O De Viron

J O Dickey

The two types of El-Nino and their impacts on the Length-of-day 1

At the interannual to decadal timescale, the changes in 2 the Earth rotation rate are linked with the El-Niño South-3 ern Oscillation phenomena through changes in the Atmo-4 spheric Angular Momentum. As climatic studies demon-5 strate that there were two types of El-Niño events, namely 6 Eastern Pacific (EP) and Central Pacific (CP) events, we in-7 vestigate how each of them affect the Atmospheric Angular 8 Momentum. We show in particular that EP events are asso-9 ciated with stronger variations of the Atmospheric Angular 10 Momentum and length-of-day. We explain this difference 11 by the stronger pressure gradient over the major mountain 12 ranges, due to a stronger and more efficiently localized pres-13 sure dipole over the Pacific Ocean in the case of EP events. 14 [Ashok et al., 2007]. These two ENSO types have differ-39 ent teleconnection patterns and climatic consequences [e.g.

Introduction

The Earth rotation is not constant in time; in particular, the Earth rotation rate, and the associated length-of-day to play a major role [START_REF] Chao | Interannual length-of-day variation with rela-247 tion to the Southern Oscillation/El-Nino[END_REF][START_REF] Chao | Correlation of Interannual Length-of-Day 250 Variation With El-Niño/Southern Oscillation, 1972-1986[END_REF], as a warm -El-Niño

24

-event has been shown associated with a longer day and a 25 cold -La Niña -event associated with a shorter day. Pacific [e.g. [START_REF] Latif | Greenhouse warm-295 ing, decadal variability, or El Nino? An attempt to under-296 stand the anomalous 1990s[END_REF], the Nino-4 region (5 • S -5 • 34 N, 160 • E to 150 • W). These are known with a variety of 35 names, Central Pacific (CP) El Niño [START_REF] Kao | Contrasting Eastern-Pacific and 282 Central-Pacific Types of ENSO[END_REF]Yu 36 and Kim, 2010], warm pool El Niño [START_REF] Kug | Two Types of El Nino 289 Events: Cold Tongue El Nino and Warm Pool El Nino[END_REF], date-37 line El Niño [START_REF] Larkin | Global seasonal temperature 292 and precipitation anomalies during El Nino autumn and win-293 ter[END_REF] or El Niño Modoki

The AAM is composed of two parts, a mass term corre-51 sponding to the AM associated with the rigid rotation of 52 the atmosphere with the solid Earth, and a motion term 53 corresponding to the relative AM of the atmosphere with 54 respect to the solid Earth.

55

Alternatively, as first proposed by [START_REF] Widger | A study of the flow of angular momentum 319 in the atmosphere[END_REF], one can 56 also consider the atmosphere as an external forcing to the 57 solid Earth. The total atmospheric torque acting on the 58 solid Earth is the sum of four effects: a pressure effect on the 59 topography, the gravitational interaction between the atmo-60 spheric and the Earth masses, the wind friction drag over the 61 Earth surface, and the interaction between the gravity wave 62 and the topography [START_REF] Barnes | Atmo-241 spheric angular momentum fluctuations, length-of-day changes 242 and polar motion[END_REF][START_REF] Huang | The 271 balance of global angular momentum in a long-term atmo-272 spheric data set[END_REF].

63

The last term is generally negligible [de Viron and Dehant,

83

The torque approach was used for understanding the 84 atmospheric angular momentum anomaly associated with 85 the ENSO phenomenon [Wolf and Smith, 1987;Ponte and 86 Rosen, 1999;de Viron et al., 2001;[START_REF] Marcus | Interannual 302 atmospheric torque and El Niño-Southern Oscillation: Where 303 is the polar motion signal?[END_REF]. Dur-

Data Preparation

In this study, we used outputs of the National Centers (1)

H mass Z = a 4 Ω g ∫ 2π 0 ∫ π 0 P surface (θ, λ) sin 3 θ dθ dλ, ( 2
)
where a is the mean Earth radius, g is the mean gravity 103 acceleration, u is the zonal wind, P surface is the surface pres-

Γ Mountain Z = a 3 ∫ 2π 0 ∫ π 0 ∂P surface (θ, λ) ∂λ h(θ, λ) sin θ dθ dλ (3) Γ Friction Z = -a 3 ∫ 2π 0 ∫ π 0 τ λ sin 2 θ dθ dλ, ( 4
)
where h is the orography and τ λ is the zonal friction drag.

The longitude derivative of the surface pressure is estimated using a using a five-point stencil [e.g. [START_REF] Burden | Numerical Analysis[END_REF]:

df (x) dx i ≃ 8fi-2 -fi-1 + fi+1 -8fi+2 12 ∆x (5)
The EP and CP Niño index are estimated, following Ren and Jin [2011], from the Niño 3 and Niño 4 index from the NOAA Climate Prediction Center, made available at the Earth System Research Laboratory website. Defining

α = { 2 5 0 where Nino3 • Nino4 < 0 NEP = Nino3 -α • Nino4 (6) NCP = Nino4 -α • Nino3 (7)
To minimize the impact of the high-frequency noise in the

120 t + X = {t : NX (t) > σN X } (8) t 0 X = {t : -σN X ≤ NX (t) ≤ σN X } (9) t - X = {t : NX (t) < -σN X } (10)
We then compute a composite anomaly by making the difference between the average positive state and the average negative state.

CX (x, y) = C(t + X , x, y) -C(t - X , x, y) (11)
where X can be either EP or CP , and C(t, x, y) is the 121 dataset at time t and coordinates (x, y).

122

ENSO induced AAM anomaly

We estimated the composite impact of the ENSO by com- of the pressure dipole for the CP events makes it weaker.

X -3 mountain torque, total and integrated over each continents, 182 are given on Table 1. The table shows that there is also some effect over the topography of Africa.

184

The friction torque shows similar features in case of CP

Conclusions

In this paper, we investigate the impact of the ENSO on East Pacific Central Pacific -3.00 -2.25 -1.50 -0.75 0.00 0.75 1.50 2.25 3.00

16(

  LOD) show fluctuations in a broad band of periods. A 17 global description of the causes at the different time scales 18 can be found in Hide and Dickey [1991]. The main cause of 19 LOD change for periods ranging from a few days to a few 20 years is the Earth atmosphere interaction. As soon as in-21 terannual fluctuations were observed in the Earth rotation 22 data, the El-Niño Southern Oscillation (ENSO) was shown 23

26

  Classical El-Niño events are characterized by maximum 27 warm water anomaly in the Eastern Pacific Ocean, and re-28 ferred as the Eastern Pacific (EP) El-Niño events, with Sea 29 Surface Temperature (SST) anomalies in the Nino-3 region 30 (5 • S -5 • N, 150 • W to 90 • W). Frequent occurrences of a 31 new type of El Niño have been observed since the 1990s, with 32 the maximum warm SST anomaly in the Central Equatorial33

  64 2003]. The topography from the atmospheric Global Circu-65 lation Models (GCMs) is classically defined with respect to 66 the geoid; consequently, the topographic torque computed 67 using such a topography is actually the sum of topogra-68 phy and gravitational torque, and is known as the mountain 69 torque. The total torque is thus computed as the sum of the 70 mountain and the friction torque. 71 Generally, the mountain torque generates the axial AAM 72 variations, which are eventually damped away by the friction 73 torque [de Viron et al., 2001; Lott et al., 2008; Marcus et al., 74 2011]. A noticeable exception is the seasonal AAM anomaly, 75 which is generated by an anomalous friction torque over the 76 Indian Ocean [de Viron et al., 2002]. Both the atmospheric 77 AM (AAM) and torques can be estimated from the output, 78 whereas the inherent accuracy limits this method at the un-79 derstanding of the physical processes but does not allow to 80 estimate Earth rotation variation with a precision allowing 81 to use it in the frame of geodetic studies [de Viron and De-82 hant, 2003].

87

  ing the ENSO event, a low pressure appears in the Eastern 88 part of the Pacific Ocean, which creates a positive torque 89 over the atmosphere and consequently increases the AAM 90 and the LOD. The increased surface wind over the North-91 ern Pacific increases the friction torque, which eventually 92 cancels the AAM anomaly.

  93

94for

  Environmental Prediction -National Center for Atmo-95 spheric Research (NCEP-NCAR) reanalysis [Kalnay et al., 96 1996], from 1948 to 2013. Data includes the zonal wind 97 field (as a function of time, pressure level, latitude, and lon-98 gitude), the surface pressure and East-West wind stress (as 99 a function of time, latitude, and longitude), and the model θ, λ) sin 2 θdp dθ dλ

  104sure, θ and λ are the colatitude and longitude, and Ω is the 105 Earth mean angular velocity. In order to be able to inves-106 tigate the space pattern of the anomaly, we also used the 107 expression of equation (1) only integrated along the longi-108 tude, corresponding to the contribution to the motion term 109 at a given latitude, pressure level, and time. 110 The axial torque are estimated from the surface pressure 111 longitude derivative and orography using 112

  113 computation, the indices are smoothed by a 1-year running 114 mean. We isolate the impact of each type of events by first 115 separating the data epochs into three categories, for each 116 index, the epochs with index values above 1σ being the pos-117 itive state, with index values below -1σ being the negative 118 state, and the value in the interval [-σ, σ] being the neutral 119 state.

  185 and EP ENSO, but they are stronger in the case of the EP 186 ENSO. The anomaly maps are shown on Figure 4. The total 187 friction torque is at the level of 10 Hadleys for EP ENSO, and about a third for CP ENSO, with maximum effect over 189 the Pacific and over the part of the Antarctic Ocean, North 190 of the Indian Ocean, as seen on Table 2. The stronger fric-191 tion in the case of EP ENSO is logical, considering that the 192 wind anomaly is stronger in the CP ENSO case. A stronger 193 friction torque is also necessary to break down the larger 194 AAM anomaly resulting from the larger mountain torque in 195 the EP ENSO case.

  196

197theFigure 1 .

 1 Figure 1. Whisker diagram of the AAM during times where indices (NEP on the left, NCP on the right) are 1σ above average, below average, or at the neutral state.

Figure 2 .Figure 3 .Figure 4 .

 234 Figure 2. Difference in surface pressure anomaly between positive and negative phase of NEP and NCP , as defined in equation(11).

AAM and torque for the two types of ENSO events

	123
	124	puting the mean AAM for t +,0,-EP	and t +,0,-CP	. Whisker dia-
	125	grams for each of them are plotted on Figure 1, the as-
	126	sociated AAM anomaly can be observed on the left axis,
	127	whereas the corresponding LOD anomaly can be read on
	128	the right axis. The above average values of both EP and
	129	CP indices are seen to be associated with anomalously high
	130	value of AAM, whereas below average index values are asso-
	131	ciated with anomalously low value of AAM. The difference
	132	is found significant at more than 99% with an ANOVA test
	133	(see Davis [1986], for example). The t 0 X are the largest set,
	134	with about 500 epochs, whereas the + and -have about 100.
	135	Due to the one-year smoothing, the epochs from the same
	136	winter are not independents; consequently, for the statis-
	137	tics, only the mean value over a given winter was kept. The
	138	ANOVA group size was subsequently of the order of 15 to
	139	20 winters for the + and -epochs, and about 100 for the 0
	140	epochs.
	141	The EP anomaly is stronger: in particular, the difference
	142	of mean between above average and below average is nearly
		2.5 time larger for EP than for CP.
	166
	167	showing the topography in a gray scale, and the pressure
	168	anomaly with color contours. The most obvious difference
	169	occurs over Himalayas: in case of the EP ENSO, there is a
	170	strong pressure gradient with the pressure on the West slope
	171	being smaller, whereas there is no such gradient in case of
	172	CP ENSO. Over the Andes, a pressure gradient exists in
	173	both cases, but it is shifted East in the case of CP ENSO,
	174	and is consequently not acting over topography, while the
	175	gradient in case of EP ENSO closely follows the coast, and
	176	the mountain range, and is consequently very efficient. Over
	177	the Rocky Mountains, a pressure gradient on the West slope
	178	can be noted in both cases, but the more westward location

143

4.

Such a difference in AAM signature finds its explana-144 tion in the torque acting on the atmosphere from the solid 145 Earth. As explained in

[START_REF] Ponte | Torques responsible for 310 evolution of atmospheric angular momentum during the[END_REF]

, the torque 146 causing the AAM anomaly in the case of an ENSO event is 147 the mountain torque associated with the pressure anomaly.

148

The Southern Oscillation is known (see for instance Clarke 149

[2008]) to be associated with a pressure East-West dipole 150 over the Pacific. However, depending on the type of events, 151 the location of this dipole is directly linked to that of the SST 152 anomaly, as shown on Figure 2. In particular, the EP neg-153 ative pole is centred on the east coast of the Pacific Ocean, 154 whereas the WP negative pole is centred on the middle of 155 the Pacific Ocean. 156 The mountain torque is generated by a longitude differ-157 ence of pressure acting over a mountain range: if the pres-158 sure over the West slope of the mountain is stronger than 159 that over the East side, it acts to push the Earth to ro-160 tate faster and slows the atmosphere rotation down. Con-161 sequently, to understand the impact of the ENSO events 162 on the AAM, mostly the pressure over the main mountain 163 ranges, Himalayas, Andes, and Rocky Mountains are rele-164 vant. 165 The Figure 3 focus over those three mountain ranges,

Table 1 .

 1 Mountain torque (in Hadley, i.e. 10 18 N m), computed from C EP and C CP of the surface pressure, computed as explained by equation (11).

	Continent East Pacific Central Pacific
	Africa	1.2	0.8
	Europe	-0.4	0.1
	N America	1.7	1.0
	S America	1.1	0.0
	Asia	1.7	0.2
	Oceania	0.2	-0.1
	Antarctica	-0.1	0.1
	Total	5.4	2.1

Table 2 .

 2 Friction torque (in Hadley, i.e. 10 18 N m), computed from C EP and C CP of the friction drag, computed as explained by equation (11). The separation map for the ocean/continent can be found in Figure3of[START_REF] Marcus | Abrupt 306 atmospheric torque changes and their role in the[END_REF].

	Continent/ocean East Pacific Central Pacific
	Africa	1.2	0.1
	Europe	-1.0	-0.1
	N America	0.5	0.1
	S America	0.0	0.3
	Asia	0.1	-0.2
	Oceania	-0.2	-0.2
	Antarctica	0.5	0.2
	N Pac	-2.0	0.2
	Eq. Pac	-3.9	-1.3
	S Pac	-1.7	-0.3
	N Atl	-0.3	-0.4
	Eq. Atl	0.3	0.2
	S Atl	-1.4	-0.9
	Indian	-2.0	-1.1
	Antarctic Ocean	0.1	-0.0
	Total	-9.9	-3.3

Acknowledgments. We gratefully acknowledge discussions

Consequently, the mountain torque associated with the EP 180 ENSO is stronger in all the three cases. The values of the with Tong Lee regarding the two ENSO type literatures. This


O De Viron
email: deviron@ipgp.fr

M Van Camp

O Francis

Revisiting absolute gravimeter intercomparisons

Keywords:

HAL is

Introduction

Measuring gravity is a challenging task, as it requires to measure very small deviations of a strong acceleration. During the 80ies, with the improvement of measurement techniques, it became possible to build ballistic absolute gravimeters, which determine the absolute value of the gravity acceleration with a precision of 10 -9 at a given place and time [1]. Such a level of precision is necessary to investigate geodynamic phenomena and for high precision metrological experiments aiming at the new definition of the kilogram, for instance [START_REF] Eichenberger | Tracing Planck's constant to the kilogram by methods[END_REF].

There is no other measurement principle which has a smaller combined standard uncertainty than the ballistic asolute gravimeters considered here, which makes it difficult to assess how accurate they are. The only possible way to estimate their accuracy is to compare them to each other on a regular basis. Since 1980, 8 International Comparisons of Absolute Gravimeters (ICAG) were organized at the Bureau International des Poids et Mesures (BIPM) [3, and references therein]. The objectives are: (1) to determine the level of uncertainty in the absolute measurements of the gravity and (2) to determine a comparison reference value (CRV) for g at the sites of the BIPM gravity network. Moreover, during the ICAGs, relative spring gravimeters participated in the determination of the vertical gravity gradient and, since 2001, of the gravity differences between the different sites. Results including relative gravimeters in the determination of the comparison reference value have been presented for the first time in the ICAG-2001 report [START_REF] Robertsson | Results from the fifth international comparison of absolute gravimeters[END_REF]. Originally, given the small number of absolute gravimeters, this was done to improve the precision of the transfer in gravity between the sites, mandatory for comparing the gravity from the different sites at the BIPM [START_REF] Robertsson | Results from the fifth international comparison of absolute gravimeters[END_REF]. Since 2003, two regional comparisons of absolute gravimeters were organized at the Underground Laboratory for Geodynamics in Walferdange (WULG) [START_REF] Francis | Results of the International Comparison of Absolute Gravimeters in Walferdange (Luxembourg)[END_REF][START_REF] Francis | Results of the European Comparison of Absolute Gravimeters in Walferdange (Luxembourg) of November 2007 Gravity, Geoid and Earth observation[END_REF]; another one will be organized in November 2011. Those aim at determining the offsets between the absolute gravimeters, but they differ from the ICAG as they do not estimate the CRV. The relative gravimeters do not participate in these comparisons; they were only used to determine the vertical gravity gradients [START_REF] Francis | Results of the International Comparison of Absolute Gravimeters in Walferdange (Luxembourg)[END_REF].

The comparison requires all the instruments to measure at exactly the same location, which is of course physically impossible simultaneously. Consequently, the comparison is done with instruments measuring the gravity at common locations but at different epochs. During the last comparisons, the time variable part of the signal has been monitored either by the absolute gravimeter measuring during comparisons always at the same location or by a superconducting gravimeter. The observed changes in the gravity residuals, after correcting for tidal and atmospheric pressure effects, were below 1 microgal due to favorable meteorological conditions (Heavy rains may indeed induce gravity changes of a few microgal in less than an hour). No corrections have been applied as the gravity changes were small enough to be ignored.

When comparing a large set of instruments, it is not mandatory that every instrument measure everywhere or at a same location provided that enough data is collected to solve for all the unknowns, i.e. the offset of each instrument and the gravity at each location. During the WULG campaigns, given that 15 sites are available, work is planned in such a way that (1) each instrument measures at least three sites, (2) each site is visited by at least three instruments, and (3) two different instruments, which occupied the same site, did not measure at another common site again. Those conditions allow a better conditioning of the equation system.

Prior 2001, for the ICAG campaigns, gravity was calculated at each site by averaging the gravity value coming from each absolute gravimeter that measured at that site. If a same gravimeter measured more than one time at the same site, each gravity value was first averaged before contributing to the general average. Then, at each site and for each gravimeter, the offsets were determined. Finally, the offsets obtained for each instrument are averaged, giving the final value of the offset of the absolute gravimeter. For details on the combined adjustment of absolute and relative gravimeter to transfer the g values at the CRV site see e.g. [START_REF] Robertsson | Results from the fifth international comparison of absolute gravimeters[END_REF].

From the ICAG 2001 [START_REF] Vitushkin | Results of the Sixth International Comparison of Absolute Gravimeters, ICAG-2001[END_REF][START_REF] Cox | The evaluation of key comparison data[END_REF], a least-square adjustment of both relative and absolute gravimeter measurements was performed, where each absolute gravity measurement is modeled as the sum of the value of the gravity at the site, the instrumental offset and the measurement error. Francis et al. [START_REF] Francis | Results of the European Comparison of Absolute Gravimeters in Walferdange (Luxembourg) of November 2007 Gravity, Geoid and Earth observation[END_REF] compared this method, referenced as "OF" in that paper and "Method 2" in this work, to the average method applied for the ICAGs before 2001 (referenced as "AG" in [START_REF] Francis | Results of the European Comparison of Absolute Gravimeters in Walferdange (Luxembourg) of November 2007 Gravity, Geoid and Earth observation[END_REF] and as "Method 1" in this paper). They evidenced slightly more dispersed offsets when using Method 2. However, neither this paper nor ICAG-related publication does further investigate the reason of this dispersion.

The present paper demonstrates that Method 1 provides incorrect estimates of the offsets, underestimating the dispersion of the gravimeter offsets. Based on a simple example (3 gravimeters measuring at 3 sites) and a more realistic one (18 gravimeters and 12 sites) with no measurement errors, we show that Method 1 provides erroneous results even in the framework of its own hypotheses, whereas the Method 2 finds the right values. Classically, Method 2 hypothesizes that the average offset is zero, in order to prevent the equation system from being undetermined. We show in this paper that this method is highly sensitive to outliers, and that better results can be obtained by imposing a zero median. This is physically sounded, as it is equivalent to chose the shift so that the L1-norm of the offsets (i.e. the sum of the absolute values of the individual offsets) is minimal, which makes sense for metrological instruments such as absolute gravimeters.

A second part of this paper consists in comparing the methods on actual measurements. We selected two campaigns: the ICAG 2005, as results were published with and without incorporating the relative gravimeters, and the WULG 2007 regional comparison, as, for the first time, the results from the average Method 1 and the least squares Method 2 were both published.

For the evaluation of comparison data, it is often referred to the procedures described by Cox [START_REF] Cox | The evaluation of key comparison data[END_REF]. He proposed guidelines for two procedures that could be used to evaluate key comparison data. Unfortunately, these procedures treat only the cases for which all the involved apparatus measure the same quantity resulting in one unique value. As previously explained, during a comparison of absolute gravimeters, several sites with different gravity values are used for practical reasons. The Cox's procedures cannot be apply directly as they require to transfer all the data to one reference site that will serve as the CRV. The drawback is that this transfer (for which the gravity ties have to be measured) will increase the uncertainty budget. Due to the specificity of the AG comparisons, we propose a third procedure in which the offsets of each gravimeter and the g values at the different sites are estimated directly from the observations avoiding the passage by a unique CRV. This approach is not new. This is what is basically done in Methods 1 and 2.

After the pilote studies, a first Key comparison was performed in 2009. We strongly believe that the procedure for the evaluation of the data of the absolute gravimeter key comparisons needs to be defined and agreed upon. This present paper is our contribution to reach this objective.

Resolution Techniques

In an ideal world, an absolute gravimeter comparison experiment would be an easy thing. We would have a standard gravity, known to a precision much better than what AG can provide, and we would simply compare the measurement with the standard. The problem is that there is no such thing as a "perfect" instrument, to which the others could be compared. Consequently, the instruments can only be compared to each other, hoping that, somehow, they are correct in average. Looking how well the AGs are doing in the measurement of g is also a way to evidence systematic errors in the instrument, the measurement method or in the data processing, if the dispersion of the results is larger than the declared uncertainties.

In a less perfect but fairly good world, we could put all the instruments at the same place, so that we could simply compare them all to each other. Unfortunately, the spatial variation of gravity would require the instrument to be exactly at the same place, which is physically impossible. In addition, in the real world, the gravity changes with time, but this is something well known, especially if some absolute or relative gravimeters are continuously measuring during the comparison campaign. So we can correct for that, in such a way that we can compare instruments that have been at the same location at different epochs [START_REF] Francis | Results of the European Comparison of Absolute Gravimeters in Walferdange (Luxembourg) of November 2007 Gravity, Geoid and Earth observation[END_REF][START_REF] Cox | The evaluation of key comparison data[END_REF].

The intercomparison consists in having N instruments that have measured at some of the M available locations, with at least 2 measurements by instrument and by site; from this information, we want to determine the individual absolute offsets, and possibly the gravity at each site.

According to Method 1, the average value of all the measurement at each site is a fair estimate of the gravity at this site. So, the difference between each individual measurement and this average value is an independent measurement of the instrumental offset. Consequently, model is based on the equation (1) here below:

g j i -ḡj = δ i + ǫ j i (1)
where g j i is the measurement value obtained at site j by instrument i, ḡj is the average of all the gravity values measured at site j, δ i is the offset of instrument i, and ǫ j i is the measurement error for the measure g j i . The Method 2 is conceptually more straightforward. It is based on a normal equation saying that the gravity value obtained by instrument i at site j is equal to the sum of the gravity at site j plus the offset of instrument i and the measurement error:

g j i = g j + δ i + ǫ j i (2)
The two methods would then be solved by a least square fit, which becomes a simple mean in Method 1. On the other hand, Method 2 has a huge problem: the system is ill-conditioned, because a true value of a quantity is by nature indeterminate. In other words, measurements will be exactly the same for any given number added to all the offsets and subtracted from all the gravity values.

In order to get a solution, we need to add one equation that prevents the system to be ill-conditioned. Many solutions can be proposed. For example, we can take one instrument as exact, with a zero offset, we can consider that the average offset is zero, or we can impose a subset of offsets to have a zero mean. As the model being exact is one of the major assumptions of the least square methods, it is not a trivial action, but we are left with no other choice.

At this point, the Method 1 could seem more appealing, as no such additional equation is required, but this is not true. Actually, Method 1 uses one similar assumption by observation site (the average of the measurements at the site is the true gravity), which makes it more dangerous than Method 2.

Simulations

3.1. Case of three gravimeters 3.1.1. Method 1 Let us suppose three absolute gravimeters G1, G2, G3, each measuring at two sites out of three (A,B,C). Let us write again g j the gravity at site j and δ i the offset of instrument i. Let us also suppose for now that there is no measurement error. The equation system of Method 1 becomes:

g A 1 - g A 1 + g A 3 2 = δ ′ 1 g B 1 - g B 1 + g B 2 2 = δ ′′ 1 g B 2 - g B 1 + g B 2 2 = δ ′ 2 (3)
Table 1. Numerical values for a simple case (all in µGal). The line g gives the "true" gravity values, the column offsets gives the "true" offsets, and the values g iX are the simulated observations. As we work in an idealistic errorless case, the values are simply the sum of the g value of the column and the offset value of the line.

A B C Offsets δ i G1 g 1A = 10 g 1B = 10 / 10 G2 / g 2B = -50 g 2C = -50 -50 G3 g 3A = -10 / g 3C = -10 - 10
g 0 0 0 / g C 2 - g C 3 + g C 2 2 = δ ′′ 2 g A 3 - g A 1 + g A 3 2 = δ ′ 3 g C 3 - g C 3 + g C 2 2 = δ ′′ 3
According to equation (1), g j i = ḡj + δ i , such that the estimator for

δ i becomes δi = δ ′ i + δ ′′ i 2 = 1 2 δ i - δ k + δ l 4 (4)
with k, l different from i. We can substitute the mean instrumental offset in this equation:

δ = δ 1 + δ 2 + δ 3 3 . ( 5
)
The solution of the system is then

δi = 3 4 δ i - 3 4 δ (6)
The estimators δi of the offsets are obviously biased: for example, the values amount 75% of the true ones for a zero mean offset. As an illustration, let us take the numerical values of Table 1.

With δ = -50/3, and substituting the numerical values into (6), we obtain:

δ 1 = 20 δ 2 = -25 δ 3 = 5
Those solutions are wrong: the absolute values are obviously not the "true" ones but, and this is even worse for the comparison purpose, the dispersion of the offsets is underestimated by about 25%. This can be explained simply. The offset values are the mean of the offset obtained at each site. When comparing G2 and G3 at the site Cboth with a negative offset, we found a mean value which is deviated to the negative values (equal to -30 rather than zero). The two offsets are then determined at site C using wrong gravity values: -20µGal for G2 (very optimistic) and 20µGal for G3 (pessimistic), which eventually affects the retrieved offsets.

Method 2

In this simple case, the equation system reads:

g A 1 = g A + δ 1 g A 3 = g A + δ 3 g B 1 = g B + δ 1 (7) g B 2 = g B + δ 2 g C 2 = g C + δ 2 g C 3 = g C + δ 3
We add the following equation, to solve the ill-conditioning of the problem:

δ 1 + δ 2 + δ 3 3 = δ = 0µGal. ( 8
)
As the observation system only allows solving for the unknown within an additive constant, we have to add an absolute constrain like equation [START_REF] Cox | The evaluation of key comparison data[END_REF]. In our synthetic case, we know that equation ( 8) is false, and that the equation ( 9) here below would be more appropriate

δ 1 + δ 2 + δ 3 3 = δ = - 50 3 µGal. (9)
Unfortunately, in the real world, the true value of the mean offset is unknown, so we have to guess the absolute information. Many other options could be chosen, for instance only one of the offsets could be imposed to be zero, but probabilistically, the variance of the mean is lower than the variance of the individual values, which makes the expected error smaller. The absolute value obtained from the experience will only be as good as this information we have guessed. The analytic solution of the system for the error free problem is

ĝX = g X + δ δi = δ i - δ (10)
Note that, as long as the absolute information is exact, i.e. δ = 0µGal, the estimates of the gravity values and of the offsets are exact. Using the numerical values of Table 1, we found the following numerical values:

δ 1 = 80 3 µGal δ 2 = - 100 3 µGal δ 3 = 20 3 µGal
In this synthetic case, we know the true value of the offset, so we can compare our estimate with the reality. We found offset estimates that are shifted by -50/3µGal=-16.7µGal from their true value, i.e. exactly the error we did on our absolute information Table 2. Numerical values in a realistic case (all in µGal). The gravity value has been set to 0 everywhere, the column offset gives the "true" offset, and the values are the simulated observations. As we work in an idealistic errorless case, the values are simply the value of the instrumental offsets. guess. On the other hand, the dispersion of the offsets is accurately estimated to 30.6 µGal: Method 2 allows determining accurately each instrumental offset relative to each other, i.e. we have an exact relative comparison, but no absolute determination of the offset.

Simulations with 18 instruments at 12 different sites

To bring the comparison between the two methods one step further, we built an additional synthetic case, more similar to the actual ICAG campaigns, with 12 sites and 18 instruments, of which two present large offsets of -90 µGal(#OO) and -10 µGal (#O) (Table 2) Again, both Methods 1 and 2 were applied to retrieve the instrumental offsets. From Method 1, we obtain an analytical solution for the system for any experiment with N measurements at m sites with n instruments, given by:

δi = α i δ i + N k = 1 k = i β k δ k + N j=1 ζ j ǫ j . (11)
The coefficients α i , β k and ζ j depend on n, m, N , and on the distribution of the measurements. The α i coefficient tends to 1 and the β k coefficients to zero when the number of equations increases for a same number of unknowns. The precise values of these coefficients depend on the distribution of the measurements. In our case, the α i are close to 0.8 and the absolute values of the β k to 0.1.

Using Method 2, the analytical solution of any experiment reads:

ĝi = g i + δ + N i=1 γ i ǫ i δi = δ i -δ + N i=1 µ i ǫ i (12)
The coefficients γ i and µ i depend on n, m, N , and on the distribution of the measurements. In this example the measurement errors ǫ i are null, as in the previous case. The numerical solutions for the instrumental offsets of the gravimeters are given in Table 3, together with the dispersion (standard deviation) of the offsets. As previously, Method 1 underestimates the dispersion ( σ Method1 = 19.9µGal), while Method 2 got the right value ( σ Method2 = 21.2µGal). As expected from the analytical solution, the results from Method 2 are shifted with respect to the right values by the true mean offset of -6.2µGal. In other words the gravimeters #1-16 are positively biased; this is due to the two outliers O and OO.

For Method 1, the underestimation can be lowered by removing obvious outliers, whenever possible. Nevertheless, the equation system used in this method will always result in an underestimation of the dispersion, as shown by equation (6).

Real cases: impact of measurement errors

Method used

As explained here above, both methods are unable to retrieve the correct absolute instrumental offsets. Method 2 estimates correctly the relative offsets and, consequently, the dispersion of the offsets, but they are estimated within an additive constant. By default, the constant is such that the mean offset is zero. Nevertheless, there is little reason to believe that the mean of the offsets is zero, but this condition has the advantage to be linear, which makes it easy to incorporate in the least square problem. So, the computation is performed that way, and we get a solution to which an arbitrary constant can be added. However, the problem with imposing a zero mean is that an outliers will move away the better gravimeters from the true solution, as shown in the previous example. This can be solved by removing obvious outliers like #OO and #O from the mean, but it does not help much when the outliers are not so obvious.

Table 3. Solution of Method 1 and 2 in the realistic synthetic example (Table 2). The values are in microgal and the dispersion is the standard deviation of the offset for both methods. For Method 2 the offsets are shifted by 6.2 µGal with respect to the "true" value.

Instrument

δ i = 0, (13)
we propose to shift the offset values obtained using that assumption by the value δ that minimizes the L 1 norm [START_REF] Barton | Nelder-Mead simplex modifications for simulation optimization Management[END_REF] :

i | δ -δ i |. ( 14
)
This method is much less sensitive to outliers, as it favors the small individual values rather than the mean value to be small. As explained for instance in [7], it is equivalent to impose a zero median rather than a zero mean. Note that both methods (zero mean or minimum L 1 norm) come down to subtracting a constant value to all the offsets: the true (unknown) mean value in the case of the zero mean method or the one that will make the L 1 norm minimum. Consequently, the error made on the absolute value is δ in the zero mean case, and δ in the minimum L 1 norm case. Our hypothesis, which we confirm using a numerical experiment here bellow, is that δ > δ in most of the realistic cases.

To test its behavior with respect to the regular values as well as to outliers, we made the following numerical experiments:

• The offsets of 15 gravimeters were randomly generated using a Gaussian distribution with a unit standard deviation;

• Respectively 5,3,1 and 0 outlying offsets were randomly generated using a Gaussian distribution with a standard deviation equal to 5; this creates sets of respectively 20, 18, 16 and 15 gravimeters. Having a few gravimeters with such outlying offsets is realistic, given the results of the ICAG 2005 [START_REF] Vitushkin | Results of the Seventh International Comparison of Absolute Gravimeters ICAG-2005 at the Bureau International des Poids et Mesures[END_REF];

• Knowing the offsets, the actual mean offset was computed and removed from the offsets of each gravimeter, then the sum of squared errors i (δ i -δ) 2 was computed;

• The same was done using instead of δ, the value δ that minimizes the L1 norm;

• The process was repeated 100,000 times, which allows us to generate histograms of the error distribution for each case.

The results are displayed in Figure 1. When applying the L 1 norm, 95% of the results lye in all cases in the [-0.52,0.52] interval, the zero mean method performs slightly better only when there is no outlier, lying in the [-0.43,0.43] interval, but its performances decrease strongly when the number of outliers increases. As expected from the L 1 norm definition, this simulation confirms that the L 1 norm method is barely sensitive to the outliers, which is not the case for the null average process. Note that the distribution in case of the L 1 norm is not as smooth as the zero mean, because applying the L1 norm is a non-linear process.

Applying this method on the two synthetic cases presented in Table 1 and Table 2, we obtain, respectively, a shift (i.e. an error) δ = -10 (instead of δ = -16.7) and δ = -0.5 (instead of δ = -6.1) respectively. Consequently, imposing the minimum L 1 norm is better than the zero mean as soon as an outlier is present, and performs slightly worse in cases where no outlier is present (see in Figure 1). The zero mean method is appropriate only if all the gravimeters present similar offsets, equally distributed around 0, but even only one outlier bias the average. The L 1 rule favors the gravimeters having small offsets, providing a better estimate of the absolute offsets, as it is more likely to have a set of instruments with small offsets than having a zero mean value for the whole set of instrument. Note that we also tried with "less outlying" outliers (with a standard deviation of 3 rather than 5), and the L 1 norm solution is still improving the results as soon as one or more outliers are present. Error (in µGal)

Relative abundance

Figure 1. Probability distribution of the error on the offset (in red) and for the minimum L 1 norm (black), for different numbers of outlier. Errors are given on the horizontal axis in unit of the standard deviation, and the number of events on the vertical axis is normalized by the number of experiment.

Applying the L 1 norm method on actual cases

In the real world, the true average value δ is unknown. The Method 2 was applied in order to determine the instrumental offsets δ i . In the least square fit, the normal equations have been weighted by the uncertainties [START_REF] Kay | Fundamentals of Statistical Signal Processing: Estimation Theory[END_REF] of the individual measurements.

As there is no indication that the measurement errors are normally distributed, we used a bootstrapping method to estimate the instrumental offsets and site gravity values. To this end, we performed 100,000 resampling of the experimental data, keeping constant the number of observations (i.e. allowing replacement, but requesting that each gravimeter appears at least once) as if the sample was the whole population (see for instance [START_REF] Moore | The practice of business statistics: Using data for decisions[END_REF]). This method allows a fairly robust estimation for large samples, even if the error distribution is far from being Gaussian. From the 100,000 estimates for the offset of each instrument and of the dispersion of those offsets, we can get a confidence interval for the individual offsets and their dispersion. The bootstrapping of the data provides mean values for the instrumental offsets δ i , then the minimum L 1 criterion is applied and provides δ. This operation does not affect the higher moments of the distribution of our results, as the same value is subtracted from every single values. The results are presented in the next subsections for the ICAG 2005 and WULG 2007 campaigns.

The ICAG 2005 experience

The ICAG 2005 was a comparison of 19 instruments on 12 sites, with 96 measurements provided in Table 2.3.1 in Jiang et al [START_REF] Jiang | Review of the Seventh International Comparison of Absolute Gravimeters (ICAG 2005) -a pilot study toward the CIPM Key Comparison[END_REF]. The equations are weighted by the expanded uncertainties U. The results of the bootstrap evaluation are shown on Figure 2 and in Table 4. Figure 2 (left panel) shows with a gray scale the probability distribution of each offset. The values corresponding to darker area are more probable. Vertical red lines are drawn at values 0 (solid), ±2 (dotted), and ±5 (dashed) µGal. The figure allows a visual comparison of the probability distributions from the different instruments, but the 95% confidence intervals are not shown on the figure, to make it more readible, as they are shown in Figure 3 and given in Table 4.

Figure 2 (right panel) also shows the probability distribution for the offset dispersion. Note that this distribution, as for the confidence interval of Table 4, was obtained from the distribution of the offset dispersion obtained from the 100,000 resamplings. The numerical results are summarized in Table 4, where the offset values (in µGal) corresponding to the 5th, the 50th, and the 95th percentile are given for each instrument. The last line shows the 5th, the 50th, and the 95th percentile for the standard deviation of the offsets. These results are compared to the values provided by Vitushkin et al [START_REF] Vitushkin | Results of the Seventh International Comparison of Absolute Gravimeters ICAG-2005 at the Bureau International des Poids et Mesures[END_REF] on Figure 3. Note that, unlike the classical least-square error estimation, the bootstrapping method used here allows non centered error bars. This occurs often when an outlying measurement is affecting the data.

The WULG 2007 experience

For this experience the weights are obtained from the mean set standard deviation plus a systematic error of 2 µGal, as done by Francis et al [START_REF] Francis | Results of the European Comparison of Absolute Gravimeters in Walferdange (Luxembourg) of November 2007 Gravity, Geoid and Earth observation[END_REF]. The results from the bootstrapping are given in Figure 4 and Table 5. The multi-modal probability distribution of the offset dispersion given on the right panel results from the outlier effect (gravimeter # 20 does not even show up on the left panel). These results are compared to the values provided by Francis et al [START_REF] Francis | Results of the European Comparison of Absolute Gravimeters in Walferdange (Luxembourg) of November 2007 Gravity, Geoid and Earth observation[END_REF] on Figure 5.

When removing the outlier #20, as done in [START_REF] Francis | Results of the European Comparison of Absolute Gravimeters in Walferdange (Luxembourg) of November 2007 Gravity, Geoid and Earth observation[END_REF], we got more a reasonable offset dispersion distribution, with the 5th percentile at 2.1, the median at 3.8 and the 95th percentile at 7.2 µGal (Figure 6). If the second outlier #18 is also removed, this becomes 1.8, 2.4 and 4.0 µGal, respectively.

Discussion and conclusions

The intercomparison experiments aim at determining the offsets of a set of absolute gravimeters by making them measuring together the same gravity values. The Method 1, used among others in [START_REF] Robertsson | Results from the fifth international comparison of absolute gravimeters[END_REF], is based on the idea that the subset of gravimeters measuring at the same site has a zero mean offset. The alternative Method 2 was used in [START_REF] Vitushkin | Results of the Sixth International Comparison of Absolute Gravimeters, ICAG-2001[END_REF] and [START_REF] Francis | Results of the International Comparison of Absolute Gravimeters in Walferdange (Luxembourg)[END_REF], and compared with Method 1 in [START_REF] Francis | Results of the European Comparison of Absolute Gravimeters in Walferdange (Luxembourg) of November 2007 Gravity, Geoid and Earth observation[END_REF], based on the assumption that the whole set of gravimeters has a zero mean offset.

We have shown both from the mathematical point of view and from the synthetic cases that the Method 1 leads to an underestimation of the absolute value of the offsets, which in turn underestimates the dispersion of the offsets. Consequently, this method should not be used in intercomparison of gravimeters.

The other method is ill-conditioned, and can only be solved if an additional constraint is applied. Since the ICAG 2001 [START_REF] Vitushkin | Results of the Sixth International Comparison of Absolute Gravimeters, ICAG-2001[END_REF], the mean offset is set to zero; this solves the ill-conditioning problem, but imposes a constant on the system: this constant is the actual (unknown) mean offset which is added in the solution to the site gravity values and subtracted to the offset values. Consequently, as the probability of having a null mean offset is weak, we know that the computation gives erroneous results.

The method is not to be blamed for this, as the physical problem is actually illconditioned. It is impossible to estimate the offset of a set of instruments simply by comparing them to each other. The Method 2 allows getting a solution, but does not solve the ill-conditioning issue. The hypothesis of zero mean offset is probably better for a larger set of instruments, but it is presently impossible to make it so large that the mean offset would not be an issue.

We proposed here another option in order to fix the constant: we minimize the L1 From the results shown here, we also confirm that a single instrument is not likely to provide alone measurements at the microgal level accuracy. On the other hand, when comparing two measurements performed with the same instrument, the time change of the gravity can be determined with such an accuracy, given that no instrumental drift (for instance on the laser) occurred and that the instrumental setup is exactly the same Offset (µGal)

. Offsets and error bars as determined by Francis et al [START_REF] Francis | Results of the European Comparison of Absolute Gravimeters in Walferdange (Luxembourg) of November 2007 Gravity, Geoid and Earth observation[END_REF] (blue), as given in Table 5 (red) and obtained by method 2 when the # 20 has been removed (green).

Table 4. ICAG 2005: 5th, 50th, and 95th percentile estimations for the offset of each instrument (in µGal). The last line shows the estimation of dispersion equivalent to the standard deviation of the offsets for the whole set of instruments. Note that this value is obtained directly from the bootstrapping, and is thus not equal to the standard deviation of the above numbers. for the two measurements.

Acknowledgments

The contribution of OdV to this study is IPGP contribution XXXX. This study benefited from a grant of the Space Campus of the University Paris Diderot. Discussions with G. Pajot-Métivier were helpful to this paper. The two referees are acknowledged for their carreful review, which helped improving this paper.

Bibliography

[1] Faller J., 2002, Thirty years of progress in absolute gravimetry: a scientific capability implemented by technological advances, Metrologia, 39, 425-428.

Table 5. WULG 2007: 5th, 50th, and 95th percentile estimations for the offset of each instrument (in µGal). The last line shows the estimation of the dispersion equivalent to the standard deviation of the offsets for the whole set of instruments. Note that this value is obtained directly from the bootstrapping, and is thus not equal to the standard deviation of the above numbers.

Figure 2 .

 2 Figure 2. : Result from the bootstrapping of the ICAG 2005 experience. Left panel: Probability distribution for the offset (darker is more likely). Right panel: Probability distribution of the offset dispersion estimated from the whole dataset.

Figure 6 .

 6 Figure 6. Probability distribution of the offset dispersion from the bootstrapping of the WULG 2007 comparison, with the gravimeters #20 (red) and #18, 20 (black) not taken into account.

Table 4 .

 4 norm of the offset values. This solution does not provide an exact solution, but it is more robust in case of outliers, as it favors instruments having the smallest offsets. We obtained an offset dispersion of 4.4 µGal (ICAG 2005) and 3.8 µGal (WULG 2007), this value is more robust for the 2005 campaign than for the 2007, where the bootstrapping has shown a strong variability under resampling, which is due to strongly biased gravimeters. This problem can be solved by excluding the biased gravimeters from the calculations.

		25
		20
		15
		10
		5
	Offset (µGal)	-5 0
		-10
			Vitushkin et al., 2010
		-15	This study
		A10#008	FG5#101	FG5#108	FG5#202	FG5#206	FG5#209	FG5#211	FG5#213	FG5#215	FG5#216	FG5#221	FG5#224	FG5#228	FGC-1	GABL	IMGC-02	JILAg#2	JILAg#6	TBG

Figure 3. Offsets and error bars as determined by Vitushkin et al [9] (blue) and as given in

  Result from the bootstrapping of the WULG 2007 regional comparison. Left panel (A): Probability distribution for the offset values (darker is more likely; for the instrument #20 the offset lies outside the graph limits). Right panel (B): Probability distribution of the offset dispersion estimated from the whole dataset. This value is strongly influenced by the gravimeter # 20.

	1
	2
	3
	4
	5
	6
	7
	8
	9
	10
	11														0.035
	12
	13														0.03
	14
	15														0.025
	16 17 18 19 20	-20	-16	-12	-8	-4	0	4	8	12	16		20	Probability distribution	0.01 0.015 0.02
						Offset (µGal)								0.005
															108 0	110	112	114	116	118	120	122	124
		0	0.01		0.02	0.03		0.04	0.05		0.06			Offset standard deviation (µGal)
			FG5#222 Francis et al., 2010 (#20 excl.) FG5#202 FG5#206 FG5#211 FG5#215 FG5#216 FG5#218 This study Figure 4. FG5#101 -15 -10 -5 0 5 10 This study (#20 excl.)	FG5#220	FG5#221	FG5#226	FG5#228	FG5#229	FG5#230	FG5#232	FG5#233	FG5#234	IMGC#2	Jilag-6

  Instrument 5 th percentile Median 95 th percentile

	A10#008	5.5	8.3	11.5
	FG5#101	-3.4	-1.5	0.2
	FG5#108	3.4	5.0	6.6
	FG5#202	2.6	4.3	6.3
	FG5#206	-1.2	1.3	4.9
	FG5#209	-7.8	-6.5	-5.1
	FG5#211	2.3	3.8	5.5
	FG5#213	-2.8	-0.3	1.7
	FG5#215	-1.7	-0.1	1.7
	FG5#216	-0.2	4.1	6.1
	FG5#221	-2.5	0.0	2.5
	FG5#224	-3.8	0.6	4.5
	FG5#228	-3.7	-2.3	-0.9
	FGC-1	-5.7	-3.1	-1.1
	GABL	3.6	6.2	8.3
	IMGC-02	-4.2	-0.5	2.9
	JILAg#2	-3.1	-1.3	0.5
	JILAg#6	-9.2	-3.6	2.8
	TBG	-6.0	6.3	14.6
	Dispersion	3.6	4.4	5

  Instrument 5 th percentile Median 95 th percentile

		0.2
						# 18,20 removed
		0.18				# 20 removed
		0.16
	Probability distribution	1 FG5#101 2 FG5#222 3 FG5#202 4 FG5#206 5 FG5#211 0.06 0.08 0.1 0.12 0.14	-0.1 -1.6 -0.2 -4.6 -0.3	2.1 0.6 3.3 -1.5 2.6		4.6 3.2 6.5 1.7 5.9
		6 FG5#215 0.04	-1.8	0.5		2.8
		7 FG5#216 8 FG5#218 0.02	-0.6 -8.0	1.5 -4.1		3.6 -0.9
		9 FG5#220 10 FG5#221 1 2 0	-0.8 -3.0 Offset standard deviation (µGal) 2.2 0.0 3 4 5 6	7	5.2 2.6	8	9
		11 FG5#226	-8.5	-3.6		0.5
		12 FG5#228	-2.8	-0.5		2.1
		13 FG5#229	-4.1	-1.5		1.1
		14 FG5#230	-3.0	-0.2		2.8
		15 FG5#232	-1.3	1.4		4.0
		16 FG5#233	-1.7	0.5		3.0
		17 FG5#234	-3.2	-0.8		2.0
		18 IMGC#2	-24.3	-12.4		-2.8
		19 Jilag-6	-3.7	0.5		4.4
		20 MPG#2	490	514.5	532.0
		Dispersion	109.4	115.5	119.8