dataesr/scientific-paragraphs-categorization huggingface dataset:
Moreover, most of the previous studies have been limited to the WAM season (e. g. ,  Knippertz et al. , 2011; Schuster et al. , 2013; van der Linden et al. , 2015; Dione et al. , 2019). This is understandable as WA is cloudiest during the WAM season. However, it was shown that daytime LLCs exist in all seasons especially over southern WA  (Danso et al. , 2019). It is therefore important to understand the nature of such daytime LLCs over the whole region and not only during the WAM season. This is particularly challenging due to the lack of a long-term surface-based observational dataset in WA, thus limiting our understanding of these cloud systems. A few studies, which were done with simulations, reanalysis, and satellite data in the region (e. g. ,  Knippertz et al. , 2011; Schuster et al.
, 2013; van de Linden et al. , 2015; Adler et al. , 2017)  have nevertheless shown results similar to those of ground observational studies (e. g. ,  Adler et al. , 2019; Babić et al. , 2019b)  to some extent. In this general context, the main contribution of this study is to enhance our understanding of daytime LLCs in WA by focusing on daytime LLCs in both the dry and wet seasons with a state-of-the-art reanalysis dataset. Firstly, we will present the seasonal and diurnal distributions of the occurrence frequency of the daytime LLCs in two areas of WA with contrasting climate regimes (the Sahelian and the Guinean regions). We will then identify synoptic-scale conditions (specifically the moisture flux) associated with the presence of the daytime LLCs. Additionally, we will investigate the impact of LLC on surface heat fluxes and estimate the percentage of incoming surface shortwave (SW) radiation that is attenuated when these clouds are present.
This paper is organized as follows: Sect. 2 describes the study area, datasets, and methodology. Section 3 presents the diurnal and monthly distribution of the occurrence of daytime LLCs, while the synoptic and local conditions associated with the occurrence of long-lasting LLCs are discussed in Sect. 4. Section 5 presents the estimated attenuation of incoming shortwave radiation during the occurrence of LLCs. Finally, conclusions are drawn in Sect. 6.
Thus, a 25 MW solar power plant is under construction in the northern part of the country in Korhogo while two solar power plant projects with a capacity of 30 MW each have been approved and will be built in the localities of Tuoba and Laboa in the northwestern part of the country (MPEER, 2019). Additionally, there are several planned and existing solar energy projects in the Sahel window, especially in Burkina Faso with the 33 MW capacity Zagtouli solar power plant  (Moner-Girona et al. , 2017) , which is currently the largest solar energy project in the Sahelian part of WA. Occurrence or non-occurrence of LLC are first identified from the extracted data points based on the quantitative value of the CF in the two selected windows. This is used to show the diurnal and monthly distribution of the LLC occurrence.
Conclusions In this paper, we made use of the state-of-the-art hourly reanalysis dataset from ECMWF-ERA5 from 2006 to 2015 to analyze the occurrence of daytime LLCs in WA. The analysis was performed for both the wet (JAS) and dry (DJF) seasons and focused on two climatically contrasting areas in the region (i. e. , Guinea and the Sahel). We have first identified events of LLC occurrence in both regions. We then analyzed some regional-and local-scale environmental conditions which occur during those events of LLC occurrence. This analysis mainly focused on the moisture flux before, during and after the LLC occurrence. We also analyzed the impacts of the LLC occurrence of on the surface heat fluxes. https://doi. org/10. 5194/esd-11-1133-2020 Earth Syst. Dynam. , 11, 1133-1152, 2020 Finally, the attenuation of incoming shortwave radiation during the occurrence of LLC in the region was estimated.
LLC Class-2 leads to mean attenuations of 49. 1 % and 44. 2 % in Guinea and the Sahel, respectively. In DJF, mean attenuations of 13. 3 % and 32 % are experienced in Guinea for LLC Class-1 and Class-2 events, respectively. In the Sahel, the occurrence of LLC Class-1 events leads to a 16. 3 % attenuation of incoming shortwave radiation. It should however be noted that ERA5 slightly overestimates cloud fraction in the lower atmosphere over WA (see  Supplement). By using the ERA5 cloud cover dataset in this study, we have provided important information on daytime LLCs in WA -a region notably known for its lack of surface observations. Previous studies have mostly focused on nighttime LLCs in the southern part of WA and only during the WAM season. Here, we expanded to the Sahel region and investigated the occurrence of LLCs during the dry season as well.
Acknowledgements. We are grateful to the  Institute of Environmental Geosciences (IGE; University of Grenoble -Alpes, France)  and to  Laboratoire de Physique de l'Atmosphère et de Mécanique des Fluides  ( LAPA-MF; University Felix Houphouet Boigny, Côte d'Ivoire ), who hosted DKD during his stay in Grenoble and Abidjan in the framework of the  International joint laboratory NEXUS  on climate, water, land, energy and climate services (LMI NEXUS). Financial support. This research has been supported by the  France National Research Institute for Sustainable Development IRD (Institut de Recherche pour le Développement, France). Review statement. This paper was edited by  Gabriele Messori  and reviewed by three anonymous referees.
Data availability. We are grateful to the institutions that provided the data used in this work: ERA5 data are publicly provided by the Copernicus Climate Data Store (CDS), https:// cds. climate. copernicus. eu/cdsapp#!/home (last access: 18 September 2019; Hersbach et al. , 2018); the 2B-GEOPROF-LIDAR data product is provided by the CloudSat Data Processing Center, http://www. cloudsat. cira. colostate. edu/data-products/level-2b/ 2b-geoprof-lidar (last access: 18 August 2020; Mace and Zhang,  2014); surface measurements of shortwave radiation are provided by the AMMA-CATCH Database (Galle et al. , 2018)  and can be accessed on request to the managers of the AMMA-CATCH Database. Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/esd-11-1133-2020-supplement.
Scientific conferences provide valuable opportunities for researchers across career stages and disciplines to present their latest work, and to network with their peers. These meetings have largely been held in-person with rapid proliferation in the number of meetings and attendees. Yet the format and quality of their organization lag behind what is possible and as a result, the current experience of attending conferences in many disciplines remains unchanged in many respects. We created a database of 270 national and international academic conferences held in-person during 2018-2019 in various disciplines and examined them for their features, costs and impact on the community. We found that many meetings could still be improved significantly in terms of diversity, inclusivity, promoting early career researcher (ECR) networking and career development, venue accessibility, and importantly, reducing the meetings' carbon footprint.
We highlight key ongoing issues and provide recommendations for improving research conferences (Figure  1 , Tables  S2-S11 )  (4). By switching from the existing in-person conference model to equitable and nearly carbon-neutral conferences, improvements will be made in a plethora of areas, with positive short-and long-term impact on research and research culture. Summary of an analysis of a database of 270 conferences held in-person and organized by over 150 scientific societies and other organizations during 2018-2019 in various scientific fields including Engineering, Physics, Mathematics, Neuroscience, Genomics, Chemistry, Ecology, Evolution, Cell Biology and Immunology (Tables  S2-S11 )  (4). These scientific societies combined have ~1,658,602 researchers registered members attracting a combined ~859,114 attendees to their meetings.
The number of attendees varies from 100 to over 30,000 for a single event (Tables  S1, S6 ) (4), and most conferences (74%, 199/270 in our database (4)) are held annually (Table  S4 ). This means that the average three-day, 1,000-person national conference generates over 580 tons of planet-warming CO2 emissions every year. The total carbon footprint from a single annual meeting of the Society for Neuroscience, hosting 31,000 attendees, is equivalent to the annual carbon footprint of 1000 medium-sized laboratories  (16). The ~859,114 attendees of 270 conferences held during 2018-2019 collectively generated ~2 million tons of CO2 in the form of air travel and other activities to attend these meetings (Table  S8 ). Multiplying these amounts of CO2 generated by the hundreds and thousands attending a single conference (Table  S1 , S8), shows why curbing emissions through diminishing scientific air travel and in-person conference attendance must become a priority for every researcher, university, scientific society, and funding agency.
Additionally, all conference materials such as abstracts, videos of talks, posters and presentation slides can be uploaded to open access platforms such as Zenodo, protocols. io. or channels dedicated to preprinting conference proceedings on bioRxiv with a Digital Object Identification (DOI). Such sharing does not entail extra work for researchers, enables citation of these outputs on manuscripts and curriculum vitae and allows review on-site or via peer-review platforms such as PREreview  (33). Digital conference libraries allow researchers in all time zones and those unable to attend these conferences asynchronously. Widespread preprinting of research results can reduce the number of conferences held overall (Table  S13 , example 14).
Create a central database for scientific conferences Conferences are often not well-characterized, evaluated or promoted within and across disciplines and countries; making it difficult and timeconsuming for researchers worldwide, in particular ECRs, to identify the most beneficial (Figures  5, 6 ) gatherings. Attendee and speaker gender and career stage statistics are seldom reported online by conference organizers. A central database (4) can help keep researchers well-informed on the past and future meetings and the key considerations of environmental sustainability and equity that all scientific conferences must possess (4).
Oftentimes not all avenues for seeking female speakers are not explored  (52). Overcoming biases involves not only awareness but also positive action. Blinding the selection of talks, removing conflicts of interest, utilizing open databases listing ECRs from underrepresented groups (53), and having fewer long talks to make time for more short talks may all help ensure that minority groups of scientists are welcomed and equally represented in conferences (20,54) (Table  S13 , example 10). Funders and meeting organizers need to pledge and enforce gender equity policies and public inclusion declarations and guidelines, and publish the names of invited speakers and session chairs in advance of the conference program. Conference attendees can express conditional attendance, lifted only after the invitation of men and women speakers in a fair and balanced manner.
Acknowledgements The authors would like to thank  Drs. Richard Sever ,  Ronald Vale ,  Alexandre Bisson Filho ,  Dominic Trépel ,  Edward Emmott ,  Renuka Kudva ,  Steven Burgess ,  Jason Fernandes ,  Andy Tay ,  Vinodh Ilangovan  and  Inez Lam  for valuable comments on an earlier version of the manuscript.
The Atmospheric Model LMDZ is the atmospheric General Circulation Model (GCM) that has been developed for about 30 years at the Laboratoire de Météorologie Dynamique (LMD). The versions of LMDZ used for Phases 5 and 6 (hereafter called 6A) of CMIP together with the configuration adopted are described in  Hourdin et al. (2006)  and  Hourdin et al. (2020). The main contribution of IPSL to CMIP5 was done with a package hereafter called AP and referred to as "IPSL-CM5A" in the CMIP5 database. Version 6A is an improved version of the "new physics" package, a preliminary version of which has been tested for CMIP5  (Hourdin et al. , 2020)  and is referred to as "IPSL-CM5B-LR" in the CMIP5 database. The changes from the AP to the "new physics" version are linked to a complete rethinking of the parameterizations of turbulence, convection, and clouds and are described in  Hourdin et al.
The Land Surface Model The land surface is described by the ORCHIDEE model v2. 0. The ORCHIDEE model v2. 0 computes primarily the fluxes of energy, water, and carbon that are exchanged between the different soil and plant reservoirs and the exchange of these fluxes with the atmosphere. In addition, it computes the stocks of water and carbon in the different soil and plant reservoirs and the energy stored in the different soil and snow layers. Model state variables are prognostic, including the Leaf Area Index (LAI), as they are updated at each time step after the calculation of the fluxes between all reservoirs. The module computing dynamically the LAI, the vegetation albedo, and the soil water stress function applied to transpiration is activated for all CMIP experiments, except for the HighResMIP one (see section 2.
4) that uses prescribed values. The vegetation properties are defined by plant functional types (PFTs), and their fraction within each grid cell is globally set from land cover maps that were derived specifically for the CMIP6 simulations  (Lurton et al. , 2020). These maps combine the historical maps from the land use harmonization database (LUH2v2h,  Hurtt et al. , 2011)  and the maps derived from satellite observations  (Bontemps et al. , 2015). See https://orchidas. lsce. ipsl. fr/dev/lccci/, for more information. The water and energy budgets are computed at the same timestep as the atmospheric physics  (Hourdin et al. , 2020)  using classical soil-vegetation-atmosphere transfer (SVAT) parameterizations. The most relevant modification since the version used for CMIP5 is related to the soil hydrology, the snow scheme, and the background albedo.
The continental ice-covered surfaces (ice sheets and glaciers) are not included in ORCHIDEE, but they are treated in a specific module within LMDZ. Momentum and heat roughness heights as well as visible and near infrared albedos are set to constant values representative of snow conditions over the Antarctic Plateau  (Vignon et al. , 2018). The heat transfer in the snow and ice is parameterized as a conductive process with a fixed thermal inertia  (Hourdin, 1992). The vertical grid is made of 11 vertical levels to represent the e-folding damping of thermal waves with typical periods from 1,800 seconds to 240 years. The value of the snow thermal inertia was calibrated to obtain realistic surface temperature and diurnal cycle amplitudes in Antarctica  (Vignon et al. , 2017). Le  Quéré et al. (2018)  have recently used a version of ORCHIDEE (referred to as Orchidee-Trunk), which is simular to the version used for CMIP6 in an intercomparison project focussing on the carbon and water fluxes where 15 other land surface models (LSMs) were involved.
fluxdata. org/data/la-thuile-dataset/)). Still in forced mode, the dynamic roughness heights impact the river discharge at the scale of individual watersheds with significant improvements for the Danube and the Mississippi watersheds (not shown). The impact of activating the dynamical roughness height in coupled simulations is limited for the considered space and time scales (see section 3. 4), but the option is activated for all CMIP6 simulations. 10. 1029/2019MS002005.
, 2016)  experiments, which is very rare compared to the (365 × 144 × 142 × 96) times that the calculation is performed in one year of simulation. The CMIP6 experiments presented in this paper have not been rerun due to the time constraint imposed by the CMIP exercise; however, an a posteriori correction method has been developed. All the CMIP6 data that have been affected by this problem have been either unpublished or corrected a posteriori and republished. The a posteriori correction method applied to the published data is given in Appendix B with an estimation of the associated uncertainties, which is of the order of several tenths of degrees for the daily values. Due to the low value of the reconstruction errors for the monthly mean values (30 times less than the reconstruction errors of the daily values), it was decided not to make a correction to the monthly values.
Setup of the Simulations To document the impact of the changes described in the previous sections, simulations forced by observed SST and SIC are produced by combining final versions of atmospheric physics (AP and 6A) and of the soil hydrology (Choi and ctrl), namely, APChoi (corresponding to the IPSL-CM5A in the CMIP5 database), APctrl, 6AChoi, and 6Actrl (corresponding to the IPSL-CM6A in the CMIP6 database) (Table  1 ). A monthly mean climatology of SST and SIC calculated over the years 1978-2008 is used for the simulations in order to minimize the impact of the interannual variability in the evaluation. The 6Actrl experiment is also compared with the results of the AMIP experiment for which a 20-member ensemble has been produced and is published in the CMIP6 database. The impact of the new developments is documented thanks to an additional set of sensitivity experiments with the 6A physics, where the new features of the LSM and the ABL are individually tested (Table  2 ).
The total column integrated water vapor is evaluated using the reanalysis and extension of the NASA Water Vapor Project (NVAP) data set which comprises a combination of radiosonde observations, Television and Infrared Operational Satellite (TIROS) Operational Vertical Sounders (TOVS), and Special Sensor Microwave/Imager (SSM/I) data sets  (Vonder Haar et al. , 2012). The river discharges are extracted from the Global Runoff Data Center (GRDC) database  (Milliman & Farnsworth, 2011). The Snow Cover Extent (SCE) is extracted from the output from the Interactive Multisensor Snow and Ice Mapping System (IMS) at the National Ice Center (NIC) processed at Rutgers University and included in the NOAA Climate Data Record (CDR) of Northern Hemisphere (NH) Snow Cover Extent. For the minimum and maximum daily temperature, we used the Climatic Research Unit (CRU) Time-Series (TS) version 4.
The cold bias is probably overestimated over Greenland, the Artic Ocean, and Antartica due to a warm bias diagnosed in ERA-I  (Jakobson et al. , 2012; Reeves Eyre & Zeng, 2017; Vignon et al. , 2018). When using ERA-5, as reference data set instead of ERA-I, the bias over Greenland is 10. 1029/2019MS002005.
Journal of Advances in Modeling Earth Systems for ctrl (Table  4 ), albeit this result is modulated at regional scale (Figure  6 ). Investigating the minimum and maximum daily temperature shows a widespread warm bias of daily minimum temperature over the midlatitude (Figure  8 ). This bias is present over the whole year for the AP physical package used for CMIP5, and only in JJA for the 6A package used for CMIP6, it is very marginally sensitive to the land surface scheme. This is consistent with the reduction of the turbulent mixing in the PBL for the stable boundary layers obtained with the 6A atmospheric physics and with the results of  Wei et al. (2017) , which suggested that a bias in the simulated PBL mixing could very likely contribute to the temperature bias common to most of the models that participated to CMIP5 with AMIP experiments.
Yet, the positive bias of all simulated discharges might rather be related to massive irrigation in this basin  (Guimberteau et al. , 2012) , which is not taken into account in these simulations. 2. The observations refer to the GPCP product for precipitation and to the Global Runoff Data Center (GRDC) database for the river discharges  (Milliman & Farnsworth, 2011). The gray shaded areas indicate the interannual variability of the observed precipitation in the basin area (upper panel) and the interannual variability of the river discharge at the measurement stations. 10. 1029/2019MS002005 Journal of Advances in Modeling Earth Systems.
For the majority of grid points and days, the OFF and ON methods give similar results. The reconstruction error lies within the range (-0. 2 K, 0. 4 K). These small differences between the two methods for daily values show that the near-surface temperature is not fundamentally modified by the OFF correction compared to what would be obtained with an on-line correction. The reconstruction errors for the monthly mean near-surface temperature would have been negligible compared to the daily errors, being 30 times smaller than daily errors. national program LEFE/INSU. The CMIP6 project at IPSL used the HPC resources of TGCC under the allocations 2016-A0030107732, 2017-R0040110492, and 2018-R0040110492 (project gencmip6) provided by GENCI. One of the authors (Y. Zhao) benefited from the French state aid managed by the ANR under the "Investissements d'avenir'' program with the reference ANR-11-IDEX-0004 -17-EURE-0006.
This study benefited from the ESPRI (Ensemble de Services Pour la Recherche l'IPSL) computing and data centre (https://mesocentre. ipsl. fr), which is supported by CNRS, Sorbonne Universite, Ecole Polytechnique, and CNES and through national and international grants. Analysis presented in 4. 14. 1 benefited from the support of the CMUG and ESA-CCI programs (http://www. esa-cmug-cci. org). We also thank two anonymous reviewers for their helpful comments on the original manuscript.
Figure13. Multiannual mean seasonal cycle of the precipitation (upper panel) and of river discharge (lower panel) observed and simulated for 14 major river basins and for the four reference experiments: 6Actrl, Choi6A, APctrl, and ChoiAP and for the "NoSnowFreez" experiment described in Table2. The observations refer to the GPCP product for precipitation and to the Global Runoff Data Center (GRDC) database for the river discharges (Milliman & Farnsworth, 2011). The gray shaded areas indicate the interannual variability of the observed precipitation in the basin area (upper panel) and the interannual variability of the river discharge at the measurement stations.
Results Transcriptional adaption after liver injury. To assess global transcriptional shifts in hepatocytes at single-cell resolution following acute liver injury, we employed scRNA-Seq to characterize response dynamics in both PH and APAP models, capturing the injury, regeneration, and termination phases of liver regeneration  4  (Fig. 1b, c ). We profiled a total of 16,019 cells across 19 different experiments to an average sequencing depth of >48,000 reads/cell (Supplementary Fig. 1a-c , Supplementary Methods). Immune and endothelial cell types, as well as low-quality cells, were filtered out from the dataset, retaining 10,762 high-quality hepatocyte transcriptomes for subsequent analyses (Supplementary Fig. 1d , e, Supplementary Data 1, Methods). Shared nearest neighbor clustering (SNN) visualized on a t-Stochastic Neighbor Embedding (t-SNE) plot revealed hepatocyte populations that cluster by injury model and post-injury time point (Fig.
Since regression changed very little, other than downweighting technical differences in cell quality, and the biological signals on which this work focuses were robust to regression, we opted to use the non-regressed dataset in our downstream analysis to avoid possible introduction of artificial variation. APAP injury resulted in pericentral necrosis after 6 h as demonstrated by histological analysis (hereafter A6; Fig. 1b, c ). Hepatocytes scoring high for a pericentral hepatocyte signature (PCHSig) were absent at 6 h post-APAP (A6, Fig. 1f ). Surprisingly, at 24 h post-APAP, the pericentral hepatocyte expression signature returned (A24, Fig. 1f ), despite histology showing persistent pericentral necrosis (A24, Fig. 1b, c ). In particular, expression of two typically pericentrally restricted genes-Cyp2e1, responsible for metabolizing APAP, and Glul, which assimilates ammonia into glutamine-was maintained, or returned, following pericentral injury.