of individual contributing factors of sea level 785 change to a set of observations of sea level change from tide gauges. The individual contributing factors are theoretically known cryospheric fingerprints from two ice sheets, 18 major glacier regions, glacial isostatic adjustment from 161 Earth rheological models and dynamic changes of sea surface height modelled by six global climate models.
Short term sea-level variations are represented in HR by https://doi. org/10. 5194/egusphere-2023-281 Preprint. Discussion started: 14 March 2023 c Author(s) 2023. CC BY 4. 0 License. extending the spatio-temporal patterns from satellite altimetry back to the year 1900 using tide gauge 790 records. We create the HLT dataset by low-pass filtering the HR dataset and high-pass filtering the CoDEC dataset before summing them. HLT shows improved agreement with tide gauge records on hourly to monthly time scales when compared to CoDEC due to the inclusion of density variations. This is most apparent for lower latitudes. The performance on interannual time scales is equal to Dangendorf et al. , 2019. 795 Default counterfactual data. To estimate the effects of historical sea-level rise on coastal systems,.
Forcing with this dataset allows for a comparison of simulated catches against the congruent(Watson,   1105    2019) reconstruction of historical fisheries catches (spanning the period 1869-2015; Watson and Tidd, 2018). To permit integration into marine ecosystem models that capture different fishing sectors, fleets, and functional groups these data include nominal active fishing effort disaggregated by location (Exclusive Economic Zone/High Seas and Large Marine Ecosystem), fishing country, fishing gear, targeted functional group, and fishing sector (coastal artisanal and industrial). Impact modellers are 1110 allowed to distribute this effort across space, time, and target organisms in any method compatible with their models' structure. The fishing effort data does not include any information about changes in the efficiency of fishing technology over time (technological creep).
Fixed 2015 direct human forcing (obsclim + 2015soc; default). To https://doi. org/10. 5194/egusphere-2023-281 https://doi. org/10. 5194/egusphere-2023-281 Preprint. Discussion started: 14 March 2023 Preprint. Discussion started: 14 March 2023 c Author(s) 2023. CC BY 4. 0 License. c Author(s) 2023. CC BY 4. 0 License. 230 235 240 types (i. e. , the type covering the largest fraction of the cell of the topmost soil layer) within each ISIMIP grid cell and the Binary masks available at 0. 25°, 0. 5°, and 1°resolution (Sherman, 2017). dominant soil types on the agricultural land within each ISIMIP grid cell. Both maps were derived from the Harmonized World Soil Database (HWSD Version 1. 1, 2009) assuming that soil types are evenly distributed within the ISIMIP grid cells. We have used version 1.