dataesr/scientific-paragraphs-categorization huggingface dataset:
Daily metrics over West Africa In the following, similar diagnostics are presented to evaluate the selected daily metrics. To reduce the number of figures in the core of the paper, some of them are presented in the Supplement (three metrics in the core of the paper, three others in the Supplement). A more complete metrics report is available at http://www. amma2050. org/content/climate-metrics. Figures  11  and 12  shows the results for the tas 95th percentile of daily values of near-surface air temperature. WFD, WFDEI, and EWEMBI provide similar values in summer (Fig. 11 ; see also Table  2 ) with the highest values over the Sahara in spring (up to 40 • C, not shown), moving northward in summer, and with weaker values in autumn (up to 32 • C; not shown). WFD values appear to be a bit higher than the two other reference datasets.
Figures 13 and 14 provide similar analysis for the 95th percentile of daily pr. WFD, WFDEI, and EWEMBI provide fields consistent with the ITCZ location including high values over the mountain areas (Fig. 13 ). WFDEI and EWEMBI have very similar fields while the range of values for WDF is very different, with values higher than 30 mm day -1 in the ITCZ in summer in contrast with values lower than 20 mm day -1 for the two other reference datasets (see also Table  2 ). A similar range of differences is present over the Guinea area in spring and to a lesser extent in autumn (not shown). Such differences are also large over the mountain areas (Fouta Djalloon, Cameroon). CDF-t bias-corrected GCM data have remaining weak biases relative to WFDEI, lower than ±2 mm day -1 , except for IPSL-CM5A-LR, where differences up to +5 mm day -1 are located north of 10 • N.
Compared to the 95th percentile of daily tas, Taylor diagrams (Fig. 14 ) again show the good performance of the CDF-t bias-correction method for the 29 GCMs, but with a bit higher distance to REF for both the Sahel and Guinea areas. ISIMIP bias-corrected GCM data are more scattered than CDF-t-corrected GCMs in relation to their respective reference dataset, WFD and WFDEI, and WFD is located far from the WFDEI REF in terms of normalized standard deviation and centred RMSE (see also Table  2 ). Finally, Figs. 15 and 16 provide similar analysis for the number of days with pr > 10 mm day -1. WFD, WFDEI, and EWEMBI provide values consistent with the ITCZ location including high values over the mountain areas (Fig. 15 ). In contrast to the previous metric, WFD has a more similar range of values relative to WFDEI and EWEMBI, with some overestimation, especially over Nigeria, Cameroon, and central Africa.
The spatial variance is higher than for the two previous metrics with a higher contrast between mountain and plain areas. Remaining biases in the CDF-t-corrected data are localized over mountain areas with mostly negative biases, but also over plains with mostly positive biases in the ITCZ area and especially extended for IPSL-CM5A-LR. Taylor diagrams (Fig. 16 ) once more show a good performance of the CDF-t correction method to remove biases and reduce inter-GCM dispersion. ISIMIP bias-corrected GCMs have a higher dispersion than CDF-t-corrected GCMs relative to their respective reference dataset.
The simulation of these processes makes SARRA-O particularly suited for the analysis of climate impacts on cereal growth and yield in dry tropical environments (see for instance  Sultan et al. , 2013). Several sen-sitivity simulations have been carried out. First SARRA-O has been forced for each year from 1979 to 2001 by WFD, WFDEI, and EWEMBI data. Second, the IPSL-CM5A-LR model has been used to force SARRA-O over the same years, with raw, CDF-t bias-corrected, and ISIMIP biascorrected data. The simulations have been compared to the "GDHY" dataset (1981-2001) of 1. 125 • gridded yield estimation. GDHY is a hybrid of FAO country yield data, satellite-derived crop-specific vegetation index and global crop datasets on crop calendar, harvested area, and production shares achieved by different growing season.
Subnational yield statistics have been used to validate the gridcell yield estimates  (Iizumi et al. , 2014). Note that SARRA-O provides potential yields that can be different from observed yields, so this comparison with the GDHY dataset must be considered as indicative only. Finally, sensitivity to individual variables has been conducted by comparing the SARRA-O simulation forced with WFDEI data with simu- lations where one WFDEI variable is replaced by the corresponding raw IPSL-CM5A-LR data. Figure  17  compares the simulated crop yields over the Sahel and Guinea areas when SARRA-O is forced either by WFD, WFDEI, or EWEMBI and by the raw, CDF-t, or ISIMIP bias-corrected IPSL-CM5A-LR model. GDHY data are also shown as an evaluation. Over the Guinea area, the differentiation of ensembles of simulations is quite clear.
The raw IPSL-CM5A-LR simulation has the highest yields (∼ 2200 kg ha -1 ) while WFD and associated ISIMIP biascorrected simulations have the lowest yields (∼ 240 and 180 kg ha -1 respectively). The four remaining simulations, based on WFDEI and associated CDF-t bias-corrected data and EWEMBI and GDHY data, have intermediate yields, between 700 and 1000 kg ha -1. Thus it is shown first that SARRA-O maize yields are quite sensitive to the different forcing datasets, second that WFD lead to simulated yields far from the GDHY data while WFDEI and EWEMBI leads to quite better yields, and finally that the raw GCM and GCM corrected with WFD are also quite far from the validation data while the GCM corrected with WFDEI has a rather good performance. The simulation forced by EWEMBI has a higher mean value than WFDEI (∼ 760 and 1030 kg ha -1 respectively), and GDHY has yields ranging between WFDEI and EWEMBI (∼ 980 kg ha -1 ), close to EWEMBI.
Over the Sahel area, the curves are closer but some similar conclusions can be drawn. WFD and associated ISIMIP biascorrected simulations provide the lowest yields (∼ 400 and 370 kg ha -1 respectively). WFDEI, EWEMBI, and CDF-t bias-corrected simulations are very close (∼ 660, 650, and 710 kg ha -1 respectively). Finally, in contrast to the Guinea area, GDHY data have the highest yields (∼ 980 kg ha -1 ), far from other simulations. The raw simulation (∼ 590 kg ha -1 ) is close to the WFDEI, EWEMBI, and CDF-t bias-corrected simulations. This last point is quite surprising since raw IPSL-CM5A-LR data have large biases. Figure  18  shows the maps of mean simulated yields for raw IPSL-CM5A-LR, WFDEI, and CDF-t bias-corrected EWEMBI simulations, GDHY data, and the difference between EWEMBI and WFDEI simulations.
1 ). Comparisons of WFDEI and EWEMBI interannual time series of yields and associated tas, pr, and rsds on individual grid points in these areas confirm that these yield differences are linked exclusively to rsds differences. Finally, maps of simulated yields from WFD and ISIMIP bias correction confirm the weak values over all of West Africa due to an underestimation of rsds south of 10 • N (not shown). To go further, a sensitivity analysis to individual variables has been conducted by comparing the SARRA-O simulation forced with WFDEI data with simulations where one of these WFDEI variables is replaced by the corresponding raw IPSL-CM5A-LR data. These variables are pr, rsds, tas-min, and tasmax, and also rsds from ISIMIP bias-corrected IPSL-CM5A-LR (using WFD as reference). Table  3  shows the mean yields for the Sahel and Guinea areas and the resulting biases relative to WFDEI simulations.
Biases are very weak with tasmin-tasmax simulations (WFDEItminmax), a bit higher for pr simulations (WFDEIpr) and for rsds simulations (WFDEIrsds) and drastically large for rsds from ISIMIP bias-corrected simulations (WFDEIWFDrsds). Thus rsds appears as a very critical variable for maize yields simulated with SARRA-O, confirming a previous study based on an older version, SARRA-H, of the crop model  (Oettli et al. , 2011). SARRA-O has also been run over the period 1950-2099 using the RCP8. 5 projection, forced by ISPL-CM5A-LR in terms of raw, CDF-t bias-corrected, and ISIMIP biascorrected data. Figure  19  shows, on the one hand, the resulting time series of maize yields over the Sahel and Guinea boxes and, on the other hand, the maps of yields from CDFt bias-corrected data over 1979-2001 and 2077-2099 and the resulting difference between these two periods.
Time series of standardized yield anomalies to their respective mean over 1979-2001 are also displayed. In agreement with the previous analysis, ISIMIP bias-corrected forcing data (with WFD as reference data) lead to the lowest yields over both the Sahel and Guinea areas at the present time but also over the whole 21st century. Over the Guinea area, the very high simulated yields coming from raw data are drastically reduced with CDF-t bias-corrected forcing data (with WFDEI as reference data) while over the Sahel area these yields are rather similar. After CDF-t bias correction, yields are quite similar over the two areas. Interannual variability in simulated yields is proportional to the mean with a very weak variability for ISIMIP yield and higher variability for CDF-t and raw simulations.
Conclusions The objectives of this paper are (i) to introduce a new biascorrected dataset for which the CDF-t correction method has been applied to CMIP5 GCM daily data for the first time over Africa, (ii) to quantify the effect of using different reference datasets on the corrected data, (iii) and to illustrate this effect on crop simulations over West Africa. This bias correction has been applied over the period 1950-2099, combining historical runs and RCP scenarios with 29/27/20 GCMs for RCP8. 5/4. 5/2. 6 respectively. It has been applied to six variables critical for agricultural impacts: daily accumulated pr, daily mean, minimum and maximum near-surface air temperature, daily mean surface downwelling shortwave radiation, and daily mean wind speed. The use of different bias-correction methods also based on different reference datasets contributes to the total uncertainty in climate projections and can contribute in some contexts more than the use of different GCMs or RCMs  (Iizumi et al.
, 2017). So using multiple bias-correction techniques   3  but for the number of days when precipitation is greater than or equal to 10 mm day -1. and reference datasets is highly recommended. In this context, CDF-t bias-corrected GCM data have been compared to the five GCMs ISIMIP bias-corrected data, and the impact of the different reference datasets, WFD (used in ISIMIP bias corrections), WFDEI (used in CDF-t bias corrections), and the more recent EWEMBI (used in a second version of ISIMIP bias corrections), has been examined in detail. Crop simulations have also been carried out to test how the impact of bias corrections in forcing data (temperature, pr, rsds) is integrated in terms of crop (maize) yields. Finally, bias corrections have also been presented in the context of RCP8. 5 scenarios.
The whole observational period, 1979-2013, has been chosen to calibrate the bias-correction process. It has been shown that using various calibration sub-periods has a weak impact, in particular on the time evolution over the 21st century. The evaluation of CDF-t bias correction applied to the 29 GCMs, both to mean seasonal data and to daily metrics, has shown that CDF-t is very effective in removing the biases in respect to the reference WFDEI data and in reducing the high inter-GCM scattering. It has also shown some distance, depending on variables and metrics, from biascorrected ISIMIP GCM data, mainly due to the differences between WFDEI and WFD reference data. WFDEI (and associated CDF-t bias-corrected GCMs) appears closer to EWEMBI than WFD (and associated ISIMIP bias-corrected GCMs).
Metrics based on temperature are very close for the three reference datasets, and some differences exist in pr-based metrics. In contrast, significant differences have been highlighted in terms of rsds. This has consequences in terms of crop (maize) yields over West Africa. Sensitivity simulations performed with one GCM have shown that bias corrections improve the yields simulated by the raw GCM. However, the ISIMIP bias-corrected GCM still underestimate them as CDF-t bias-corrected GCMs do but with yield estimates closer to observed ones. EWEMBI provides the closest yields to observed estimates. This is mainly due  to rsds whose values are underestimated in WFDEI south of 10 • N. Finally, in agreement with maize yield sensitivity simulations, projections of future yields over West Africa have quite different levels depending on the bias-correction method.
However, they all show a similar relative decreasing trend over the 21st century. The main perspective of this work is to go on exploring the uncertainty linked to bias-correction methods and their associated reference data in RCP climate scenarios by producing a second version of this bias-corrected 29-GCM ensemble over Africa using more recent reference data like EWEMBI or others like those used in AgMIP based on other reanalyses (AgMERRA or AgCFSR;  Ruane et al. , 2015). The main divergence among all those reference datasets is probably expected from rsds. Bias correction for other variables useful for user-based metrics like specific humidity is also scheduled. Comparison between CDF-t and ISIMIP bias-correction methods based on the same reference dataset is also ongoing. The CFD-t bias correction has been applied independently for each of the six variables.
However, this may be a problem since existing spatial coherency and dependence among variables may be destroyed by the application of univariate calibrations. Recently, to address this issue, improved calibrations have been developed in terms of multivariate cor- rection and spatial and/or temporal dependences (see for instance Vrac and Friederichs, 2015, for a synthesis). Implementation of more sophisticated methods using multivariate correction is also ongoing. This work constitutes a first step in producing biascorrected datasets over Africa within AMMA-2050. An atlas is in preparation that will provide extensive results over Africa to the FCFA stakeholders and end-user communities. These communities will be accompanied by FCFA climate scientists in order to be aware of the way to use these data and their limitations.
Acknowledgements. The research leading to these results has received partial funding from the  NERC/DFID   Future Climate For Africa programme  under the  AMMA-2050  project, grant number  NE/M019934/1. The lead author has been also supported by  IRD. Mathieu Vrac  has been partly funded by the  ANR   StaRMIP  project. We acknowledge the  World Climate Research Programme 's Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modelling groups (listed in Table 1 of this paper) for producing and making their model output available. For CMIP the  US Department of Energy's Program for Climate Model Diagnosis and Intercomparison  provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth.
Data availability. The ISIMIP Fast Track data are available at https://doi. org/10. 5880/PIK. 2016. 001 (Hempel et al. , 2013b)   and the EWEMBI dataset at https://doi. org/10. 5880/pik. 2016. 004 (Lange, 2016). The CDF-t bias-corrected CMIP5 data over Africa are available at http://amma2050. ipsl. upmc. fr/. To access the data, users must contact the lead author at moflod@locean-ipsl. upmc. fr.
ACKNOWLEDGMENTS The project leading to this publication has received funding from  Excellence Initiative of Aix-Marseille University -A*MIDEX, a  French "Investissements d'Avenir" programme. It has been partly funded by  France Life Imaging Grant  No. ANR-11-INBS-0006. It has received funding from the  European Union 's  Horizon 2020 Research and Innovation programme  under Grant Agreement No. 736937. We also thank  Institut Carnot STAR  for support. R. A. , M. D. , and S. E. worked on the physics and the basic principles of the HMA for  UHF-MRI. M. D. , R. A. , L. L. , A. V. , and S. E. worked on the modeling and the design of the HMA. MRI experiments were conducted by L. L. , Z. R. , and A. V. A. A. and F. M. developed the B þ.
6 au from the Sun, where Rosetta encountered it, to an active comet near perihelion (1. 24 AU) on 2015 August 13 and went back to its quiet state at 3. 8 au from the Sun in September 2016. The data collected during this 2-yr escort period provided us with a unique opportunity to assess the evolution and variability over seasons, spatial locations, and heliocentric distances. The Rosetta Plasma Consortium (RPC) instruments monitored the plasma environment during the full mission. Among them, the RPC-Mutual Impedance Probe (MIP;  Trotignon et al. 2007 ) and the RPC-LAngmuir Probe (LAP;  Eriksson et al. 2007)  were used to derive electron densities and electron temperatures  (Galand et al. 2016; Eriksson et al. 2017; Gilet et al. 2017; Heritier et al. 2017a). The bulk of the electron population comprises two distinct components.
To assess the photo-ionization of the coma, we need a daily observation of the spectral solar flux. It is continuously measured by the Thermosphere Ionosphere Mesosphere Energetics and Dynamics (TIMED)-Solar EUV Experiment (SEE;  Woods et al. 2005) , from Earth's orbit, adjusted to 1 au from the Sun. Most of the recent literature uses the V11. 02 of the Level 3 (L3) TIMED-SEE solar irradiance datasets and complementary solar models. A new version, V12. 0, was released on 2017 October 31, featuring solar fluxes with overall higher intensity. The effect of the update is assessed in this section. Alternatively, the Flare Irradiance Spectral Model (FISM;  Woods et al. 2006; Peterson et al. 2008 ) also provides solar irradiance datasets in the EUV range. FISM uses observations from TIMED-SEE, the Solar Stellar Irradiance Comparison Experiment (SOLSTICE) on the Upper Atmospheric Research Satellite (UARS) and, the Solar Dynamics Observatory (SDO) EUV Variability Experiment (EVE).
The ionization frequencies are computed using the three different datasets introduced earlier: TIMED-SEE L3 V11. 02, its recently-updated version TIMED-SEE L3 V12. 0, and FISM. The photo-ionization at 1 au is subject to a decrease over the solar cycle (see Fig. 2 ) while the photo-ionization at the location of the comet is mostly sensitive to the heliocentric distance and peaks close to perihelion (2015 August 13), unlike the outgassing rate that peaks slightly after (see Fig. 1 ). For a given heliocentric distance, the photo-ionization frequency at the comet is found to be higher pre-perihelion than post-perihelion. The effects of the time shift applied to extrapolate the solar flux from Earth's orbit to the position of comet 67P are visible in Fig. 3  through the phase differences in the oscillations of the ionization frequencies.
The phase shift is maximum (180 • or 12 days) in February 2015, when comet 67P and the Earth are located at exactly opposite sides of the Sun, and minimum (0 • or 0 days) in March 2016, when the two bodies are aligned on the same side of the Sun. There is a significant increase of about 30% for the photo-ionization frequencies computed with TIMED-SEE between V11. 02 and V12. 0. FISM lies in between these two versions in terms of magnitude. In Sect. 3, we chose to use TIMED-SEE V12. 0 as it is the most recent released version of the EUV solar flux. Nevertheless, differences between the different solar irradiance datasets, in terms of ionospheric densities, are not significant at the large heliocentric distances cases treated in this paper. However, they should be important near perihelion.
Post-perihelion ionizing sources Since pre-perihelion cases were studied by  Galand et al. (2016) , in this section we focus on the ionospheric population post-perihelion measured by the Rosetta instruments at large heliocentric distances (>2. 5 au). The ionospheric population at small heliocentric distances (near perihelion) will be the object of a separate study as not only dissociative recombination needs to be taken into account (with an accurate electron temperature, see Sect. 2. 3) but also because solar flux becomes the main ionizer (see Sect. 4) and there is uncertainty on its local magnitude, due to possible attenuation through dust near perihelion  (Johansson et al. 2017). We first give an introduction to the databased model (Sect. 3. 1) that is used. We then give a brief description of the instruments used to measure the total plasma densities (RPC-MIP and RPC-LAP) and the way the data has been processed (Sect.
When these conditions are not fulfilled, the average is not computed and no value for the ionospheric density is provided. The moving averages are plotted in Fig. 10  (purple dots). As expected, most of the moving averages between 2016 July 10 14:00 UT and 2016 July 11 00:00 UT cannot be computed due to these blindness effects. ν hν + ν e -n(r) r -r 0 u = n i ( Such blindness effects on RPC-MIP are common and it is useful to use RPC-LAP electron densities to fill the missing data gaps and build consistency within the existing RPC-MIP data. We use Langmuir probe bias voltage sweeps to determine dI/dU  (Eriksson et al. 2007). Assuming a drifting Maxwellian distribution for the ion population, dI/dU is proportional to n i /u i  (Jacobsen et al. 2009) , where n i is the ion number density and u i is the effective ion velocity (combining thermal and drift velocities).
Acknowledgements. Work at  Imperial College London  is supported by  Imperial College President's scholarship, STFC of UK  under grants  ST/K001051/1 ,  ST/N000692/1 ,  ST/P002250/1 ,  ESA  under contract No. 4000119035/16/ES/JD ,  ISSI International Team  on the plasma environment of comet 67P after Rosetta, number  402. We are thankful to  David Stansby , from  Imperial College London , for reviewing the article and correcting the English writing. Work at LPC2E/CNRS was supported by  ESEP ,  CNES , and  ANR  under the financial agreement  ANR-15-CE31-0009-01. The MIP-LAP cross calibration was funded by  ESA  through the  RPC Enhanced Archive activity. Work on ROSINA  at the  University of Bern  was funded by the  State of Bern , the  Swiss National Science Foundation , and the  ESA PRODEX Program.
The  Rosetta Plasma Consortium (RPC)  instrument package consists of five sensors (RPC-ICA, RPC-IES, RPC-LAP, RPC-MAG, RPC-MIP) and a control unit (RPC-PIU). RPC is operated by  Imperial College London  in cooperation with SwRI, IRF,  IGEP_TU Braunschweig ,  IRFU ,  LPC2E. The lead funding agencies are CNES, SNSB,  NASA ,  BMWE  and  DLR , and  STFC. We are indebted to the RPC and ROSINA operation and data processing teams. We are also very grateful to the  TIMED/SEE PI ,  Tom Woods , and his team for providing us with the solar flux dataset, associated routines of extrapolation to planets, and constructive insight on the solar dataset (http://lasp. colorado. edu/see/). We acknowledge the staff of CDDP and  Imperial College London  for the use of AMDA and the  RPC Quicklook database  (provided by a collaboration between the  Centre de Données de la Physique des Plasmas , supported by  CNRS ,  CNES ,  Observatoire de Paris  and  Université Paul Sabatier , Toulouse and  Imperial College London , supported by the  UK Science and Technology Facilities Council ).