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Predictability of North Atlantic/Arctic Ocean Surface State

This project aims to assess the predictability of key oceanic and atmospheric quantities related to the North Atlantic/Arctic Ocean surface state. We will analyze hindcast predictions, near-future forecasts, and observations to understand the mechanisms underlying predictability in the North Atlantic ocean, sea-ice, atmosphere system.

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Predictability of North Atlantic/Arctic Ocean Surface State

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  1. Core Theme 1Predictability of key oceanic and atmospheric quantities related to the North Atlantic/Arctic Ocean surface state M.N. Houssais (UPMC) C. Frankignoul (UPMC) K. Lohmann (MPI-M), Y. Gao (NERSC), D. Stammer (UHAM) J. Jungclaus (MPI-M)

  2. Overview Subpolar Gyre Sea Surface Temperature (M. Payne, DTU) Decadal to multi-decadel variations in North Atlantic SSTs are important not only for European climate,…

  3. Overview …but also for ocean ecosystems and socio-economic factors

  4. Near-term predictions Anomaly correlation between observed and multi-model ensemble mean prediction of surface temperature at 2-5 year lead time, trend removed. Promising results for North Atlantic and Sub Polar Gyre region related to AMOC, heat content changes in SPG Hazeleger et al., THOR D27, 2012

  5. CMIP5 predictions and uncertainties Impact of initialization (initialized minus initialized ensemble means) on forecasts of the period 2012 to 2016. Common skill in North Atlantic, but many uncertainties elsewhere, incl. Nordic Seas Smith et al., 2012

  6. Different regional flavors of variability Models show pronounced differences in details, e.g. pathway of NAC, SPG, exchange with Nordic Seas Branstator and Teng, 2012

  7. Regional aspects: Subpolar Gyre SPG 0-500m temperature anomalies from MetOffice ocean analysis (black), DePreSys (red) and No Assim (blue) hindcasts Prediction systems show usefull skills in reproducing regional aspects and mechanisms (AMOC, SPG)… Robson and Smith, 2012

  8. Processes: Labrador Sea Water formation LSW in observations and in ocean model runs with data assimilation (left). Comparing data assimilation simulation with hindcasts (right) …and allow to infer relationships between the observed indices of variabilty and other parameters/processes. Matei et al., in preparation

  9. Regional aspects: Sea Ice Predicted annual-mean northern-hemisphere sea-ice extent. Black: original RCP4.5 run, solid lines: ensemble hindcasts, dashed lines: persistence Sea ice predictions appear to be tricky beyond a few months. Strong events, slow changes due to ocean heat transport changes may have some longer-term predictability Tietsche at al, submitted, 2012

  10. W.P. 1.1: Key points of heat- and fresh water exchanges Langehaug et al., 2012 CT1 will look at key regions of the North Atlantic/Arctic region; identify mechanisms, and assess uncertainty in model results comparing with THOR/NACLIM observations

  11. W.P. 1.2: Predictability of the atmosphere related to the ocean state MCA analyses of AMOC and SLP in THOR models for the most significant atmospheric response to the AMOC. CT1 will identify SST, SST, sea-ice patterns affecting the atmosphere and assess the predictability in climate models, observations, and idealized studies Gastineau and Frankignoul, 2012

  12. WP 1.1 : Predictability of key oceanic quantities related to North Atlantic/Arctic ocean surface state (MPI-M, NERSC) • 1.1.1 Quantify hindcast predictability and uncertainties in near-future predictions of North Atlantic/arctic ocean state • - predictability of SST, SST, sea-ice coverage in North Atlantic sector in multi-model CMIP5 hindcasts • - evaluation of uncertainties in CMIP5 near-future forecasts •  Evaluation of state-of-the-art prediction skills for subpolar North Atlantic and the exchange between Atlantic and Arctic • 1.1.2 Quantify hindcast predictability and uncertainties in near-future predictions of key oceanic quantities controlling North Atlantic/arctic ocean surface state: • - AMOC • - Subpolar Gyre • Heat and fresh water transport in key sections (e.g. GSR, Barents Sea) • Processes and model uncertainties •  Asses mechanisms underlying predictability in the North Atlantic ocean, sea-ice, atmosphere system. How : CMIP5 hindcasts, near-future predictions, control expmts., dedicated coupled experiments, NACLIM observations

  13. WP 1.2 : Predictability of the atmosphere related to the North Atlantic/Arctic surface stateUPMC, UHAM, NERSC Identify the sensitivities of European climate to North Atlantic SST, sea ice and sea surface salinityusing the THOR adjoint assimilation system. Identify the surface state patterns that most impact the atmosphere, and where observations constrain or improve predictions Identify the patterns of SST, SIC, snow cover, and western boundary current changes that most impact the observed atmospheric circulation in the N. Atlantic/European sector.Use GCM experiments to understand the mechanisms of the atmospheric response to SST and SIC changes, and their back interaction on the ocean Compare the observed surface state impact to that in CMIP5 climate models to assess their ability to represent decadal fluctuations. Use observations to downscale and possibly correct model predictions to local scales of interest for impact studies Estimate the part of the climate changes in CMIP5 multi-model forecast experiments which is due to ocean-driven boundary forcing Understand the influence of Arctic sea ice and SST on observed polar meso-cyclones activity and Polar lows and establish their links to large-scale weather regimes and the main modes of surface variability. Infer the related impact of climate changes in climate simulations How : Obs., THOR adjoint assimilation system, coupled simulations

  14. WP 1.3 : Mechanisms of ocean surface state variability (seasonal to decadal time scales) UHAM, UPMC • 1.3.1 Spatial patterns of ocean surface state (SST, sea ice) variability • - Extreme September sea ice events • - Persistence of sea ice anomalies from season to season • - Determine relevant sea ice parameters (SIC, thickness, age, …) •  determine dominant modes of interannual to decadal variability • 1.3.2 Link between ocean surface state and key ocean quantities : • - AMOC • - Ocean gyre indices (Beaufort Gyre, Subpolar Gyre) • - Atlantic and Pacific water inflow to • - Ocean stratification (Arctic halocline) •  apply the analysis to the ocean surface changes that most influence the atmosphere (WP 1.2) • 1.3.3 Impact of the atmosphere on ocean surface state variability • - Identification of atmospheric modes driving ocean surface variability (incl. potential feedbacks) • - Role of the atmosphere in extreme sea ice events (September minimum) • - Response of stand-alone ocean models to these dominant modes How : Obs., THOR adjoint assimilation system, coupled & ocean-only simulations

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