On the robustness of near term climate predictability regarding initial state uncertainties
- 340 Downloads
A set of four ensemble simulations has been designed to assess the relative importance of atmospheric, oceanic, and deep ocean initial state uncertainties, as represented by spatial white noise perturbations, on seasonal to decadal prediction skills in a perfect model framework. It is found that a perturbation mimicking random oceanic uncertainties have the same impact as an atmospheric-only perturbation on the future evolution of the ensemble after the first 3 months, even if they are initially only located in the deep ocean. This is due to the fast (1 month) perturbation of the atmospheric component regardless of the initial ensemble generation strategy. The divergence of the ensemble upper-ocean characteristics is then mainly induced by ocean–atmosphere interactions. While the seasonally varying mixed layer depth allows the penetration of the different signals in the thermocline in the mid-high latitudes, the rapid adjustment of the thermocline to wind anomalies followed by Kelvin and Rossby waves adjustment dominates the growth of the ensemble spread in the tropics. These mechanisms result in similar ensemble distribution characteristics for the four ensembles design strategy at the interannual timescale.
KeywordsClimate predictability Uncertainties Ensemble spread Initial condition perturbation Prediction reliability Ensemble generation
The data used in this study are freely available: the authors can send them upon request. This work has been funded by the European community 7th framework programme (FP7) through the SPECS (Seasonal-to-decadal climate Prediction for the improvement of Climate Service) project under Grant agreement 308378 and by the Natural and Environmental Research Council UK (MESO-CLIP, NE/K005154/1 and SMURPHS, NE/N005767/1). We also thank the TGCC for computing resources and the IPSL model pole. The authors would like to thank Pablo Ortega and Javier Garcia-Serrano for helpful discussions and two reviewers for there helpful comment on the manuscript. They also thank J. Annan and W. Connolley for interesting exchanges about their experiment with HadCAM3. A.G. also wishes to thank the University of Southampton and the National Oceanography Centre Southampton and especially the PO and MSM teams for their welcome and the facilities they provided in order to help collaboration.
- Abraham JP, Baringer M, Bindoff NL, Boyer T, Cheng LJ, Church JA, Conroy JL, Domingues CM, Fasullo JT, Gilson J, Goni G, Good SA, Gorman JM, Gouretski V, Ishii M, Johnson GC, Kizu S, Lyman JM, Macdonald AM, Minkowycz WJ, Moffitt SE, Palmer MD, Piola AR, Reseghetti F, Schuckmann K, Trenberth KE, Velicogna I, Willis JK (2013) A review of global ocean temperature observations: implications for ocean heat content estimates and climate change. Rev Geophys 51(3):450–483. doi: 10.1002/rog.20022 CrossRefGoogle Scholar
- Aumont O, Bopp L (2006) Globalizing results from ocean in situ iron fertilization studies. Glob Biogeochem Cycles 20, GB2017. doi: 10.1029/2005GB002591
- Collins M, Botzet M, Carril AF, Drange H, Jouzeau A, Latif M, Masina S, Otteraa AH, Pohlmann H, Sorteberg A, Sutton R, Terray L (2006) Interannual to decadal climate predictability in the North Atlantic: a multimodel-ensemble study. J Clim 19(7):1195–1203. doi: 10.1175/JCLI3654.1 CrossRefGoogle Scholar
- Dufresne JL, Foujols M-A, Denvil M-AS, Caubel A, Marti O, Aumont O, Balkanski Y, Bekki S, Bellenger H, Benshila R, Bony S, Bopp L, Braconnot P, Brockmann P, Cadule P, Cheruy F, Codron F, Cozic A, Cugnet D, de Noblet N, Duvel J-P, Ethé C, Fairhead L, Fichefet T, Flavoni S, Friedlingstein P, Grandpeix J-Y, Guez L, Guilyardi E, Hauglustaine D, Hourdin F, Idelkadi A, Ghattas J, Joussaume S, Kageyama M, Krinner G, Labetoulle S, Lahellec A, Lefebvre M-P, Lefevre F, Levy C, Li ZX, Lloyd J, Lott F, Madec G, Mancip M, Marchand M, Masson S, Meurdesoif Y, Mignot J, Musat I, Parouty S, Polcher J, Rio C, Schulz M, Swingedouw D, Szopa S, Talandier C, Terray P, Viovy N, Vuichard N (2013) Climate change projections using the IPSL-CM5 Earth System Model: from CMIP3 to CMIP5. Clim Dyn 40(9–10):2123–2165. doi: 10.1007/s00382-012-1636-1 CrossRefGoogle Scholar
- Kirtman B, Power SB, Adedoyin JA, Boer GJ, Bojariu R, Camilloni I, Doblas-Reyes FJ, Fiore AM, Kimoto M, Meehl GA, Prather M, Sarr A, Schär C, Sutton R, van Oldenborgh GJ, Vecchi G, Wang HJ (2013) Near-term climate change: projections and predictability. In: Stocker TF, Qin D, Plattner G-K, Tignor M, Allen SK, Boschung J, Nauels A, Xia Y, Bex V, Midgley PM (eds) Climate change 2013: the physical science basis. Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press, CambridgeGoogle Scholar
- Madec G (2008) NEMO ocean engine, Technical note, IPSL. Available at http://www.nemo-ocean.eu/content/download/11245/56055/file/NEMO_book_v3_2.pdf
- Mignot J, Garcia-Serrano J, Swingedouw D, Germe A, Nguyen S, Ortega P, Guilyardi E, Ray S (2015) Decadal prediction skill in the ocean with surface nudging in the IPSL-CM5A-LR climate model. Clim Dyn. doi: 10.1007/s00382-015-2898-1
- Valcke S (2006) OASIS3 user guide (prism_2-5), technical report TR/CMGC/06/73, PRISM report no 2. CERFACS, Toulouse, p 60Google Scholar