Dominique Dumas
email: dominique.dumas@univ.lyon3.fr

Changes in temperature and temperature gradients in the French Northern Alps during the last century

Keywords: Alps, climate change, temperature, gradient, trends, statistical significance

In moutain environments, local factors such as topography or exposure to the sun influence the spatial distribution of temperatures. It is therefore difficult to characterise the global evolution of temperatures over several decades. Such local effects can either accentuate or attenuate thermal contrasts between neighbouring areas. The present study uses two regional thermal indicatorsthermal gradients and temperatures reduced to sealevel -to monitor the monthly evolution of minimum and maximum temperatures in the French Northern Alps. Measures were calculated for the period extending from 1960 to 2007 based on data from 92 measuring stations. Temperature gradients were computed and further used to monitor the altitudinal evolution of temperatures. A characteristic regional temperature was determined for the whole of the French Northern Alps based on temperatures reduced to sea-level, and changes in temperatures since 1960 were assessed. Multiple linear regression models made it possible to extend measurements over a longer period, and to make enhanced calculations of temperature changes in the mountains since 1885. This is the first study to examine temperature changes in the French Northern Alps over such an extended period. Gradient data suggest that over the last 50 years temperatures have changed at all altitudes. In addition, the evaluation of the temperature rise over 100 years reveals that minimal and maximal monthly temperatures trends are only significant a few months of the year.

INTRODUCTION

Interpreting temperatures in mountainous areas has never been straightforward. Previous studies of temperature have illustrated the complex relationship between temperature and relief [START_REF] Angot | Sur la de´croissance de la température de l'air avec la hauteur[END_REF][START_REF] Harding | The variation of the altitudinal gradient of temperature within the British Isles[END_REF]Douguedroit and Saintignon, 1984;[START_REF] Bücher | Secular trend of surface temperature at an elevated observatory in the Pyrenees[END_REF][START_REF] Beniston | An analysis of regional climate change in Switzerland[END_REF][START_REF] Beniston | Climatic change at high elevation sites: an overview[END_REF][START_REF] Weber | 20th-century changes of temperature in the mountain regions of central Europe[END_REF][START_REF] Diaz | Temperature variations during the last century at high elevation sites[END_REF][START_REF] Böhm | Regional temperature variability in the European Alps 1760-1998 from homogenised instrumental time series[END_REF][START_REF] Rolland | Spatial and Seasonal Variations of Air Temperature Lapse Rates in Alpine Regions[END_REF][START_REF] Beniston | Mountain weather and climate: a general overview and a focus on climatic change in the Alps[END_REF][START_REF] Barry | Mountain weather and climate[END_REF]. Local factors, such as topography, can interfere with the overall interpretations made at regional scales. For instance, valleys in close relation to one another might show very different intra-or inter-annual climate evolutions. In addition, a number of factors can come into play to modulate the temperatures measured by weather stations in mountain areas: altitude, orientation, valley shape, and location [START_REF] Diaz | Temperature variations during the last century at high elevation sites[END_REF][START_REF] Weber | 20th-century changes of temperature in the mountain regions of central Europe[END_REF]. Altitude is nevertheless the most influential of these parameters, as montly and yearly temperatures are closely related to altitude.

There has been a great amount of scientific research into environmental changes in the Alps, mostly using either proxy data or data obtained from sites equipped with measuring devices (examples include the annual mass balance of a number of glaciers, modifications in Alpine vegetation, dendrochronological studies, monitoring of the snow mantle, modifications in hydrological and hydrosedimentary flows, etc.). This often obliges one to link observed environmental changes with the evolution of temperatures on a regional scale. The aim of the present study was to provide data enabling one to relate the observed environmental changes and the evolution of temperature at a regional scale. Such data should prove particularly useful to understand the environmental changes at the scale of the French Northern Alps since the late 19 th century.

Based on the measures of temperatures of nearly one hundred stations of known altitudes, it has been possible to make an accurate assessment of the inter-annual temperature evolutions for the entire area of the French Northern Alps (Figure 1). Temperature changes between 1960 and 2007 will be described first, based on two regional monthly indicators, thermal gradient and temperature reduced to sealevel [START_REF] Dumas | Evolution des températures minimales dans les Alpes du Nord depuis 1960[END_REF][START_REF] Dumas | Les températures dans les Alpes du Nord: influence de l'altitude et évolution depuis 1960. Geographia Technica[END_REF]. The calculation of reduced temperatures enabled us to follow the temporal evolution of temperatures over the entire French Northern Alps region, while the calculation of thermal gradients revealed the relation between temperature and altitude. The evolution of temperature variations related to altitude will thus be described with monthly and annual temperature gradients for a period of almost 50 years. Second, monthly values of reduced temperatures were extrapolated over the period extending from 1885 to 2007. The evolution of maximum and minimum regional temperatures can therefore be described for a period of over 120 years.

DATA AND METHODS

The study was carried out in the French Northern Alps using meteorological data gathered in the French departments of Isère, Savoie, Haute-Savoie and Hautes-Alpes. This area does not cover all of the French Alps, but the aim was to work on a rather homogenous climate zone (Douguedroit and [START_REF] Douguédroit | Les gradients de températures et de précipitations en montagne[END_REF][START_REF] Dumas | Evolution des températures minimales dans les Alpes du Nord depuis 1960[END_REF][START_REF] Beniston | Mountain weather and climate: a general overview and a focus on climatic change in the Alps[END_REF][START_REF] Dumas | Les températures dans les Alpes du Nord: influence de l'altitude et évolution depuis 1960. Geographia Technica[END_REF]. The area is large enough to include a variety of topographical features: valley floors, slopes and summits.

The first step was to identify over 150 MétéoFrance (the French Meteorological Agency) stations in the study area. Mean monthly temperatures were computed from daily minimum, maximum and average temperatures. Data from a few stations were set aside for the following reasons: deviations in the records, measurement periods of less than eight years, or excessively long gaps in the records. A total of 92 stations were selected for the period between 1960 and 2007. The stations selected were ordered according to their altitude, exposure and topographical features. Altitudes ranged from 134 m (Sablons) to 2800 m (St-Martin-de-Belleville), although 95% of the stations were below 2000 m, and 15% were between 1500 m and 2000 m.

Gaps were filled in on the monthly data so as to build up monthly indicators with a homogenous data set. The homogeneity of series was verified in monthly steps using the method of cumulative residuals [START_REF] Bois | Une méthode de contrôle de séries chronologiques utilisées en climatologie et en hydrologie[END_REF][START_REF] Buishand | Tests for detecting a shift in the mean of hydrological time series[END_REF][START_REF] Hubert | Segmentation des séries hydrométéorologiques. Application à des séries de précipitations et de débits de l'Afrique de l'Ouest[END_REF]. The detection of possible deviations was improved beforehand by deseasonalising temperatures [START_REF] Ladiray | Seasonal adjustment with the X-11 Method[END_REF]. In order to carry this out, data were standardised (i.e. centred with the mean and then reduced using the standard deviation of monthly values). This enabled the heterogeneity of several series to be detected and corrected. It should be noted that the impact of such corrections on the final results was quite limited as on one hand the most heterogeneous series were left out of the study, while on the other hand the large number of measurement points greatly limited the possible influence of untypical stations on the results.

Calculating two regional indicators

For the period extending from 1960 to 2007, based on linear models monthly means of the minimum (Tn), maximum (Tx) and mean (Tg) daily temperatures were used to calculate a temperature reduced to sea level, as well as a gradient of the fall in temperature (Figure 2). These monthly estimations, calculated on the basis of the altitude of the stations, could have been refined by also taking topographical variations into account. For example, including such topographical data in a multiple linear regression model would have yielded a lower residual variance. However, a study dissociating sites from their topographical features would not have given an overall view of climate changes in the French Northern Alps on the basis of only two parameters.

In addition, the two indicators stemming from a linear regression remain relevant provided that the relationship be verified and validated by statistical tests. In the present study, the linear regression models between weather-station altitudes and their mean monthly temperatures were systematically verified using four tests: Bravais-Pearson, Fisher-Snedecor, Student tests on origin and slope. The significance level was set at 1%. -for Tx: gradient = 0.62°C.100m -1 , regional temperature = 21.3°C -for Tn: gradient = 0.46°C.100m -1 , regional temperature = 10.4°C

Reconstructing monthly mean temperatures from 1885 to 2007

The period from 1885 to 2007 was chosen to define the climate changes having taken place in the French Northern Alps. The reconstruction relies on the data from three stations whose observations cover the whole of the 20th century, and whose observational conditions have remained completely or largely unchanged. Data from two of the stations, Annecy (elevation: 458m) and Lyon (elevation: 207m), were taken from the MétéoFrance data bank, and that of the third, Geneva (elevation: 420m), from the Météo Suisse data bank. The three series had already been homogenised using algorithms belonging to MétéoFrance [START_REF] Mestre | Méthodes statistiques pour l'homogénéisation de longues séries climatiques[END_REF][START_REF] Caussinus | Detection and correction of artificial shifts in climate series[END_REF] and Météo Suisse (in the "Data Warehouse" database). These three stations were left out of the calculations of gradients and reduced temperatures beforehand so as not to introduce any bias or establish regression models from non-independent variables. It was thus possible to not only study the temperature changes in the French Northern Alps since the 1960s, but also over a longer period by reconstructing temperatures. Such reconstitutions cannot, however, be carried out with the same robustness for the temperature gradients. This would have requires a large number of observations from stations spanning a wide range of altitudes. Data spanning the 120 years' period were only available for the three stations of Annecy, Lyon and Geneva, spanning an elevation range of 250 m only, as compared to 92 stations distributed over a maximum elevation range of 2500 m.

To reconstruct the temperatures since 1885, we used a validation procedure based on observations that were fully independent on the models. The first step consisted in dividing the 92 stations into two comparable and homogeneous groups. The stations were ordered based on their altitude, and every other station was then allotted to one group while the other half was allotted to the other group. Figure 3 illustrates the actual altitudinal distribution of the stations of the two groups for the maximal temperature of June 1960. The first group of stations was used as the training group. Multiple linear regressions were computed monthly over the 1960 -2007 period with the data collected from the three stations providing information from 1885 to 2007. Annual values were then calculated from mean monthly values.

Regional minimum and maximum temperatures calculated monthly from correlated with the three observation series (Table 1; Figures 4 and54). The monthly correlation coefficients between regional temperatures and temperatures from the Annecy, Lyon and Geneva stations always exceed 0.93; at least 86% of the variance of the regional temperatures can therefore be explained by the series of temperatures used in the regression models (Table 1). In addition to the Bravais-Pearson test, the linear regression models were validated using the Fischer-Snedecor test with a level of significance set below 1% (significant at p < 0.01).

Table 1. Monthly values of the correlation coefficient R and the F ratio (Fischer-Snedecor test) for minimum, maximum and average monthly temperatures (Tn, Tx and Tg) The monthly models were then tested on the set of independent data from the test group. As shown on Figure 6 for the minimal July temperatures, the values estimated by the model were also strongly correlated with the observations of the test group. This held true for the whole set of data, as can be seen on Table 2. Thus, the validation procedure revealed highly significant correlations between the calculated and observed values in the test group, confirming the robustness of the procedure. Therefore, it was possible to reliably extend and extrapolate the temperatures' series to the 1885-2007 period.

Month R F R F R F 1 0,
y = 0,9856x R 2 = 0,

Estimating trends

Inter-annual evolution and warming rates throughout the French Northern Alps can be examined over 48 years for gradients and 123 years for temperatures. The total change based on the mean trend during the observation period (1960-2007 or 1885-2007) was obtained by multiplying the slope of the regression line by 100.

The trend analysis was performed using a linear regression analysis and Mann-Kendall test. The Mann-Kendall test (MK), a non-parametric test for trend detection in a time series, has been widely used in environmental sciences [START_REF] Mann | Nonparametric tests against trend[END_REF][START_REF] Kendall | Rank Correlation Methods[END_REF][START_REF] Sneyers | Sur l'analyse statistique des séries d'observations[END_REF][START_REF] Vialar | Calcul des probabilités et statistiques[END_REF][START_REF] Kundzewicz | Detecting trends and other changes in hydrological data[END_REF][START_REF] Böhm | Regional temperature variability in the European Alps 1760-1998 from homogenised instrumental time series[END_REF]. The comparison of several tests applied to hydrological and climate series has frequently shown MK's value and strength [START_REF] Hirsch | Nonparametric trend test for seasonal data with serial dependence[END_REF][START_REF] Gerstengarbe | Estimation of the beginning and end of recurrent events within a climate regime[END_REF][START_REF] Yue | Power of Mann-Kendall and Spearman's ro tests for detecting monotonic trends in hydrological series[END_REF][START_REF] Kundzewicz | Trend detection in river flow time-series: 1. annual maximum flow[END_REF]. MK generally turns out to be more robust than Student or Spearman tests, as it is less affected by abnormal values. Nonparametric MK can be used to detect monotonic trends that are not necessarily linear. The null hypothesis in MK is that the data are independent and randomly ordered. In the present study, trends are considered to be statistically significant at p < 0.05, and highly significant at p < 0.01.

RESULTS

Intra-seasonal and inter-annual gradient evolution

The seasonal evolution obtained from the 1960-2007 monthly values revealed an annual dynamics. Such a dynamic had not yet been fully described for the Alps (Figure 7). Based on reduced temperature, the mean annual temperature amplitude in this area can be estimated as 16.0°C for the minima, and 22.3°C for the maxima. The mean annual temperature is estimated as 7.3°C for the minima and 17.8°C the maxima. Although the mean annual gradients (Table 3) are 0.43°C per 100m for Tn, and 0.52°C per 100m for Tx, seasonal variations are clearly noticeable.

The gradients show an annual cycle which can also be found in other mountain regions [START_REF] Harding | The variation of the altitudinal gradient of temperature within the British Isles[END_REF]. The cycle observed in the Alps is slightly out of step with respect to the seasonal evolution of temperatures, with a fairly rapid rise early in the year, followed by a slower drop towards the end of the year (Figure 7). Mean monthly gradients are shown on Table 3. They fluctuate between 0.37 and 0.47°C per 100m for Tn, and between 0.32 and 0.66°C per 100m for Tx. The gradients' evolution remains progressive and regular. Regarding the minimum and maximum temperatures, the smaller gradients are observed in December-January, and the greatest in April. Such seasonality has not always been described in the Alps, some authors reporting more complex, if not random, monthly variations of the gradients [START_REF] Angot | Sur la de´croissance de la température de l'air avec la hauteur[END_REF]Saintignon, 1976;Dougédroit and Saintignon, 1981;[START_REF] Bisci | Le gradient thermique dans les Marches méridionales (Italie Centrale)[END_REF]. However, other studies found similar trends, with lower gradients in winter and higher ones in summer [START_REF] Harding | The variation of the altitudinal gradient of temperature within the British Isles[END_REF][START_REF] Rolland | Spatial and Seasonal Variations of Air Temperature Lapse Rates in Alpine Regions[END_REF][START_REF] Barry | Mountain weather and climate[END_REF]. This type of seasonal cycle can partly be explained by more frequent thermal inversions in the cold season and by the particularly unstable nature of the atmosphere in spring, favouring a more active vertical mingling. However, it is also a consequence of the radiative and turbulent heat exchanges which progressively change over the year with the path of the sun and the multiple interactions of radiations with the relief. However, there does not seem to be a direct link with the presence of a snow cover, suggested in some studies of thermal gradients [START_REF] Douguédroit | Décroissance des températures mensuelles et annuelles avec l'altitude dans les Alpes du Sud et en Provence (séries 1959-1978). Eaux et Climat: mélanges géographiques offerts en hommage a[END_REF][START_REF] Bisci | Le gradient thermique dans les Marches méridionales (Italie Centrale)[END_REF][START_REF] Rolland | Spatial and Seasonal Variations of Air Temperature Lapse Rates in Alpine Regions[END_REF] (Figure 8). As can be seen, the greatest gradients, in April, are not related to the specific state of the snow cover, and their decrease continues smoothly after the snow has melted in May, or somewhat later at greater altitudes. Although their seasonal evolutions are similar, maximum temperature gradients are greater than minimum gradients throughout the year, except for the months of January and December. Minima are more frequently linked to higher relative humidity, which tends to attenuate the decrease of temperatures with altitude. For instance, in adiabatic conditions at ground level, the humid adiabatic gradient at a temperature of 15°C is around 0.5°C per 100m, whereas the dry adiabatic gradient is close to 1.0°C per 100m [START_REF] Barry | Atmosphere, weather and climate[END_REF][START_REF] Triplet | Météorologie générale[END_REF].

On an inter-annual scale, scarcely studied in the literature and never in the French Northern Alps, mean monthly and yearly gradients all exhibit a high degree of variability. Depending on the year and month, monthly temperature gradients can vary by ratios of 1 to more than 3, and even 1 to 9 for maximum temperature gradients in January. Monthly standard deviations (Table 3) are greater in autumn and winter (from October to February), reflecting the gradients' greater interannual variability linked to more frequent thermal inversions in the valleys. Such inversions are influenced by a number of climatic parameters such as cloud cover, wind speed and direction, and air temperature.

Overall, monthly and annual gradient variability (Figure 9) do not show any significant upward or downward trend (Table 3). Such relative gradient stability is noteworthy as it could indirectly indicate that the recent climatic changes in this mountain area are not more pronounced in low than in more elevated areas. The temperature changes observed over the last 50 years would thus concern all of the French Northern Alps, with no altitudinal distinction, and would have taken place in a relatively homogeneous way at all ranges of altitude. Other authors have however suggested that the warming trends could be different depending on the elevation (e.g., [START_REF] Rebetez | Changes in daily and nightly day-to-day temperature variability during the twentieth century for two stations in Switzerland[END_REF]2006). Some studies of climate change in upland areas in other Alpine regions have shown that warming is sometimes more marked at higher altitudes [START_REF] Bücher | Secular trend of surface temperature at an elevated observatory in the Pyrenees[END_REF][START_REF] Weber | 20th-century changes of temperature in the mountain regions of central Europe[END_REF]. Similarly, based on data collected in Switzerland over the past 30 years, Rebetez and Reinhard, (2007) further argue that below 1100 m, the low stations display a stronger trend than the more elevated areas, whereas above this level the warming is greater at higher than at lower elevations. Such conclusions are however always drawn from a small number of stations. In addition, they refer to models developed for very high elevations (above 4000 m) in the tropical Andes [START_REF] Bradley | Projected temperature changes along the American cordillera and the planned GCOS network[END_REF]2006). These conflicting hypotheses could be directly tested for the French Northern Alps through a specific analysis of temperatures' evolution as a function of altitude.

Another factor may influence the gradient stability, specifically the time of year, as significant tendencies of monthly gradients were observed between 1960 and 2007 in November, March and April (Table 3). Indeed, in November, the minimum temperature gradient shows a trend towards a significant drop (-0.013°C.100m - 1 .10yrs -1 ). In March and April, on the other hand, the evolution of maximum temperature gradients shows a significant rising trend (+0.012 and +0.011°C.100m - 1 .10yrs -1 ). The general evolution of thermal anomalies established for the French Northern Alps closely, and synchronically, follows the mean anomalies calculated for the continental Northern Hemisphere. At the hemispheric scale, temperatures fluctuate within a narrow amplitude of around 0.5°C, whereas the amplitude is sometimes 1.5°C over the French Northern Alps region. In other words, the amplification of the climatic signal recorded for the continental Northern Hemisphere is five times smaller than for the Alps.

Since 1885, there has been a significant rise in annual temperatures, with a highly significant warming rate of respectively 0.87, 0.84 and 0.93°C per 100 years for the mean, minimum and maximum temperatures (Table 4). This figure is similar to the rate of warming observed for the whole of France over the 20th century, but had not been established so accurately for the mountain area [START_REF] Moisselin | Les changements climatiques en France au XXe siècle[END_REF]. Between 1885 and 2007, temperature changes in the Alps were therefore close to the 0.85°C in 100 years observed over the whole of the Northern Hemisphere's continental regions. (CRUTEM3-NH data provided by the UK Meteo Office's Hadley Centre: Brohan et al. 2006;[START_REF] Jones | Surface air temperature and its variations over the last 150 years[END_REF][START_REF] Jones | Hemispheric and large-scale surface air temperature variations: An extensive revision and an update to 2001[END_REF][START_REF] Rayner | Globally complete analyses of sea surface temperature, sea ice and night marine air temperature, 1871-2000[END_REF][START_REF] Rayner | Improved analyses of changes and uncertainties in marine temperature measured in situ since the mid-nineteenth century: the HadSST2 dataset[END_REF]. Nevertheless, the evolution of the temperatures in the Alps departs from that observed in the Northern Hemisphere starting in the 1970s. Such congruence of the temperature evolution in a mountain area with the global temperatures over the XXth century, followed by an uncoupling in the 70s and 80s has also been described in other mountainous regions [START_REF] Weber | 20th-century changes of temperature in the mountain regions of central Europe[END_REF][START_REF] Beniston | Climatic change at high elevation sites: an overview[END_REF][START_REF] Beniston | Mountain Climates and Climatic Change: An Overview of Processes Focusing on the European Alps[END_REF][START_REF] Bhutiyani | Long-term trends in maximum, minimum and mean annual air temperatures across the Northwestern Himalaya during the twentieth century[END_REF]. It has also been reported that, within a century timeframe, the rise in temperatures in mountainous areas is not always greater than that of the surrounding plains [START_REF] Weber | 20th-century changes of temperature in the mountain regions of central Europe[END_REF][START_REF] Colombo | Study of temperature and precipitation variations in Italy based on surface instrumental observations[END_REF][START_REF] Rangwala | Twentieth Century Temperature Trends in Colorado's San Juan Mountains[END_REF].

The evolution of temperatures on a monthly scale does not appear as clear-cut. First, depending on the month, the rise does not occur with the same intensity (Figure 12). Second, for some months, a rise in temperatures cannot always be statistically validated by the Mann-Kendall test (Table 4). The results of this test suggest that neither minimum nor maximum temperatures have risen significantly in February and November. Nor can the rise be confirmed for maximum temperatures in April, June and September. Over the whole year, except for February and November, minimum temperatures show significantly greater and more regular warming than maximumu temperatures. Important variations in monthly warming rates can also be found for the period from 1960 to 2007 (Figure 12). It appears that annual temperature means have been raising throughout the last fifty years in the French Northern Alps, and that a noticeable and highly significant increase in recorded figures has progressively taken place. The mean rise is estimated at 3.4°C over 100 years for minimum temperatures and 3.9°C over 100 years for maximum temperatures. Annual warming over the same period has been 3.6°C, that is, two thirds greater than the rise of 2.4°C over 100 years observed for the whole of the Northern Hemisphere's continental areas (Table 5).

Warming has not been observed with the same rate at a monthly scale, depending on the months and specific temperature variable. Between 1960 and 2007, the highest significant monthly warming rate peaked at 7.8°C per 100 years for maximum temperatures in March, compared with 3.7°C per 100 years for the lowest significant monthly rate observed for minimum temperatures in June. Nevertheless, as for the period from 1885 to 2007, on a monthly scale the temperatures observed over the last fifty years have not always shown indisputable warming (Table 5):

-In February and November, the warming rates of maximum and minimum temperatures cannot always be confirmed by the Mann-Kendall test. Warming rates for both these months tend moreover to be minimal, and are always smaller than those observed for the continental Northern Hemisphere.

-The rise in mean temperatures (Tg) is statistically established in March as well as from May to August and in December.

For the other months of the year, although the trends may be real (e.g., in January the equivalent rise is 4.6°C over 100 years), they are not confirmed by MK.

-The great warming rates of monthly minimum temperatures (Tn), always above 3.7°C per 100 years, are statistically significant in only seven months out of 12: in December and January, from May to August, and in October.

-Finally, for maximum temperatures monthly warming rates, even though sometimes very strong, are significant for only four months of the year: March, May, June, and August. It cannot not be statistically demonstrated that the other months' maximum temperatures have risen since 1960.

CONCLUSION

The evolutions of regional temperatures and gradients in the French Northern Alps greatly vary depending on the month of the year, the specific temperature measure (Tn, Tx, or Tg), as well as the period of time investigated.

The inter-annual evolution of gradients over the last fifty years is undoubtedly the trickiest to evaluate. Annual and monthly gradients show large inter-annual variations, and apart from minimum temperatures in November, and maximum temperatures in March-April, there seems to have been no significant evolution since 1960. The stability of the gradients over the last fifty years could thus indirectly reflect the fact that temperature changes in the French Northern Alps affect the entire area in a relatively uniform way, a hypothesis that contrasts with conclusions drawn from smaller scale observations made in other Alpine areas. However, the seasonal variations of these gradients are easier to interpret. Shallow winter gradients and steeper summer ones reflect, among other factors, the influence of thermal inversions on the minima. This is not a novel observation regarding mountain area, but is nevertheless the first time that temperature gradients are precisely assessed for the French Northern Alps.

Between 1885 and 2007, and since 1960, temperature changes in the French NorthernAlps have been more conspicuous at an annual scale. For the 1885-2007 period, the Alpine range sustained the global modifications due to climate change at an annual scale without much amplification. The same cannot however be said for the period starting in 1960, where observed warming rates have clearly increased. Over the last few decades, this amplification has become even more noticeable at a monthly scale, with warming rates often exceeding 4.0°C per 100 years. This is to be compared to a mean monthly warming rate of 2.4°C per 100 years for the Northern Hemisphere's continental regions. The increase in monthly temperatures is nevertheless much more variable depending on the month. In addition, whatever the time period studied, all monthly temperatures do not always indicate significant, indisputable warming.

Figure 1 .

 1 Figure 1. The location of the 92 MétéoFrance weather stations used in the present study (snow stations ; 1 -Col de Porte, 2 -Saint Christophe)

Figure 2 .

 2 Figure 2. The correlation of Tn and Tx temperatures as a function of the altitude of measurement stations. An example of monthly temperatures in September 2001:-for Tx: gradient = 0.62°C.100m -1 , regional temperature = 21.3°C -for Tn: gradient = 0.46°C.100m -1 , regional temperature = 10.4°C

Figure 3 .

 3 Figure 3. Distribution of the stations in two groups: example based on June 1960 maximal temperatures.

Figure 4 .Figure 5 .

 45 Figure 4. The time series of minimum annual temperatures in the northern Alps (Tn Alps) calculated and reconstructed for 1960-2007.

Figure 7 .

 7 Figure7. The mean monthly evolution of gradients and regional temperatures in the northern Alps: Tn is on the left, Tx on the right.

Figure 8 .

 8 Figure 8. Mean monthly evolution of the snow cover computed over the period 1961-2010 from the daily data at the Col de Porte (1325 m: data Météo-France, CNRS, CNRM-GAME, CEN) on the left, and Saint Christophe (1570 m: data Météo-France) stations.

Figure 9 .Figure 10 .Figure 11 .

 91011 Figure 9. The evolution of annual mimimum and maximum temperature gradients in the northern Alps between 1960 and 2007

Figure 12 .

 12 Figure 12. Yearly evolution of the monthly trends of the Tn, Tx, Tg and CRUTEM3-NH temperatures computed for 1960-2007 and 1885-2007 periods.

Table 2 .

 2 Figure 6. Example of the mean minimal July temperatures. Relation between the computed and observed monthly temperatures for the 1960-2007 period. Correlation coefficients (R) and values of the Fisher-Snedecor test (F) for the monthly computed and observed temperatures of the test group over the 1960-2007 period.

		20,0
	Tn group 2 (°C)	14,0 16,0 18,0	9016
		12,0
		12,0	13,0	14,0	15,0	16,0	17,0	18,0	19,0
					Tn es t imat e (°C)
			Tn			Tx		Tg
	Month	R	F	R	F		R	F
	1	0,956	0,611	0,978	0,536		0,979	0,589
	2	0,977	0,553	0,990	0,423		0,989	0,609
	3	0,962	0,869	0,993	0,258		0,993	0,990
	4	0,915	0,626	0,991	0,245		0,983	0,952
	5	0,935	0,555	0,988	0,648		0,985	0,906
	6	0,955	0,912	0,987	0,548		0,989	0,876
	7	0,940	0,819	0,980	0,538		0,980	0,819
	8	0,931	0,982	0,978	0,966		0,983	0,838
	9	0,949	0,815	0,986	0,289		0,984	0,885
	10	0,964	0,913	0,979	0,701		0,985	0,868
	11	0,950	0,661	0,970	0,287		0,976	0,753
	12	0,967	0,526	0,975	0,899		0,982	0,597

Table 3 .

 3 Monthly gradients (in °C/100m) of minimum (Tn) and maximum (Tx) temperatures. Extreme values, standard deviations and trends for the period from 1960 to 2007

		Mean 1960-2007			Tn 1960-2007					Tx 1960-2007
		Tg	Tn	Tx	min	max	σ Trend °C/100m per 10 years	u(t)	MK	min	max	σ Trend °C/100m per 10 years	u(t)	MK
	January February March April May June July August September October November December	0,35 0,46 0,53 0,56 0,54 0,54 0,52 0,51 0,48 0,42 0,39 0,35	0,37 0,45 0,46 0,47 0,45 0,46 0,44 0,42 0,41 0,41 0,40 0,38	0,34 0,46 0,59 0,66 0,64 0,62 0,61 0,59 0,54 0,44 0,39 0,32	0,18 0,19 0,35 0,35 0,39 0,38 0,37 0,31 0,32 0,30 0,27 0,22	0,55 0,66 0,58 0,54 0,50 0,54 0,53 0,50 0,51 0,51 0,50 0,55	0,08 0,08 0,05 0,04 0,02 0,03 0,03 0,04 0,04 0,05 0,06 0,07	0,012 0,005 0,000 -0,003 0,000 -0,002 0,000 -0,001 0,000 -0,002 -0,013 0,004	1,53 1,01 0,30 -0,12 φ φ φ φ -0,34 φ -0,39 φ 0,94 φ -0,07 φ 0,07 φ -0,43 φ -2,06 * 0,07 φ	-0,06 0,27 0,46 0,55 0,57 0,46 0,47 0,43 0,45 0,33 0,20 0,14	0,50 0,62 0,68 0,73 0,72 0,77 0,73 0,73 0,62 0,59 0,55 0,53	0,11 0,09 0,05 0,04 0,04 0,04 0,05 0,05 0,04 0,06 0,09 0,09	0,009 0,005 0,012 0,011 0,002 -0,006 -0,011 -0,005 0,002 0,002 -0,007 0,005	0,62 φ 0,57 φ 2,03 * 2,20 * 0,60 φ -1,33 φ -1,69 φ -1,23 φ 0,57 φ 0,68 φ -0,82 φ 0,52 φ
	Min	0,35	0,37	0,32	0,18	0,50	0,02	-0,013			-0,06	0,50	0,04	-0,011
	Max Annual	0,56 0,47	0,47 0,43	0,66 0,52	0,39 0,36	0,66 0,47	0,08 0,02	0,012 0,000	-0,05 φ	0,57 0,44	0,77 0,58	0,11 0,03	0,012 0,015	0,78 φ

Significance levels of the Mann-Kendall test (MK): ** is 0.01; * is 0.05; φ >0.05

Table 4 .

 4 Linear trend (°C per 100 years), 1885-2007 estimates, based on the data of the monthly models for the 1885-1960 period and on the sea level temperature from the 92 stations for the 1960-2007 period.

		CRUTEM3 NH		Tg			Tn			Tx
		trend			trend			trend			trend
		°C per 100 years	u(t)	MK	°C per 100 years	u(t)	MK	°C per 100 years	u(t)		°C per 100 years	u(t)	MK
	January	1,12	6,53 **	1,52	2,76	**	1,96	3,39	**	0,90	1,69
	February	1,32	7,25 **	0,55	1,03		0,53	0,71		0,53	0,92
	March	1,27	8,62 **	0,98	2,45	*	0,57	1,57		1,45	2,50	*
	April	0,93	8,17 **	0,32	0,84		0,22	0,68		0,78	1,68
	May	0,76	8,46 **	0,63	1,58		0,53	1,35		0,82	1,72
	June	0,66	7,76 **	0,52	0,79		0,37	1,19		0,54	0,39
	July	0,53	6,40 **	1,29	3,15	**	1,41	4,88	**	1,11	1,99	*
	August	0,61	7,38 **	1,26	3,36	**	1,44	5,32	**	1,08	2,03	*
	September	0,52	6,11 **	0,53	1,45		0,54	1,61		0,54	1,33
	October	0,68	6,74 **	1,35	3,02	**	1,05	2,21	*	1,75	3,42 **
	November	0,87	6,48 **	0,24	0,71		0,18	0,43		0,34	0,89
	December	0,88	6,08 **	1,21	2,94	**	1,31	2,89	**	1,26	2,71 **
	Annual	0,85	9,84 **	0,87	4,92	**	0,84	5,50	**	0,93	4,25 **

Table 5 .

 5 Linear trend (°C per 100 years), 1960-2007 calculated based on the sea level temperatures obtained from the 92 stations.

		CRUTEM3 NH	Tg		Tn		Tx
		trend		trend		trend		trend
		°C per 100 years	u(t) MK	°C per 100 years	u(t) MK	°C per 100 years	u(t)	°C per 100 years	u(t) MK
	January	3.18	5.14 **	4.64	1.96	4.86	2.28 *	4.41	1.72
	February	3.08	4.21 **	1.80	0.66	0.92	0.02	2.67	0.75
	March	2.98	5.35 **	5.68	2.92 **	3.54	1.81	7.81	3.06 **
	April	2.64	6.13 **	3.09	1.92	2.02	1.69	4.17	1.83
	May	2.14	6.81 **	5.20	3.02 **	5.01	3.59 **	5.39	2.42 *
	June	2.13	6.22 **	4.77	2.99 **	3.73	3.20 **	5.82	2.52 *
	July	2.05	5.94 **	4.22	2.56 *	4.40	3.34 **	4.04	1.78
	August	2.17	5.94 **	4.59	2.97 **	4.52	4.05 **	4.66	2.15 *
	September	1.94	5.48 **	1.65	1.35	2.06	1.71	1.23	0.84
	October	2.05	5.12 **	3.30	1.94	3.93	2.47 *	2.66	1.37
	November	2.05	4.09 **	0.15	0.12	0.36	-0.05	-0.05	0.07
	December	2.58	5.23 **	4.58	2.31 *	5.09	2.51 *	4.07	1.78
	Annual	2.42	6.54 **	3.64	5.05 **	3.37	5.33 **	3.91	4.21 **
				Mann

-

  Kendall test (MK) significance levels: ** is 0.01; * is 0.05; blank is >0.05


Carolina O Dufour
email: carolina.dufour@hmg.inpg.fr

Julien Le Sommer

Jan D Zika

Marion Gehlen

James C Orr

Pierre Mathiot

Bernard Barnier

J Le Sommer

Standing and Transient Eddies in the response of the Southern Ocean Meridional Overturning to the Southern Annular Mode

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

Introduction

The Southern Annular Mode (SAM) is the dominant mode of atmospheric variability in the Southern Hemisphere. It is characterized by an annular structure centered over Antarctica and is evident in many atmospheric fields. In positive phases of the SAM, the polar vortex contracts, resulting in poleward-intensified zonal winds over the circumpolar ocean [START_REF] Hall | Synchronous Variability in the Southern Hemisphere Atmosphere, Sea Ice, and Ocean Resulting from the Annular Mode[END_REF][START_REF] Sen Gupta | Coupled Ocean-Atmosphere-Ice Response to Variations in the Southern Annular Mode[END_REF]. Observations reveal a trend in the SAM towards greater dominance of positive phases over the past decades [START_REF] Thompson | Annular Modes in the Extratropical Circulation[END_REF][START_REF] Cai | The Response of the Antarctic Oscillation to Increasing and Stabilized Atmospheric CO 2[END_REF][START_REF] Marshall | Trends in the Southern Annular Mode from Observations and Reanalyses[END_REF], a tendency resulting from a non-linear combination of natural and anthropogenic forcing [START_REF] Thompson | Interpretation of recent Southern Hemisphere climate change[END_REF][START_REF] Marshall | Causes of exceptional atmospheric circulation changes in the Southern Hemisphere[END_REF]).

Changes in its dynamics are likely to affect the Southern Ocean's capacity as a sink of CO 2 [START_REF] Metzl | The annual f CO 2 cycle and the air-sea CO 2 flux in the sub-Antarctic Ocean[END_REF][START_REF] Quéré | Saturation of the southern ocean CO 2 sink due to recent climate change[END_REF]). The response of the Southern Ocean carbon cycle to the upward trend in the SAM is thought to be driven largely by changes in upwelling, which brings deep water that is rich in dissolved inorganic carbon (DIC) to the surface [START_REF] Lenton | Role of the Southern Annular Mode (SAM) in Southern Ocean CO 2 uptake[END_REF][START_REF] Lovenduski | Enhanced CO2 outgassing in the Southern Ocean from a positive phase of the Southern Annular Mode[END_REF][START_REF] Lovenduski | Toward a mechanistic understanding of the decadal trends in the Southern Ocean carbon sink[END_REF]. Thus the response is driven by changes in the meridional overturning circulation (MOC).

The MOC cannot be directly deduced from observations though; it must be investigated indirectly, in particular through modeling studies, which can assess the response of the MOC to changes in winds. Along with a poleward intensification of zonal winds, Ekman transport and Ekman pumping are expected to be enhanced and shifted poleward. This would be followed by tightening of isopycnal slopes and poleward shift of isopycnals, resulting in enhanced and poleward shifted Antarctic Circumpolar Current (ACC) transport [START_REF] Hall | Synchronous Variability in the Southern Hemisphere Atmosphere, Sea Ice, and Ocean Resulting from the Annular Mode[END_REF][START_REF] Spence | Southern Ocean response to strenghening winds in an eddy-permitting global climate model[END_REF]. Thus the MOC is likely to be enhanced and shifted as well [START_REF] Sen Gupta | Coupled Ocean-Atmosphere-Ice Response to Variations in the Southern Annular Mode[END_REF][START_REF] Hallberg | The Role of Eddies in Determining the Structure and Response of the Wind-Driven Southern Hemisphere Overturning: Results from the Modeling Eddies in the Southern Ocean (MESO) Project[END_REF]. Modeling studies indicate that during a positive phase of the SAM, there is an increase in vertical tilt of isopycnals, which intensifies the ACC [START_REF] Hall | Synchronous Variability in the Southern Hemisphere Atmosphere, Sea Ice, and Ocean Resulting from the Annular Mode[END_REF][START_REF] Meredith | Circumpolar response of Southern Ocean eddy actvity to a change in the Southern Annular Mode[END_REF][START_REF] Yang | Delayed baroclinic response of the Antarctic circumpolar current to surface wind stress[END_REF][START_REF] Spence | Southern Ocean response to strenghening winds in an eddy-permitting global climate model[END_REF][START_REF] Farneti | The role of mesoscale eddies in the rectification of the southern ocean response to climate change[END_REF], and a shift of the ACC poleward by a few degrees [START_REF] Spence | Southern Ocean response to strenghening winds in an eddy-permitting global climate model[END_REF]). An acceleration and shift in the position of the ACC coincident with positive phases of the SAM have indeed been reported (e.g. [START_REF] Meredith | Changes in the ocean transport through drake passage during the 1980s and 1990s, forced by changes in the Southern Annular Mode[END_REF][START_REF] Sallée | Response of the Antarctic Circumpolar Current to Atmospheric Variability[END_REF]. Likewise, the MOC is shown to intensify and change its structure due to poleward intensification of winds, thus implying substantial changes in properties and rates of formation of water masses [START_REF] Oke | Oceanic Response to Changes in the Latitude of the Southern Hemisphere Subpolar Westerly Winds[END_REF][START_REF] Sen Gupta | Coupled Ocean-Atmosphere-Ice Response to Variations in the Southern Annular Mode[END_REF][START_REF] Hallberg | The Role of Eddies in Determining the Structure and Response of the Wind-Driven Southern Hemisphere Overturning: Results from the Modeling Eddies in the Southern Ocean (MESO) Project[END_REF][START_REF] Garabato | Variability of Subantarctic Mode Water and Antarctic Intermediate Water in the Drake Passage during the Late-Twentieth and Early-Twenty-First Centuries[END_REF]).

In contrast, recent observed trends in isopycnal slopes suggest that ACC transport and the Southern Ocean's MOC may be insensitive to decadal changes in wind stress [START_REF] Böning | The response of the Antarctic Circumpolar Current to recent climate change[END_REF]. A possible explanation may be the role played by eddies, which have not been accounted for explicitly in studies with coarse-resolution models. Recent studies using high resolution models have shown that eddies may play a major role in the dynamical response of the Southern Ocean to the SAM [START_REF] Hallberg | The Role of Eddies in Determining the Structure and Response of the Wind-Driven Southern Hemisphere Overturning: Results from the Modeling Eddies in the Southern Ocean (MESO) Project[END_REF][START_REF] Farneti | The role of mesoscale eddies in the rectification of the southern ocean response to climate change[END_REF].

Indeed, eddies are thought to counteract the steepening of isopycnal slopes induced by the increase in Ekman transport. The MOC's response to changes in winds is also thought to be largely moderated by eddies [START_REF] Hallberg | The Role of Eddies in Determining the Structure and Response of the Wind-Driven Southern Hemisphere Overturning: Results from the Modeling Eddies in the Southern Ocean (MESO) Project[END_REF][START_REF] Farneti | The role of mesoscale eddies in the rectification of the southern ocean response to climate change[END_REF].

It has been proposed that the apparent discrepancy between the results of coarse-resolution models and the limited observations, is due to the inability of such models to resolve eddies. Resolving eddies, not simply parameterizing their effect, may be crucial when studying the response of the Southern Ocean to changes in the SAM. However, if this latter point has clearly emerged from the recent literature, little is known as for the respective roles of standing and transient eddies in the response of the Southern Ocean dynamics to changes in winds.

Moreover, when investigating the response of the Southern Ocean to the SAM, part of the discrepancy between model studies may be due to their different approaches of representing the SAM forcing itself. In model studies, these forcings generally fall into three categories: highly idealized perturbations [START_REF] Oke | Oceanic Response to Changes in the Latitude of the Southern Hemisphere Subpolar Westerly Winds[END_REF][START_REF] Hallberg | The Role of Eddies in Determining the Structure and Response of the Wind-Driven Southern Hemisphere Overturning: Results from the Modeling Eddies in the Southern Ocean (MESO) Project[END_REF][START_REF] Klinger | Decadal response of global circulation to southern ocean zonal wind stress perturbation[END_REF], either a homogeneous increase or a poleward shift in winds), realistic perturbations [START_REF] Lefebvre | Influence of the Southern Annular Mode on the sea ice-ocean system: the role of the thermal and mechanical forcing[END_REF][START_REF] Farneti | The role of mesoscale eddies in the rectification of the southern ocean response to climate change[END_REF], both more intense and poleward shifted winds), and simulated wind anomalies within climate simulations [START_REF] Farneti | The role of mesoscale eddies in the rectification of the southern ocean response to climate change[END_REF][START_REF] Spence | Southern Ocean response to strenghening winds in an eddy-permitting global climate model[END_REF]. Hence, there remains a need to document the effect of such differences in wind forcings onto Southern Ocean dynamics, and in particular the effect of the spatial distribution of wind anomalies.

Here we assess the dynamical response of the Southern Ocean to the SAM focusing on (i) how eddies affect this response and (ii) the extent to which changes in the spatial pattern of the SAM matters. To do so we run simulations with a regional model of the Southern Ocean at two eddy-permitting resolutions, independently varying Southern Hemisphere wind intensity and patterns, consistent with a positive phase of the SAM. Analysis focuses on the corresponding changes in ACC transport and Southern Ocean MOC.

Methods

a. Ocean model

Our study uses the NEMO system (Nucleus for European Modeling of the Ocean, Madec ( 2008)) which couples the hydrostatic, primitive equation ocean model OPA [START_REF] Madec | OPA8.1 Ocean General Circulation Model reference manual[END_REF] with the Louvain-la-Neuve sea-ice model LIM [START_REF] Fichefet | Sensitivity of a global sea ice model to the treatment of ice thermodynamics and dynamics[END_REF]. For atmospheric forcing, we use the DRAKKAR Forcing Set 3 (DFS3), a combined product using air temperature, humidity, and 10-m winds from ERA-40 (until year 2001) then ECMWF (European Center for Medium-Range Weather Forecasts, from 2002 to 2004) along with precipitation and radiation variables from satellite observations [START_REF] Brodeau | An ERA-40 based atmospheric forcing for global ocean circulation models[END_REF].

Simulations performed in this study are consistent with the hierarchy of models developed during the DRAKKAR project (Drakkar [START_REF] Group | Eddy-permitting Ocean Circulation Hindcasts of past decades[END_REF]. That is, we use a regional extraction from the global grid, designated ORCA, which includes all ocean south of 30 • S and is designated PERIANT. This regional model is used here at two different nominal horizontal resolutions of 0.5 • (PERIANT05) and 0.25 • (PERIANT025). Both have 46 vertical levels with refined spacing at the surface (6 m) and increasing with depth to 250 m in the deepest level. Table 1 shows that our model resolutions are comparable to the resolutions used by the other high-resolution model configurations that have been used elsewhere for similar studies on the Southern Ocean sensitivity to winds. To facilitate comparison between simulations at 0.5 • and 0.25 • horizontal resolutions, we avoided using any eddy induced advection parameterization, such as that of [START_REF] Gent | Isopycnal Mixing in Ocean Circulation Models[END_REF]. Vertical mixing is represented using a TKE (Turbulent Kinetic Energy) scheme and a background vertical mixing coefficient of 10 -5 m 2 s -1 [START_REF] Madec | Nemo ocean engine. Note du Pôle de modelisation[END_REF] with enhanced mixing in case of static in-stability.

In both 0.5 • and 0.25 • regional simulations, the northern boundary at 30 • S is open and forced under radiation conditions [START_REF] Treguier | An eddy-permitting model of the Atlantic circulation: Evaluating open boundary conditions[END_REF] with the output of the corresponding DRAKKAR global simulations ORCA05-G70 and ORCA025-G70, respectively [START_REF] Barnier | Impact of partial steps and momentum advection schemes in a global ocean circulation model at eddy permitting resolution[END_REF][START_REF] Biastoch | Causes of Interannual-Decadal Variability in the Meridional Overturning Circulation of the Midlatitude North Atlantic Ocean[END_REF]. Those global simulations have been shown to be in reasonable agreement with the observations (e.g. [START_REF] Lombard | Regional patterns of observed sea level change: Insights from a 1/4 • global ocean/sea-ice hindcast[END_REF] in particular in the Southern Ocean (e.g. [START_REF] Treguier | Southern Ocean overturning across streamlines in an eddying simulation of the Antarctic Circumpolar Current[END_REF]).

For all simulations, the ocean starts at rest and temperature and salinity are initialised with the National Oceanographic Data Center World Ocean Atlas 1998 [START_REF] Levitus | World ocean database[END_REF].

Simulations were limited to the period 1980-2004 to avoid potentially erroneous interseasonal variability of Southern Ocean winds in the pre-satellite era [START_REF] Bromwich | Strong trends in the Skill of the ERA-40 and NCEP-NCAR Reanalyses in the High and Midlatitudes of the Southern Hemisphere[END_REF].

For analysis, we focus on the 1995-2004 period, after the circulation has stabilized following a 15-year spin-up (Fig. 3).

A common issue in z-coordinate ocean models is their inability to capture the descent of dense waters that become Antarctic Bottom Water (AABW, [START_REF] Winton | Simulation of Density-Driven Frictional Downslope Flow in Z-Coordinate Ocean Models[END_REF][START_REF] Lee | Spurious diapycnal mixing of the deep waters in an eddy-permitting global ocean model[END_REF]. This artefact results in a slow downward trend in ACC transport in the global configurations ORCA05 and ORCA025 [START_REF] Treguier | Response of the Southern Ocean to the Southern Annular Mode: interannual variability and multidecadal trend[END_REF], of which PERIANT05 and PERIANT025 are regional extractions. To overcome this common difficulty, some models relax deep-water temperature and salinity towards observations to maintain realistic values near the ocean bottom (e.g. [START_REF] Hallberg | The Role of Eddies in Determining the Structure and Response of the Wind-Driven Southern Hemisphere Overturning: Results from the Modeling Eddies in the Southern Ocean (MESO) Project[END_REF]. In all our simulations, we apply a 3-D relaxation of AABW temperature and salinity towards climatological annual-mean values from [START_REF] Gouretski | Woce global hydrographic climatology, a technical report[END_REF] (see Appendix).

b. Synthetic forcing

For both horizontal resolutions, we carried out three sets of simulations: REFxxx refers to the set of control simulations where the reanalyzed atmospheric winds are unperturbed; WINDxxx refers to the set of simulations where the winds are increased homogeneously everywhere; and SAMxxx refers to simulations where winds are perturbed following the observed pattern of the SAM (poleward intensification). The suffix xxx designates other characteristics related to resolution and forcing intensity of individual simulations that will be detailed later. But first let us focus on how the synthetic forcing for the WINDxxx and SAMxxx simulations was constructed.

The ocean model forcing depends largely on the wind velocity at 10 m in the atmosphere, U(x, y, t). In the REFxxx simulations, the 10-m wind velocity is simply that from the atmospheric reanalysis (U ar (x, y, t)). In the WINDxxx simulations, the magnitude of U is permanently and homogeneously increased over the domain. In the SAMxxx simulations, a pattern P Uar which describes the spatial wind anomaly pattern of the SAM is added permanently. Hence the general formula for U may be written:

U(x, y, t) = αU ar (x, y, t) + βP Uar (x, y), (1)
In equation ( 1), α and β are constants. Following [START_REF] Lefebvre | Influence of the Southern Annular Mode on the sea ice-ocean system: the role of the thermal and mechanical forcing[END_REF] Figure 1.a shows the zonal component of the regression pattern, P Uar , onto the SAM index. The pattern is extremely zonal with positive coefficients in southern regions near the ACC (around 60 • S) and negative coefficients on the north side of the Southern Ocean. This overall structure corresponding to a positive phase of the SAM, is poleward intensification of zonal winds over the circumpolar ocean and weakening of zonal winds farther north, as described by many authors (e.g. [START_REF] Thompson | Annular Modes in the Extratropical Circulation[END_REF][START_REF] Hall | Synchronous Variability in the Southern Hemisphere Atmosphere, Sea Ice, and Ocean Resulting from the Annular Mode[END_REF][START_REF] Sen Gupta | Coupled Ocean-Atmosphere-Ice Response to Variations in the Southern Annular Mode[END_REF].

The WINDxxx simulations use the forcing strategy of [START_REF] Hallberg | The Role of Eddies in Determining the Structure and Response of the Wind-Driven Southern Hemisphere Overturning: Results from the Modeling Eddies in the Southern Ocean (MESO) Project[END_REF], except that we modify wind velocity instead of the wind stress. Our model uses bulk formulae for ocean surface boundary conditions, implying that the wind velocity enters into calculations of wind stress, the momentum transfer coefficient, evaporation, sensible heat, and latent heat. Hence our modification of mechanical forcing (wind velocities) affects the thermodynamic forcing via surface fluxes. Additionally, bulk formulae provide a feedback from the ocean to the atmosphere via surface fluxes, even though we do not use a coupled ocean-atmosphere model. The SAMxxx simulations adopt the forcing strategy of [START_REF] Lefebvre | Influence of the Southern Annular Mode on the sea ice-ocean system: the role of the thermal and mechanical forcing[END_REF], except that we only built of series of synthetic forcings for wind whereas they also did the same for air temperature. We did not adjust air temperature. In conclusion, the two forcing strategies applied to a consistent set of simulations (WINDxxx and SAMxxx) represent a novel forcing approach that allows us to investigate the response of Southern Ocean dynamics to changes in the strength and pattern of the winds.

Details of the characteristics of these series of simulations are summarized in Table 2. At the 0.5 • resolution, three simulations were made applying increasing wind intensity for each of the WINDxxx and SAMxxx series. At the 0.25 • resolution, we made only two simulations to conserve computional resources. Thus we applied only the strongest wind anomaly to each type of perturbation. Thus the linearity of the response of the Southern Ocean response to changes in wind is only studied at the 0.5 • resolution. [START_REF] Ducet | Global high resolution mapping of ocean circulation from Topex/Poseidon and ERS-1 and -2[END_REF]. In both models and observations, the most energetic regions are the latitudes of the ACC, the Agulhas Retroflection, the Brazil-Malvinas confluence, and the East Australian Current. At both resolutions, simulated regional patterns generally agree well with those observed. Zonally integrated near-surface EKE averaged between 35 • S and 65 • S is 27 cm 2 s -2 for REF05

and 69 cm 2 s -2 for REF025, i.e. ∼1/4 and ∼2/3 of observed values, respectively. The reasonable match of REF025 with the observed pattern and magnitude of EKE helps confirm its adequacy for this study. Similar patterns, despite the expected lower magnitude of EKE in REF05, suggests that this intermediate-resolution version of the same model serves as an appropriate first step into the eddying regime. Using both models allows us to say more about the resolution that is needed to address how the SAM affects ocean dynamics.

Figure 3 shows the time series of the annual mass transport at Drake Passage in REF05 and REF025 regional simulations (PERIANT05 and PERIANT025), and in the corresponding ORCA05-G70 and ORCA025-G70 global simulations (ORCA05 and ORCA025). Global simulations were run for a longer period ) than the regional simulations , but that cannot explain differences in the trends. After 8 years of spin-up, the trend flattens out in the regional simulations, whereas in the global simulations there is a downward trend even after 15 years of spin-up. The difference is due to the 3-D relaxation of AABW towards observed temperature and salinity (see Appendix) which was applied to regional simulations only. This relaxation maintains the deep meridional gradient of density in the Southern Ocean, stabilising ACC transport. Consequently, Drake Passage transport is more intense in the regional simulations than in the global simulations, by at least 10 Sv

(1 Sv = 10 6 m 3 s -1 ). In REF05 and REF025, the transport at Drake Passage averaged over 1995-2004 is 142.6 Sv and 143.2 Sv, respectively. Observational estimates range from 110 Sv to 150 Sv [START_REF] Whitworth | Monitoring the Transport of the Antarctic Circumpolar Current at Drake Passage[END_REF][START_REF] Whitworth | Volume transport of the Antarctic Circumpolar Current from bottom pressure measurements[END_REF][START_REF] Cunningham | Transport and variability of the Antarctic Circumpolar Current in Drake Passage[END_REF]). Moreover, Drake Passage transport differs by only less than 1 Sv between REF05 and REF025.

In previous model studies, this ACC transport differs by tens of Sv depending in part on model resolution (e.g. [START_REF] Hallberg | The Role of Eddies in Determining the Structure and Response of the Wind-Driven Southern Hemisphere Overturning: Results from the Modeling Eddies in the Southern Ocean (MESO) Project[END_REF][START_REF] Farneti | The role of mesoscale eddies in the rectification of the southern ocean response to climate change[END_REF].

Figure 4 shows the mean 1995-2004 salinity along the 115 For detailed validation of the global simulations ORCA05-G70 and ORCA025-G70, see [START_REF] Barnier | Impact of partial steps and momentum advection schemes in a global ocean circulation model at eddy permitting resolution[END_REF] and [START_REF] Biastoch | Causes of Interannual-Decadal Variability in the Meridional Overturning Circulation of the Midlatitude North Atlantic Ocean[END_REF].

3. Southern Ocean dynamical response to changes in wind pattern and intensity a. Response of the ACC transport 1) Shift of the ACC mean position

The Polar Front (PF) and Subantarctic Front (SAF) are the two main fronts that compose the ACC, carrying between them up to 75% of the total ACC transport south of Australia [START_REF] Rintoul | Baroclinic transport variability of the Antarctic Circumpolar Current south of Australia (WOCE repeat section SR3)[END_REF]. Thus the position of those fronts provides a good indicator of ACC position. Here, we calculate the PF and SAF positions using a comparable method to that of [START_REF] Sallée | Response of the Antarctic Circumpolar Current to Atmospheric Variability[END_REF], computing the surface dynamic height anomaly referenced to 1500 m. Following [START_REF] Sallée | Response of the Antarctic Circumpolar Current to Atmospheric Variability[END_REF], the PF is defined by the 0.95-m contour and the SAF by the 1.20-m contour. Figure 2 shows the mean PF and SAF positions for the period 1995-2004 for REF05 and SAM05+++ simulations and their corresponding simulations at 0.25 • . Also shown are the same front positions, but as given by [START_REF] Orsi | On the meridional extent and fronts of the Antarctic Circumpolar Current[END_REF]'s climatology (Fig. 2.e). In REF05 and REF025, the mean positions of the PF and SAF as well as their meanders are generally quite similar to those proposed by [START_REF] Orsi | On the meridional extent and fronts of the Antarctic Circumpolar Current[END_REF], although simulated fronts are shifted northward by 2-3 • (see also Table 2). On average, the simulated ACC is wider than indicated by hydrographic observations from [START_REF] Orsi | On the meridional extent and fronts of the Antarctic Circumpolar Current[END_REF]. The simulated ACC being wider than the observed appears due to the model's ability to resolve frontal meanders whereas those structures in observed climatologies are probably smoothed by averaging. Closer inspection of differences between the two resolutions reveals that the ACC in REF025 is much more spatially variable, with larger meanders and varying more in width along its course, e.g., being narrower than REF05 in some places (at Drake Passage, on the Kerguelen Plateau) and wider in others (southeast Indian Ocean, south Australia and southeast Pacific). REF025 is able to better represent this variability because of its more energetic mesoscale activity and its better resolution of the bathymetry.

During positive phases of the SAM, fronts tend to shift poleward in the Atlantic and Indian basins but equatorward in the Pacific basin (Fig. 2). The response of the ACC mean position to positive SAM events is thus not zonally coherent. This may be due to the strong variations in the latitude of ACC fronts around the circumpolar belt, thus being exposed to different SAM-induced wind anomalies along their pathways, as [START_REF] Sallée | Response of the Antarctic Circumpolar Current to Atmospheric Variability[END_REF] suggested.

In addition, frontal variability driven by SAM positive events is stronger in SAM025+++ than in SAM05+++ simulation. Yet in many regions where major topographic highs extend well above the abyssal plain, such as over the South Atlantic mid-ocean ridge or Kerguelen Plateau, ACC fronts show no spatial variability. Besides, the WIND05+++ and WIND025+++ simulations both show similar large equatorward meanders downstream of the major topographic features as well as stronger responses than in the SAMxxx simulations (not shown).

Table 2 gives Our results thus show that bathymetry may mainly determine the ACC pathway. However, downstream of major topographic highs, fronts become more sensitive to atmospheric forcing and thus more spatially variable [START_REF] Sallée | Response of the Antarctic Circumpolar Current to Atmospheric Variability[END_REF]. In those regions, it is mainly the SAM-driven wind anomaly patterns that induce meridional shifts of fronts.

2) Acceleration of the ACC shows a large range of responses of the ACC transport to winds. However, the strongest response is a 12% increase in the ACC transport (from REF05 to SAM05+++).

To learn about the relative importance of the mechanisms controlling the SAMs influence on the ACC, we can compare the two sets of simulations, SAM05 and WIND05. In both sets, there is a strong linear relationship between the mean zonal wind stress and the mean ACC transport (the coefficient of determination R 2 = 0.98 for WIND05, and R 2 = 0.93 for SAM05). But it is already known that larger zonal wind stress yields greater ACC transport (e.g. [START_REF] Marshall | Residual-mean solutions for the antarctic circumpolar current and its associated overturning circulation[END_REF]. What is more telling is that the slope of the SAM05 series of simulations is far steeper than that for the WIND05 series. Thus SAM-driven anomalies in wind patterns are more efficient in accelerating the ACC than are homogeneous wind anomalies. This demonstrates that the spatial pattern of wind, and not only its magnitude, can play a major role in changing the ACC transport.

Likewise, comparing SAM05+++ to SAM025+++ demonstrates that by moving to the enhanced resolution, reduces the response of ACC transport to poleward intensified winds by nearly half, from +12% to +5%. Other modeling studies also indicate that the response of ACC transport to increased wind forcing is reduced as model resolution increases [START_REF] Hallberg | The Role of Eddies in Determining the Structure and Response of the Wind-Driven Southern Hemisphere Overturning: Results from the Modeling Eddies in the Southern Ocean (MESO) Project[END_REF][START_REF] Spence | Southern Ocean response to strenghening winds in an eddy-permitting global climate model[END_REF][START_REF] Farneti | The role of mesoscale eddies in the rectification of the southern ocean response to climate change[END_REF]). Yet the resolution increase between our WIND05+++ and WIND025+++ simulations produces the opposite tendency: increased resolution increases ACC transport response, i.e. for the case where there is a spatially homogenous increase in winds. Although the difference in ACC transport between WIND05+++ and WIND025+++ is small and further experiments are required to confirm the systematic nature of this result, this finding does provide a counter-example to the necessary reduction in a model's ACC transport sensitivity with increasing resolution.

b. Response of the meridional overturning circulation

It is appropriate to consider the MOC in potential density, rather than in depth space, to obtain a relevant representation of the transport of water masses (e.g. [START_REF] Treguier | Southern Ocean overturning across streamlines in an eddying simulation of the Antarctic Circumpolar Current[END_REF]. Following the lead of [START_REF] Döös | The Deacon Cell and the Other Meridional Cells of the Southern Ocean[END_REF], we analyze results in terms of the total meridional overturning streamfunction, Ψ tot , expressed as:

Ψ tot (y, σ 2 ) = z(x,y,σ 2 ) -H v dz dx, (3)
where σ 2 is the 2000-m depth referenced potential density, H is the spatially varying ocean bottom depth, z is the depth of the σ 2 isopycnal, v is the meridional velocity, and the overbar denotes a temporal average at constant depth. Thus for some variable ξ over a period τ , ξ = τ 0 ξdt/τ . Then, the transient eddy-induced circulation Ψ * , i.e. that due to deviations from the temporal mean, is defined following [START_REF] Treguier | Southern Ocean overturning across streamlines in an eddying simulation of the Antarctic Circumpolar Current[END_REF] as:

       Ψ(y, σ 2 ) = z(x,y,σ 2 ) -H
vdz dx,

Ψ * = Ψ tot -Ψ (4)
where Ψ is the mean Eulerian circulation computed from the time-mean velocity and the time-mean potential density field. ing denser, as they move southward while flowing along the ACC. This upper cell is not apparent when the MOC is computed along streamlines rather than latitude lines [START_REF] Treguier | Southern Ocean overturning across streamlines in an eddying simulation of the Antarctic Circumpolar Current[END_REF]). However, it does constitute a genuine overturning in density space. The mean Eulerian overturning (Fig. 6, middle column) shows the same general structure as the total overturning, but it is partly compensated by the transient-eddy overturning (Fig. 6, right column) as detailed in equation ( 4). (2006) found in their study. Thus the overlying subptropical cell transforms denser waters in the SAMxxx simulations (∼35.8 kg m -3 ) than in the REFxxx and WINDxxx simulations (∼35.5 kg m -3 and ∼35.2 kg m -3 , respectively). This water mass densification consequent to a poleward shift of zonal winds may be related to a cooling of SAMW as suggested by [START_REF] Oke | Oceanic Response to Changes in the Latitude of the Southern Hemisphere Subpolar Westerly Winds[END_REF].

The density of newly formed water masses is affected by any anomalous intensification of zonal winds over the ACC latitudes, which leads to a change in wind curl and thus a change in the location of the Ekman divergence. Hence deep waters coming from the north upwell at higher latitudes where they interact with a colder atmosphere, release more heat, and are transformed into denser water masses. However, when the wind is increased everywhere homogeneously over the Southern Ocean (our WINDxxx simulations), the ACC's fronts shift equatorward, forcing downwelling of intermediate waters to occur at more northern latitudes and along lighter isopycnal surfaces.

Therefore, SAM positive events can indeed imply significant changes in water mass properties and in turn, affect their rate of formation, as shown by previous studies [START_REF] Oke | Oceanic Response to Changes in the Latitude of the Southern Hemisphere Subpolar Westerly Winds[END_REF][START_REF] Sen Gupta | Coupled Ocean-Atmosphere-Ice Response to Variations in the Southern Annular Mode[END_REF][START_REF] Hallberg | The Role of Eddies in Determining the Structure and Response of the Wind-Driven Southern Hemisphere Overturning: Results from the Modeling Eddies in the Southern Ocean (MESO) Project[END_REF]. However, our results also point out that the MOC density structure responds in a very different way to homogeneous wind anomalies than to SAM-induced wind anomalies.

2) Intensification of the subpolar MOC

The increase in the Southern Ocean carbon sink with rising atmospheric CO 2 may be diminished by the SAM-induced intensification of the MOC, which enhances upwelling of DIC-rich deep waters to the surface [START_REF] Lenton | Role of the Southern Annular Mode (SAM) in Southern Ocean CO 2 uptake[END_REF][START_REF] Lovenduski | Enhanced CO2 outgassing in the Southern Ocean from a positive phase of the Southern Annular Mode[END_REF][START_REF] Lovenduski | Toward a mechanistic understanding of the decadal trends in the Southern Ocean carbon sink[END_REF].

As upwelling of deep water is effected by the subpolar cell; we focus on its response to wind anomalies.

To estimate subpolar MOC intensity, we computed the maximum of Ψ tot in the subpolar cell between 40 • S and 55 • S. Results are shown in Figure 5.b for each simulation as a function of the mean zonal wind stress over the model domain. Simulated responses of the subpolar MOC to winds vary greatly, with the strongest response being a 153% increase in the subpolar MOC in the WIND05+++ simulation. Our results exhibit much greater sensitivity of the subpolar MOC to changes in winds relative to previous modeling studies [START_REF] Hallberg | The Role of Eddies in Determining the Structure and Response of the Wind-Driven Southern Hemisphere Overturning: Results from the Modeling Eddies in the Southern Ocean (MESO) Project[END_REF][START_REF] Farneti | The role of mesoscale eddies in the rectification of the southern ocean response to climate change[END_REF].

Comparing results in simulations at the 0.5 • resolution indicates nearly linear relationships within each set, but different sensitivities between them. Linear fits between mean subpolar MOC intensity versus mean zonal wind stress result in a coefficient of determination R 2 > 0.99 for the WIND05 simulations and R 2 = 0.91 for the SAM05 runs. Thus, wind largely determines MOC intensity, as discussed previously [START_REF] Gnanadesikan | On the Relationship of the Circumpolar Current to Southern Hemisphere Winds in Coarse-Resolution Ocean Models[END_REF][START_REF] Karsten | The Role of Eddy Transfer in Setting the Stratification and Transport of a Circumpolar Current[END_REF]). Yet the slope for the WIND05 series is steeper than that for the SAM05 series, implying that a spatially homogeneous increase in wind anomalies intensifies the subpolar MOC more than does the SAM-driven change in their spatial pattern. Indeed, in the WIND05 series of simulations, wind stress is increased over a greater range of lati-tudes. Thus, intensification in Ekman pumping both north and south of the ACC is spread out over a wider range of latitudes in the WIND05 simulations, perhaps leading to a stronger change in the subpolar MOC.

In contrast, the effect of resolution on the subpolar MOC is mixed between the two types of wind perturbations (Fig. 5b). Better resolving eddies attenuates the response of the subpolar MOC to a spatially homogeneous increase in winds (WIND025+++ vs. WIND05+++) but enhances its response to a SAM-induced pattern change in winds (SAM025+++ vs. SAM05+++). Enhanced mesoscale activity (better resolution) is expected to attenuate the response of the subpolar MOC to increased wind stress, as reported by [START_REF] Hallberg | The Role of Eddies in Determining the Structure and Response of the Wind-Driven Southern Hemisphere Overturning: Results from the Modeling Eddies in the Southern Ocean (MESO) Project[END_REF]

Discussion

As illustrated in equation ( 4), the total circulation Ψ tot can be decomposed into a mean

Eulerian circulation Ψ and a transient eddy-induced circulation Ψ * . The Ψ term represents the temporal mean flow. Thus it includes both the contribution of the zonally averaged flow (mainly the Ekman transport) and the meridional excursions from the zonal mean (standing eddies, [START_REF] Ivchenko | The Dynamics of the Antarctic Circumpolar Current[END_REF]). In the same way that the MOC can be separated into its temporal mean and transient eddy (perturbation) components, so can the temporal mean be separated into its zonally averaged and standing eddy components (see [START_REF] Lee | Eddy mass transport for the southern ocean in an eddy-permitting global ocean model[END_REF] for a similar decomposition):

       Ψ =< Ψ > +Ψ SE < Ψ > (y, σ 2 ) = <z(y,σ 2 )> -H < v > d < z > dx, (5)
where the angular brackets denote a zonal average at constant depth. A zonal average for a function ξ and latitude circle of length L is thus < ξ >= ξdx/L. The streamfunction < Ψ > is effectively the mean Eulerian MOC zonally averaged at constant depth and projected onto the zonally averaged density. At latitudes of Drake Passage, < Ψ > essentially corresponds to a northward Ekman transport on shallow layers and a southward geostrophic transport on layers whose mean depth is below the Drake Passage. The streamfunction Ψ SE accounts for the standing eddy overturning circulation. Thus, the total circulation can be defined in terms of three main contributions,

Ψ tot =< Ψ > +Ψ SE + Ψ * , (6)
i.e. from the zonal-average mean Eulerian flow, standing eddies, and transient eddies.

Figure 7 shows < Ψ > and Ψ SE for SAM05+++ and SAM025+++, averaged over 1995-2004. In both simulations, < Ψ > is partly compensated by Ψ SE . At the higher resolution, the standing eddy-driven circulation is reinforced. As in Section 2, for an index of the MOC intensity for each < Ψ > and Ψ SE , we compute the maximum value between 40 • S and 55 • S. Enhancing resolution (moving from SAM05+++ to SAM025+++) enhances the compensation of zonal-average mean Eulerian flow (< Ψ >) achieved by standing eddy flow (Ψ SE ) by more than 10% (see also Fig. 7). Therefore, higher resolution allows for meridional transport from standing eddies to better compensate for enhanced Ekman transport from intensified winds. Similar results are found by comparing the WIND05+++ and WIND025+++ simulations (not shown).

Although higher resolution in our models enhances meridional fluxes from standing eddies, it reduces meridional fluxes from transient eddies (Fig. 6). The majority of the overturning circulation is thus a balance of Ekman transport and the time-mean geostrophic flow. Although the dominance of standing eddies in the eddy compensation of the enhanced wind-driven overturning has not been pointed out in the literature (to our knowledge), this result does find some support in previous studies. [START_REF] Farneti | The role of mesoscale eddies in the rectification of the southern ocean response to climate change[END_REF], for instance, subjected two fine-resolution models to present day and SAM-like forcing anomalies (CTL and SHW3X simulations). They found a transient eddy driven overturning response of only 2-3 Sv (their Fig. 10), indicating that the standing eddy driven overturning might balance almost all the wind-driven overturning. Moreover, using an eddy-permitting global ocean model, [START_REF] Lee | Eddy mass transport for the southern ocean in an eddy-permitting global ocean model[END_REF] highlighted the role of standing eddies in cancelling the wind-driven circulation.

Enhanced resolution alters standing eddy meridional fluxes because it allows for (1) enhanced deviations from the zonal mean of velocity (v-< v >) and (2) enhanced deviations in the zonal mean of isopycnal thickness (h-< h >, where h = dz dσ 2 ), i.e., stratification.

Higher resolution implies finer topography, thus better resolving standing eddies (see Fig. 2)

and allowing them to be more vigorous (changing v-< v >). Enhanced resolution may also cause large-scale changes in stratification, perhaps in part from transient eddies.

Figure 8 summarises the results on the intensity in ACC transport and MOC subpolar cell responses to a wind modification at the two eddy-permitting resolutions. The simulated response of Southern Ocean dynamics to a wind anomaly is not straightforward. In particular, this response depends on both numerical resolution and spatial pattern of wind anomalies. This response also dramatically differs when considering either ACC or MOC responses. Hence, our results show that there is no simple link between the two. Moreover, the MOC appears to be more sensitive to wind changes than the ACC. This result is in agreement with the study of [START_REF] Meredith | Sensitivity of the overturning circulation in the southern ocean to decadal changes in wind forcing[END_REF] which used theoretical arguments to suggest that in an eddy-saturated Southern Ocean (that is with the ACC being only weakly sensitive to wind stress change), the response of the Southern Ocean overturning to wind stress changes is likely to be substantial. Indeed, dynamical and thermodynamical processes may not set at the same order the respective balances of the MOC and ACC transports.

Conclusion

A regional ocean-sea ice model of the Southern Ocean configured at two eddy-permitting resolutions (0.5 • and 0.25 • ) was forced by atmospheric reanalysis products during and perturbed to study how SAM pattern and intensity affect the ACC and MOC.

During positive phases of the SAM, ACC transport increases by ∼10% and is weakly shifted poleward by less than 0.5 • as a result of a stronger wind stress and modified wind curl.

The increased wind stress intensifies the subpolar MOC, confirming that the overturning circulation is set at first order by the wind-driven circulation. There are also substantial In a recent study by [START_REF] Böning | The response of the Antarctic Circumpolar Current to recent climate change[END_REF], an apparent small sensitivity of the ACC to changes in the Southern Hemisphere winds is observed. Those authors use this result to argue that the MOC may not be highly sensitive to wind changes either. Our results demonstrate that the ACC and MOC can exhibit very different sensitivities to wind perturbations. Hence, it is not straightforward to infer a change in the MOC from a change in ACC transport.

Finally, modifying the pattern of wind anomalies leads to opposing responses of the ACC and MOC. SAM-driven wind anomalies are found to be far more efficient at accelerating the ACC than homogeneous wind anomalies. Thus simulations that aim to simulate the response of the Southern Ocean to the SAM should account not only for the increase in winds but also the change in the pattern (driven by the contraction of the polar vortex).

Moreover, our result implies that differences between wind pattern tendencies, as resolved by climate models, may induce significant discrepancies in the projected Southern Ocean dynamics.

climatology of [START_REF] Gouretski | Woce global hydrographic climatology, a technical report[END_REF] as follows:

       ∂ t Θ(t, x, y, z) = ... + M (x,y,z) τ (Θ clim (x, y, z) -Θ(t, x, y, z)) ∂ t S(x, y, z) = ... + M (x,y,z) τ (S clim (x, y, z) -S(t, x, y, z)) (A3)
where Θ and S are potential temperature and salinity computed by the model, respectively, Θ clim and S clim are potential temperature and salinity from the annual climatology of Gouretski and Koltermann ( 2004), and τ is the restoring time scale.

Figure 9 shows the ratio of water depth restored with a time scale of less than ten years in our simulations, i.e., with a total restoring coefficient M (x,y,z) τ > 3.17 10 -9 s -1 , corresponding to a mask coefficient M(x,y,z) > 0.2 and a restoring time scale τ = 2 years. The constraint on the model AABW is strongest around the Antarctic mainland and gradually weakens to the north. This gradient is due to very dense water masses which forming on the Antarctic ice shelves that then spread northward along the continental slope. Yet restoring is weak in the latitude band of the ACC.

The efficiency of our restoring method was tested in regional simulations of the Southern Ocean over 1991-2000 using the same modelling system (Section 2 details the model and configuration used). With this model we made sensitivity tests to refine the potential density criterion σ ref (determining the volume of water restored) and the restoring time scale τ . All of theses tests required shorter spin-ups than did the reference simulation; the ACC transport stabilized after only 8 years (see Fig. 3). After the spin-up, there was no longer a trend in ACC transport as opposed to the non-restored simulation. Interannual variability of ACC transport was not affected by restoring of bottom-water temperature and salinity.

Yet the larger the restoring constraint, the greater was the ACC transport. Thus the σ ref and τ criteria were chosen to yield reasonable ACC transport intensity while maintaining values that provided only a weak constraint on the model. A similar approach was also used by [START_REF] Hallberg | The Role of Eddies in Determining the Structure and Response of the Wind-Driven Southern Hemisphere Overturning: Results from the Modeling Eddies in the Southern Ocean (MESO) Project[END_REF] who applied weak sponges around the Antarctic ice shelves to ensure the formation of AABW. They also noticed that this restoring influenced the net ACC transport. [START_REF] Hallberg | The Role of Eddies in Determining the Structure and Response of the Wind-Driven Southern Hemisphere Overturning: Results from the Modeling Eddies in the Southern Ocean (MESO) Project[END_REF]

Figure 1 .

 1 Figure 1.b shows the 1995-2004 average for zonal profiles of wind stress for the complete

  an estimation of the ACC mean position computed by averaging the PF and SAF mean positions over1995-2004. Increasing winds narrow and strengthen the ACC.At a horizontal resolution of 0.5 • positive SAM events shift the ACC mean position by less than 0.5 • , either poleward or equatorward. Considering the 0.5 • resolution though, this shift is insignificant and may be ascribed to the turbulent nature of the ACC. Conversely, results from SAM025+++ indicate a potentially significant poleward shift of 0.3 • of the ACC mean position. However, to infer a systematic poleward shift, more simulations would be needed at 0.25 • , following the approach used with the 0.5 • model. Besides, at both resolutions, the WINDxxx simulations show a significant and systematic equatorward shift of the ACC mean position of more than 1 • .In each experiment (WINDxxx or SAMxxx), the simulated frontal shifts are small. The response of the ACC mean position to the SAM positive forcing anomaly is less than 0.5 • , despite the large increase in wind intensity and the poleward shift of zonal winds (3 • ) applied in our simulations. In particular, we find smaller poleward shifts in the ACC's mean position than previous estimates of 1.0 • ±0.9 • (Fyfe and Saenko 2006) and 3.5 •[START_REF] Spence | Southern Ocean response to strenghening winds in an eddy-permitting global climate model[END_REF]).

Figure 5 .

 5 Figure 5.a shows the time-averaged mass transport at Drake Passage for each simulation

Figure 6

 6 Figure6shows the resulting total meridional overturning computed from 5-day-averaged

  The subtropical, subpolar, and deep cells of the total overturning are similar in REF05 and REF025 simulations, each cell transporting up to ∼15 Sv, ∼8 Sv, and ∼10 Sv, respectively. While intensities of both subtropical and subpolar cells of the mean Eulerian overturning do not change while increasing resolution (∼-15 Sv and ∼12 Sv), the upper cell linked to the surface ACC pathway shows an intensification of up to ∼-5 Sv in REF025.1) Modification of the MOC density structureIn the 0.25 • model, the SAM-induced poleward shift in winds (the move from REF025 to SAM025+++) results in a ∼4 • southward shift in the southernmost extent of the subpolar cell of the total overturning (Fig.6). That southward shift is similar to the ∼3 • shift found by[START_REF] Farneti | The role of mesoscale eddies in the rectification of the southern ocean response to climate change[END_REF] with their CM2.4 model. Yet there is no such shift in our 0.5 • model, i.e., between REF05 to SAM05+++. This southward shift is related to the significant, albeit weak, poleward shift in the ACC mean position in the SAM025+++ simulation (see Section 3.1). Additionally, the subpolar cell transforms deep into intermediate waters in different ways in the different sets of simulations. The subsequent intermediate waters are denser in the SAMxxx simulations (∼35.9 kg m -3 ) than in the REFxxx simulations (∼35.7 kg m -3 ). Conversely, much lighter intermediate waters are found in the WINDxxx simulations (∼35.4 kg m -3 ). This last result is consistent with what Hallberg and Gnanadesikan

  [START_REF] Farneti | The role of mesoscale eddies in the rectification of the southern ocean response to climate change[END_REF]. They argue that such an attenuation occurs because enhanced resolution allows transient eddy fluxes to better compensate for the increased Ekman transport in the MOC. Yet in our SAM025+++ simulation, the role of transient eddies in the meridional overturning becomes almost negligible (Fig. 6, bottomright panel). This reduction of the transient eddy meridional overturning with resolution occurs despite an increase in EKE and reduced ACC transport, both of which indicating a more active transient eddy field. Thus some process besides meridional fluxes from transient eddies must compensate for the large increase in zonal Ekman transport, as discussed below.

  changes in the MOC's density structure: the subpolar cell shifts poleward by ∼4 • while the overturning draws on denser intermediate waters.As resolution increases, standing eddy fluxes more efficiently balance the Ekman flow; conversely, meridional transient eddy fluxes are not enhanced with increasing winds and resolution. Indeed, only standing eddies compensate meridional fluxes from the wind-driven circulation when the spatial pattern of the winds associated with the SAM is applied to the 0.25 • model (simulation SAM025+++). Thus standing eddies play a crucial role in the meridional overturning of the Southern Ocean.

4

  Mean simulated salinity(1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004) along a meridional section at 115 • E in the REF05 (top panel) and REF025 (bottom panel) with a zoom on the upper 1000 m. Solid black contours indicate some salinity contours. The colorbar indicates salinity on the practical salinity scale. . . . . . . . . . . . . . . . . 43 5 (a) Mass transport at Drake Passage (in Sv) and (b) Maximum transport of the subpolar cell between 40 • S and 55 • S (in Sv), each given as the 1995-2004 mean and plotted against mean zonal wind stress in N m -2 for the same period. Symbols indicate the series of simulations: REFxxx (circles), WINDxxx (triangles) and SAMxxx (squares). Colors indicate resolution: 0.5 • (black) and 0.25 • (grey). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 6 The MOC (in Sv) in σ 2 potential density coordinates for (left) the total circulation Ψ tot , (middle) the mean Eulerian circulation Ψ, and (right) the transient eddy-induced circulation Ψ * . Simulations are indicated for from top to bottom as REF05, WIND05+++, SAM05+++, REF025, WIND025+++ and SAM025+++. Red cells indicate clockwise flow while blue cells indicate counter-clockwise flow. (Top left panel, in grey) Nomenclature of the four distinct cells are given and are described in section 3.b. . . . . . . . . . . . . 45 7 The mean Eulerian circulation (in Sv) in σ 2 potential density coordinates separated into (left) zonal-average mean Eulerian circulation < Ψ > and (right) the standing eddy-driven circulation Ψ SE for SAM05+++ (top) and SAM025+++ (bottom). Red indicates clockwise flow and blue indicates counterclockwise flow. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

Fig. 1 .

 1 Fig. 1. (a) Regression pattern (m s -1 std -1 (I SAM ); I SAM being the SAM index) of 1980-

Fig. 2 .

 2 Fig. 2. Mean EKE over 1992-2002 in (cm 2 s -2 ) from (a-d) the simulations at 10 m and

Fig. 3 .

 3 Fig. 3. Time series of annual mass transport at Drake Passage (in Sv) for the DRAKKAR

Fig. 4 .

 4 Fig. 4. Mean simulated salinity (1995-2004) along a meridional section at 115 • E in the

Fig. 5 .

 5 Fig. 5. (a) Mass transport at Drake Passage (in Sv) and (b) Maximum transport of the

Fig. 6 .

 6 Fig. 6. The MOC (in Sv) in σ 2 potential density coordinates for (left) the total circulation

Fig. 7 .

 7 Fig. 7. The mean Eulerian circulation (in Sv) in σ 2 potential density coordinates separated

Fig. 9 .

 9 Fig. 9. Percent of water depth that is restored with a time scale of less than ten years, i.e., with a total restoring coefficient of M (x,y,z) τ

  , P Uar is the regression pattern of U ar onto the SAM index, I SAM , such that:

	for Environmental Prediction (NCEP) 1 .
	(U ar (x, y, t) -I SAM (t) • P(x, y)) t (U ar (x, y, t) -I SAM (t) • P(x, y))	(2)
	is minimized over the period 1980-2001 and over the model domain (south of 30 • S). The
	index I SAM is the 1980-2001 monthly SAM index built from products of the National Center

  • E section of the Southern Ocean in the REF05 and REF025 simulations. Salty and weakly stratified waters are found at around 42 • S at the subsurface corresponding to the Subantarctic Mode waters (SAMW).

	The low salinity layer (34.2-34.4 psu) which appears at intermediate depth is typical of
	Antarctic Intermediate waters (AAIW) and can be traced back from the surface. A salty
	tongue (more than 34.7 psu) is found at around 2000 m and corresponds to Circumpolar

Deep Water (CDW) which flow southward and upwell near the Polar Front. The REF05 and REF025 simulations show few differences: fronts are more localized in REF025; SAMW is shifted northward in REF05 and is less salty than in REF025; AAIW extends deeper and is further south in REF05 than in REF025. Simulated structure and magnitude of salinity generally agree with observations (section I9 of the Southern Ocean WOCE Hydrographic Atlas,

[START_REF] Orsi | Hydrographic Atlas of the World Ocean Circulation Experiment[END_REF]

), although simulated AAIW and SAMW are slightly denser.

Table 1 .

 1 Comparison of various eddying model configurations in terms of grid type (column 2) and grid-cell size at 60 • S (column 3). A Mercator grid is isotropic, i.e., dy is refined as dx decreases poleward. Studies using these models to study the sensitivity of Southern Ocean circulation to winds are indicated in the final column.

	M odel	Grid type	dy	Ref erence
	ORCA2-LIM	Mercator 100 km	(Lefebvre and Goose 2005)
	PERIANT05	Mercator 28 km	this study
	MESO 1/2 •	Mercator	28 km (Hallberg and Gnanadesikan 2006)
	OCCAM 1/4 •	lon,lat	27 km	-
	UVic ESCM 0.2 • x0.4 •	lon,lat	22 km	(Spence et al. 2010)
	PERIANT025	Mercator 14 km	this study
	CM2.4	Mercator	14 km	(Farneti et al. 2010)
	MESO 1/4 •	Mercator	14 km

http://www.cpc.ncep.noaa.gov/products/precip/CWlink/daily ao index/aao/monthly.aao.index.b79.current.ascii

Acknowledgments.

We would like to thank J.-M. Molines for his modelling support. For this project, C.O.

Dufour is supported by the CEA (Commissariat à l'Energie Atomique). J. Le Sommer and B. Barnier are supported by the CNRS (Centre National de la Recherche Scientifique). J.D.

Zika is supported by the SouthernCross project of the Agence Nationale de la Recherche (contract ANR-08-JCJC-0777-01). We hereby acknowledge the Centre National d'Etudes Spatiales (CNES) for its constant support to the DRAKKAR project. Simulations were carried out at the CNRS IDRIS super computer facility in Orsay, France and at the CINES super computer facility in Montpellier, France.

APPENDIX

3-D thermohaline restoring of AABW

Thermohaline properties of Antarctic Bottom Waters (AABW) are restored towards the observed climatology of [START_REF] Gouretski | Woce global hydrographic climatology, a technical report[END_REF] as described below.

Following [START_REF] Orsi | Circulation, mixing, and roduction of Antarctic Bottom Water[END_REF], in the Southern Ocean, AABW are the water mass having σ 2 > 37.16 kg m -3 , where σ 2 is the potential density referenced at 2000 m. With this density criterion, we constructed a mask (M) of the domain as follows:

where σ 2 is computed from the annual climatology of [START_REF] Gouretski | Woce global hydrographic climatology, a technical report[END_REF], Although the NODC World Ocean Atlas 1998 (WOA1998) monthly climatology was used to initialize model temperature and salinity fields, we used the annual climatology of [START_REF] Gouretski | Woce global hydrographic climatology, a technical report[END_REF] to build this deep restoring mask and apply the restoring in our simulations, because it was computed with an interpolation method that avoids creating spurious water masses as in WOA1998 [START_REF] Levitus | World ocean database[END_REF].

Throughout each simulation, model temperature and salinity are restored to the annual

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