Rémi Meynadier

Gaëlle De Coëtlogon

Marion Leduc-Leballeur

Laurence Eymard

Serge Janicot

Seasonal influence of the sea surface temperature on the low atmospheric circulation and precipitation in the Eastern Equatorial Atlantic

The Air-Sea interaction in the Gulf of Guinea and its role in setting precipitation at the Guinean coast is investigated in the present paper. This study is based on satellite observations and WRF simulations forced by different sea surface temperature (SST) patterns.

It shows that the seasonal cold tongue setup in the Gulf of Guinea, along with its very active northern front, tends to strongly constrain the low level atmospheric dynamics between the equator and the Guinean coast. Underlying mechanisms including local SST effect on the marine boundary layer stability and hydrostatically-changed meridional pressure gradient through changes in SST gradient are quantified in WRF regarding observations and CFSR reanalyses. Theses mechanisms strongly impact moisture flux convergence near the coast, leading to the installation of the first rainy season of the West African Monsoon (WAM) system. The current study details the mechanisms by which the Atlantic Equatorial cold tongue plays a major role in the pre-onset of the boreal WAM.

Introduction

The northern Gulf of Guinea is marked by a heavy coastal rainfall in April -May, which is generally intense during 2 to 3 weeks, and stops about one week before the onset of the Sahelian phase of the West African monsoon (Thorncroft et al. 2010, Nguyen et al. 2011). The latter is suspected to be strongly conditioned by the continent, with an important role played by the Saharan Heat Low in West Africa [START_REF] Lavaysse | Seasonal cycle of the West African heat low: a climatological perspective[END_REF]). The monsoon jump (i.e the rapid shift of precipitation from the Guinean coast to the Sahel) occurs also because of dynamical instabilities developing at the end of the coastal precipitation phase [START_REF] Cook | Role of inertial instability in the West African monsoon jump[END_REF].

Coastal convection is likely inhibited because of the subsidence of dry air induced by a westward Rossby wave generated after the Indian monsoon onset [START_REF] Flaounas | The West African monsoon onset in 2006: Sensitivity to surface albedo, orography, SST and synoptic dry-air intrusions using WRF[END_REF]. It has been shown that the sea surface temperature (SST) in the Gulf of Guinea played a key role in the West African Monsoon. The intensification and the northward seasonal migration of the South Atlantic anticyclone (Sta Helena) induces an acceleration of the Trade winds in the southeastern Tropical Atlantic in boreal spring and summer, cooling the SST into an equatorial "cold tongue" between roughly 4°S and 1°N [START_REF] Caniaux | Coupling between the Atlantic cold tongue and the West African monsoon in boreal spring and summer[END_REF][START_REF] Marin | Why were sea surface temperatures so different in the eastern equatorial Atlantic in June 2005 and 2006?[END_REF][START_REF] De Coëtlogon | Intraseasonal variability of the oceanatmosphere coupling in the Gulf of Guinea during boreal spring and summer[END_REF][START_REF] Jouanno | Intraseasonal Modulation of the Surface Cooling in the Gulf of Guinea[END_REF][START_REF] Giordani | Intraseasonal mixed layer heat budget in the equatorial Atlantic during the cold tongue development in 2006[END_REF]. A consequence of the northern front of the cold tongue is to accelerate the overlying surface wind, as suggested for interannual [START_REF] Caniaux | Coupling between the Atlantic cold tongue and the West African monsoon in boreal spring and summer[END_REF]) and intraseasonal [START_REF] De Coëtlogon | Intraseasonal variability of the oceanatmosphere coupling in the Gulf of Guinea during boreal spring and summer[END_REF][START_REF] De Coëtlogon | Atmospheric response to sea surface temperature in the Eastern Equatorial Atlantic at quasi-biweekly timescales[END_REF] timescales. Moreover, the development of the cold tongue during the boreal spring controls the Guinean coastal rainfall (GCR) in May-June (Thorncroft et al. 2010[START_REF] Leduc-Leballeur | Observation of the marine atmospheric boundary layer in the Gulf of Guinea during the 2006 boreal spring[END_REF], through the onset of a low-level atmospheric circulation (LLAC) between the equator and the coast [START_REF] Leduc-Leballeur | Air-sea interaction in the gulf of Guinea at intraseasonal time-scales : wind bursts and coastal precipitation in boreal spring[END_REF]. The implied mechanism is linked to changes in Sea Level Pressure (SLP) gradient induced by the strengthened meridional SST gradient in the Northern Front of the Cold Tongue (NFCT, de Coëtlogon et al. 2014[START_REF] Caniaux | Coupling between the Atlantic cold tongue and the West African monsoon in boreal spring and summer[END_REF], according to the mechanism described in [START_REF] Lindzen | On the role of sea surface temperature gradients in forcing low-level winds and convergence in the tropics[END_REF]. It then increases the transport of moisture towards the coast (Thorncroft et al., 2010), encouraging convection to develop there. This mechanism will be called afterward "LN" (as "Lindzen and Nigam").

Another mechanism of Tropical Atlantic SST influence on surface wind is linked with the vertical mixing of the wind momentum in the marine atmospheric boundary layer (MABL), which decreases over a colder SST because of an increased stability in the lower atmospheric column [START_REF] Sweet | Air-Sea Interaction Effects in the Lower Troposphere Across the North Wall of the Gulf Stream[END_REF], Wallace et al. 1987, Hayes et al. 1987). Stability is also increased by tropospheric subsidence occurring at the equator, suppressing deep convection when the cold tongue is well established [START_REF] Yin | Spatial Variability of Atmospheric Boundary Layer Structure over the Eastern Equatorial Pacific[END_REF]. This mechanism will be called afterward "SW" (as "Sweet and Wallace"). De [START_REF] De Coëtlogon | Atmospheric response to sea surface temperature in the Eastern Equatorial Atlantic at quasi-biweekly timescales[END_REF] showed significant hints of both of these mechanisms in daily observations and reanalyses at short timescales (a few days), but not at seasonal timescales. The connection between the NFCT and the GCR is therefore investigated in this study at the seasonal scale.The tested hypothesis is that the NFCT forces both the wind speed increase between the equator and the Guinean coast, and the northward migration of the precipitation toward the coastal region. Numerical simulations are performed with the non-hydrostatic regional Weather Research and Forecasting WRF model [START_REF] Skamarock | A description of the advanced research WRF version 3[END_REF] forced by different observed SST. The analysis aims at quantifying the cold tongue influence on low level dynamics and further explore its impact on the water cycle.

The numerical simulations discussed in this paper are based on composites around a reference date, thereafter Tref, of the LLAC and Guinean coastal rainfall (GCR) onset, which was defined in [START_REF] Leduc-Leballeur | Air-sea interaction in the gulf of Guinea at intraseasonal time-scales : wind bursts and coastal precipitation in boreal spring[END_REF]. Section 3 thoroughly describes the Tref definition and the air-sea coupling for a 2-month composite period around Tref in the observations, the meteorological reanalyses, and the WRF simulations. Section 4 focuses on the mechanisms modifying the low-level atmospheric circulation induced by SST seasonal variability and the quantitative estimation of the underlying processes in the WRF simulation. The importance of the atmospheric forcing at the model domain boundaries versus the SST influence are tested in four additional experiments, detailed in section 5. The last section presents a short discussion and summary of the results.

Data

Surface wind vectors were provided by the QuikSCAT (Quick Scatterometer) satellite [START_REF] Liu | Atmospheric manifestation of tropical instability wave observed by QuikSCAT and Tropical Rain Measuring Mission[END_REF] available on the Remote Sensing Systems website (www.remss.com), as 3day running mean on a 0.25°x0.25° grid between 2000 and 2009. A two-dimensional cubicspline interpolation first filled the gaps due to the clouds for each available day, and then a linear temporal interpolation at each grid point completed the 15 missing days over the decade.

The high-resolution SST product developed using optimum interpolation, the Advanced Very High Resolution Radiometer (AVHRR) infrared satellite SST data, and in situ data from ships and buoys [START_REF] Reynolds | Daily highresolution blended analyses for sea surface temperature[END_REF]) have been retrieved thanks to the NOAA-NCDC (National Oceanic and Atmospheric Administration -National Climatic Data Center) ftp site (http://www.ncdc.noaa.gov), with a spatial grid resolution of 0.25° and at a daily temporal resolution.

In addition, the daily averaged TRMM 3B42 V7 product on a 0.25° x 0.25° grid was used. It mixes satellite measurements from microwave sensors and infrared geostationary instruments with data from ground radars [START_REF] Huffman | The TRMM Multisatellite Precipitation Analysis (TMPA): Quasi-global, multiyear, combined-sensor precipitation estimates at n e scales[END_REF]), which guarantees the best possible quality for precipitation data. It was retrieved from the website trmm.gsfc.nasa.gov.

Reanalysis from the Climate Forecast System Reanalysis (CFSR, [START_REF] Saha | Supplement to The NCEP Climate Forecast System Reanalysis[END_REF] were retrieved from the National Centre for Environmental Prediction (NCEP) site (www.ncep.noaa.gov). One of the distinctive CFSR characteristics is that it was performed with a coupled ocean-atmosphere model, which was shown to better describe the air-sea interaction in the eastern Tropical Atlantic than the ERA-interim (European centre for meddium-range weather forecasts Re-Analysis, de [START_REF] De Coëtlogon | Atmospheric response to sea surface temperature in the Eastern Equatorial Atlantic at quasi-biweekly timescales[END_REF]. The data are available on a 0.5°x0.5° horizontal grid, with vertical atmospheric profiles retrieved on 27 levels from 1000 to 100 hPa. Daily means of the 6-hourly parameters were used over the 2000-2009 decade.

Composites around the LLAC / GCR onset date (Tref)

a. Observations composite [START_REF] Leduc-Leballeur | Air-sea interaction in the gulf of Guinea at intraseasonal time-scales : wind bursts and coastal precipitation in boreal spring[END_REF] defined a reference date to study the abrupt change observed in the surface wind pattern between April and July in the eastern equatorial Atlantic, due to the cold tongue and NFCT development. This date, hereafter referred as Tref, corresponds to the onset of strong southerlies between the equator and the Guinean coast (more exactly, Tref is the date when southerlies become -and remain until fall -stronger at 2°N-3°N than just south of the equator, see their paper for more details). They showed that this date corresponds to the GCR onset using satellite precipitation data, and to the LLAC installation between the equator and the Guinean coast as seen in atmospheric reanalyses (NCEP CFSR and ERA-Interim). The seasonal composite around Tref (i.e. daily averages around Tref for the period 2000-2009) marks clearly the time of a strong SST cooling between roughly 4°S and 1°N (Fig. 3 bottom row), while the wind accelerates north of the equator and decelerates further south (middle row). In addition, Tref corresponds to a northward seasonal shift of the rainfall (top row): after Tref, no more rainfall is indeed observed south of 2°N in the Gulf of Guinea. The intense GCR lasts about 2 weeks before the Sahelian "monsoon jump". In this composite, the mean observed seasonal GCR clearly lasts about 2 weeks, while the rain completely stops south of 2°N-3°N (top left). The coastal shift of precipitation also coincides with the intensification of the oceanic NFCT (more than 0.6 K per lat. deg. after Tref, bottom left).

In addition to this clear seasonal evolution around Tref, a very large intraseasonal variability can be observed yearly (not shown) in the equatorial region and further south, with a dominant timescale of about 2 weeks, which is probably due to a strong SST / surface wind coupling (with a negative feedback) in the equatorial region (see [START_REF] De Coëtlogon | Intraseasonal variability of the oceanatmosphere coupling in the Gulf of Guinea during boreal spring and summer[END_REF][START_REF] De Coëtlogon | Atmospheric response to sea surface temperature in the Eastern Equatorial Atlantic at quasi-biweekly timescales[END_REF]. Although these quasi-biweekly timescales are considerably damped in the 2000-2009 composites around Tref, as seen in Fig. 3 (left column), they are however still visible in the observed surface wind and SST composites, emphasizing the importance of the quasibiweekly oscillations for the phasing of the GCR onset, as discussed in [START_REF] Leduc-Leballeur | Air-sea interaction in the gulf of Guinea at intraseasonal time-scales : wind bursts and coastal precipitation in boreal spring[END_REF]. However, the intraseasonal variability will not be further discussed here, as the scope of this study is to rule out the mechanisms behind the rapid seasonal shift around Tref.

b. WRF simulation and CFSR composites

The configuration of WRF version 3.3 and parameterizations used in this study are chosen to produce realistic surface wind and rainfall patterns in May -June (see [START_REF] Meynadier | Sensitivity testing of WRF parametrizations on air-sea interaction and its impact on water cycle in the gulf of Guinea[END_REF]).

WRF physics is based here on Betts-Miller-Janjic (Betts,1986 ;Betts et Miller, 1986 ;Janjic , 1994) scheme for the cumulus scheme, Yonsei University (YSU, Hong et al., 2006a) nonlocal closure PBL scheme, RRTMG for the short and long-wave radiation schemes and the single moment six-class (WSM6) scheme (Hong et al., 2006b) for microphysics. The horizontal resolution is set at 25 km with 35 vertical sigma levels and a top-of-atmosphere at 50 hPa. The simulation domain is similar to the one used in [START_REF] Meynadier | Sensitivity testing of WRF parametrizations on air-sea interaction and its impact on water cycle in the gulf of Guinea[END_REF] and was chosen to be wide enough in latitude and longitude for WRF to fully simulate "by itself" the large scale atmospheric features (such as the African Easterly Jet) of the West African climate system. A 2-month numerical simulation with WRF was designed to represent the average seasonal evolution around Tref. ERA Interim data for years 2000 to 2009 were averaged in 6-hour composites around Tref (1 month before and 1 month after), and used to force the regional model boundaries, except for the SST field. For the latter, the surface input forcing over the ocean was composited around Tref using satellite-based daily Reynolds dataset (seen in Fig.

3, bottom row).

A time-latitude diagram of this WRF simulation for the 10°W-0 meridional band shows that the precipitation simulated by the model is almost twice too abundant. Its evolution during the seasonal transition (phasing around Tref + latitudinal distribution) matches the observed composite quite well (Fig. 3, top middle). The evolution of the simulated surface wind pattern is also in agreement with the observations, except that the wind speed is about 1 to 3 m/s too weak (middle row). This northerly bias is the strongest between the equator and the Guinean coast, where the actual southerlies can actually reach 8 m/s on average. A similar bias was also found in reanalyses (see [START_REF] De Coëtlogon | Atmospheric response to sea surface temperature in the Eastern Equatorial Atlantic at quasi-biweekly timescales[END_REF], as can be seen here for the CFSR reanalyses (Fig. 3, right column), in spite of the assimilation of QuikSCAT surface wind observations. One can note that WRF SST is not exactly similar to Reynolds SST used to force the model but shows slightly smaller values. WRF SST shown here is actually surface skin temperature (SKT) from the WRF ouput.

c. Transition patterns around the LLAC onset

The abrupt seasonal transition around Tref was examined by comparing the parameters at two different times, before and after Tref: The two periods (also sketched in Fig. 1) are 2 weeks long each, thereby partly damping the large quasi-biweekly variability observed in the equatorial region. The surface wind speed is generally too weak in the simulation, as mentioned in the previous section, by about 1 m/s, and up to 3 m/s north of the equator (Fig. 4, bottom row). Before Tref (period 1), this bias stands south of 5°N, with a slightly stronger-than-normal wind near the Guinean coast; but after Tref, the bias largely increases near the coast (period 2). Since the wind bias is actually maximal downstream of the SST front, particularly after Tref near the coast, it may suggest that the NFCT impact on the surface wind acceleration is not strong enough in the model,compared to the observations. The bias could also come from flaws in the forcing fields at the model boundaries, or from the model itself, especially near West Africa coast, where the numerical representation of the continental surface wet conditions still remains a challenge (Taylor 2008) and could impact the near-shore low-level atmospheric circulation.

Although much too abundant in the simulation, the precipitation spatial distribution is very similar to the observations (Fig. 5): with a realistic slight deviation from the zonal symmetry, it exhibits a clear northward migration from as far south as the equator before Tref to a maximum near the Guinean coast after Tref, and no more rainfall south of 2-3°N. The PWAT pattern also follows this steep northward shift before and after Tref, with a pattern similar in the simulation than in the observations, pending a small dry bias. The vertical extension of the SST impact is documented with latitudinal-vertical sections of the model output, averaged between 10°W and 0°E. Fig. 6 shows period 1, period 2, and their difference (i.e. [period 2] minus [period 1]), for the meridional velocity (shadings) and meridional gradient of geopotential height (thereafter dZ/dy, black contours) up to 500 hPa. It shows that dZ/dy is negative south of the equator in the whole troposphere, traducing the presence of the South Atlantic anticyclone (Fig. 6a and6b). But further north, close to and over the continent (north of 5°N), its pattern is more complex than would give a "classical Hadley circulation", with a symetric positive dZ/dy: because of the strong meridional thermal contrast between the eastern Tropical Atlantic and West Africa, dZ/dy is strongly negative within or near the atmospheric boundary layer, and strongly positive overhead.

The seasonal abrupt transition of the meridional low-level atmospheric circulation from period 1 to period 2 is clearly seen in Fig. 6: the middle of the LLAC (i.e. subsident/ convective cell between the equator and the Guinean coast) is indeed shifted from around 0.5°N in period 1 to around 2°N in period 2 (Fig. 6a and6b), thereby strongly increasing the subsidence, or inhibiting the convection, between the equator and 3°N, and increasing the convection north of 4°N (Fig. 6c). The difference in time (period 2 minus period 1, or Δ ) of the meridional SST gradient (dSST/dy) in Fig. 6d fits very well the pattern of Δ dZ/dy: the largest value of |dZ/dy| is indeed found between 1°N and 2°N, slightly north of the Δ dSST/dy maximum at 0.5°N-1°N: this is in agreement with a pressure anomaly forced by the SST front through the LN mechanism, which likely controls the strong northward acceleration of southerlies between the equator and the coast. The impact of the SST front extends up to 850 hPa at 1°N, which is even deeper than the boundary layer (which top is usually around 900 -950 hPa, not shown). Conversely, south of the front (4°S-Eq), the pressure gradient slightly loosens, with a pattern matching the decrease in dSST/dy, which weakens down the southerlies, but within a much shallower layer. The latter signal is in agreement with the LN mechanism, but it also fits with the SW mechanism between 2°S and the equator, just over the coldest part of the cold tongue, where only a thin surface layer (under 975 hPa) seems influenced. As was previously found in the observations (de Coëtlogon et al. 2014), both mechanisms appear indeed to control the surface wind in this area, with a time lag spanning from a few hours (when the SW dominates) to 1 -2 days (when the LN takes control).In summary, the surface divergence induced by the SST front (seemingly through both SW and LN mechanisms) around the equator creates a strong anomalous secondary circulation in the low troposphere (< 700 hPa), subsiding at the equator and convecting at the coast, as well as a strong deep subsidence over the NFCT. This is in very good agreement with the LLAC found in ERA-interim and CFSR reanalyses, as described in [START_REF] Leduc-Leballeur | Air-sea interaction in the gulf of Guinea at intraseasonal time-scales : wind bursts and coastal precipitation in boreal spring[END_REF]: before Tref, they found a large loop subsiding over the Gulf of Guinea south of 2°S and convecting between the equator and the coast, with a northward monsoon flux near the surface and a return southward branch around 700 mb. But after Tref, the LLAC is framed between the equator and the coast, with its southern edge probably pushed north by the NFCT. This seasonal shift of the LLAC in the Gulf of Guinea has a strong effect on precipitation, as Fig. 5 already suggested. In addition, Fig. 7 presents the vertical cross-section of meridionalvertical wind vectors and horizontal moisture flux divergence, highlighting the large control of the latter on the precipitation profiles, averaged between 10°W and 0°E. Before Tref, a low-level divergent / convergent humidity flux pattern straddles the equator in the low atmosphere (<850 hPa). As suggested in Thorncroft et al. (2010), the SST between the equator and 3°N is warmer than 28°C (see Fig. 3), which can trigger deep atmospheric convection: the convergence maximum is located just south of 3°N, framed by two divergent areas (south of the equator and north of 4°N), in agreement with the diabatic heating described for example in Gill (1981). After Tref, the SST cooling within the cold tongue leads the southerlies strengthening north of the SST front (or NFCT) as discussed previously, hence to the reinforcement of the divergent pattern and its northward shift by more than 200 km, at the equator. This likely leads the atmospheric subsident branch to migrate northward, up to 2°N, thereby reinforcing the moist convection inhibition (in addition to the SST cooling).

A strong reinforcement of the low-level moisture flux convergence is also visible between 3°N and 5°N: it results from the wind strenghtening around 3°N on the one hand, and the deceleration of the low-level wind as the air moves toward the Guinean coast at 4°N-5°N on the other hand.

The latter probably comes from an increased coastal convergence, since the continental surface friction considerably weakens the surface wind (and if the upstream wind is stronger, then the deceleration due to the continental friction is larger as well, thereby increasing the convergence). But in addition, there is also a clear positive dZ/dy signal that counters southerlies north of about 3.5°N (Fig. 6c), with maximal values on the continental side of the Guinean coast, of comparable magnitude than the ones created by the NFCT: clearly, this dZ/dy inversion around 3.5°N is a major contributor of the low-level convergence between 3°N and 5°N after Tref.

Very interestingly, the continental dZ/dy maximal values, around 5.5°N and 6.5°N, coincide with huge negative peaks in the meridional gradient of SKT (Fig. 6,bottom). This suggests that a flaw in the representation of the WRF wet continental conditions (or from the tooabundant precipitation ?) may well explain the strong northerly bias in the model surface wind north of 5°N after Tref, in comparison with satellite data, with a too-strong dZ/dy inversion close to the Guinean coast.

Note that the patterns of the atmospheric mass divergence and of the humidity flux divergence are very similar (not shown), which emphasizes the fact that the precipitation are strongly controlled by the low-level atmospheric dynamics in this region. If thermodynamics are likely important before Tref for driving the deep atmospheric convection (precipitation oceanic phase described in Thorncroft et al. 2010 and[START_REF] Nguyen | Guinean coast rainfall of the West African monsoon[END_REF], these results strongly suggests that the NFCT apparition drives the increased coastal convergence and the Guinean coastal rainfall after Tref.

Quantitative estimations of the SST influence on surface wind

In order to compare the simulation with observations more easily, we attempt here to quantify the impact of the SST and dSST/dy on the surface wind speed where their influence is maximal: the cold tongue for SST, and its northern front for dSST/dy. Surface wind speed, SST and dSST/dy were zonally averaged between 10°W and 0°E, in three different latitude bands: 1°S-Eq (SST + wind speed), Eq-1°N (dSST/dy), and 2.5°N-3.5°N (wind speed). This leads to 61 daily values around Tref (i.e. from Tref -30 days until Tref + 30 days), from the 2-months WRF simulation, or from the composites computed with observations and CFSR reanalysis.

A linear regression was performed on the surface wind speed versus SST in the [1°S-Eq] band, allowing to estimate the amplitude of the local wind response to the small-scale SST features within a few hours through the SW mechanism (Fig. 8, left). Note that before Tref, the equatorial SST is rather uniformly warm: the equatorial wind is then mostly driven by the large-scale atmospheric circulation. Only post-Tref values were therefore taken into account for the linear regression fits (i.e. days plotted with dots in Fig. 8, left, whereas circles represent the days before Tref). In the observations, the SST / wind speed fit has a slope of almost 1 m/s/K, which indicates a cooling of the SST by about 1K inducing a weaker wind speed by more than 1 m/s (blue line). De [START_REF] De Coëtlogon | Atmospheric response to sea surface temperature in the Eastern Equatorial Atlantic at quasi-biweekly timescales[END_REF] found that both ERA-Interim and CFSR tend to underestimate the wind response to the SST fluctuations at intraseasonal timescales, which is also found here at the seasonal scale with a slope of less than ½ m/s/K for CFSR (green line). On the contrary, the WRF model exhibits a decreased wind when the SST has cooled down with a slope slightly larger than 1 m/s/K, which broadly fits the observations pending uncertainties (red line).

The surface wind response to dSST/dy through the LN mechanism was similarly estimated, but in taking into account the delayed response in time (1-2 days) and space (advection by the mean atmospheric flow, Fig. 8, right): dSST/dy is then averaged in [Eq-1°N], and the wind response is measured by substracting the wind speed just south of the front (at [1°S-Eq]) from the wind speed further north (at [2.5°N-3.5°]), thereby isolating the wind strenghtening due to the SST gradient from the large-scale atmospheric circulation.

The regression fit matches very well the observations, with a slope of 4 to 5 m.(lat. deg.)/s/K (Fig. 8 right, simulation in red and observations in blue), whereas the CFSR reanalysis clearly appears to underestimate the mechanism amplitude (green line). This suggests that the wind strenghtening when crossing the NFCT (due to the LN mechanism) in the model is in good agreement with the observations. Note that the set of WRF parameterizations were carefully chosen in order to get the characteristic weak / strong seasonal wind pattern on each side of the NFCT [START_REF] Meynadier | Sensitivity testing of WRF parametrizations on air-sea interaction and its impact on water cycle in the gulf of Guinea[END_REF], but this did not guarantee to get realistic linear amplitudes of the wind response to both LN and SW forcing mechanisms.

The surface wind is usually considered as resulting from a balance between the pressure gradient force, the Coriolis force, and the friction forces (drag at the surface, and momentum entrainment from the wind shear above), usually neglecting the advection (inertial) term, as in the simple model of Stevens et al. (2002). The SST influences the wind through a local modification of the pressure gradient (LN mechanism), opposing the large-scale pressure gradient -rather controlled by the free troposphere above the MABL. In addition, through the SW mechanism and the stratification in the MABL, the SST partly controls the momentum entrainment from above. Therefore, a flaw in the representation of the atmospheric circulation in the free troposphere, above the MABL top -where the vertical gradients, such as humidity gradient are very strong [START_REF] Meynadier | Sensitivity testing of WRF parametrizations on air-sea interaction and its impact on water cycle in the gulf of Guinea[END_REF], -could have a large impact on the surface wind representation. Eventually, we found that the horizontal advection (inertial) term

⃗ U . ∇ ⃗
U is also very important for this region in the WRF simulation. Fig. 9 shows its mean value during the 2-month simulation: it can amount to as much as half of the pressure gradient magnitude 1 ρ ∥ ∇ SLP∥ , as shown in Fig. 9. In particular, this ratio is of about 35% around 3°N between 10°W and 0°E on average in the 2-month simulation, with a larger value (up to 50%) if period 2 only is considered, because the wind is stronger (not shown).

The advection is therefore actually far from being negligible in the Eastern Equatorial Atlantic, compared to SST or free troposphere influences, which would probably reinforce any bias induced by the representation of these 2 terms.

In short, the SST influence on the surface wind appears to match well the observations in agreement with the SW and LN mechanisms amplitudes. But quite strong biases remain in the surface wind model compared to the observations, which questions the influence of largerscale environmental factors, such as the free tropospheric circulation or the continental surface. In particular, the next section investigates if the characteristic wind pattern evolution around Tref is actually due to the cold tongue development in the SST, or rather to a remote forcing.

Sensitivity cross-experiments : SST vs external forcing

In order to test if the results of previous sections are really due to the change in SST, and not to the change in the forcing parameters at the model boundaries, four additional WRF simulations were performed. All were made with a stationary external forcing, averaged either in period 1 (before Tref) or in period 2 (after Tref), with a permanent diurnal cycle (the same composite day of period 1 was repeated during 8 weeks, and the same was done for the composite day of period 2):

* external forcing of period 1 with the SST of period 1 (thereafter "case1") * external forcing of period 2 with the SST of period 2 (thereafter "case2"), * external forcing of period 1 with the SST of period 2 (thereafter "case1_SST2"), * external forcing of period 2 with the SST of period 1 (thereafter "case2_SST1").

This way, the influence of the SST change can be tested in the conditions of constant period 1 or constant period 2, i.e. without any change in the external forcing (case1_SST2 minus case1, or case2 minus case2_SST1), whereas the influence of the external forcing can be tested in the conditions of a constant SST (case2_SST1 minus case1, or case2 minus case1_SST2). Indeed these effects are certainly not completely linear, and the method misses their non-linear dynamics, but some of their main characteristics can nonetheless be depicted.

The significance of these differences were validated by a t-test, based on rms estimated for case1 and case2 simulations, the maximal value between the 2 being kept at each grid point.

One degree of freedom per day was taken (which correspond to an exponential decrease of less than one day in autocorrelations, checked for a few SLP and meridional surface wind gridpoints, not shown). The null hypothesis (i.e. non significant difference between the 2 values) was only rejected when the difference exceeded the 95% t-value, thereby taking into account the large level of noise in the simulations due to internal variability in the model. Cross-test experiments were also investigated vertically: they show that the NFCT impact is very strong around the equator, and significant from as far south as 6°S, up to the coast at 5°N

(not shown). However, the strong signals seen over the continent in Fig. 6 and 7 (north of 5°N) appear due to the seasonal change of the external forcing (surface conditions ?), and probably to the Saharan Heat Low reinforcement as well in period 2 (not shown).

Cross-test experiments were then analyzed regarding the water cycle components. ). An explanation of this could be that the simulation with everyday similar warm SST forcing before Tref leads to large amount of ocean precipitation using the BMJ cumulus scheme.

Conclusion

The air-sea interaction mechanisms in the Gulf of Guinea and their role in the coastal precipitation are investigated from WRF experimental simulations. It is shown here that the WRF simulation captures very well the seasonal transition of the rainfall from an oceanic phase to the Guinean coastal one, as well as the transition of the surface wind speed from a rather uniform distribution in the gulf of Guinea to a very characteristic pattern across the NFCT (weak around the equator, strong further north). The increased low-level convergence near the coast has for effect to set the Guinean coastal rainfall described in [START_REF] Nguyen | Guinean coast rainfall of the West African monsoon[END_REF]. Boundary and lateral forcing cross-test experiments made with WRF clearly show that the SST seasonal evolution solely pushes precipitation toward the Guinean coast and suppresses moist convection south of 4°N. This is due to a change in moisture flux convergence directly connected to the low-level wind changes under the influence of the SST seasonal variability.

The amplitude of the seasonal cold tongue impact on surface wind through the SW mechanism is estimated with linear regression, leading to a value of about 1 m/s/K, in agreement with the estimations made at intraseasonal timescales in de [START_REF] De Coëtlogon | Atmospheric response to sea surface temperature in the Eastern Equatorial Atlantic at quasi-biweekly timescales[END_REF]. It is about twice as large as the SST impact on the overlying wind speed as measured in extratropical regions [START_REF] Perlin | Modeling the Atmospheric Boundary Layer Wind Response to Mesoscale Sea Surface Temperature Perturbations[END_REF][START_REF] Small | Air sea interaction over ocean fronts and eddies[END_REF]). In addition, through the LN mechanism, the NFCT accelerates the surface wind by 4 to 5 m.(lat. deg.)/s/K. These values compare very well with the ones found in the observations, whereas the CFSR reanalysis exhibits a clear tendency to underestimate the impact of both mechanisms, which is consistent with the northerly surface wind bias found between the equator and the coast in the reanalysis and may be partly due to a lower resolution [START_REF] De Coëtlogon | Atmospheric response to sea surface temperature in the Eastern Equatorial Atlantic at quasi-biweekly timescales[END_REF]. However, putting aside the hypothesis that observations could be wrong there (since the QuikSCAT product is known to be taken with great cautions in the 100-km band off the coasts, as all scatterometers products), a similar strong northerly wind bias is also found in the WRF simulation in the northern Gulf of Guinea. As discussed in section 3c, the latter is likely linked to a strong decrease in the meridional gradient of the low-level pressure near the Guinean coast in the WRF simulation after Tref (Fig. 6c), which would excessively weaken down the upstream marine southerlies north of 3.5°N. This large change in the meridional pressure gradient

Fig. 1

 1 Fig. 1 defines the Tref date for each year, from 2000 to 2009. Among them, note two particular years : the earliest Tref in 2005 (May 11), and the latest in 2006 (June 25). Note that years 2005 and 2006 are known for exhibiting a large difference in the properties of the equatorial cold tongue. Marin et al. (2009) showed that far colder temperatures were observed in 2005 than in 2006. This difference results mainly from a time shift in the development of the cold tongue between the two years, due to different surface wind speed conditions in April-May, thus explaining why Tref is earlier in 2005 than in 2006. This large interannual variability prevents the regular daily seasonal average in May-June of being pertinent for a clear view of the seasonal changes in the observations, hence the need for a composite built around Tref.

period 1 =

 1 [Tref-3 weeks : Tref-1 week]. On average : 10-24 May. period 2 = [Tref : Tref+2 weeks]. On average : 1-14 June.

Fig. 4 (

 4 Fig. 4 (surface wind and SST) and Fig. 5 (precipitation and precipitable water, thereafter

  Let us remind here the two main mechanisms through which the SST possibly influences the surface wind: * Sweet and Wallace (SW) mechanism: the cold tongue reduces the wind speed through the vertical stability of the lower atmospheric boundary layer. As discussed for example in de Coëtlogon et al. (2014), this effect is immediate and local, and confined in the lower part of the boundary layer (or mixed layer), and it is well observed in the vicinity of the cold tongue, where the SST cooling and wind speed weakening are maximal (i.e. in the [1°S-Eq] band). * Lindzen and Nigam (LN) mechanism: an increased SST gradient accelerates the overlying wind through a hydrostatically-induced increased pressure gradient (in absolute values), possibly advected by the mean atmospheric flow. Here, on average, the meridional SST gradient is maximal in [Eq-1°N], and the southerlies crossing the equator in [2.5°N-3.5°N] (i.e. 100 to 200 km downstream of the SST front).

Fig. 10

 10 Fig. 10 shows the difference in SLP patterns due to a change in SST (top row) or in the external forcing (bottom row). The seasonal evolution of the SLP is clearly due to the SST change, since the maximal SLP fluctuations (1 to 1.5 mb) are located close to the minimal SST ones for both periods (about -2K, as shown in black contours). In the simulations only differing by the external forcing (the SST is the same), the SLP does not increase significantly in the equatorial region (bottom row), by less than 0.3 -0.4 mb. Only the southern Eastern Equatorial Atlantic (south of 5°S) appears significantly influenced by the external forcing.

  Figure 12b clearly shows that with the set-up of the NFCT, the SST seasonal evolution solely pushes precipitation toward the Guinean coast and suppresses moist convection south of 4°N. Precipitation patterns are spatially well correlated with vertically integrated moisture flux anomalies (figure 12 a and b). In term of spatial structures and amplitude, the change of moisture flux convergence (or divergence) due to the change of SST exhibits indeed a very consistent pattern with the rainfall changes. Note that the vertically integrated moisture flux field (represented by vectors in Figure 12a) is very close to the surface wind pattern due to dSLP/dy: change before and after Tref in moisture flux convergence or divergence (and then precipitation) is directly connected to the low-level wind changes due to the SST seasonal evolution. The vertical structure of moisture convergence (Figure 12c) confirms the influence of the SST changes over the ocean, even if the amplitude of the low level moisture divergence and the associated suppressed rain (between the equator and 3°N) is likely overestimated, compared to Fig. 7 (right panel

  seems to be of inland origin, which could suggest a flaw in the model representation of the continental response to excessive coastal convergence and convection in the Guinean coastal region in the model. This flaw could also explain why the secondary circulation between the equator and the coast, or LLAC, after Tref, appears to be weaker in the model than in the reanalyses (ERA interim as well as CFSR), since this cell variability is probably driven not only by the surface pressure gradients but also by internal thermodynamics (it would indeed be partly driven by the humidity convergence at the coast, see[START_REF] Leduc-Leballeur | Air-sea interaction in the gulf of Guinea at intraseasonal time-scales : wind bursts and coastal precipitation in boreal spring[END_REF][START_REF] De Coëtlogon | Atmospheric response to sea surface temperature in the Eastern Equatorial Atlantic at quasi-biweekly timescales[END_REF]). Eventually, it is important to keep in mind that the advection of momentum by the mean atmospheric flow plays a very significant role in the cold tongue region.Unhappily, too few observations of the low troposphere are available in this region to properly document the LLAC, whith only a few radiosoundings launched from the AMMA 2006 (EGEE) and PIRATA 2014 campaigns. Further investigations and a better documentation of the low troposphere dynamics in the northern Gulf of Guinea are therefore strongly needed regarding the structure of the LLAC, especially after Tref, since its misrepresentation could be a clue in the general difficulty of the models to correctly represent the West African Monsoon later on in the season (by, for example, advecting not enough moisture from the Gulf).

  Fig ure 1 : ref ere nce dat es Tre f for eac h par tic ula r yea r, fro m 2000 to 2009 (black star), with « period 1 » (from [Tref -3 weeks] until [Tref -1 week], blue line) and « period 2 » (from Tref until [Tref + 2 weeks], red line).

Figure

  Figure

  Figure 2: diagra m time / latitud e of TRM M precip itation (top), QuikS CAT surfac e wind (middl e, speed in shadin gs) and Reyno lds SST (bottom), 10°W-0, in May-June-July-August 2005 (left) and 2006 (right). White contour in the lower row indicates a SST meridional gradient of 0.6 K per latitude degree. Vertical black line points Tref, and black dotted lines frame a period of 2 months around Tref.

Figure 3 :

 3 Figure 3: time / latitude diagrams of ~2-month composites around the date of reference Tref, 10°W-0: in the observations (left column), in the WRF simulation (middle), and in the CFSR ranalysis (right) ; of PPT (top row), surface wind (middle) and SST (bottom, SKT for WRF). Black vertical lines indicate Tref. White contours in lower frames indicate level 0.6 K per latitude-degree for the SST (OBS and CFSR) or SKT (WRF output) meridional gradient.

Figure 4 :

 4 Figure 4: Top row: observed QuikSCAT surface wind velocity (speed in shadings, m/s) and Reynolds SST (black contours), averaged in Period 1 (i.e. from Tref-3 weeks to Tref-1 week, left), Period 2 (average from Tref to Tref+2 weeks, middle column), and their difference (right).Middle row: similar than top row, but for the WRF simulation with SKT instead of SST (SKT not plotted over the continent for clarity). Bottom row: differences between top and middle rows (WRF-OBS).

Figure 5 :

 5 Figure 5: similar than Fig. 4, but for precipitation (shadings, in mm/day, observations from the TRMM B42 product) and precipitable water vapor (black contours, intervals of 2 kg/m2).

Figure 6 :

 6 Figure 6: 10°W-0, WRF simulation composites « Period 1 » (a), « Period 2 » (b) and «Period 2 » minus « Period 1 » (c) of meridional wind (shadings) and meridional gradient of geopotential height (black contours, intervals of 0.1 m per lat. degree for a and b, or 0.05 m per lat. degree for c, level 0 in heavy black). (d): «Period 2 » minus « Period 1 » of the meridional gradient of SKT (K per lat. deg.). Vertical dashed line stands fo the equator latitude, plain for the Guinean

Figure 7 :

 7 Figure 7: 10°W-0 WRF simulation averages in period 1 (left), period 2 (middle) and their difference (right), of the meridional humidity divergence (shadings, kg/m^3/s), the meridional circulation (arrows) and the precipitation (magenta profile, mm/day).

Figure 8 :

 8 Figure 8: Left: composites of daily surface wind speed versus daily SST, 2000-2009, 10°W-0 / 1°S-Eq, 30 days before (circles) and 30 days after (dots) Tref, in the observations (blue) and CFSR reanalyses (green), or in the corresponding WRF simulation (red). Lines : linear « least square fit » regressions. Right: similar, but for the surface wind speed difference ([2.5°N-3.5°N] minus [1°S-Eq]) versus dSST/dY ([Eq-1°N]). The coefficient of determination is indicated for each regression.

Figure 9 :

 9 Figure 9: In the WRF simulation, fraction of the advection (inertial) term ∥U . ∇ U∥ , versus the pressure gradient term 1 ∥∇ SLP∥ where = 1.12 kg/m 3 (shading). Mean U (arrows) and wind speed ∥U∥ (black contours, intervals of 0.5 m/s). All terms were first computed at the simulation output frequency (i.e. 8 values per day during the 2-month simulation around Tref), and then averaged over the whole period.

Figure 10 :

 10 Figure 10: WRF simulation surface pressure (colours) and SST (black contours, intervals of 0.2 K) differences between: * testing the influence of the SST : case 1 with SST2 -case 1 (top left), and case 2 -case 2 with SST1 (top right); * testing the influence of the external forcing : case 2 -case 1 with SST2 (bottom left), case 1 with SST2 -case 2 (bottom right).

Figure 11 :

 11 Figure11: same as Fig.5for the absolute value of pressure gradient (shadings) and wind speed (black contours, intervals of 0.5 m/s, heavy line marks null wind speed difference).

Figure 12 :

 12 Figure 12: In WRF simulation : testing the influence of the SST with « Case 2 » minus « Case 2 with SST1 » for : a) vertically integrated Moisture flux convergence (divergence) in blue (red), in mm/day, with integrated moisture flux ( kg /m 2 /s ), (b) precipitation (mm/day), (c) zonal average (10°W-0W) of vertical cross section of horizontal moisture flux divergence (shading, EMBED opendocument.MathDocument.1 10 8 s -1 ), meridional (m/s) -vertical (×100 Pa/s)


Marwa Said

Laurent Cassayre

Jean-Louis Dirion

Ange Nzihou

Xavier Joulia

Influence of Nickel on Biomass Pyro-Gasification: Coupled Thermodynamic and Experimental Investigations

The present research falls within the scope of the chemical influence of heavy metals on thermochemical conversion of biomass. Specifically, the impact of nickel during pyrolysis and CO 2 -gasification of willow samples was studied by experimental and thermodynamic approaches. For this purpose, willow wood samples were impregnated with various contents of nickel. The pyro-gasification tests were performed in a laboratory fixed bed reactor, providing the chemical composition of solid residue and major gases. Our thermodynamic simulations, based on a specific calculation procedure, were found to corroborate most of the experimental data. In addition to these methodological findings, the main results of the study are (i) Ni has a noticeable catalytic activity even at low contents (between 0.016 and 0.086 mol per kg of wood), (ii) due to the low oxygen potential of the system C + CO 2 , Ni stays in an active metal form as long as some carbon is left in the reactor, and (iii) Ni prevents the volatilization of sulfur during the gasification, due to the formation of a stable Ni 3 S 2 solid phase.

INTRODUCTION

Biomass is considered to be the only natural and renewable carbon resource that can effectively substitute fossil fuels. Additionally, it is especially abundant and stands as the third energy resource after oil and coal. [START_REF] Radmanesh | A Unified Lumped Approach in Kinetic Modeling of Biomass Pyrolysis[END_REF] Among the many options for biomass processing, several thermochemical conversion processes are appealing, such as combustion for heat and electricity production, and pyro-gasification with the addition of a gasification agent such as H 2 O or CO 2 . Moreover, biomass gasification in the presence of CO 2 would offer a potential solution for the alleviation of greenhouse gas emission.

Wastes, such as contaminated biomass, constitute widely available resources for these conversion processes. Plants used for phytoextraction represent an accessible resource of such contaminated biomass. These plants are used to extract contaminants from polluted soils, and heavy metals (HM) accumulate inside the roots, stems and leaves. [START_REF] Gupta | Phytoextraction Capacity of the Plants Growing on Tannery Sludge Dumping Sites[END_REF] For example, the concentration of zinc in crops grown on contaminated soils can reach 4 g•kg -1 in contaminated willow leaves, [START_REF] Lievens | Fast Pyrolysis of Heavy Metal Contaminated Willow: Influence of the Plant Part[END_REF] and up to 10 g•kg -1 of nickel in the tissues of some hyperaccumulators. [START_REF] Zhang | Increasing Purity of Ammonium Nickel Sulfate Hexahydrate and Production Sustainability in a Nickel Phytomining Process[END_REF] Houzelot et al. [START_REF] Houzelot | Effect of up-Scaling on the Quality of Ashes Obtained from Hyperaccumulator Biomass to Recover Ni by Agromining[END_REF] even studied specific plants allowing nickel content as high as 150 g•kg -1 . Waste biomass such as demolition wood, chromated copper arsenate timber, deinking sludge, sewage sludge, or spent pot liner constitute many other examples of contaminated biomass that can contain high amounts of HM.

Currently, contaminated biomass is hardly used in thermochemical conversion processes, due to its potentially high content in HM. Given the large quantities of these resources, many studies investigate the behavior and the effect of the HM during their thermochemical conversion. It has been shown that HM distribute unevenly between ash, gases and tars. Their behavior mainly depends on their initial content in the biomass, and on their chemical form during the thermal conversion. [START_REF] Gupta | Phytoextraction Capacity of the Plants Growing on Tannery Sludge Dumping Sites[END_REF][START_REF] Lievens | Fast Pyrolysis of Heavy Metal Contaminated Willow: Influence of the Plant Part[END_REF] The HM vaporization, and more generally the inorganic elements behavior, has been widely studied with the aim of not only reducing the emissions of toxic elements in the environment during industrial combustion processes [START_REF] Wu | Inhibiting Evaporation of Heavy Metal by Controlling Its Chemical Speciation in MSWI Fly Ash[END_REF][START_REF] Toledo | The Partitioning of Heavy Metals in Incineration of Sludges and Waste in a Bubbling Fluidized Bed. 2. Interpretation of Results with a Conceptual Model[END_REF] but also managing related technical issues (agglomeration of fluidized beds, slag formation, deposition and corrosion phenomena on heat exchangers). [START_REF] Pohorěly | Behavior of Heavy Metals in Steam Fluidized Bed Gasification of Contaminated Biomass[END_REF][START_REF] Yuan | Partition and Size Distribution of Heavy Metals in the Flue Gas from Municipal Solid Waste Incinerators in Taiwan[END_REF] A body of work has also shown that some HM have a significant influence on reaction kinetics. Depending on their nature, these HM have a catalytic or inhibitory effect on thermochemical conversion reactions. [START_REF] Nzihou | The Fate of Heavy Metals during Combustion and Gasification of Contaminated Biomass-a Brief Review[END_REF] As a consequence, finely divided particles of nickel, cobalt or iron [START_REF] Sutton | Review of Literature on Catalysts for Biomass Gasification[END_REF] are sometimes used as catalysts in thermochemical conversion processes.

To improve the understanding of HM behavior and/or influence during thermochemical conversion of biomass, two main methods (sometimes combined) are usually applied. The first method consists of carrying out measurements and analysis of pyro-gasification products in laboratory or industrial installations. The TGAs (thermogravimetric analyzers) are usually used to identify the kinetics of the gasification reaction. [START_REF] Lahijani | CO 2 Gasification Reactivity of Biomass Char: Catalytic Influence of Alkali, Alkaline Earth and Transition Metal Salts[END_REF][START_REF] Huo | Study on CO 2 Gasification Reactivity and Physical Characteristics of Biomass, Petroleum Coke and Coal Chars[END_REF] The fixed-bed reactor is widely used thanks to its ease of operation. Other studies are carried out in fluidized bed reactors, which is a widely developed industrial gasification technology. [START_REF] Pohorěly | Willow Trees from Heavy Metals Phytoextraction as Energy Crops[END_REF] For example, S ̌yc et al. [START_REF] Pohorěly | Willow Trees from Heavy Metals Phytoextraction as Energy Crops[END_REF] studied the behavior of Cd, Cu, Ni, Pb, and Zn during steam gasification of a mixture of oak and beech wood in a fluidized bed reactor. They showed that cadmium is the most volatile metal and that it is mainly present in the gas phase, whereas nickel is found in the ashes. The authors concluded that the vaporization of the heavy metals decreases as follows: Cd ≫ Pb > Zn > Cu > Ni. Collard et al., [START_REF] Collard | Influence of Impregnated Metal on the Pyrolysis Conversion of Biomass Constituents[END_REF] Bru et al., [START_REF] Bru | Pyrolysis of Metal Impregnated Biomass: An Innovative Catalytic Way to Produce Gas Fuel[END_REF] and Richardson et al. [START_REF] Richardson | In Situ Generation of Ni Metal Nanoparticles as Catalyst for H 2 -Rich Syngas Production from Biomass Gasification[END_REF] inserted intentionally by impregnation, some HM (Ni, Cu, and Fe) into the biomass to improve the contact between the catalyst and the biomass and optimize the conversion efficiency. Other researchers [START_REF] Huang | Effects of Metal Catalysts on CO 2 Gasification Reactivity of Biomass Char[END_REF][START_REF] Lahijani | Catalytic Effect of Iron Species on CO 2 Gasification Reactivity of Oil Palm Shell Char[END_REF] investigated the reactivity of various biomass chars, in the presence of different metals, in CO 2 atmosphere using different kinetic models to fit with the reactivity data. They found that the gasification reactivity of metal-containing chars was higher than that of the chars obtained from raw biomass.

The second approach relies on the investigation of thermodynamic calculations, which may be a valuable computational approach to predict and understand the behavior of elements during thermal treatment of biomass and waste. In some recent studies, [START_REF] Ma | Thermochemical Equilibrium Study of Slag Formation during Pressurized Entrained-Flow Gasification of Woody Biomass[END_REF][START_REF] Konttinen | Trace Element Behavior in the Fluidized Bed Gasification of Solid Recovered Fuels -A Thermodynamic Study[END_REF][START_REF] Koukkari | CHEMSHEET -An Efficient Worksheet Tool for Thermodynamic Process Simulation[END_REF][START_REF] Kangas | Modeling Biomass Conversion during Char Gasification, Pyrolysis, and Torrefaction by Applying Constrained Local Thermodynamic Equilibrium[END_REF] specific calculations procedure were developed, to take into account the highly nonequilibrium behavior of organic matter during thermal treatment and its influence on inorganics' behavior.

In the present research, we focus on understanding the impact of the nickel content on the thermal decomposition of a biomass heated in a CO 2 atmosphere. The methodology combines experimental measurements (in a thermogravimetric device and a fixed bed reactor) and thermodynamic simulations. The main research questions concern (i) the determination of the chemical form of nickel during the main steps of the thermal treatment (pyrolysis up to 450 °C, gasification up to 950 °C), (ii) the influence of nickel on the main conversion reactions, and (iii) on the behavior of other inorganic elements.

The selected biomass is willow, which exhibits high HM absorption capacity. Because natural contaminated biomass is very heterogeneous and usually contains many metals whose roles can interfere, [START_REF] Yung | Review of Catalytic Conditioning of Biomass-Derived Syngas[END_REF] the willow samples were prepared by impregnation of various Ni contents. On the basis of our previous study, [START_REF] Said | Effect of Nickel Impregnation on Wood Gasification Mechanism[END_REF] we chose impregnation conditions adapted to the understanding of the effect of Ni without modifying the wood structure.

In the following sections, our experimental data are discussed in the light of a thermodynamic approach that we developed in previous research (databases aspects, [START_REF] Said | Behavior of Heavy Metals during Gasification of Phytoextraction Plants: Thermochemical Modelling[END_REF] calculation procedure aspects [START_REF] Said | On the Relevance of Thermodynamics to Predict the Behaviour of Inorganics during CO 2[END_REF] ).

MATERIALS AND METHODS

2.1. Experimental Procedure. 2.1.1. Samples Preparation. The willow wood pellets, supplied by "peṕinieres-Naudet" company (France), were dried, crushed, and sieved to a range of particle size of 0.5-1 mm. About 30 g of willow wood particles were then impregnated in 300 mL of nickel nitrate aqueous solutions, prepared with three different amounts of Ni(NO 3 ) 2 •6H 2 O (Sigma-Aldrich, 99% purity). The obtained mixtures were stirred at ambient temperature for 24 h.

According to our previous study, [START_REF] Said | Effect of Nickel Impregnation on Wood Gasification Mechanism[END_REF] the Ni concentration in the aqueous solution had to be below 0.1 mass %, to avoid significant modifications of the wood structure and noticeable washing of some elements (mostly Ca, K, and S). Therefore, the selected solution concentrations were 0.01, 0.05, and 0.07 mass % of Ni, respectively. The resulting Ni concentration in the impregnated wood samples were measured by elemental analysis (see section 2.1.3) and correspond to 0.09, 0.34, and 0.48 wt %, respectively. The Ni concentrations are close to those found in the literature for phytoextraction plants, where the Ni contents are usually between 0.1 and 1 wt %. [START_REF] Degroot | The Influence of Exchangeable Cations on the Carbonization of Biomass[END_REF][START_REF] Mahar | Challenges and Opportunities in the Phytoremediation of Heavy Metals Contaminated Soils: A Review[END_REF] In the following, the samples are labeled "wood-X-Ni", X being the Ni concentration (in wt %) in the wood samples. "Raw-wood" corresponds to a nonimpregnated wood.

2.1.2. Pyro-Gasification Devices. Thermal treatments were carried out using two devices: a fixed-bed reactor, with 5 g of wood, and a thermo-gravimetric analyzer (TGA), with 15 mg of wood.

The reactor is composed of a quartz tube, in which the wood samples are held by a sintered porous quartz disk (see our previous work [START_REF] Said | Effect of Nickel Impregnation on Wood Gasification Mechanism[END_REF] for more details). The wood samples are first heated at 5 °C•min -1 , under N 2 flow (6 L•h -1 ), up to 450 °C. After 1 h at 450 °C, a mixed atmosphere (5.4 L.h -1 of CO 2 and 0.6 L•h -1 of N 2 ) is flushed in the reactor, and the furnace is heated at 5 °C•min -1 , from 450 to 950 °C. The sample is then kept for 1 h at 950 °C, before cooling. An additional experiment was carried out with wood-0.34-Ni and stopped when the temperature reached 800 °C. These operating conditions were selected to complete the sample pyrolysis before the start of the gasification. Even if there is some interference between the thermal degradation step and the gasification step, this protocol is the best compromise to illustrate the influence of Ni content on both types of reactions, and for the two reactors.

Solid residues are collected after treatment at 450 °C (henceforth char) and at 950 °C (henceforth ash). All solid samples are weighed and characterized.

The gases are collected in successive 0.5 L sampling bags and analyzed by a gas chromatograph equipped with two analytical columns. This protocol provides the quantitative amount of major gases N 2 , O 2 , H 2 , CO, CH 4 , CO 2 , C 2 H 4 , and C 2 H 6 .

The thermogravimetric measurements are carried using a TGA/DTA analyzer (SDT Q600) under atmospheric pressure. About 15 mg of sample is placed in a platinum crucible. The analyses are performed in the same conditions (temperature and atmosphere) as in the pyro-gasification reactor. Each TGA is repeated three times, and the mean standard deviation of weight loss is lower than 1%. Due to the low initial amount of wood, the solid residues are not characterized.

2.1.3. Sample Analyses. The CHN contents of the different samples (raw wood, impregnated wood, char, and ash) are determined with an elemental analyzer (Flash 2000). The ash content of each sample is measured according to the standard FN-EN-ISO-18122, which consists of an air oxidation at 550 °C. The oxygen content is determined by mass balance.

For the determination of inorganics, the samples are mineralized with a specific mineralization protocol and analyzed on inductively coupled plasma optical emission spectroscopy (ICP-OES). For wood and char, 200 mg samples are immersed in 2 mL of oxygenated water and 8 mL of nitric acid. The mixture is placed in a special fluoroplastic flask (PTFE), which is inserted in a pressure digestion vessel made of high alloy stainless steel SS 316 Ti (DAB-2). The vessel is heated in an electric heating block for 4 h at 200 °C under high pressure (max 200 bar). For ashes, 40 mg samples are attacked by a mixture of hydrogen peroxide and nitric acid and then heated for 10 h at 200 °C under the same conditions as those described for char. Each measurement is repeated three times.

The samples compositions are provided in Table 1, in mol per kg of wood (mol•kgw -1 ). For all elements (except Ni), the reported composition is the mean value measured in the four wood samples (Raw wood, Wood-X-Ni with X = 0.09, 0.34 and 0.48). The precision indicated in Table 1 corresponds thus to the standard deviation of twelve measurements (three analyses of each four samples).

All solid residues are also characterized by X-ray diffraction, with a Philips PANalytical X'Pert MPD diffractometer using Cu (Kα) radiation. Data acquisition is performed between 10 and 70°in 2θ with a step of 0.017°(2θ).

2.2. Thermodynamic Calculation Methodology. In our previous work, [START_REF] Said | On the Relevance of Thermodynamics to Predict the Behaviour of Inorganics during CO 2[END_REF] a specific calculation methodology was selected out of several options by comparing equilibrium calculations with the elemental composition (27 elements listed in Table 1) of raw willow wood ashes after CO 2 pyrogasification. The main features of the selected procedure are presented in this section.

2.2.1. Software and Database. Equilibrium calculations are performed with FactSage 7.0. [START_REF] Bale | FactSage Thermochemical Software and Databases  Recent Developments[END_REF] To describe the complex system composed of 27 elements, a specific selection of the compounds available within the FactSage package led to the final choice of 373 gaseous, 247 liquid, and 1115 solid species. Within this selection, there are 10 gaseous, 10 liquid, and 66 solid Ni-containing compounds. Two liquid solution phases were also selected: (i) SALTF, which describes liquid salts solutions (Na, K//F, Cl, CO 3 ) with low melting points, and (ii) SLAGA, which describes liquid silicates solutions (Si, K, Ca// O, S) with high melting points. [START_REF] Lindberg | Towards a Comprehensive Thermodynamic Database for Ash-Forming Elements in Biomass and Waste Combustion  Current Situation and Future Developments[END_REF] 2.2.2. Calculations Conditions. The calculations are performed on 1 kg of wood. The input composition, for which Ni is the only variable, is provided in Table 1. The fixed conditions for the calculations are the pressure (1 bar) and the nature of the gasification agent (CO 2 ). The procedure consists of calculating the equilibrium every 10 °C, from 450 to 950 °C. At each calculation step, a constant amount ⟨A⟩ of CO 2 (in mol•kgw -1 ) is added in the system, and the equilibrium composition is recorded. After each computation step, the gas phase is removed from the system to simulate an open system in which the gaseous species are not in contact with the condensed phases (Figure 1). Five additional steps are computed at 950 °C, to simulate the temperature plateau.

2.2.3. Determination of ⟨A⟩. During CO 2 gasification of char, the main reaction is the oxidation of carbon according to the so-called Boudouard reaction:

+ = C(s) CO (g) 2CO(g) 2
When C(s) has fully reacted, the exhaust gas is thus mainly composed of unreacted CO 2 (g). As illustrated in Figure 2, the amount of CO 2 which is added in the system at each calculation step (⟨A⟩) plays an important role on the temperature at which total carbon cunsumption occurs.

In our simulations, ⟨A⟩ is adjusted so that the total consumption of carbon occurs at the same temperature as in the experimental reactor. Adjusting ⟨A⟩ is thus a simple way of introducing a kinetic restriction on the main gasification reaction. It can be noted from Figure 2 that if the temperature step is different from 10 °C, the ⟨A⟩ value will be modified proportionally: ⟨A⟩ is divided by 2 for a temperature step of 5 °C, and multiplied by 2 for a temperature step of 20 °C. This is consistent with the Boudouard equilibrium, because the carbon consumption at a given temperature is only depending on the CO 2 partial pressure.

The measured variation of CO(g) and CO 2 (g) concentration in the exhaust gases is plotted in Figure 3. The total conversion of carbon occurs when the temperature reaches 950 °C. ⟨A⟩ was thus set to 1.25 mol•kgw -1 (Figure 2), which is smaller than the experimental amount of CO 2 (1.78 mol•kgw -1 ) flushed through the reactor during the 2 min necessary to raise the temperature by 10 °C, indicating that, on average, about 30% of the CO 2 does not take part in the gasification reaction.

It can be noted that for another reactor or operating conditions, the same methodology could be applied. ⟨A⟩ would then be obtained from experimental measurements of CO 2 and CO concentration profiles in the exhaust gases. These data are easy to obtain for any reactor with standard chromatography techniques.

The discrepancy between calculated and measured gas composition is large (temperature shift up to 200 °C), and is the direct consequence of kinetics control of gasification reactions. However, the main tendency (i.e., a large increase of CO proportion, followed by a significant drop after total carbon consumption) is represented with such calculation procedure, using one single parameter, which is not the case with closed system calculations. [START_REF] Said | On the Relevance of Thermodynamics to Predict the Behaviour of Inorganics during CO 2[END_REF]

RESULTS AND DISCUSSION

3.1. Catalytic Effect of Ni. 3.1.1. Thermogravimetric Analyses. Figure 4 shows the thermal analyses of the wood samples, which is divided into three main zones: the first temperature increases from 30 to 450 °C (A), the plateau at 450 °C for 1 h (B), and the second temperature raise to 950 °C, with a CO 2 flush (C). The pyrolysis step (A and B) was carried out under N 2 , and the gasification step (C) under 90% CO 2 and 10% of N 2 .

In Zone A, after a slight mass loss at about 100 °C corresponding to the water evaporation, a major mass loss occurs between 200 and 450 °C. As already finely described in the literature, this step corresponds to the hemicellulose and cellulose pyrolysis. [START_REF] Yang | Characteristics of Hemicellulose, Cellulose and Lignin Pyrolysis[END_REF] In Zone B, the mass loss at the end of the isotherm increases by 6% for the wood-0.34-Ni and by 8% for the wood-0.48-Ni samples compared to that for raw wood. This feature is a clear indication of the influence of Ni on the pyrolysis step.

The gasification step (zone C) starts with the input of CO 2 at t = 144 min. The differential of the TG signal (DTG) is presented in Figure 5 for zone C. For the impregnated samples wood-0.34-Ni and wood-0.48-Ni, an additional reaction is evidenced in the temperature range 470-660 °C. The DTG signal is more intense at high Ni concentrations. Furthermore, the main gasification reaction, which reaches a maximum rate at around 910 °C on raw wood, is shifted to lower temperatures with increasing amount of impregnated Ni (respectively 880, 845, and 820 °C for wood-0.09-Ni, wood-0.34-Ni, and wood-0.48-Ni). Figure 5 also shows that the increase of the Ni content in the samples enhances the gasification rate and, as a consequence, the variation of the gasification reaction kinetics.

These results are in-line with previous findings dealing with CO 2 gasification kinetics. Indeed, Figueiredo et al. [START_REF] Figueiredo | Gasification of Active Carbons of Different Texture Impregnated with Nickel, Cobalt and Iron[END_REF] showed that the reactivity of impregnated carbon increases with the amount of Ni catalyst, with a decrease of the activation energy from 260 kJ•mol -1 without Ni to about 110 J•mol -1 for the Ni- catalyzed gasification reaction. Many other studies led to the conclusion that the kinetic parameters of the gas-solid reactions during CO 2 gasification are deeply affected by the presence and the amount of inorganic elements (either alkaline/alkaline-earth metals [START_REF] Duman | The Effect of Char Properties on Gasification Reactivity[END_REF] or transition metals [START_REF] Lahijani | Catalytic Effect of Iron Species on CO 2 Gasification Reactivity of Oil Palm Shell Char[END_REF][START_REF] Figueiredo | Gasification of Active Carbons of Different Texture Impregnated with Nickel, Cobalt and Iron[END_REF] ). Di Blasi [START_REF] Di Blasi | Combustion and Gasification Rates of Lignocellulosic Chars[END_REF] stated that the char reactivity depends on three main characteristics: its porosity, its chemical structure, and the quantity of inorganic constituents. As evidenced in previous studies, [START_REF] Richardson | In Situ Generation of Ni Metal Nanoparticles as Catalyst for H 2 -Rich Syngas Production from Biomass Gasification[END_REF][START_REF] Duman | The Effect of Char Properties on Gasification Reactivity[END_REF] all those characteristics change when the wood or the char contains a high amount of a metal element.

The effect of Ni on the pyrolysis and gasification products is further discussed in the following sections on the basis of the data collected with the fixed-bed reactor.

3.1.2. Fixed Bed Reactor Experiments Analysis. The same experimental protocol (described in section 2.1.2) for the TG analysis is used for the bed reactor experiments.

3.1.2.a. Pyrolysis

Step in Fixed Bed Reactor. The mass balance of the pyrolysis products, taking account gases, char, and tars, is presented in Table 2. Around 18-20% of the initial mass is missing. This is attributed to the condensation of tars on the reactor walls and in the gas circuit. Tars recovery is indeed a major and recurrent experimental difficulty, which requires the development of specific devices that were not available in our configuration. Nevertheless, the impact of Ni is rather clear from our experimental data: it leads to an increase of the total amount of gas and a decrease of the final amount of char.

The organic elemental composition and the ash content of the four samples of chars produced at 450 °C-60 min are presented in Table S1. The data show that both carbon and hydrogen contents in the chars decrease with the rise of the Ni content, in favor of the ash content. The highest variation is observed for hydrogen, with a decrease of 30% in the wood-0.48-Ni sample compared to that in the raw char. This variation of the elemental composition of the chars agrees well with the amount and composition of gases produced during pyrolysis. The comparison of the impact of Ni impregnation on the total amount of H 2 and CO (in moles of gas formed per kg of wood) is presented in Figure 6. Additional data concerning CO 2 , CH 4 , C 2 H 4 , and C 2 H 6 yields are plotted in Figure S1. The gas composition analysis shows a strong increase of the H 2 yield (multiplied by 6 and 14 times for wood-0.09-Ni and wood-0.48-Ni, respectively) and, to a lesser extent, of the CO formation (about 5% for the wood-0.48-Ni sample).

Given all these results, we can conclude that Ni impregnation in willow wood leads to significant changes during the pyrolysis step: (i) an increase of the total amount of gas produced and of the H 2 content in this gas and (ii) a drop of the char amount and of the H content in this char.

These phenomena are explained by a catalytic effect of Ni on biomass pyrolysis reactions (and more specifically on tars cracking inside the wood), as already well documented in the literature. For instance, Bru et al. [START_REF] Bru | Pyrolysis of Metal Impregnated Biomass: An Innovative Catalytic Way to Produce Gas Fuel[END_REF] and Collard et al. [START_REF] Collard | Influence of Impregnated Metal on the Pyrolysis Conversion of Biomass Constituents[END_REF] studied respectively the influence of Ni on the pyrolysis reactions of beech wood at 700 °C and oak wood at 600 °C. They both found a significant decrease in tar production in favor of gases, which is consistent with our results. Furthermore, Richardson et al. [START_REF] Richardson | In Situ Generation of Ni Metal Nanoparticles as Catalyst for H 2 -Rich Syngas Production from Biomass Gasification[END_REF] showed that nickel metal nanoparticles act as a catalytic active phase for enhancing H 2 production during biomass pyrolysis. They also demonstrate that Ni is an active catalyst exclusively in its metal form related to a reduction reaction occurring between 450 and 600 °C.

In these studies, Ni concentrations ranged from 0.07 to 0.65 mol•kgw -1 and pyrolysis temperatures from 600 to 700 °C. In our study, we show that the catalytic effect of Ni on pyrolysis reactions is effective at lower temperature (450 °C) with Ni contents as low as 0.016 mol•kgw -1 .

3.1.2.b. Gasification in Fixed Bed Reactor. Due to the low amounts of tars and ashes, as well as the difficulty to recover them, the mass balance of the gasification part of the experiment (ramp 450-950 °C + isotherm at 950 °C with CO 2 flush) was not established.

The total production of H 2 and CO is presented in Figure 7. The nickel content has no significant impact on the overall CO yield. However, the catalytic effect of Ni is noticeable on the H 2 yield with an increase of 1% and 22% for the wood-0.09-Ni and wood-0.48-Ni samples, respectively.

It is well attested that the CO 2 gasification of char leads to the formation of CO by the Boudouard reaction and results in low values of H 2 /CO ratio. [START_REF] Jang | Combined Steam and Carbon Dioxide Reforming of Methane and Side Reactions: Thermodynamic Equilibrium Analysis and Experimental Application[END_REF] In the present study, the H 2 /CO ratio is around 0.05, with a content of CO 2 in the gas injected into the reactor equal to 88%. This is rather consistent with the data compiled by Butterman et al., [START_REF] Butterman | CO 2 as a Carbon Neutral Fuel Source via Enhanced Biomass Gasification[END_REF] which mention a H 2 /CO ratio in the range 0.1-1.0 for CO 2 contents varying from 30 to 50% in the inlet gas, and a clear decrease of this ratio with increasing CO 2 content. Furthermore, the increase of the Ni content in the sample raises the H 2 /CO value by 23% compared to that of raw wood.

The production profiles of H 2 and CO as a function of the temperature presented in Figure 8 provide more insights into the influence of the Ni content. Data for CH 4 are provided in Figure S2. Significant changes related to Ni are evidenced on

L•h -1 of CO 2 + 0.6 L•h -1 of N 2 .
Table 2. Mass Balance of the Products Obtained from the Willow Wood Samples Pyrolysis in a Fixed-Bed Reactor Where T Ranges from Ambient to 450 °C in an Inlet Gas Flow of 6 L•h -1 (N 2 ). these profiles, essentially between 500 and 800 °C. The H 2 yield increases by 55-118% at 600 °C for wood-0.34-Ni and wood-048-Ni compared to that of raw wood, and H 2 production stops at lower temperature in the presence of Ni.

Similarly, the CO yield increases up to 1 and 1.5 mol•kgw -1 at 600 °C for wood-0.34-Ni and wood-0.48-Ni samples, respectively, whereas it is negligible at low Ni content. The CO production reaches a maximum at 900 °C for the wood-0.34-Ni sample and at 800 °C for the wood-0.48-Ni sample.

For the wood-0.09-Ni and raw wood, the maximum of CO production is detected at 900 and 950 °C respectively.

Combined with TGA analysis, the data collected from the fixed-bed reactor experiments provide a rather clear picture of the catalytic influence of Ni on gasification. Indeed, for Ni contents higher than 0.09 mol•kgw -1 , an additional reaction occurs between 500 and 600 °C. This reaction results in a significant mass loss of the char and produces noticeable amounts of H 2 (g) and CO(g). Furthermore, the presence of nickel in the samples decreases the temperature onset of the main gasification reaction, from 920 °C in raw wood down to 830 °C with wood-0.48-Ni. [START_REF] Said | Effect of Nickel Impregnation on Wood Gasification Mechanism[END_REF] The catalytic effect of nickel on both pyrolysis and gasification is thus clearly evidenced and characterized by quantification of the major reaction products (char, tars, H 2 , CO) during the process. Furthermore, this catalytic effect depends on the initial quantity of nickel.

L•h -1 CO 2 + 0.6 L•h -1 N 2 .
3.2. Ni Speciation. 3.2.1. Speciation by XRD Characterizations. The XRD (X-ray diffraction) analyses of the char samples obtained by pyrolysis at 450 °C are presented in Figure 9. The diffraction patterns of the four samples show the presence of a dominant amorphous phase. The presence of graphite carbon, calcium carbonate, and potassium carbonate is detected in all samples. A crystalline phase corresponding to Ni metal (with fcc structure) is also evidenced in wood-0.34-Ni and wood-0.48-Ni samples, but it is not detectable in the wood-0.09-Ni sample.

Furthermore, the diffractogram of the residue obtained at 800 °C also show the presence of nickel in its metal form (Figure 10).

The X-ray diffractograms of the ashes recovered at the end of the pyro-gasification reaction are presented in Figure 11. They indicate the presence of nickel oxide (NiO) in the three impregnated samples, which shows that Ni metal undergoes oxidation at some point during the process.

3.2.2. Speciation by Thermodynamic Calculations. The calculated temperature evolution of the chemical speciation of nickel for three samples is presented in Figure 12. The Ni speciation results for the wood-0.48-Ni sample are similar to those found for the wood-0.34-Ni sample. For this reason, the speciation results of the wood-0.48-Ni sample are not included in Figure 12.

A first comment is that, independently of its initial quantity, Ni fully remains in condensed phases, either in solids or in the slag solution phase.

In any case, Ni forms intermetallic compounds with Sb (NiSb) and Sn (Ni 3 Sn x , with 1 ≤ x ≤ 3), which are the most stable compounds that bind to Ni among the 26 elements considered in the biomass. A consequence is that, for raw wood (for which the Sb and Sn content is higher than the Ni content), there is no free Ni metal on the entire temperature range.

When more Ni is available in the system (wood-0.09-Ni), a second type of compound forms (Ni 3 S 2 ), which is stable up to about 700 °C.

With higher Ni contents (wood-0.34-Ni and wood-0.48-Ni), Ni is predominantly present in its metal form through the whole temperature range.

At the end of the pyro-gasification reaction, when all carbon is consumed, nickel progressively oxidizes into NiO, as illustrated in Figure 12. According to the model, part of the oxide dissolves in the liquid slag phase. Figure 13 gives an additional insight into the behavior of Ni in the pyrogasification reactor. Indeed, in the 450-950 °C temperature range, the equilibrium oxygen partial pressure (PO 2 ) resulting of the equilibrium between CO 2 and wood is lower than the equilibrium PO 2 between Ni and NiO. This means that the stable form of nickel is the metal form. However, when all carbon has been consumed, the atmosphere switches to pure CO 2 (g), with a higher oxygen potential that leads to oxidation of Ni into NiO.

3.2.3. Discussion. The combination of XRD characterization and thermodynamic calculations provides a concordant picture on the behavior of Ni during the pyro-gasification. Indeed, nickel is detected in its metal form in solid residues recovered at 450 and 800 °C, provided that Ni quantity is sufficient for detection. The final ashes contain predominantly nickel oxide, due to its oxidation at the end of the process. As already mentioned, Ni must be in its metal form to cause a catalytic effect on the pyro-gasification reactions. The thermodynamic speciation indicates that, at low temperature, sulfur has a high affinity with Ni. As reported in several experimental studies carried out on various Ni-based catalysts, [START_REF] Yang | Characteristics of Hemicellulose, Cellulose and Lignin Pyrolysis[END_REF][START_REF] Figueiredo | Gasification of Active Carbons of Different Texture Impregnated with Nickel, Cobalt and Iron[END_REF] sulfur can be an important inhibitor of its catalytic activity. Therefore, a threshold content of Ni is required in the biomass to observe the catalytic effect of Ni. Below this threshold value, Ni is trapped in sulfide compounds. Furthermore, it is expected that at temperature higher than 700 °C, the decomposition of nickel sulfide enhances the catalytic effect of Ni. Apart from S, whose behavior will be further discussed in the next section, both calculated and measured data show that there is no major influence of the initial Ni amount on the content of the main elements of the ashes.

As discussed in our previous work, 27 the adopted calculation procedure leads to a satisfactory reproduction of trace elements contents measured in raw wood ashes recovered after CO 2 gasification. The wood samples impregnated with Ni studied in the present work did not lead to major changes in the behavior of trace elements either. Some of them were shown to fully or partially volatilize: S, Cd, Pb, Zn, Cl, Sn, K, Na and F.

3.3.2. Influence of Ni on Sulfur Behavior. As discussed in details by Kaknics et al., [START_REF] Kaknics | Inorganic Phase Transformation in Miscanthus Ash[END_REF] sulfur has been shown to volatilize to a large extent during pyro-gasification at low temperature, in the form of H 2 S(g) and COS(g), when placed in the reducing environment constituted by C(s) and CO 2 (g). Our experimental data confirm that, for raw wood, more than 50% of the initial sulfur is not found in the final solid residue. Furthermore, the amount of sulfur in the ashes increases significantly with the Ni content. These results are consistent with the calculated amount of S in gas phase presented in Figure 15. Indeed, Ni promotes the trapping of sulfur in a Ni 3 S 2 solid phase up to about 650 °C. At higher temperatures, S is then trapped in a CaS solid phase. When the initial nickel content is significantly higher than the sulfur content, most of sulfur is thus trapped in the solid residue. This behavior is rather similar to the observations from Mortensen [START_REF] Mortensen | Industrial Scale Experience on Steam Reforming of CO 2 -Rich Gas[END_REF] and Woolcock 40 in the case of nickel catalysts for methane re-forming. The authors observed sulfur trapping on nickel, resulting in the catalyst's poisoning.

CONCLUSIONS

The combination of experimental and thermodynamic approaches helped to evaluate the role and the behavior of Ni during the pyro-gasification reaction of impregnated willow samples in a CO 2 atmosphere. The experimental tests allowed us to quantify the solid and gaseous products during pyrogasification. The thermodynamic simulation methodology consisted of (i) considering an open system to simulate a gas flow in the system and (ii) determining the amount of gasification agent added at each calculation step with experimental measurements of CO 2 and CO concentration profiles, these data being easy to obtain for any reactor with standard chromatography techniques. The thermodynamic simulations were consistent with most of the experimental data (chemical analysis of ashes, XRD of the solid residues at 450, 800, and 950 °C), even if the exact amount of Ni linked with a catalytic activity could not be determined with accuracy.

Based on this methodological approach, the main results of the study are

• Ni has a noticeable catalytic activity even at low content (between 0.016 and 0.086 mol•kgw -1 ) • Ni stays in an active metal form as long as some carbon is left in the reactor due to the low oxygen potential of the system C + CO 2 • Ni prevents the evaporation of sulfur during the gasification, due to the formation of a stable Ni 3 S 2 (s) phase Our methodological contribution constitutes a powerful approach that would allow for future investigation of the influence of other metals such as Cu and Fe during pyrogasification reactions, as well as detecting potential synergies between inorganic elements such as the Ni-S interactions evidenced in this work .

2 Figure 1 .

 21 Figure 1. Principle of open system equilibrium calculations.

Figure 2 .

 2 Figure 2. Evolution of the temperature of full carbon conversion with the amount v of CO 2 added at each calculation step.

Figure 3 .

 3 Figure 3. Comparison of the temperature evolution of CO 2 and CO molar fraction in the gas phase (i) measured in fixed bed reactor with raw wood (symbols and dashed lines) and (ii) calculated with ⟨A⟩ = 1.25 mol•kgw -1 (continues lines).

Figure 4 .

 4 Figure 4. TG profiles of willow wood samples during pyro-gasification.

Figure 5 .

 5 Figure 5. DTG profiles of willow wood samples during gasification. Inlet gas flow is composed of 5.4 L•h -1 of CO 2 + 0.6 L•h -1 of N 2 .

Figure 6 .

 6 Figure 6. Influence of Ni content on the total production of H 2 and CO during pyrolysis willow wood samples in a fixed-bed reactor where T ranges from ambient to 450 °C using an inlet gas flow of 6 L•h -1 (N 2 ).

Figure 7 .

 7 Figure 7. Influence of Ni content on the total production of H 2 and CO during gasification of willow wood samples in a fixed-bed reactor where T ranges from 450 to 950 °C while the inlet gas flow is 5.4 L•h -1 of CO 2 + 0.6 L•h -1 of N 2 .

Figure 8 .

 8 Figure 8. Influence of temperature on the amount of H 2 and CO produced during gasification of willow wood samples in a fixed-bed reactor using an inlet gas flow of 5.4L•h -1 CO 2 + 0.6 L•h -1 N 2 .[START_REF] Said | Effect of Nickel Impregnation on Wood Gasification Mechanism[END_REF]

Figure 9 .

 9 Figure 9. XRD analyses of the char samples produced at 450 °C-60 min.

Figure 10 .

 10 Figure 10. XRD analyses of the chars produced at 450 °C-60 min and 800 °C for the wood-0.34-Ni sample.

Figure 11 .

 11 Figure 11. XRD analyses of the ashes samples produced at 950 °C-60 min.

3 . 3 .

 33 Ni Effect on the Behavior of Inorganic Elements. 3.3.1. Influence of Ni on Ashes Composition. The elemental composition of ashes (measured and calculated with FactSage) is presented in Figure 14. The chemical analyses performed by ICP-OES indicate that the ashes are mainly composed of Ca, K, Mg, P, S, and Si, plus a growing proportion of Ni for impregnated samples. This is consistent with the major phases identified by XRD (Figure 12): Ca 5 (PO 4 ) 3 (OH), K 2 SO 4 , Ca 3 Mg(SiO 4 ) 2 , and CaO, plus NiO. The calculated elemental composition of the ashes, determined as the sum of all solid compounds at 950 °C, reproduces remarkably well the experimental data.

Figure 12 .

 12 Figure 12. Calculated Ni speciation as function of Ni content, for T = 450-950 °C. Additional calculation steps for T = 950 °C are represented on the right extremity of the x-axis.

Figure 13 .

 13 Figure 13. Influence of temperature on oxygen partial pressure (PO 2 ) at equilibrium: (i) Ni-NiO equilibrium; (ii) raw wood.

  is available free of charge on the ACS Publications website at DOI: 10.1021/acs.iecr.7b05201. Table S1, elementary composition of the chars; Figure S1, influence of Ni content on the total production of C n H m and CO 2 during pyrolysis; Figure S2, influence of temperature on the amount of CH 4 produced during gasification (PDF) ■ AUTHOR INFORMATION Corresponding Author *L. Cassayre. E-mail address: laurent.cassayre@ensiacet.fr ORCID Laurent Cassayre: 0000-0001-6876-6086NotesThe authors declare no competing financial interest.

Figure 14 .

 14 Figure 14. Comparison between experimental and calculated ashes elemental composition.

Figure 15 .

 15 Figure 15. Calculated influence of temperature and Ni content on the proportion of sulfur in the gaseous phase.

Table 1 .

 1 Elemental Composition of Wood Samples

	element	mol•kgw -1	element	mol•kgw -1	element	mol•kgw -1