Isabelle Bertrand
email: isabelle.bertrand@univ-lorraine.fr

Julie Challant

Helene Jeulin

Cédric Hartard

Laurence Mathieu

Severine Lopez

Evelyne Schvoerer

Sophie Courtois

Christophe Gantzer

Hélène Jeulin

Séverine Lopez

Scientific Interest

Group Obépine

Yvon Maday

Epidemiological surveillance of SARS-CoV-2 by genome quantification in wastewater applied to a city in the northeast of France: Comparison of ultrafiltration-and protein precipitation-based methods

Keywords: SARS-CoV-2, Wastewater, Concentration Methods, Prevalence

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

In December 2019, the first outbreak of a new coronavirus disease (COVID-19) was reported in Wuhan, China. A new coronavirus linked to Severe Acute Respiratory Syndrome (SARS-CoV-2) was identified. It displayed more than 80% sequence homology with the previous SARS-CoV, identified in 2003(Cheung et al., 2020). By March 2020, 114 countries reported outbreaks and the WHO declared a pandemic linked to SARS-CoV-2 (WHO, 2020). France was severely affected, with more than 2,550,000 confirmed COVID-19 infections and more than 62,000 deaths by the end of 2020.

Even though many cases of COVID-19 are asymptomatic, when symptomatic it is characterized mainly by fever and respiratory symptoms, but there are also a variety of other symptoms frequently associated with the disease. These include gastrointestinal complaints, including anorexia, diarrhoea, and vomiting/nausea at frequencies of up to 27%, 12% and 10%, respectively (Cheung et al., 2020). SARS-CoV-2 is able to productively replicate in human gut enterocytes [START_REF] Lamers | Science. SARS-CoV-2 productively infects human gut enterocytes[END_REF] and the SARS-CoV-2 genome has been detected in the stools of both symptomatic and asymptomatic patients (Cai et al., 2020;[START_REF] Gao | 2019 Novel coronavirus infection and gastrointestinal tract[END_REF][START_REF] Holshue | First case of 2019 novel coronavirus in the United States[END_REF][START_REF] Tang | Detection of novel coronavirus by RT-PCR in stool specimen from asymptomatic child, China[END_REF][START_REF] Wölfel | Virological assessment of hospitalized cases of coronavirus disease 2019[END_REF][START_REF] Wu | SARS-CoV-2 Titers in Wastewater Are Higher than Expected from Clinically Confirmed Cases[END_REF]. Viral RNA may be detected in stools up to 10 days after viral clearance from the respiratory tract, regardless of the severity of the disease (Cheng et al., 2020).

The viral concentration can be high, potentially reaching 10 7 genome copies (gc)/g [START_REF] Wölfel | Virological assessment of hospitalized cases of coronavirus disease 2019[END_REF].

As expected, very high concentrations of the SARS-CoV-2 genome have also been detected in wastewater [START_REF] Ahmed | Detection of 2019 novel coronavirus (2019-nCoV) by realtime RT-PCR[END_REF][START_REF] Hindson | COVID-19: faecal-oral transmission?[END_REF][START_REF] Medema | Presence of SARS-Coronavirus-2 RNA in sewage and correlation with reported COVID-19 prevalence in the early stage of the epidemic in The Netherlands[END_REF][START_REF] Nemudryi | Temporal detection and phylogenetic assessment of SARS-CoV-2 in municipal wastewater[END_REF][START_REF] Randazzo | SARS-CoV-2 RNA titers in wastewater anticipated COVID-19 occurrence in a low prevalence area[END_REF]Wu et al., 2020a;[START_REF] Wurtzer | Evaluation of lockdown effect on SARS-CoV-2 dynamics through viral genome quantification in waste water[END_REF]. Concentrations may reach close to 10 6 gc/L (Wu et al., 2020a;[START_REF] Wurtzer | Evaluation of lockdown effect on SARS-CoV-2 dynamics through viral genome quantification in waste water[END_REF]. The presence of SARS-CoV-2 in wastewater indicates a potential health risk, but also a data source that can be used for epidemiological purposes [START_REF] Lodder | SARS-CoV-2 in wastewater: potential health risk, but also data source[END_REF][START_REF] Thompson | Making waves: Wastewater surveillance of SARS-CoV-2 for population-based health management[END_REF]. Wastewater-based epidemiology is not a new idea. It has been used to detect chemicals [START_REF] Ahmed | Detection of 2019 novel coronavirus (2019-nCoV) by realtime RT-PCR[END_REF] and other viruses, such as norovirus, hepatitis A virus, poliovirus, and Aichivirus (Asghar et al., 2014;Hellmer et al., 2014;[START_REF] Lodder | Feasibility of quantitative environmental surveillance in poliovirus eradication strategies[END_REF][START_REF] Lodder | Aichi virus in sewage and surface water, the Netherlands[END_REF]. Many countries (including The Netherlands, Spain, France, Australia, and Israel) now support the idea of using this type of epidemiological approach in the surveillance of human populations and to possibly launch an early warning system to predict future outbreaks [START_REF] Ahmed | Detection of 2019 novel coronavirus (2019-nCoV) by realtime RT-PCR[END_REF]Bar Or et al., 2020;[START_REF] Medema | Presence of SARS-Coronavirus-2 RNA in sewage and correlation with reported COVID-19 prevalence in the early stage of the epidemic in The Netherlands[END_REF][START_REF] Wu | SARS-CoV-2 Titers in Wastewater Are Higher than Expected from Clinically Confirmed Cases[END_REF][START_REF] Wurtzer | Evaluation of lockdown effect on SARS-CoV-2 dynamics through viral genome quantification in waste water[END_REF].

Whatever its purpose -whether epidemiological or to assess public health risk -the first step is to define a method of quantifying SARS-CoV-2 in wastewater. The methodologies that have been used to date are highly diverse. The concentration methods were based on various principles, including ultrafiltration [START_REF] Ahmed | Detection of 2019 novel coronavirus (2019-nCoV) by realtime RT-PCR[END_REF][START_REF] Medema | Presence of SARS-Coronavirus-2 RNA in sewage and correlation with reported COVID-19 prevalence in the early stage of the epidemic in The Netherlands[END_REF][START_REF] Nemudryi | Temporal detection and phylogenetic assessment of SARS-CoV-2 in municipal wastewater[END_REF][START_REF] Trottier | Post-lockdown detection of SARS-CoV-2 RNA in the wastewater of Montpellier, France[END_REF]; protein precipitation [START_REF] Kumar | First proof of the capability of wastewater surveillance for COVID-19 in India through detection of genetic material of SARS-CoV-2[END_REF]La Rosa et al., 2020b;Wu et al., 2020a); electronegative membranes [START_REF] Ahmed | Detection of 2019 novel coronavirus (2019-nCoV) by realtime RT-PCR[END_REF] and ultracentrifugation [START_REF] Wurtzer | Evaluation of lockdown effect on SARS-CoV-2 dynamics through viral genome quantification in waste water[END_REF].

Recovery rates are currently unknown or extrapolated from those of similar viruses such as porcine epidemic diarrhoea virus (PEDV) and mengovirus (MgV) [START_REF] Randazzo | SARS-CoV-2 RNA titers in wastewater anticipated COVID-19 occurrence in a low prevalence area[END_REF].

The aim of this study was to define the dynamics of viral concentration in wastewater during the first lockdown of the French population. This objective required a simple, specific, and sensitive approach to quantifying the SARS-CoV-2 genome in wastewater. In the present study, we took advantage of the wide circulation of SARS-CoV-2 in north-eastern France to compare two methods of virus concentration. We assessed the recovery rate of SARS-CoV-2 by virus quantification directly in four unconcentrated wastewater samples. We also included an internal process control, using Fspecific RNA phages of genogroup II (FRNAPH GGII). We applied the most reliable method to investigate the temporal variations of SARS-CoV-2 genome concentrations in wastewater over 12 weeks in an area of France with 250,000 inhabitants, which has been significantly impacted by COVID-19.

Material and methods

Sewage samples

Samples were collected between 2 April and 28 May 2020 in the French Grand Est region. In that region, the population was under lockdown from 17 March to 10 May 2020. A total of 12 water samples (400 mL) were collected from a wastewater treatment plant (WWTP) weekly or twice weekly. The average volume of influent treated in this WWTP was ~118,000 m 3 /day (~ 250,000 inhabitants). The wastewater samples were captured at regular intervals, just after decantation, but before activated sludge treatment. The samples were stored at ˗20°C until analysis.

Clinical samples

At the same time, 19,850 samples were examined by the laboratory of the local University Hospital to ascertain the presence of the SARS-CoV-2 genome in nasopharyngeal swabs. These samples were collected between week 10 and week 22 from suspected COVID-19 patients hospitalized in the local university hospital (60%) and from retirement home residents (40%). More than 95% of the people sampled during this period were resident in the urban area of the WWTP. The procedure used for the detection of the SARS-CoV-2 genome was based on primers and probes designed to target two RdRp (RNA-dependent RNA polymerase) gene segments (RdRp_IP2 and RdRp_IP4), in accordance with a procedure developed by the French National Reference Centre for Respiratory Viruses (Institut Pasteur, Paris, France).

Concentration procedures

Two concentration procedures, based on ultrafiltration and on PEG 6000 precipitation, respectively, were compared in the present study.

For the ultrafiltration procedure, a Centricon® Plus-70 centrifugal ultrafilter with a cut-off of 100 kD (Merck Millipore) was used. Before processing the samples, the ultrafilter was washed with 60 mL deionised water by centrifugation at 1,500 × g for 15 min to remove the trace amounts of glycerine, in accordance with the manufacturer's recommendations. Two volumes of 50 mL wastewater were both filtered by centrifugation at 1,500 × g for 15 min. After each centrifugation step, the concentrate was recovered by inverting the system and applying centrifugation (1,000 × g for 2 min).

The resulting concentrate's volume was around 1.5 mL. The ultrafilter was then washed with 3.5 mL of deionised water. The washing solution was added to the 1.5 mL concentrate to produce the final concentrate sample (5 mL). In order to recover the maximum amount of virus genome from the ultrafilter, two further washing steps were undertaken, each using 5 mL NucliSENS® lysis buffer (bioMérieux) for an incubation time of 5 min. The entire volume (15 mL) was then used for nucleic acid extraction. Twelve water samples were subjected to this procedure.

PEG 6000 precipitation was performed in a 250-mL centrifuge bottle containing 3 g beef extract powder, 3 g NaCl, and 0.37 g glycine for 100 mL of wastewater. After the dissolution of the beef extract powder, 20 g of PEG 6000 were added. The sample was gently stirred at 4°C for 2 h and then maintained at 4°C overnight. The pellet obtained after centrifugation at 4,500 × g and 4°C for 45 min was resuspended in deionised water to obtain a concentrated 5 mL sample. Ten millilitres of NucliSENS® lysis buffer were then added to the concentrate. After incubation for 10 min at room temperature, the entire volume (15 mL) was used for nucleic acid extraction. This concentration method was tested on the first four water samples of our study.

In tandem with each concentration procedure, 5 mL samples of unconcentrated water were used for viral genome extraction. They were submitted to the same procedure as the concentrated samples. Ten millilitres of NucliSENS® lysis buffer was added to the water, which was incubated at room temperature for 10 min, prior to the nucleic acid extraction.

Nucleic acid extraction

Both concentrated and unconcentrated wastewater samples reached the same volume of 15 mL after the lysis step. Phenol-chloroform-isoamyl alcohol purification was then undertaken with both concentrated and unconcentrated samples. This was realized in a 50 mL conical tube, containing 4 g of a mixture of high-vacuum silicon grease (Dow Corning®) and silicon dioxide (Sigma) (90:10 w/w), which was labelled the separation tube. The sample in lysis buffer (15 mL) was transferred to the separation tube and 15 mL phenol-chloroform-isoamyl alcohol (25:24:1 pH 7.8-8.2, Acros Organics™) were added. After 15 s of vigorous stirring by hand and centrifugation (3,500 × g for 5 min), the hydrophile supernatant (15 mL) was recovered. The nucleic acid extraction was continued using 70 µL of magnetic silica beads and the NucliSENS® easyMAG™ platform (bioMérieux). The extracted nucleic acids were eluted in 100 µL of elution buffer.

A complementary step was applied to some of the samples to remove residual environmental inhibitors using OneStep PCR Inhibitor Removal kit (ZymoResearch). The RNA samples were stored at ˗80°C until viral genome quantification. A negative control made of sterile phosphate buffered saline was included at the beginning of each nucleic acid extraction procedure.

Viral genome quantification

Viral RNA quantification was performed using real-time RT-PCR and RT-digital droplet PCR (RT-ddPCR).

For the FRNAPH GGII genome, the VTB4-Fph GII set published by [START_REF] Wolf | Viral multiplex quantitative PCR assays for tracking sources of fecal contamination[END_REF] was used. For the SARS-CoV-2 genome, two primer sets were selected: the "RdRp_IP4" set developed by the Pasteur Institute (Paris, France: https://www.who.int/docs/default-source/coronaviruse/real-timert-pcr-assays-for-the-detection-of-sars-cov-2-institut-pasteur-paris.pdf?sfvrsn=3662fcb6_2) for the RdRp gene and the "E" set developed by [START_REF] Wölfel | Virological assessment of hospitalized cases of coronavirus disease 2019[END_REF] for the envelope protein (E) gene. The specificity of these sets for other respiratory viruses, including human coronaviruses, has been previously described [START_REF] Wölfel | Virological assessment of hospitalized cases of coronavirus disease 2019[END_REF]Etievent et al., 2020). The SARS-CoV-2 and FRNAPH GGII genomes were quantified in the same RNA extracts. For the RdRp_IP4, E, and VTB4-Fph GGII sets, quantification was performed using an RNA UltraSens™ One-Step Quantitative RT-PCR system (Applied Biosystems™). The RdRp_IP4 and E sets were applied to 5 µL of RNA in a 25-µL reaction volume with final concentrations of 0.4 µM for each primer and 0.2 µM for the probe. The VTB4-Fph GGII set was used on 2 µL of RNA in a 20-µL reaction volume with final concentrations of 1 µM for each primer and 0.3 µM for the probe. A StepOnePlus Real-Time PCR System (Applied Biosystems™) was used for these three real-time RT-PCR assays. For the RdRp_IP4 and E sets, the RT step was performed at 50 °C for 20 min and PCR amplification was performed at 95°C for 2 min, followed by 50 cycles of 15 s at 95°C and 30 s at 58 °C. For the VTB4-Fph GGII set the RT step was performed at 50 °C for 30 min and PCR amplification was performed at 95°C for 2 min, followed by 45 cycles of 15 s at 95°C and 40 s at 58 °C. Negative and positive controls were included in each experiment. Quantification was carried out using standard curve ranges. RNA extracted from patients who had tested positive for SARS-CoV-2 was quantified using ddRT-PCR with the E set, as described below. Quantified RNA was then used to obtain the standard curve for both RdRp_IP4 and E genes. The nCoV-ALL-Control plasmid (Eurofins genomic) was also used for the standard curve of the E gene. This plasmid containing ampicillin resistance gene was maintained in TOP10 chemically competent E. coli (Invitrogen) and quantified using Qubit 4 fluorometer (Invitrogen). At last, RNA extracted from GA phage suspension was used to obtain the standard curve for FRNAPH GGII. RNA extracted from GA phage suspension was quantified by ddRT-PCR using VTB4-Fph the GGII set as described below. The standard curves ranged from 1 × 10 -1 to 1 × 10 4 gc/reaction for the RdRp_IP4 and E genes of SARS-CoV-2 and from 2.8 × 10 -1 to 2.8 × 10 4 gc/reaction for the GA phage.

The limit of detection (LoD) was 1 gc/RT-qPCR reaction for the RdRp and E genes. The limit of quantification (LoQ) ranged from 1 to 10 gc/RT-qPCR reaction for the RdRp gene and reached 1 gc/RT-qPCR reaction for the E gene. When we take the analytical volumes of the wastewater sample concentrations subjected to RT-qPCR analysis into consideration, the LoQ ranged from 2 × 10 2 to 2 × 10 3 gc/L of wastewater for the RdRp gene and reached 2 × 10 2 gc/L of wastewater for the E gene.

The ddRT-PCR assays were performed using the E set of SARS-CoV-2 and the VTB4-Fph GGII set for GA phage. Amplifications were carried out in a 20-µL reaction mixture containing 5 µL of RNA and 15 µL of One-Step RT-ddPCR™ Kit for Probes (Bio-Rad). The reaction mix contained 0.9 μM of each primer and 0.3 μM of the probe. The samples were placed in the droplet generator using 70 µL of generator oil to generate up to 20,000 droplets per sample. The resulting picolitre droplet emulsions (40 µL) were transferred to a Veriti 96-Well Thermal Cycler (Applied Biosystems). After amplification, the plate was transferred to the QX100TM Droplet Reader (Bio-Rad) and QuantaSoft™ Software (Bio-Rad) was used to measure the number of positive droplets per well. Droplets were designated positive or negative based on their fluorescence amplitude, using thresholding. The starting concentration of each target RNA molecule was then calculated, by modelling a Poisson distribution.

For each sample and each targeted virus (SARS-CoV-2 and FRNAPH GGII) the recovery rate was calculated as follows: recovery rate = genome copies (gc) in 5 mL unconcentrated sample × 20 × 100 / gc in 100 mL of the concentrate. The high concentration of the FRNAPGH GGII genome allowed for the determination of the recovery rate in all wastewater samples. It was also possible to determine the recovery rate for SARS-CoV-2 in 5 of the 12 samples.

Estimation of RT-qPCR inhibition

The estimation of RT-qPCR inhibition in the wastewater samples was based on the concentrations of FRNAPH GGII detected. Logarithmic dilutions (1/10 and 1/100) were performed in PCR-grade water following viral RNA extraction. The RT-qPCR assay was then carried out on both undiluted and diluted RNA extracts. The percentage of inhibition was estimated for undiluted and 1/10 samples by taking the concentration obtained from the 1/100 samples as a reference, due to the high dilution of potential inhibitors.

Statistical analysis

The statistical analyses were performed using XLSTAT 2020.3.1.2 software. As normal distribution of the data and the homogeneity of variances could not be met, two non-parametric tests were used. A Kruskal-Wallis test was performed on k-independent samples (k>3) to compare the viral genome copy values obtained using the different concentration and purification methods. A Wilcoxon signed-rank test was performed on paired samples to compare the recovery rates following the two methods and the genome concentrations of the two targeted genes. P values < 0.05 were considered statistically significant.

Results and discussion

Concentration method for SARS-CoV-2 quantification in wastewater

The epidemiological surveillance of SARS-CoV-2 by quantifying its genome in wastewater requires reliable methods of concentration and detection. Beginning our study in a region that was highly impacted by COVID-19 allowed us to estimate recovery rates for SARS-CoV-2.

FRNAPH are usually present in wastewater at concentrations that are relatively stable over time and around the world [START_REF] Lucena | Occurrence and densities of bacteriophages proposed as indicators and bacterial indicators in river waters from Europe and South America[END_REF]. We obtained just under 2.5 × 10 7 gc/L which was high enough to allow us to evaluate both recovery rates and PCR inhibition problems. Using these bacteriophages, the presence of PCR inhibitors was detected at even a low volume (5 mL) of unconcentrated wastewater. Indeed, the PCR inhibition varied between 88% and 100% in undiluted RNA extracts (n=5) and between 14% and 42% in 1/10 diluted RNA extracts (n=5). Following phenolchloroform purification, the PCR inhibitors had been completely removed from 5 mL of wastewater.

In these five wastewater samples, the level of contamination by SARS-CoV-2 also enabled the quantification of the virus in 5 mL unconcentrated samples and showed that, following phenolchloroform purification, the concentrations of SARS-CoV-2 genome detected in the undiluted samples had multiplied by between a factor of 2 and a factor of 10. The positive impact of this method on viral RNA extraction from environmental samples such as wastewater, sediments and animal stool samples has already been shown by previous studies [START_REF] Miura | Development of an effective method for recovery of viral genomic RNA from environmental silty sediments for quantitative molecular detection[END_REF][START_REF] Hartard | Occurrence of and sequence variation among Fspecific RNA bacteriophage subgroups in feces and wastewater of urban and animal origins[END_REF]. The phenol-chloroform purification method was systematically applied in the following experiments. A complementary method for the removal of PCR inhibitors in RNA extracts was tested during the comparison of the concentration methods (as described below).

Two concentration procedures, based on protein precipitation using PEG 6 000 and on ultrafiltration using the Centricon® 70-Plus 100 kD device, respectively, were compared. Recovery rates for both FRNAPH GGII and SARS-CoV-2 could be determined (Table 1) for both methods in four distinct wastewater samples collected in April 2020, since these samples tested positive for the SARS-CoV-2 genome even when only 5 mL of unconcentrated wastewater was analysed. The mean recovery rate of FRNAPH GGII reached 47.7 ± 18.5% and 39.2 ± 23.9% using ultrafiltration and protein precipitation, respectively. Because some of the results obtained for FRNAPH GGII showed residual PCR inhibition, the RNA extracts previously obtained by both methods were then purified using a PCR inhibitor removal kit. This treatment led to mean recovery rates of 47.1 ± 10.4% and 43.3 ± 20.3% for ultrafiltration and protein precipitation, respectively. This supplementary treatment therefore led to a slight increase in the mean recovery rate of protein precipitation. It also led to a decrease in the standard deviation in both concentration methods. The concentration values obtained for FRNAPH GGII using the two concentration methods, with or without the use of the PCR inhibitor removal kit, were not significantly different (p value = 0.098, Kruskall-Wallis test).

For SARS-CoV-2, the mean recovery rates of the RdRp_IP4 gene reached 64.1 ± 50.2% and 32.4% ± 20.2% using ultrafiltration and protein precipitation, respectively. Additional purification of the RNA extracts led to mean recovery rates of 55.8 ± 46.9% and 23.5 ± 15.0% for ultrafiltration and protein precipitation, respectively. Thus, the mean recovery rate obtained for SARS-CoV-2 (RdRp_IP4 gene) using ultrafiltration were twice as high as those obtained using protein precipitation. Moreover, the highest recovery rate was obtained using ultrafiltration in the four wastewater samples. As observed in the case of the phage, additional purification reduced the standard deviation values. The genome concentration values were not significantly different between the two concentration methods (p value = 0.088, Kruskall-Wallis test). Amplification of the E gene was then performed on the purified RNA extracts, resulting in mean recovery rates of 64.0 ± 41.6% and 45.0 ± 44.6% for ultrafiltration and protein precipitation, respectively (Table 1).

The statistical analysis of the data obtained for both the RdRp_IP4 and E genes showed significantly higher recovery rates for ultrafiltration than for protein precipitation (p value = 0.009, Wilcoxon signed-rank test).

Recovery rates and the inhibition of molecular detection methods applied for SARS-CoV-2 in wastewater have been poorly described. Nevertheless, our SARS-CoV-2 recovery rate was higher than those described in a recent review paper (La Rosa et al., 2020a) reporting five studies that described recovery rates for coronavirus in water matrices. But these recovery rates were all defined using artificial spiking with different types of coronaviruses (bovine enteric coronavirus, transmissible gastroenteritis virus [TGEV], murine hepatitis virus, and SARS-CoV). The recovery rate did not exceed 28% for bovine enteric coronavirus seeded in wastewater (Collomb et al., 1986) and it varied from 0 to 21.4% for SARS-CoV seeded in wastewater (n=4) [START_REF] Wang | Concentration and detection of SARS coronavirus in sewage from Xiao tang Shan hospital and the 309th hospital[END_REF]. Our higher recovery rates for the SARS-CoV-2 naturally present in the wastewater samples could be primarily explained by the choice of concentration method. Increasing PEG concentration might lead to a decrease in the standard deviation values as suggested by the recovery rate of 41.9 ± 6.5% for TGEV seeded in concentrated surface water (Blanco et al., 2019). Two of the studies reviewed by La Rosa and colleagues (2020a) used adsorption/elution on glass wool with the objective of analysing large volumes (5-50 L) of surface water (Blanco et al., 2019) and dechlorinated tap water (Abd-Elmaksoud et al., 2014). Such methods are commonly used as primary concentration methods and are followed by secondary concentration methods -PEG precipitation in the case of these two studies.

However, the combination of concentration methods used on large volumes of water commonly leads to a decrease in recovery rates. By working on municipal wastewater spiked with different enveloped and non-enveloped viruses, [START_REF] Ye | Survivability, partitioning, and recovery of enveloped viruses in untreated municipal wastewater[END_REF] have shown that ultrafiltration is a more suitable method for wastewater than ultracentrifugation or PEG precipitation because the recovery rate of ultrafiltration is 25% compared with 5% by ultracentrifugation or PEG precipitation. Since Ye and colleagues (2016) used culture as their detection method, the low recovery rates may be explained by the possible inactivation of coronavirus by the greater ultracentrifugal forces and the lower precipitation capacity of enveloped viruses compared with non-enveloped viruses in PEG precipitation. Wurtzer and colleagues (2020) showed the ability of ultracentrifugation to concentrate SARS-CoV-2 from wastewater, but the recovery rate was not given, and detection was done using RT-qPCR instead of culture methods. It is important to note that the ultrafiltration method could be used for the detection of infectious SARS-CoV-2 in wastewater, as shown with FRNAPH by [START_REF] Medema | Presence of SARS-Coronavirus-2 RNA in sewage and correlation with reported COVID-19 prevalence in the early stage of the epidemic in The Netherlands[END_REF]. The environmental conditions may have a negative impact on the integrity of the SARS-CoV-2 particles, with implications for detection methods and viral risk assessment; this requires further research.

The procedure combining ultrafiltration, phenol-chloroform purification, and additional purification of the RNA extracts was chosen to monitor the concentrations of the SARS-CoV-2 genome in wastewater during longitudinal wastewater sampling. Our approach based on ultrafiltration may be performed with different devices such as Centricon® [START_REF] Ahmed | Detection of 2019 novel coronavirus (2019-nCoV) by realtime RT-PCR[END_REF][START_REF] Medema | Presence of SARS-Coronavirus-2 RNA in sewage and correlation with reported COVID-19 prevalence in the early stage of the epidemic in The Netherlands[END_REF], Corning® Spin-X® UF [START_REF] Nemudryi | Temporal detection and phylogenetic assessment of SARS-CoV-2 in municipal wastewater[END_REF] or Vivaspin® [START_REF] Trottier | Post-lockdown detection of SARS-CoV-2 RNA in the wastewater of Montpellier, France[END_REF] concentrators.

Quantification of the SARS-CoV-2 genome in wastewater

Our longitudinal study was performed in a French geographical area with one of the highest prevalence rates of COVID-19. The population was placed under lockdown from 17 March until 11 May 2020, but the number of cases detected at the University Hospital increased continuously until the end of March, when the wastewater sampling period started. Twelve wastewater samples, collected between 2 April and 28 May 2020, were analysed in one replicate using both the ultrafiltration procedure on 100 mL and the direct analysis of 5 mL to quantify the SARS-CoV-2 and FRNAPH GGII genomes.

The FRNAPH GGII genome could be quantified in both 5 mL and 100 mL of wastewater in all the samples (Figure 1). The mean concentrations reached 2.1 × 10 7 ± 1.1 × 10 7 gc/L and 1.6 × 10 7 ± 1.4 × 10 7 gc/L in unconcentrated and concentrated samples, respectively. From these concentration values, the recovery rates of FRNAPH GGII ranged from 14.1% to 133.8%.

We propose a recovery rate of over 10% as a quality control, to validate the results. This goes beyond the current ISO standards for the molecular detection of viruses in water and food, which require recovery efficiencies of over 1% (International Organization for Standardization -ISO 2019;[START_REF] Lowther | Validation of EN ISO method 15216 -Part 1 -Quantification of hepatitis A virus and norovirus in food matrices[END_REF]. Quantifying FRNAPH at the same time as SARS-CoV-2 also allowed for the normalisation of faecal material content, which may vary daily, depending on rainfall or variations in faecal content. In our samples, FRNAPH genomes ranged from 1.1 × 10 7 to 6.7 × 10 7 gc/L, showing that the faecal pollution was relatively stable during the period in question. No significant rainfall events were reported during the sampling period. To compare the variations in SARS-CoV-2 genome concentrations in different wastewater samples may require the normalisation of genome content.

Other variables, such as temperature, pH, turbidity, total suspended solids, 5-day biological oxygen demand (BOD5), antecedent dry days, and conductivity may explain the variability of faecal indicator bacteria (FIB) [START_REF] Mohammed | Comparative predictive modelling of the occurrence of faecal indicator bacteria in a drinking water source in Norway[END_REF][START_REF] Paule-Mercado | Monitoring and predicting the fecal indicator bacteria concentrations from agricultural, mixed land use and urban stormwater runoff[END_REF] and probably of other faecal pathogens.

The SARS-CoV-2 genome could be quantified in 5 mL of wastewater, without using any concentration procedures, between 2 April and 30 April (Figure 1). By analysing 100 mL of wastewater, a decrease in the concentration of the SARS-CoV-2 genome was observed over the course of 8 weeks (Figure 1). The SARS-CoV-2 genome was even undetectable during one week ( 22and 25 May) and present but below the LoQ in the last sample (28 May). The concentrations of RdRp and E genes in the twelve samples appeared to be similar (p value = 0.496, Wilcoxon signed-rank test). Moreover, the concentration values obtained using real-time RT-qPCR and ddRT-PCR for the E gene were similar (p value = 0.734, Wilcoxon signed-rank test). This suggests that RT-PCR inhibitors were effectively removed by our protocol. Indeed, ddRT-PCR is far less influenced by inhibitors than RT-qPCR [START_REF] Sun | Extended direct lysis method for virus detection on berries including droplet digital RT-PCR or real time RT-PCR with reduced influence from inhibitors[END_REF].

The decrease in the genome concentrations detected in wastewater may be related to the decrease in the number of COVID-19 cases observed at the Nancy University Hospital (Figure 1). Between 2 April and 23 April, the number of cases decreased from 677/week to 286/week and the concentration values of the SARS-CoV-2 genome detected in 100 mL of wastewater decreased from 1.2 × 10 4 gc/L to 3.0 × 10 3 gc/L. During this period, the virus genome could be detected in only 5 mL of wastewater and decreased only from 1 × 10 4 gc/L to 7 × 10 3 gc/L. The analysis of only 5 mL of wastewater could be less representative of the variation in viral genome concentration. After a single data point showing an increase in genome concentration in wastewater in both 5 mL and 100 mL (30 April), the SARS-CoV-2 genome was no longer detected in 5 mL of wastewater and the concentrations dropped below 10 3 gc/L in the 100 mL analysis. For three of the samples during this period, either no genes were detected or only one gene was detected, and the number of cases varied between 27/week and 52/week. Nevertheless, SARS-CoV-2 was still detected in wastewater in the last sample (28 May) collected 17 days after the end of the lockdown. We observed a parallel decrease in cases in patients and genome concentration in wastewater, confirming the link between the circulation of the virus in the human population and its presence in wastewater. This clearly confirms the findings of other studies showing such a relationship [START_REF] Wurtzer | Evaluation of lockdown effect on SARS-CoV-2 dynamics through viral genome quantification in waste water[END_REF]. One of the first studies on the detection of the SARS-CoV-2 genome in wastewater [START_REF] Medema | Presence of SARS-Coronavirus-2 RNA in sewage and correlation with reported COVID-19 prevalence in the early stage of the epidemic in The Netherlands[END_REF]) even showed that it was possible to detect the virus in wastewater during the early stages of the COVID-19 epidemic.

Conclusion

We developed a method of concentrating SARS-CoV-2 from wastewater with one of the highest recovery rates described in the literature. The quantification of the SARS-CoV-2 genome was carried out in both concentrated and unconcentrated wastewater which was naturally contaminated; this gives our results a high degree of reliability. Moreover, this study provides additional data to validate proof-of-concept for a link between the outbreak in the human community and the concentrations of faecally excreted viruses in wastewater. This method can contribute to the monitoring of the epidemic and improve the management of potential viral recirculation.

Declaration of interest

  https://doi.org/10.1053/j.gastro.2020.03.065 Choi, P.M., Thomas, K.V., O'Brien, J.W., Mueller, J.F., 2020. Mining population exposure and community health via wastewater-based epidemiology. In: Jiang G., Li X. (eds) A New Paradigm for Environmental Chemistry and Toxicology. Springer, Singapore.

Figure 1 .

 1 Figure 1. Quantitative time-course monitoring of SARS-CoV-2 and FRNAPH-GGII genomes in

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Acknowledgements

The authors would like to thank Sibel Berger for her contribution to the acquisition of the RT-qPCR assays for SARS-CoV-2 at LCPME. The authors would also like to thank Christophe Merlin and Hélène Guilloteau for their contributions to the plasmid replication.

Funding

This work has benefited from the financial support of the French Government's Ministry of Higher Education and Research and Innovation (Ministère de l'enseignement supérieur, de la recherche et de l'innovation [MESRI]). This study was supported by the French scientific interest group Obépine for the surveillance of SARS-CoV-2 in wastewater. All the authors' salaries were funded by either Lorraine University (France), the University Hospital of Nancy (France) or SUEZ (France).


Benjamin Sultan

Ben Parkes

Marco Gaetani

Direct and indirect effects of CO2 increase on crop yield in West Africa

Keywords: Burkina Faso, Cameroon, Senegal, Mali, Niger

Climate change directly threatens food security in West Africa through a negative impact on productivity of the main staple food crops. However, providing consistent future crop yield projections in the region remain challenging because of uncertainty in the response of the regional climate to the CO2 increase and in the response of the cultivated crop to this altered climate with more CO2 in the atmosphere. Here, we analyse a set of idealised climate simulations to investigate the effect of CO2 concentration increase on the West African monsoon and potential impacts on crop yields of maize. On the one hand, simulations with prescribed SST and quadrupled CO2 concentration are analysed to study the atmospheric response to direct radiative forcing induced by increasing CO2 concentration, not mediated by ocean heat capacity. On the other hand, simulations with prescribed SST augmented by 4 K are analysed to study the atmospheric response to the global ocean warming expected as a consequence of the increasing CO2 radiative forcing. We show that if CO2 concentration increase has a positive impact on crop yield due to the fertilisation effect, it also has a direct effect on the monsoon which acts to increase (decrease) rainfall in the Eastern (Western) part of the Sahel and increase (decrease) crop yields consequently. Finally, we show that SST warming acts to reduce rainfall and increase local temperatures leading to strong reduction of crop yield. The reduction of crop yield is more important in the Eastern part of the Sahel where the warming is more intense than in the Western part of the Sahel. Overall, positive effects are weaker and more uncertain than the negative effects in the analysed simulations.

Introduction

Sub-Saharan Africa is particularly vulnerable to climate change which directly threatens food security of the rapidly growing population [START_REF] Ipcc | Climate change 2014: synthesis report[END_REF]. Here, multiple environmental, political, and socioeconomic stressors interact to increase the region's susceptibility and limits its economic and institutional capacity to cope with and adapt to climate variability and change (Connolly-Boutin and Smit 2016; [START_REF] Müller | Climate Change Impacts on Agricultural Yields[END_REF][START_REF] Challinor | Assessing the vulnerability of food crop systems in Africa to climate change[END_REF]. Achieving food security in several African countries will depend partly on the effective adaptation of agriculture to climate change, as crop yields of major staple food crops in the Tropics are expected to decrease in a warmer climate [START_REF] Challinor | A metaanalysis of crop yield under climate change and adaptation[END_REF]). However, one of the limits of adaptation planning -such as breeding more resilient crop varieties or promoting more resistant existing varieties and practices [START_REF] Barnabás | The effect of drought and heat stress on reproductive processes in cereals[END_REF]-is the high uncertainty in regional scenarios of crop production under climate change. Indeed, although there are robust evidences of a decrease of crop production due to the global warming [START_REF] Challinor | A metaanalysis of crop yield under climate change and adaptation[END_REF][START_REF] Knox | Climate change impacts on crop productivity in Africa and South Asia[END_REF], the spread of crop yield responses remains very large as found by [START_REF] Müller | Climate Change Impacts on Agricultural Yields[END_REF] which showed that projected impacts relative to current African production levels range from -100% to +168%. Most of the uncertainty is led by the difficulty to estimate the twofold effect of CO2 concentration increase, i.e. the response of the regional climate to the CO2 increase and the response of the cultivated crop to this altered climate with more CO2 in the atmosphere [START_REF] Berg | Projections of climate change impacts on potential C4 crop productivity over tropical regions[END_REF]). Here we investigate those two effects of CO2 increase in West Africa where crop yield is projected to decrease under global warming [START_REF] Roudier | The impact of future climate change on West African crop yields: what does the recent literature say?[END_REF] but also where there are large discrepancies in future climate scenarios [START_REF] Sultan | Agriculture in West Africa in the Twenty-first Century: climate change and impacts scenarios, and potential for adaptation[END_REF]. The variability of the West African climate during the 20th century has been deeply tied to the CO2 concentration increase, but the response of the regional atmospheric dynamics is particularly complex and still debated [START_REF] Gaetani | West African Monsoon dynamics and precipitation: the competition between global SST warming and CO2 increase in CMIP5 idealized simulations[END_REF]. Indeed, the CO2 concentration increase has a twofold and conflicting effect on the West African monsoon dynamics. Interestingly, whereas the radiative forcing mediated by the Global Ocean warming weakens the monsoonal circulation, the direct radiative forcing at the land surface acts locally to enhance precipitation [START_REF] Gaetani | West African Monsoon dynamics and precipitation: the competition between global SST warming and CO2 increase in CMIP5 idealized simulations[END_REF]. Specifically, the Tropical Ocean warming heats the troposphere and imposes stability, reducing moisture transport and deep convection over land, ultimately weakening the monsoonal circulations [START_REF] Held | Simulation of Sahel drought in the 20th and 21st centuries[END_REF]. Over land, CO2 radiative forcing leads to local increased evaporation and vertical instability, resulting in enhanced precipitation [START_REF] Giannini | Mechanisms of climate change in the Semiarid African Sahel: the local view[END_REF]. Climate variability in West Africa during the 20th century was characterised by large variability, alternating wet periods with droughts [START_REF] Nicholson | Rainfall over the African continent from the 19th through the 21st century, Global and Planetary Change[END_REF]. After a devastating drought characterising the 70s and peaking in the mid-80s [START_REF] Held | Simulation of Sahel drought in the 20th and 21st centuries[END_REF], West Africa experienced a recovery in summer monsoon precipitation during the 90s and at the turn of the 21st century [START_REF] Fontaine | Recent changes in precipitation, ITCZ convection and northern tropical circulation over North Africa (1979-2007)[END_REF]. Dry anomalies were caused by the weakening of the monsoonal circulation in West Africa, driven by the warming of the Tropical Ocean [START_REF] Giannini | Oceanic forcing of Sahel rainfall on interannual to interdecadal time scales[END_REF]. The concomitant negative phase of the Atlantic Multidecadal Oscillation (AMO), which reduced the boreal summer northward migration of the intertropical convergence zone (ITCZ) and its associated rain belt, exacerbated the drying trend, resulting in a long lasting drought [START_REF] Mohino | Sahel rainfall and decadal to multi-decadal sea surface Temperature variability[END_REF]. Conversely, the recent precipitation recovery has been related to the faster warming of the northern hemisphere, which favoured the northern displacement of the ITCZ (Park et al. 2015), and to the local CO2 radiative forcing over land (Dong and Sutton 2016). In this context, how the competing effects of ocean-mediated and local CO2 radiative forcing combine is still unclear, and the likely further future increase in CO2 concentration casts uncertainties on the rainfall projections for the 21st century [START_REF] Biasutti | Forced Sahel rainfall trends in the CMIP5 archive[END_REF].

Rising CO2 concentration in the atmosphere has an impact of the cultivated crop through a direct and an indirect effect. The direct effect is the potential of atmospheric CO2 to increase crop water productivity by enhancing photosynthesis and reducing leaf-level transpiration of plants ( [START_REF] Deryng | Regional disparities in the beneficial effects of rising CO 2 concentrations on crop water productivity[END_REF]. Although the amplitude of this effect depends on the region, the scale and the crop, most of the recent modelling studies found significant increases of crop yield in West Africa due to elevated CO2 [START_REF] Deryng | Regional disparities in the beneficial effects of rising CO 2 concentrations on crop water productivity[END_REF][START_REF] Deryng | Climate Change Impacts on Crop Productivity in Global Semi-Arid Areas and Selected Semi-Arid Economies[END_REF][START_REF] Sultan | Robust features of future climate change impacts on sorghum yields in West Africa[END_REF]Muller et al. 2010). It is particularly true for C3 crops such as cotton [START_REF] Gerardeaux | Positive effect of climate change on cotton in 2050 by CO 2 enrichment and conservation agriculture in Cameroon[END_REF]. C4 crops such as maize, sorghum or millet are less sensitive to atmospheric CO2 concentration however there are impacts as a result of stomatal closure and soil moisture conservation [START_REF] Leakey | Rising atmospheric carbon dioxide concentration and the future of C4 crops for food and fuel[END_REF]. The indirect effect is the response of the crop to the altered climate due to atmospheric CO2 concentration increase. Most studies find yield losses under future climate scenarios [START_REF] Sultan | Agriculture in West Africa in the Twenty-first Century: climate change and impacts scenarios, and potential for adaptation[END_REF][START_REF] Challinor | A metaanalysis of crop yield under climate change and adaptation[END_REF][START_REF] Roudier | The impact of future climate change on West African crop yields: what does the recent literature say?[END_REF][START_REF] Knox | Climate change impacts on crop productivity in Africa and South Asia[END_REF][START_REF] Challinor | Assessing the vulnerability of food crop systems in Africa to climate change[END_REF][START_REF] Kotir | Climate change and variability in Sub-Saharan Africa: a review of current and future trends and impacts on agriculture and food security[END_REF][START_REF] Müller | Climate Change Impacts on Agricultural Yields[END_REF], because of the adverse role of higher temperatures which reduce the crop cycle duration and increase evapotranspiration [START_REF] Schlenker | Robust negative impacts of climate change on African agriculture[END_REF][START_REF] Roudier | The impact of future climate change on West African crop yields: what does the recent literature say?[END_REF][START_REF] Berg | Projections of climate change impacts on potential C4 crop productivity over tropical regions[END_REF][START_REF] Sultan | Assessing climate change impacts on sorghum and millet yields in the Sudanian and Sahelian savannas of West Africa[END_REF]. Although uncertain, changes in rainfall modulate the spatial distribution of climate change impacts on crop yields [START_REF] Sultan | Agriculture in West Africa in the Twenty-first Century: climate change and impacts scenarios, and potential for adaptation[END_REF][START_REF] Sultan | Robust features of future climate change impacts on sorghum yields in West Africa[END_REF]. Indeed, yields losses of millet and sorghum are particularly high in the western Sahel as a result of the combination of warming and decreased precipitation at the beginning of the rainy season [START_REF] Sultan | Robust features of future climate change impacts on sorghum yields in West Africa[END_REF]). In the central Sahel, temperature and precipitation operate in opposite directionswarming causes yield loss whereas increased rainfall at the end of the rainy season is favourable for growing millet and sorghum [START_REF] Sultan | Robust features of future climate change impacts on sorghum yields in West Africa[END_REF]. Thus, direct and indirect effects of rising atmospheric CO2 act in a competing way with benefits of elevated levels of CO2 through increased crop water productivity while resulting warmer mean temperatures are likely to lead to crop yield losses.

Here, we use a set of idealised climate simulations of five climate models combined with a crop model to investigate the effect of CO2 concentration increase on the West African monsoon and potential impacts on maize yields. Maize is an important staple crop of West Africa and is grown extensively in Burkina Faso, Mali, Niger and Nigeria. The climate model experimental set-up allows to separate the direct local CO2 radiative forcing on the West African climate from the indirect forcing mediated by global Sea Surface Temperature (SST). The aim of this study is to investigate how these decoupled climate signals propagate through a crop model rather than producing realistic maize simulations. In addition, the crop model experimental set-up allows to evaluate the potential of the CO2 fertilisation effect on the crop water productivity in combination with the impacts of regional climate change in West Africa.

In the next section we introduce the experimental set-up and the crop model (GLAM model) used in this study. In section 3, we analyse the simulations by separating (i) the direct effect of CO2 increase on crop and monsoon and (ii) the effect of SST warming on the regional climate and crop productivity. Finally, in section 4, we discuss our conclusions.

Materials and Methods

AMIP Simulations

The competition between the SST-mediated and direct CO2 effect on the West African climate is studied by analysing idealised numerical experiments from a set of five climate models selected in the CMIP5 archive [START_REF] Taylor | An overview of CMIP5 and the experiment design[END_REF]) (see Table 1 for details on the models). Models are run in atmospheric-only configuration, with observed SST and sea ice prescribed for the period 1979 to the 2008. The simulations take into account the observed evolution in the atmospheric composition (including CO2), due to both anthropogenic and natural influences, and the changes in solar forcing, emissions and concentrations of aerosols, and land use. This experimental set-up is used as the control simulation (CTL) for two sensitivity experiments run either by prescribing uniform 4K increase in global SST (4K experiment), or by quadrupling the CO2 atmospheric concentration while P e e r R e v i e w O n l y maintaining the SST unchanged (4xCO2 experiment). The use of such an idealised design is intended to isolate in a straightforward manner the climate responses to, respectively, the direct local CO2 radiative forcing and to the global SST increase, which would not be possible in ocean-atmosphere coupled simulations. Indeed, in coupled simulations, direct and SST-mediated effects of CO2 forcing on the climate system are mixed, and the competitive aspects of the CO2 influence on the West African monsoon cannot be disentangled. Specifically, in the 4xCO2 experiment, the Global Ocean is not allowed to warm and store heat, so that the climate system only responds to the local radiative forcing on land surface induced by the quadrupling of the CO2 concentration (Fig. S1-S5 in Supplementary Material). Conversely, by fixing CO2 concentration at present-day values and increasing SST, the climate system only responds to the ocean surface warming, with no direct forcing from increasing CO2 concentration (Fig. S6-S10 in Supplementary Material). The experimental setup is described in detail in [START_REF] Taylor | An overview of CMIP5 and the experiment design[END_REF]. Extreme idealised forcing is imposed in the sensitivity experiments to magnify the response of the climate system to the global SST warming (in 4K), and to the local direct CO2 radiative forcing (in 4xCO2), respectively. These conditions are comparable with the situation expected in 2100 in the RCP8.5 emission scenario [START_REF] Riahi | RCP 8.5-A Scenario of Comparatively High Greenhouse Gas Emissions[END_REF], with the CO2 concentration augmented from 390 ppm in 2011 to more than 1000 ppm (more than +260 %), and more than 3K global SST warming (IPCC 2014). Model selection is based on the experiment availability, thus considering only models for which the three experiments (CTL, 4K and 4xCO2) are available. Moreover, the availability of daily data for the variables used to force the GLAM model (see Section 2.2) represents a further constraint, leading to the final selection of the model ensemble. Availability of multiple realisations is also limited (for the baseline experiment, 6 members are respectively available for HadGEM2-A and IPSL-CM5A-LR, and 2 members for MIROC5; for sensitivity experiments, 2 members are available for IPSL-CM5A-LR and IPSL-CM5B-LR, respectively). Therefore, one realisation of each experiment is used for each model, not to bias the results toward models for which more realisations are available. However, the analysis of precipitation and temperature outputs from HadGEM2-A and IPSL-CM5A-LR shows that idealised perturbations in the sensitivity experiments are large enough to overcome model internal variability (not shown), and choosing different realisations for baseline and sensitivity experiments would not change the conclusions of the paper. The selected models correctly simulate all the main features of the monsoonal dynamics, although the comparison with observational and reanalysis products shows some biases (see Supplementary Material in [START_REF] Gaetani | West African Monsoon dynamics and precipitation: the competition between global SST warming and CO2 increase in CMIP5 idealized simulations[END_REF]. Particularly, HadGEM2-A and IPSL-CM5B-LR simulate a significant weaker monsoon, while MIROC5 is affected by significant wet biases. Specific analysis of these biases is beyond the scope of the paper. However, biases in model simulation of the West African monsoon are related to the coarse resolution, which limit the model ability in producing intense and organised convective systems [START_REF] Vellinga | Sahel decadal rainfall variability and the role of model horizontal resolution[END_REF], to the poor representation of the global SST teleconnections [START_REF] Rowell | Simulating SST teleconnections to Africa: what is the state of the art?[END_REF]). The differences in grid resolution among models are harmonised by using a first-order conservative remapping to regrid all the datasets to a 1° regular grid. Although caution should be used when regridding the coarse resolution of the IPSL models to 1°, we consider this choice an appropriate compromise to conserve climate model information and respond to the crop model needs. Finally, we highlight that the purpose of this study is to analyse the sensitivity of crop yield to idealised conditions representing the competing effects of the CO2 concentration increase, rather than a realistic productivity assessment.

GLAM Simulations

GLAM is the Global Large Area Model for annual crops [START_REF] Challinor | Design and optimisation of a largearea process-based model for annual crops[END_REF]. GLAM is a processbased model that was developed for use with climate scale data. GLAM requires soil data, a crop parameter set and meteorological inputs. The soil data is derived from the Digital Soil Map of the World and gridded to the meteorological data grid. The planting dates for the crops were derived from the Global Gridded Crop Model Intercomparison project dates for maize (Elliott et al 2015). GLAM runs are performed at 1° regular grid resolution using climate simulations outputs as meteorological inputs. These inputs are maximum daily temperature, minimum daily temperature, downwelling shortwave radiation at the surface and precipitation. The maize parameter set is identical to the one used in [START_REF] Parkes | Projected changes in crop yield mean and variability over West Africa in a world 1.5 K warmer than the pre-industrial era[END_REF] and based on the parameter set used in Vermulen et al. (2013). In this study GLAM was run with an idealised crop where the yield gap parameter is set to 1 instead of being calibrated to observed crop yields [START_REF] Challinor | Crop yield response to climate change varies with cropping intensity[END_REF][START_REF] Parkes | Crop failure rates in a geoengineered climate: impact of climate change and marine cloud brightening[END_REF]. We are using a maize parameterisation that is idealised in that we are not attempting to replicate observed yields but instead are simulating the theoretical maximum yield value for a crop in those circumstances. This removes the effects of pest/diseases and management techniques. This method was selected to show the meteorological signals consistently across the domain. GLAM uses a triangular profile to determine growth at a given temperature. For these simulations the base, optimum and maximum temperature were set at 8, 34 and 44 C respectively. If the mean daily temperature is above the optimum temperature then reduced growth is expected. A high temperature stress routine is also used and can further reduce yields if temperatures are above 37 C during flowering. The high temperature stress routine is described fully in [START_REF] Challinor | Simulation of the impact of high temperature stress on annual crop yields[END_REF].

Carbon dioxide fertilisation in C4 crops is less significant than in C3 crops. However, C4 crops do respond to carbon dioxide fertilisation due to stomatal closure and conservation of soil moisture. The relationship between transpiration efficiency and carbon dioxide fraction for C4 grasses increases to a maximum before levelling off. The increased transpiration efficiency values are based on response ratio of the transpiration efficiency to carbon dioxide for water limited maize in GLAM generated by Julian Ramirez-Villegas (personal communication, 2015). The curve of this relationship was modelled using a negative square term to find the maximum transpiration efficiency. This maximum is at 850 ppm CO2 and results in a transpiration efficiency of 11.06 pa (from 6.5 pa). In addition, the maximum transpiration efficiency was increased from 9.0 g/kg to 15.31 g/kg where the fractional increase in transpiration efficiency (pa) is maintained for the increase in maximum transpiration efficiency (kg).

The six scenarios

Six scenarios are used in this study to investigate the role of CO2 concentration increase on crop yield in West Africa (Table 2):

• The control scenario (t0c1f1): Here GLAM runs are performed using climate inputs from the most realistic configuration of a set of climate models forced with observed SST and sea ice prescribed for the period 1979 to the 2008 which takes into account anthropogenic and natural influences.

It allows to have a realistic climate forcing as a baseline to further sensitivity experiments. • The scenario with fertilisation effect of CO2 on the crop (t0c1f4): Here GLAM runs are performed using baseline climate but the crop is experiencing four times higher levels of CO2 concentration which increase transpiration efficiency in the GLAM model and thus increase crop yield. When compared to the control scenario, this simulation gives the fertilising effect of CO2 increase on crop yield in the GLAM model for unchanged climate conditions. • The warmer climate scenario with no effect of CO2 on the crop transpiration (t4c1f1): Here GLAM runs are performed using the +4K climate conditions and atmospheric CO2 concentration is similar to the control scenario. When compared to the control scenario, this simulation allows to point out the impact on crop yield of the climate system response to the global SST warming. efficiency in the GLAM model. When compared to the control scenario, this simulation gives the combined effects of warmer climate and increase of transpiration efficiency on crop yield in the GLAM model.

• The direct CO2 effect on the monsoon with no effect of CO2 on the crop transpiration (t0c4f1):

Here GLAM runs are performed using climate conditions responding to the quadrupling of CO2 concentration while SST is unchanged, and CO2 concentration remains unchanged compared to the control scenario. When compared to the control scenario, this experiment allows to isolate the impact on crop yield of the response of the monsoon to the local direct CO2 radiative forcing.

• The direct CO2 effect on the monsoon added to the effect of CO2 on the crop transpiration (t0c4f4): Here GLAM runs are performed using climate conditions responding to quadrupled CO2 concentration and crop is experiencing four times higher levels of CO2 concentration which increase transpiration efficiency in the GLAM model. When compared to the control scenario, this experiment simulates the combined effects of the CO2 increase on the monsoons dynamics and on the transpiration efficiency.

Results

The control simulation

The control simulation reveals important differences in total rainfall and mean temperatures between the five climate models (Table 3). In particular, HadGEM2 simulates the lowest annual precipitation value (461 mm/year) while the MIROC5 model is the wettest (671 mm/year) and the hottest model (29.1C). Even if climate models are forced with observed SST and are thus likely more realistic than coupled model simulations, there are still important biases compared to observations in the control run. Indeed all models are too dry and too hot compared to annual rainfall and mean temperature computed using the reference WFDEI dataset (707 mm/year and 27.1C respectively). As a result of these important differences between climate models, the simulated yield varies strongly from one model to another since yield is highly sensitive to rainfall and temperature variations (Figure 1). In the GLAM crop model, crop yield increases as total rainfall amount during the growing season increases, following an exponential fit, until reaching values where the water constraint is not limiting anymore. Simulated potential crop yield follows a more linear fit with temperature and shows a decrease of crop yield as temperature decreases. As a result, the HadGEM2 model has the lowest mean yield because of its low annual rainfall compared to the four other models.

The direct effect of CO2 increase on crop and monsoon

Elevated concentration of CO2 in the GLAM crop model under the control climate (t0c1f4 simulation) has a clear positive effect on crop yield (red bars in Figure 2). The multi-model mean shows an increase of +26.79% of simulated potential crop yield compared to the control simulation (t0c1f1 simulation) when aggregating results over whole West Africa. This crop yield increase is found using any of the five climate models although the amplitude of the yield gain varies across the models. The yield increase exceeds 40% using the IPSL CM5A model while it less than 23% using the CNRM CM5 model. The CO2 fertilisation effect is linked to the water stress and therefore it can explain why the responses are model dependent.

An increase of atmospheric CO2 concentration has also a direct effect on the monsoon which has in turn an impact on simulated potential crop yields. The t0c4f1 simulation isolates this effect by removing the fertilisation effect on the crop model. When aggregating results over whole West Africa, the multi-model mean shows a very weak response of crop yields (-0.67%) which mainly result from a high dispersion across the response of individual models (grey bars in Figure 2). Indeed, the monsoon effect can lead to yield gain of about 22% using the HadGEM2 model while it results to a P e e r R e v i e w O n l y yield loss of more than 15% in the MIROC5 model. This yield response is mainly driven by the rainfall change in the model with elevated levels of CO2 (Figure 3; Table 4). The HadGEM2 model is highly sensitive to atmospheric CO2 increase which produces more rainfall (+19.82%) while the same CO2 increase lead to a reduction of rainfall of about 2% in the MIROC model. In average over West Africa, there is a positive linear relationship between rainfall changes and yield changes between the t0c4f1 simulation and the control t0c1f1 simulation (R²=0.98; Figure 3).

The combination of the direct effect of CO2 increase on the crop and on the monsoon (t0c4f4 simulation) leads to a large increase of simulated crop yields (+26.79%) over West Africa in the multimodel mean (Figure 2). It indicates that the fertilisation effect on the crop dominates the yield response rather than the effect of the monsoon. However individual model runs show that the monsoon effect can largely modulate the fertilisation effect (blue bars in Figure 2). The benefits of the fertilisation effect are almost cancelled in the MIROC model (24,04% in t0c1f4 simulation and 6,39% in t0c4f4 simulation) which simulates a decrease of rainfall with the CO2 increase. On the opposite, the benefits are doubled in the HadGEM2 model (24,45% in t0c1f4 simulation and 49,68% in t0c4f4 simulation) which simulates more rains with the CO2 increase.

The spatial patterns of yield change due to CO2 increase show some important regional disparities (Figure 4). Although the yield increase due to the CO2 fertilisation effect (Figure 4a) is widespread over the Soudano-Sahelian zone, it is slightly less important in the North where water stress is too high for being compensated by the CO2 increase. The CO2 fertilisation effect is also reduced in the wettest areas along the Guinean coast and in southern Atlantic coast where on the opposite there is no water stress and thus where the crop cannot benefit from the reduction of transpiration expected by the CO2 atmospheric concentration increase. In Figure 4b, yield change is driven by the effect of CO2 on the monsoon without taking into account the fertilisation effect (t0c4f0 simulation). The spatial pattern opposes the western part of West Africa and to a less extent the Guinean Coast where potential crop yield losses are expected with the Central Sahel where crop yield gains are expected.

When averaging across West Sahel and East Sahel boxes (see Figure 4b for the localisation of the boxes), we can see that the direct effect of the monsoon leads to potential crop yield losses in the West Sahel (-5.27%) and to yield increases in East Sahel (+12.07%). It is highly variable across model and the response depends on rainfall change (Table 5). This spatial pattern is very close to the precipitation change due to CO2 increase effect on the monsoon (Figure 4c). [START_REF] Gaetani | West African Monsoon dynamics and precipitation: the competition between global SST warming and CO2 increase in CMIP5 idealized simulations[END_REF] shows that the response of the WAM precipitation to the quadrupling of the CO2 concentration is the northward migration of the precipitation belt, driven by the intensification of the meridional energy gradient across West Africa, and resulting in positive (negative) precipitation anomalies in the Sahel (Guinean coast). The positive precipitation anomalies in the Sahel are also modulated along the zonal direction, being stronger to the east than to the west (see Figure 2 in [START_REF] Gaetani | West African Monsoon dynamics and precipitation: the competition between global SST warming and CO2 increase in CMIP5 idealized simulations[END_REF]). This feature is associated with an anomalous zonal cell triggered by strengthened convection over West Africa, which connects with subsidence over Tropical Atlantic. This results in a quasi-zonal anomaly in the monsoonal flow, which favours moisture convergence in central-eastern Sahel (see Figures 4 and8 in [START_REF] Gaetani | West African Monsoon dynamics and precipitation: the competition between global SST warming and CO2 increase in CMIP5 idealized simulations[END_REF]. Precipitation patterns in model responses to quadrupled CO2 concentration are then produced by the combination of the circulation response in the zonal and meridional directions, which is in turn driven by the model regional response to the CO2 forcing. When we combine the direct effect of CO2 increase on the crop and on the monsoon (t0c4f1 simulation), yield is increasing almost everywhere in West Africa (Figure 4d) except in Western part of the Sahel where the reduction of rainfall induced by the CO2 increase dominates the yield benefit of the fertilisation effect. A warmer ocean (+4K increased SSTs in the t4c1f1 experiment) leads to particularly detrimental climate conditions for the crop (Figure 5; Table 6). An important warming is simulated in West Africa with annual temperatures changes ranging between 4K and 5K in the coastal areas of the Atlantic Ocean and reaching +6K, up to +7K in the more continental areas (Figure 5a). The multi-model mean shows that the +4K warming of SSTs leads to a +5.54K local growing season warming in average over West Africa (Table 6). A warmer ocean induces a reduction of rainfall all over West Africa (-18.97% in average) except in the North Eastern part of the Sahel (Niger) where rainfall increases. The rainfall deficit is particularly important in South West Sahel in Senegal, Gambia and Guinea-Bissau where a reduction of rainfall greater than 40% is simulated in the t4c1f1 experiment (see also [START_REF] Gaetani | West African Monsoon dynamics and precipitation: the competition between global SST warming and CO2 increase in CMIP5 idealized simulations[END_REF].

The effect of SST warming

These warm and dry conditions lead to large potential crop yield losses (Figure 6; Table 6) everywhere in West Africa. The multi-model mean shows that a +4K warming of SSTs leads to a reduction of 56% of crop yield in average over West Africa and a shortening of the crop season duration of 9 days. This reduction is only partly (-40.89%) compensated by the fertilisation effect of CO2.

It is interesting that even if the rainfall deficit is the greatest in the Western part of the Sahel, yield loss is more pronounced in the Eastern part of the Sahel when averaged simulations over the same West and East boxes shown in Figure 4 (Table 7). The reduction of the potential yield is the most important in the East Sahel (-69.8%) and slightly hampered by CO2 fertilisation effect (-54.7% of crop yield loss). The yield loss is less important in the West Sahel reaching -52.6% without taking into account CO2 effect on crop and -38.6% with the CO2 fertilisation effect. Simulations show that if the monsoon rains are more affected in the Western Sahel (a reduction of 12 days and more than 25% of the rainfall) than in the Eastern Sahel (a reduction of 3 days and about 16% of rainfall), the warming is more important in the East Sahel (+5.9K against 5.4K in the West Sahel). With such levels of warming, temperatures changes drive the yield variability in the crop model as illustrated by Figure 7 which depicts a linear relationship between temperatures and yield changes in the simulations. It might explain why even if the monsoon rainfall is less affected in the East Sahel, the impact on crop yield is more important since the warming is more intense.

Conclusion

Rising CO2 concentration in the atmosphere leads to two opposite effects on potential crop yield in West Africa. On one hand, benefits could be expected through an increase of rainfall driven by the direct effect of CO2 radiative forcing on the monsoonal dynamics. Our simulations showed that, without increase of temperature, positive impacts will be more likely in Central and East Sahel where annual rainfall are strongly enhanced by elevated levels of CO2. Indeed, monsoonal precipitation in West Africa responds to increasing CO2 concentration migrating northwards to the Sahel, driven by the strengthened energy meridional gradient associated with the CO2 radiative forcing over land. Enhanced deep convection triggers an anomalous zonal cell which intensifies the westerly moisture flow from the tropical Atlantic, resulting in a wetter response in central-eastern Sahel [START_REF] Gaetani | West African Monsoon dynamics and precipitation: the competition between global SST warming and CO2 increase in CMIP5 idealized simulations[END_REF]. Yield gains are also expected through the CO2 fertilisation effect which act to reduce crop transpiration in the crop model and thus increase drought resistance. Although the amplitude of the expected benefits of the CO2 fertilisation is certainly crop model dependent and still debated in the literature [START_REF] Deryng | Regional disparities in the beneficial effects of rising CO 2 concentrations on crop water productivity[END_REF], we found that they are far greater than those expected from the direct effect on the monsoon. In the Central East Sahel for instance, our simulations showed an increase of +43.5% of crop yield with the fertilisation effect against a yield gain of +12.1% due to the rainfall increase. On the other hand, negative impacts are expected from the elevation of temperatures. Detrimental conditions for the crop were obtained by warming up the ocean of +4K leading to drought conditions in the Western part of the Sahel and to an increase mean surface temperature of more than +5.5K in West Africa with particularly warm conditions in continental regions. This warming could lead to yield loss of more than 56% which can only be partly hampered by the fertilisation effect (-40.9% of yield loss by taking into account the fertilisation effect).

With such competing effects, which are not always additive, providing reliable climate change impacts scenarios on crop yields is challenging. The differences between climate models in the estimation of the effects of direct and SST-mediated effects of CO2 were found to be very large with for instance the HadGEM2 model simulating an increase of +49.7% of the yield through the increase of rainfall (t0c4f4 simulations) and a decrease of -56.1% with the increase of temperatures (t4c1f4).

Overall we found that positive effects in the analysed simulations are weaker and more uncertain than the negative effects. Indeed, simulated positive effects on crop yield range from +6.4% using the MIROC5 model to +49.7% in the HadGEM2 model while the negative effects range from -51.7% to -62.9% using the same two climate models respectively. We also found that temperatures increase will likely have a more important impacts on crop yield than rainfall changes as shown in previous studies [START_REF] Schlenker | Robust negative impacts of climate change on African agriculture[END_REF][START_REF] Roudier | The impact of future climate change on West African crop yields: what does the recent literature say?[END_REF][START_REF] Berg | Projections of climate change impacts on potential C4 crop productivity over tropical regions[END_REF][START_REF] Sultan | Assessing climate change impacts on sorghum and millet yields in the Sudanian and Sahelian savannas of West Africa[END_REF]. We highlight that, by construction, the idealised simulations analysed in this paper do not account for climate feedbacks to the increasing CO2 concentration. In particular, the global SST response to CO2 forcing in past and future climate simulations is far from the homogeneous warming prescribed in the 4K experiment, and this may lead to different results for the monsoonal dynamics and crop productivity. For instance, it has been shown that, in the presence of overall global ocean warming, while the warming of the Tropical belt inhibits precipitation in West Africa, the differential warming of the Northern Hemisphere, and in particular of the North Atlantic and Mediterranean, is favourable to rainfall [START_REF] Giannini | A unifying view of climate change in the Sahel linking intra-seasonal, interannual and longer time scales[END_REF][START_REF] Park | Northern-hemispheric differential warming is the key to understanding the discrepancies in the projected Sahel rainfall[END_REF][START_REF] Park | Anthropogenic Mediterranean warming essential driver for present and future Sahel rainfall[END_REF]. Ocean-atmosphere coupled simulations of future climate in West Africa include all the climate feedbacks, so that the uncertainties in AMIP idealised simulations discussed in this paper are exacerbated, undermining mitigation and adaptation strategies in the region. Whereby in AMIP simulations the responses to an idealised forcing are concordant, though different in amplitude, coupled model simulations for the end of the 21st century range from dry to very wet projections, characterised by spatial inhomogeneity (Monerie et al. 2017). Coupled climate models are generally skilful in simulating the relationship between the regional atmospheric dynamics and the Sahelian rainfall [START_REF] Biasutti | The role of the Sahara low in summertime Sahel rainfall variability and change in the CMIP3 models[END_REF], while SST teleconnections are poorly simulated [START_REF] Rowell | Simulating SST teleconnections to Africa: what is the state of the art?[END_REF], mainly because of the model biases in simulating ocean dynamics [START_REF] Roehrig | The present and future of the West African monsoon: a process-oriented assessment of CMIP5 simulations along the AMMA transect[END_REF]. Moreover, coupled climate simulations are generally performed not considering dynamic vegetation and land use, which are instead key ingredients of the monsoonal dynamics [START_REF] Koster | Regions of strong coupling between soil moisture and precipitation[END_REF]). Fixing model shortcomings and improving model design should be then prioritised in the next CMIP6 exercise (Eyring et al. 2016).

Every modelling study has its limitations and we recognize some caveats in our experiments. First of all, we use a limited number of GCMs (only five) within the full list of models participating to the CMIP5 exercise (more than 30). If different results with different or with more models are still possible, [START_REF] Gaetani | West African Monsoon dynamics and precipitation: the competition between global SST warming and CO2 increase in CMIP5 idealized simulations[END_REF] showed a general agreement among models in their response to the idealized conditions, which demonstrates the robustness of the mechanisms linking the WAM dynamics to the SST and CO2 idealized forcings, whatever the model physics or performance.

Another limitation is the use of only one ensemble member from each GCM which does not ensure that most of the plausible scenarios are captured. However, we are here critically limited by the availability of ensemble members in the CMIP5 archive, which does not allow to perform a full exhaustive analysis of the internal variability within each GCM. Finally, a caution is necessary when interpreting the crop simulation results presented in this study. Crop yields results have to be interpreted as potential crop yield response to two aspects of climate change on the crops grown in West Africa, i.e. the increase in temperatures and the increase in atmospheric carbon dioxide levels, and not as a realistic crop yield prediction for the future. yields, without calibration. The parameter set is the same as the one used in [START_REF] Parkes | Projected changes in crop yield mean and variability over West Africa in a world 1.5 K warmer than the pre-industrial era[END_REF], this includes the high temperature stress routine. This routine reduces crop yields as a result of high temperature stress during flowering. The potential yields are much higher than real yields and therefore the magnitude of reductions in yield as a result of high temperature stress is expected to be higher than for calibrated crops. This is expected to reduce the yields in the t4c1f1 and t4c1f4 experiments and may lead to an overestimate of the impact of increased temperatures.

P

Figure 1 :Figure 2 :Figure 3 :Figure 4 :Figure 5 :Figure 6 :Figure 7 :

 1234567 Figure 1: Crop yield response to rainfall and temperature variations in the GLAM model. Pixel by pixel difference against the domain average for mean yield and total growing season rainfall (left) and mean temperature (right). Values are then averaged over the 30 years of the control experiment to give more than 400 values expressed in percentage.

  The crop model is simulating potential

	P e
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	n
	l y

Table 1 :

 1 Models analysed. CMIP5 model information and outputs are available through the Earth System Grid Federation archive (http://cmip-pcmdi.llnl.gov/cmip5)

	e
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Table 2 :

 2 The experiments with GLAM and AMIP runs. In control climate (ctl), SST and CO2 are prescribed at the 1979-2008 observed values[START_REF] Taylor | An overview of CMIP5 and the experiment design[END_REF]).

			Sea Surface Temperature in AGCM	CO2 atmospheric in AGCM concentration	CO2 concentration model in GLAM crop	Short description of the scenario
		t0c1f1	ctl	ctl	ctl	The control scenario
	Control climate	t0c1f4	ctl	ctl	ctl x4	The scenario of direct effect on CO2 on the crop
						The warmer climate
	Altered climate	t4c1f1	ctl +4K	ctl	ctl	scenario with no effect of CO2 on the crop
	with +4K warmer					transpiration
	SST but control					The warmer climate
	CO2 concentration	t4c1f4	ctl +4K	ctl	ctl x4	scenario with direct effect of C02 on the crop
						transpiration
						The direct CO2 effect of
	Altered climate with 4 times higher levels of CO2 concentration but control SST	t0c4f1 t0c4f4	ctl ctl	ctl x4 ctl x4	ctl ctl x4	the monsoon with no effect of CO2 on the crop transpiration The direct CO2 effect of the monsoon with no effect of CO2 on the crop
						transpiration

Table 3 :

 3 Simulated yield (kg/ha), annual rainfall (mm/year) and mean surface temperature (degC) in West Africa in the t0c1f1 control simulations. The values are averaged over the domain: Longitude 15W to 20E and latitude 4N to 15N

	Climate	Mean yield	Total precipitation	Mean temperature
	model	(kg/ha)	(mm/year)	(degC)
	CNRM CM5	3994,0	650	27,2
	HadGEM2	2921,5	461	28,7
	IPSL CM5A	3706,6	645	27,6
	IPSL CM5B	3342,6	569	27,4
	MIROC5	3480,3	671	29,1

Table 4 :

 4 Yield and annual precipitation change (%) in West Africa (20W-15E ; 4N-15N) in the t0c4f1 simulations comparing to CTL simulation. MMM is the multi-model mean.

			Total
		Mean yield	precipitation
	Climate models	change (%)	change (%)
	CNRM CM5	-0,57	6,59
	HadGEM2	21,91	19,82
	IPSL CM5A	1,42	5,43
	IPSL CM5B	-7,68	1,60
	MIROC5	-15,26	-2,08
	MMM	-0,67	5,49

Table 5 :

 5 Yield and annual precipitation change (%) in West Sahel and East Sahel (see boxes on Figure4) in the t0c4f1 simulations comparing to the control CTL simulation. MMM is the multi-model mean.

			Total
		Climate	precipitation	Mean yield
	Domain	models	change (%)	change (%)
	West Sahel CNRM CM5	4,29	-6,79
		HadGEM2	16,07	9,21
		IPSL CM5A	6,78	-1,02
		IPSL CM5B	-2,07	-11,94
		MIROC5	-1,81	-14,34
		MMM	4,01	-5,27
	East Sahel	CNRM CM5	10,34	8,11
		HadGEM2	33,45	90,31
		IPSL CM5A	13,23	10,09
		IPSL CM5B	12,27	0,86
		MIROC5	1,60	-20,64
		MMM	12,75	12,07

Table 6 :

 6 Crop season duration (day), annual precipitation (%), mean temperature (K) and yield change (%) in the t4c1f1 and t4c1f4 simulations (only yield differs) in West Africa comparing to the control CTL simulation. MMM is the multi-model mean.

		Growing season	Total	Mean	t4c1f1	t4c1f4
	Climate	duration change	precipitation	temperature	Mean yield	Mean yield
	models	(day)	change (%)	change (K)	change (%)	change (%)
	CNRM CM5	-14,3	-15,3	5,7	-54,7	-43,1
	HadGEM2	-2,9	-16,6	6,0	-62,9	-52,6
	IPSL CM5A	-12,7	-18,6	5,3	-55,4	-32,3
	IPSL CM5B	-10,9	-27,2	5,5	-57,0	-39,9
	MIROC5	-3,5	-17,5	5,2	-51,7	-38,6
	MMM	-8,9	-19,0	5,5	-56,1	-40,9

Table 7 :

 7 Crop season duration (day), annual precipitation (%), mean temperature (K) and yield change (%) in the t4c1f1 and t4c1f4 simulations (only yield differs) in West and East Sahel comparing to control CTL simulation. MMM is the multi-model mean.

			Growing
			season	Total	Mean	t4c1f1	t4c1f4
		Climate	duration	precipitation	temperature	Mean yield	Mean yield
	Domain	models	change (day)	change (%)	change (K)	change (%)	change (%)
	West	CNRM CM5	-15,7	-24,9	5,4	-50,9	-41,6
	Sahel	HadGEM2	-6,9	-25,8	5,8	-60,9	-51,5
		IPSL CM5A	-14,2	-18,1	5,2	-50,2	-26,0
		IPSL CM5B	-13,8	-28,1	5,3	-51,8	-35,2
		MIROC5	-9,6	-30,6	5,2	-50,4	-40,6
		MMM	-12,0	-25,5	5,4	-52,6	-38,6
	East	CNRM CM5	-10,3	-10,5	6,1	-62,7	-47,8
	Sahel	HadGEM2	5,4	-10,4	6,4	-85,9	-78,0
		IPSL CM5A	-10,1	-28,0	5,5	-69,2	-50,8
		IPSL CM5B	-5,6	-39,8	5,9	-81,1	-69,7
		MIROC5	6,1	1,6	5,3	-63,4	-44,0
		MMM	-2,9	-16,3	5,9	-69,8	-54,7

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International Journal of Climatology -For peer review only

Acknowledgements

M.G. has been supported by the LABEX project, funded by Agence Nationale de la Recherche (French National Research Agency, Grant ANR-10-LABX-18-01). The research leading to these results has received funding from the European Union Seventh Framework Programme FP7/2007-2013 under grant agreement n o 603864. (HELIX: High-End cLimate Impacts and eXtremes; http://www.helixclimate.eu).