dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusions In this paper we examine nearly 4 decades of global LAI changes under rising atmospheric CO 2 concentration. We find that Earth's greening trend is weakening and clusters of browning are beginning to emerge and, importantly, have expanded during the last 2 decades. With one exception, all analyzed satellite observation datasets confirm these results but with different signal strengths. Leaf area is primarily decreasing in the pan-tropical green belt of dense vegetation. Leaf area gain is occurring mostly in sparsely vegetated regions in cold and/or arid climatic zones and in temperate forests. Thus, vegetation greening is occurring mainly in regions of low LAI, whereas browning is seen primarily in regions of high LAI. Consequently, these opposing trends are decreasing the texture of leaf area distribution in natural vegetation.
, 2017). The uncertainty in future projections is large, ranging from a stable CO 2 fertilization-driven carbon sink to a collapse of the system at a certain CO 2 concentration  (Cox et al. , 2000). Concerning leaf area, the models project a steady greening of the tropical forests in the high-end CO 2 emissions scenario (business as usual) and a slight browning in the low-end scenario (mitigation) by the end of the century  (Piao et al. , 2019). Altogether, the tropical forests have the potential to crucially influence the evolution of climate throughout the 21st century and should be a vital issue for future research. Code and data availability. All data used in this study are available from public databases or the literature, which can be found with the references provided in the respective "Materials and methods" subsection.
Processed data and analysis scripts are available from the corresponding author upon request, and the repository was published under https://zenodo. org/record/5348210 together with this article. Correspondence and requests for materials should be addressed to Alexander J. Winkler (alexander. winkler@mpimet. mpg. de or awinkler@bgc-jena. mpg. de). Video supplement. The animation shows how the leaf area index on the African continent has evolved over the last 4 decades. The spotlight is on tropical forests, where the initial greening signal during the first 2 decades shifts to a declining trend in the course of the last 2 decades (https://doi. org/10. 5446/51213; Böttinger and Winkler, 2021). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/bg-18-4985-2021-supplement.
Acknowledgements. We thankfully acknowledge  Taejin Park  and  Chi Chen  for their help with remote sensing data (GIMMS LAI). We also acknowledge  Qian Zhao ,  Zaichun Zhu , and  Zhiqiang Xiao , who provided further remote sensing LAI datasets (GLOBMAP LAI and GLASS LAI), and thank  Ulrich Weber  for his assistance in processing various datasets. We thank  Philippe Peylin ,  Matthias Rocher ,  Andrew J. Wiltshire ,  Sebastian Lienert , and  Anthony P. Walker  for providing model output as part of the TRENDYv7 ensemble. Alexander J. Winkler  wishes to thank  Thomas Raddatz  and  Veronika Gayler  for their support in working with the  MPI-M Earth System Model. We gratefully acknowledge  Thomas Riddick  for his review and valuable comments on the manuscript.
Some groups suggested using circulating soluble ACE2 to capture as many viruses as possible in plasma, restricting their fixation on cell-membrane ACE2 and thus limiting cell infection. In vitro studies showed that genetically engineered recombinant soluble ACE2 could be a useful therapeutic option  [17, 30]. Moreover, soluble ACE2 might favor the angiotensin-(1-7)/ACE2/MasR pathway over the angiotensin II/ACE/AT1R/aldosterone pathway, preventing or treating severe inflammatory tissue lesions  [11]. Human recombinant soluble ACE2 is an FDA-approved treatment since 2013, with a 2017 phase-II trial in acute respiratory distress syndrome  [31] , which would allow rapid transfer to COVID-19 application. A dedicated clinical trial must start soon (NCT04287686). Angiotensin II is also hypothesized to confer protection in COVID-19 infection.
Some studies reported high levels of ACEi/ARB in COVID-19 cases, and especially in severe forms requiring hospital or ICU admission, although it should be noticed that arterial hypertension is very common in the general population, especially in the elderly, for which ACEi/ARB is prescribed in 25-30% of cases  [5]. Thus the positive or negative impact of ACEi/ARB in COVID-19 in humans remains unknown and appears complex  [50] , possibly even depending on clinical stage (viral contamination phase versus tissue inflammation phase)  [51, 52]. Studies of this question are forthcoming (NCT04330300). Early data extracted from a retrospective cohort performed in China are in favor of a protective impact on overall mortality of ACEis/ARBs but these results must be consider with caution because of the presence of several potential confounders factors not included in analyses  [53].
In line with in vitro results, it is suggested that ARBs but not ACEis may confer protection in COVID-19 infection by preventing onset of severe tissue lesions  [25, 54] ; two clinical trials of losartan (ARB) in COVID-19, with or without hospital admission, are due to be launched (NCT04312009 and NCT04311177). Thus, many scientific societies, including the French Society of Arterial Hypertension (SFHTA)  [55]  and European Society of Cardiology  [56] , and many publications  [5, 56, 57]  advise against stopping maintenance treatments in an attempt to ''prevent'' COVID-19 infection, especially as imbalance in blood pressure and heart failure may be deleterious. There remains the difficult and unanswered question of long-course ACEi/ARB therapy in case of COVID-19 infection, especially when severe.
(1994) ,  Sloan et al. (1996) ,  and Haywood et al. (2000) , the complexity and number of climate models used to study the MP has since increased substantially (e. g. Haywood and Valdes, 2004). This progression culminated in 2008 with the initiation of a co-ordinated international model intercomparison project for the Pliocene (Pliocene Model Intercomparison Project, PlioMIP). PlioMIP Phase 1 (PlioMIP1) proposed a single set of model boundary conditions based on the U. S. Geological Survey PRISM3D dataset  (Dowsett et al. , 2010)  and a unified experimental design for atmosphere-only and fully coupled atmosphere-ocean climate models  (Haywood et al. , 2010 (Haywood et al. , , 2011)). PlioMIP1 produced several publications analysing diverse aspects of MP climate. The large-scale temperature and precipitation response of the model ensemble was presented in  Haywood et al.
g. Lunt et al. , 2012). Haywood et al. (2013a, b)  discussed the possible contributing factors to the noted discrepancies in DMCs, noting three primary causal groupings: uncertainty in model boundary conditions, uncertainty in the interpretation of proxy data and uncertainty in model physics (for example, recent studies have demonstrated that this model-proxy mismatch has been reduced by including explicit aerosol-cloud interactions in the newer generations of models;  Sagoo and Storelvmo, 2017; Feng et al. , 2019). These findings substantially influenced the experimental design for the second phase of PlioMIP (PlioMIP2). Specifically, PlioMIP2 was developed to (a) reduce uncertainty in model boundary conditions and (b) reduce uncertainty in proxy data reconstruction. To accomplish (a), state-of-the-art approaches were adopted to generate an entirely new palaeogeography (compared to PlioMIP1), including accounting for glacial isostatic adjustments and changes in dynamic topography.
Boundary conditions All model groups participating in PlioMIP2 were required to use standardized boundary condition datasets for the core midPliocene-eoi400 experiment (for wider accessibility, this experiment will hereafter be referred to as Plio Core ). These were derived from the U. S. Geological Survey PRISM dataset, specifically the latest iteration of the reconstruction known as PRISM4  (Dowsett et al. , 2016). They include spatially complete gridded datasets at 1 • × 1 • of latitudelongitude resolution for the distribution of land versus sea, topography and bathymetry, as well as vegetation, soils, lakes and land ice cover. Two versions of the PRISM4 boundary conditions were produced which are known as enhanced and standard. The enhanced version comprises all PRISM4 boundary conditions including all reconstructed changes to the land-sea mask and ocean bathymetry.
Experimental design The experimental design for Plio Core and associated PI control experiments (hereafter referred to as PI Ctrl ) was presented in  Haywood et al. (2016a; this special issue) , and the reader is referred to this paper for full details of the exhttps://doi. org/10. 5194/cp-16-2095-2020 Clim. Past, 16, 2095-2123, 2020 perimental design. In brief, participating model groups had a choice of which version of the PRISM4 boundary conditions to implement (standard or enhanced). This approach was taken in recognition of the technical complexity associated with the modification of the land-sea mask and ocean bathymetry in some of the very latest climate and earth system models. A choice was also included regarding the treatment of vegetation. Model groups could either prescribe vegetation cover from the PRISM4 dataset (vegetation sourced from  Salzmann et al.
To assess the relationship across the ensemble between the reported ECS and the modelled ESS, we correlate reported ECS across the ensemble with the associated Plio Core -PI Ctrl temperature anomalies. We do this on global, zonal mean Clim. Past, 16, 2095 Past, 16, -2123 Past, 16, , 2020  https://doi. org/10. 5194/cp-16-2095-2020 and local scales. A strong correlation at a particular location would suggest that MP proxy data at that location could be used to derive proxy-data-constrained estimate of ECS (similar to an emergent constraint), while a weak correlation would suggest that proxy data at that location could not be used in ECS estimates.
3a. Overall, the ensemble mean anomaly (dashed black line) is fairly constant throughout the year; however, models within the ensemble have very different characteristics in terms of the monthly and seasonal distribution of the warming. Some members of the ensemble have a relatively flat seasonal cycle in SAT (e. g. NorESM-L, NorESM1-F, COSMOS); however, others show a very strong seasonal cycle. The models that show a very strong seasonal cycle do not agree on the timing of the peak warming. For example, EC-Earth3. 3 has the peak warming in October, CESM2 has peak warming in July, and MRI2. 3 has peak warming in January/February. The lack of consistency in the seasonal signal of warming has interesting implications in terms of whether PlioMIP2 outputs could be used to examine the potential for seasonal bias in proxy datasets.
In many cases, the tropical oceans show a temperature anomaly more strongly related to ECS than the land, although this is not always the case. 4 Data-model comparison  Haywood et al. (2013a, b)  proposed that the proxy data and climate model comparison in PlioMIP1 could include discrepancies owing to the comparison between time averaged PRISM3D SST and SAT data and climate model representations of a single time slice. In order to improve the integrity of the data-model comparisons in PlioMIP2,  Foley and Dowsett (2019)  synthesized alkenone SST data that can be confidently attributed to the Marine Isotope Stage (MIS) KM5c time slice that experiment Plio Core is designed to represent. Foley and Dowsett (2019)  provide two different SST datasets. One dataset includes all SST data for an interval of 10 000 years around the time slice (5000 years to either side of the peak of MIS KM5c), and the other covers 30 000 years (up to 15 000 years to either side of the peak; this latter dataset will hereafter be referred to as F&D19_30).
Age models used in the compilation are those originally released with the datasets, but later modifications of age models or the integration of additional data could result in mean SST values different from those reported in F&D19_30. All SST estimates are calibrated using  Müller et al. (1998). Prescott et al. (2014)  demonstrated that due to the specific nature of orbital forcing 20 000 years before and after the peak of MIS KM5c, age and site correlation uncertainty within that interval would be unlikely to introduce significant errors into SST-based DMC. Given this and in order to maximize the number of ocean sites where SST can be derived, we carry out a point-based SST data-model comparison using the F&D19_30 dataset. We compare the multi-model mean SST anomaly to a proxy SST anomaly created by differencing the F&D19_30 dataset from observed pre-industrial SSTs derived for years 1870-1899 of the NOAA Extended Reconstructed Sea Surface Temperature (ERSST) version 5 dataset  (Huang et al.
Nevertheless, what emerges from the comparison of the PlioMIP2 SST ensemble to the F&D19_30 SST dataset is a nuanced picture of widespread data-model agreement with specific areas of concern. Data model comparisons undertaken for PlioMIP1 indicated that the PlioMIP1 ensemble overestimated the amount of SST change as a zonal mean in the tropics  (Dowsett et al. , 2012 (Dowsett et al. , , 2013;; Fedorov et al. , 2013). In PlioMIP2 pointbased comparisons, there is little indication of a systematic mismatch between the data and the models. Models and proxy data appear to be broadly consistent in the tropics. The F&D19_30 dataset is comprised of alkenone-based SSTs. In contrast, the PRISM3D dataset used for DMC in PlioMIP1 was time averaged and composed of estimates from a combination of faunal analysis, Mg/Ca and alkenone-based SSTs.
Tierney et al. (2019)  demonstrated that the PlioMIP1 ensemble compared well to alkenone-based SST estimates in the tropical Pacific for the whole mid-Pliocene Warm Period, not just the PlioMIP2 time slice, when the alkenone-based temperatures were recalculated using the BAYSPLINE calibration. Therefore, the choice of proxy and inter-proxy calibration alone can be enough to alter the interpretation of the extent to which the model and data agree. In addition, comparing the PlioMIP2 results to an additional dataset of published SSTs for the time slice  (McClymont et al. , 2020) , we see that the first order outcome of data-model comparison is the same as that shown by the comparison to F&D19_30. The Pliocene minus pre-industrial SST anomaly will not only depend on which SST dataset is chosen to represent the Pliocene but also on the choice of observed SST dataset used for the pre-industrial era.
Another region of data-model mismatch noted in PlioMIP1 was the North Atlantic Ocean (NA). Haywood et al. (2013a)  noted a difference in the model-predicted (multimodel mean) versus proxy-reconstructed (PRISM3D) warming signal of between 2 to 7 • C in the NA. The PlioMIP2 multi-model mean SST anomaly appears to be broadly con-sistent with the F&D19_30 dataset with an SST anomaly at two sites matching to within 1 • C and the other to within 3 • C (Fig. 8 ). There are several possible ways to account for this apparent improvement. Firstly, the total number of sites in the NA in F&D19_30 is reduced compared to the PRISM3D SST dataset  (Dowsett et al. , 2010). The site that led to the 7 • C difference noted in  Haywood et al. (2013a)  is not present in the F&D19_30 dataset. Secondly, the PlioMIP2 experimental design specified both the Canadian Archipelago and Bering Strait as closed.
Past, 16, 2095 Past, 16, -2123 Past, 16, , 2020  https://doi. org/10. 5194/cp-16-2095-2020 upper ocean temperature gradient in the region today. This lends some credence to the idea that the observed mismatch between PlioMIP2 SST and the F&D19_30 proxy-based anomaly could arise from the complexities and/or uncertainties associated with interpreting alkenone-based SSTs in the region as simply an indication of mean annual SST  (Leduc et al. , 2014). However, we note that no seasonal bias has been identified in the modern dataset in the Benguela region (Tierney and Tingley, 2018).
For PlioMIP1, Hargreaves and Annan (2016) also found that modelled Plio Core -PI Crtl SST anomalies over the tropics (30 • N-30 • S) were correlated with modelled ECS, according to ECS = α T 30 • N -30 • S + C + ε, (2) where α and C are constants and ε represents all errors in the regression equation. They then used Eq. (  2 ) along with tropical SST data from PRISM3D (an interpolated dataset of Pliocene proxy SST) to provide a Pliocene-data-constrained estimate of ECS of 1. 9-3. 7 • C. In order to constrain ECS from the data and modelling used in PlioMIP2, we slightly amend the Hargreaves and Annan (2016) methodology because PlioMIP2 proxy data are more sparsely distributed than PlioMIP1 proxy data, and we cannot obtain a reliable estimate of tropical average SST from the data available. To estimate ECS for PlioMIP2, we instead rely on point-based observations (Fig.
6-4. 8 • C. This is larger than the values suggested from PlioMIP1 (1. 9-3. 7 • C). -The PlioMIP2 model ensemble shows broad agreement on the polar amplification of the global warming signal and tropical enhancement of rainfall anomalies. Intermodel differences in simulated temperature are mostly found in polar regions and where the land-sea mask and orography of Pliocene palaeogeography differ from today. -The PlioMIP2 ensemble appears to be broadly reconcilable with new temporally specific records of sea surface temperatures. Significant agreement between simulated and reconstructed temperature change is seen with notable local signals of data-model disagreement. Differences between observed pre-industrial sea surface temperature datasets are large enough to have a significant impact on how well models reproduce proxyreconstructed ocean temperature changes.
Acknowledgements. We acknowledge the use of NOAA_ERSST_V5 data provided by the  NOAA/OAR/ESRL PSD, Boulder, Colorado, USA , from their website at https://www. esrl. noaa. gov/psd/(last access: 12 September 2019). Alan M. Haywood ,  Julia C. Tindall ,  Aisling M. Dolan ,  Stephen J. Hunter  and  Daniel J. Hill  acknowledge the  FP7 Ideas  programme:  European Research Council  (grant no. PLIO-ESS, 278636 ), the  Past Earth Network  ( EPSRC  grant no. EP/M008. 363/1 ) and the  University of Leeds Advanced Research Computing service. Julia C. Tindall  was also supported through the  Centre for Environmental Modelling and Computation (CEMAC) ,  University of Leeds. Harry J. Dowsett  and  Kevin M. Foley  acknowledge support from the  USGS Climate Research and Development Program. This research used samples and/or data provided by the  Ocean Drilling Program (ODP)  and  International Ocean Discovery Program (IODP).
Statistical Analyses of Phenotypic Data To test for a WD effect at the phenotypic level, a two-way analysis of variance (ANOVA) was performed for each trait separately. The level and significance of the G × W interaction was assessed with the following model: y ij = µ + G i + W j + GxW ij + ε ij. In this model, y ij represents the phenotype of genotype i (G i ) in watering condition j (W j ) and GxW ij and ε ij are the genotype × watering regime interaction and residual errors, respectively. For each trait, phenotypic plasticity (PP) was calculated according to the following formula: PP i = (y WDy control )/y control , where PP i represents the plasticity (WD response) for each genotype (i) and y WD (or y control ) the average phenotype in the WD (or control) condition. The average response of the eight genotypes was further evaluated by computing the relative stress impact: RSI = Y WD -Y control /Y control , where Y represents the average value across all genotypes for a trait, given a condition.
Differential Gene Expression Analysis Differential expression (DE) analysis was performed using a negative binomial generalized linear model with the Bioconductor and R package DESeq2 1. 14. 1  [32]. The impact of WD on transcriptome variation was evaluated in fruit and leaf samples separately. The analysis was restricted to genes with at least 20 read counts across samples, encompassing 23,552 and 22,864 genes for leaf and fruit samples, respectively. The impact of genotype and watering condition on transcriptome variation was first graphically evaluated through principal component analysis (PCA) on the normalized gene expression. Normalized counts were transformed with the variance stabilizing transformation (VST) before performing the PCA analysis. Thereafter, genes showing differential expression according to the watering condition were identified for each genotype and for fruit and leaf organs separately.
Gene Ontology Enrichment Analysis Gene ontology (GO) enrichment analysis was performed using the R-Bioconductor package goseq (version 1. 36. 0) [34]. Enriched GO terms were investigated for the sets of DEGs in fruit and leaves separately. For each organ, the DEGs were separated into three different categories (up, down and up-down) depending on the pattern of the expression regulation. The up (or down) category involves all DEGs that were consistently up-(or down-) regulated across genotypes or in at least one genotype. The up-down category involved DEGs showing different regulation patterns (i. e. , upregulated in one genotype and downregulated in another and vice-versa). Only annotated genes were considered for the GO analysis. Significant GO terms for biological processes (BPs) and molecular function (MF) were selected after multiple testing correction by setting an FDR threshold at a 5% cutoff with the Benjamin and Hochberg method  [33].
Funding: This work was performed in collaboration with the  GeT core facility, Toulouse, France  (http://get. genotoul. fr), and was supported by  France Génomique National  infrastructure, funded as part of the " Investissement d'avenir" program  managed by  Agence Nationale pour la Recherche  (contract  ANR-10-INBS-09 ). "The work was also supported by the  ANR  project  ADAPTOM   ANR-13-BSV7-012. I. D. was supported by the  WAAPP (West Africa Agricultural Productivity Project)  fellowship and hosted as Ph. D. student in the INRA UR1052, GAFL.
This study focuses on daytime low-level clouds (LLCs) that occur within the first 2 km of the atmosphere over West Africa (WA). These daytime LLCs play a major role in the earth's radiative balance, yet their understanding is still relatively low in WA. We use the state-of-the-art ERA5 dataset to understand their occurrence and associated drivers as well as their impact on the incoming surface solar radiation in the two contrasting Guinean and Sahelian regions of WA. The diurnal cycle of the daytime occurrence of three LLC classes namely No LCC, LLC Class-1 (LLCs with lower fraction), and LLC Class-2 (LLCs with higher fraction) is first studied. The monthly evolutions of hourly and long-lasting LLC (for at least 6 consecutive hours) events are then analyzed as well as the synoptic-scale moisture flux associated with the long-lasting LLC events.
g. ,  Schrage and Fink, 2012; Schuster et al. , 2013; Kalthoff et al. , 2018; Dione et al. , 2019; Lohou et al. , 2020; Pedruzo-Bagazgoitia et al. , 2020). The majority of those studies were carried out within the framework of two major field campaigns; the African Monsoon Multidisciplinary Analysis (AMMA)  (Redelsperger et al. , 2006)  in 2006 and the Dynamics-Aerosol-Chemistry-Cloud Interactions in West Africa (DACCIWA)  (Knippertz et al. , 2015)  in 2016. Both of these field campaigns provided an unprecedented database of surface-based observations of different cloud properties. Other studies on LLCs in the region have also been performed with satellite observations and model data  (van der Linden et al. , 2015; Adler et al. , 2017; Hannak et al. , 2017). These studies suggest that LLC formation is linked to a number of processes including but not limited to cooling caused by horizontal cold-air advection and the occurrence and strengthening of the nocturnal low-level jet  (Schrage and Fink, 2012; Adler et al.