Prévisibilité décennale

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1 Prévisibilité décennale
Initialiser un modèle couplé pour prévoir la variabilité naturelle du climat Didier Swingedouw, Juliette Mignot, Sonia Labetoule, Aurélie Persechino, Eric Guilyardi, Benoit Vannière, Jérôme Servonnat (bientôt)

2 Variabilité climatique
Température globale Variabilité décennale en plus d’une tendance liée aux GHG Forçage? (aérosols, soleil) Variations naturelles du système climatique superposées à la réponse aux GHG IPCC 2007, Chapitre 2

3 Variabilité climatique
Température globale modèle GFDL (A1B) Simulations d’un même modèle sous le même scenario avec conditions initiales différentes Variabilité décennale

4 Prévisibilité Deux types de prévisibilité (cf. Edward Lorenz):
Première espèce : conditions initiales Deuxième espèce : conditions aux limites Meehl et al. 2009

5 Sources d’incertitude
Hawkins & Sutton 2009

6 Variabilité mutidécennale
1ière EOF SST par bassin Atlantic Multi-decadal Oscillation Pacific Decadal Oscillation The "Pacific Decadal Oscillation" (PDO) is a long-lived El Niño-like pattern of Pacific climate variability. While the two climate oscillations have similar spatial climate fingerprints, they have very different behavior in time. Fisheries scientist Steven Hare coined the term "Pacific Decadal Oscillation" (PDO) in 1996 while researching connections between Alaska salmon production cycles and Pacific climate (his dissertation topic with advisor Robert Francis). Two main characteristics distinguish PDO from El Niño/Southern Oscillation (ENSO): first, 20th century PDO "events" persisted for 20-to-30 years, while typical ENSO events persisted for 6 to 18 months; second, the climatic fingerprints of the PDO are most visible in the North Pacific/North American sector, while secondary signatures exist in the tropics - the opposite is true for ENSO. Several independent studies find evidence for just two full PDO cycles in the past century: "cool" PDO regimes prevailed from and again from , while "warm" PDO regimes dominated from and from 1977 through (at least) the mid-1990's. Shoshiro Minobe  has shown that 20th century PDO fluctuations were most energetic in two general periodicities, one from 15-to-25 years, and the other from 50-to-70 years. Major changes in northeast Pacific marine ecosystems have been correlated with phase changes in the PDO; warm eras have seen enhanced coastal ocean biological productivity in Alaska and inhibited productivity off the west coast of the contiguous United States, while cold PDO eras have seen the opposite north-south pattern of marine ecosystem productivity. Causes for the PDO are not currently known. Likewise, the potential predictability for this climate oscillation are not known. Some climate simulation models produce PDO-like oscillations, although often for different reasons. The mechanisms giving rise to PDO will determine whether skillful decades-long PDO climate predictions are possible. For example, if PDO arises from air-sea interactions that require 10 year ocean adjustment times, then aspects of the phenomenon will (in theory) be predictable at lead times of up to 10 years. Even in the absence of a theoretical understanding, PDO climate information improves season-to-season and year-to-year climate forecasts for North America because of its strong tendency for multi-season and multi-year persistence. From a societal impacts perspective, recognition of PDO is important because it shows that "normal" climate conditions can vary over time periods comparable to the length of a human's lifetime .

7 Régionalisation AMO index
Température moyenne sur 30 stations représentatives en France Figure de Christophe Cassou

8 Prévision décennale Grandes tendances
Premières tentatives: Effet global et régional Pas d’accord entre les prévisions à cause de : modèles différents méthodes d’initialisation différentes : AMOC proxy (Latif et al. 2006) Decadal prediction examples. Observed and hindcast values of (A) ten year mean global mean surface temperature and (B) an Atlantic sea surface temperature (SST) dipole index. The latter is a proxy for MOC fluctuations and is defined as the average SST difference for 60-10W,40-60N minus 50-0W, 40-60S. Hindcasts begin in 1982 (1955) in Smith et al (Keenlyside et al. 2008), with a four (three) member forecast every season (five years); shading (error bars) indicates the ensemble range. The error bars centered on 2015 represent actual forecasts for the period Hindcasts for Smith et al (Keenlyside et al. 2008) are adjusted to have the observed means over the ( ) period. Note the different axis used in (A) for Keenlyside et al Observations are from HadISST 1.1 and HadCRU3. Meehl et al. 2009

9 Prévisibilité décennale à l’IPSL: outils
Test préminaires : (résultats présentés ici) 96 x 95 x L39 ORCA2 + PISCES 144 x 142 x L39 Objectifs Preliminary tests (here): control simulations only, with fixed external forcing

10 Prévisibilité décennale : rôle de l’AMOC
Figure d’aurélie Persechino Corrélation d’ensemble AMOC

11 Initialisation : « nudging »
Rappeler le modèle vers des observations Méthode la plus simple : mettre un terme de rappel dans les équations Choix à l’IPSL : SST et « avec ou sans » vent Anomalies plutôt que valeur totale (problème de dérive initiale)

12 Initialisation des modes de variabilité en SST
Observations Observations SST SST SST+wind SST+wind et ds le CTRL ??? montrez que ce n’est pas corrélé du tout, mais que l’on a la bonne struucture EOF 1 par bassin

13 Variabilité AMOC récente
Pas de mesures continues de l’AMOC sur longue période de temps Estimation via assimilation de données Huck et al. 2008 Nudging initialise bien notre AMOC !

14 Et la NAO ? SST SST+wind NCEP ERA40 Signif 90% (pas 99%)

15 Expériences CMIP5 de prévisibilité du climat
1960 1965 1970 1985 1980 1975 2000 1995 1990 2005 2010 2030 A l’IPSL : on ne met pas de forçage radiatif = prévisibilité première espèce

16 Hindcasts pour SST’ Max(AMOC) AMO

17 Prévisibilité en mode hindcast
Observations 1960 1965 1970 1985 1980 1975 2000 1995 1990 2005 2010 2030

18 Hindcasts Carte de corrélation moyenne d’ensemble sur 10 ans (sans forçage radiatif !) pour chaque date de départ avec observations Keenlyside et al. 2008 SST’ nudging + wind stress forcing SST’ nudging Figures de Juliette Mignot SST’ + WS fait moins bien (?)

19 Perspectives Inclure le forçage radiatif ! (Sonia Labetoule)
Initialisation en SSS (Sonia Labetoule & Didier Swingedouw) Etudes en modèle parfait plus poussées (Juliette Mignot & Aurélie Persechino) Compréhension de l’initialisation de l’AMOC (Didier Swingedouw) Mécansime de prévisibilité dans l’atmopshère (Jérôme Servonnat, EPIDOM)

20 Merci

21 AMO and AMOC (coupled GCMs)

22 Forecasting with SST nudging only?
HadCM3 idealised experiments Comparison of assimilation of different amounts of ocean data Dunstone and Smith 2010 - Fail of global SST assimilation with six hourly relaxation: strong increase of MOC Still strong increase if SST assimilated poleward of 60°N only. Better with 96hrs relaxation, but not good reproduction of variability.

23 AMOC variability 1960-2005 AMOC 45°N Pohlmann et al 2010 SST’
*(3yr running means) Anomalies normalisées SST’ SST’ + WS SST’ + WS * 2 SST’ + WS, different I.C. Control simulation

24 AMOC variability 1960-2005 AMOC 45°N Pohlmann et al 2010 SST’
*(3yr running means) Anomalies normalisées SST’ SST’ + WS SST’ + WS * 2 SST’ + WS, different I.C. SST’ equatorward (60°) Control simulation

25 initialized simulation
Hindcasts SST nudging g = 40W/m2/K + wind stress forcing SST nudging g = 40W/m2/K Keenlyside et al 2008 10YR averages SST correlations with the control initialized simulation

26 initialized simulation
Hindcasts 30S 30N 60N 30S 30N 60N corelations with the control initialized simulation Oceanic heat content 0-300m SST nudging g = 40W/m2/K + wind stress forcing SST nudging g = 40W/m2/K Relative skill de SST’ + WS a bit better for OHC 0-300m? SST

27 SST’ + WS fait bcp mieux pour la SSS?
Hindcasts SST nudging g = 40W/m2/K + wind stress forcing SST nudging g = 40W/m2/K SST’ + WS fait bcp mieux pour la SSS? SSS

28 Conclusions SST nudging gives reasonable initialization when the nudging strength is set with care Still, induces artifical feedbacks and would require SSS nudging for consistency Benefit of additional wind stress forcing still needs to be precisely assessed (pb OHC du run initialisé, mais predicitve skill plutôt meilleure pour SSS?) Promising hindcasts results on North Atlantic SSTs / heat content (and AMOC)

29 Next steps Better asses the mechanisms of predictability: perfect model studies Better assess the impact of surface initialization: perfect model initialization Better assess the predictability skill over land / atmosphere Better asses the initialization and predictability skill of the AMOC Perform full exercice with consistent external forcing and enhanced atmospheric resolution Test other ways of initializing at the surface: repeated initialization cycles, SSS nudging, bulk formulaes.

30 “ Worst case” (no assimilated data) Other panels show difference between assimilated AMOC and “truth” as a funcMon of observing system “BEST” (Argo plus atmosphere temp and winds)