ACCORD Institut Non Linéaire de Nice; Laboratoire de ... · ACCORD Institut Non Linéaire de Nice; Laboratoire de Météorologie Dynamique (CNRS) 166 Partner: Laboratoire de Météorologie

ACCORD Institut Non Linéaire de Nice; Laboratoire de Météorologie Dynamique (CNRS)

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Partner: Laboratoire de Météorologie Dynamique (LMD) du CNRS (04)Subcontractor: Institut Non Linéaire de Nice (INLN) CNRSResponsible Scientists: Guy Plaut (INLN) and Robert Vautard (LMD)Scientific Staff: Guy Plaut, Robert Vautard, Eric SimonnetAddress: INLN, 1361 route des Lucioles, F 06560 Valbonne, FranceTelephone: 33 (0) 4 92 96 73 10Fax: 33 (0) 4 93 65 25 17Email: [email protected]

1. Original Objectives and Extent to Which They Have Been Achieved ...................................................... 1681.1 Original objectives................................................................................................................................ 1681.2 Extent to which they have been achieved ............................................................................................. 168

2. Weather Regimes (Task 1.3) ..................................................................................................................... 1692.1 Introduction........................................................................................................................................... 1692.2 Data and methodology .......................................................................................................................... 1692.3 Weather regimes ................................................................................................................................... 169

2.3.1 Z700 and Z500 ............................................................................................................................ 1692.3.2 SLP weather regimes................................................................................................................... 170

2.4 Comparison between different pressure level weather regimes............................................................ 1702.5 SLP weather regimes over the last 120 years........................................................................................ 171

3. Weather Regimes and Local Climate (Tasks 3.2 and 3.5)......................................................................... 1723.1 Introduction, data and methodology ..................................................................................................... 1723.2 Weather regime influences on local temperature.................................................................................. 173

3.2.1 French station temperatures and SLP weather regimes............................................................... 1733.2.2 Gridded European temperatures and SLP weather regimes ........................................................ 1733.2.3 Weather regimes with more severe membership criteria............................................................. 173

3.3 Z700 weather regimes influence on precipitation ................................................................................. 1743.3.1 Wet day percentage at French stations ....................................................................................... 1743.3.2 Heavy precipitation over the Alps ............................................................................................... 174

3.4 Weather regimes and local climate: a summary.................................................................................... 175

4. Intense Events LSC Classification (Tasks 3.2 and 3.5) ............................................................................. 1754.1 Introduction........................................................................................................................................... 1754.2 Data processing and methodology ........................................................................................................ 1764.3 Savoy, the Alpes Maritimes and Queyras ............................................................................................. 176

4.3.1 The LSC clusters for IPE over Savoy.......................................................................................... 1764.3.2 The LSC clusters for IPE over the Alpes Maritimes ................................................................... 1764.3.3 IPE trends and comparison with Queyras....................................................................................1774.3.4 Alternative to IPE LSC classification.......................................................................................... 177

4.4 IPE over other Alpine sub-regions........................................................................................................ 1784.5 Weather regimes and intense events ..................................................................................................... 179

4.5.1 Introduction ................................................................................................................................. 1794.5.2 Very cold days and weather regimes........................................................................................... 1794.5.3 Direct classification of very cold days LSC ................................................................................ 1794.5.4 The case of precipitation intense events ...................................................................................... 180

4.6 Summary of Section 4 conclusions....................................................................................................... 180

5. Low-Frequency Oscillations (LFO) (Task 2.2) ......................................................................................... 1815.1 Introduction........................................................................................................................................... 1815.2 Comparison of intraseasonal LFO at various levels.............................................................................. 181

5.2.1 Methodology ............................................................................................................................... 1815.2.2 The main SLP intraseasonal oscillations ..................................................................................... 1825.2.3 Interactions between LFO ........................................................................................................... 182


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5.2.4 SLP LFO further in the past ........................................................................................................ 1825.2.5 Comparison between SLP and Z700 LFO................................................................................... 182

5.3 SLP interannual LFO ............................................................................................................................ 1835.4 Intraseasonal LFO and weather regimes ............................................................................................... 1835.5 Intraseasonal LFO and local surface climate ........................................................................................ 184

5.5.1 Temperature ................................................................................................................................ 1845.5.2 Precipitation ................................................................................................................................ 184

5.6 Multi-channel wavelet analysis............................................................................................................. 185

6. Conclusions and Relevance to ACCORD Objectives................................................................................ 185

7. Acknowledgements.................................................................................................................................... 187

8. References ................................................................................................................................................. 187

9. List of ACCORD Publications .................................................................................................................. 188

Appendix A: Mutual Information....................................................................................................................... 189


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1. Original Objectives and Extent to Which They Have Been Achieved

1.1 Original objectivesThe major scientific objectives of INLN and LMD were the following:

• To classify circulation at the regional scale (Task 1.3).• To use Multi-Channel Singular Spectrum Analysis (MSSA) to examine the occurrence of

oscillations and variability over the last 120 years (Task 2.2).• To address the question of the linkage between large-scale circulation and extreme

weather events (Task 3.2).• To explore downscaling schemes for surface weather conditions over Western Europe and

the Mediterranean (Task 3.5).• To explore new approaches such as "mutual information" (Task 3.5).

1.2 Extent to which they have been achievedWe started in ACCORD with expertise in MSSA and automatic classification. We alsobrought our powerful package ANAXV which allows automation of almost any numericalexperiment using data. PCA and MSSA, as well as the dynamical cluster algorithm, are alsoimplemented in ANAXV. It was made available to Partners 3 and 7.

Task 1.3 was addressed throughout the ACCORD contract period. Circulation classificationprovides the basis of Sections 2 and 3 of this report. The work reported in Section 4 alsoaddresses (particular daily) circulation classification. It should be noted that the classificationis always performed at the supra-regional scale, not the regional scale. Regional-scaleclassification was undertaken, however, the linkages between clusters and mesoscale weatherwere systematically optimized using the largest available scale circulation patterns. Task 2.2was dealt with by Eric Simonnet and many original new results are reported in Section 5. Theinnovative work and conclusions regarding Task 3.2 are reported in Section 4. Thedevelopment of Task 3.5 is described in Sections 3 and 4 (exploration of downscalingschemes) and in Appendix A (mutual information). However, downscaling schemes for theMediterranean were not explored because of the lack of long period high-resolution pressuredata.

Our research in the ACCORD program framework, as well as our main conclusions, arebrought together in three main report sections:

• Large-Scale Circulation (LSC) classification of all days into clusters and weather regimes(Section 2) and investigation of the links between these weather regimes and local weather(Section 3). One of our most important conclusions is that this approach provides a muchfiner description of local climate than just the average climate.

• An original approach to downscaling schemes, intended for forecasting Intense Events (e.g.heavy precipitation or cold spells) or, at a later date, for evaluating the possible local changesin Intense Event occurrence due to greenhouse warming, was developed. It consists of theclassification into clusters of LSC for Intense Event days only. Intense Events LSC clustercentres were found to have high discriminating power in many cases (Section 4).

• Several LSC classifications (defined using NCEP Z500 and Z700 Reanalysis fields, as wellas the 120 year daily SLP data set from the Climatic Research Unit (CRU)) were submitted to


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MSSA. Intraseasonal oscillations at different pressure levels, as well as at differentfrequencies, were compared. Interannual as well as interdecadal oscillations were alsoinvestigated. The connections with mesoscale weather patterns were also studied (Section 5).In the same section, we say a few words about an innovative technique, the multichannelwavelet transform, which was applied to the 120 year SLP data set. Preliminary, but verypromising, results were obtained linking intraseasonal wavelet activity fluctuations to knowninterannual climate oscillations (Section 5.6).

• Finally, the "mutual information" approach was developed and preliminary results arepresented in Appendix A.

2. Weather Regimes (Task 1.3)

2.1 IntroductionFollowing Vautard (1990) and Michelangeli et al. (1995), we define weather regimes as thecluster of central patterns obtained when classifying all the LSC patterns of a data set. In thisway we identify the most recurrent LSC patterns. As in Michelangeli et al. (1995), we use thedynamical cluster algorithm implemented in the ANAXV package, which allows a fullyobjective procedure without any a priori hypothesis about the classes to be found. Wecompare the classifications obtained for the different fields, as well as the regimes at differenthistorical periods for SLP. We also consider low and very low frequency variability over thelast 120 years.

2.2 Data and methodologyThe daily Z500 and Z700 data come from the NCEP/NCAR Reanalysis project and extendfrom 1958 onwards. To make comparison with the results of Michelangeli et al. (1995) easier,we use the same 100° longitude by 40° latitude window centred on 50°N 10°W and weclassify November to March daily LSC. Thus our weather regimes are extended winter ones.The SLP data were obtained from the CRU and cover a longer period (1880-1997).

We first perform a spatial filtering, retaining only a small number of EOFs (10, or even sixlater without any major ramifications). We then perform the classification of LSC patternswithin this 10 dimensional PC-space. Given a prescribed number of clusters k, the goal of thedynamical cluster algorithm is to find a partition of the data set into k clusters that minimizesthe sum of variance within the clusters. The Euclidian distance is used first as a similaritymeasure. Other criteria can be used as well (Robertson and Ghil, 1999), such as thecorrelation distance dc = 1 - corr, where corr is the anomaly pattern correlation (actually acosine in the PC-space); dc ranges from 0 for perfectly correlated patterns up to 2 for inverselycorrelated patterns. We use dc extensively when producing probability or compositehistograms. We also check the differences between the classifications obtained with the twogeopotential height fields (Section 2.3.2). The best number k of clusters is checked using a rednoise test which provides confidence thresholds for a classifiability index c* (Michelangeli etal., 1995).

2.3 Weather regimes

2.3.1 Z700 and Z5006140 extended winter Z700 maps were classified. Figure 1a shows the classifiability index c*

versus k, the number of classes, together with the 10-90% confidence level limits. Only the


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choice k = 4 allows one to get more significant classes than the red noise test. The fourweather regimes identified are the same as those found by Vautard (1990) who looked forweather regimes as quasi-stationary points in the 10 leading PC space. The same classes werealso found by Michelangeli et al. (1995) using the dynamical cluster algorithm. FollowingVautard (1990), we define the patterns shown in Figure 2 as follows: AR (Atlantic Ridge)inducing north-westerlies over western Europe; BL (Blocking) with a maximum positiveanomaly over Scandinavia; GA (Greenland Anticyclone); and, ZO (Zonal) with enhancedzonal flow.

The Z500 maps classified span the same period as the Z700 ones. With k = 4 clusters, thepatterns correspond exactly to the Z700 ones. However, the red noise test indicates that k = 5is preferred (Figure 1b). The fifth selected regime displays a strong positive anomaly close toNewfoundland, together with a less important one over eastern Europe, and is referred to asGT (Greenwich Trough). This regime does not persist when all year LSCs are classified, andtherefore appears less robust than other ones. The five weather regime anomaly patterns areshown in Figure 3, together with the corresponding full-field Z500 patterns.

2.3.2 SLP weather regimesAlthough the CRU daily SLP data set covers 120 years, we first only classify winters after1957 in order to make the comparisons with Z700 and Z500 more meaningful. In this case, k= 5 classes is preferred (Figure 1c). It may be seen in Figure 4 that four of the SLP regimeshave anomaly patterns rather similar to the Z700 ones. The most marked difference is the factthat the SLP regime we call BL corresponds, on average, to an anticyclonic cell with its centreover Eastern Europe (near the Ukraine), creating southeasterly flow (not that cold in manycases) over western Europe. A fifth regime which we call WBL (West Blocking), but whichcould also have been called Blocking, favours much colder conditions over Europe, with ananticyclonic cell centred over Scotland on average. The SLP BL weather regime could alsohave been called European Anticyclone (EA).

Before turning to a comparison of weather regimes at different pressure levels, let us brieflymention that we also performed daily LSC classification into weather regimes for the wholeyear. Very similar patterns (not shown here) were found. The significance checks (leading tothe best choice for k) are summarized in the right-hand panels of Figure 1. Three points areworth emphasizing: i) classifications using an angular or Euclidean distance lead to almostindistinguishable patterns; ii) five clusters are always preferred for SLP, the same as thewinter ones above; iii) for Z700 and Z500, three clusters are preferred, the patterns areidentical at both pressure levels, two of them are the already known BL and ZO and the thirdone may be called AR-GA.

2.4 Comparison between different pressure level weather regimesContingency tables of weather regime occurrence at different pressure levels have beencomputed and the 5% and 95% significance thresholds established by randomly shuffling thedays 100 times (Tables 1-3). Numbers in italics are below the 5% confidence level thresholdand provide a significantly smaller number of coincidences than random; bold numbers areabove the 95% level.

• Z700-Z500 (Table 1): the four regimes AR, BL, GA, and ZO for Z700 and Z500 show anobvious one-to-one correspondence. This is also suggested by visual inspection of Figures 2and 3. The GT weather regime seems to correspond to a combination of the Z700 AR and BL


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regimes.• Z700-SLP (Table 2): similar conclusions apply when we compare Z700 and SLP data. WBLappears as a combination of certain Z700 AR and BL members.

• Z500-SLP (Table 3): once again, the new (Z500) regime GT members are mainly takenfrom the SLP AR and blocking-like regimes (BL and WBL). This is in agreement with bothprevious comparisons since the Z700 BL regime takes members from both BL and WBL SLPregimes.

We conclude this section by noting that there are highly significant correlations betweenclassifications of LSCs at different pressure levels. For instance, ZO days at one level arealmost always classified as ZO at other levels, whereas WBL days (for SLP) never coincidewith ZO days at other levels. Such conclusions were not a priori obvious, since the LSCs forall days have to belong to one of four or five classes, which forces classes to hold some badlycorrelated members. In the next section we use more severe class membership conditions,with the drawback that not all days will be classified.

2.5 SLP weather regimes over the last 120 yearsIn order to compare the SLP regimes for the period 1958-1997 with those obtained for theearlier periods 1880-1918 and 1918-1957, we repeat the SLP analysis for these two periods.Data are processed in the same way for each period. We first subtract the extended wintermean field at each gridpoint, then perform a PCA, and finally classify within the 10-leadingPC space:

• 1880-March 1918. The dynamical cluster algorithm selects only four clusters for this period.A visual inspection shows that the regime GA is no longer selected although the WBL pattern(Figure 5, left column) has been a little distorted into a pattern somewhat intermediarybetween the WBL and GA patterns of Figure 4.

• November 1918-1957. For this more recent period, five clusters are preferred. They areactually indistinguishable from the five clusters obtained for the preceding period (comparethe right column anomaly maps of Figures 5 and 4).

This comparison of weather regimes for the last three 40-year periods, leads us to theimportant conclusion that the most recurrent LSC patterns have been very stable during atleast the last 120 years. Four rather than five clusters are preferred for the earliest period but ifone nevertheless uses k = 5 for this period, the same five patterns are obtained as for the othertwo periods.

Since the weather regimes found over the three periods are very similar, we classify all winterLSCs between 1880 and 1998 according to the five classes of the most recent period. InFigure 6 we plot the number of occurrences of a given weather regime per winter, togetherwith its 10-year moving average. Low-frequency variability clearly appears. An SSA(Vautard et al., 1992) produces interdecadal peaks around 20 years for GA, and 15 and 14years for AR and BL. Similar interdecadal variability was found by Vautard et al. (1992) forthe IPCC global surface air temperature record. Peaks at 14 years have also been identified byPlaut et al. (1995) in the Manley-Parker Central England Temperatures time series (Manley,1974; Parker et al., 1992). Interdecadal variability was also identified by Moron et al. (1998).Very low-frequency variability dominates the WBL and ZO weather regimes. Notice, inparticular, the increase of ZO over the last 15 years (well documented in numerous North


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Atlantic Oscillation (NAO) studies), and the corresponding decrease of WBL after a "plateau"during the break in Northern Hemisphere warming. (Notice, however, that it is during therecent 1995-1996 winter that one sees the highest WBL frequency for 120 years.) An SSAalso produces Quasi-Biennial Oscillation-like (QBO-like) peaks at approximately 2.5 yearsfor all five regimes (not shown). A 2-3 year oscillation is also present in the NAO index(Higuchi, 1999).

To summarise, the winter frequencies of the five SLP weather regimes show stronginterannual as well as interdecadal (and even slower) variability. The weather regimesthemselves (i.e. the most recurrent LSC patterns) appear very stable over the last 120 years.

3. Weather Regimes and Local Climate (Tasks 3.2 and 3.5)

3.1 Introduction, data and methodologyIn order to check the way in which weather regimes influence local climate, we first classifylocal temperatures into terciles (warm, mean and cold) and consider the relative change offrequency of a given temperature category for each weather regime. For brevity, we considercold tercile occurrences at 30 French stations and also at 460 NCEP Reanalysis gridpointsspanning Western Europe between 35°N and 71°N, 10°W and 33°E.

Then we turn to precipitation and compare the percentage of "wet" days (as opposed to "dry"days) and also “heavy precipitation" days, defined as those belonging to the last decile ofprecipitation amounts.

In addition to the large-scale fields processed in Section 2, we use four data sets:

• Mean daily temperature recorded at 30 Météo-France stations from 1949 to 1996.

• NCEP daily air temperature at 2 m on a Gaussian grid, restricted to a Western Europe area(see above). These data cover the period 1958 to 1997 and are Reanalysis data processedusing the NCEP/NCAR model in the same way as the LSC geopotential height fields.

• Daily precipitation values from the 30 Météo-France stations.

• The Alpine Precipitation Climatology (APC) from ETH Zürich (Frei and Schär, 1998)which covers the European Alpine area and the adjacent foreland. It is a compilation of 6678daily station records on a 25 km grid for the period 1971-1995. Each gridpoint corresponds onaverage to six stations.

Once that temperatures are classified into terciles, we compute for each gridpoint or stationlocation the 3x5 contingency tables associated with the SLP weather regimes. We count, forinstance, the number of days with simultaneous occurrence of a cold temperature at theselected location and ZO, and then the percentage of cold days for ZO, which we comparewith its climatic average of 33%. If this percentage falls to say 11%, we conclude that it haschanged by a factor -66%. Note that changes may be larger than +100%. We also compute the95% significance thresholds based on randomly reshuffling the days 100 times.


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3.2 Weather regime influences on local temperature

3.2.1 French station temperatures and SLP weather regimesThe relative changes of cold day occurrence probability for the five SLP weather regimes areplotted in Figure 7. Non-significant departures from the climatic average tercile are indicatedby the shaded areas. One of the most outstanding changes is the almost doubling (100%increase) of cold days with WBL at most stations (the increase is slightly smaller in thesouth). In contrast, ZO cold days are very rare (a decrease of more than 50%, sometimes75%). Other weather regimes show less pronounced (but often significant) changes.

A first conclusion seems to emerge. At least for France, the weather regime approach allows afiner description of the local climate than simply average climate. The atmosphere does notmerely evolve around a mean state, it spends more time around a few particular (large-scale)states with well defined consequences for local weather.

3.2.2 Gridded European temperatures and SLP weather regimesFigure 8 shows the consequences of each of the five SLP weather regimes for the cold dayoccurrence probability. AR mainly brings warmer conditions (a 50% decrease in cold days)over Eastern Europe. GA has more contrasting influences: cold day occurrence increases byup to 70% over Scandinavia, but decreases by 40% over Iberia. BL is warmer from the BritishIsles to Scandinavia, but much colder in southeastern Europe (remember that the BL regime ischaracterized by an anticyclonic cell over eastern Europe, providing mild oceanic flows overnorthwestern and northern Europe). ZO and WBL have their most outstanding consequencesover western France, in good agreement with what was observed for station data in Figure 7.Also for other weather regimes, the agreement between Figures 7 and 8 (over France) isremarkable, especially if one considers the different origins and nature of the two data sets.

3.2.3 Weather regimes with more severe membership criteriaAs Robertson and Ghil (1999), we modified the criteria required for weather regimemembership by introducing a rather severe angular filtering in the PC space, requiring amaximum angle of 45° (cos > 0.7, or dc < 0.3) between a given daily anomaly pattern and theparticular weather regime pattern. In this way, one eliminates days not clearly belonging toany one of the weather regimes. From now on, no more than 22-28% of the patterns areclassified, but this strong requirement for membership has interesting consequences. One cansee in Figure 9 that for GA the probability of cold days doubles over Scandinavia. Incontrast, with the new ZO class, the probability of a cold day decreases by more than 80%over a large area extending from western France and southeast England eastward to southernScandinavia, western Poland, and Austria. WBL has similar (but opposite) strong localconsequences, extending over almost all Europe. From northwestern France to Poland thecold day probability increases by up to 130-150% compared to its climatic average.

All these features reinforce our view that the classification of LSCs into weather regimesprovides an efficient and coherent way of describing local departures of climate from average.Weather regimes could then be used in downscaling approaches to describe local climate, oreven local climate changes, starting from GCM output.


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3.3 Z700 weather regimes influence on precipitation

3.3.1 Wet day percentage at French stationsWe now study precipitation using the same methodology as for temperature. A first glance atFigure 10 indicates an outstanding contrast between the Mediterranean region stations and theremaining ones. With AR, both western and southeastern France have fewer wet days,whereas the extreme northeastern French records have up to 15% more wet days than average.These features are not surprising if one considers (see Figure 2) that during AR days, westernEurope is submitted to northwesterly flow, mostly anticyclonic to the southwest, but morecyclonic to the northeast. The particular orography of France may explain the small wet dayincrease also observed near the Pyrennees and to the west and north of the Massif Central.With BL, precipitation probability decreases strongly almost everywhere. The largestdecrease is up to 60% to the north, but there is a sharp increase in the Roussillon region,where the easterlies due to BL favour heavy precipitation (see Section 3.3.2) or even extremeflooding (such as occurred in mid-November 1999). With GA, there is a large increase in wetdays over all the country. This increase is much smaller over Roussillon due to orographiceffects, but reaches a maximum over the extreme southeast where a cluster with a GreenlandHigh favours intense precipitation (see Section 3.3.2). Finally, with ZO days, there is a sharpcontrast between most Mediterranean regions and the remaining ones. Wet day probabilityrises by up to 40% to the northwest, but decreases by up to 20% to the southeast.

Correlation patterns (not shown) between the total November-March precipitation amountsand the number of occurrences of each Z700 weather regime were also computed. Theyappear very similar to the patterns shown in Figure 10 and again suggest that the weatherregime approach is a relevant one if one wishes to describe coherently the local climatedeparture from average. At least a large amount of this departure for a given period is verylikely to originate in anomalous weather regime frequencies. Consider for instance theparticular case of ZO. The correlation patterns (not shown) divide France into two parts: onethird (roughly the southeast) with decreased rainfall amounts and the remaining two thirds(north, west and southwest) with a correlation between precipitation amounts and the numberof ZO occurrences exceeding 0.5 in most northern stations. This is coherent with the recentincrease of both winter NAO index and northwest European winter precipitation (togetherwith the corresponding decrease in several southern European sub-regions, see Section 3.3.2).

3.3.2 Heavy precipitation over the AlpsHere we consider days with precipitation amounts belonging to the highest precipitationamount decile. Two outstanding features are seen in Figure 11. First, a lot of meso-structuresappear. They characterize the way in which local precipitation data are connected to LSC.Second, almost the same particularities appear for the Mediterranean regions as in Figure 10.Let us first discuss the case of BL. Heavy precipitation probability (hereafter HPP) decreasesby roughly 80% in a wide northern third of the Alpine Precipitation Climatology (APC) area,whereas the wet day occurrence (not shown) decreases by only 30% on average. InRoussillon, HPP increases by more than 40%. BL indeed favours intense precipitation overthis coastal area most exposed to easterlies (see Section 3.3.3 for further discussion of thispoint). For AR, HPP increases by almost 60% over the most northeasterly third of the APCdomain. For GA, the increase is over 100% over the French southern Alps. With ZO, the HPPincrease is over 40% in a rather small northwestern sector, whereas a significant decreaseoccurs over the French southern Alps, Languedoc-Roussillon and also the Italian Piedmont.


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3.4 Weather regimes and local climate: a summaryWe conclude that a small number (four or five) of large-scale circulation patterns (weatherregimes) are able to describe many features of European sub-regional weather patterns oreven Alpine meso-climates. The description one gets using weather regimes allows a muchfiner description of the local climate than simple average climate. The atmosphere does notmerely evolve around its mean state, but spends much more time around a few characteristicstates with well defined consequences for local weather. This appears true for precipitation aswell as for temperature. In this way, interannual, or even interdecadal, local climate variabilitymay originate above all from fluctuations in the relative occurrence of weather regimes. Oneis then tempted to conclude that such an approach may provide a reliable framework forbuilding downscaling algorithms for local climate change study purposes, using GCM outputas a proxy, since GCM-simulated LSC changes are likely be more relevant than the sameGCM mesoscale features.

The above conclusions about weather regimes reinforce the interest in dynamical systemsapproaches to climate change studies, such as suggested by Palmer (1983). During the last120 years, the unstable fixed points (the weather regimes) did not really change, whereas theprobability of any given weather regime was certainly not stationary.

A question remains, however. If one's interest is not in average weather, but in intense events(if not extreme ones for which a statistical description would be questionable), do theseweather regimes (the most frequently observed large-scale patterns) provide an accurateframework in order to build tentative downscaling algorithms? We tackle this question in thenext section.

4. Intense Events LSC Classification (Tasks 3.2 and 3.5)

4.1 IntroductionSuppose from now on that one is interested in the statistical forecasting (through somedownscaling scheme) of Intense Events (IE) starting from LSC patterns. The followingquestion may be raised. What is the most efficient way of proceeding? Is it the previous one(namely a classification of LSC for all days into a few classes leading to rather similarconsequences for local weather patterns within a class)? Or does it consist in a prior selectionof IE days LSC followed by a classification of these peculiar LSCs alone? We adopt the laterapproach in this section, and test it with particular events that we call Intense PrecipitationEvents (IPE) over a few small Alpine sub-regions. We want a criterion to establish whichapproach is the most efficient. For this purpose, we will evaluate the extent to which theconditional probability of IE occurrence departs from its climatological mean either for dayswith LSCs close (from the correlation measure point-of-view, see the definition of dc inSection 2) to weather regimes central patterns, or for days close to IE LSC clusters centralpatterns. The difference between the two approaches lies in the fact that one classifiesthousands of daily patterns (without any prior selection), but for the other only a few hundred.In the latter case one may get clusters pointing towards sparsely populated phase spaceregions, since selected patterns correspond to a priori infrequently visited phase spacedirections (do not forget that IE are rare events), whereas the often visited weather regimepatterns are likely to correspond to maxima of the PDF.


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4.2 Data processing and methodologyWe use the APC data set which combines daily precipitation records from thousands ofstations (see Section 3.1 for a brief description). Small, hopefully homogeneous, Alpine sub-regions corresponding to 10-15 gridpoints are selected (Figure 12). IPE over given sub-regions are defined using some objective criterion. The one we choose results from acompromise between the extreme character of precipitation and the necessity to keep a largeenough ensemble in order to get significant classifications. We define IPE, say over the AlpesMaritimes, as any day when at least one gridpoint received more than a given precipitationthreshold. With the commonly used value of 40 mm as a threshold, we are left for the AlpesMaritimes with about 12 IPE per year on average. (Raising the threshold value to 50 mm,although giving fewer IPE, leads to indistinguishable LSC cluster patterns.) Once the IPEhave been identified, we select the corresponding Z700 LSCs (in their 10 leading PC's space)and turn to their classification, again using the dynamical cluster algorithm. New significancechecks are defined in order to check the robustness of the classification, based upon acomparison between the classifiability index of the IPE LSC and that of a set made up of thesame number of LSC patterns, but randomly selected from the data set.

In order to estimate the usefulness of a classification, we introduce the concept ofdiscriminating power. We ask ourselves if any practical conclusion may be firmly stated whenthe large-scale circulation (analysed or forecast) resembles one of the cluster centres. If this isthe case, that is if patterns quite similar to a given cluster centre correspond to IPE with a highprobability (see panels c of Figures 13 and 15), and also lead to high precipitation compositesover the given sub-domain (see panels d of Figure 13 and 15), we will say that the cluster inquestion has a high discriminating power.

4.3 Savoy, the Alpes Maritimes and Queyras

4.3.1 The LSC clusters for IPE over SavoyFor both Savoy and Alpes Maritimes, the random test selects k = 2 clusters, with aclassifiability index c* well above the 90% significance limit. For IPE over Savoy, the mostoutstanding feature of cluster 1 is a strong negative height anomaly (-150 m) over the NorthSea, whereas cluster 2 may be characterized by its weak negative anomaly over westernEurope, with a light anomalous southerly flow over the Alps. Daily anomalies almost neverlook very similar to cluster 2 centre since there are only 10 days (during 25 years) with d2

< 0.2 (i.e. a pattern correlation > 0.8), none of them being an IPE. Looking at the grey bars inFigure 13, one realizes that this cluster has very low discriminating power. It may be viewedas an aggregate of scattered LSC leading to heavy precipitation mainly from March to earlyautumn (Figure 14), with a pronounced peak in September.

In contrast, anomaly patterns similar to that of the cluster 1 centre do occur in the actualanomaly maps and mostly correspond to rather heavy precipitation days. More than a quarterof those days with d1 < 0.2 are indeed IPE and the histogram of rain composites shown inFigure 13 display a pronounced peak for small d1 values. Most of the (late) autumn and winterheavy precipitation events correspond to class 1 IPE (Figure 14), in such a way that thiscluster is responsible for the great bulk of heavy snowfalls.

4.3.2 The LSC clusters for IPE over the Alpes MaritimesThe situation is not very different over the Alpes Maritimes, but with a very different patternat least for the cluster with the highest discriminating power. Cluster 1 could be called GASC(Greenland Anticyclone - Sole Cyclone) and actually points towards a highly discriminating


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phase space direction since close to this direction (d1 < 0.2), more than 50% of days areindeed IPE (Figure 15c). The composites in Figure 15d confirm the interest of GASC. Class 1events are predominantly autumn or cold season events (Figure 14) and may be accompaniedby heavy snowfalls over the Southern Alps in the later case, such as in January 1994 whenfour class 1 IPE occurred, giving rise to avalanches and a lot of damage to power lines inmountainous areas of the Alpes Maritimes.

The IPE probability is much lower for cluster 2 (grey bars of lower panels of Figure 15)which could be called QP (Quadrupole). If one tries classification into more than two clusters,cluster 1 remains very robust, whereas cluster 2 is broken into smaller clusters. The cluster 2centre could perhaps be viewed as an average direction between a few fuzzy sub-clusters.

4.3.3 IPE trends and comparison with QueyrasAlthough the APC covers only 25 years, we tentatively turn to a discussion of possible trendseither in IPE frequency, or in total precipitation amounts from IPE. If one observes that therecent Northern Hemisphere warming mainly occurred after 1980, looking at IP trends overthe last 25 years makes some sense. In order to get more sensible conclusions, we consideraverage precipitation amounts for the whole set of gridpoints of a sub-region. In this waypossible trends will be more confidently established, since local extreme events will be mostlydissolved within yearly averages involving 10 to 15 gridpoints and almost as many intenseevents (in addition each gridpoint precipitation amount holds for a weighted average alreadyinvolving several rain gauges).

Opposite linear trends appear in the left and right panels of Figure 16. Annual intenseprecipitation amounts begin with roughly 2.5 times larger amounts for the Alpes Maritimesthan for Savoy-Mont Blanc, whereas they end with similar values for both sub-regions. TheSavoy and Mont Blanc region, which lie on the northern flank of the Alps, thus recordedincreasing intense precipitation during the last 25 year period, as did the northwestern Europecountries, whereas an opposite tendency was observed over the Alpes Maritimes. The lattertrend, which amounts to almost -150 mm for annual amounts between the beginning and theend of the period, would have been even more pronounced without the heavy autumn andwinter precipitation of the early 1990s. If the precipitation records for 1999 were available,these opposing tendencies would appear even more pronounced!

We end this comparison with some remarks about a similar classification of LSCs of IPE overan intermediate Alpine sub-region, the so-called Queyras and surrounding massifs (see Figure12). Since the Queyras is sheltered by other massifs both from northerly and southerly flows,one cannot expect many IE, at least if the same threshold is used. We performedclassifications of IPE LSCs for the Queyras, using an identical IPE definition. Three was thebest choice of clusters number. Figure 17 shows some features of clusters 1 and 2. Annualamounts from both these clusters are indeed much lower than for the previous Alpine sub-regions. However, it is enlightening to observe that Queyras cluster 1 centre looks rathersimilar to Savoy-Mont Blanc cluster 1, whereas Queyras cluster 2 is almost identical to theAlpes Maritimes cluster 1. Thus one should not be surprised to observe the opposite lineartrends exhibited by these two clusters contributions to intense precipitation.

4.3.4 Alternative to IPE LSC classificationWe discuss briefly a few alternative ways of dealing with IPE LSCs.


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• Compositing all IPE LSCs. With such an approach, smaller LSC domains are preferredsince, in most cases, significant anomalies only extend over a limited geographical area.Figure 18 displays all IE composite maps for both the Alpes Maritimes and Savoy. At firstglance, the composite approach seems rather challenging in the former case. Any day with dc

< 0.2 has a high probability of being an IPE, and the gridpoint precipitation amount histogramof panel c) exhibits a sharp peak at small dc values. On closer inspection (not shown), onerealizes, however, that the number of days with dc < 0.2 is much smaller (by a factor < 1/3)than the number of days with d1 (the distance to cluster 1 centre) smaller than 0.2, so that thisapparently higher discriminating power is almost inoperant as compared to that of cluster 1(GASC). Other advantages in favour of the cluster approach are: i) the different seasonalbehaviour displayed by different clusters (Figure 14); and, ii) the more subtle understandingof underlying dynamical processes which is made possible through the classificationapproach, remember in particular the insight one gets into the origin of the opposite trendsexhibited by Queyras clusters 1 and 2.

• Using a narrower domain never improved the cluster properties; they were at best almostunchanged; some Alpine sub-regions were more sensitive to the western parts of the domain,others to the eastern part.

• Using SLPs instead of Z700 tends to make cluster discriminating power only a little lower.

• Other possibilities such as thickness (e.g. Z300 - Z700) only resulted in some improvementsfor temperature (not precipitation) IE (see Section 4.5).

4.4 IPE over other Alpine sub-regionsTogether with Partner 7, the LSC of IPE over Ticino were also classified. Tests on theclassification index lead us to keep only gridpoints lying south of the mountain ridgeseparating the Swiss Rhone valley from the Piedmont flank. The region around LagoMaggiore is one of the most likely to experience IPE which may occur almost all through theyear (with maxima in October and May). Three clusters were preferred, one of them beingvery similar to GASC. However, no significant trend could be observed for Ticinoprecipitation amounts from IE. If one takes three clusters for the Alpes Maritimes (althoughclassification into three clusters lead to a classifiability index below the 90% significancethreshold, the clusters are still fully robust), they can be put in a one-to-one correspondencewith the Ticino clusters (Figure 19).

The left panel of Figure 20 shows the three cluster centres responsible for IE over RivieraLevante. Once again, they look very similar to the Ticino or Alpes Maritimes clusters.However, as for the Alpes Maritimes, this sub-region experienced a pronounced negativetrend for IE annual amount, which decreased by more than 300 mm on average between 1971and 1996 (Figure 21, left histograms). These observations contrast with what may beobserved for Roussillon where IE LSC clusters (Figure 20, right panel) do not correlate withthe previous ones. The main patterns responsible for IE over Roussillon look very similar toBL or to anti-zonal flows (remember that IPE increased over this region during the negativeNAO 1995-1996 winter). Over Roussillon, precipitation amounts from IE, although limited,increased by two during the APC period (Figure 21, right histograms).

At this point, it may seem surprising that with very similar LSC clusters for IE, both the AlpesMaritimes and the Riviera Levante experience decreasing annual precipitation amounts


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whereas over Ticino amounts from IE do not decrease. Starting from the fact that IE mostlyoccur during autumn and winter over the former regions which experience summer droughts,whereas they occur all year round over Ticino, we tried to deal separately with autumn andcold season events (from October to March) and spring and summer ones (April toSeptember) over Ticino. In Figure 22, the year is divided into two extended seasons. One cansee that precipitation amounts from the former season IE (i.e. autumn-winter IE) actuallydisplay a (rather small) negative trend, whereas the later season (spring-summer) IEprecipitation amounts show a pronounced positive trend (+200 mm in 25 years). One couldtentatively interpret these features in the following way. During the period 1971-1996,Mediterranean Alpine regions tended to experience lower precipitation amounts from IPE. Inaddition to the ordinary summer drought, autumn and winter precipitation shortagesthreatened. During these seasons, similar features emerged for Ticino which however receivedincreasing amounts from IPE during the complementary season when precipitation almoststops along the Mediterranean coastal areas.

As to the Roussillon, which also experiences IE mostly during autumn and cold seasons, andwhich also lies beside the Mediterranean Sea, the reverse trend is likely to be associated withthe very different large-scale patterns yielding heavy precipitation there.

4.5 Weather regimes and intense events

4.5.1 IntroductionWe observed in the previous section that the classification of IE-alone LSC often leads toclusters with high discriminating power, in the sense that days with LSC similar enough tothese cluster patterns could be days with a high probability of IE. But what about theconnections between weather regimes and intense local or sub-regional events? We tackle thisquestion from the point-of-view of both temperature and precipitation below. For brevity, weonly consider very particular cases here, although our conclusions were found to be verygeneral.

4.5.2 Very cold days and weather regimesAnother kind of (local) IE consists of very warm days (VWD) or very cold days (VCD) at agiven station. Since we want some objective definition, we define VCD as any day with amean temperature anomaly below -1.6 standard deviations. Several Météo-France stationrecords were analysed and the same conclusions reached. Thus only histograms for Nice aredisplayed here. Figure 23 shows VCD probability histograms against intervals of dc, thecorrelation distance between the LSC of a given day and that of a weather regime (centre)LSC. The thin horizontal line corresponds to the climatological average, and we look forappreciable departures from it. Such departures do not occur for BL or GA. ZO has moresignificant consequences concerning VCD occurrence, since VCD appear quite forbidden fordc < 0.4, whereas their probability is enhanced by a factor up to 6 or 7 for days with the ZOpattern reversed (anti-zonal days). Since WBL looks very much like a reversed ZO, it is notvery surprising that WBL-like patterns highly favour VCD occurrence.

4.5.3 Direct classification of very cold days LSCSince we want to check the relevance of weather regimes in forecasting IE, we now turn tothe concurrent approach. We classify only large-scale patterns of VCD. More precisely, wenow classify thickness patterns (Z300 - Z700), which are a little more appropriate fortemperature. Two classes of thickness anomaly patterns for VCD in Nice are displayed in


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Figure 24a (top and middle anomaly maps). The second one (which occurred quite oftenduring the long January 1985 cold spell) has a high discriminating power since more than50% of days with dc < 0.2 are VCD (this is also true for Paris and Lyon). A comparisonbetween this last histogram and both previous ones, suggests that the classification of VCDalone LSC (or LST: Large Scale Thickness) may be more convenient if one is interested inVCD downscaling forecasts. In this case, it turns out that the largest departures fromclimatological averages occur if one chooses the VCD thickness composite (Figure 24, lowermap and panel d). With this choice, the VCD probability rises up to 2/3 for 0.2 < dc < 0.4.One can also notice that dc never gets smaller than 0.2 with the VCD thickness composite.Unlike cluster centres, composite patterns often correspond to somewhat unphysical states, tothe extent that one cannot find actual patterns with a dc below 0.2 (i.e. a correlation > 0.8). Weconclude this discussion about temperature events by saying that although weather regimesmay, in some cases, provide appropriate patterns for downscaling forecasts of VCD, the mostefficient patterns are found if one deals with VCD LSC patterns alone.

4.5.4 The case of precipitation intense eventsFollowing Sections 4.5.2 and 4.5.3, it only remains to check the weather regimesdiscriminating power for IPE studies. For sake of brevity, we again limit our discussion to theFrench Alpes Maritimes. Very similar conclusions may, however, be drawn for other sub-alpine domains. The IPE probabilities are displayed against the correlation distance to eachZ700 weather regime pattern in the Figure 25 histograms. It is obvious that none of them issuitable if one intends to forecast IPE through downscaling.

In contrast, as was shown in Section 4.3, the so-called GASC cluster does point towards aphase space direction with a high discriminating power. If one compares IPE probability to itsclimatological mean, it is enhanced by a percentage of almost 1500% for days the LSCs ofwhich correlate with GASC with dc < 0.2 (Figure 15).

4.6 Summary of Section 4 conclusionsThe analyses performed in Sections 3 and 4 lead to the following conclusions. When thepurpose is a better description of, or downscaling approach to, local climate, the classificationof all daily LSC into a small number of classes, the so-called weather regimes, provides avery attractive approach. However, if the main interest lies in somewhat rare events, such asIntense Precipitation, the classification of Intense Events Days LSC alone provides a muchmore attractive approach. Of course the clusters one gets point towards less populated phasespace regions, but the corresponding patterns often correspond to highly enhanced IEprobability. This is illustrated in the left panel of Figure 26, where one observes that the PDFof all LSCs against the correlation distance to the GASC cluster has very few events close todc = 0, in comparison to the right panel where dc is relative to ZO. Moreover, one shouldnotice the greater bulk of events close to dc = 1 (i.e. corr = 0) for GASC. Most LSCs arepoorly correlated with the GASC pattern, whereas they do correlate to ZO. However, theprobability that an IPE occurs over the Alpes Maritimes is so much enhanced with patternssimilar to GASC, that remarkably robust clusters do emerge when one classifies IPE LSCsinto clusters.

These clusters of mesoscale Intense Events LSCs are in some sense data adaptive, and verydifferent patterns correspond to different sub-regions (such as the Alpes Maritimes, Mont-Blanc and Roussillon). The cluster patterns are objectively established, which contrasts withnumerous downscaling approaches where one classifies according to some a priori criteria


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such as the flow orientation. In view of the poverty of deterministic IPE forecasts in manycases, downscaling algorithms using IPE LSC classes could provide challenging alternatives.They could at least allow one to examine the possible meso-scale consequences of climatechange as simulated by GCMs.

5. Low-Frequency Oscillations (LFO) (Task 2.2)

5.1 IntroductionIn previous sections, we have been interested in statistical descriptions of the atmosphere,climate and weather. However, we know that the atmosphere is also well described usingdynamical systems theory. The existence of a strange attractor of finite dimension with acomplex topological structure is now recognized to be at the core of the whole (nonlinear)dynamics of the atmosphere. Spells of regular activities such as quasi-stationary behaviour(unstable fixed points) and regular oscillations (unstable limit cycles) are characteristic of thisattractor. Weather regimes could be connected in a natural way with unstable fixed points.Low-frequency oscillations (LFO) related to unstable limit cycles have become a majorsubject of interest. They cover a large frequency range from intramonthly, interannual up tointerdecadal and longer periods. The weather regime occurrences could be favoured byparticular phases of oscillations, and their transitions could be influenced by the succession ofphases of LFO. These questions have been tackled by Plaut and Vautard (1994, referred to asPV94 hereafter) using a 32-year set of National Meteorological Center (NMC) analyses forthe Z700 geopotential height.

Here, we first extract and compare intraseasonal oscillations corresponding to the three LSCfields (SLP, Z700 and Z500) (Section 5.2). For the 120-years SLP data set, we then studyinterannual LFO (Section 5.3). Then, we try to understand the connections between theseoscillations and the weather regimes studied in previous sections (Section 5.4). Section 5.5focuses on the connection between LFO and local surface climate (temperature andprecipitation). In Section 5.6, we briefly introduce a new powerful tool based on waveletanalysis, namely the multichannel wavelet transform, and we say a few words about thespectral activity of the main intraseasonal LFO during the last 120 years.

5.2 Comparison of intraseasonal LFO at various levels

5.2.1 MethodologyFor a description of MSSA we refer to PV94. MSSA diagonalizes a multi-channels multi-lagscross-covariance matrix. The eigenvectors, ST-EOF, correspond to adaptive space-timepatterns of length [win], where [win] is the time window of the MSSA. This parametercorresponds to the maximum time lag involved in the cross-covariance matrix. To keep thesize of the matrix reasonable, a PCA is first performed. Here, we retain only the six leadingPCs, without any loss of generality. Then we adopt the notation SLP_[win] for a particularMSSA of the field SLP with a window length [win]. A nice feature of MSSA is its ability toextract oscillatory behaviour, provided that intermittent but recurrent oscillatory space-timepatterns are present within the data set. The LFO manifest themselves through the existenceof pairs of (almost) oscillatory ST-EOF with (almost) the same period. The correspondingcross-covariance matrix eigenvalues are (almost) degenerated. We use the same criterion asPV94 to select the oscillatory pairs corresponding to LFO. We then use "reconstructedcomponents" (PV94) as a basic tool to define eight phases for an oscillation, and compute thecorresponding phase composites of any field (the involved LSC field itself, or temperature,precipitation, etc).


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5.2.2 The main SLP intraseasonal oscillationsTo make the comparison with Z700 and Z500 easier, we here consider the period 1958-1997.We performed three MSSA with respective window lengths of 75, 150, and 300 days (notevery day is sampled for large window lengths). Three robust LFO were detected. The firstone with a 31-day period was found in all three MSSA. The longer periods 66-day and 133-day LFO were only detected with SLP_150 and SLP_300 (unless very regular, an LFO cannotbe detected with a MSSA the window length of which is smaller that the LFO period). Allthese oscillations are robust and emerge with the same frequency and patterns if one variesthe MSSA parameters, including the number of LSC PCs analysed. The space-time patternsof LFO are best described through the phase-compositing techniques mentioned above. Figure27 displays the eight phases SLP anomaly composites for the 30-31day LFO. One recognizesthe characteristic westwards propagation of successive positive and negative anomalies(PV94). One also recognizes, in the 66-day LFO space-time patterns of Figure 28, thecharacteristic northward drift of successive positive and negative anomalies of the 70-dayoscillation of PV94 over the North Atlantic, although in the latter study, Z700 heights, notSLP, were submitted to MSSA.

5.2.3 Interactions between LFOFor brevity, we only compare the two 31-day and 66-day LFO. We study the correspondencebetween the eight phases of the first oscillation and the eight phases of the second one. It maybe seen on the 8x8 contingency table (Table 4) that the two LFO show a clear tendency to bephase-locked. The contingency table of simultaneous occurrences of the two oscillationsshows two preferred phase categories of the 66-day mode for each phase category of the 31-day one. A similar behaviour was observed by PV94 for the Z700 field 35-day and 70-dayoscillations which exhibited similar space-time patterns and phase-locking. We also observe aclear phase-locking between the 133-day oscillation and the 66-day one. The threeoscillations should thus be considered as belonging to a single limit cycle.

5.2.4 SLP LFO further in the pastWe now perform the same MSSA, but for the historical periods 1880-1918 and 1918-1958.For the first period, we obtain the same phase-locked LFO, but with shorter periods of around25 days, 50 days, and 100 days instead of 31, 66, and 133 days above. The second periodMSSA also provides similar patterns. We find at least three distinct and robust modes withspace-time patterns similar to the 66-day ones above and a northward propagation, but withdifferent periods of roughly 40, 60 and 80 days. One should note that the corresponding ST-EOFs are almost exactly similar, but with different periods, and probably represent the samephysical oscillation with its frequency modulated. Such variations of the frequency with timesuggest the use of wavelet transforms (see Section 5.6).

5.2.5 Comparison between SLP and Z700 LFOWe also submitted the Z700 LSC field to MSSA. The Z700 LFO look very similar to the SLPones. In the experiment Z700_300, one finds again 31, 66, and 133-day oscillations makingup a triad limit cycle. For brevity, we only show in Table 5 the 8x8 contingency table betweenthe SLP and Z700 66-day oscillations. It is obvious that the same oscillation is involved atboth levels. An MSSA of Z500 heights (not shown) again recovers the same LFO.


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5.3 SLP interannual LFOThe length of the SLP CRU data set allows one to explore interannual or even interdecadalSLP variability. We perform several MSSA with time windows between 5 and 10 years. Mostoften, we sample every month average. The most robust LFO we find is a Quasi-Biennial one(QBO). Its period is around 30 months, and it is almost a standing wave with a NAO-likepattern, although there are indications for a fast north-south anomaly propagation at half-cycle. This LFO and its links with the tropical QBO should be further investigated.

We also choose a 40 year window length from which emerges one very robust LFO with aperiod of 7.5-8 years. The phase composites shown in Figure 29 indicate a clockwise rotationof a dipole pattern. We compare this mode with the 7-8-year oscillation detected in the 333year long Manley-Parker series of Central England Temperatures (CET) (Plaut et al., 1995).The reconstruction of this oscillation (more exactly its first PC) is displayed in Figure 30together with the (fully independent) reconstruction of the CET 7-8 year LFO (details aboutthe reconstruction procedure may be found in PV94). The phase-locking between the twoseries is striking (and is very robust against changes in the window lengths and sampling ratesof both the SLP MSSA and the CET series SSA). We also compute the CET monthlyanomaly composites keyed to the phases of the SLP oscillation. The results are shown inFigure 31, together with the uncertainties on these composites. The 7.5-8 year SLP oscillationis significantly linked to CET variations of up to 0.4 degrees. The coldest phases (two andthree) correspond to a SLP positive anomaly centred between Greenland and Iceland (that iswith cold anomalous north-easterlies, see Figure 29). The warmest phases (seven and eight)correspond to reinforced zonal flows. Even summer or winter months alone composites aresignificant (not shown). As suggested by Plaut et al. (1995), this oscillation is due to internallow-frequency variability of the Atlantic ocean (Moron et al., 1998). It is also observed innumerical simulations of the wind-driven ocean circulation (Speich et al., 1995; Simonnet etal., 1999). We thus arrive at the important conclusion that a (local) fluctuation oftemperatures in Central England is indeed forced by a genuine atmospheric oscillation whichin turn seems to be due to the internal dynamics of the wind-driven mid-latitude oceaniccirculation. It is a confirmation of the importance of the ocean in atmospheric as well as localtemperature variability. It may be that such ocean-atmosphere linkages involve interannualLFO influences on the weather regime frequencies.

5.4 Intraseasonal LFO and weather regimesWe now turn to an investigation of the links between intraseasonal LFO and the transitionsbetween weather regimes (Section 2.3.2). Let us first imagine a situation where thereconstruction of a given LFO would represent the full SLP field. Then the atmosphere wouldevolve through the successive phase composite patterns corresponding to this oscillation. Onecan observe in Figures 27 and 28 that these phase composite patterns often display obvioussimilarities with one and only one of the weather regime patterns of Figure 4. The 31-daymode of Figure 27 would thus induce the cyclic transitions:

ZO → BL → WBL → GA → ZO. If the only excited atmospheric space-time component wasthe 65-day mode, the transitions would be: ZO → AR → WBL → GA → ZO. At this stage,one can remark that both oscillations induce rather similar weather regime sequences. Let usnow look at the true atmosphere. For this purpose, we build two contingency tables giving thenumbers of days with simultaneous occurrence of a given LFO phase category (rows)together with a given SLP weather regime (columns). An inspection of Table 6 shows nettendencies for preferred weather regime sequences during one given LFO life cycle.Moreover, these preferred sequences actually coincide with the cyclic transitions which would


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occur if the LFO corresponded to the full SLP anomaly field as described above. Puttingtogether both contingency tables of Table 6, we get the preferred sequence within theatmosphere:

ZO → BL (AR) → WBL → GA → ZO.

A similar preferred cycle was also observed by PV94 for the Z700 field and by Vautard(1990). These studies suggest that the preferred transitions between (LSC) weather regimesare likely to mainly originate from the most recurrent intraseasonal LFO dynamics. A similarpreferred weather regime cycle also occurs at higher tropospheric levels (not shown).

5.5 Intraseasonal LFO and local surface climateSince the development of LFO favours preferred successions of weather regimes whichthemselves favour definite local climatic conditions, we now look at the influence of LFO onthe local surface weather conditions. We use the surface data sets and the methodologydescribed in Section 3.1. The only difference is that here the four (or five) weather regimesare replaced by the LFO eight phase categories. For brevity, we only show one figure fortemperature and one for precipitation.

5.5.1 TemperatureWe use the NCEP reanalysis (T2m) gridpoint data set spanning Western Europe. The relativechanges of the warm tercile occurrence for each phase category of the 66-day oscillation aredisplayed in the maps of Figure 32. The sharp decrease of warm day occurrence probabilityover most of Europe for categories two and three is explained by the correspondinganomalous north-easterlies generated by the categories two and three composite fields shownin Figure 28. Conversely, the increase of warm day occurrence probability for categories sixand seven originates from the anomalous south-westerlies visible on the corresponding mapsof Figure 28. The influence of the 31-day LFO (not shown) is somewhat less pronounced,although significant.

5.5.2 PrecipitationWe use the Météo-France station data set. The relative changes of wet day occurrenceprobabilities over France for each phase category of the 66-day oscillation are shown inFigure 33. Most features have natural explanations if one considers the corresponding SLPanomaly composites of Figure 28. In phase two, for instance, the anomalous easterlies giverise to more dry days over most of France. The exception for the Roussillon was mentionedpreviously (Section 3.3.1). In phase four, the anomalous southerlies favour precipitationfurther to the east over Mediterranean coastal areas. In the same way, the anomalouswesterlies of phase six favour wet days to the north and west, whereas the Mediterraneancoastal areas are sheltered by the mountain massifs.

As a conclusion of Section 5.5, we can say that the development of intraseasonal LFO exertssignificant influences on local surface weather conditions. It was mentioned in PV94 thatintraseasonal LFO tends to stop during the warm season and that their amplitudes exhibitpronounced interannual variability. It is of course during the high amplitude bursts of theseLFO that their influence on local weather is most pronounced.


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5.6 Multi-channel wavelet analysisThe intermittent character of LFO, together with the pronounced interannual variability oftheir amplitudes mentioned above, and the possible drift of their peak frequency (Section5.2.4) leads us in a natural way to look for some analysis tool such as wavelet transform(Meyer, 1990; 1992), in order to analyse possible drift of the frequency of an LFOcorresponding to a given propagation scheme. In the same way that (one channel) SSA wasnot able to extract the dominant space-time modes from the atmospheric data set, the waveletanalysis has to be generalized to multi-dimensional signals in some way. The task is far fromself-evident and remains at a preliminary stage. Let us, however, say that we were able todefine an “activity” corresponding to propagation schemes of a given kind (such as the south-north propagation of the 66-day LFO anomaly patterns), taking possible frequency drift intoaccount. This “activity” is a positive parameter depending on time only. The larger theamplitude of the LFO corresponding to the selected propagation scheme, the larger the“activity”. We arrived at an intriguing result. This “activity” displays pronounced interannualoscillatory behaviour, with a 7.5-8 year period. The corresponding oscillatory componentreconstruction is displayed in Figure 34, together with the Manley-Parker CET 7.5 year one.Until 1940, both oscillations are perfectly in phase, and then in phase opposition, whichsuggests a dynamical link, still to be understood.

6. Conclusions and Relevance to ACCORD ObjectivesIn Sections 2 to 4 of this report, we describe an extensive study of two kinds of Large ScaleCirculation (LSC) patterns automatic classification. The first approach classifies LSCs for alldays into weather regimes. We classified Z500, Z700, as well as SLP LSC. A red-noisesignificance test was performed, and it was concluded that the most significant classificationswere into three (Z500, Z700 all year anomaly patterns) to four (Z700, winter) or five (Z500,winter; SLP, all cases) weather regimes. Classifications performed at different pressure levelswere found to be remarkably compatible. With the 120 years of SLP daily maps, the fiveweather regimes one gets for each of the 40 year historical periods are almostindistinguishable. While the annual frequencies of the weather regimes do exhibit low-frequency variability, even at the interdecadal scale, the weather regime patterns themselveshave been exceedingly stationary over the last 120 years. A challenging description of localclimate emerges. The most frequent LSC patterns have indeed very particular consequencesfor meso-scale climate, in such a way that the local departures from the climatic averages (weverified this point for temperature as well as for precipitation) are most often highly correlatedwith the LSC weather regimes. The atmosphere does not merely evolve around its mean state,it spends more time around a few characteristic recurrent states with well definedconsequences for local weather. As a consequence, a large amount of interannual or eveninterdecadal local climate variability may originate from fluctuations in relative occurrencesof weather regimes. The relevance of weather regimes for a better description of localclimates is also linked to the atmospheric tendency to slow down when it evolves in thevicinity of a weather regime centre, which helps to increase the time the atmosphere spendsclose to these centres which correspond to PDF maxima (Figure 26a).

However, it was observed in Section 4 that in many cases the weather regime patterns are notthe most relevant ones if one is interested in Intense Events (IE) studies or forecasting. Insuch a case, it appears one is more advised to first select LSCs of IE days (for a station or asub-region such as Savoy or Ticino), and then classify this restricted set of LSCs. In this way,one often gets a small (typically two or three) number of clusters with, for some of them(often one of them), a high discriminating power. The probability that any day with a LSC


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similar enough to this cluster centre be an IE day may be enhanced by a factor up to 15 ormore as compared to its climatic mean. The clusters one gets may be very different fordifferent sub-regions, such as for the Alpes Maritimes and Roussillon for instance. Of course,they most often point towards scarcely populated phase space regions within the atmosphericattractor, since their occurrence corresponds mainly to IE that are rather rare events.

The existence of such clusters appears very attractive from the point-of-view of climatechange studies at the sub-regional scale, since most GCMs are likely to better simulate large-scale pattern occurrences than local climate, especially for precipitation which depends uponmore or less accurate sub-grid scale parametrization schemes. Downscaling algorithms couldbe constructed in order to forecast local climate changes, starting from LSCs. The highdiscriminating power of certain circulation patterns allows one to foresee strategies forexploring possible local consequences of climate change. Validation runs could even helptaking model defects into account.

At least for the last 25 years, an important trend towards less autumn-winter IntensePrecipitation (IP) was observed over several sub-regions lying over the southern flank of theAlps which owe their most intense precipitation to autumn and winter southerly flows. ForTicino, this was compensated by spring and summer rainfall, but this was not the case formost Mediterranean coastal areas, except perhaps for regions such Roussillon which owes itsIP mostly to easterlies. In contrast, the most northern massifs such as Savoy and Mont-Blancsuffer increasing autumn-winter IP. Another attractive feature of IE alone LSC classificationinto clusters lies in the different seasonal behaviour of different clusters (Figure 14). Thedirect classification of (sub-regional) precipitation patterns appeared quite unattractive incomparison.

Then, in Section 5 we submitted the 120 year CRU SLP data set to MSSA at several timescales. We first turned to Intraseasonal Low Frequency Oscillations (LFO). The same space-time patterns were found as in Plaut and Vautard (1994) for the most recent period (1958-1998), with a dominant westward propagation for 31-day and south-north propagation for 66-day and 133-day modes. Further in the past (back to 1880), the same propagation schemeswere found, but with frequency drifts. This lead us to tentatively introduce a multichannelwavelet transform (Section 5.6).

The intraseasonal LFO exert definite influences on the succession of weather regimes, as wellas on the local surface weather. The most favoured sequence of weather regime transitions isin good agreement with the development of intraseasonal dominant LFO. These LFO usuallystop during the warm season, and their amplitudes show important interannual variability.

Interannual LFO were also extracted. The most important one is a 7.5-8 year one. The alreadyknown 7-8 year oscillation of the Central-England Temperature Manley Parker time serieswas found to be perfectly phase-locked with this oscillation, which is most likely due to theinternal dynamics of the wind-driven mid-latitude oceanic circulation. It is a confirmation ofthe importance of the ocean in atmospheric as well as local climate variability.

The MSSA wavelet transform, although at a preliminary stage, led us to the surprisingconclusion that a parameter measuring the activity of intraseasonal LFO also exhibited apronounced 7.5-8 year frequency peak.

To conclude, our most important results may be summarized as follows:


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i) The weather regime approach demonstrates the strong linkage between the (three tofive) most frequent LSC patterns and the instantaneous local surface weatherconditions departure from their climatic averages. Weather regimes supply a muchfiner description of local climate than simply average climate. The weather regimepatterns did not change during the last 120 years, but their annual frequenciesdisplayed strong interannual as well as interdecadal variability.

ii) Mesoscale Intense (and Extreme) Events (such as intense precipitation and very coldtemperatures) mostly lie out of the scope of the weather regime approach which isconcerned with the most recurrent LSC patterns. However, the probability of IntenseEvent occurrence displays strong linkages with LSC. These linkages are mostaccurately investigated through objective classification of restricted sets of LSCs,those corresponding to Intense Events over the involved region or location.

iii) Both these conclusions clearly point at the relevance of our approach to develop newdownscaling algorithms aimed at:• local Intense Events forecasting, and• local climate change investigation.

iii) Application of MSSA to the 120 year CRU SLP data set led to the finding of a 7.5-8year oscillation perfectly phase-locked with the already known 7-8 year oscillation ofthe CET time series. This result confirms the importance of the ocean in atmosphericas well as local temperature variability.

7. AcknowledgementsWe would like to thank the CRU for making available their SLP 120 year daily data set, inaddition to NCEP Reanalyses. We also thank C. Frei and C. Schär, ETH Zürich for the AlpinePrecipitation Climatology, and Météo-France for making available their French station dailyobservations data set.

8. ReferencesFrei, C. and Schär, C., 1998: ‘A precipitation climatology for the Alps from high-resolution

rain-gauge observations’, International Journal of Climatology, 18, 873-900.Higuchi, K., 1999: ‘A wavelet characterization of the North Atlantic Oscillation variation and

its relationship to the North Atlantic sea surface temperature’, International Journal ofClimatology, 19, 1119-1129.

Manley, G., 1974: ‘Central England Temperatures: monthly means 1659 to 1973’, QuarterlyJournal of the Royal Meteorological Society, 100, 389-405.

Meyer, Y., 1990: Ondelettes, Hermann.Meyer, Y., 1992: Les Ondelettes, Algorithmes et Application, Armand Collin.Michelangeli, P-A., Vautard, R. and Legras, B., 1995: ‘Weather regimes: recurrence and

quasi stationarity’, Journal of Atmospheric Science, 52, 1237-1256.Moron, V., Vautard, R. and Ghil, M., 1998: ‘Trends, interdecadal, and interannual oscillations

in global sea-surface temperatures’, Climate Dynamics, 14, 545-569.Palmer, T., 1993: ‘Extended-range atmospheric prediction and the Lorenz model’, Bulletin of

the American Meteorological Society, 74, 49-65.Parker, D.E., Legg, T.P. and Folland, C.K., 1992: ‘A new daily Central England Temperature


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series, 1772-1991’, International Journal of Climatology, 12, 317-342.Plaut, G. and Vautard, R., 1994: ‘Spells of low-frequency oscillations and weather regimes in

the Northern Hemisphere’, Journal of Atmospheric Science, 51, 210-235.Plaut, G., Ghil, M. and Vautard, R., 1995: ‘Interannual and interdecadal variability in 335

years of Central England Temperatures’, Science, 268, 710-713.Robertson, A.W. and Ghil, M., 1999: ‘Large-scale weather regimes and local climate over the

western United States’, Journal of Climate, 12, 1796-1813.Shannon, C.E. and Weaver, W., 1949: The Mathematical Theory of Communication,

University of Illinois Press.Silverman, B.W., 1986: Density Estimation for Statistic and Data Analysis, Chapman and

Hall, 175pp.Simonnet, E., Temam, R., Wang, S., Ghil, M. and Ide, K., 1999: ‘Successive bifurcations in a

2.5-layer shallow-water model of the wind-driven ocean circulation’, in preparation.Speich, S., Dijkstra, H.A. and Ghil, M., 1995: ‘Successive bifurcations in a shallow-water

model applied to the wind-driven ocean circulation’, Nonlinear Processes in Geophysics,2, 241-268.

Vautard, R., 1990: ‘Multiple weather regimes over the North Atlantic: Analysis of precursorsand successors’, Monthly Weather Review, 118, 2056-2081.

Vautard, R., Yiou, P. and Ghil, M., 1992: ‘Singular spectrum analysis: A toolkit for short,noisy chaotic signals’, Physica D, 58, 95-126.

9. List of ACCORD PublicationsDoctor, M., Plaut, G. and Schuebach, E., 2000: ‘Atmospheric circulation associated with

heavy precipitation events in the southern Alps in Switzerland and comparison with theAlps in the southeastern France’, Climate Research, in preparation.

Plaut, G., 1999: ‘Classification of large-scale circulation for heavy precipitation events overthe French Alps’, abstract prepared for the 1999 European Geophysical Society GeneralAssembly.

Plaut, G., 2000: ‘Intense precipitation over some Alpine sub-regions, large-scale circulationclassification and downscaling’, Climate Research, in preparation.

Simonnet, E. and Plaut, G., 2000: ‘Large-scale circulation weather regimes, MSSA and localclimate over some parts of Western Europe’, Climate Research, in preparation.


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Appendix A: Mutual InformationAs was stated in the Introduction (Section 1), the concept of mutual information wasdeveloped for meteorological quantities, but results are still at a preliminary stage. In thisappendix, we briefly introduce mutual information theory and comment on some preliminaryresults.

In many forecasting problems, one often has to answer the preliminary question: what is thebest predictor? One is also often interested in measuring the amount of information betweentwo given quantities: such as two meteorological stations or a large-scale circulation (scalar)quantity and a local one like temperature or precipitation. For instance, it is more convenientto classify thickness patterns (Z300-Z700) in order to study VCD and temperature IE than,let's say, SLP fields. This should be reflected in a greater amount of information between thetwo quantities. Information here, has to be understood in the abstract. Tossing a coin wouldgive you one byte of information. Information is indeed deeply related to probabilitymeasures and PDFs. A tool has already been developed by Shannon and Weaver (1949) and isessentially used in information theory. It is called Shannon Entropy and is a measure of theamount of information that a system with irregular behaviour may have. Mutual information(MI) is defined using Shannon entropy. It allows one to measure the amount of informationbetween two statistical quantities. It also gives the reduction of uncertainty on a quantity oncewe know another quantity. Note that if these two quantities are statistically independent, theircorresponding MI is zero. In order to measure the MI between two statistical variables, say Xand Y, one should estimate their associated PDFs. If pX, pY are the PDFs associated with X, Yand pX;Y the joint PDF of X,Y, the MI I is defined as

In the case of two independent variables, pX;Y = pXpY and thus I(X,Y) = 0. In order toevaluate I, we estimate the PDFs using Kernel Density Methods (Silverman, 1986) andcompute (1) by numerical quadrature. Note that (1) is invariant under the transformations X0 =ƒ(X) and Y0 = g(Y), in particular Ι(λX,µY) = I(X,Y).

We now conduct the following experiment. We consider the three LSC quantities (SLP, Z500and Z700) and the daily mean temperature anomalies from 30 Météo-France stations for theperiod 1958 to present and for the whole year. We perform a PCA on the LSC fields, aftersubtracting the seasonal trend, and consider the first three PCs. The corresponding EOFs are:an NAO-like pattern, an east-west dipole and another NAO-like dipole but dominated by alarger southern anomaly extending from the Atlantic to eastern Europe. The first three EOFsexplain 50% of the total variance. The three corresponding PCs have roughly the samevariances and are ordered in different ways at different levels. We then compute the quantity(1) where X represents a PC series and Y the temperature anomalies of a given Météo-Francestation. This quantity thus gives the amount of information between a LSC quantity and alocal one. If one knows a given PC, MI could also be interpreted as a measure of the reductionof uncertainty on the local temperature. However, the fact is that the MI obtained is notinteresting in itself if one does not compare it either with other station locations or other LSCPCs, i.e. Z500, Z700 or SLP.

(1) pXpY

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The first result is that it is the NAO-like dipole PC which is the most "correlated" withtemperature anomalies and for all 30 stations with MI values twice as high as for the otherPCs. If we now sum the three MI values corresponding to the three PCs at a given location,we obtain the result that it is the SLP field which is better "correlated" with temperature. Z500and Z700 have very similar MI intensity. The question one would like to ask is, is this resultrobust? We also recover similar properties when taking different samples (winter time) andalso considering data centred differently (grand mean removed). Finally, we must add that theMI values differ significantly from one station to another.


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Table 1: Contingency table of simultaneous occurrences of Z700 (rows) and Z500 (columns)weather regimes. 100 random reshufflings have been done in order to assess statisticalconfidence limits: bold (italic) numbers indicate statistically significant (at the 5% level) large(small) numbers of coincidences.

Z700/Z500 AR BL GA GT ZOAR 1056 22 46 386 47BL 62 1114 41 436 25GA 45 86 964 32 79ZO 43 143 4 191 1318

Table 2: As for Table 1, but for Z700 (rows) and SLP (columns) weather regimes.

Z700/SLP AR BL GA WBL ZOAR 915 59 44 461 31BL 28 1039 97 405 57GA 21 40 885 167 68ZO 287 273 56 0 1031

Table 3: As for Table 1, but for Z500 (rows) and SLP (columns) weather regimes.

Z500/SLP AR BL GA WBL ZOAR 663 64 62 358 14BL 37 926 110 181 62GA 21 16 731 223 50GT 261 242 85 269 165ZO 269 163 94 2 896

Table 4: Contingency table of simultaneous occurrences of the eight phase categories of the31-day (rows) and 66-day (columns) SLP oscillations. Same conventions as for Table 1.

31-day/66-day(5%-95%)

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1 235 258 168 212 234 275 190 2252 207 275 226 181 204 263 230 1983 193 246 290 148 186 233 266 2124 212 210 279 200 195 204 257 2165 211 192 226 293 181 194 259 2406 188 187 192 281 214 218 227 2657 234 191 203 252 283 198 216 2458 264 190 191 209 268 241 214 214


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Table 5: Contingency table between the eight phase categories of the SLP 66-day (rows) andthe Z700 66-day (columns) oscillations. Same conventions as for Table 1.

SLP 66-day/Z700 66-day(5%-95%)

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1 781 122 60 33 21 14 87 5992 565 803 148 37 20 30 41 1133 119 538 835 141 59 50 33 434 40 78 528 828 160 56 46 355 35 38 80 568 761 153 76 156 25 43 32 92 643 795 139 847 115 50 43 39 71 656 817 1118 98 77 49 38 30 71 620 815

Table 6: Contingency table between the eight phase categories of the SLP 31-day (left) and66-day (right) LFO and the five SLP weather regimes (columns). Same conventions as forTable 1.

31-day BL ZO GA AR WBL 66-day BL ZO GA AR WBL1 496 388 237 344 332 1 326 312 234 388 4842 506 257 197 359 465 2 351 201 352 271 5743 362 199 229 405 579 3 315 196 558 218 4884 285 239 366 334 549 4 338 276 594 263 3055 370 278 494 314 340 5 381 445 426 284 2296 302 409 506 327 228 6 424 557 283 338 2247 337 568 421 319 177 7 473 490 179 455 2628 364 578 333 319 197 8 414 439 157 504 301


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Figure 1: a,b,c) Red noise significance checks for classification of extended winter dailyLSCs into weather regimes. Black circles: classifiability index. Grey bars: 10%-90%significance level. d,e,f) Same for classification for the whole year; black circles andcorresponding bars: classification using Euclidean distance as for a,b,c; triangles: angulardistances using dc (see text for details).

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Figure 2: The four Z700 weather regimes for extended winters 1958-1998. Left column:cluster centre anomalies. Right column: full Z700 fields of cluster centres.

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Figure 3: The five Z500 weather regimes (same period as Figure 2) Left column: clustercentre anomalies. Right column: full Z500 fields of cluster centres.

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Figure 4: The five sea level pressure weather regimes (same period as Figure 2). Left column:cluster centre anomalies. Right column: full SLP fields of cluster centres.

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Figure 5: The five sea level pressure weather regimes anomaly patterns. Left: historical period1880-March 1918. Right: November 1918-1957.

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Figure 7: Relative change in frequency of cold days for each SLP weather regime, full lines:relative increase (%); dashed lines: relative decrease; changes are relative to the climatic mean1/3. Station data from Météo-France.

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Figure 8: Relative change in frequency of cold days for each SLP weather regime, full lines:relative increase (%); dashed lines: relative decrease; changes are relative to the climatic mean1/3. Gridded data from NCEP Reanalysis.

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Figure 10: Relative changes in frequency of wet days for each Z700 weather regime, ascompared to local climatic average; full lines: relative increase (%); dashed lines: relativedecrease. Station data from Météo-France.

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Figure 12: ETH Zürich APC gridpoints defining each of the Alpine sub-domains.

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Figure 13: a,b) Geopotential anomaly of cluster centres for Savoy_Mont Blanc IntensePrecipitation Events; c) Probability against di of any day being a class i IPE. d) All 25 yearsdays rain composites against di (at the Savoy_Mont Blanc sub-domain gridpoint marked witha large circle in Figure 12). Black bars: cluster 1; Grey bars: cluster 2.

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0

0

25-125

-100 -75

-75-5

0

-50 -25

-25

-25

0

0

0 300˚

330˚0˚

30˚

60˚

30˚ 30˚

60˚ 60˚

0

0

0

25

25

-50

-25

-25

0

0

0

Pro

babi

lity

(%)

di

0

25

50

0 0.4 0.8 1.2 1.6 2

Rai

n C

ompo

site

(m

m)

di

0

5

10

15

20

0 0.4 0.8 1.2 1.6 2


206

Figure 14: Seasonal dependence of class 1 (black) and class 2 (grey bars) IPE occurrences forboth the Alpes Maritimes and the Savoy_Mont Blanc.

J F M A M J J A S O N D

per

Cal

enda

r M

onth

in 2

5 ye

ars

0

10

20

30

Alpes Maritimes

J F M A M J J A S O N D

Num

ber

of I

PE

s pe

r C

lass

and

0

10

20

30

Savoy_Mont Blanc


207

Figure 15: Same as Figure 13, but for the Alpes Maritimes Intense Precipitation Events.

a) b)

c) d)

300˚

330˚0˚

30˚

60˚

30˚ 30˚

60˚ 60˚

0 00

0

2525 50

-125

-100

-75

-75

-50

-50

-25-25

-250 00

0 300˚330˚

0˚30˚

60˚

30˚ 30˚

60˚ 60˚

00

0

0

25

25

25

25

5050

-25

-25

00

0

0

Pro

babi

lity

(%)

di

0

25

50

0 0.4 0.8 1.2 1.6 2

Rai

n C

ompo

site

(m

m)

di

0

5

10

15

20

0 0.4 0.8 1.2 1.6 2


208

Figure 16: Number of Intense Precipitation Events (IPE) per year (top), average amount perIPE over the domain (middle), and annual amount from IPE also averaged over the domaingridpoints (bottom), together with the trends. Domains: Alpes Maritimes (left) andSavoy_Mont Blanc (right). Data from ETH Zürich APC.

Eve

nts

per

year

0

10

20

30

40

1970 1975 1980 1985 1990 1995

Alpes MaritimesA

v. P

reci

p. p

er e

vent

(m

m)

0

20

40

60

1970 1975 1980 1985 1990 1995

Ann

ual a

mou

nt (

mm

)

0

300

600

900

1200

year1970 1975 1980 1985 1990 1995

1970 1975 1980 1985 1990 1995

Savoy_Mont Blanc

1970 1975 1980 1985 1990 1995

year1970 1975 1980 1985 1990 1995


209

Figure 17: Anomaly maps and yearly histograms (as Figure 16) for two classes of IPE overthe Queyras sub-domain.

Events per year

0

5

1970 1975 1980 1985 1990 1995

Av.

Pre

cip.

per

eve

nt (

mm

)

0

20

40

60

1970 1975 1980 1985 1990 1995

Annual amount (mm)

0

200

Cluster 1

1970 1975 1980 1985 1990 1995

00

0

25

50

75-125

-100-75

-75

-50

-50

-25

-25

-25 00

01970 1975 1980 1985 1990 1995

1970 1975 1980 1985 1990 1995

Cluster 2

1970 1975 1980 1985 1990 1995

0

0

0

25

50 -125

-100-75 -50

-50

-25

-25

0

0

0


210

Figure 18: a) Composite geopotential anomaly for all IPE over the Alpes Maritimes; b)Probability against dc of any day being an IPE; c) All 25 years days rain composite of largecircle gridpoints against dc. d,e,f) Same for Savoy_Mont Blanc.

a) d)

b) e)

c) f)

330˚

0˚

30˚

30˚

60˚0

0

0

25

-100

-75

-75

-50

-50

-25

-25

-25

0

0

0

330˚

0˚

30˚

30˚

60˚

0

0

0

25

-100

-75

-50

-50

-25

-25

-25

0

0

0

Pro

babi

lity

(%)

d c

0

25

50

75

0 0.4 0.8 1.2 1.6 2

Alpes Maritimes

Rai

n C

ompo

site

(m

m)

d c

0

5

10

15

20

25

0 0.4 0.8 1.2 1.6 2

Pro

babi

lity

(%)

d c

0

25

50

75

0 0.4 0.8 1.2 1.6 2

Savoy_Mont BlancR

ain

Com

posi

te (

mm

)

d c

0

5

10

15

20

25

0 0.4 0.8 1.2 1.6 2


211

Figure 19: The three cluster centres obtained when one classifies Z700 anomaly patterns of allIPE over Ticino (left panel), or the Alpes Maritimes (right panel).

300˚

330˚0˚

30˚

60˚

0

0

25

50

75

-75

-50

-50-2

5

-25

0

0

’GASC’

300˚

330˚0˚

30˚

60˚0

0

0

25

25

25

25

5050

5075

-25

-25

0

0

0

’QP’

300˚

330˚0˚

30˚

60˚

0 00

25

25-100

-75

-50

-50-25

-25-25

0 00

’IL’

Ticino

300˚

330˚0˚

30˚

60˚

0

00

0

2550

75

-150

-125

-100-75

-75

-50

-50

-25 -25

-25

0

00

0

300˚

330˚0˚

30˚

60˚

0 0

0

0

25

2525

25

50

50

50

75

-50

-25

-250 0

0

0

300˚

330˚0˚

30˚60

˚

0 0

0

25

25

25

50

50

75

-75

-50

-50

-25-25

0 0

0

Alpes Maritimes


212

Figure 20: The three cluster centres provided when one classifies Z700 anomaly patterns ofall IPE over Riviera Levante (left panel), or Roussillon (right panel).

300˚

330˚0˚

30˚

60˚

002550

75100

-125

-100-75

-75

-50

-50

-25

-25

-25 00

’GASC’

300˚

330˚0˚

30˚

60˚

0

0

00

0

25

25

25 50

-25

-25

0

0

00

0

’QP’

300˚

330˚0˚

30˚

60˚0

0

025

-125

-100-75

-75

-50

-50

-25

-25-25

0

0

0

’IL’

Riviera Levante

300˚

330˚0˚

30˚

60˚

0

025

25

50

50

75

75

100125

-50

-25

-25

-25

0

0

300˚

330˚0˚

30˚

60˚0

025

25

25

50

50

75

75

100

125

-75

-50

-50

-25

-25

0

0

300˚

330˚0˚

30˚60

˚

0

0

0

0

2525

-25

-25

0

0

0

0

Roussillon


213

Figure 21: Number of Intense Precipitation Events (IPE) per year (top), average amount perIPE over the domain (middle), and annual amount from IPE also averaged over the domaingridpoints (bottom), together with the trends. Domains: Riviera Levante (left) and Roussillon(right). Data from ETH Zürich APC.

Eve

nts

per

year

0

10

20

30

40

1970 1975 1980 1985 1990 1995

Riviera Levante

Av.

Pre

cip.

per

eve

nt (

mm

)

0

20

40

60

1970 1975 1980 1985 1990 1995

Ann

ual a

mou

nt (

mm

)

0

300

600

900

1200

year1970 1975 1980 1985 1990 1995

1970 1975 1980 1985 1990 1995

Roussillon

1970 1975 1980 1985 1990 1995

year1970 1975 1980 1985 1990 1995


214

Figure 22: Same as Figure 21 but for Ticino, dividing years into autumn-winter (left: October-March precipitation) and spring-summer (right: April-September).

Eve

nts

per

year

0

5

10

15

20

1970 1975 1980 1985 1990 1995

Ticino: oct._march

Av.

Pre

cip.

per

eve

nt (

mm

)

0

20

40

60

1970 1975 1980 1985 1990 1995

Ann

ual a

mou

nt (

mm

)

0

300

600

900

1200

year1970 1975 1980 1985 1990 1995

1970 1975 1980 1985 1990 1995

Ticino: april_sept.

1970 1975 1980 1985 1990 1995

year1970 1975 1980 1985 1990 1995


215

Figure 23: Histograms of Very Cold Day probability in Nice against angular distance dc tofour of the five SLP weather regime centres. Data from Météo-France. Thin line: climaticaverage.

a) b)

c) d)

Pro

babi

lity

(%)

dc

BL

0

10

20

30

40

50

0 0.4 0.8 1.2 1.6 2

Pro

babi

lity

(%)

dc

GA

0

10

20

30

40

50

0 0.4 0.8 1.2 1.6 2

Pro

babi

lity

(%)

dc

WBL

0

10

20

30

40

50

0 0.4 0.8 1.2 1.6 2

Pro

babi

lity

(%)

dc

ZO

0

10

20

30

40

50

0 0.4 0.8 1.2 1.6 2


216

Figure 24: Histograms of Very Cold Day (VCD) probability in Nice against angular distancedc to two of the five Nice VCD thickness anomaly clusters (b, c) and to Nice VCD thicknessanomaly composite (d). Thin line: climatic average. Panel a: corresponding thicknessanomaly patterns.

a) d)

c)

b)

Pro

babi

lity

(%)

dc

Cluster 1

0

10

20

30

40

50

0 0.4 0.8 1.2 1.6 2

Pro

babi

lity

(%)

dc

Cluster 2

0

10

20

30

40

50

0 0.4 0.8 1.2 1.6 2

Pro

babi

lity

(%)

dc

composit

0

10

20

30

40

50

60

0 0.4 0.8 1.2 1.6 2

300˚

330˚0˚

30˚

60˚0

0

0

25

25

2550

50

50

75

100

-125

-100 -75

-75

-50

-50

-25-25

0

0

0

300˚

330˚0˚

30˚

60˚0

00

25

25

255075

100

125

150

-100

-75 -50

-50

-25

-25

-25

0

00

300˚

330˚0˚

30˚

60˚

00

0

25

25

-100

-75-50

-50

-25-25

00

0


217

Figure 25: Histograms of winter Intense Precipitation Events (see text for definition).Probability over the Alpes Maritimes against angular distance dc to the four Z700 weatherregime centres. Thin line: climatic average. Data from ETH Zürich APC.

a) b)

c) d)

Pro

babi

lity

(%)

dc

AR

0

10

20

30

40

50

0 0.4 0.8 1.2 1.6 2P

roba

bilit

y (%

)dc

BL

0

10

20

30

40

50

0 0.4 0.8 1.2 1.6 2

Pro

babi

lity

(%)

dc

GA

0

10

20

30

40

50

0 0.4 0.8 1.2 1.6 2

Pro

babi

lity

(%)

dc

ZO

0

10

20

30

40

50

0 0.4 0.8 1.2 1.6 2


218

Figure 26: Two examples of PDF against dc intervals. The correlation distance dc is computedrelative to a) the ZO SLP Weather Regime centre; and b) the Z700 GASC cluster of LSCs ofIntense Precipitation Events over the Alpes Maritimes.

a) b)

PD

F(%

) pe

r 0.

2 in

terv

al

dc

ZO (SLP)

0

4

8

12

16

20

0 0.4 0.8 1.2 1.6 2

PD

F(%

) pe

r 0.

2 in

terv

al

dc

’GASC’ (Z700)

0

4

8

12

16

20

0 0.4 0.8 1.2 1.6 2


219

Figure 27: Composite of the SLP anomalies keyed to phase categories 1-8 of the 31-dayoscillation. Contour interval: 0.5 hPa.

300˚

330˚ 0˚30

˚

00

0.50.5

0.5

1

1

-1-0.5 0

0

1

300˚

330˚ 0˚

30˚

0

00

0.5

0.50.5

1

1

1

1.51.5

1.5

2

2

2.5

2.5

2.5

3

0

00

2

300˚

330˚ 0˚

30˚

0

00.5

0.5

1

1

1

1.5

1.5

1.5

2

2

2

2.5

2.5

3

3

3.54

4.5

-0.50

0

3

300˚

330˚ 0˚

30˚

0

0

0

0.50.5 1

1

1.5

1.5

22.5 3

3.5

-1

-1

-0.5

-0.5

0

0

0

4

300˚

330˚ 0˚

30˚

00.5

1

-1

-1

-0.5

-0.5

-0.5

-0.5

0

5

300˚

330˚ 0˚

30˚

0

0

-2.5

-2

-2

-1.5

-1.5

-1.5

-1-1

-1

-0.5

-0.5

-0.5

0

0

6

300˚

330˚ 0˚

30˚

0

0

0

0.5

-4

-3.5 -3

-3

-2.5

-2.5

-2-2 -1.5

-1.5

-1.5

-1-1

-1

-0.5-0.5

0

0

0

7

300˚

330˚ 0˚

30˚

0

0

0

0.5

0.5

-3.5

-3 -2.5

-2.5

-2

-2

-1.5

-1.5

-1-1 -0.5

-0.5

0

0

0

88


220

Figure 28: Composite of the SLP anomalies keyed to phase categories 1-8 of the 66-dayoscillation. Contour interval: 0.5 hPa.

300˚

330˚ 0˚30

˚

0

0

0.5

0.5

0.5

1

1

1

1.5

1.52

2

2.5 0

0

1

300˚

330˚ 0˚

30˚00 0.5

0.51

1

1.5

1.5

2

2

2.5

2.5

3

3

3.5

4

-1.5

-1

-1

-1

-0.5

-0.5

00

2

300˚

330˚ 0˚

30˚

000.5

0.5

11.522.5

3

3.5

4

4.5

-2.5-2

-2 -2

-1.5

-1.5-1-1

-0.5

-0.500

3

300˚

330˚ 0˚

30˚

00.5

1

-3

-2.5

-2.5

-2

-2

-2 -1.5

-1.5

-1.5

-1

-1

-1-0.5

-0.5

-0.5-0.5 0

4

300˚

330˚ 0˚

30˚

0

0

-3.5

-3-2.5 -2

-2

-1.5-1.5

-1

-1

-1

-0.5

-0.5

-0.5

0

0

5

300˚

330˚ 0˚

30˚

0

00.5

0.5

11

-3

-2.5

-2

-2

-1.5

-1.5

-1-1-0.5

-0.5 0

0

6

300˚

330˚ 0˚

30˚

0

0

0.5

0.5

1

1

1 1.5

1.5

1.5

1.52

2

2.5

-3.5

-3

-2.5-2 -1.5-1-0.5 0

0

7

300˚

330˚ 0˚

30˚

0

0

0.5

0.5

1

1

1

1.5

1.5

1.5

2

2

2.5

2.5

3

-2-1.5

-1-0.5 0

0

88


221

Figure 29: One period of the SLP anomalies 7.5-8-year oscillation (reconstructed frommonthly data). Contour interval: 0.5 hPa.

300˚

330˚ 0˚

30˚

0

0

0.05

0.05

0.1

0.1

0.15

0.15

0.2

0.25

0.3

-0.2 -0.1

5

-0.15-0.1 -0.1

-0.1

-0.0

5

-0.05

0

0

phase 1

300˚

330˚ 0˚

30˚

0

0

0.05

0.05

0.1

0.1

0.150.20.250.30.35

0.40.45

0.5 0.

55

-0.3

-0.25-0.25

-0.25

-0.25

-0.2

-0.2

-0.2

-0.2

-0.15

-0.15

-0.15

-0.1

-0.1-0

.05-0.050

0

phase 2

300˚

330˚ 0˚

30˚

0

0

0.050.10.150.20.25

0.30.35

0.40.45

0.5

-0.3

-0.3

-0.25 -0.2

-0.2

-0.15

-0.15

-0.15

-0.1

-0.1-0.1 -0.05

-0.050

0

phase 3

300˚

330˚ 0˚

30˚

0

0

0.05

0.050.1

0.15

-0.15

-0.1-0.05 0

0

phase 4

300˚

330˚ 0˚

30˚

00

0.05

0.05

0.1

0.1

0.1

0.15

0.15

0.2

-0.3

-0.2

5

-0.2-0.15

-0.1-0

.05

-0.0

5

00

phase 5

300˚

330˚ 0˚

30˚

0

0

0.05

0.050.10.1

0.15

0.15

0.15

0.2

0.2

0.2

0.2

0.25

0.25

0.250.3

0.3

0.35

-0.6

-0.5

5-0

.5-0.45-0.4-0.35-0.3 -0.25-0.2-0.15-0.1-0.050

0

phase 6

300˚

330˚ 0˚

30˚

00.05

0.05

0.1

0.10.1

0.15

0.15

0.150.20.2

0.25

0.25

0.3

0.350.35 0.4

-0.6

-0.5

5

-0.5-0.45-0.4-0.35-0.3-0.25-0.2-0.15-0.1-0.050

phase 7

300˚

330˚ 0˚

30˚

0

0

0.050.10.150.2

0.20.25

-0.3

-0.25

-0.2-0.15-0.1

-0.050

0

phase 8phase 8


222

Figure 30: First reconstructed PC of the 7.5-8 year SLP oscillation against CET 7-8-yearreconstructed oscillation (1880-1996).

Figure 31: CET monthly anomalies phase composites for the eight phases of the 7.5-8-yearSLP oscillation. Broken lines: statistical uncertainties.

1880 1900 1920 1940 1960 1980 2000−0.5

−0.4

−0.3

−0.2

−0.1

0

0.1

0.2

0.3

0.4

0.5

YEARS

CE

T a

mpl

itude

in d

egre

s)

Reconstructed components of CET and SLP 7.5−8−year oscillations

slp

cet

1 2 3 4 5 6 7 8 9−0.3

−0.25

−0.2

−0.15

−0.1

−0.05

0

0.05

0.1

0.15

0.2

SLP 8−year LFO PHASE INDEX

CE

T m

onth

ly a

nom

alie

s (d

egre

s)


223

Figure 32: Relative change in frequency of warm days over Europe for each 66-day LFOphase category; full lines: relative increase (%); dashed lines: relative decrease; changes arerelative to the climatic mean 1/3. Gridded data from NCEP Reanalysis.

-32

-30

-28

-28

-26-26

-26-26

-24

-24

-24

-22

-22

-20

-20

-18

-18

-16

-16

-14

-14

-14

-12

-12

-12-12

-12

-10

-10-10

-8-8

-8

-8

-6

-6

-6

-6

-4

-4

-4

-4

-4

-2

-2

-2

-2

-2 0

0

02

22

2

2

2

2

4

44

6

-54

-52

-52

-50

-50

-50-48

-48

-48

-46 -46

-46

-44

-44

-44

-44

-42

-42

-42

-42

-40

-40

-40

-40

-38

-38

-38

-38

-38

-36

-36

-36

-36-36

-34

-34

-34

-34

-32

-32

-32

-32-3

2

-30

-30

-30

-30

-28

-28-28

-28

-26

-26

-26

-24

-24

-24

-24

-22

-22-22

-22

-20

-20

-20

-18

-18

-18

-18

-16

-16

-16

-14

-14

-14

-12

-12

-10

-10

-8

-8

-6

-6

-4

-4

-2 2 4

6

-50

-48

-46

-44

-44

-42-42 -40-40

-40

-38-38

-38

-36

-36

-36

-34

-34

-34

-32

-32

-32

-30

-30

-30

-28-28

-28

-26-26

-26

-24-24

-24

-22-22

-22

-20-20

-20

-20

-18-18

-18

-16-16

-16

-16

-16

-14

-14

-14

-12

-12

-12

-10

-10

-10

-8

-8

-6

-6

-6

-4

-4

-4

-2

-2

-2

0

0

2

2

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SLP 66-day LFO (warm days)


224

Figure 33: Same as Figure 32 but for the wet day percentages over France. Data from Météo-France.

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15Phase 1 Phase 2

Phase 3 Phase 4

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SLP 66-day LFO (wet days)


225

Figure 34: Multi-channel wavelet “activity” (see text) reconstructed 7.5 year oscillatorycomponent (full line) together with the CET 7-8 year reconstructed oscillatory component(dotted line).

1880 1900 1920 1940 1960 1980 2000−1.5

−1

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norm

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time (year)

7.5−year CET oscillation and 7.5−year intraseasonal spectral activity

Documents

ACCORD Institut Non Linéaire de Nice; Laboratoire de ... · ACCORD Institut Non Linéaire de Nice; Laboratoire de Météorologie Dynamique (CNRS) 166 Partner: Laboratoire de Météorologie