Analyse statistique des sries de mesuresPhilippe KeckhutUniversit Paris 6Pierre et Marie CurieUniversit Versailles Saint-QuentinInstitut Pierre-Simon Laplace des Sciences de lEnvironnementLATMOS

Analyse de rgression : tendance T2 = R2 * Fc / S (ti-tm)2 incertitude sur la tendance variablit n d temps

Auto-correlation Longues sries temporelle (plusieurs dcenies) t Optimisation si la variabilit est limite la variabilit atmosphrique R Mesures frquentes -> moyennes mensuelles Dcroissance exponentielle Temps caractristique joursFc=(1+/(1-

Echelle temporelle et signification statistiqueTemps ncessaire pour une dtection significative (2s)

ParametresrgionVariabilit rsiduelleTendance attendue(/dcennie)Temps ncessaire(annes)Temprature

Ozone

VentHaute stratoMsosphre

Basse stratoHaute strato3-8 K8-10 K

20 %7%

5-15 m/s1-2 K1-3 K

5-20%5-10%

< 1m/s8-1715-30

7-205-10

20-50

Analyse multi-rgressionForages naturels (solaire, volcanique) et anthropiques

Ramaswamy et al, Rev. Geoph., p71, 2001

Erreur MthodologiqueLerreur mthodologique dpend du bruitAprs 20 ans lerreur mthodologique est ngligeable

DiscontinuitsPrincipaux facteurs de discontinuit :Amlioration de la prcision, sensibilit, et chantillonnage Amlioration instrumentaleChangement des algorithmesChangements des protocoles et modes opratoirs

Eviter les changements instrumentaux Date des changements (Metafile) Mesures redondantes

Srie alatoire dcale de 1 s en

LAZANTE John R., International journal of climatology,

vol. 16, 1197-1226, 1996

Changements Instrumentaux des fusesEstimated from the time serie analysesEstimated from the aerothermic calculations

Regression multi-paramtres

T(t) = m + St + ATrend + BSolar + CQBO + DENSO + EAO + F.Step(ti) + Nt les termes A, B, C, D, E, F reprsente lamplitude des tendances / les facteurs de variabilit et les rsidus et biais.Les rsidus (AR(1)) correspondent la variabilit non considre dans le modle.Les limites de ces analyses correspondent : Linadquation du modle La linarit de la rponse atmosphrique Les biais dues au la non indpendance des forages (mthodologie) Discontinuits

Mares atmosphriquesLes mares sont dues labsorption du rayonnement solaire par lozone et la vapeur deauCe forage induit des oscillations en phase avec le cycle diurneCe forage induit de larges amplitudes dans la haute stratosphre et dans la msosphreCes signatures sobservent partir des lidars et les satellitesLidar La Runion GSMWMLS/UARS 1 hPa6KLes mares induisent des biais dans les comparaisons de donnes, les tandances et la validation satellitaire

Interfrences dues aux maresTime of launchAveraged temperature 45-55 km2:0010:0015:00Kubicki et al., submitted toJASTP , 2004.

Influences des mares sur la tendanceKeckhut et al., J. Geophys. Res., p447, 1999

Tendances en fonction de la latitudeVolgogradOHP, _ _Wallops, ---Riory, .US tropicalUS tropicalWallopsOHPVolgogradRiorySummerWinterKubicki et al., submitted toJASTP, 2004.US tropical: 8S-34NWallops Island: 37,5NRyori, Japan: 39NOHP, France 44NVolgograd 49N

Tendance lchelle globaleNCEP analyses at 1 hPa (50 km)Keckhut et al., J. Geophys. Res., p546, 2001

Evolution des forages

Tendance ou variabilit naturelle?~43-57 km

Tendance ou variabilit naturelle?~43-57 km

Tendance non-linaireLestimation de tendances non-linaires est possible Dans le domaine 2 et dpend de la longueur de la srie et du bruitDans le domaine 3 dpendant uniquement du bruit rsiduel

Rsultats concernant la tempratureTendances linaires varient en fonction de la dure des sriesTerme non-linaire indique l'inflexion des tendances et stabilise la tendance gnrale

Stratgie de surveillance lors dune variabilit importante et multipleDistribution des anomaliesCycle hydrologiqueTransport verticalTransporthorizontalSources

Cas de la haute troposphre

Evolution des pics dozoneThe stratospheric origin increasesThe long-term tropospheric transport was minimum in 1980sBL origin remains constant since 1970sColette, et al. Atmos. Env., 2005

Evolution de la temprature 44N

Dviation standard de la temprature

EOF of T profiles Summer (April to Oct)

EOF of T profiles Winter (Dec-Jan-Feb)

Mode msosphriqueMesospheric inversion

Mode Stratosphrique-msosphriqueStratospheric warming

Rponse de latmosphre aux cycles de 27-joursWavelet analysesMgII solar fluxLidar temperatureInterferences avec les ondes plantaires

Variabilit de lOzoneTOMS Total Ozone Wavelet analysisAnalyse par ondelettes Changements de la variabilit associs ltablissement dun vortex Mise en vidence de changements significatifs de la dure de lhiver et de lt Quantification de la variabilit

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Rsultats de lanalyse de la variabilit1. Ozone variability is associated to the occurrence of stratospheric warmings2. The winter variability sems to be asscociated with solar cysle

Observations des cirrus par lidar

Climatologie des cirrusMid-latitude (44N)(from Goldfarb et al., GRL, 2001)Southern tropic (21S)(from Cadet et al., GRL, 2002)

Graph1

54.285714285716

64.285714285712

6014

4010

57.89473684217

502

24.32432432430

46.15384615380

55.88235294124

48.2758620691

6011

57.142857142923

OHP

La Runion

Month

Occurence (%)

Feuil1

Jan54.2857142857161935

Feb64.2857142857122742

Mar60142440

Apr40101025

May57.894736842171119

Jun5021938

Jul24.32432432430937

Aug46.153846153801839

Sep55.882352941241934

Oct48.27586206911429

Nov60111525

Dec57.1428571429231628

Feuil1

OHP

La Runion

Month

Occurence (%)

Feuil2

Feuil3

Cirrus lOHP 1997-1999 Les cirrus sub-visibles correspondent une paisseur optique t < 0.03 Distribution et frquence doccurrences indpendant de la saison 3 classes identifies, fonction de laltitude et paisseur des nuagesClasse dpaisseur optique de A F

Type de cirrus

Distribution des anomalies

Classes de cirrusKeckhut et al., J. Appl. Meteo., 2005 (in press)

ClassIII IIIOccurrence (%)362735Height (km)8.6 0.99.8 0.711.5 0.9Altitude relative to tropopause-78-0.513+716Thickness (km)0.9 0.63.2 0.90.9 0.6Temperature (C)-41 6-50 6-58 6

ConclusionsAnalyse critique des donnesMetadonnesSources de biaisStationnarit des sriesAnalyse statistique des donnesDistribution des donnesIndpendance des donnesApport de la simulationAnalyse mathmatique interprtable physiquementAnalyse de la variabilitSources multiplesSparation des sourcesAdaptation des mthodesTF et OndelettesClassificationComposantes principales

Le Lidar rtrodiffusionOccurrence des nuages + diffusion molculaire

Lidar Rayleigh : Profil de tempratureSapplique pour de la diffusion molculaireDensit et la pression sont des mesures relativesLa temprature est dduite partir de la lois des gaz parfait est une estimation absolue