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

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1 Partner: Laboratoire de Météorologie Dynamique (LMD) du CNRS () Subcontractor: Institut Non Linéaire de Nice (INLN) CNRS Responsible Scientists: Guy Plaut (INLN) and Robert Vautard (LMD) Scientific Staff: Guy Plaut, Robert Vautard, Eric Simonnet Address: INLN, 131 route des Lucioles, F Valbonne, France Telephone: 33 () Fax: 33 () plaut@inln.cnrs.fr 1. Original Objectives and Extent to Which They Have Been Achieved Original objectives Extent to which they have been achieved.... Weather Regimes (Task 1.3) Introduction Data and methodology Weather regimes Z7 and Z SLP weather regimes Comparison between different pressure level weather regimes SLP weather regimes over the last 1 years Weather Regimes and Local Climate (Tasks 3. and 3.) Introduction, data and methodology Weather regime influences on local temperature French station temperatures and SLP weather regimes Gridded European temperatures and SLP weather regimes Weather regimes with more severe membership criteria Z7 weather regimes influence on precipitation Wet day percentage at French stations Heavy precipitation over the Alps Weather regimes and local climate: a summary Intense Events LSC Classification (Tasks 3. and 3.) Introduction Data processing and methodology Savoy, the Alpes Maritimes and Queyras The LSC clusters for IPE over Savoy The LSC clusters for IPE over the Alpes Maritimes IPE trends and comparison with Queyras Alternative to IPE LSC classification IPE over other Alpine sub-regions Weather regimes and intense events Introduction Very cold days and weather regimes Direct classification of very cold days LSC The case of precipitation intense events.... Summary of Section conclusions.... Low-Frequency Oscillations (LFO) (Task.) Introduction Comparison of intraseasonal LFO at various levels Methodology The main SLP intraseasonal oscillations Interactions between LFO... 1

2 .. SLP LFO further in the past..... Comparison between SLP and Z7 LFO....3 SLP interannual LFO Intraseasonal LFO and weather regimes Intraseasonal LFO and local surface climate Temperature..... Precipitation.... Multi-channel wavelet analysis.... Conclusions and Relevance to ACCORD Objectives Acknowledgements References List of ACCORD Publications... Appendix A: Mutual Information

3 1. Original Objectives and Extent to Which They Have Been Achieved 1.1 Original objectives The 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 1 years (Task.). To address the question of the linkage between large-scale circulation and extreme weather events (Task 3.). To explore downscaling schemes for surface weather conditions over Western Europe and the Mediterranean (Task 3.). To explore new approaches such as "mutual information" (Task 3.). 1. Extent to which they have been achieved We started in ACCORD with expertise in MSSA and automatic classification. We also brought our powerful package ANAXV which allows automation of almost any numerical experiment using data. PCA and MSSA, as well as the dynamical cluster algorithm, are also implemented in ANAXV. It was made available to Partners 3 and 7. Task 1.3 was addressed throughout the ACCORD contract period. Circulation classification provides the basis of Sections and 3 of this report. The work reported in Section also addresses (particular daily) circulation classification. It should be noted that the classification is always performed at the supra-regional scale, not the regional scale. Regional-scale classification was undertaken, however, the linkages between clusters and mesoscale weather were systematically optimized using the largest available scale circulation patterns. Task. was dealt with by Eric Simonnet and many original new results are reported in Section. The innovative work and conclusions regarding Task 3. are reported in Section. The development of Task 3. is described in Sections 3 and (exploration of downscaling schemes) and in Appendix A (mutual information). However, downscaling schemes for the Mediterranean were not explored because of the lack of long period high-resolution pressure data. Our research in the ACCORD program framework, as well as our main conclusions, are brought together in three main report sections: Large-Scale Circulation (LSC) classification of all days into clusters and weather regimes (Section ) 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 much finer 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 changes in Intense Event occurrence due to greenhouse warming, was developed. It consists of the classification into clusters of LSC for Intense Event days only. Intense Events LSC cluster centres were found to have high discriminating power in many cases (Section ). Several LSC classifications (defined using NCEP Z and Z7 Reanalysis fields, as well as the 1 year daily SLP data set from the Climatic Research Unit (CRU)) were submitted to

4 MSSA. Intraseasonal oscillations at different pressure levels, as well as at different frequencies, were compared. Interannual as well as interdecadal oscillations were also investigated. The connections with mesoscale weather patterns were also studied (Section ). In the same section, we say a few words about an innovative technique, the multichannel wavelet transform, which was applied to the 1 year SLP data set. Preliminary, but very promising, results were obtained linking intraseasonal wavelet activity fluctuations to known interannual climate oscillations (Section.). Finally, the "mutual information" approach was developed and preliminary results are presented in Appendix A.. Weather Regimes (Task 1.3).1 Introduction Following Vautard (199) and Michelangeli et al. (199), we define weather regimes as the cluster of central patterns obtained when classifying all the LSC patterns of a data set. In this way we identify the most recurrent LSC patterns. As in Michelangeli et al. (199), we use the dynamical cluster algorithm implemented in the ANAXV package, which allows a fully objective procedure without any a priori hypothesis about the classes to be found. We compare the classifications obtained for the different fields, as well as the regimes at different historical periods for SLP. We also consider low and very low frequency variability over the last 1 years.. Data and methodology The daily Z and Z7 data come from the NCEP/NCAR Reanalysis project and extend from 19 onwards. To make comparison with the results of Michelangeli et al. (199) easier, we use the same 1 longitude by latitude window centred on N 1 W and we classify 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 (-1997). We first perform a spatial filtering, retaining only a small number of EOFs (1, or even six later without any major ramifications). We then perform the classification of LSC patterns within this 1 dimensional PC-space. Given a prescribed number of clusters k, the goal of the dynamical cluster algorithm is to find a partition of the data set into k clusters that minimizes the sum of variance within the clusters. The Euclidian distance is used first as a similarity measure. Other criteria can be used as well (Robertson and Ghil, 1999), such as the correlation distance d c = 1 - corr, where corr is the anomaly pattern correlation (actually a cosine in the PC-space); d c ranges from for perfectly correlated patterns up to for inversely correlated patterns. We use d c extensively when producing probability or composite histograms. We also check the differences between the classifications obtained with the two geopotential height fields (Section.3.). The best number k of clusters is checked using a red noise test which provides confidence thresholds for a classifiability index c* (Michelangeli et al., 199)..3 Weather regimes.3.1 Z7 and Z 1 extended winter Z7 maps were classified. Figure 1a shows the classifiability index c * versus k, the number of classes, together with the 1-9% confidence level limits. Only the 19

5 choice k = allows one to get more significant classes than the red noise test. The four weather regimes identified are the same as those found by Vautard (199) who looked for weather regimes as quasi-stationary points in the 1 leading PC space. The same classes were also found by Michelangeli et al. (199) using the dynamical cluster algorithm. Following Vautard (199), we define the patterns shown in Figure as follows: AR (Atlantic Ridge) inducing north-westerlies over western Europe; BL (Blocking) with a maximum positive anomaly over Scandinavia; GA (Greenland Anticyclone); and, ZO (Zonal) with enhanced zonal flow. The Z maps classified span the same period as the Z7 ones. With k = clusters, the patterns correspond exactly to the Z7 ones. However, the red noise test indicates that k = is preferred (Figure 1b). The fifth selected regime displays a strong positive anomaly close to Newfoundland, together with a less important one over eastern Europe, and is referred to as GT (Greenwich Trough). This regime does not persist when all year LSCs are classified, and therefore appears less robust than other ones. The five weather regime anomaly patterns are shown in Figure 3, together with the corresponding full-field Z patterns..3. SLP weather regimes Although the CRU daily SLP data set covers 1 years, we first only classify winters after 197 in order to make the comparisons with Z7 and Z more meaningful. In this case, k = classes is preferred (Figure 1c). It may be seen in Figure that four of the SLP regimes have anomaly patterns rather similar to the Z7 ones. The most marked difference is the fact that the SLP regime we call BL corresponds, on average, to an anticyclonic cell with its centre over Eastern Europe (near the Ukraine), creating southeasterly flow (not that cold in many cases) over western Europe. A fifth regime which we call WBL (West Blocking), but which could also have been called Blocking, favours much colder conditions over Europe, with an anticyclonic cell centred over Scotland on average. The SLP BL weather regime could also have been called European Anticyclone (EA). Before turning to a comparison of weather regimes at different pressure levels, let us briefly mention that we also performed daily LSC classification into weather regimes for the whole year. Very similar patterns (not shown here) were found. The significance checks (leading to the best choice for k) are summarized in the right-hand panels of Figure 1. Three points are worth emphasizing: i) classifications using an angular or Euclidean distance lead to almost indistinguishable patterns; ii) five clusters are always preferred for SLP, the same as the winter ones above; iii) for Z7 and Z, three clusters are preferred, the patterns are identical at both pressure levels, two of them are the already known BL and ZO and the third one may be called AR-GA.. Comparison between different pressure level weather regimes Contingency tables of weather regime occurrence at different pressure levels have been computed and the % and 9% significance thresholds established by randomly shuffling the days 1 times (Tables 1-3). Numbers in italics are below the % confidence level threshold and provide a significantly smaller number of coincidences than random; bold numbers are above the 9% level. Z7-Z (Table 1): the four regimes AR, BL, GA, and ZO for Z7 and Z show an obvious one-to-one correspondence. This is also suggested by visual inspection of Figures and 3. The GT weather regime seems to correspond to a combination of the Z7 AR and BL 17

6 regimes. Z7-SLP (Table ): similar conclusions apply when we compare Z7 and SLP data. WBL appears as a combination of certain Z7 AR and BL members. Z-SLP (Table 3): once again, the new (Z) regime GT members are mainly taken from the SLP AR and blocking-like regimes (BL and WBL). This is in agreement with both previous comparisons since the Z7 BL regime takes members from both BL and WBL SLP regimes. We conclude this section by noting that there are highly significant correlations between classifications of LSCs at different pressure levels. For instance, ZO days at one level are almost always classified as ZO at other levels, whereas WBL days (for SLP) never coincide with ZO days at other levels. Such conclusions were not a priori obvious, since the LSCs for all days have to belong to one of four or five classes, which forces classes to hold some badly correlated members. In the next section we use more severe class membership conditions, with the drawback that not all days will be classified.. SLP weather regimes over the last 1 years In order to compare the SLP regimes for the period with those obtained for the earlier periods -19 and , we repeat the SLP analysis for these two periods. Data are processed in the same way for each period. We first subtract the extended winter mean field at each gridpoint, then perform a PCA, and finally classify within the 1-leading PC space: -March 19. 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, left column) has been a little distorted into a pattern somewhat intermediary between the WBL and GA patterns of Figure. November For this more recent period, five clusters are preferred. They are actually indistinguishable from the five clusters obtained for the preceding period (compare the right column anomaly maps of Figures and ). This comparison of weather regimes for the last three -year periods, leads us to the important conclusion that the most recurrent LSC patterns have been very stable during at least the last 1 years. Four rather than five clusters are preferred for the earliest period but if one nevertheless uses k = for this period, the same five patterns are obtained as for the other two periods. Since the weather regimes found over the three periods are very similar, we classify all winter LSCs between and 199 according to the five classes of the most recent period. In Figure we plot the number of occurrences of a given weather regime per winter, together with its 1-year moving average. Low-frequency variability clearly appears. An SSA (Vautard et al., 199) produces interdecadal peaks around years for GA, and 1 and 1 years for AR and BL. Similar interdecadal variability was found by Vautard et al. (199) for the IPCC global surface air temperature record. Peaks at 1 years have also been identified by Plaut et al. (199) in the Manley-Parker Central England Temperatures time series (Manley, 197; Parker et al., 199). Interdecadal variability was also identified by Moron et al. (199). Very low-frequency variability dominates the WBL and ZO weather regimes. Notice, in particular, the increase of ZO over the last 1 years (well documented in numerous North 171

7 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 the recent winter that one sees the highest WBL frequency for 1 years.) An SSA also produces Quasi-Biennial Oscillation-like (QBO-like) peaks at approximately. years for all five regimes (not shown). A -3 year oscillation is also present in the NAO index (Higuchi, 1999). To summarise, the winter frequencies of the five SLP weather regimes show strong interannual as well as interdecadal (and even slower) variability. The weather regimes themselves (i.e. the most recurrent LSC patterns) appear very stable over the last 1 years. 3. Weather Regimes and Local Climate (Tasks 3. and 3.) 3.1 Introduction, data and methodology In order to check the way in which weather regimes influence local climate, we first classify local temperatures into terciles (warm, mean and cold) and consider the relative change of frequency of a given temperature category for each weather regime. For brevity, we consider cold tercile occurrences at 3 French stations and also at NCEP Reanalysis gridpoints spanning Western Europe between 3 N and 71 N, 1 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 of precipitation amounts. In addition to the large-scale fields processed in Section, we use four data sets: Mean daily temperature recorded at 3 Météo-France stations from 199 to 199. NCEP daily air temperature at m on a Gaussian grid, restricted to a Western Europe area (see above). These data cover the period 19 to 1997 and are Reanalysis data processed using the NCEP/NCAR model in the same way as the LSC geopotential height fields. Daily precipitation values from the 3 Météo-France stations. The Alpine Precipitation Climatology (APC) from ETH Zürich (Frei and Schär, 199) which covers the European Alpine area and the adjacent foreland. It is a compilation of 7 daily station records on a km grid for the period Each gridpoint corresponds on average to six stations. Once that temperatures are classified into terciles, we compute for each gridpoint or station location the 3x contingency tables associated with the SLP weather regimes. We count, for instance, the number of days with simultaneous occurrence of a cold temperature at the selected location and ZO, and then the percentage of cold days for ZO, which we compare with its climatic average of 33%. If this percentage falls to say 11%, we conclude that it has changed by a factor -%. Note that changes may be larger than +1%. We also compute the 9% significance thresholds based on randomly reshuffling the days 1 times. 17

8 3. Weather regime influences on local temperature 3..1 French station temperatures and SLP weather regimes The relative changes of cold day occurrence probability for the five SLP weather regimes are plotted in Figure 7. Non-significant departures from the climatic average tercile are indicated by the shaded areas. One of the most outstanding changes is the almost doubling (1% increase) of cold days with WBL at most stations (the increase is slightly smaller in the south). In contrast, ZO cold days are very rare (a decrease of more than %, sometimes 7%). 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 a finer description of the local climate than simply average climate. The atmosphere does not merely evolve around a mean state, it spends more time around a few particular (large-scale) states with well defined consequences for local weather. 3.. Gridded European temperatures and SLP weather regimes Figure shows the consequences of each of the five SLP weather regimes for the cold day occurrence probability. AR mainly brings warmer conditions (a % decrease in cold days) over Eastern Europe. GA has more contrasting influences: cold day occurrence increases by up to 7% over Scandinavia, but decreases by % over Iberia. BL is warmer from the British Isles to Scandinavia, but much colder in southeastern Europe (remember that the BL regime is characterized by an anticyclonic cell over eastern Europe, providing mild oceanic flows over northwestern and northern Europe). ZO and WBL have their most outstanding consequences over 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 (over France) is remarkable, especially if one considers the different origins and nature of the two data sets Weather regimes with more severe membership criteria As Robertson and Ghil (1999), we modified the criteria required for weather regime membership by introducing a rather severe angular filtering in the PC space, requiring a maximum angle of (cos >.7, or d c <.3) between a given daily anomaly pattern and the particular weather regime pattern. In this way, one eliminates days not clearly belonging to any one of the weather regimes. From now on, no more than -% of the patterns are classified, but this strong requirement for membership has interesting consequences. One can see in Figure 9 that for GA the probability of cold days doubles over Scandinavia. In contrast, with the new ZO class, the probability of a cold day decreases by more than % over a large area extending from western France and southeast England eastward to southern Scandinavia, western Poland, and Austria. WBL has similar (but opposite) strong local consequences, extending over almost all Europe. From northwestern France to Poland the cold day probability increases by up to 13-1% compared to its climatic average. All these features reinforce our view that the classification of LSCs into weather regimes provides 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, or even local climate changes, starting from GCM output. 173

9 3.3 Z7 weather regimes influence on precipitation Wet day percentage at French stations We now study precipitation using the same methodology as for temperature. A first glance at Figure 1 indicates an outstanding contrast between the Mediterranean region stations and the remaining ones. With AR, both western and southeastern France have fewer wet days, whereas the extreme northeastern French records have up to 1% more wet days than average. These features are not surprising if one considers (see Figure ) that during AR days, western Europe is submitted to northwesterly flow, mostly anticyclonic to the southwest, but more cyclonic to the northeast. The particular orography of France may explain the small wet day increase also observed near the Pyrennees and to the west and north of the Massif Central. With BL, precipitation probability decreases strongly almost everywhere. The largest decrease is up to % 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.) or even extreme flooding (such as occurred in mid-november 1999). With GA, there is a large increase in wet days over all the country. This increase is much smaller over Roussillon due to orographic effects, but reaches a maximum over the extreme southeast where a cluster with a Greenland High favours intense precipitation (see Section 3.3.). Finally, with ZO days, there is a sharp contrast between most Mediterranean regions and the remaining ones. Wet day probability rises by up to % to the northwest, but decreases by up to % to the southeast. Correlation patterns (not shown) between the total November-March precipitation amounts and the number of occurrences of each Z7 weather regime were also computed. They appear very similar to the patterns shown in Figure 1 and again suggest that the weather regime approach is a relevant one if one wishes to describe coherently the local climate departure from average. At least a large amount of this departure for a given period is very likely to originate in anomalous weather regime frequencies. Consider for instance the particular case of ZO. The correlation patterns (not shown) divide France into two parts: one third (roughly the southeast) with decreased rainfall amounts and the remaining two thirds (north, west and southwest) with a correlation between precipitation amounts and the number of ZO occurrences exceeding. in most northern stations. This is coherent with the recent increase of both winter NAO index and northwest European winter precipitation (together with the corresponding decrease in several southern European sub-regions, see Section 3.3.) Heavy precipitation over the Alps Here we consider days with precipitation amounts belonging to the highest precipitation amount decile. Two outstanding features are seen in Figure 11. First, a lot of meso-structures appear. 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 1. Let us first discuss the case of BL. Heavy precipitation probability (hereafter HPP) decreases by roughly % in a wide northern third of the Alpine Precipitation Climatology (APC) area, whereas the wet day occurrence (not shown) decreases by only 3% on average. In Roussillon, HPP increases by more than %. BL indeed favours intense precipitation over this coastal area most exposed to easterlies (see Section for further discussion of this point). For AR, HPP increases by almost % over the most northeasterly third of the APC domain. For GA, the increase is over 1% over the French southern Alps. With ZO, the HPP increase is over % in a rather small northwestern sector, whereas a significant decrease occurs over the French southern Alps, Languedoc-Roussillon and also the Italian Piedmont. 17

10 3. Weather regimes and local climate: a summary We conclude that a small number (four or five) of large-scale circulation patterns (weather regimes) are able to describe many features of European sub-regional weather patterns or even Alpine meso-climates. The description one gets using weather regimes allows a much finer description of the local climate than simple average climate. The atmosphere does not merely evolve around its mean state, but spends much more time around a few characteristic states with well defined consequences for local weather. This appears true for precipitation as well as for temperature. In this way, interannual, or even interdecadal, local climate variability may originate above all from fluctuations in the relative occurrence of weather regimes. One is then tempted to conclude that such an approach may provide a reliable framework for building downscaling algorithms for local climate change study purposes, using GCM output as a proxy, since GCM-simulated LSC changes are likely be more relevant than the same GCM mesoscale features. The above conclusions about weather regimes reinforce the interest in dynamical systems approaches to climate change studies, such as suggested by Palmer (193). During the last 1 years, the unstable fixed points (the weather regimes) did not really change, whereas the probability 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 these weather regimes (the most frequently observed large-scale patterns) provide an accurate framework in order to build tentative downscaling algorithms? We tackle this question in the next section.. Intense Events LSC Classification (Tasks 3. and 3.).1 Introduction Suppose from now on that one is interested in the statistical forecasting (through some downscaling scheme) of Intense Events (IE) starting from LSC patterns. The following question 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 similar consequences for local weather patterns within a class)? Or does it consist in a prior selection of IE days LSC followed by a classification of these peculiar LSCs alone? We adopt the later approach in this section, and test it with particular events that we call Intense Precipitation Events (IPE) over a few small Alpine sub-regions. We want a criterion to establish which approach is the most efficient. For this purpose, we will evaluate the extent to which the conditional probability of IE occurrence departs from its climatological mean either for days with LSCs close (from the correlation measure point-of-view, see the definition of d c in Section ) to weather regimes central patterns, or for days close to IE LSC clusters central patterns. The difference between the two approaches lies in the fact that one classifies thousands 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 space regions, since selected patterns correspond to a priori infrequently visited phase space directions (do not forget that IE are rare events), whereas the often visited weather regime patterns are likely to correspond to maxima of the PDF. 17

11 . Data processing and methodology We use the APC data set which combines daily precipitation records from thousands of stations (see Section 3.1 for a brief description). Small, hopefully homogeneous, Alpine subregions corresponding to 1-1 gridpoints are selected (Figure 1). IPE over given subregions are defined using some objective criterion. The one we choose results from a compromise between the extreme character of precipitation and the necessity to keep a large enough ensemble in order to get significant classifications. We define IPE, say over the Alpes Maritimes, as any day when at least one gridpoint received more than a given precipitation threshold. With the commonly used value of mm as a threshold, we are left for the Alpes Maritimes with about 1 IPE per year on average. (Raising the threshold value to mm, although giving fewer IPE, leads to indistinguishable LSC cluster patterns.) Once the IPE have been identified, we select the corresponding Z7 LSCs (in their 1 leading PC's space) and turn to their classification, again using the dynamical cluster algorithm. New significance checks are defined in order to check the robustness of the classification, based upon a comparison between the classifiability index of the IPE LSC and that of a set made up of the same number of LSC patterns, but randomly selected from the data set. In order to estimate the usefulness of a classification, we introduce the concept of discriminating power. We ask ourselves if any practical conclusion may be firmly stated when the large-scale circulation (analysed or forecast) resembles one of the cluster centres. If this is the case, that is if patterns quite similar to a given cluster centre correspond to IPE with a high probability (see panels c of Figures 13 and 1), and also lead to high precipitation composites over the given sub-domain (see panels d of Figure 13 and 1), we will say that the cluster in question has a high discriminating power..3 Savoy, the Alpes Maritimes and Queyras.3.1 The LSC clusters for IPE over Savoy For both Savoy and Alpes Maritimes, the random test selects k = clusters, with a classifiability index c* well above the 9% significance limit. For IPE over Savoy, the most outstanding feature of cluster 1 is a strong negative height anomaly (-1 m) over the North Sea, whereas cluster may be characterized by its weak negative anomaly over western Europe, with a light anomalous southerly flow over the Alps. Daily anomalies almost never look very similar to cluster centre since there are only 1 days (during years) with d <. (i.e. a pattern correlation >.), none of them being an IPE. Looking at the grey bars in Figure 13, one realizes that this cluster has very low discriminating power. It may be viewed as an aggregate of scattered LSC leading to heavy precipitation mainly from March to early autumn (Figure 1), with a pronounced peak in September. In contrast, anomaly patterns similar to that of the cluster 1 centre do occur in the actual anomaly maps and mostly correspond to rather heavy precipitation days. More than a quarter of those days with d 1 <. are indeed IPE and the histogram of rain composites shown in Figure 13 display a pronounced peak for small d 1 values. Most of the (late) autumn and winter heavy precipitation events correspond to class 1 IPE (Figure 1), in such a way that this cluster is responsible for the great bulk of heavy snowfalls..3. The LSC clusters for IPE over the Alpes Maritimes The situation is not very different over the Alpes Maritimes, but with a very different pattern at 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 17

12 phase space direction since close to this direction (d 1 <.), more than % of days are indeed IPE (Figure 1c). The composites in Figure 1d confirm the interest of GASC. Class 1 events are predominantly autumn or cold season events (Figure 1) and may be accompanied by heavy snowfalls over the Southern Alps in the later case, such as in January 199 when four class 1 IPE occurred, giving rise to avalanches and a lot of damage to power lines in mountainous areas of the Alpes Maritimes. The IPE probability is much lower for cluster (grey bars of lower panels of Figure 1) which could be called QP (Quadrupole). If one tries classification into more than two clusters, cluster 1 remains very robust, whereas cluster is broken into smaller clusters. The cluster centre could perhaps be viewed as an average direction between a few fuzzy sub-clusters..3.3 IPE trends and comparison with Queyras Although the APC covers only years, we tentatively turn to a discussion of possible trends either in IPE frequency, or in total precipitation amounts from IPE. If one observes that the recent Northern Hemisphere warming mainly occurred after 19, looking at IP trends over the last years makes some sense. In order to get more sensible conclusions, we consider average precipitation amounts for the whole set of gridpoints of a sub-region. In this way possible trends will be more confidently established, since local extreme events will be mostly dissolved within yearly averages involving 1 to 1 gridpoints and almost as many intense events (in addition each gridpoint precipitation amount holds for a weighted average already involving several rain gauges). Opposite linear trends appear in the left and right panels of Figure 1. Annual intense precipitation amounts begin with roughly. times larger amounts for the Alpes Maritimes than for Savoy-Mont Blanc, whereas they end with similar values for both sub-regions. The Savoy and Mont Blanc region, which lie on the northern flank of the Alps, thus recorded increasing intense precipitation during the last year period, as did the northwestern Europe countries, whereas an opposite tendency was observed over the Alpes Maritimes. The latter trend, which amounts to almost -1 mm for annual amounts between the beginning and the end of the period, would have been even more pronounced without the heavy autumn and winter precipitation of the early 199s. 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 over an intermediate Alpine sub-region, the so-called Queyras and surrounding massifs (see Figure 1). 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 performed classifications of IPE LSCs for the Queyras, using an identical IPE definition. Three was the best choice of clusters number. Figure 17 shows some features of clusters 1 and. Annual amounts from both these clusters are indeed much lower than for the previous Alpine subregions. However, it is enlightening to observe that Queyras cluster 1 centre looks rather similar to Savoy-Mont Blanc cluster 1, whereas Queyras cluster is almost identical to the Alpes Maritimes cluster 1. Thus one should not be surprised to observe the opposite linear trends exhibited by these two clusters contributions to intense precipitation..3. Alternative to IPE LSC classification We discuss briefly a few alternative ways of dealing with IPE LSCs. 177

13 Compositing all IPE LSCs. With such an approach, smaller LSC domains are preferred since, in most cases, significant anomalies only extend over a limited geographical area. Figure displays all IE composite maps for both the Alpes Maritimes and Savoy. At first glance, the composite approach seems rather challenging in the former case. Any day with d c <. has a high probability of being an IPE, and the gridpoint precipitation amount histogram of panel c) exhibits a sharp peak at small d c values. On closer inspection (not shown), one realizes, however, that the number of days with d c <. is much smaller (by a factor < 1/3) than the number of days with d 1 (the distance to cluster 1 centre) smaller than., so that this apparently 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 seasonal behaviour displayed by different clusters (Figure 1); and, ii) the more subtle understanding of underlying dynamical processes which is made possible through the classification approach, remember in particular the insight one gets into the origin of the opposite trends exhibited by Queyras clusters 1 and. Using a narrower domain never improved the cluster properties; they were at best almost unchanged; some Alpine sub-regions were more sensitive to the western parts of the domain, others to the eastern part. Using SLPs instead of Z7 tends to make cluster discriminating power only a little lower. Other possibilities such as thickness (e.g. Z3 - Z7) only resulted in some improvements for temperature (not precipitation) IE (see Section.).. IPE over other Alpine sub-regions Together with Partner 7, the LSC of IPE over Ticino were also classified. Tests on the classification index lead us to keep only gridpoints lying south of the mountain ridge separating the Swiss Rhone valley from the Piedmont flank. The region around Lago Maggiore is one of the most likely to experience IPE which may occur almost all through the year (with maxima in October and May). Three clusters were preferred, one of them being very similar to GASC. However, no significant trend could be observed for Ticino precipitation amounts from IE. If one takes three clusters for the Alpes Maritimes (although classification into three clusters lead to a classifiability index below the 9% significance threshold, the clusters are still fully robust), they can be put in a one-to-one correspondence with the Ticino clusters (Figure 19). The left panel of Figure shows the three cluster centres responsible for IE over Riviera Levante. 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 negative trend for IE annual amount, which decreased by more than 3 mm on average between 1971 and 199 (Figure 1, left histograms). These observations contrast with what may be observed for Roussillon where IE LSC clusters (Figure, right panel) do not correlate with the previous ones. The main patterns responsible for IE over Roussillon look very similar to BL or to anti-zonal flows (remember that IPE increased over this region during the negative NAO winter). Over Roussillon, precipitation amounts from IE, although limited, increased by two during the APC period (Figure 1, right histograms). At this point, it may seem surprising that with very similar LSC clusters for IE, both the Alpes Maritimes and the Riviera Levante experience decreasing annual precipitation amounts 17

14 whereas over Ticino amounts from IE do not decrease. Starting from the fact that IE mostly occur 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 and cold season events (from October to March) and spring and summer ones (April to September) over Ticino. In Figure, the year is divided into two extended seasons. One can see that precipitation amounts from the former season IE (i.e. autumn-winter IE) actually display a (rather small) negative trend, whereas the later season (spring-summer) IE precipitation amounts show a pronounced positive trend (+ mm in years). One could tentatively interpret these features in the following way. During the period , Mediterranean Alpine regions tended to experience lower precipitation amounts from IPE. In addition to the ordinary summer drought, autumn and winter precipitation shortages threatened. During these seasons, similar features emerged for Ticino which however received increasing amounts from IPE during the complementary season when precipitation almost stops along the Mediterranean coastal areas. As to the Roussillon, which also experiences IE mostly during autumn and cold seasons, and which also lies beside the Mediterranean Sea, the reverse trend is likely to be associated with the very different large-scale patterns yielding heavy precipitation there.. Weather regimes and intense events..1 Introduction We observed in the previous section that the classification of IE-alone LSC often leads to clusters with high discriminating power, in the sense that days with LSC similar enough to these cluster patterns could be days with a high probability of IE. But what about the connections between weather regimes and intense local or sub-regional events? We tackle this question from the point-of-view of both temperature and precipitation below. For brevity, we only consider very particular cases here, although our conclusions were found to be very general... Very cold days and weather regimes Another kind of (local) IE consists of very warm days (VWD) or very cold days (VCD) at a given station. Since we want some objective definition, we define VCD as any day with a mean temperature anomaly below -1. standard deviations. Several Météo-France station records were analysed and the same conclusions reached. Thus only histograms for Nice are displayed here. Figure 3 shows VCD probability histograms against intervals of d c, the correlation 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 for appreciable departures from it. Such departures do not occur for BL or GA. ZO has more significant consequences concerning VCD occurrence, since VCD appear quite forbidden for d c <., whereas their probability is enhanced by a factor up to or 7 for days with the ZO pattern reversed (anti-zonal days). Since WBL looks very much like a reversed ZO, it is not very surprising that WBL-like patterns highly favour VCD occurrence...3 Direct classification of very cold days LSC Since we want to check the relevance of weather regimes in forecasting IE, we now turn to the concurrent approach. We classify only large-scale patterns of VCD. More precisely, we now classify thickness patterns (Z3 - Z7), which are a little more appropriate for temperature. Two classes of thickness anomaly patterns for VCD in Nice are displayed in 179

15 Figure a (top and middle anomaly maps). The second one (which occurred quite often during the long January 19 cold spell) has a high discriminating power since more than % of days with d c <. are VCD (this is also true for Paris and Lyon). A comparison between this last histogram and both previous ones, suggests that the classification of VCD alone LSC (or LST: Large Scale Thickness) may be more convenient if one is interested in VCD downscaling forecasts. In this case, it turns out that the largest departures from climatological averages occur if one chooses the VCD thickness composite (Figure, lower map and panel d). With this choice, the VCD probability rises up to /3 for. < d c <.. One can also notice that d c never gets smaller than. with the VCD thickness composite. Unlike cluster centres, composite patterns often correspond to somewhat unphysical states, to the extent that one cannot find actual patterns with a d c below. (i.e. a correlation >.). We conclude this discussion about temperature events by saying that although weather regimes may, in some cases, provide appropriate patterns for downscaling forecasts of VCD, the most efficient patterns are found if one deals with VCD LSC patterns alone... The case of precipitation intense events Following Sections.. and..3, it only remains to check the weather regimes discriminating power for IPE studies. For sake of brevity, we again limit our discussion to the French Alpes Maritimes. Very similar conclusions may, however, be drawn for other subalpine domains. The IPE probabilities are displayed against the correlation distance to each Z7 weather regime pattern in the Figure histograms. It is obvious that none of them is suitable if one intends to forecast IPE through downscaling. In contrast, as was shown in Section.3, the so-called GASC cluster does point towards a phase space direction with a high discriminating power. If one compares IPE probability to its climatological mean, it is enhanced by a percentage of almost 1% for days the LSCs of which correlate with GASC with d c <. (Figure 1).. Summary of Section conclusions The analyses performed in Sections 3 and lead to the following conclusions. When the purpose is a better description of, or downscaling approach to, local climate, the classification of all daily LSC into a small number of classes, the so-called weather regimes, provides a very attractive approach. However, if the main interest lies in somewhat rare events, such as Intense Precipitation, the classification of Intense Events Days LSC alone provides a much more attractive approach. Of course the clusters one gets point towards less populated phase space regions, but the corresponding patterns often correspond to highly enhanced IE probability. This is illustrated in the left panel of Figure, where one observes that the PDF of all LSCs against the correlation distance to the GASC cluster has very few events close to d c =, in comparison to the right panel where d c is relative to ZO. Moreover, one should notice the greater bulk of events close to d c = 1 (i.e. corr = ) for GASC. Most LSCs are poorly correlated with the GASC pattern, whereas they do correlate to ZO. However, the probability that an IPE occurs over the Alpes Maritimes is so much enhanced with patterns similar to GASC, that remarkably robust clusters do emerge when one classifies IPE LSCs into clusters. These clusters of mesoscale Intense Events LSCs are in some sense data adaptive, and very different patterns correspond to different sub-regions (such as the Alpes Maritimes, Mont- Blanc and Roussillon). The cluster patterns are objectively established, which contrasts with numerous downscaling approaches where one classifies according to some a priori criteria

16 such as the flow orientation. In view of the poverty of deterministic IPE forecasts in many cases, downscaling algorithms using IPE LSC classes could provide challenging alternatives. They could at least allow one to examine the possible meso-scale consequences of climate change as simulated by GCMs.. Low-Frequency Oscillations (LFO) (Task.).1 Introduction In 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 using dynamical systems theory. The existence of a strange attractor of finite dimension with a complex 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 this attractor. 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 major subject of interest. They cover a large frequency range from intramonthly, interannual up to interdecadal and longer periods. The weather regime occurrences could be favoured by particular phases of oscillations, and their transitions could be influenced by the succession of phases of LFO. These questions have been tackled by Plaut and Vautard (199, referred to as PV9 hereafter) using a 3-year set of National Meteorological Center (NMC) analyses for the Z7 geopotential height. Here, we first extract and compare intraseasonal oscillations corresponding to the three LSC fields (SLP, Z7 and Z) (Section.). For the 1-years SLP data set, we then study interannual LFO (Section.3). Then, we try to understand the connections between these oscillations and the weather regimes studied in previous sections (Section.). Section. focuses on the connection between LFO and local surface climate (temperature and precipitation). In Section., we briefly introduce a new powerful tool based on wavelet analysis, namely the multichannel wavelet transform, and we say a few words about the spectral activity of the main intraseasonal LFO during the last 1 years.. Comparison of intraseasonal LFO at various levels..1 Methodology For a description of MSSA we refer to PV9. MSSA diagonalizes a multi-channels multi-lags cross-covariance matrix. The eigenvectors, ST-EOF, correspond to adaptive space-time patterns of length [win], where [win] is the time window of the MSSA. This parameter corresponds to the maximum time lag involved in the cross-covariance matrix. To keep the size of the matrix reasonable, a PCA is first performed. Here, we retain only the six leading PCs, without any loss of generality. Then we adopt the notation SLP_[win] for a particular MSSA of the field SLP with a window length [win]. A nice feature of MSSA is its ability to extract oscillatory behaviour, provided that intermittent but recurrent oscillatory space-time patterns are present within the data set. The LFO manifest themselves through the existence of pairs of (almost) oscillatory ST-EOF with (almost) the same period. The corresponding cross-covariance matrix eigenvalues are (almost) degenerated. We use the same criterion as PV9 to select the oscillatory pairs corresponding to LFO. We then use "reconstructed components" (PV9) as a basic tool to define eight phases for an oscillation, and compute the corresponding phase composites of any field (the involved LSC field itself, or temperature, precipitation, etc). 1