Daniel Duprez

F Epron

S Rijo Gomes

Nicolas Bion

Hydrogen production from hydrocarbons over Rh supported on Ce-based oxides for automotive applications

Keywords: Hydrogen production, catalytic fuel reforming, ceria-based supports, rhodium

Hydrogen production from hydrocarbons over Rh supported on Ce-based oxides for automotive applications

Introduction

In vehicles Exhaust Gas Recirculation (EGR) technique was initially developed for decreasing the temperature in the combustion chamber to limit NOx production in Diesel engines. It is now commonly used for gasoline engines with direct injection spark ignition working in lean burn conditions, for which the conventional three way catalysts cannot work efficiently. EGR also reduces throttling losses in gasoline engine, and consequently fuel consumption [1]. On-board Hydrogen production by reacting fuel with exhaust gas [2][3][4][5][6][7] in the presence of a catalyst process may enhance the efficiency of EGR systems. In this process, schematized in Fig. 1, hydrogen is virtually produced by reaction of hydrocarbons with the main components of the exhaust gas, CO2 and H2O, by dry and steam reforming, respectively. These two reactions being endothermic, this allows to recover energy from the exhaust gas [7]. Other reactions, such as total, partial oxidation or water-gas-shift reactions may also occur but their contribution is minority in the whole process [7]. Small amount gasoline may be added in the system to increase the amount of hydrogen produced. The combustion in the engine with a gas enriched in H2, by this process named Reforming of Exhaust Gas Recirculation (REGR), increases the ratio H/C of the fuel, decreasing the production of CO and the fuel consumption by an increase of combustion efficiency [2,7,8].

Rhodium-based mono or bimetallic catalysts have proven their efficiency for producing hydrogen in the REGR conditions [9][10][11], i.e. in the presence of a fuel, water, CO2, O2 and N2.

In the REGR conditions, mainly steam and dry fuel reforming may occur. We have shown [12] that Rh (1 wt%) supported on zirconia doped with 26.2 wt% of a mixture of La2O3, Nd2O3 and Y2O3 (ZLNY support) exhibited high activity for REGR gasoline process application, using isooctane (C8H18) as a model molecule. Despite a partial C8H18 conversion, high H2 yields were obtained, with CH4 yields lower than those predicted at the thermodynamic equilibrium. The study (i) on the effect of water, CO2 and REGR condition, i.e. the study of the steam reforming, dry reforming and REGR reactions separately, in various conditions of temperature and contact time and (ii) on the conversion of methane and its production by CO or CO2 methanation, or by C8H18 hydrogenolysis or decomposition, enabled us to propose a reaction scheme in 3 successive steps for isooctane conversion in REGR conditions, i.e. the reforming of isooctane in the presence of water, carbon dioxide and oxygen [13], yielding H2 and CO2, CO and CH4:

-Step 1, corresponding to reforming reactions i.e. steam reforming (eq. ( 1) and dry reforming (eq.( 2)) yielding H2 and mainly CO2, dry reforming reaction being negligible :

Step 2, corresponding to the reverse water gas shift (RWGS) reaction, yielding CO:

O H CO H CO 2 2 2    (3)
Step 3, corresponding to methanation reactions, especially with CO, yielding CH4:

2 4 2 2 2 CO CH H CO    (4)
The partial and total oxidation reactions were negligible, except for low isooctane conversions, due to the small amount of O2 in the gas phase compared to those of H2O and CO2 in REGR reaction. A slight deactivation was observed, which was attributed to either the metal sintering or a poisoning of the Rh/support interface.

The stability of reforming catalysts depends strongly on the nature of the support and on the metal-support interactions. Ceria-based supports present interesting properties for reforming reactions since (i) ceria favors strong metal-support interactions, thus limiting metal sintering [14] and (ii) is able to release oxygen and reduce its valence from Ce 4+ to Ce 3+ in oxygen-poor environments, and re-oxidize in oxygen-rich environments, which helps preventing deactivation by coke deposition [15].

In the present study, production of hydrogen from isooctane will be investigated in the presence of rhodium catalysts supported on cerium-based oxides with alumina in REGR conditions, as the previous ones [12,13]. Alumina was added to improve the stability of the ceria based support. The supports will be CeO2-Al2O3, which has demonstrated high ceria reducibility [16,17] and CeO2-ZrO2 (50:50)/Al2O3, CeO2-ZrO2 (50:50) presenting high oxygen mobility [18] and favoring high performances for REGR reaction [19].

Experimental conditions

Catalysts

Five supports were used: Al2O3 , CeO2, Al2O3(80%)-CeO2(20%), Al2O3(20%)-CeO2(80%), Ce0.5Zr0.5O2/Al2O3(10%). In the following they will be named: Al2O3, CeO2, Al(80)Ce (20), Al(20)Ce(80), these four supports corresponding to the Al(Cex) series, and CeZrAl, respectively.

Al2O3 was provided by Axens and CeO2 by Solvay (Rare-earth systems). Al(80)Ce (20) was prepared by impregnation of Ce(NO3)3.6H2O on the commercial Al2O3 support in excess of water. Al(20)Ce(80) and CeZrAl were prepared by a sol-gel method adapted from [20]. After evaporation of water, supports were dried and then calcined at 800°C in a muffle furnace for 5h.

Rhodium (1 wt% Rh) was impregnated on the support by wet impregnation technique using rhodium(III) chloride (Alfa Aesar). After evaporation of the solvent, the as-made sample was dried, and then calcined at 650 °C, under air flow (60 cm 3 min -1 ) for 4 h (temperature ramp: 2 °C min -1 ).

Characterization

The specific surface area of the catalysts was estimated by the BET (Brunauer-Emmett-Teller) model using nitrogen adsorption at -196 •C in a Tristar 3000 Micromeritics apparatus. The pore size distribution was calculated using BJH (Barret-Joyner-Halenda) model.

The metal content was determined by elemental analysis using inductively coupled plasma atomic emission spectroscopy (ICP-AES) technique in a Perkin Elmer Optima 2000 DV apparatus.

Rh accessibility was measured by H2 chemisorption. Typically 120 mg of sample were reduced in flowing hydrogen at 400 °C (1 h, heating rate 10 °C/min), flushed under Ar (3 h), and then cooled down to room temperature under flowing Ar. The H2 uptake measurements were performed at -86 •C by injecting a first series of hydrogen pulses (0.267cm 3 ) up to saturation (HC1). The low temperature of chemisorption was required to suppress hydrogen spillover on the support [21]. A second series of pulses was injected over the sample, after 10 min of purging under pure Ar, to determine the reversible part of chemisorbed hydrogen (HC2). The irreversible part was taken as HC=HC1 -HC2.

Temperature Programmed Reduction (TPR) experiments were performed in a Micromeritics AutoChem II apparatus. Before analysis, the samples (∼200 mg) were treated under flowing oxygen from RT to 500 °C for 30 min with the heating rate of 10 °C min -1 . Catalysts were then cooled down to RT under O2 (30 mL.min -1 ) and outgassed under argon for 30 min (30 mL.min -1 ). Finally TPR was performed from RT up to 1000 °C at a rate of 5 °C min -1 for 30 min under 1% H2/Ar (30 mL min -1 ). H2 consumption was quantified by gas chromatography using a Thermal Conductivity Detector (TCD).

X-ray diffraction measurements were performed at room temperature in a Bruker AXS D5005 X-ray diffractometer, working with CuKα radiation (λ = 1.54184 Å), generated at 40 kV and 40 mA. Signal is recorded for 2θ between 15° and 85° with a step of 0.04° and a step time of 6 s. For sample identification, diffraction patterns were compared to the ICDD (International Center for Diffraction Data) database integrated in the EVA software. The crystallite size was calculated using the Debye-Scherrer equation.

Catalytic test

The model feed of gasoline exhaust gas, given by PSA Peugeot Citroën Group, contains 2.2 vol.% of isooctane, 13.5 vol.% of CO2, 12 vol.% of H2O, 1 vol.% of O2 and 71.3 vol.% of N2 (REGR conditions). Anhydrous isooctane (C8H18) was supplied by Carlo Erba (99.5%); CO2, O2 and N2 were provided by Air Liquide. Ultra pure water was used. The total gas flow rate was 250 Ncm 3 min -1

Reforming of exhaust gas recirculation (REGR) reaction was carried out in a quartz tubular continuous flow reactor. The catalyst (150 mg) was activated under the REGR conditions, i.e.

in the presence of all the reactants, or under hydrogen, by heating with a ramp of 10ºC min -1 , from 130ºC to the reaction temperature. The experiments were performed at 580°C, with a total pressure of 1.3 bar and a weight hourly space velocity of 138 h -1 , which corresponds to a gas hourly space velocity of roughly 150000 h -1 , considering an average density of the solids of 1.5

g mL -1 . The experimental details for the experiments in the tubular continuous flow reactor are given in details in [12].

The catalyst performances were characterized by:

-The product yield :

in H C out x x F F Y 18 8



Where in H C F 18 8 is the molar flow rate of isooctane at the inlet of the reactor and out x F the molar flow rate of the product x at the outlet -The isooctane, oxygen, carbon dioxide and water conversion, in % (example for isooctane) :

100 x 18 8 18 8 18 8 18 8 in H C out H C in H C H C F F F X  
Experimental values are compared to the values at the thermodynamic equilibrium obtained by calculation based on the minimization of the Gibbs free energy.

Results and discussion

Characteristics of the catalysts

The characteristics of the supports and of the catalysts in terms of surface area, volume and size of pores are reported in Table 1. Supports present BET surface area between 34 (Al(20)Ce(80)) and 256 m 2 g -1 (CeO2). The introduction of rhodium, followed by activation treatments, leads to a slight decrease of the BET surface, except on CeO2 support, for which the specific surface area significantly decreases to 42 m 2 g -1 . This is due to the fact that the CeO2 support was not calcined, before introduction of rhodium. When cerium is added to aluminium oxide, it can be seen (Fig. 2) that the BET surface of the Rh/AlCex catalysts decreases from 175 to less than 50

m 2 g -1 .
XRD patterns are displayed in Fig. 3. -Al2O3 (ICDD n° 00-029-0063) is clearly identified on the Rh/Al2O3 pattern. The cell parameter (a = 0.791 nm), experimentally determined from the most intense peak (2 = 66.9°), corresponds to the (4,4,0) plane. The crystallite size is estimated at ~ 9.9 nm. Rh/CeO2 presents sharp diffraction lines corresponding to cerianite ceria (ICDD n° 00-043-1002) crystallizing in a fluorite type fcc structure, with a crystallite size of 25.6 nm.

Rh/Al(80)Ce (20) presents both type of ceria and alumina phases, which indicates that cerium is not incorporated in the alumina structure. The XRD pattern of Rh/Al(20)Ce(80) shows only peaks characteristic of ceria, showing that alumina is likely to be amorphous. The cell parameters of Rh/Al(80)Ce (20) and Rh/Al(20)Ce(80), are 0.539 nm and 0.540 nm, similar to ceria (cerianite), and the mean crystallite size of the pseudo-cerianite phase of 21.6 nm and 27.3 nm, respectively. Rh/CeZrAl presents diffraction lines typical of Ce0.5Zr0.5O2 (ICDD n° 00-038-1436), showing that the alumina added to this support is amorphous and does not modify the oxide structure. In Rh/CeZrAl, CeZr is a homogeneous solid solution with a mean crystallite size of 18.7 nm, and cell parameters (a = b = 0.372 nm; c = 0.527 nm) similar to the ICDD (a = b = 0.372 nm ; c = 0.530 nm).

TPR profiles of the Al(Cex) series and CeZrAl, are presented in Fig. 3 and the results detailed in Table 2. No peak is observed on the alumina support, due to the non-reducibility of this support. It can be seen in Fig. 3 that CeO2 presents a first reduction peak with a maximum at 371°C and a second one, starting at 660ºC. The first peak may be attributed to the reduction of surface Ce 4+ . Generally, CeO2 presents two peaks during the reduction process : a first one, around 400°C, attributed to surface reduction and the second one, above 750°C, corresponding to the reduction of the bulk [22,23]. It can be seen in Table 2 that only 7% of ceria is reduced at the first peak attributed to surface ceria, which is little and may be attributed to the low temperature of this first reduction peak. Mixed Al(80)Ce (20) and Al( 20)Ce(80) supports start to be reduced at temperatures higher than that of CeO2, with a first maximum of reduction peak 538 and 580ºC, respectively, attributed to the reduction of surface ceria, and a second one at 986 and 990ºC to bulk ceria, in accordance with the results obtained by Yao et al. [22] on the same type of support. For Al(80)Ce (20), total consumption of hydrogen is similar to the theoretical one, indicating that all ceria has been reduced. On the contrary, the experimental one for Al (20)Ce(80) (1303 µmol/g) is much lower than the theoretical one , which may be explained, by the desorption of hydrogen as for pure CeO2, or by an incomplete reduction of Ce 4+ . For CeZrAl, two peaks are observed with maxima at 657ºC and at 1088ºC, whereas it is known that the presence of Zr allows starting the reduction of Ce 4+ at lower temperature [24].This different behaviour may be explained by the presence of 10 wt% of Al2O3 that may hinder the reduction of Ce0.5Zr0.5O2. However, the first peak (657ºC), which may be attributed to the surface reduction, corresponds to 26% of the total consumption, which is much higher than that observed for the Al(Cex) series (3% -7%). Consequently, the first peak most probably corresponds to the reduction of ceria surface and subsurface [24]. As for Al(80)Ce (20), the reduction of CeO2 is complete, with an experimental total H2 consumption similar to the theoretical one . Fig. 4 displays the TPR profile of the catalysts after impregnation of Rh(1wt%). Compared to the bare supports (Fig. 4) the presence of the metal favours the reduction of Ce 4+ . Two domains of temperature can be identified. In the first one (T< 350ºC), the first reduction peak in the 120-244ºC range, is attributed to the simultaneous reduction of Rh2O3 and of the surface ceria, the presence of the metal favouring the spillover of hydrogen towards the support. It can be seen that, except for Rh/Al(20)Ce(80) presenting the lowest temperature of reduction, the temperature of the maximum of the first peak decreases as the cerium content increases. In the second part (T>350°C), the reduction of bulk ceria can occur. The experimental amount of hydrogen consumed in the first domain (T<350°C) and H2/Rh molar ratio are reported in Table 3 for each catalyst. Considering the total oxidation of Rh to Rh2O3 during the oxidizing pretreatment, the total reduction of this oxide must correspond to a consumption of 146 mol H2 g -1 , and H2/Rh = 1.5. Except for Rh/Al2O3, this molar ratio is superior to 1.5, due to the reduction of surface CeO2 (spillover), which occurs at low temperature. This phenomenon is particularly noticeable for Rh/CeZrAl (H2/Rh = 6.1). The low H2 consumption for Rh/Al2O3 is due to the calcination temperature of 650°C. It is known that, when Rh/Al2O3 is oxidized at temperatures higher than 600°C, Rh 3+ easily diffuses into the spinel structure of Al2O3 and is then difficult to reduce [25][26][27][28].

All the catalysts, except Rh/CeO2, present a metal dispersion of roughly 40%, corresponding to a particle size of ~22 Å (table 4). The low dispersion of Rh/CeO2 (18%) is attributed to the support sintering, which occurs during the calcination process, leading to a drastic decrease of the BET surface area (from 256 to 42 m 2 g -1 , Table 1).

Catalytic performances

The composition of the gas phase in the reforming conditions using isooctane as model molecule are summarized in Table 5 along with the one obtained after a blank test, i.e. a reaction in the standard conditions but without any catalyst. It can be seen that without catalyst, 6% of C8H18 and 100% of O2 are converted, and the reaction products are CO (0.24 mol/ mol C8H18), H2 (0.27 mol/ mol C8H18 0), CO2 (0.23 mol/ mol C8H18) and H2O ( 0.3 mol/mol C8H18). It can be noticed that without O2 in the gas phase, no conversion is observed, which indicates that the only reaction occurring without catalyst is an oxidation reaction. Thus the reaction products are obtained by oxidation, and as H2/CO is equal to 1.12, it can be inferred that H2 is produced by isooctane partial oxidation, according to the reaction (Eq. ( 5)):

2 2 18 8 9 8 4 H CO O H C    (5)
However, total oxidation also occurs since H2O and CO2 are produced. Some other products were also detected but in small amounts (< 0.01 mol/ mol C8H18), such as ethylene, propylene, isobutene and C7-C8 products.

Fig. 6 shows the typical evolution of the conversion and products distribution obtained in the presence of a Rh-supported catalyst, when the catalyst is activated in situ by reduction under hydrogen before the catalytic test. The conversion of isooctane and water is not total and decreases as a function of time-on -stream (TOS), which leads to a slight decrease in the yield of products. Moreover, as observed for blank reaction, other products, such as C2-C4 and C7-C8 hydrocarbons are produced in small amounts (< 0.1 mol/ mol C8H18).

When the catalyst is activated by heating up to 580°C under the reaction conditions, i.e. in the presence of all the reactants instead of pure hydrogen, the conversion of isooctane and water is noticeably decreased (Table 6), which causes a decrease in the formation of all the products and especially H2. This loss of performance may be due to an incomplete reduction of rhodium or to a deactivation of the catalyst that can start before the temperature of 580°C is reached.

conditions, all the results presented hereafter were obtained after such a pretreatment.

The yields in H2 obtained for the Rh/Al(Cex) series and for Rh/CeZrAl catalyst are reported in Fig. 7 as a function of TOS, and the complete results obtained after 7h of TOS are summarized in Table 7. Rh/Al2O3 is the less efficient catalyst in terms of isooctane conversion and H2 production. Rh/CeO2 allows obtaining high initial yield in H2 but deactivates rapidly. Catalysts containing Al2O3 and CeO2, Rh/Al(80)Ce (20) and Rh/Al( 20)Ce(80), present higher performances than Rh supported on single oxides, with an initial yield in H2 (~ 6.5 mol H2/mol C8H18) similar to the one obtained with Rh/CeO2 but a superior stability. The addition of zirconium to the Ce-Al support improves the initial yield in H2 (7.3 mol H2/ mol C8H18), but it decreases rapidly to reach the one observed with Al(80)Ce (20) and Al( 20)Ce(80). Results

summarized in Table 7 show that, whatever the catalyst, a complete conversion of isooctane is never reached and the best yields in H2, obtained after 7h of TOS with catalysts containing both ceria and alumina (Rh/Al(80)Ce (20), Rh/Al(20)Ce(80),CeZrAl) are below the value predicted by calculation at the thermodynamic equilibrium. It can also be seen that the amount of methane produced is very small, much lower than the value predicted by the thermodynamic calculation.

During the reaction in the REGR conditions, methane may be produced by isooctane decomposition or hydrogenolysis or by methanation of CO or CO2 [13], and then consumed by steam or dry reforming. All these steps were studied in details in [13] and it was demonstrated that the low amount of methane is due to the limitation of its formation, isooctane decomposition and hydrogenolysis and CO2 methanation being strongly disfavored in the reaction conditions in the presence of a Rh-based catalyst. On this type of catalyst, methane is nearly exclusively produced by CO hydrogenation. Whatever the support, the conversion of H2O is ~ 50%, whereas CO2 conversion is ~0. One can note that a negative conversion indicates that the amount of CO2 produced is higher than its consumption.

In order to understand the origin of the deactivation observed during the catalytic tests, the metal dispersion was evaluated at the end of the catalytic test. Results are reported in Figure 8 where they are compared to the initial metal dispersion. It can be seen that, whatever the support, a decrease in metal dispersion is observed during the reaction. This effect is more pronounced for CeO2, for which the metal dispersion dropped from 18% to 4 % (variation  = 77%). Consequently, for this catalyst, the metal-support interaction is rather low and does not allow limiting the metal sintering. Supports containing mainly alumina (Al2O3 and Al(80)Ce( 20)) present a dispersion drop of 30%, whereas those containing more ceria, Al(20)Ce( 80) and CeZrAl a one of 23% (Fig. 8a). Rh sintering is probably due to the presence of steam at relatively high temperature, as proposed in [29] for Pt/CeZrO2 catalyst. It can be seen from Fig. 8b that the conversion of C8H18 is directly related to the metal dispersion before and after catalytic test. Although all the rhodium supports are different, the higher the metal dispersion, the higher the C8H18 conversion. For this series of catalysts, the support does not play a direct role in the reaction but an indirect one, on the metal dispersion. These results also show that the deactivation may be related to the loss in metal dispersion. The effect of particle size has been demonstrated on the methane steam reforming reaction, the reaction rate per active site increasing with the dispersion on many metal catalysts, including rhodium [30].

However, an opposite result was obtained with Ni/CeZrO2 for the autothermal reforming (ATR), i.e. the steam reforming in the presence of oxygen, of isooctane [31]. Indeed, it was demonstrated by varying the particle size but keeping the number of active sites constant, that the yield in CO and H2 is higher on largest particles, which may be explained either (i) by the fact that isooctane is a large molecule needing large metal ensembles to be adsorbed or (ii) or by the ability of small Ni particles to be oxidized in the ATR conditions, leading to a loss of active sites. In fact, it was demonstrated on Ni/Al2O3 for isooctane conversion by partial oxidation [32] that active catalysts need to be both well dispersed and highly reducible, which are two opposite tendencies. This explanation seems valid for the Rh catalysts of the present study.

Conclusion

Rh catalysts supported on alumina, ceria or oxides containing both ceria and alumina were used supports. These better performances were directly linked to the higher initial Rh dispersion obtained on this type of supports as well as to the higher stability, due to lower metal sintering during the catalytic test. This metal sintering was less important for the supports containing the highest amount of cerium (Al( 20)Ce(80) and CeZrAl) and the role of alumina is to improve the thermal stability of the support.

EGR valve

Gasoline engine

Air + gasoline Exhaust gas Gasoline

  for producing hydrogen in the presence of isooctane, used as model molecule of gasoline fuel, and various gases representative of vehicle exhaust gases. The best results in terms of conversion, stability, and yield in hydrogen were obtained on catalysts containing both ceria and alumina, namely CeO2-Al2O3 (Al(20 or 80)Ce (80 or 20)) and Ce0.5Zr0.5O2-Al2O3 (CeZrAl)

Figure 3 :

 3 Figure 3 : X-Ray diffraction patterns of the Rh/Al(Cex) series and of the Rh/CeZrAl catalyst ; (■) : -Al2O3 ; () : CeO2 ; () : Ce0.5Zr0.5O2.

Figure 4 :Figure 5 :Figure 6 :Figure 7 :

 4567 Figure 4: TPR profiles of the Al(Cex) series and CeZrAl.

Figure 8 :

 8 Figure 8 : Comparison of the metal dispersion of the fresh (initial : A1, B1, C1, D1 and E1) and used (final : A2, B2, C2, D2 and E2) for Rh/Al(Cex) and Rh/CeZrAl catalysts (a) and evolution of the conversion of C8H18 as a function of initial and final metal dispersions.

Table 1 :

 1 Surface area of catalysts and supports and volume and size of pores for the supports.

	Supports and catalysts	BET surface area (m 2 /g)	Volume of pores (cm 3 /g)	Size of pores (Å)
	Al2O3	184	0.49	108
	Rh/Al2O3	175	-	-
	CeO2 a	256	0.17	147
	Rh/CeO2	42	-	-
	Al(80)Ce(20)	103	0.32	124
	Rh/Al(80)Ce(20)	102	-	-
	Al(20)Ce(80)	34	0.07	76
	Rh/Al(20)Ce(80)	29	-	-
	CeZrAl	66	0.20	125
	Rh/CeZrAl	57	-	-
	a not calcined

Table 2 :

 2 TPR results of Al(Cex) series and CeZrAl.

	Support	CeO2	Theo. H2 consumption for CeO2	Tmax		Exp. H2 consumption	% of reduction of surface Ce 4+ to Ce 3+ a
		(wt%) (µmol H2/g)	pic 1 (°C)	pic 2 (°C)	TOTAL (µmol H2/g)	pic 1 (µmol H2/g)	pic 1 (%)
	Al2O3	0%	-	-	-	-	-	-
	CeO2	100%	2905	371	1025	-b	217	7%
	Al(80)Ce(20)	20%	581	538	986	634	40	7%
	Al(20)Ce(80)	80%	2324	580	990	1303	75	3%
	CeZrAl	51.9%	1508	657	1088	1572	399	26%
	a calculated from the theoretical value
	b consumption not finished at the end of the experiment

Table 3 :

 3 Amount of H2 consumption and H2/Rh molar ratio at the first reduction peak (Tmax <

	350°C)
	Reduction at Tmax <	Exp. H2 cons.	H2/Rh
	350ºC	(µmol H2/g)	(mol/mol)
	Rh/Al2O3	51	0.5
	Rh/CeO2	369	3.8
	Rh/Al(80)Ce(20)	160	1.6
	Rh/Al(20)Ce(80)	252	2.6
	Rh/CeZrAl	595	6.1

Table 4 :

 4 metal contents, metal dispersion and metal particle size of Rh/Al(Cex) and Rh/CeZrAl catalysts.

	Catalysts	Metal content (ICP)	Dispersion a	Particle size b (Ǻ)
	Rh/Al2O3	1.10%	39%	24
	Rh/CeO2	1.01%	18%	51
	Rh/Al(80)Ce(20)	1.09%	43%	21
	Rh/Al(20)Ce(80)	1.03%	42%	22
	Rh/CeZrAl	1.01%	46%	20
	a considering H/Rh = 1
	b calculated from the dispersion value

Table 5 :

 5 Composition of the gas in the REGR condition and of the outlet gas (blank reaction without catalyst at 580°C, P = 1.3 bar, WHSV= 137.8 h -1 ) Effect of the cerium content in Rh/Al(Cex) catalysts on the BET surface.

	Component	Inlet composition (mol.%)	Inlet composition (mol/mol C8H18 0)	Products after blank reaction (mol/mol C8H18 0)
	C8H18	2.2%	1.00	0.94
	CO2	13.5%	6.15	6.38
	H2O	12.0%	5.45	5.75
	O2	1.0%	0.45	0
	N2	71.3%	32.42	32.42
	H2	-	-	0.27
	CO	-	-	0.24
	CH4	-	-	-

/g) wt.% of Ce

ACKNOWLEDGEMENTS ADEME (PREDIT Program n° 06 06 C0137) is gratefully acknowledged for financial support.


Gunnar Myhre

Olivier Boucher

François-Marie Bréon

Drew Shindell

Declining uncertainty in transient climate response as CO 2 dominates future climate change

Carbon dioxide (CO 2 ) has been the largest contributor to radiative forcing and surface temperature change over the industrial era but other anthropogenic drivers have had a significant role 1,2 . The large uncertainty in the total forcing makes it difficult to derive climate sensitivity from historical observations 3-7 . Based on data from Intergovernmental

Panel of Climate Change (IPCC) reports, we show that the evolution of increased anthropogenic forcing and its reduced relative uncertainty between the Fourth and Fifth

Assessment Reports 1,8 can be expected to continue into the future, driven by the greater ease of reducing air pollution than CO 2 emissions, long lifetime of CO 2 , and hence a stronger dominance of CO 2 forcing. Here we present, using a statistical model, that the relative uncertainty in anthropogenic forcing of more than 40% quoted in the latest IPCC report for 2011 will be reduced by almost half by 2030, even without further improvement in scientific understanding. Absolute forcing uncertainty will also decline for the first time assuming projected decreases in aerosols occur. Other factors being equal, this stronger constraint on forcing will bring a significant reduction in the uncertainty of observation-

based estimates of transient climate response, with a 50% reduction in its uncertainty range expected by 2030.

Equilibrium climate sensitivity (ECS) and transient climate response (TCR) are two key measures that are used to evaluate how much the world might warm. TCR, which corresponds to the warming at the time of a doubling CO 2 in a 1%-per-year CO 2 increase scenario, is more policy-relevant than ECS to gauge the strength of climate change over coming decades.

Although there is high confidence in the human contribution to climate change, current IPCC estimates of TCR show a large uncertainty range of 1.0 to 2.5 ¡C (5-95% confidence interval) for a doubling of CO 2 (ref. 2 ), which translates into an equivalent range of 0.27 to 0.68 ¡C (W m -2 ) -1 for the normalized definition of TCR that we adopt in this paper. Different methods and data sets have been used to derive estimates of TCR. Observation-based studies analyze the historical temperature record combined with information on the radiative forcing (RF) [START_REF] Knutti | The equilibrium sensitivity of the Earth's temperature to radiation changes[END_REF] . The high uncertainty in historical RF is the main contributor to the uncertainty in the estimate of TCR and ECS through such methods [START_REF] Andreae | Strong present-day aerosol cooling implies a hot future[END_REF][START_REF] Otto | Energy budget constraints on climate response[END_REF] . Recent studies have shown that uncertainties in climate sensitivity will be reduced in the future based on longer available time series of surface temperature [START_REF] Urban | Historical and future learning about climate sensitivity[END_REF] . Here, we show that narrowing the uncertainty in RF can have a larger effect on the diagnosed TCR uncertainty.

Recently the IPCC 5 th Assessment Report assessed historical RF and its uncertainty 1 . In this paper we evaluate how uncertainty estimates have evolved between IPCC reports and estimate how we expect uncertainty estimates in RF to change in the future. We then evaluate the consequences of these trends on future uncertainty in diagnosing TCR from the available temperature observations record.

Climate change can be driven by a wide range of emitted compounds as well as by physical and chemical processes 1,[START_REF] Forster | Changes in Atmospheric Constituents and in Radiative Forcing[END_REF] . The increase in well-mixed greenhouse gas abundances leads to a documented RF with small relative uncertainty (≈10%) with all uncertainties presented here covering 5-95% ranges and all relative uncertainties given as half the 5-95% relative ranges unless otherwise stated. However, several of these greenhouse gases affect atmospheric chemistry leading to indirect effects that add to the RF uncertainty [START_REF] Isaksen | Atmospheric composition change: Climate-Chemistry interactions[END_REF][START_REF] Raes | Atmospheric chemistry-climate feedbacks[END_REF][START_REF] Shindell | Improved Attribution of Climate Forcing to Emissions[END_REF] . The positive RF from greenhouse gas increases since pre-industrial time has partly been counteracted by an overall negative RF by anthropogenic aerosols [START_REF] Crook | A balance between radiative forcing and climate feedback in the modeled 20th century temperature response[END_REF][START_REF] Huber | Anthropogenic and natural warming inferred from changes in Earth's energy balance[END_REF][START_REF] Hansen | Earth's energy imbalance and implications[END_REF][START_REF] Ramanathan | Atmosphere -Aerosols, climate, and the hydrological cycle[END_REF] ; however the scattering and absorbing effects of atmospheric aerosols, including the component due to aerosol-cloud interactions, have uncertainties [START_REF] Boucher | Clouds and Aerosols[END_REF] that are much larger (~100% relative uncertainty) than those associated with CO 2 (see Supplementary Figure 1).

In the two most recent IPCC assessment reports (AR4 and AR5) best estimates for anthropogenic RF, together with their uncertainties, have been provided 1,8 . Similar estimates have been provided for earlier IPCC assessments [START_REF] Haywood | Causes of the reduction in uncertainty in the anthropogenic radiative forcing of climate between IPCC (2001) and IPCC[END_REF][START_REF] Boucher | On summing the components of radiative forcing of climate change[END_REF] . The changes in RF estimates and their uncertainties between the reports are combinations of evolution in our scientific understanding and temporal change of the forcing agents between the RF evaluation years.

Forcing estimates in the IPCC assessment are based on observations and modelling, and estimates constrained from observed climate change 20,21 are ignored.

Relative to pre-industrial (1750) the total anthropogenic RF in AR5 (for year 2011) is larger than in AR4 (for year 2005) and TAR (for year 1998) and Figure 1 shows that further increases are expected under two extreme Representative Concentration Pathways (RCPs) for 2030 (see Methods). This increased total anthropogenic forcing from TAR and AR4 to AR5 is due to increases in greenhouse gases as well as increased scientific evidence for a less negative aerosol forcing 1 . In AR5 more aerosol processes are included in the forcing estimates (allowing for rapid adjustments in the atmosphere) relative to previous IPCC assessments, which resulted in a less negative RF estimate (-0.9 (-1.9 to -0.1) W m -2 in AR5 versus -1.2 (-2.4 to -0.6) W m -2 ). Importantly, the relative uncertainty is reduced from TAR and AR4 to AR5 as shown in the right panel of Figure 1. Projections using the RCPs indicate that this reduction will continue and, by 2030, the relative uncertainty in the total anthropogenic RF will be approximately halved relative to the latest IPCC assessments assuming no change in the scientific understanding of the forcing mechanisms (based on RCP2.6 and RCP8.5).

Despite improvements in understanding, individual RF uncertainties changed relatively little between the assessment reports; yet the relative uncertainty in total RF has narrowed and can be expected to exhibit an even stronger decrease by 2030.

The main cause of reduction in the relative uncertainty of the total RF is due to the increasing share of the CO 2 contribution to the total as shown in Figure 2. In the last decades of the 20 th century, non-CO 2 greenhouse gases made substantial contributions to the total, with rapid RF increases, while aerosols offset part of the greenhouse gas RF. The first decade in this century and projections for the next few decades show limited RF changes for non-CO 2 greenhouse gases and a decrease in the offsetting negative aerosol forcing combined with an enhancement in the CO 2 RF. This dramatic change in the relative RF contributions is due to fairly stable or declining recent and projected emissions of short-lived aerosols and aerosol precursors and most non-CO 2 GHGs, in contrast with continuous increases in CO 2 emissions [START_REF] Peters | COMMENTARY: The challenge to keep global warming below 2 degrees C[END_REF] coupled with its long lifetime. The forcing due to aerosols including their influence on clouds is better understood and quantified than in AR4, but uncertainties remain large [START_REF] Boucher | Clouds and Aerosols[END_REF] . Over the last two decades, there has been a large change in the distribution of aerosols, linked to reduced anthropogenic emissions in Europe and North America and increased emissions over South and East Asia. These opposite trends over the last decades are expected to more-or-less balance each other in terms of global mean RF 1,23 .

Figure 3 illustrates the time evolution of forcing used in this paper and its standard deviation.

A maximum in the standard deviation (and hence absolute uncertainty) was reached around 2011 and is expected to decline further despite the increase in forcing. This leads to continued reduction in the relative uncertainty which, based on AR5 estimates, has been declining since about 1970. Figure 3 In the following we take the trends in anthropogenic RF estimates described above and examine their implications for estimating TCR from the historical record. TCR, when derived from historical observations or simulations relates the temperature change ( T) and the RF at a given time as follows:

TCR = T / RF (1)
The method used to estimate TCR here is similar to that used in recent studies [START_REF] Otto | Energy budget constraints on climate response[END_REF] . Note that in the above equation TCR is expressed per unit of RF rather than for a doubling of CO 2 abundance. It assumes quasi linear changes in T and RF over a chosen time period. Often TCR is assumed to be similar for all climate forcing mechanisms, although this may not be the case [START_REF] Shindell | Inhomogeneous forcing and transient climate sensitivity[END_REF] (see further discussion below and in the Supplementary Material). It may also depend on the rate of change of forcing [START_REF] Forster | Evaluating adjusted forcing and model spread for historical and future scenarios in the CMIP5 generation of climate models[END_REF] , which introduces a further small uncertainty term not accounted for here. The relative uncertainty in TCR (d TCR / TCR), where d refers to half the uncertainty of the 5-95% uncertainty range, is shown in Figure 4a Figure 4b shows that the absolute range in TCR will be at least 25% lower than the AR5 range over the RCP8.5 temperature ranges for 2030. For small temperature changes, the absolute uncertainty in TCR will see a greater reduction than the relative uncertainty in TCR between AR5 and the RCP for 2030 (up to 56%).

Including an enhanced response to forcing in the Northern Hemisphere extratropics [START_REF] Shindell | Inhomogeneous forcing and transient climate sensitivity[END_REF] would increase the uncertainty in present-day TCR calculations, but would lead to an even greater narrowing of the TCR uncertainty moving to 2030 RCP conditions (see Supplementary Figure 2). The combination of air quality policies, the Montreal Protocol, trends in emission of climate related compounds, and most importantly the differentiated lifetime of the compounds suggests that the current evolution is likely to continue over the next few decades. Our findings illustrate that the stronger domination of CO 2 RF over the other forcing terms leads to a better quantification of TCR.

A better quantification of TCR will have a pronounced impact on the probability distribution of estimates of the amount of permissible CO 2 emissions for a given temperature target, e.g the 2 o C target agreed to under the UNFCCC. Currently these emissions are highly uncertain [START_REF] Meinshausen | Greenhouse-gas emission targets for limiting global warming to 2 degrees[END_REF] , but the expected CO 2 domination will bring about (by itself) a better quantification of TCR and future projections of climate change.

Methods

All forcing values and their uncertainties used for figures and analysis are given in the IPCC AR5 in chapter text, supplementary or annex 1,27 , except for one case as described below for RCP2.6. The time evolution of historical and future RF is also from IPCC AR5. Projections for 2030 are based on the two most extreme RCPs, namely RCP2.6 and RCP8.5 [START_REF] Van Vuuren | The representative concentration pathways: an overview[END_REF] . These two RCP represent lower and upper projections over the next decades, respectively in terms of where the only change in estimate arise from atmospheric compositional change. Aerosols RF for RCP2.6 is not provided in the IPCC AR5 annex [START_REF] Prather | IPCC 2013: Annex II: Climate System Scenario Tables[END_REF] and is derived based on the difference in aerosol forcing from 2010 to 2030 as derived from one model (OsloCTM2) [START_REF] Skeie | Anthropogenic radiative forcing time series from pre-industrial times until 2010[END_REF] and thus also made consistent with the AR5 estimate. The main source of the time evolution of historical and future forcing of aerosols and ozone for IPCC AR5 was a multi-model study [START_REF] Shindell | Radiative forcing in the ACCMIP historical and future climate simulations[END_REF] .

The time series of uncertainty used in Figure 3 are derived from a Monte-Carlo method, based on converting IPCC AR5 uncertainty ranges in RF for 2011 into fractional error PDFs. We then sample these to generate plausible RF time series.

For the calculations of changes in uncertainties in TCR probability distribution functions (PDFs) of TCR from PDFs of temperature change and RF is derived using a Monte Carlo random sampling approach. The values adopted to derive the PDFs are given in Supplementary Table 1. Supplementary Figure 3 shows PDFs of TCR derived in this way from PDFs of RF and temperature change. The 5-95% interval is derived from the PDFs of TCR.

Relative uncertainties are given as half the 5-95% relative ranges. In Figure 4 the full 5-95% relative range is added to the relative uncertainty. In Figure 1 the relative uncertainties are calculated as half the 5-95% confidence range, divided by the best estimate.

  clearly shows how the reduction in relative uncertainty is caused by the increasing dominance of CO 2 in the total RF. Overall, the combination of enhanced CO 2 forcing and weak magnitude of the non-CO 2 and aerosols forcing both contributed to the recent reduction in uncertainty in anthropogenic forcing and likewise for the trend in the coming decades. Whereas the change in the relative uncertainty in the anthropogenic forcing from AR4 to AR5 is a combination of larger CO 2 domination and improved quantification of the aerosol forcing, estimated further change by 2030 is solely due to a change in atmospheric abundances and no assumed change in scientific understanding about the individual drivers of climate change.

  for RF relative uncertainties for AR4, AR5 and two RCPs for 2030 as a function of temperature change using the Monte Carlo simulations described and discussed in the Methods and Supplementary Material. Figure4ashows that the relative uncertainty in TCR for the two RCPs is about half that found for AR5 data. The uncertainties related to temperature decrease as the temperature change increases, as can be seen for the two RCPs. However, the contribution from temperature uncertainties is less than 10% of the change in the relative uncertainty in TCR between AR5 and the RCPs for 2030, emphasizing that changes in the RF uncertainties are the dominant cause of the differences between AR5 and the RCPs for 2030. The difference in RF uncertainty between AR4, AR5 and the two RCPs for 2030 translates into a large difference in the 5-95% uncertainty range of the TCR for AR5 present-day temperature changes and best estimate RF as shown in the inset in Figure 4. The only difference in these ranges in TCR is caused by the declining uncertainty in RF. The better quantification of RF has the largest impact on the upper range of the derived TCR in absolute terms. Upper ranges of TCR are associated with low values of RF for which the lower bound of the aerosol RF is particularly relevant. A relatively symmetric distribution of RF leads to more asymmetric shape of the distribution of TCR 7 . The two RCPs uncertainty estimates in 2030 provide rather similar uncertainty ranges for TCR. The AR5 likely range of TCR can be reduced by about 50% based on climate data from two additional decades solely due to expected RF trends without further improvements in understanding (subject to continued availability of global surface temperature observations), see inset in Figure 4b. The absolute change in TCR is more dependent on temperature changes than the relative change in TCR.

  CO 2 emissions and to some extent other climate relevant species. The development over the last decade is closest to RCP8.5 in terms of CO 2 emissions. Other emission scenarios based on realistic development until 2030 have little impact on our findings. AR5 forcing estimates for aerosols and contrail induced cirrus include rapid adjustments and thus use the effective radiative forcing concept 1,17 , whereas in AR4 and previous IPCC reports rapid adjustments were not quantified. This makes some of the forcing estimates not entirely comparable, but allowing for the difference in treatment of rapid adjustment is the most consistent method for the aerosols between the IPCC reports. Forcing estimates for the two RCPs and AR5 are derived consistently with the same forcing concept and relative uncertainty for the individual drivers. The combined forcing from ozone and stratospheric water vapour in Figure1has a small change in the relative uncertainty between AR5 and the RCPs caused solely by abundance changes. The best estimate of the total anthropogenic RF for the various IPCC reports and the two RCPs is calculated based on the sum of the best estimate of each component. The range of the total anthropogenic RF is derived from the square root of the sum of the square of the upper and lower range deviation from the best estimate for the individual component. This allows for a consistent treatment of the best estimate and range, but may differ slightly from the report values in previous IPCC reports. The best estimate and uncertainty ranges for the two RCPs for 2030 are derived consistently with AR5 estimates,

Figure 1 :

 1 Figure 1: Anthropogenic forcing for four phases of IPCC reports and two RCPs. Aerosols, ozone and stratospheric water vapour, well-mixed greenhouse gases (WMGHG), land use change and total forcing are given for SAR (1750-1993), TAR (1750-1998), AR4 (1750-2005) and AR5 (1750-2011)) and two RCPs for 2030. . All the forcing values are based onbest estimates reported in the IPCC reports, but with a consistent approach to calculate the total forcing which may differ slightly from reported values (see Methods). In SAR land use change was not estimated and thus not included in the total. Further, the RF of a given CO 2 concentration was estimated to be 15% higher in SAR compared to the recent IPCC reports, adding 0.24 Wm -2 to the total RF quoted in SAR. Estimate for AR5 and the two RCPs for 2030 includes rapid adjustments in the RF, whereas these have not been quantified earlier in SAR, TAR, and AR4. The probability density function for SAR and TAR are based on Boucher and Haywood[START_REF] Boucher | On summing the components of radiative forcing of climate change[END_REF] and their simulation C1.5. The relative uncertainties are shown in the right panel. All uncertainty ranges correspond to 5-95% confidence intervals with relative uncertainties given as half the 5-95% relative range.

Figure 2 :

 2 Figure 2: Decadal RF change between 1970 and 2010 and for 2020 to 2030 for two RCPs. The forcing is given for 1970 to 1980, 1980 to 1990, 1990 to 2000, and 2000 to 2010 and for 2020 to 2030 based on IPCC AR5 forcing values (see Methods). RF for ozone includes changes in the troposphere as well as in the stratosphere. Other WMGHG includes CH 4 , N 2 O and halocarbons. All process associated with aerosol-radiation and aerosol-cloud interactions taken into account in the IPCC assessments are included for aerosol, except black carbon on snow. Consistent treatment is applied for RCPs for 2030 and AR5. Forcing mechanisms other than those shown in the figure are small (see Supplementary Information).

Figure 3 :

 3 Figure 3: Time evolution in RF and standard deviation in RF. RF for total anthropogenic, CO 2 , the combined non-CO 2 greenhouse gases (GHG) such as CH 4 , N 2 O, halocarbons, ozone, and stratospheric water vapour, the others such as land use changes, black carbon on snow and ice, and contrails, and finally aerosols over the period 1850 and 2030 (a); the time evolution of the standard deviation of RF (b) and the ratio of the standard deviation of RF to the total RF (c). All the time evolutions of forcing are taken from IPCC AR5 (see Methods). RCP8.5 and RCP2.6 are shown with solid and dashed lines, respectively.

Figure 4 :

 4 Figure 4: The relative a) and absolute b) uncertainty in TCR for the indicated conditions as a function of temperature change. The results are based on Monte Carlo simulations of the PDF of TCR as a function of temperature change and relative uncertainty in RF for AR4, AR5, and RCP2.6 and RCP8.5 for 2030 (see methods for source of RF values). The uncertainties in RF for the two RCPs are based on the scientific knowledge in AR5 and projected abundance changes. Observed temperature changes and their uncertainties from AR4 and AR5 are adopted in the calculations of the relative uncertainty in TCR, whereas for RCP2.6 and RCP8.5 results are shown for CMIP5 simulated temperature changes. The absolute uncertainty in temperature change for 2030 is assumed to be same as in AR5. The relative uncertainties and best estimates of TCR are shown as horizontal lines with ranges shown for lower and upper bound of the relative uncertainty in TCR and TCR (lines for AR4 and AR5 whereas bands for the two RCPs for 2030). The inset shows the TCR and the uncertainty range from Equation 1 for temperature changes and RF at the time of AR5 but with different relative uncertainty in RF from AR4, AR5, and RCP2.6 and RCP8.5 for 2030. The diamond symbol shows the TCR with the best estimate RF and is thus constant to illustrate solely the difference in relative uncertainty in RF.

1 Myhre, G. et al., Anthropogenic and Natural Radiative Forcing, in Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, edited by T. F. Stocker et al. (Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 2013), pp. 659-740. Acknowledgements GM was supported from the Norwegian Research Council project SLAC (208277)

Correspondence and requests for material should be addressed to GM (gunnar.myhre@cicero.oslo.no)

Author contributions

GM, FMB, DS initiated the study with additional contribution on the design on the study from PF and OB. GM, OB, FMB, PF, and DS performed the analysis and wrote the paper.