dataesr/scientific-paragraphs-categorization huggingface dataset:
Since the majority of lakes lacked nearby weather stations, the uniform EWEMBI data allowed us to include a broader diversity of lakes and avoid cumbersome data harmonization. Since the EWEMBI dataset was also used to help bias-correct the future climate scenarios used in the ISIMIP2b simulation round  (Frieler et al. , 2017; Lange, 2019b) , the performance of calibrated lake models can be indicative of their ability to simulate past and future climate change when forced by the ISIMIP bias-corrected data. In addition to the calibration runs that were limited to periods when observed water temperature data were available, the local sector modellers were encouraged to drive their lake models with the complete EWEMBI data record between 1979-2016. This was aimed at evaluating the lake models' abilities to reproduce effects of observed meteorologic variability and extreme events on thermal simulations.
These simulations could also be used for benchmarking simulations forced with modelled future climate conditions from GCMs. In addition to the EWEMBI dataset, five other reanalysis datasets were provided in ISIMIP2a for modellers to use as inputs according to their capacities, with the goal of exploring the effect of input data choice on simulation outcomes. All datasets are described and referenced in the simulation protocol document (https://www. isimip. org/protocol/2a/, last access: 23 May 2022). Data providers supplied historical measured water temperature profiles for 62 lakes (Fig. 1a , Table  S1 ). Lake data had to meet two criteria to be included in the local lake dataset: (1) data needed to overlap with the EWEMBI time span, and (2) temperature profiles needed to encompass at least two consecutive years in the case of (sub)-daily sampling frequency, or at least five consecutive years in the case of (sub)-monthly sampling frequency.
These criteria enabled intra-and inter-annual variability to be captured in water and meteorologic conditions in the model calibration procedure. A few lakes from under-represented geographical locations (e. g. tropics) were included despite shorter water temperature records. Water temperature data were harmonized to a uniform data format and visually quality-controlled to remove outliers. In addition to water temperature, the data providers supplied detailed information of the lake depth and hypsometric data to characterize the morphometry of lake basins, which are required as input to most of the lake models.
Climate input data for ISIMIP2b were derived from four GCMs, namely GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-LR, and MIROC5  (Taylor et al. , 2012; Frieler et al. , 2017)  that were available from CMIP5. These GCMs were chosen since they best met the needs of all sectors participating in the ISIMIP and provided the necessary scenario length at daily temporal resolution. They had a wide range of projected warming rates, with GFDL-ESM2M and HadGEM2-ES representing the lower and higher ends of the warming spectrum, respectively. The data management team at the Potsdam Institute for Climate Impact Research (PIK) biasadjusted the GCM data with a reference dataset of atmospherics observations (EWEMBI, Lange, 2019a) using the statistical transfer functions by  Hempel et al. (2013)  modified to correct known biases in modelled variables  (Frieler et al.
ISIMIP3a/b The protocol for the third simulation round of ISIMIP, ISIMIP3, is currently ongoing. ISIMIP3 is largely similar to the ISIMIP2 protocol but includes counterfactual climate forcing in ISIMIP3a, and the next generation (CMIP6) of climate model forcing and various emission scenarios in ISIMIP3b. Below, we highlight the main differences between both simulation rounds. In ISIMIP3a, the observational climate forcing covers the period 1901-2016 and consists of the Global Soil and Wetness Project version 3 (GSWP3;  Dirmeyer et al. , 2006) , homogenized to W5E5 for the period  1901 -1978 (Lange, 2019a) )  and a combination of the W5E5 dataset  (Lange, 2019b; Cucchi et al. , 2020)  for the period 1979-2016 and GSWP3 before that. This observational dataset was bias-corrected using observations from the Global Precipitation Climatology Centre (GPCC) (more details at https://climatedataguide.
Climate data application All bias-adjusted meteorologic forcing data provided by ISIMIP sectors have a daily temporal resolution and a spatial resolution matching the ISIMIP grid scale. While most models in the Lake Sector performed simulations at daily time steps, some models required temporally disaggregated forcing data at sub-daily time steps. The modelling teams performed temporal disaggregation using their customary approaches (see Sect. 4). For simulations in the local domain, data were extracted for grid cells corresponding with the lakes' geographic locations. No further downscaling or local corrections were applied to ensure consistency in the forcing data applied to all local lakes.
Bathymetry Most lake hydrodynamic models require the hypsographic relationship between depth and surface area, which is critical for determining layer volumes and storage and the vertical transfer of heat. Data providers supplied these bathymetric data for each lake in the local domain. The two versions of air2water models did not require information on lake bathymetry. For global lake simulations in ISIMIP2, the bathymetry of the representative lakes in each grid cell was derived from a rasterized version of the Global Lake and Wetland Database (GLWD;  Lehner and Döll, 2004; Toptunova, 2019). Specifically, for each grid cell, average lake depth and lake surface area values were calculated from all GLWD lake data contained within the grid cell. Lake bathymetry for each generic lake then was assumed to be cylindrical.
isimip. org/protocol/2b/, last access: 23 May 2022) or was directly outputted by the lake model (Tables  4, S4 ). The Lake Sector simulation protocol provides the model performance metrics used during calibration of lakes in the local spatial domain (Table  3 ). A full list of variables simulated within ISIMIP2b is summarized in Table  S4. This diversity of GCM input datasets, emissions scenarios, lake models, and their output variables means that the total ensemble of impact simulations under the Lake Sector requires considerable storage space. In addition, those appreciable computing resources should be anticipated by potential future collaborators. For example, the global lake simulations for ISIMIP2b take up 14 TB of storage space. This means that applications with simulations under multiple GCMs, lake models, and scenarios for a given variable will require high-performance computing resources.
For running simulations, computing times may vary depending on the scale of one's contribution. On the one hand, simulating a local, calibrated lake with FLake for a single scenario and GCM combination may take seconds on a laptop  (Thiery et al. , 2014b) , but, on the other hand, global simulations from CLM4. 5 for one such scenario and GCM combination will require several weeks using 144 compute cores on a high-performance computer, substantiating both computational costs and resources for dataset storage. These technical prerequisites, in addition to individual model feasibility issues for local versus global domain simulations, explain the discrepancy in model availability across the ISIMIP2 local and global simulations. • • * Air2water 6par (L) • • * ALBM (G) • • • • • • • • • • ALBM (L) • • • • • • • • • • CLM4.
While the multi-model mean goodness of fit was reasonable for most lakes, 16 % of lakes showed RMSE larger than 2 • C, indicating less certain predictions (Tables  3, S1 ). For individual models, the number of lakes with RMSE exceeding 2 • C varied from 3 lakes (5 %, air2water4par) to 40 lakes (65 %, FLake). Although the ISIMIP2a forcing data used a daily time step at the ISIMIP grid scale, the prediction errors in water temperature were relatively small (Table  3 ), even though these input data and their resolutions are, in general, less than optimal for the simulations of individual lakes  (Bruce et al. , 2018 ). An exception is the air2water model that, owing to its statistical and data-driven calibration of its model's parameters, has been shown to be able to provide the same projections irrespective of the nature of the air temperature dataset used to drive the model  (Piccolroaz et al.
These results show the importance of using an ensemble of models to increase the robustness of simulated past and future changes and making interpretations less dependent on a single or small suite of the lake models used  (Trolle et al. , 2014). The common fundamental output from all lake models was water temperature, and for most models, this output is in the form of a full vertical profile at a daily time step. These data and other related model output were also aggregated to different metrics describing lake hydrodynamics, e. g. thermocline depth, the onset of stratification, energy and heat fluxes at the air-water interface, and lake ice characteristics and dynamics (Table  4 ). The methods to calculate these metrics are defined in the Lake Sector protocol (https://www. isimip. org/protocol/, last access: 23 May 2022), and for additional metrics, the full lake water temperature profiles can be further processed by users.
The ISIMIP Lake Sector database has also been used to assess present and future alterations of lake mixing regimes (Woolway and Merchant, 2019) and the shifts in lake stratification and their climatic drivers  (Woolway et al. , 2021b). Finally, both event and trend attribution of lake heatwaves  (Woolway et al. , 2021b (Woolway et al. , , 2022) )  and lake ice cover changes  (Grant et al. , 2021) , respectively, have been undertaken using ISIMIP simulations in combination with global observational datasets to confirm the role of anthropogenic climate change in observed lake changes.
Model response to observational vs. simulated forcing data In addition to simulations using ISIMIP2a forcing, the ALBM and FLake models were also used for simulations forced by EWEMBI observational data. This will allow for assessment of the difference in model output when used with observational forcing data compared to simulations with GCM forcings during the historical time period. Given that impacts under past and future climates are modelled with bias-adjusted GCMs, a comparison with simulations using the observed data used for bias correction will allow an assessment of how simulations forced with the GCM historical inputs compare with those forced using observed (historical) climate (see also  Piccolroaz et al. , 2018  for a similar analysis). This can give an estimate of the uncertainty in the ISIMIP GCM scenarios and the bias correction method.
There are so far no studies for this application of the ISIMIP2a simulations, but the existing simulation output https://doi. org/10. 5194/gmd-15-4597-2022 Geosci. Model Dev. , 15, 4597-4623, 2022 Figure  3. Maps of lake area (a), lake volume (b), and mean lake depth (c) used in the ISIMIP3 simulation round. In the input data for ISIMIP3 simulations, a single lake is assigned to each grid cell. However, here we show a modified version of the dataset to delineate large lakes in the global map. The maps are derived from HydroLAKES  (Lehner and Messager, 2016)  and GLOBathy  (Khazaei et al. , 2022)  datasets using the ISIMIP3 lake mapping methods described in "Code and data availability". archives are publicly accessible and hold potential for further study.
Global-scale calibration and validation Important steps can be made in the development of a globalscale dataset for calibration and validation purposes. There are a few challenges to overcome in the future  (Janssen et al. , 2015). First, due to project-based research, long-term measurements are rare as often measurement campaigns stop when projects are over. Second, data are often locked within institutes, meaning that a consistent global database requires corporation between various parties. Similarly, in situ data that have not been properly indexed and stored, sometimes referred to as "dark data", require rescue efforts to extend back our measurement period of lakes. Third, data are gathered inconsistently, for instance by using different methods, measuring over different periods, or collecting at different spatial scales.
Remote sensing could overcome these issues to some extent, as they can provide long-term, global observations. Remote sensing datasets for lakes are increasing  (Dörnhöfer and Oppelt, 2016). Examples of already existing datasets are datasets for lake temperature  (Sharma et al. , 2015) , ice phenology  (Wang et al. , 2021) , and even biological indicators  (Fang et al. , 2022; Hou et al. , 2022). A disadvantage is that remote sensing is limited to proxy values, which still require ground truthing by in situ monitoring data. Moreover, remote sensing performs variably depending on the measurement system and weather conditions and are variable in assessment. While optical imagery is easily obscured by cloud cover, active microwave systems can be used in all-weather conditions for some variables such as ice cover  (Kilic et al.
, 2018; Murfitt and Duguay, 2021). Therefore, satellite observations must be combined with highly spatiotemporally resolved in situ measurements from buoys, field sampling programs, and long-term monitoring networks  (Rand et al. , 2022). Specifically, in situ measurements are essential for observing lake processes below the water surface (such as stratification and mixing), to improve understanding of complex air-water energy fluxes (such as evaporation), and to maintain long-term perspectives that began prior to the advent of satellites and regardless of weather conditions that adversely impact some satellite measurements. First attempts at such databases are for example the HydroLAKES database, which already has water discharge into lakes  (Messager et al. , 2016).
Conclusions Modelling the impact of climate change at a global scale using an ensemble of lake models requires data, vision, ambition, and a strong collaborative network of researchers from a range of disciplines. The first ensemble model simulations in the Lake Sector of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) has leveraged such a network to design and execute a protocol that has now provided state-of-the-art scientific evidence of climate change impacts on lakes under low to high greenhouse gas emission scenarios. The Lake Sector protocol in the local domain allows for the calibration of lake models forced with historical ISIMIP2a inputs and parametrized using site-specific data of bathymetry. Comparison of simulated and measured water temperature from 62 well-studied lakes facilitated detailed calibration and evaluation of the models in the local domain.
In future global simulations, these locally derived parameter and coefficient values could improve the full ensemble of models that have so far been uncalibrated in their global domain applications. Simulations in the global domain provided daily outputs of lake thermal conditions at the ISIMIP grid scale. Our simulations at both the local and global resolution quantify past and future changes in water temperature, energy and heat fluxes, and ice with unprecedented geographical coverage. Simulations by the ISIMIP Lake Sector also provide previously unattainable opportunities to evaluate the levels of uncertainty in simulations related to the differences in forcing data between reanalyses, GCMs, emission scenarios, and in model structure and parameterization among lake models. Simulations by the ISIMIP Lake Sector continue to estimate the range and robustness of plausible lake responses to global warming either at 1.
5 • C above pre-industrial levels as defined by the ISIMIP2b protocol or for any other future greenhouse gas emission scenarios. This work furthers the state of the art in freshwater science  (Vanderkelen et al. , 2020; Grant et al. , 2021; Woolway et al. , 2021b). Here, we have described the protocol of the Lake Sector in ISIMIP2 and ISIMIP3, which includes the simplifying assumption that hydrologic inputs from the lake watershed had minimal effects on the simulated thermal structure. While this is a reasonable assumption for lake hydrodynamic simulations, it will not be sufficient for simulations of lake biogeochemistry and ecology that strongly depend on the nutrient inputs from the lake watershed. Under the ISIMIP framework's provision of consistent climate forcing datasets and scenarios, the climate change impacts simulated in the Lake Sector are comparable with simulation results from other ISIMIP sectors, supporting cross-sectoral assessments of climate change impacts  (Lange et al.
A full list of models is available at https://www. isimip. org/impactmodels (The Inter-Sectoral Impact Model Intercomparison Project, 2022). All inputs to ISIMIP impact models and model output simulations can be found here: https://data. isimip. org/ (last access: 23 May 2022). Background information and citations for ongoing ISIMIP3 developments are at https://doi. org/10. 48364/ISIMIP. 263794. 1 (  Vanderkelen and Schewe, 2020a)  for ISIMIP3a and at https://doi. org/ 10. 48364/ISIMIP. 383948 for ISIMIP3b  (Vanderkelen and Schewe, 2020b). For global lake mapping in ISIMIP3 (see Sect. 3. 5. 1, "Bathymetry"), the code for processing lake simulation input data is publicly available and can be found here: https://doi. org/10. 5281/ zenodo. 6457813 (Marcé et al. , 2022d). Supplement. The supplement related to this article is available online at: https://doi.
Acknowledgements. We thank and acknowledge the support of  Stefan Lange ,  Matthias Büchner , and  Iliusi Vega del Valle  for their roles in producing, coordinating, and making available the ISIMIP climate scenarios. We also thank  Jorrit Mesman  for enabling ISIMIP3 simulations through LakeEnsembleR. Malgorzata Golub  thanks  Marcus Lundberg  and  Douglas Scofield at Uppmax  for their assistance with codes optimization and software installation, which was made possible through application support provided by  SNIC. The  Archbold Biological Station ,  Evelyn Gaiser ,  Vivi-enne Sclater , and  Monika LaPlante  are thanked for sharing the data on Lake Annie. We thank  Jacob Zwart  of  USGS  for reviewing this paper. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the  U.
S. Government. Finally , we thank  Willem van Verseveld ,  Bertrand Guenet , and  Peter Düben  for reviewing this paper and  Charles Onyutha  for acting as editor. Financial support. The funds from the  WATExR  project ( ERA4CS ,  JPI Climate)  provided the financial support to  Malgorzata Golub ,  Rafael Marcé ,  Don Pierson ,  Tadhg Moore ,  Eleanor Jennings , and  Daniel Mercado-Bettin. The computations and data handling were enabled by resources provided by the  Swedish National Infrastructure  for Computing (SNIC) at Uppmax partially funded by the  Swedish Research Council  through grant agreement no. 2018-05973. Inne Vanderkelen  is a research fellow at the  Research Foundation Flanders  (grant no. FWOTM920 ). Zeli Tan is supported by the  US DOE's Earth System Modeling program  through the  Energy Exascale Earth System Model  ( E3SM ) project.
Annette B. G. Janssen  is supported by the  NWO talent grant Veni  (project number  VI. Veni. 194. 002 ). Bram Droppers is supported by the  Wageningen Institute for Environment and Climate Research (WIMEK ; grant no. 5160957551 ). Hampus Markensten  was funded by  Department of Public Technology , at  Mälardalen University, ABB Västerås ,  Västmanlands Avfallsaktiebolag ,  Eskilstuna Energi & Miljö  and the  EU   REFLECT  project (Response of European Freshwater Lakes to Environmental and  Climatic Change , contract no. ENV4-CT97-0453 ). Josef Hejzlar  received support from the  European Regional Development Fund (ERDF)  and the  European Social Fund  ( ESF ) project  Biomanipulation  as a tool for improving water quality of dam reservoirs (grant no. CZ. 02. 1. 01/0. 0/0. 0/16_025/0007417 ).
Partial funding for this work was provided by the  Israeli Ministry of Energy  grant no. 215-17-017. High-frequency data from  Windermere  and  Esthwaite Water  are currently funded by the  UK Natural Environment Research Council  as part of the  UK-SCAPE  programme (grant no. refNE/R016429/1 ). Huaxia Yao  and  James A. Rusak  were supported by the  Ontario Ministry of the Environment, Conservation and Parks. Robert Ladwig  was supported through an  NSF   ABI development grant  (grant no. DBI 1759865 ). Victor M. Stepanenko  is partially supported by the  Russian Ministry for Higher Education and Science  (contract no. 075-15-2021-1399 ). Publisher's note: the article processing charges for this publication were not paid by a Russian or Belarusian institution. ISIMIP data preparation and curation is supported by the  German Federal Ministry of Education and Research (BMBF)  under the  ISI-Access research  project (grant no.
16QK05 ). Data from lakes Annecy, Bourget, and Geneva were collected and stored with support from the  © OLA-IS, AnaEE-France ,  INRAE of Thonon-les-Bains , CIPEL, SILA, and CISALB (Rimet et al. , 2020(Rimet et al. , , https://doi. org/10. 4081/jlimnol. 2020(Rimetet al. ,. 1944)). Data from Lake Kivu were collected with support from  Jean-Pierre Descy  and the  Belgian Science Policy Office  through the research project  EAGLES  (grant no. CD/AR/02A ). Alo Laas and data collection from Estonian lakes was funded by  Estonian Research Council  (grant nos. PSG32 ,  PRG709 , and  PRG1167 ). Data from Lake Burley Griffin were collected by the  National Capital Authority, Canberra, Australia. Dale Robertson  was supported by the.
Code and data availability. The global ISIMIP2a lake sector simulations are available at https://doi. org/10. 48364/ISIMIP. 530719(Marcé et al. , 2022a). The global ISIMIP2b lake sector simhttps://doi. org/10. 5194/gmd-15-4597-2022Geosci. Model Dev. , 15, 4597-4623, 2022.
Computational experience Computational experiments were conducted on a Workstation HP Intel(R)-Xeon(R) at 3. 5 GHz with 64 GB RAM (Win 10 Pro, 64 bits) with a processor with 6 cores, and considering only one thread. The solution algorithms have been implemented in C++ with ILOG Concert Technology API  (CPLEX 12. 10). The reminder of this section is organized as follows. In Section 6. 1, we provide a description of the test instances. Section 6. 2 summarizes the results of the tests performed to evaluate the proposed formulations, and to calibrate the main KSHFP parameters (kernel and buckets size). Finally, Section 6. 3 describes the final performance comparison of the different solution algorithms. All the instances used for the tests as well as detailed preliminary and final results can be accessed at https: //github.
Acknowledgments The research of D. L. Huerta-Muñoz  has been partially supported by the  Mexican National Council for Science and Technology (CONA-CYT). The research of  E. Fernández  has been partially funded by the  Spanish MINECO  and  ERDF  funds through grant  MTM2019-105824GB-I00. The research of  F. Perea  has been partially funded by the  Spanish ''Ministerio de Ciencia ,  Innovación y Universidades '' through grants  RTI2018-094940-B-100  and  MTM2016-74983 , and from the '' Generalitat Valenciana, Spain '' through grant  AICO/2020/049. This support is gratefully acknowledged. The authors also wish to thank the anonymous reviewers whose comments helped improving a former version of the paper.