Methodology, Model Validation, Climatology, and Trends for Air Temperature and Precipitation

VOLUME 48 JOURNAL OF APPLIED METEOROLOGY AND CLIMATOLOGY MARCH 2009

Reanalysis of 44 Yr of Climate in the French Alps (1958–2002): Methodology, Model Validation, Climatology, and Trends for Air Temperature and Precipitation

YVES DURAND,MARTIN LATERNSER,GE´ RALD GIRAUD,PIERRE ETCHEVERS, BERNARD LESAFFRE, AND LAURENT ME´ RINDOL GAME/CNRM-CEN (CNRS/Me´te´o-France), Saint-Martin d’He´res, France

(Manuscript received 19 June 2007, in ﬁnal form 18 August 2008)

ABSTRACT

Since the early 1990s, Me´teó-France has used an automatic system combining three numerical models to simulate meteorological parameters, snow cover stratification, and avalanche risk at various altitudes, aspects, and slopes for a number of mountainous regions in France. Given the lack of sufficient directly observed long-term snow data, this ‘‘SAFRAN’’–Crocus–‘‘MEPRA’’ (SCM) model chain, usually applied to operational avalanche forecasting, has been used to carry out and validate retrospective snow and weather climate analyses for the 1958–2002 period. The SAFRAN 2-m air temperature and precipitation climatology shows that the climate of the French Alps is temperate and is mainly determined by atmospheric westerly flow conditions. Vertical profiles of temperature and precipitation averaged over the whole period for altitudes up to 3000 m MSL show a relatively linear variation with altitude for different mountain areas with no constraint of that kind imposed by the analysis scheme itself. Over the observation period 1958–2002, the overall trend corresponds to an increase in the annual near-surface air temperature of about 18C. However, variations are large at different altitudes and for different seasons and regions. This significantly positive trend is most obvious in the 1500–2000-m MSL altitude range, especially in the northwest regions, and exhibits a significant relationship with the North Atlantic Oscillation index over long periods. Precipitation data are diverse, making it hard to identify clear trends within the high year-to-year variability.

1. Introduction A 10-yr snow climatology (1981–91) of the French Alps has been established on the basis of modeled snow Since the early 1990s, Me´te´o-France has used an au- data alone [i.e., not taking into account any snow mea- tomatic system based on three numerical models to surements (Martin 1995)]. These data have been used to simulate meteorological parameters, snow cover stratig- test snow sensitivity to input meteorological parameters raphy, and avalanche risk at various altitudes, aspects, (Martin et al. 1994). Similar studies have been carried out and slopes for a number of mountainous regions (mas- for Switzerland with a particular emphasis on long-term sifs) in France (Durand et al. 1999). This SAFRAN– trends (Laternser and Schneebeli 2003). Crocus–MEPRA1 (SCM) model chain, usually applied As far as we know, no practical climatological studies to operational avalanche forecasting, is used here for on combined snow and meteorological parameters have retrospective snow and weather climate analyses. been carried out for the French Alps. Classical climatological studies in France concentrate more on the predominant low-elevation regions of the country and 1 Here, SAFRAN stands for Syste`me d’Analyse Fournissant des focus mainly on air temperature and precipitation. Renseignements Atmosphe´riques a` la Neige (Analysis System Pro- Moisselin et al. (2002) and Schmidli et al. (2002) discuss viding Atmospheric Information to Snow) and MEPRA stands for precipitation trends in detail for the entire European Mode`le Expert de Pre´vision du Risque d’Avalanche (Expert System for Avalanche Hazard Estimation). Alps. Frei and Scha¨r (1998) have determined a high- resolution precipitation climatology for the Alps based on daily analyses using a methodology similar to ours Corresponding author address: Yves Durand, Me´te´o-France CNRM-CEN, 1441 rue de la Piscine, 38400 Saint-Martin d’He´res, for this parameter. They also present a very complete France. description of several prior climatological studies over E-mail: [email protected] the Alps. Martin Beniston has also widely investigated

DOI: 10.1175/2008JAMC1808.1

Ó 2009 American Meteorological Society 429 Unauthenticated | Downloaded 09/26/21 12:32 AM UTC 430 JOURNAL OF APPLIED METEOROLOGY AND CLIMATOLOGY VOLUME 48 mountain climates with particular emphasis on the en- data available from various observation networks (in- tire Alps in France and Switzerland (Beniston 2005; cluding radar and satellite data) over the considered el- Rebetez and Beniston 1998). Some regional studies evations and aspects of the different massifs. SAFRAN on precipitation have also been carried out (Berthelot combines the observed information with a preliminary 2004) in the southern French Alps. estimation generally provided by numerical weather The present study analyzes long-term climate series over forecasting models. The analysis method combines an the entire French Alps. Using 44 yr of newly reanalyzed optimal interpolation every 6 h and a variational inter- atmospheric model data from the 40-yr European Centre polation over 6-h windows, providing hourly data for the for Medium-Range Weather Forecasts (ECMWF) re- main relevant atmospheric parameters affecting snow analysis (ERA-40) project (ECMWF 2004), the SCM surface changes (i.e., air temperature, wind speed, air model chain has been run on an hourly basis for a period humidity, cloudiness, snow and rain precipitation, long- starting in winter 1958/59. Results include air tempera- wave radiation, and direct and scattered solar radiation). ture and precipitation trends, as well as average condi- Crocus (Brun et al. 1989, 1992) is a numerical snow tions (spatial variability) and long-term trends (temporal model used to calculate changes in energy and mass in variability) for various snow-cover parameters. the snow cover. It uses only the meteorological data The SCM chain has already been validated on nu- provided by SAFRAN and simulates the evolution of merous occasions but only over the 1981–95 period in temperature, density, liquid water content profiles, and mountainous areas. As few validation stations are avail- layering of the snowpack at different elevations, slopes, able prior to 1980, the SAFRAN meteorological model and aspects, including the internal metamorphism pro- was validated using specific procedures. The results of cesses. It is assumed that each simulated slope is free of these comparisons show that the air temperature error snow on 1 August of each year. The simulated snowpack (RMS) is between 1.58 and 28C and that the precipita- then evolves every hour from the first snowfall until tion error (only for nonzero precipitation) is unbiased. complete melting without reinitialization. The com- These values are satisfactory even if few validation puted snow state for a given hour is thus based only on stations are used by the model. Furthermore, these the snow state of the previous hour and on the atmo- magnitudes of error have been corroborated by other spheric forcing of the current hour. studies over other geographical areas. Because of their In the present paper, the following output data will be pertinence for snowpack evolution, these validation presented: air temperature and precipitation (total or tests only involve two of the nine parameters analyzed. snow), all provided by SAFRAN; snow depth at ground The obtained results show the ability of the SAFRAN level as computed by Crocus will be in a forthcoming model to reproduce the main climatological features for paper. All these parameters are modeled for all 23 massifs this mountainous region and to provide valid input data of the French Alps in 300-m-altitude steps over eleva- for the Crocus snow model. tions ranging at the most from 300 to 3600 m MSL (Fig. 1; Because of the amount of information involved, this Table 1). These different massifs have been defined for study is divided into two papers. The first paper (this one) their climatological homogeneity, especially with regard focuses on data description, methodology, and validation to precipitation fields (Pahaut et al. 1991). They are those in relation to the SAFRAN meteorological model and used for operational avalanche hazard estimation in presents meteorological trends for air temperature and France and their characteristics have been well known precipitation (total and snow). A forthcoming paper will for many years by local forecasters. Their boundaries co- deal with the results of the Crocus snowpack model, incide well with the main topographic features as shown providing a comprehensive snow climatology and long- by Fig. 1. However, for each massif, only existing eleva- term snow trends. Results will be discussed, snow trends tions are considered and no ‘‘fictitious’’ extrapolations are considered in the light of the air temperature and pre- made to higher or lower elevations, which can make cipitation trends revealed by this paper, and compari- comparisons difficult between massifs for certain elevation sons made with international snow studies (Rebetez and ranges. The output has an hourly resolution from 1 August Beniston 1998). The MEPRA expert system for avalanche 1958 to 31 July 2002 and covers 44 winter periods. By risk forecasting (Giraud 1993) is not used in this part of convention, winters are referred to by the year of the main the study and is therefore not discussed in this paper. part of the winter (e.g., 1959 means winter 1958/59).

2. Models used 3. Data and methods SAFRAN (Durand et al. 1993) is a meteorological The meteorological analyses are based on both con- application that performs an objective analysis of weather ventional observations and numerical atmospheric weather

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FIG. 1. The 23 massifs of the French Alps between Lake Geneva to the north and the Mediterranean Sea to the south and their underlying orographic features. model outputs. Conventional observations include var- analysis software such as SAFRAN that uses informa- ious kinds of datasets extracted from the operational tion available on a daily basis, even if sparse, without databases of Me´teó-France and ECMWF. They cover the constraint of full and homogeneous observation the French Alps and adjacent areas of neighboring Italy series. However, since it takes observation errors into and Switzerland within a grid of 43.158–47.08N and account statistically, SAFRAN is not a ‘‘perfect’’ in- 4.458–8.08E. Data are concatenated into several differ- terpolator scheme [i.e., it will never give the observation ent file types according to their contents and source. The initial data had not been checked properly in terms of TABLE 1. Details of the mountainous massifs of the French Alps quality (apart from quality flags at ECMWF and some used in the SCM chain with their elevation range and geographic region (cf. Fig. 1). routine consistency checks at Me´teó-France); however, this was done automatically during subsequent SAFRAN Massif Elev (m) Region modeling. Chablais 600–2700 m NW foothills All available conventional observations have been Aravis 900–2700 m NW foothills used and are recorded in several files and databases Bauges 600–2100 m NW foothills (Table 2). Air pressure, air temperature, wind (meridian Chartreuse 600–2100 m NW foothills and zonal components), humidity, snow depth, new Vercors 600–2400 m NW foothills Mont-Blanc 1200–3600 m North snow, and various parameters for weather type and Beaufortin 900–3000 m North cloudiness are available at their own observation fre- Haute-Tarentaise 900–3600 m North (interior) quency (hourly or by steps of 3 or 6 h). Precipitation, Haute-Maurienne 1200–3600 m North (central, interior) snow depths, and minimum–maximum temperatures are Vanoise 900–3600 m North available only on a daily basis. Radiosonde and pilot Maurienne 600–3000 m North (central) Belledonne 600–3000 m North balloon data from Lyon, Montelimar, Nıˆmes, Payerne Grandes-Rousses 900–3300 m Central (Switzerland), and Torino (Italy) are also used. The Thabor 1500–3000 m Central (interior) number of stations providing available data varies Oisans 900–1200 m South (central) greatly with the hour, day, and year considered and is Pelvoux 1200–3600 m South (central) thus given only as a general indication. Individual files Champsaur 1200–3300 m South De´voluy 600–3000 m South are incomplete in roughly two-thirds of all cases, in Queyras 1200–3000 m South (interior SE) particular snow, weather type, and cloudiness along Parpaillon 900–3300 m South (interior SE) with minimum–maximum temperature and new snow Ubaye 1200–3000 m South (interior SE) amount. All these missing data and short observation Alpes-Azureénnes 600–2700 m Far south series are the main reason for using meteorological Mercantour 1200–3000 m Far south

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TABLE 2. Characteristics of the different observation sources level at ’8.5 km MSL) within a regular grid of 1.58 (see text). latitude–longitude were extracted over the entire pe- Network, Temporal Period of No. of stations riod. Horizontal and vertical downscaling operators observation resolution (h) observation (approx) have also been developed to adapt these data from the Synoptic, surface 6 1958–2002 30–200 ECMWF MARS archive system to the concerned pa- Automatic, surface 1 1958–2002 30–200 rameters and area of the Alps. Climatologic, surface 24 1958–2002 150–300 Even if the analyzed results cover the entire annual Vertical atmospheric 12 (6) 1958–2002 0–5 period on an hourly time step and are therefore available at that scale, the results presented here in the different figures are mainly yearly or seasonal averages or value (except by chance)], even if the observation point amounts at different elevations of two selected varia- corresponds exactly to an analysis point. bles: near-surface air temperature (sometimes referred Prior to this study, the SCM chain had been run since to only as air temperature or temperature) and 24-h 1981 using different numerical guess fields provided rainfall (sometimes referred to only as precipitation). by the available numerical weather prediction models Some finer results will be discussed in the text, in partic- in use at Me´teó-France. The most widely used is the ular for half-seasons such as early summer or midwinter, Action de Recherche Petite Echelle Grande Echelle but will not be illustrated so as to avoid complicating (ARPEGE) model (Courtier et al. 1991), with a present the figures. Elevation ranges are often referred to as dynamic resolution of about 20 km. This guess field has low (,1000 m), mid- (1000–2000 m), and high alti- more particularly been used by Quintana-Seguı´ et al. tudes (.2000 m); however, these terms should not be (2008) for their work with SAFRAN. However, as taken too literally since they only represent a rough output from this model is not available prior to 1991, graduation. we chose to use retrospective analyses from ERA-40 (ECMWF 2004) that provide a uniform coverage of 4. Model validation our entire study period even if their spatial resolution is coarser. This version of the ECMWF assimilation Before running the two first models in coupled mode, scheme uses both satellite and conventional observa- each was carefully validated in different contexts. Two tions to provide a full set of validated meteorological well-instrumented automatic sites, Col de Porte (1340 m, analysis parameters from the surface to the 0.1-hPa Chartreuse massif) and Col du Lac Blanc (2800 m, level (’65 km MSL) dating back to 1958 (the Interna- Grandes Rousses massif), are not included in the anal- tional Geophysical Year). For our purposes, six parame- ysis system and are used for a daily local validation of ters (P, Z, T, U, V, H; see Table 3) over a maximum of SAFRAN (Durand et al. 1993). Crocus has been vali- 16 elevation levels (from the surface up to the 300-hPa dated at Col de Porte over several winter seasons (Brun

TABLE 3. ERA-40 output data parameters and corresponding elevation levels used in this study.

Level P (air pressure) Z (geopotential) T (air temperature) U (meridional wind) V (zonal wind) H (humidity) Surface x X X X X 1000 hPa X X X X X X 925 hPa X X X X X X 850 hPa X X X X X X 775 hPa X X X X X X 700 hPa X X X X X X 600 hPa X X X X X X 500 hPa X X X X X X 400 hPa X X X X X X 300 hPa X X X X X X Model level X No. 51 900 hPa* X No. 50 880 hPa* X No. 48 820 hPa* X No. 47 790 hPa* X No. 45 720 hPa* X No. 43 650 hPa* X

* The model level P depends on the surface pressure; therefore the values given are only approximate (ECMWF 2004).

Unauthenticated | Downloaded 09/26/21 12:32 AM UTC MARCH 2009 D U R A N D E T A L . 433 et al. 1989, 1992) using measured meteorological data formed and the results illustrate both the quality of the from automatic weather stations. The SAFRAN and analysis and the pertinence of the observations. Crocus models were assessed in coupled mode by comparing simulated and measured snow depths at 37 a. Air temperature sites over the 1981–91 period (Martin et al. 1994). The quality of the simulations is satisfactory except at loca- Figure 2 shows the RMS values of the difference be- tions where snowdrifting is very frequent or where the tween measurements (43 sites, some with sometimes local meteorology significantly differs from regional sporadic data) and SAFRAN analyzed fields for the (i.e., here massif) meteorology. Results are better in the annual mean air temperature over the 44 yr for the two northern Alps than in the southern Alps because of a datasets (WITH represented by the solid line and higher density of the snow weather observation network WITHOUT by the broken line). No constraint is ap- in the northern Alps. As mentioned by Martin et al. plied to the analysis scheme and the presented RMS (1994), the maximum snow depth errors of the 37 sites values therefore include the observation errors of the are usually less than 20 cm for test sites below 1500 m, concerned sites, which increase the results. It is difficult and 30 cm for other sites. The corresponding mean error to separate the relative share of these two errors. As a values are, respectively, 10 and 13 cm, corresponding to general indication, the values of RMS temperature ob- 18% and 12% of the observed mean snow depths. This servation error used in our area of the Alps range from encouraging snow depth evaluation represents an indi- 18 to 1.58C depending on the site and result from our rect validation of SAFRAN meteorological parameters own monitoring and experience. These values are close presented here, especially precipitation. The other results to those suggested, for instance by Fuentes and Heimann concerning snow parameters will be presented in a (1996). The WITH set exhibits higher quality because of forthcoming paper. A global validation of SAFRAN ca- the additional information of the test sites, but the RMS pacities over all of France has also been performed difference between the two sets is low, from 0.18Cin by Quintana-Seguı´ et al. (2008) and has confirmed the 2000–09 to 0.38C in the 1960s. The SAFRAN analyses unbiased characteristic of the results in a hydrological are globally better at the end of the millennium because context. Their results show an average value of the RMS of the improvement of the snow and weather network difference between SAFRAN output and observations at with an increasing number of meteorological observa- about 1.58C for temperature, but at the same time point tions. For extreme values (details not shown here), the out some problems concerning precipitation over moun- RMS values vary from 0.18C in 1996 for WITH simu- tainous areas related to the sub-massif-scale variability. lations for a site in the southern Alps to 4.78C in 1986 for Given the small number of previous studies and the WITHOUT simulations for a site in the central Alps. lack of widely distributed mountain meteorological The minimum (TN) and maximum (TX) daily air tem- observations, especially over the 1958–80 period, the peratures have also been compared. The bias is gener- SAFRAN results have been validated mainly in terms ally positive for TN (mean value of 1.28C for WITH and of air temperature and precipitation. The choice of 1.98C for WITHOUT set) and negative for TX (mean these two parameters is also related to their impact on value of 21.08C for WITH and 21.48C for WITHOUT snow evolution. As no observed data are directly rep- set). resentative of the massif scale, results were validated us- The relative decrease in performance over the 1980s ing the ability of the SAFRAN model to simulate precise has been identified to a lack of information in the da- geographical locations by including their main surround- tabases, mainly due to the demise of the French manual ing topographical features (Durand et al. 1999) through observation network that had not yet been compensated appropriate downscaling procedures. In a first step, 43 for by the deployment of the new snow weather ob- such sites (Table 4) were selected using two criteria: servation network in mountainous areas—which clearly available meteorological data during most of the period, produced a positive impact during the 1990s. The au- and sites well distributed over the whole Alps. tomatic observation network was also, at that time, only For the entire 44-yr period, a first run was carried out in its infancy, with data difficult to integrate in the without the observations from these selected sites. This analysis scheme. As described farther on (and also vis- first experiment and the corresponding results will be ible in Fig. 9), the 1980s are also representative of a net hereinafter referred to as ‘‘WITHOUT.’’ In a second change in the warming temperature trend (Trenberth step, a new run referred to as ‘‘WITH’’ was carried et al. 2007), especially concerning the daily minima that out using all the observations. Objective comparisons are not used explicitly by SAFRAN. This phenomenon between the raw observed data and corresponding could also partially explain the bad RMS values of that SAFRAN WITH and WITHOUT data were then per- critical period if we could establish the sensitivity of this

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TABLE 4. List of the 43 selected validation sites used in Fig. 2 (comparisons WITH–WITHOUT insertion in the analysis scheme) with their main characteristics. The 21 sites with the longest observed series and used in Fig. 4 (comparison with observations) are in italic. The seven ‘‘department representative’’ sites used in the SAFRAN validation (Tables 5, 6) are in boldface.

Name Department Massif Alt (m MSL) Lat (8N) Lon (8E) Aspect Les Gets Haute Savoie Chablais 1172 46.17 6.67 E Les Contamines Haute Savoie Chablais 1180 45.77 6.73 N La Clusaz Haute Savoie Aravis 1180 45.9 6.42 NW Vallorcine Haute Savoie Mt Blanc 1300 46.03 6.93 NE Chamonix Haute Savoie Mt Blanc 1042 45.91 6.86 F Megeve Haute Savoie Mt Blanc 1104 45.85 6.62 SW Les Houches Haute Savoie Mt Blanc 1008 45.89 6.81 F Beaufort Savoie Beaufortin 1030 45.69 6.57 F Bourg St Maurice Savoie Beaufortin 868 45.61 6.76 F Val d’lse`re Savoie Hte Tarentaise 1850 45.44 6.98 F Pralognan Savoie Vanoise 1420 45.38 6.72 F Peisey-Nancroix Savoie Vanoise 1350 45.32 6.46 F St Martin de B. Savoie Vanoise 1500 45.27 6.51 F Bonneval Savoie Hte Tarentaise 1830 45.37 7.04 NW Bessans Savoie Hte Tarentaise 1715 45.32 6.99 F Valloire Savoie Maurienne 1296 45.08 6.43 F St Sorlin Savoie Maurienne 1650 45.22 6.23 S Chambery Savoie Bauges 239 45.65 5.88 F St Pierre de C Ise`re Chartreuse 945 45.33 5.82 F St Pierre d’E Ise`re Chartreuse 644 45.38 5.85 F Revel Ise`re Belledonne 630 45.19 5.87 F La Ferrie`re Ise`re Belledonne 1082 45.28 6.07 F SMH Ise`re Chartreuse 200 45.17 5.77 F Allemond Ise`re Belledonne 1270 45.16 6.08 F Autrans Ise`re Vercors 1090 45.17 5.54 F Villard de L. Ise`re Vercors 1050 45.07 5.55 F Besse Ise`re Grdes Rousses 1525 45.07 6.17 F Vaujany Ise`re Grdes Rousses 772 45.13 6.08 N Bourg d’Ois Ise`re Grdes Rousses 720 45.03 6.18 F St Christ. En O. Ise`re Oisans 1570 44.95 6.17 SW La Grave Hautes Alpes Oisans 1780 45.03 6.3 SW St Etienne en D. Hautes Alpes Devoluy 1300 44.7 5.93 S Lus La Cr. Hautes Alpes Devoluy 1059 44.68 5.71 SW Briancxon Hautes Alpes Le Pelvoux 1324 44.91 6.61 F Le Monetier Hautes Alpes Le Pelvoux 1490 44.97 6.5 F Orcie`res Hautes Alpes Champsaur 1435 44.68 6.32 F St Veran Hautes Alpes Queyras 2010 44.7 6.87 SW Arvieux Hautes Alpes Queyras 1675 44.75 6.75 F Ceillac Hautes Alpes Queyras 1665 44.67 6.77 SE Embrun Alpes Hte Provence Parpaillon 871 44.57 6.5 S Barcelonnette Alpes Hte Provence Ubaye 1155 44.39 6.67 F Beuil Alpes Maritimers Alpes-Asure´ennes 1465 44.1 7 F Luceram Alpes Maritimers Mercantour 1420 43.93 7.37 S

particular parameter with respect to the final result observation errors. As a whole, the magnitude of these in the framework of a reduced observation network; differences is very close to those obtained by Quintana however, this point is still under investigation. (RMS value of about 1.58C), who used SAFRAN over The presented results are also an indirect evaluation all of France with a majority of low-elevation areas as of SAFRAN accuracy according to the information previously indicated (Quintana-Seguı´ et al. 2008). used and of the sensitivity of the analysis scheme to its Some detailed results concerning seven selected sites input data. When the observation network is sparse, as (one for each French Alps department) are shown during the 1980s, the analysis error is close to the guess- in Table 5 and concern TN–TX and mean daily tem- field error. On the other hand, a denser observation perature (TM) verifications. The values correspond to network implies less analysis error in the vicinity of the RMS differences and bias differences. Results of the

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FIG. 2. Global comparison of the RMS evolution of annual mean air temperature (8C) for the two SAFRAN runs with and without the 43 selected observations. comparisons for the WITH and WITHOUT experi- b. 24-h precipitation ments are indicated in a similar way. The results of the As 24-h precipitation is not a continuous daily pa- comparisons are obviously better for WITH experi- rameter, direct comparisons are not easy. We therefore ments (because of the use of the additional observations used two types of comparisons. in the analysis scheme), except for Luceram, a site re- cently set up in the southern French Alps (detailed re- For the sites previously selected in each French Alps sults not shown) with few available data and for which department (seven sites, already used in Table 5), insertion in the SAFRAN data decreases analysis per- objective comparisons are computed between the formance. Except for this particular site, the bias is al- observed and the WITH and WITHOUT corre- ways positive for TN and negative for TX, as already sponding SAFRAN analyzed quantities only when mentioned. This supports our previous idea concerning the daily quantity is higher than 0.2 mm. Table 6 the possible weaknesses of the diurnal cycle analyzed by shows this comparison using different statistical SAFRAN (underestimation of the amplitude, but less parameters such as the average of observed data and error on the mean temperature value) and the possible SAFRAN analyzed data, standard deviation of the improvements that could be achieved by using explicitly difference, and the correlation coefﬁcient. As for the information on these extremes. This also shows that temperature, the Luceram site shows the worst re- the analysis process is not trivial and that the charac- sults for all statistical parameters, which reinforces teristics of each site have to be carefully taken into ac- our previous doubts concerning the use of these data count, which is not yet the case for Luceram. The same in the analysis scheme. However, the weak differ- analyses were performed using only winter data and ences between WITH and WITHOUT results are both bias and RMS show similar values (not shown representative of the good stability of the SAFRAN here). scheme, especially in relation to precipitation, for

TABLE 5. Illustration of the spatial variability of the SAFRAN analyses; the table shows for seven sites the rms and bias difference for minimum (TN), maximum (TX), and average (TM) air temperature (8C) over the whole period and the two experiment sets (WITH and WITHOUT).

WITH WITHOUT No. of TN (8C) bias TX (8C) bias TM (8C) bias TN (8C) bias TX (8C) bias TM (8C) Site cases (rms) (rms) (rms) (rms) (rms) bias (rms) Chamonix 13 695 12.2 (3.1) 21.0 (2.3) 10.7 (1.7) 12.9 (3.8) 21.2 (2.6) 10.8 (1.9) Bessans 6921 13.4 (4.6) 20.5 (2.8) 11.5 (2.8) 13.6 (4.9) 20.5 (2.9) 11.6 (2.9) Villard de Lans 13 138 11.0 (2.8) 21.0 (2.1) 20.0 (1.7) 12.2 (3.7) 21.3 (2.5) 10.5 (1.9) Lus La Croix Haute 15 930 10.6 (2.0) 20.3 (1.5) 10.2 (1.2) 11.9 (3.4) 20.8 (2.2) 10.5 (1.7) Orcie`res 12 249 10.6 (2.3) 22.2 (3.1) 20.8 (1.7) 11.4 (2.7) 22.6 (3.4) 20.6 (1.7) Barcelonnette 14 246 13.9 (4.9) 21.7 (2.7) 11.1 (5.8) 14.9 (5.8) 22.6 (3.6) 11.2 (2.3) Luceram 2791 22.5 (3.6) 10.6 (1.8) 21.0 (1.7) 22.4 (3.4) 10.1 (1.8) 21.1 (1.9)

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21 TABLE 6. Illustration of the spatial variability of the SAFRAN analyses and observations for the precipitation parameter (mm day ); the table shows for seven sites the observed and modeled mean values, the averaged differences (bias), the standard deviations (std), and correlations over the whole period and the two experiment sets (WITH and WITHOUT).

WITH WITHOUT Mean Mean Mean observed analyzed analyzed No. of values values Difference bias values Difference bias Site cases (mm day21) (mm day21) (std) Correlation (mm day21) (std) Correlation Chamonix 7881 6.98 8.56 21.58 (4.22) 0.91 8.52 21.54 (4.58) 0.90 Bessans 7591 5.18 5.91 20.73 (3.94) 0.90 5.47 20.29 (4.80) 0.84 Villard de Lans 7874 7.47 7.8 20.33 (3.95) 0.93 7.92 20.45 (4.76) 0.89 Lus La Croix 7268 6.66 7.02 20.36 (4.45) 0.90 6.7 20.40 (4.71) 0.89 Haute Orcie`res 6156 8.87 8.38 10.49 (4.30) 0.94 8.27 10.60 (4.80) 0.92 Barcelonnette 5814 4.72 6.35 21.63 (4.76) 0.92 6.39 21.67 (4.82) 0.92 Luceram 1280 5.38 5.38 22.38 (9.62) 0.77 7.73 22.35 (10.21) 0.73

which characteristic spatial lengths are smaller than of drawing conclusions with only the modeled fields at for temperatures. locations or areas where no observations are available. For all the 43 sites, contingency tables of daily pre- For this, 21 observation sites with more than 10 000 data cipitation have been compiled to compare obser- (two observations per day) were selected from the ini- vations (rows in the table) and SAFRAN analyzed tial list of 43 sites previously used (Table 4). For ex- data (columns). Seven classes from near 0 (#0.2 ample, Fig. 3 shows the annual observed temperature mm) to high precipitation (.40 mm) were defined. for representative locations of the three main geo- Table 7 shows the result in terms of percentages in graphical areas: Nice for the southern Alps, Annecy for the different classes for the WITH and WITHOUT the northern Alps, and Villard-de-Lans for the central simulations. For the WITH results, the value of the Alps. All locations exhibit a clear temperature increase Hansen and Kuiper Skill Score (Wilks 1995) of over the past 40 yr of about 1.58C for Annecy and Nice 0.607 as well as the percentage of well-classified and 1.18C for Villard-de-Lans (with higher variability). cases of 68.2% (based on the diagonal elements of These results, required for the same period as the Table 7) are globally quite satisfactory. Concerning modeled results, are relevant for the last 45 yr, but the WITHOUT experiment, the comparisons be- cannot be extrapolated to longer periods such as the tween the two sets show that the SAFRAN analy- whole century. They exclude particularly the important ses without using the data of the additional sites are slightly worse with a Hansen and Kuiper skill score TABLE 7. Daily precipitation contingency table between obser- of 0.575. vations (lines) and SAFRAN (columns) for the two experiments sets (WITH and WITHOUT) according to seven classes (mm In both experiments, SAFRAN analyses give values 2 day 1). lower than measurements, especially for high values of precipitation. Despite this, these validations globally show #0.2 0.2–2 2–5 5–10 10–20 20–40 .40 the ability of SAFRAN to reproduce the mountain me- WITH mm mm mm mm mm mm mm teorological climatology of precipitation since 1958 but #0.2 mm 81.5 16.4 1.7 0.4 0.1 0.0 0.0 with a slight bias generally due to local effects. However, 0.2–2 mm 9.5 60.8 23.4 5.5 0.8 0.1 0.0 the real-time operational runs show that this does not 2–5 mm 0.6 18.8 47.5 27.8 5.0 0.3 0.0 5–10 mm 0.2 2.9 19.9 50.6 24.8 1.6 0.0 affect the results for climatological purposes (Martin et al. 10–20 mm 0.1 0.4 2.8 19.1 61.0 16.4 0.2 1994; Martin 1995; Quintana-Seguı´ et al. 2008). 20–40 mm 0.1 0.1 0.3 2.3 25.2 65.5 6.5 .40 mm 0.3 0.3 0.3 0.4 2.6 38.1 58.1 WITHOUT c. Analyzed temperature and precipitation trends #0.2 mm 81.5 16.0 1.9 0.5 0.1 0.0 0.0 0.2–2 mm 12.7 56.6 22.9 6.5 1.2 0.1 0.0 Before discussing the final SAFRAN output analyzed 2–5 mm 1.5 21.4 43.3 27.1 6.2 0.4 0.0 on the massif scale in terms of temporal trends for 5–10 mm 0.4 4.8 21.1 46.2 25.3 2.1 0.1 precipitation and temperature, a quick overview of the 10–20 mm 0.3 1.0 4.0 20.1 57.3 17.2 0.3 observation series will be provided to evaluate the 20–40 mm 0.1 0.3 0.7 3.2 26.5 62.4 6.9 modeled results. The purpose is to assess the possibility .40 mm 0.1 0.2 0.3 0.8 3.7 39.3 56.4

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FIG. 3. Annual mean air temperature with its increase and temporal trend for three representative observed series locations: (a) Nice, (b) Villard-de-Lans, and (c),(d) Annecy.

1940s and 1950s decades, as can be seen when com- Similar remarks can be made for the precipitation paring Figs. 3c and 3d for the Annecy series. trends shown in Fig. 4b, especially in the northern Alps Figure 4a exhibits the mean temperature trend for 21 where both SAFRAN analyses and observations show a observation sites and the corresponding SAFRAN an- small temporal increase, often overestimated by the alyzed values downscaled at these points. As explained, model. Trends are rather weak in the southern Alps for it is difficult to simulate precise geographical locations this parameter. As very few observation series cover the with the modeled results, which do not take into account full temporal period and as the observations are above small-scale orographic effects at these locations. In ad- all representative of the winter season, these values are dition, some observation sites have also been greatly difficult to interpret both in time and spatially. The influenced by surrounding urbanization, as is probably mean precipitation trends, presented in Fig. 4b, are the case for Megeve. However, the mean air tempera- 11.6 mm yr21 for the observed data and 12.6 mm yr21 for ture trends for the 21 sites are 0.0258Cyr21 for the the SAFRAN simulated data. Note the clear latitudinal observed data and 0.0288Cyr21 for the SAFRAN difference with a positive trend both for the observa- simulated data. The results are therefore of the same order tions of the northern Alps (those from about 1 to 30 on of magnitude even if the analysis overestimates the values the x axis in Fig. 4b) and the SAFRAN results, and no for many points such as those in the Vercors massif. real trend in the south. However, even if the positive

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FIG. 4. Observed and SAFRAN-analyzed annual mean (a) temperature and (b) precipitation trends for 21 sites in the French Alps since 1958. trend values are consistent with those of Fig. 9, they are 5. Massif-scale climatology not statistically signiﬁcant. Moisselin et al. (2002) point a. SAFRAN air temperature climatology out the lack of consistency and of signiﬁcance of most of the observed precipitation series over the southeast of The climate of the French Alps is temperate (Fig. 5) France and these data are the main SAFRAN inputs. It with annual mean air temperature at 1800 m MSL is therefore impossible to draw valid conclusions on the varying from 3.48C in the north (Chablais massif) to scale of the observation site concerning these trends. 5.18C in the south (Mercantour massif) near the Medi- However, considering cross validations only, which is terranean Sea. This latitudinal variability is globally our purpose here, we observe a consistent positive trend consistent with that observed for France as a whole for precipitation in the northern Alps and no trend at lower elevations (annual mean air temperature of in the southern Alps, both for observed data and 12.98C for the city of Toulouse in the south and 10.08C SAFRAN results. for the city of Lille, 850 km to the north). The variations

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FIG. 5. SAFRAN (left) annual and (right) (top) winter and (bottom) summer mean values for air temperature at an elevation of 1800 m with the same color code for each. are slightly higher in winter (from 21.48 to 10.48C; Fig. precipitation can be very high (commonly 100% for 5, top right panel) than in summer (from 18.38 to annual data and much more seasonally) and regional 19.98C; Fig. 5, bottom right panel). This low variability trends exist (next Fig. 9b). Frei and Scha¨r (1998), along with latitude over these two seasons is partially due to with Beniston (2005), also insist on the dynamic inter- the fact that results shown concern near surface condi- action between weather systems and mountains and on tions at a constant midaltitude elevation, which implies the influence of sea moisture, especially during south- a partial influence of the more smoothed free atmos- erly conditions. At 1800 m MSL, the maximum annual phere conditions. In addition, the strongest latitudinal precipitation amounts to nearly 2000 mm in the north- gradient occurs mainly during the intermediate seasons western foothills (particularly Chartreuse and Aravis), according to the latitudinal variations of the polar front and decreases to less than half that amount toward the over France. southeast (831 mm for Queyras). A secondary maximum is located in the extreme southeast associated with b. SAFRAN precipitation climatology the occurrence of northward Mediterranean flows. Note The climate of the French Alps is mainly determined the small difference between summer and winter in the by a northwesterly atmospheric flow as can seen in massif precipitation distribution despite the differences Fig. 6, which shows annual mean precipitation at the in meteorological patterns and types of precipitation. massif scale. This influence is visible both in summer The snow fraction is about half in the northwest and and winter (rhs of Fig. 6), with more convective pre- only one-third in the south. In this respect, the three cipitation in summer. Year-to-year variability of annual southernmost massifs (Ubaye, Alpes-Azureénnes, and

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FIG. 6. As in Fig. 5, but for precipitation.

Mercantour) get less snow than the overall driest massif sidered logical, except perhaps for Belledonne, which (Queyras); Ubaye receives only an average of around has close ties to Chablais-Mt Blanc (rather than to its 280 mm of snow water equivalent, as compared with immediate neighbors), and De´voluy that is closely re- 944 mm of total precipitation. lated to Queyras-Parpaillon. For each area (Fig. 7a) thus representative of the c. Mean vertical gradients snowpack conditions, low-atmosphere vertical gradients Four main areas of the Alps (Fig. 7a) regrouping the have been computed for the near-surface temperature different massifs of Fig. 1 have been determined by (e.g., the massif averaged air temperature at 2 m at expert meteorological clustering. The first split sepa- different surface elevations) and for the annual rainfall. rates areas of greatest dissimilarity and divides roughly As shown in Figs. 7b,c, these gradients are very linear the northern from the southern areas. This line does not over the entire area. Note that this is not imposed by the really run W–E, but rather SW–NE. Note that Haute- analysis scheme (Durand et al. 1993) and results directly Maurienne in the central east has a pronounced south- from the processing of the observations and the ERA- ern influence. The second split separates the north- 40 fields. From north to south, the mean near-surface western foothills from the central ranges. Whereas vertical temperature gradient varies from 25.08 to 25.58C Belledonne, Beaufortin, and Mont-Blanc have a rather (1000 m)21. These rates are very close to those com- foothill character, Vercors (the southernmost foothill puted by Rolland (2003) over the Italian Alps. The massif) resembles more the central massifs. The third annual vertical rainfall gradients exhibit a larger lat- split divides the southern Alps, notably with Ubaye in- itudinal dependence with respective values from north cluded in the far south. These subdivisions can be con- to south of 294, 195, 172, and 178 (1000 m)21.

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FIG. 7. (a) The four main areas of the Alps and the SAFRAN-averaged vertical gradient for the (b) near-surface air temperature and (c) annual rainfall.

6. Temperature and precipitation trends available online at http://www.cpc.noaa.gov/products/ precip/CWlink/pna/nao_index.html) variations over the SAFRAN analyzed temperature and precipitation whole study period to better explain observed features. trends are shown in detail for the entire French Alps and the different areas defined in Fig. 7a at midaltitude (1800 As explained by Beniston (2005), NAO is well repre- m MSL). When appropriate, the situations for particular sentative of the decadal-scale variability of the climate massifs and at other elevations will be also discussed. in the Alps, especially at high elevation. Even if its in- All the SAFRAN analyzed values are compared with fluence is more pronounced during the winter season the North Atlantic Oscillation (NAO) index2 (freely when the westerly meteorological flows are more in- tense, all the results presented here cover the complete

2 The NAO index used here is the one computed daily by the year. In addition, the daily variability of all the involved National Oceanic and Atmospheric Administration/National parameters is such that a temporal filter has to be used Weather Service/Climate Prediction Center and is constructed by to remove interannual noise. Figure 8 shows the varia- projecting the daily (0000 UTC) 500-hPa height anomalies over tion of the correlation coefficient between the daily the Northern Hemisphere onto the first leading modes of the ro- tated empirical orthogonal function (REOF) analysis of monthly- NAO index and spatially averaged SAFRAN parame- mean 500-hPa heights over the 1950–2000 period. ters at 1800 m MSL according to different temporal

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FIG. 8. Correlations (vertical axis) between the daily NAO index and spatially averaged SAFRAN parameters at 1800 m MSL with different temporal ﬁlter lengths (horizontal axis, in days): (a) temperatures over the entire French Alps and the other areas deﬁned in Fig. 7a and (b) precipitation of the same areas with the same color code.

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filter lengths for temperature (Fig. 8a) and precipitation have identified a relationship between strong westerly (Fig. 8b) and for the different geographical areas pre- flows across the North Atlantic and a positive NAO viously defined in Fig. 7a. We can see that without the index, which results in a correlation between NAO and filtering of several years (see the x axis in days), the air temperature that is quite large and positive in the correlations are not significant. On the other hand, long north of the Alps but smaller in the south. Beniston and filtering periods are impossible with our sample of about Jungo (2002) widely studied the relationship between 45 yr. However, we can observe that for temperature NAO index and the temperature and pressure fields in (Fig. 8a), a minimal total sampling interval of 4 yr is Switzerland with a particular emphasis on the increased necessary to reach a minimum correlation over the en- values since the 1980s. They successfully linked high- tire Alps and their northern and central areas whereas value NAO periods over the entire Alps to high pressure the southern Alps exhibit weaker values. Precipitation blocking events accompanied by vertical circulation in- (Fig. 8b) does not show any significant value, especially ducing compressional warming, subsiding velocities, and in the southern areas. These features have already been decreasing cloudiness and thus positive temperature identified by several authors including Beniston (2005, anomalies. Scherrer et al. (2006) have also shown an and references therein) using observed series and are enhanced occurrence of blocking-type high pressure mainly due to the lower influence of the strong Atlantic systems over Europe and its link with NAO. According flows on the southern Alps climate. The discrepancy to Fig. 8a, the correlation between filtered NAO index between temperature and precipitation is mainly due to and temperature over our working area is about 0.7, which their different horizontal characteristic scales. corroborates the previous results and indicates a mutual For this part of the study, 3 supplementary years of influence on this finer scale. The latitudinal variability is analyzed values have been added to the previous 44 consistent with the discussion in Pro¨ mmel et al. (2007). available years, extending the study period to 2005. This Detailed results (not shown here) show an overall rise was done because we thought it was important to take of about 11.58C for Chablais (the northernmost French into account in our results the severe decrease of the NAO massif) over the last 30 yr. The winter half-year increase index during these years and the corresponding tem- was almost 128C and the summer increase about 11.58C perature variations. However, given that no ERA-40 with a constant very limited variation but higher guess field was available at the time for SAFRAN, we variability during late summer. All foothill massifs used the operational daily ARPEGE fields (Courtier (Chablais–Vercors) including Mont-Blanc, Beaufortin, et al. 1991) as described in Durand et al. (1999). and Belledonne show in general the same behavior. The total 4-yr sampling interval (triangular shape, 2 yr Chartreuse is the most extreme massif with a net before, 2 yr after) was chosen for all the following ana- winter rise of almost 12.58C. The Mercantour massif is lyses and figures concerning the annual variability of well representative of the central and southern massifs. 1800 m MSL near-surface temperature and precipita- The most striking difference to the northern massifs is tion over different areas of the Alps and for the NAO a strong temperature decrease in early winter (228C) index. A larger value would not have been significant since the mid-1980s followed by only a slight increase given the 48 available years. In this section, total pre- in midwinter but an increasing trend in late winter (up cipitation (rain and snow) is mainly discussed. to 138C), which implies only a slight increase (10.58C) on the scale of the overall winter season. All central a. Temperature trends and southern massifs roughly follow this pattern with SAFRAN filtered daily temperatures over several Haute Tarentaise-Vanoise-Maurienne being the least areas are presented in Fig. 9a together with the filtered distinctive and Queyras-Parpaillon-Ubaye being the NAO index. The mean values over the entire Alps most pronounced. (black curve) exhibit the classical shape of the last years b. Precipitation trends (rain and snow) characterized by a plateau until the 1970s, followed by a more pronounced increase of about 118C. All the dif- Figure 9b shows the same results for precipitation. As ferent regional areas show the same features modulated in several other studies, no clear temporal trend or clear by the latitudinal variability and temporal smoothing. relationship with the NAO index can be found for any These characteristics have already been pointed out by of the concerned areas that exhibit only a clear lat- Trenberth et al. (2007) for a larger spatial scale but with itudinal variability between the northern and southern the same magnitude for the temperature trend, and are Alps. A linear fitting procedure was performed for the mainly due to the increase of the daily minimum tem- curves representative of the northern and southern peratures as quoted by Moisselin et al. (2002) and areas in relation to the validation process presented in Beniston (2005). On a large scale, Pro¨ mmel et al. (2007) Fig. 4 and the very small trends observed in the northern

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FIG. 9. Annual variability of the NAO index (right vertical axis) and spatially averaged SAFRAN values at 1800 m MSL with a temporal filter of 4 yr: (a) temperatures for the entire French Alps and the other areas defined in Fig. 7a; and (b) precipitation for the same areas with the same color code. In addition, a linear fit for the northern and southern areas is shown in (b). observations. However, these indications of a possible showed, over a much larger area of the Alps (about 15 small positive trend in the north and of a very flat shape times bigger than ours), a clear negative correlation in the south are not statistically significant and allow no between NAO index and the first component of an EOF conclusions to be drawn. decomposition of the winter precipitation field. In fact, These results could appear to be contrary to other the numerous differences with our experiment, in par- studies such as that of Quadrelli et al. (2001), who ticular our study area for which the main climatological

Unauthenticated | Downloaded 09/26/21 12:32 AM UTC MARCH 2009 D U R A N D E T A L . 445 precipitation features are more represented by their which denotes shorter intermediate seasons, consistent second EOF component and the use of yearly precipi- with the present personal feelings of many inhabitants. tation at midaltitudes (1800 m MSL) over 44 yr, make The precipitation (Fig. 10b, same areas as in Fig. 10a) comparisons and conclusions difficult because of these does not exhibit any temporal structure and we see scale and decomposition effects. mainly the latitudinal gradient as well as the main fea- However, all these considerations and study com- tures of the southern Alps: dry in summer and winter and parisons lead to questions, especially for this precipita- storms in autumn. Particularly in winter, year-to-year tion parameter presenting high local variability and variability can be very high and appears to have in- links with large-scale circulation patterns that are diffi- creased even more in recent years. While the last de- cult to state. Pro¨ mmel et al. (2007) mention the north- cade is generally marked by low precipitation, some ern deviation of the westerly winds in the southern Alps outstanding maximum years clearly stand out. The early together with decreased precipitation amounts. In their winters of 1997, 1998, and 2001 have beaten all records study of accumulated new snow totals (a quantity rela- in the south, but below 2000 m MSL precipitation fell tively well related to precipitation) over the Swiss Alps, predominantly in the form of rain. The midwinters of Scherrer and Appenzeller (2006) observe a correlation 1995 and 1999 brought record precipitation in the north of their first orthogonal mode with surface pressure falling as snow down to 1000 m MSL and the far south anomalies over southeastern Europe. Quadrelli et al. received large snow amounts down to low levels in 1993 (2001), over their large area, show a good correlation and 1995. The year 2001 was an outstanding year for between their first precipitation mode and north–south late-winter record snowfalls throughout the French fluctuations of the Atlantic midlatitude westerlies, Alps except in the far south (Mercantour, Alpes- whereas their second mode, much less correlated to Azureénnes) and Haute-Maurienne in the east. Even if NAO, is more influenced by northwest flows. Indeed, it is standard to split the Alps into a northern and the latter meteorological situations are basically the southern part, the central massifs, in particular, can rainiest over our working area (Fig. 6) during winter, show major deviations. Particularly in early summer, whereas the summer season is more influenced by con- these central massifs can differ considerably from both vection. These two points can partially explain our very northern and southern massifs (results not illustrated weak correlation with NAO. here), showing a strong increase (up to 100% over the Detailed results (not shown here) show flat mean whole period for Grandes-Rousses and Pelvoux). shapes in Chablais both for winter and summer seasons d. Vertical trends but with larger interannual and interseasonal variations. The Grande Rousses massif presents a significant in- At different elevations (from 600 to 3600 m MSL), crease during the summer period (;70 mm per decade) Table 8 presents, among other features, the Spearman’s and is one of the only massifs to show a small positive rank correlation coefficient ‘‘r’’ computed for the daily trend, whereas Mercantour shows a small negative trend near-surface SAFRAN analyzed temperatures over the especially during the winter season. However, Chablais entire area of the Alps for the new 47-yr period. This presents two extreme values for the last two winters coefficient is simply a special case of the Pearson product- (2001 and 2002) and Mercantour includes three very moment coefficient in which the data are converted to high values during recent winters (1997, 1998, 2001) rankings before calculation, and has been widely used while its snowfall rises to a high point at the end of the by many authors such as Moisselin et al. (2002) for trend 1970s before dropping. detection. Here, its vertical variation shows a clear positive increase with time especially at midelevations c. Annual distribution (1500–2000 m MSL). The corresponding significance The annual distribution of monthly mean tempera- has been evaluated through a Student’s t test with a ture and precipitation at 1800 m MSL is presented in 95% confidence interval [the corresponding t values are Fig. 10. The previously seen marked temperature in- presented in Table 8 in the t(r) column]. As the t crease is clear in Fig. 10a (left: entire Alps; top right: threshold corresponding to our sample size is about 2, northern Alps; bottom right: southern Alps) both in all levels except the highest (3600 m MSL) present a winter and summer seasons. The winter season exhibits significant positive near-surface temperature increase fewer cold events, begins a bit later in the north, and over the limited study period. Even though Spearman’s ends earlier in the south. The summer season becomes method does not require the assumption that the re- clearly warmer over a longer time. The transition period lationship between the variables is linear, many studies between winter and summer temperatures appears to be (such as Trenberth et al. 2007 and references therein) decreasing (as shown by the ‘‘green’’ area) in all regions, have computed linear fits, but generally over longer

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FIG. 10. Annual distribution of SAFRAN monthly-mean values at 1800 m MSL over the entire French Alps (23 massifs) for (a) temperature (left: entire Alps; top right: northern Alps; bottom right: lower southern Alps) and (b) daily mean values for precipitation (same geographical display). Years on horizontal axis and months on vertical axis.

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TABLE 8. Temperature trends for the entire Alps study area at pressure situations and of the induced vertical tempera- different elevations (from 600 to 3600 m MSL). The ‘‘r’’ column ture inversions for which the tops are generally located r indicates Spearman’s rank coefficient, and ‘‘t( )’’ represents the within these midelevations. These phenomena could also corresponding Student’s t function. The ‘‘a’’ and ‘‘b’’ columns in- dicate, respectively, the linear trend (8Cyr21) and the residual (8C) be increased by the winter snow cover decrease at these of the associated linear fit. The ‘‘6a’’ column (8Cyr21) represents elevations and by increased summer dryness (not pre- the confidence interval (95%) of the a parameter and the column sented here). 2 R is the square of the correlation coefficient of the linear fit. The The mean values obtained at midelevations corre- computation is performed over 47 yr. spond to those given by Trenberth et al. (2007) but with Alt a larger confidence interval, mainly due to our short (m) r t(r) a (8Cyr21) 6ab(8C) R2 time series. Rebetez and Reinhard (2007) find a slightly 21 600 0.48 3.6 0.020 0.010 9.7 0.24 higher value (0.0578Cyr ) for 12 Swiss stations over the 900 0.49 3.8 0.020 0.010 8.2 0.24 1975–2004 period. Beniston and Jungo (2002) also deter- 1200 0.63 5.5 0.026 0.011 6.8 0.35 mined an altitudinal variation of temperature anomalies 1500 0.70 6.6 0.034 0.011 5.4 0.46 with minimum values at low elevations. These results 1800 0.70 6.6 0.033 0.010 3.9 0.47 2100 0.66 6.0 0.031 0.010 2.7 0.44 canalsobecomparedtotheobservedtrendvaluesinFig.4, 2400 0.61 5.1 0.029 0.010 1.0 0.39 which well illustrate the variability in our mountainous 2700 0.49 3.7 0.020 0.010 20.7 0.24 area. 3000 0.41 3.1 0.016 0.011 22.2 0.17 The similar study for precipitation (not shown here) 3300 0.35 2.5 0.014 0.011 23.9 0.12 does not show any significant results for our area over 3600 0.22 1.5 0.009 0.010 25.7 0.06 the same considered time period. e. Link between temperature and precipitation trends periods. We have also determined such fits at the dif- Looking at snow precipitation trends in the light of ferent elevations despite the 47 available years, which temperature trends reveals that in the north, falling tem- implies results only representative of this period. The peratures are associated with slightly rising snowfalls limited accuracy of the linear assumption is visible through (early winter) and rising temperatures cause diminish- the values of the square of the correlation coefficient ing snowfalls (midwinter–early summer). Constant late- (column R2 in Table 8) between raw and fitted values summer temperatures show no impact on snow precipi- where only midelevation values are of little significance. tation trends, as would be expected. However, the ex- However, all vertical levels (except the highest) exhibit ample of Mercantour in the far south shows that strongly a positive linear trend (the a column) corroborated by dropping early winter temperatures do not necessarily their 95% confidence interval (6a column) with a clear result in increasing snowfalls, since total precipitation is emphasis at midelevation and a weaker signal higher. also decreasing. At Grandes-Rousses, in the central Discussion of these results is hampered by the char- part, we see that strongly rising late-winter temperatures acteristics of the analyzed temperature, which here is have hardly any effect either on snow or rain precipita- representative of the near-surface conditions but at dif- tion, but a strong early summer temperature increase is ferent mountainous elevations. It is therefore the ‘‘sub- accompanied by a very strong rainfall increase and a tle’’ result of surface and free atmosphere conditions with slight snowfall decline. Finally, near-constant late-sum- the interaction of the orographic features and effects mer temperatures are accompanied by a strong positive such as sun occultation or meteorological-induced cir- rainfall trend but have no effect on snowfall. culation. The highest elevations can therefore be as- Beniston (2003) studied the possible impacts of these sumed to be more representative of the free atmosphere climatic trends in mountainous areas on hydrology, snow conditions, which implies a reduced, or very weak, pos- conditions, glacier vegetation, and tourism; he mentions itive temperature trend. At low elevations, the temper- more particularly several research works with SAFRAN- ature trend is superimposed on other phenomena such as Crocus. Some elements of our study are in common with valley effects, boundary layer processes, local observa- those of Beniston, especially the uncertainties concern- tion site characteristics, and less sun radiance, which in- ing precipitation and the NAO–temperature link. troduce noise in the positive signal. A complementary explanation of this vertical variability can be found in the 7. Summary behavior of the NAO index for which the fluctuations are linked to pressure field anomalies. Over a large part of The validations presented here and based on the our study period, the observed positive NAO fluctuations SAFRAN analysis process show the robustness of the (Fig. 9) are thus representative of more frequent high models used and their ability to reproduce the main

Unauthenticated | Downloaded 09/26/21 12:32 AM UTC 448 JOURNAL OF APPLIED METEOROLOGY AND CLIMATOLOGY VOLUME 48 meteorological features of several mountainous obser- make it a reliable tool. We are also indebted to the vation sites even when data are deliberately omitted ECMWF, who carried out the ERA-40 simulations that from the analyses. The analyzed results on the massif are the basis for this study, to the NOAA/NWS/CPC for scale can be considered to be representative of the cli- the daily NAO index, and to several colleagues of matology of the French Alps study area at different Me´teó-France who helped us to collect and process dif- elevations during the considered period. ferent climatological series. We also thank numerous The annual mean air temperature at 1800 m MSL people together with the three anonymous reviewers, the varies from 3.48C in the north (Chablais massif) to 5.18C editor, and the native English translator who all helped in the south (Mercantour massif). 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