Abstract
Abstract. We present statistical methods to determine climate regimes for the last glacial period using three temperature proxy records from Greenland: measurements of δ18O from the Greenland Ice Sheet Project 2 (GISP2), the Greenland Ice Core Project (GRIP) and the North Greenland Ice Core Project (NGRIP) using different timescales. A Markov Chain Monte Carlo method is presented to infer the number of states in a latent variable model along with their associated parameters. By using Bayesian model comparison methods we find that a model with 3 states is sufficient. These states correspond to a gradual cooling during the Greenland Interstadials, more rapid temperature decrease into Greenland Stadial and to the sudden rebound temperature increase at the onset of Greenland Interstadials. We investigate the recurrence properties of the onset of Greenland Interstadials and find no evidence to reject the null hypothesis of randomly timed events.
Highlights
Measurements of δ18O from Greenland ice cores for the last glacial are a good proxy for regional temperature
We propose a statistical modelling strategy able to identify different climate regimes corresponding to differing rates of temperature change
The method allows for uncertainty in the detection of events, avoiding the sensitivity of results due to absolute inclusion or exclusion of a single event according to an authors preferred interpretation of the data
Summary
Greenland Interstadial (GIS), the warm phase of the glacial period. An alternation between GIS and the colder Greenland Stadial (GS) will be referred to as a DO cycle. Livina et al (2010) used GRIP and NGRIP δ18O data to study the number of states in the climate for the last 60 kyrs using a polynomial fitting algorithm to windows of the data They detect the two states corresponding to the stadial and interstadials of the last glacial and find that these merge to a single state around 25 kyrs BP. The aim of this paper is to study models that are able to capture the features of the cycles (sudden temperature changes and small fluctuations) while including the associated uncertainty in the identification of climate states. We generate an ensemble of data sets derived from NGRIP, GRIP or GISP2 using Gaussian Process regression This incorporates variability into the data and reflects our uncertainty about the dating and measuring process while retaining the characteristic features of the DO cycles.
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