dataesr/scientific-paragraphs-categorization huggingface dataset:
One further aim of the refactoring is to better enable scalable development to different architectures. On the physics side, the use of thin layers in the upper ocean, which is possible with the implemented new z * coordinate, the inclusion of river discharge, and a better representation of energy and ocean mixing will be in focus. An important aspect of the conducted simulations is workflow. Often data problems are data management problems; for instance, orders of magnitude improvements in data access can be achieved by intelligently structuring and indexing the data. Moreover, encoding and decoding approaches from machine learning are being developed to interpolate from sparse data, allowing for a reduction of disk space usage. Despite the remaining technical challenges, ICON-Sapphire already allows performing unique multi-decadal climate simulations, with explicit interactions between small and large scales as well as between the components of the Earth system -ocean, atmosphere, land, and cryosphere -including carbon at the forefront of exascale climate computing.
A valuable perspective would be to study the dynamics of roach under chronic anthropogenic noise to test whether the behavioural responses we observed ultimately decrease survival and/or reproductive success  [61] , and result in densitymediated cascading effects. The data that support the findings of this study are open available in Zenodo at https://doi. org/10. 5281/zenodo. 6760791. Sididae, chao: Chaoborus larvae, ostra: ostracods, daph: Daphniidae). See Table  1  for the percentages of variance accounted for and the significance levels. FIGURE LEGENDS.
ACKNOWLEDGEMENTS We thank the  CEREEP Ecotron Ile-De-France  ( CNRS/ENS UMS 3194 ) for access to the PLANAQUA experimental facilities and help during the experiments. A special thanks to  Beatriz Decencière  and  Jacques Mériguet  for their help in the realization of the experiment. We are also grateful to  Mathieu Mullot  for assistance during the PRC analysis.
Abstract. Heat waves can be one of the most dangerous climatic hazards affecting the planet, having dramatic impacts on the health of humans and natural ecosystems as well as on anthropogenic activities, infrastructures and economy. Based on climatic conditions in West Africa, the urban centres of the region appear to be vulnerable to heat waves. The goals of this work are firstly to assess the potential uncertainties encountered in heat wave detection and secondly to analyse their recent trend in West Africa cities during the period 1993-2020. This is done using two state-of-the-art reanalysis products, namely the fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA5) and Modern-Era Retrospective analysis for Research and Applications (MERRA), as well as two local station datasets, namely Dakar-Yoff in Senegal and Aéroport Félix Houphouët-Boigny, Abidjan, in Côte d'Ivoire.
An estimate of station data from reanalyses is processed using an interpolation technique: the nearest neighbour to the station with a land sea mask ≥ 0. 5. The interpolated temperatures from local stations in Dakar and Abidjan show slightly better correlation with ERA5 than with MERRA. Three types of uncertainty are discussed: the first type of uncertainty is related to the reanalyses themselves, the second is related to the sensitivity of heat wave frequency and duration to the threshold values used to monitor them, and the last one is linked to the choice of indicators and the methodology used to define heat waves. Three sorts of heat wave have been analysed, namely those occurring during daytime, nighttime, and both daytime and nighttime concomitantly. Four indicators have been used to analyse heat waves based on 2 m temperature, humidity, 10 m wind or a combination of these.
(2018)  investigated the intraseasonal variability of large-scale heat waves during spring using the Berkeley Earth Surface Temperatures (BEST) gridded dataset and the following reanalyses: the European Centre for Medium-Range Weather Fore-casts interim reanalysis (ERA-Interim), Modern-Era Retrospective analysis for Research and Applications (MERRA; see Sect. 2. 2 for more details), and the National Centers for Environmental Prediction Reanalysis 2 (NCEP-2). They defined heat waves using anomalies of minimum/maximum values of the 2 m temperature. They found some discrepancies in the characteristics, variability and climatic trends of heat waves in the different products. Largeron et al. (2020)  analysed the April 2010 heat wave in North Africa using both the BEST dataset and climate simulations from the atmospheric component of the Centre National de Recherches Météorologiques (CNRM) climate model.
They showed a strong link between heat waves over the Sahara and the incoming heat surface fluxes. Another important result of this work is the radiative effect of water vapour on minimum temperatures during the heat wave period. This can lead to extreme heat conditions during the night and the death of elderly people. Guigma et al. (2020)  analysed the characteristics and thermodynamics of Sahelian heat waves using different thermal indices based on temperature, wind speed and relative humidity derived from fifth-generation ECMWF reanalysis (ERA5; see Data section for more details). They found that most of the regions in the Sahel experience on average one or two heat waves per year with a duration of 3-5 d and severe magnitude. They have also shown that the eastern Sahel experiences more frequent and longer events.
03 (CRU TS4. 03) and BEST datasets as well as the following reanalysis datasets: ERA5, MERRA-2 and the Japanese Meteorological Agency's 55-year reanalysis (JRA-55). They highlighted large differences in both the trend and the temporal evolution of heat wave indices between the different reanalyses. They found a peak of heat over northern and western Africa in 2010 as well as in 2016 over eastern and southern Africa. They noticed significant warming and an increase in heat wave occurrence in all the regions in Africa. However,  Engdaw et al. (2022)  focused only on dry heat waves over a large domain of West Africa (10 • S-15 • N, 20 • W-20 • E); the duration of heat waves was not addressed and nor was the evolution of wet heat waves. The most lethal heat waves are due not only to high temperatures but also to the effect of humidity  (Steadman, 1979a, b) ; hot and humid conditions (as is the case in coastal regions) can be more dangerous than equivalently hot but dry conditions  (Wehner et al.
To achieve these objectives, we first assess the biases in the reanalyses (ERA5, MERRA) using ERA5 as a reference; then, a sensitivity analysis of the frequency of heat waves with respect to the threshold values, indicators and methods applied to define heat waves is addressed. Finally, we assess the spatial and temporal variability (seasonal and interannual) of heat waves and their characteristics in different climatic regions over West Africa. The remainder of this article is organized as follows: in Sect. 2, we present the regions of interest and the data used for this work; the description of the methodology is also provided. Section 3 contains the main results of this study following the methodology described in Sect. 2. In Sect. 4, the uncertainty in the reanalyses and the origin of coastal heat waves are discussed.
25 • × 0. 25 •. ERA5 is produced using 4D-Var data assimilation and the Cycle 41r2 (Cy41r2) of the ECMWF Integrated Forecasting System (IFS), which was operational in 2016. MERRA reanalysis has a spatial resolution of 0. 625 • × 0. 5 • with 42 standard pressure levels. MERRA uses an upgraded version of the Goddard Earth Observing System model version 5 (GEOS-5) data assimilation system and the Gridpoint Statistical Interpolation (GSI) analysis scheme of  Wu et al. (2002). MERRA is produced using a 3D-Var data assimilation algorithm. These two reanalyses datasets can be accessed through the Climserv database from the Institut Pierre-Simon Laplace (IPSL) server. To be consistent in our analyses, we transformed the spatial resolution of MERRA from 0. 625 • × 0. 5 • to 0. 25 • × 0. 25 • to match the one of ERA5; this is done using a first-order conservative interpolation.
We use hourly data covering the period from 1 January 1993 to 31 December 2020 for both ERA5 and MERRA. Our choice of ERA5 and MERRA to conduct this study is supported by some previous work showing that these two reanalyses included among the most relevant used in African regions (e. g. Barbier et al. , 2018; Ngoungue Langue et al. , 2021; Engdaw et al. , 2022). As the main objective here is to process heat wave detection, we focus on atmospheric variables at the surface such as 2 m temperature (T 2 m ), 2 m relative humidity (RH), 2 m dewpoint temperature, 2 m specific humidity, 10 m wind components, and water vapour pressure (e) from which wet-bulb temperature (T w ) and apparent temperature (AT;  McGregor et al. , 2015)  were derived. These atmospheric variables have a significant impact on human thermal comfort  (McGregor et al.
263 • p • q • exp 17. 67 • (T -T 0 ) T -29. 65 -1 (5) T 0 = 273. 16 K. T ( • C), T d ( • C), T 0 (K), p (hPa), W s (m s -1 ) and q are the ambient temperature, dewpoint temperature, reference temperature, pressure, wind speed and specific humidity respectively. The land sea mask dataset used in this work was derived from ERA5 reanalysis; it can be accessed on the Copernicus Climate Data Store (CDS). T 2 m daily maximum and minimum observations at Dakar-Yoff station in Senegal and Aéroport Félix Houphouët-Boigny (FHB) station in Côte d'Ivoire were used to evaluate our interpolation method. This is due to the fact that we do not have access to other station data in these regions. The data from Dakar-Yoff extend from 1 January 1973 to 31 December 2018 and contain almost 16 % missing values, and the data from Aéroport FHB extend from 1 January 2005 to 31 December 2017 and contain 0.
Estimation of atmospheric variables at the scale of cities Reanalysis datasets used for weather studies are generally run at a global scale; therefore information at a local scale is missing in many regions; this is a critical issue in regions where there is a lack of observation stations as is the case for African cities. To overcome this problem, downscaling methods can sometimes be used. In this work, we study phenomena at the scale of the cities, and reanalyses (ERA5 and MERRA) have too coarse a spatial resolution. The scales of the reanalyses are more representative of the spatial variability of a heat wave occurring in a city than at an isolated local station. Nevertheless, some validation of the test stations needs to be done, in particular to find the best interpolation technique to estimate local temperatures from the reanalyses.
This is especially important over the coastal regions. Indeed, most of the cities used in this study are located along the coast and influenced by the ocean air masses (see Fig. 1 ). The evaluation of the spatial variability of the correlation between the local-scale variable (station) and reanalyses (ERA5) for T 2 m , for example, showed high correlation values over the continent (Fig. S2 )  (Dakar, Abidjan). This suggests that the station data are well correlated with ERA5 grid points which are located on the continent; it is therefore necessary to know whether an ERA5 grid point is over the continent or not before applying an interpolation technique. To estimate the proportion of land on a grid point, we used the land sea mask (lsm) with values ranging from 0 to 1. The land sea mask is a measure of the land occupation on a grid point.
5) (panels c and f in Fig. S3a ), the selection of the nearest grid point of reanalyses from the station with different values of lsm (≥ 0. 5, 0. 75 and 1; we show only results for lsm ≥ 0. 5) (panels'b and e in Fig. S3a ), a dynamical interpolation approach taking into account the effect of winds (not shown). The interpolation method was applied to ERA5 and MERRA, and the resulting estimated data were compared to the station data by correlation analysis. We found that ERA5 appears to be slightly better than MERRA at both stations (Dakar and Abidjan) for minimum and maximum T 2 m values (Fig. S3b ).
bool max,t,w and bool min,t,w are Boolean time series which contain 0 if the day is not part of a heat wave and 1 if the day is part of a heat wave for maximum and minimum daily values of the indicators respectively. bool min-max,t,w is a Boolean time series which indicates 1 if the day is part of a heat wave detected simultaneously with both the minimum and the maximum values of indicators and 0 if this is not the case. N d is the length in days of the study period. The mean duration and intensity are used to assess the severity of the heat wave. 2. 3. 4 Evaluation of the products using statistical metrics (hit rate, ACC, GSS) Most regions in Africa suffer from a lack of observations due to a small number of available weather stations. To access information over a large domain, we use ERA5 and MERRA reanalysis datasets, which are very consistent in representing large-scale processes in the Saharan area  (Ngoungue Langue et al.
Hit rate The hit rate, also known as the "hit", is a measure of the fraction of events detected in an evaluated dataset knowing that the events occur in the reference at the same time. It is given by the following formula: hit = TP TP + FP. ( 10 ) TP denotes true positives, which are events correctly detected by the two datasets at the same time; FP denotes false positives, which are events not detected by the evaluated dataset but that occurred in the reference. Hit values range from 0 to 1; hit = 1 means that all the events observed in the evaluated dataset occurred in the reference. Previous work such as  Olauson (2018)  and  Ramon et al. (2019)  have shown that ERA5 provides a good representation of various near-surface meteorological variables including near-surface humidity and wind speed, in comparison to others reanalyses, including MERRA.
This means that daytime and nighttime heat waves occur frequently and simultaneously over the Sahel with T w. From this result, we can deduce that humidity plays a major role in the occurrence of concomitant heat waves, which are very dangerous for human health. In this section, we show the high sensitivity of heat wave detection to the methodology applied and to the variables used as indicators. The role of humidity in heat wave occurrence in the coastal region has also been highlighted. Similar results are found with MERRA reanalysis (not shown). In summary, the heat wave detection is influenced by many parameters: the dataset, threshold values, indicators and methodology used to define such an event. There is a high dependency between these parameters and the climatic region investigated.
Acknowledgements. This work is supported by the  French National Research Agency  in the framework of the  STEWARd project  under grant  ANR-19-CE03-0012  ( 2020-2024 ). Financial support. This research has been supported by the  Agence Nationale de la Recherche  (STEWARd; grant no. ANR-19-CE03-0012 ). Review statement. This paper was edited by  Maria-Carmen Llasat  and reviewed by two anonymous referees.
Code availability. The codes to perform heat wave detection, data interpolation and the rest of the analyses are available on request; please feel free to contact us. The codes were implemented using CDO, R (heatwaveR) and Python libraries. Data availability. The data used in this study are mostly open access. Data from the ERA5 and MERRA reanalyses are available online at the Climate Data Store (CDS: https://doi. org/10. 24381/cds. adbb2d47, Hersbach et al. , 2023)  and on the NASA (https://doi. org/10. 5067/VJAFPLI1CSIV, GMAO,  2015)  website respectively. Local station data are not freely available; feel free to write to us if you need them. Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/nhess-23-1313-2023-supplement.
Anthropogenic activities, in particular rising CO 2 emissions, provoke ocean warming and acidi cation 1,2 , altering plankton habitats and threatening calcifying species 3,4 such as planktonic Foraminifera (PF). Whether they can cope with these unprecedented rates of environmental change, through lateral migrations and vertical displacements, is unresolved. Here we show, using over a century of data from the FORCIS 5 global census counts, that PF display evident poleward migratory behaviours, increasing their diversity at mid to high latitudes, and for some symbiont-barren species descending in the water column. Global PF abundance decreased by 24. 24±0. 11% over the last decades. Beyond lateral migrations 6 , our study uncovers intricate vertical migration patterns among PF species, presenting a nuanced understanding of their adaptive strategies.
Shift in latitudinal diversity gradient The effects of anthropogenic changes on plankton can be investigated through the analysis of long-term time series of observations  20, 21. The FORCIS database comprises approximately 188,000 PF subsamples drawn from oceans across the globe since 1910  5, 19. Speci cally, regional data from the North Atlantic Ocean stands out for its remarkable temporal and spatial resolution, boasting PF time series that extend, locally, over half a century (Extended Data Fig. 1 ). Atlantic mid-latitudes (30°N to 50°N) emerge as a key biodiversity hotspot, containing a wide spectrum of subtropical and temperate species (Fig. 1A ), although reported measured abundances are not particularly high (Extended Data Fig. 2 ). When comparing the species diversity of modern PF samples taken from the water column in the FORCIS database with the species diversity of PF samples from surface sediments (ForCenS database  22  ), an evolution in the latitudinal diversity gradient during the early Anthropocene is revealed (Fig.
1B  and Extended Data Fig. 3 ). Over the last century, species diversity in the mid to high latitudes has slightly exceeded pre-industrial levels, with 3 to 24 species (between 48° to 80°N), compared to 4 to 18 species in pre-industrial times (Fig. 1B ). Excluding samples with only a single adult specimen, which may be artifacts resulting from the transport of dead individuals from elsewhere, modern diversity surpasses pre-industrial levels by up to 10 species in the modern North Atlantic and Paci c regions (Extended Data Fig. 3 ). Several PF species show a poleward shift between the preindustrial (ForCenS database) and the modern era (FORCIS) (Extended Data Fig. 4 ), implying a change between Holocene and modern distribution patterns. Notably, G. scitula, a mixed-layer dweller in the temperate oceans, has expanded its modern ecological niche in FORCIS, now appearing at high latitudes (80°N) (Extended Data Figs.
92±0. 94 km/year 31 and 7. 20±1. 35 km/year 9 , outpacing terrestrial species that migrated polewards at about 1. 11 ± 0. 96 km/year  31. This migration is consistent with recent modeling work  15  , who predicted a median poleward migration speed of 3. 5 km/year for all plankton. The upper limit of the PF poleward migration speed (10. 28 km/year) corresponds exactly to the pace of thermal shifts of the oceanic isotherms (about 10 km/year)  32. This strongly suggests that PF closely tracks warming fronts, outpacing other planktonic groups. A unique feature of the FORCIS database is the possibility to track vertical changes in the planktonic distribution over time and at the species level. At low latitudes, only 3 species appear to descend to greater depths over the past decades, the most robust signal being the 45±21 m vertical deepening, between samples taken before and after 1997, for the symbiont-barren species G.
2B ). The vertical descent of certain species, whose habitats align with the deep chlorophyll maximum, matches the anthropogenically driven deepening of the thermocline  7. While speci c cases exhibit notable vertical migrations, a comprehensive analysis of the PF in the FORCIS database indicates limited changes in overall vertical distribution where most of the PF species were not showing a deepening in their depth habitat. This suggests that vertical migration is in uenced by the trophic regime of each species, and in any case slower than the deepening of isotherms, estimated at -6. 6+18. 8 m/decade between 1980 and 2015, predicted to accelerate to -32 m/decade by the end of the century under a high emission scenario  33  , which we speculate will limitate the ability of PF to survive or reproduce in these warmer environments.
Methods 1. Data The data is sourced from FORCIS  5, 19  and ForCenS  22  databases covering the post-industrial and the preindustrial time-periods, respectively. Seasons for both databases were distinguished between the Northern and Southern Hemispheres and follow the meteorological seasons. For the Northern Hemisphere autumn is de ned by September, October, November; winter by December, January, February; spring by March, April, May and summer by June, July, August. For the Southern Hemisphere, spring is de ned by September, October, November; summer by December, January, February; autumn by March, April, and winter by June, July, August.
FORCIS database (post-industrial) More than 188 000 subsamples (one single plankton aliquot collected within a depth range, time interval, size fraction range, at a single location) coming from ~163 000 samples collected from different oceanographic environments by plankton nets (~22 000 subsamples from ~6 000 samples), Continuous Plankton Recorders (CPR) (~157 000 subsamples), sediment trap (~9 000 subsamples), and plankton pump (~400 subsamples) from the global ocean (Extended Data Fig. 1 ), were extracted from the FORCIS database  5, 19. This data include literature published and unpublished data between 1910 and 2018. Total abundance of the PF and species-speci c counts for these samples were extracted from FORCIS.
Data and taxonomy harmonization To perform data harmonization and address methodological and taxonomic biases, we converted the abundance data extracted from FORCIS into number concentration (individuals/m 3 ). Speci cally, where coarse fractions were sampled using a mesh size greater than 100 μm, we employ the approach described in  49  to standardize them. This allows us to estimate the number concentration of the coarse fractions down to 100 μm to a high degree of accuracy, based on eld calibration, and reconstruct the assemblage as it would have appeared if the material had been sampled with a 100 μm net. Predicting the abundance of very small or rare species is still challenging, and those data are not interpreted here. Moreover, the FORCIS dataset has been assessed and corrected for taxonomic discrepancies over time  5  , and though some species might have been misidenti ed, it appears quite unlikely as overall mostly large PF, whose taxonomy is easier to identify, were collected in the early part of the record (i.
Environmental data Temperature and Ω calcite estimates associated with each PF North Atlantic sample (Fig. 4 ) were taken from the IPSL-CM6A-LR global climate model  51  , in its CMIP6 historical simulation, version r22i1p1f1, with a monthly resolution. The model output was converted to the World Ocean Atlas spatial grid, with a 1x1° resolution. Ω calcite was computed by dividing the modeled in situ carbonate ion concentration (denoted co3 in the CMIP6 nomenclature) by the carbonate ion concentration at saturation with respect to calcite (denoted co3satcalc). Temperature and Ω calcite were then associated with each sample of the FORCIS database by linearly interpolating the model output temperature to the latitude, longitude, depth and time of sampling. For multinet, CPR and pump data, we used the averaged sampled depth.