Laure Zanna

Abstract:

The impact of uncertainties in air-sea fluxes and ocean model parameters on the ocean circulation and ocean heat uptake (OHU) is assessed in a novel modeling framework. We use an ocean-only model forced with the simulated sea surface fields of the CMIP5 climate models. The simulations are performed using control and 1% CO 2 warming scenarios. The ocean-only ensemble adequately reproduces the mean Atlantic Meridional Overturning Circulation (AMOC) and the zonally integrated OHU. The ensemble spread in AMOC strength, its weakening, and Atlantic OHU due to different air-sea fluxes is twice as large as the uncertainty range related to vertical and mesocale eddy diffusivities. The sensitivity of OHU to uncertainties in air-sea fluxes and model parameters differs vastly across basins, with the Southern Ocean exhibiting strong sensitivity to air-sea fluxes and model parameters. This study clearly demonstrates that model biases in air-sea fluxes are one of the key sources of uncertainty in climate simulations.

Abstract:

Predictability of Atlantic Ocean sea surface temperatures (SST) on seasonal and decadal timescales is investigated using a suite of statistical linear inverse models (LIM). Observed monthly SST anomalies in the Atlantic sector (between 22(Formula presented.)S and 66(Formula presented.)N) are used to construct the LIMs for seasonal and decadal prediction. The forecast skills of the LIMs are then compared to that from two current operational forecast systems. Results indicate that the LIM has good forecast skill for time periods of 3–4 months on the seasonal timescale with enhanced predictability in the spring season. On decadal timescales, the impact of inter-annual and intra-annual variability on the predictability is also investigated. The results show that the suite of LIMs have forecast skill for about 3–4 years over most of the domain when we use only the decadal variability for the construction of the LIM. Including higher frequency variability helps improve the forecast skill and maintains the correlation of LIM predictions with the observed SST anomalies for longer periods. These results indicate the importance of temporal scale interactions in improving predictability on decadal timescales. Hence, LIMs can not only be used as benchmarks for estimates of statistical skill but also to isolate contributions to the forecast skills from different timescales, spatial scales or even model components.

Abstract:

Multidecadal variability in the Atlantic meridional overturning circulation (AMOC) is shown to differ significantly between the 4 × CO2 and preindustrial control simulations of the GFDL Earth System Model, version 2M (ESM2M) general circulation model (GCM). In the preindustrial simulation, this model has a peak in the power spectrum of both AMOC and northward heat transport at latitudes between 26° and 50°N. In the 4 × CO2 simulation, the only significant spectral peak is near 60°N. Understanding these differences is important for understanding the effect of future climate change on climate variability, as well as for providing insight into the physics underlying AMOC variability. Transfer function analysis demonstrates that the shift is predominantly due to a shift in the internal ocean dynamics rather than a change in stochastic atmospheric forcing. Specifically, the reduction in variance from 26° to 45°N is due to an increased stratification east of Newfoundland that results from the shallower and weaker mean overturning. The reduced AMOC variance that accompanies the reduced mean value of the AMOC at 4 × CO2 differs from predictions of simple box models that predict a weaker circulation to be closer to a stability bifurcation point and, therefore, be accompanied by amplified variability. The high-latitude variability in the 4 × CO2 simulation is related to the advection of anomalies by the subpolar gyre, distinct from the variability mechanism in the control simulation at lower latitudes. The 4 × CO2 variability has only a small effect on midlatitude meridional heat transport, but does significantly affect sea ice in the northern North Atlantic.

Abstract:

Observed and predicted increases in Southern Ocean winds are thought to upwell deep ocean carbon and increase atmospheric CO2. However, Southern Ocean dynamics affect biogeochemistry and circulation pathways on a global scale. Using idealised MITgcm simulations, we demonstrate that an increase in Southern Ocean winds reduces the carbon sink in the North Atlantic sub-polar gyre. The increase in atmospheric CO2 due to the reduction of the North Atlantic carbon sink is shown to be of the same magnitude as the increase in atmospheric CO2 due to Southern Ocean outgassing. The mechanism can be described as follows: The increase in Southern Ocean winds leads to an increase in upper ocean northward nutrient transport. Biological productivity is therefore enhanced in the tropics, which alters the chemistry of the sub-thermocline waters that are ultimately upwelled in the subpolar gyre. The results demonstrate the influence of Southern Ocean winds on the North Atlantic carbon sink and show that the effect of Southern Ocean winds on atmospheric CO2 is likely twice as large as previously thought in past, present, and future climates.

Abstract:

Stochastic parametrization provides a methodology for representing model uncertainty in ensemble forecasts. Here we study the impact of three existing stochastic parametrizations in the ocean component of a coupled model, on forecast reliability over seasonal timescales. The relative impacts of these schemes upon the ocean mean state and ensemble spread are analyzed. The oceanic variability induced by the atmospheric forcing of the coupled system is, in most regions, the major source of ensemble spread. The largest impact on spread and bias came from the Stochastically Perturbed Parametrization Tendency (SPPT) scheme - which has proven particularly effective in the atmosphere. The key regions affected are eddy-active regions, namely the western boundary currents and the Southern Ocean where ensemble spread is increased. However, unlike its impact in the atmosphere, SPPT in the ocean did not result in a significant decrease in forecast error. Whilst there are good grounds for implementing stochastic schemes in ocean models, our results suggest that they will have to be more sophisticated. Some suggestions for next-generation stochastic schemes are made.