Time-dependent response of a zonally averaged ocean–atmosphere–sea ice model to Milankovitch forcing

Author Posting. © The Author(s), 2010. This is the author's version of the work. It is posted here by permission of Springer-Verlag for personal use, not for redistribution. The definitive version was published in Climate Dynamics 6 (2010): 763-779, doi:10.1007/s00382-010-0790-6.An ocean-atmosphere-sea ice model is developed to explore the time-dependent response of climate to Milankovitch forcing for the time interval 5-3 Myr BP. The ocean component is a zonally averaged model of the circulation in five basins (Arctic, Atlantic, Indian, Pacific, and Southern Oceans). The atmospheric component is a one-dimensional (latitudinal) energy balance model, and the sea-ice component is a thermodynamic model. Two numerical experiments are conducted. The first experiment does not include sea ice and the Arctic Ocean; the second experiment does. Results from the two experiments are used to investigate (i) the response of annual mean surface air and ocean temperatures to Milankovitch forcing, and (ii) the role of sea ice in this response. In both experiments, the response of air temperature is dominated by obliquity cycles at most latitudes. On the other hand, the response of ocean temperature varies with latitude and depth. Deep water formed between 45°N-65°N in the Atlantic Ocean mainly responds to precession. In contrast, deep water formed south of 60°S responds to obliquity when sea ice is not included. Sea ice acts as a time-integrator of summer insolation changes such that annual mean sea-ice conditions mainly respond to obliquity. Thus, in the presence of sea ice, air temperature changes over the sea ice are amplified, and temperature changes in deep water of southern origin are suppressed since water below sea ice is kept near the freezing point.This work was supported by an NSERC Discovery Grant awarded to L.A.M. We also thank GEC3 for a Network Grant

Predicting climate change using response theory: global averages and spatial patterns

The provision of accurate methods for predicting the climate response to anthropogenic and natural forcings is a key contemporary scientific challenge. Using a simplified and efficient open-source general circulation model of the atmosphere featuring O(105105) degrees of freedom, we show how it is possible to approach such a problem using nonequilibrium statistical mechanics. Response theory allows one to practically compute the time-dependent measure supported on the pullback attractor of the climate system, whose dynamics is non-autonomous as a result of time-dependent forcings. We propose a simple yet efficient method for predicting—at any lead time and in an ensemble sense—the change in climate properties resulting from increase in the concentration of CO22 using test perturbation model runs. We assess strengths and limitations of the response theory in predicting the changes in the globally averaged values of surface temperature and of the yearly total precipitation, as well as in their spatial patterns. The quality of the predictions obtained for the surface temperature fields is rather good, while in the case of precipitation a good skill is observed only for the global average. We also show how it is possible to define accurately concepts like the inertia of the climate system or to predict when climate change is detectable given a scenario of forcing. Our analysis can be extended for dealing with more complex portfolios of forcings and can be adapted to treat, in principle, any climate observable. Our conclusion is that climate change is indeed a problem that can be effectively seen through a statistical mechanical lens, and that there is great potential for optimizing the current coordinated modelling exercises run for the preparation of the subsequent reports of the Intergovernmental Panel for Climate Change

Variability of dense water formation in the Ross Sea

The paper presents results from a model study of the interannual variability of High Salinity Shelf Water (HSSW) properties in the Ross Sea.Salinity, potential temperature and volume of HSSW formed in the western Ross Sea show oscillatory behaviour at periods of 5-6 and 9 years superimposed on long-term fluctuations.While the shorter oscillations are induced by wind variability, variability on the scale of decades appears to be related to air temperature fluctuations.At least part of the strong decrease of HSSW salinities deduced from observations for the period 1963-2000 is shown to be an aliasing artefact due to an undersampling of the periodic signal.While sea ice formation is responsible for the yearly salinity increase that triggers the formation of High Salinity Shelf Water, interannual variability of net freezing rates hardly affects changes in the properties of the resulting water mass.Instead, results from model experiments indicate that the interannual variability of dense water characteristics is predominantly controlled by variations in the shelf inflow through a sub-surface salinity and a deep temperature signal.The origin of the variability of inflow characteristics to the Ross Sea continental shelf can be traced into the Amundsen and Bellingshausen Seas.The temperature anomalies are induced at the continental shelf break in the western Bellingshausen Sea by fluctuations of the meridional transport of Circumpolar Deep Water with the eastern cell of the Ross Gyre.Upwelling in the centre of this gyre carries the signal into the surface layer where it causes anomalies of brine release near the sea ice edge in the Amundsen Sea, which results in a sub-surface salinity anomaly.With the westward flowing coastal current, both the sub-surface salinity and deep temperature signals are advected onto the Ross Sea continental shelf.Convection carries the signal of salinity variability into the deep ocean, where it interacts with Modified Circumpolar Deep Water upwelled onto the continental shelf as the second source water mass of HSSW.Sea ice formation on the Ross Sea continental shelf thus drives the vertical propagation of the signal rather than determining the signal itself

Uncertainties due to transport-parameter sensitivity in an efficient 3-D ocean-climate model

A simplified climate model is presented which includes a fully 3-D, frictional geostrophic (FG) ocean component but retains an integration efficiency considerably greater than extant climate models with 3-D, primitive-equation ocean representations (20k years of integration can be completed in about a day on a PC). The model also includes an Energy and Moisture Balance atmosphere and a dynamic and thermodynamic sea-ice model. Using a semi-random ensemble of 1,000 simulations, we address both the inverse problem of parameter estimation, and the direct problem of quantifying the uncertainty due to mixing and transport parameters. Our results represent a first attempt at tuning a 3-D climate model by a strictly defined procedure, which nevertheless considers the whole of the appropriate parameter space. Model estimates of meridional overturning and Atlantic heat transport are well reproduced, while errors are reduced only moderately by a doubling of resolution. Model parameters are only weakly constrained by data, while strong correlations between mean error and parameter values are mostly found to be an artefact of single-parameter studies, not indicative of global model behaviour. Single-parameter sensitivity studies can therefore be misleading. Given a single, illustrative scenario of CO2 increase and fixing the polynomial coefficients governing the extremely simple radiation parameterisation, the spread of model predictions for global mean warming due solely to the transport parameters is around one degree after 100 years forcing, although in a typical 4,000-year ensemble-member simulation, the peak rate of warming in the deep Pacific occurs 400 years after the onset of the forcing. The corresponding uncertainty in Atlantic overturning after 100 years is around 5 Sv, with a small, but non-negligible, probability of a collapse in the long term