State-dependence of climate sensitivity: attractor constraints and palaeoclimate regimes

Equilibrium climate sensitivity (ECS) is a key predictor of climate change. However, it is not very well constrained, either by climate models or by observational data. The reasons for this include strong internal variability and forcing on many time scales. In practise this means that the 'equilibrium' will only be relative to fixing the slow feedback processes before comparing palaeoclimate sensitivity estimates with estimates from model simulations. In addition, information from the late Pleistocene ice age cycles indicates that the climate cycles between cold and warm regimes, and the climate sensitivity varies considerably between regime because of fast feedback processes changing relative strength and time scales over one cycle. In this paper we consider climate sensitivity for quite general climate dynamics. Using a conceptual Earth system model of Gildor and Tziperman (2001) (with Milankovich forcing and dynamical ocean biogeochemistry) we explore various ways of quantifying the state-dependence of climate sensitivity from unperturbed and perturbed model time series. Even without considering any perturbations, we suggest that climate sensitivity can be usefully thought of as a distribution that quantifies variability within the 'climate attractor' and where there is a strong dependence on climate state and more specificially on the 'climate regime' where fast processes are approximately in equilibrium. We also consider perturbations by instantaneous doubling of CO$_2$ and similarly find a strong dependence on the climate state using our approach.Comment: 32 pages, 10 figure

Progress in paleoclimate modeling

International audienceThis paper briefly surveys areas of paleoclimate modeling notable for recent progress. New ideas, including hypotheses giving a pivotal role to sea ice, have revitalized the low-order models used to simulate the time evolution of glacial cycles through the Pleistocene, a prohibitive length of time for comprehensive general circulation models (GCMs). In a recent breakthrough, however, GCMs have succeeded in simulating the onset of glaciations. This occurs at times (most recently, 115 kyr B.P.) when high northern latitudes are cold enough to maintain a snow cover and tropical latitudes are warm, enhancing the moisture source. More generally, the improvement in models has allowed simulations of key periods such as the Last Glacial Maximum and the mid-Holocene that compare more favorably and in more detail with paleoproxy data. These models now simulate ENSO cycles, and some of them have been shown to reproduce the reduction of ENSO activity observed in the early to middle Holocene. Modeling studies have demonstrated that the reduction is a response to the altered orbital configuration at that time. An urgent challenge for paleoclimate modeling is to explain and to simulate the abrupt changes observed during glacial epochs (i.e., Dansgaard-Oescher cycles, Heinrich events, and the Younger Dryas). Efforts have begun to simulate the last millennium. Over this time the forcing due to orbital variations is less important than the radiance changes due to volcanic eruptions and variations in solar output. Simulations of these natural variations test the models relied on for future climate change projections. They provide better estimates of the internal and naturally forced variability at centennial time scales, elucidating how unusual the recent global temperature trends are

Tracking Icebergs and Sea Ice in the Mid-Pleistocene Bering Sea Suggests Sea Ice Affects Ice Sheet Growth

Ice sheets are losing volume and regions of sea ice cover are shifting; these changes in Arctic and sub-Arctic regions amplify climate change through positive feedback mechanisms. A history of sea ice cover and iceberg activity in high latitude seas would help to predict climate change. Sea ice and icebergs entrain and dump sediment into the oceans; both transport sand, but glacial ice is much more likely to entrain gravel (\u3e2 mm) than sea ice. This study uses siliciclastics \u3e63 μm from Bering Sea Site U1343 to track icebergs and sea ice during the Mid-Pleistocene, a period of change in global climate cycles. Fine sand (63\u3c\u3c250 μm) averages 10% of the bulk sediment at U1343 from 910 to 860 ka. A new method uses a ratio (fine sand/gravel count) to indicate the more likely presence of sea ice. Icebergs are likely present during Marine Isotope Stage (MIS) 23 and in the latter part of MIS 22 when four pulses of gravel-size ice rafted debris come before the deglaciation. A period of low ice rafted debris in MIS 22 when the sea level is decreasing rapidly is interpreted as a time of more open water. Sea ice is suggested at the coldest time in MIS 22 and at the start of the deglaciation, consistent with the Sea Ice Switch (Gildor and Tziperman 2000) hypothesis for the Mid-Pleistocene climate transition. Abundant fine sand in MIS 21 suggests the presence of sea ice in the early interglacial

On the state dependency of fast feedback processes in (palaeo) climate sensitivity

Palaeo data have been frequently used to determine the equilibrium (Charney) climate sensitivity $S^a$, and - if slow feedback processes (e.g. land ice-albedo) are adequately taken into account - they indicate a similar range as estimates based on instrumental data and climate model results. Most studies implicitly assume the (fast) feedback processes to be independent of the background climate state, e.g., equally strong during warm and cold periods. Here we assess the dependency of the fast feedback processes on the background climate state using data of the last 800 kyr and a conceptual climate model for interpretation. Applying a new method to account for background state dependency, we find $S^a=0.61\pm0.06$ K(Wm$^{-2}$)$^{-1}$ using the latest LGM temperature reconstruction and significantly lower climate sensitivity during glacial climates. Due to uncertainties in reconstructing the LGM temperature anomaly, $S^a$ is estimated in the range $S^a=0.55-0.95$ K(Wm$^{-2}$)$^{-1}$.Comment: submitted to Geophysical Research Letter

Sea-ice response to climate change in the Bering Sea during the Mid-Pleistocene Transition

This is the final version. Available on open access from Elsevier via the DOI in this recordData availability: All data presented in this paper is available in the Supplementary Materials.Sea-ice is believed to be an important control on climatic changes through the Mid-Pleistocene Transition (MPT; 0.6–1.2 Ma). However, the low resolution/short timescale of existing reconstructions prevents a full evaluation of these dynamics. Here, diatom assemblages from the Bering Sea are used to investigate sea-ice evolution on millennial timescales. We find that sea-ice was primarily controlled by ice-sheet/sea level fluctuations that modulated warm water flow into the Bering Sea. Facilitated by an amplified Walker circulation, sea-ice expansion began at ∼1.05 Ma with a step-increase during the 900 kyr event. Maximal pack ice was simultaneous with glacial maxima, suggesting sea-ice was responding to, rather than modulating ice-sheet dynamics, as proposed by the sea-ice switch hypothesis. Significant pack ice, coupled with Bering Strait closure at 0.9 Ma, indicates that brine rejection played an integral role in the glacial expansion/deglacial collapse of intermediate waters during the MPT, regulating subarctic ocean-atmospheric exchanges of CO2.Natural Environment Research Council (NERC)British Geological Surve

The middle Pleistocene transition as a generic bifurcation on a slow manifold

The final publication is available at Springer via http://dx.doi.org/10.1007/s00382-015-2501-9The Quaternary period has been characterised by a cyclical series of glaciations, which are attributed to the change in the insolation (incoming solar radiation) from changes in the Earth’s orbit around the Sun. The spectral power in the climate record is very different from that of the orbital forcing: prior to 1000 kyr before present most of the spectral power is in the 41 kyr band while since then the power has been in the 100 kyr band. The change defines the middle Pleistocene transition (MPT). The MPT does not indicate any noticeable difference in the orbital forcing. The climate response to the insolation is thus far from linear, and appears to be structurally different before and after the MPT. This paper presents a low order conceptual model for the oscillatory dynamics of the ice sheets in terms of a relaxation oscillator with multiple levels subject to the Milankovitch forcing. The model exhibits smooth transitions between three different climate states; an interglacial (i), a mild glacial (g) and a deep glacial (G) as proposed by Paillard (Nature 391:378–381, 1998). The model suggests a dynamical explanation in terms of the structure of a slow manifold for the observed allowed and “forbidden” transitions between the three climate states. With the model, the pacing of the climate oscillations by the astronomical forcing is through the mechanism of phase-resetting of relaxation oscillations in which the internal phase of the oscillation is affected by the forcing. In spite of its simplicity as a forced ODE, the model is able to reproduce not only general features but also many of the details of oscillations observed in the climate record. A particular novelty is that it includes a slow drift in the form of the slow manifold that reproduces the observed dynamical change at the MPT. We explain this change in terms of a transcritical bifurcation in the fast dynamics on varying the slow variable; this bifurcation can induce a sudden change in periodicity and amplitude of the cycle and we suggest that this is associated with a branch of “canard oscillations” that appear for a small range of parameters. The model is remarkably robust at simulating the climate record before, during and after the MPT. Even though the conceptual model does not point to specific mechanisms, the physical implication is that the major reorganisation of the climate response to the orbital forcing does not necessarily imply that there was a big change in the environmental conditions

Consequences of pacing the Pleistocene 100 kyr ice ages by nonlinear phase locking to Milankovitch forcing

Author Posting. © American Geophysical Union, 2006. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Paleoceanography 21 (2006): PA4206, doi:10.1029/2005PA001241.The consequences of the hypothesis that Milankovitch forcing affects the phase (e.g., termination times) of the 100 kyr glacial cycles via a mechanism known as “nonlinear phase locking” are examined. Phase locking provides a mechanism by which Milankovitch forcing can act as the “pacemaker” of the glacial cycles. Nonlinear phase locking can determine the timing of the major deglaciations, nearly independently of the specific mechanism or model that is responsible for these cycles as long as this mechanism is suitably nonlinear. A consequence of this is that the fit of a certain model output to the observed ice volume record cannot be used as an indication that the glacial mechanism in this model is necessarily correct. Phase locking to obliquity and possibly precession variations is distinct from mechanisms relying on a linear or nonlinear amplification of the eccentricity forcing. Nonlinear phase locking may determine the phase of the glacial cycles even in the presence of noise in the climate system and can be effective at setting glacial termination times even when the precession and obliquity bands account only for a small portion of the total power of an ice volume record. Nonlinear phase locking can also result in the observed “quantization” of the glacial period into multiples of the obliquity or precession periods.E.T. is funded by NSF Paleoclimate program, grant ATM-0455470 and by the McDonnell Foundation. P.H. is supported by the NOAA Postdoctoral Program in Climate and Global Change. C.W. is supported by the National Ocean Partnership Program (NOPP). M.E.R. is supported by NSF grant ATM-0455328