Climate field reconstruction of North Atlantic sea surface temperatures

Author(s):  
Tine Nilsen ◽  
Stefanie Talento

<p>Pseudo-proxy experiments test the skill and sensitivity of two extended climate field reconstruction (CFR) methodologies in reconstructing northern North Atlantic Summer sea surface temperatures (SSTs). The Summer target data originate from one millennium-long simulation of the CESM LME (Otto-Bliesner et al. 2016) .  The experiments test the reconstruction skill systematically for input data mimicking SST marine proxies and instrumental observations, including characteristics such as sparse distribution in space, varying signal-to noise ratio and age uncertainties.</p><p>The Bayesian hierarchical model BARCAST assumes implicitly that the target variable is described as an AR(1) process in time (Tingley & Huybers 2010), while the proxy surrogate reconstruction (PSR) method makes no such assumption (Graham et al. 2007). The PSR selects climate analogues from the simulated instrumental period in our study.</p><p>Results show that both methodologies generate skillful reconstructions for perfectly dated input data, and the PSR is superior when realistic noise levels are chosen for the input data. When the input is perturbed with age-uncertainties, the methodologies are unable to generate acceptable skillful reconstructions. Facilitating in form of data clustering is tested for both methodologies in the attempt of improving reconstruction skill. This proves successful for the PSR methodology, with the best skill obtained using n=3 clusters over the reconstruction region.</p><p>Additionally, and addressed as a topic for discussion, we detect weak temporal persistence in the input data and the BARCAST reconstructions. The lack of SST persistence is found to be partly due to the input data sampling frequency: Summer means (June, July, August) averaged for every year. Analyses show that the simulated SST data exhibit weaker memory from one Summer to the next, compared to year-to-year variability based on annual means. Similar results are also found for instrumental observations. This finding stands in contrast to results of previous studies on terrestrial reconstruction, where climate reconstructions and individual proxy records exhibit strong persistence properties, also targeting the Summer season (Werner et al. 2018, Nilsen et al. 2018)<sup>,</sup>.</p><p> </p><p>References:</p><p>Graham, N.E. et al. (2007), Clim. Change, 83, 241-285, doi: 10.1007/s10584-007-9239-2</p><p>Otto-Bliesner, B.L. et al (2016), Bull. Amer. Meteor. Soc., 97, 735-754, doi: 10.1175/BAMS-D-14-00233.1</p><p> Nilsen, T. et al. (2018), Clim. Past, 14, 947-967, doi: 10.5194/cp-14-947-2018</p><p>Tingley, M. P. and P. Huybers (2010a), J. Clim., 23, 2759–2781, doi: 10.1175/2009JCLI3015.1</p><p>Werner, J. P. et al. (2018), Clim. Past, 14, 527-557, doi: 10.5194/cp-14-527-2018</p>

2018 ◽  
Vol 14 (6) ◽  
pp. 901-922 ◽  
Author(s):  
Mari F. Jensen ◽  
Aleksi Nummelin ◽  
Søren B. Nielsen ◽  
Henrik Sadatzki ◽  
Evangeline Sessford ◽  
...  

Abstract. Here, we establish a spatiotemporal evolution of the sea-surface temperatures in the North Atlantic over Dansgaard–Oeschger (DO) events 5–8 (approximately 30–40 kyr) using the proxy surrogate reconstruction method. Proxy data suggest a large variability in North Atlantic sea-surface temperatures during the DO events of the last glacial period. However, proxy data availability is limited and cannot provide a full spatial picture of the oceanic changes. Therefore, we combine fully coupled, general circulation model simulations with planktic foraminifera based sea-surface temperature reconstructions to obtain a broader spatial picture of the ocean state during DO events 5–8. The resulting spatial sea-surface temperature patterns agree over a number of different general circulation models and simulations. We find that sea-surface temperature variability over the DO events is characterized by colder conditions in the subpolar North Atlantic during stadials than during interstadials, and the variability is linked to changes in the Atlantic Meridional Overturning circulation and in the sea-ice cover. Forced simulations are needed to capture the strength of the temperature variability and to reconstruct the variability in other climatic records not directly linked to the sea-surface temperature reconstructions. This is the first time the proxy surrogate reconstruction method has been applied to oceanic variability during MIS3. Our results remain robust, even when age uncertainties of proxy data, the number of available temperature reconstructions, and different climate models are considered. However, we also highlight shortcomings of the methodology that should be addressed in future implementations.


2018 ◽  
Vol 31 (20) ◽  
pp. 8313-8338 ◽  
Author(s):  
Isla R. Simpson ◽  
Clara Deser ◽  
Karen A. McKinnon ◽  
Elizabeth A. Barnes

Multidecadal variability in the North Atlantic jet stream in general circulation models (GCMs) is compared with that in reanalysis products of the twentieth century. It is found that almost all models exhibit multidecadal jet stream variability that is entirely consistent with the sampling of white noise year-to-year atmospheric fluctuations. In the observed record, the variability displays a pronounced seasonality within the winter months, with greatly enhanced variability toward the late winter. This late winter variability exceeds that found in any GCM and greatly exceeds expectations from the sampling of atmospheric noise, motivating the need for an underlying explanation. The potential roles of both external forcings and internal coupled ocean–atmosphere processes are considered. While the late winter variability is not found to be closely connected with external forcing, it is found to be strongly related to the internally generated component of Atlantic multidecadal variability (AMV) in sea surface temperatures (SSTs). In fact, consideration of the seasonality of the jet stream variability within the winter months reveals that the AMV is far more strongly connected to jet stream variability during March than the early winter months or the winter season as a whole. Reasoning is put forward for why this connection likely represents a driving of the jet stream variability by the SSTs, although the dynamics involved remain to be understood. This analysis reveals a fundamental mismatch between late winter jet stream variability in observations and GCMs and a potential source of long-term predictability of the late winter Atlantic atmospheric circulation.


2004 ◽  
Vol 23 (20-22) ◽  
pp. 2113-2126 ◽  
Author(s):  
Matthias Moros ◽  
Kay Emeis ◽  
Bjørg Risebrobakken ◽  
Ian Snowball ◽  
Antoon Kuijpers ◽  
...  

2008 ◽  
Vol 23 (3) ◽  
pp. n/a-n/a ◽  
Author(s):  
Marci M. Robinson ◽  
Harry J. Dowsett ◽  
Gary S. Dwyer ◽  
Kira T. Lawrence

2019 ◽  
Author(s):  
Lucia Bellino ◽  
◽  
Maria Makarova ◽  
Kenneth G. Miller ◽  
Yair Rosenthal ◽  
...  

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