Using data assimilation to investigate the causes of Southern Hemisphere high latitude cooling from 10 to 8 ka BP

Abstract

Abstract. From 10 to 8 ka BP (thousand years before present), paleoclimate records show an atmospheric and oceanic cooling in the high latitudes of the Southern Hemisphere. During this interval, temperatures estimated from proxy data decrease by 0.8 °C over Antarctica and 1.2 °C over the Southern Ocean. In order to study the causes of this cooling, simulations covering the early Holocene have been performed with the climate model of intermediate complexity LOVECLIM constrained to follow the signal recorded in climate proxies using a data assimilation method based on a particle filtering approach. The selected proxies represent oceanic and atmospheric surface temperature in the Southern Hemisphere derived from terrestrial, marine and glaciological records. Two mechanisms previously suggested to explain the 10–8 ka BP cooling pattern are investigated using the data assimilation approach in our model. The first hypothesis is a change in atmospheric circulation, and the second one is a cooling of the sea surface temperature in the Southern Ocean, driven in our experimental setup by the impact of an increased West Antarctic melting rate on ocean circulation. For the atmosphere hypothesis, the climate state obtained by data assimilation produces a modification of the meridional atmospheric circulation leading to a 0.5 °C Antarctic cooling from 10 to 8 ka BP compared to the simulation without data assimilation, without congruent cooling of the atmospheric and sea surface temperature in the Southern Ocean. For the ocean hypothesis, the increased West Antarctic freshwater flux constrainted by data assimilation (+100 mSv from 10 to 8 ka BP) leads to an oceanic cooling of 0.7 °C and a strengthening of Southern Hemisphere westerlies (+6%). Thus, according to our experiments, the observed cooling in Antarctic and the Southern Ocean proxy records can only be reconciled with the reconstructions by the combination of a modified atmospheric circulation and an enhanced freshwater flux.

Towards the assimilation of tree-ring-width records using ensemble Kalman filtering techniques

“…To date, several very diverse DA schemes have been tested on climate models-such as pattern nudging (von Storch et al 2000), forcing singular vectors (Barkmeijer et al 2003), adjoint method (Kurahashi-Nakamura et al 2014), particle filter Mathiot et al 2013) and ensemble Kalman filter (Bhend et al 2012;Pendergrass et al 2012)-providing very encouraging results regarding the potential constraint of the inter-annual internal variability. As for the observations employed in these experiments, the dominant approach has been to use either pseudo-proxies or statistically reconstructed climate Abstract This paper investigates the applicability of the Vaganov-Shashkin-Lite (VSL) forward model for treering-width chronologies as observation operator within a proxy data assimilation (DA) setting.…”

Multi-timescale data assimilation for atmosphere–ocean state estimates

“…Among DA techniques, we are unaware of any method specifically designed for the challenges of paleoclimate reconstructions that incorporates proxies across an arbitrary range of timescales. DA-based reconstructions have so far used only a single uniform timescale (e.g., Goosse et al, 2012) or have performed separate reconstructions at different uniform timescales (Mathiot et al, 2013). Traditional DA adjoint methods as applied in weather forecasting can and do include multiple timescales (Kalnay, 2003, p. 181-184), but these timescales are very short by comparison to the timescales involved in paleoclimate.…”

Assimilation of pseudo-tree-ring-width observations into an atmospheric general circulation model

“…So far, several very diverse paleo-DA schemes have been investigated, including pattern nudging (von Storch et al, 2000), forcing singular vectors (Barkmeijer et al, 2003;van der Schrier and Barkmeijer, 2005), 4D-Var (Paul and Schäfer-Neth, 2005;Kurahashi-Nakamura et al, 2014), particle filters (Annan and Hargreaves, 2012;Dubinkina et al, 2011;Dubinkina and Goosse, 2013;Mathiot et al, 2013;Matsikaris et al, 2015) and ensemble Kalman filter techniques (EnKF; Huntley and Hakim, 2010;Bhend et al, 2012;Pendergrass et al, 2012;Steiger et al, 2014; see Hughes and Ammann, 2009;Widmann et al, 2010;Hakim et al, 2013 for further references).…”