A multi-model assessment of last interglacial temperatures

• Published in: Climate of the Past
• Main Authors: Lunt, DJ, Abe-Ouchi, A, Bakker, P, Berger, A, Braconnot, P, Charbit, S, Fischer, N, Herold, N, Jungclaus, JH, Khon, VC, Krebs-Kanzow, U, Langebroek, PM, Lohmann, G, Nisancioglu, KH, Otto-Bliesner, N, Park, W, Pfeiffer, M, Phipps, SJ, Prange, M, Rachmayani, R, Renssen, H, Rosenbloom, N, Schneider, B, Stone, EJ, Takahashi, K, Wei, W, Yin, Q, Zhang, ZS
• Format: Article in Journal/Newspaper
• Language: English
• Published: Copernicus GmbH 2013
• Subjects: Earth Sciences; Physical geography and environmental geoscience; Palaeoclimatology; Arctic
• Online Access: https://doi.org/10.5194/cp-9-699-2013; http://ecite.utas.edu.au/104719

The last interglaciation (~130 to 116 ka) is a timeperiod with a strong astronomically induced seasonal forcingof insolation compared to the present. Proxy records indicatea significantly different climate to that of the modern,in particular Arctic summer warming and higher eustatic sealevel. Because the forcings are relatively well constrained, itprovides an opportunity to test numerical models which areused for future climate prediction. In this paper we compile aset of climate model simulations of the early last interglaciation(130 to 125 ka), encompassing a range of model complexities.We compare the simulations to each other and toa recently published compilation of last interglacial temperatureestimates.We show that the annual mean response of themodels is rather small, with no clear signal in many regions.However, the seasonal response is more robust, and there issignificant agreement amongst models as to the regions ofwarming vs cooling. However, the quantitative agreement ofthe model simulations with data is poor, with the models ingeneral underestimating the magnitude of response seen inthe proxies. Taking possible seasonal biases in the proxiesinto account improves the agreement, but only marginally.However, a lack of uncertainty estimates in the data does notallow us to draw firm conclusions. Instead, this paper pointsto several ways in which both modelling and data could beimproved, to allow a more robust model-data comparison.