scholarly journals Implementation of a multivariate data assimilation scheme for isopycnic coordinate ocean models: Application to a 1993–1996 hindcast of the North Atlantic Ocean circulation

2003 ◽  
Vol 108 (C3) ◽  
Author(s):  
Jean-Michel Brankart
Keyword(s):  
Data Assimilation ◽  
Atlantic Ocean ◽  
Ocean Circulation ◽  
North Atlantic ◽  
North Atlantic Ocean ◽  
Multivariate Data ◽  
The North ◽  
The North Atlantic ◽  
Ocean Models ◽  
Assimilation Scheme

Changes in the Bathymetry and Volume of Glacial Lake Agassiz Between 11,000 and 9300 14C yr B.P.

2000 ◽  
Vol 54 (2) ◽  
pp. 174-181 ◽  
Author(s):  
David W. Leverington ◽  
Jason D. Mann ◽  
James T. Teller
Keyword(s):  
Atlantic Ocean ◽  
Great Lakes ◽  
Ocean Circulation ◽  
North Atlantic ◽  
North Atlantic Ocean ◽  
Glacial Lake ◽  
Lake Agassiz ◽  
The North ◽  
Glacial Lake Agassiz ◽  
The North Atlantic

The volume and surface area of glacial Lake Agassiz varied considerably during its 4000-year history. Computer models for seven stages of Lake Agassiz were used to quantify these variations over the lake's early history, between about 11,000 and 9300 14C yr B.P. (ca. 13,000 to 10,300 cal yr B.P.). Just after formation of the Herman strandlines (ca. 11,000 14C yr B.P.), the volume of Lake Agassiz appears to have decreased by >85% as a consequence of the abrupt rerouting of overflow to its eastern outlet from its southward routing into the Mississippi River basin. This drainage released about 9500 km3 of water into the North Atlantic Ocean via the Great Lakes and Gulf of St. Lawrence. Following closure of this eastern routing of overflow, the lake reached its maximum size at about 9400 14C yr B.P. with an area of >260,000 km2 and a volume of >22,700 km3. A second major reduction in volume occurred shortly after that, when its volume decreased >10% following the opening of the Kaiashk outlet to the east into the Great Lakes, and 2500–7000 km3 of water was released into the North Atlantic Ocean. These discharges may have affected ocean circulation and North Atlantic Deep Water production.

Higher Laurentide and Greenland ice sheets strengthen the North Atlantic ocean circulation

2015 ◽  
Vol 45 (1-2) ◽  
pp. 139-150 ◽  
Author(s):  
Xun Gong ◽  
Xiangdong Zhang ◽  
Gerrit Lohmann ◽  
Wei Wei ◽  
Xu Zhang ◽  
...  
Keyword(s):  
Atlantic Ocean ◽  
Ocean Circulation ◽  
North Atlantic ◽  
North Atlantic Ocean ◽  
Ice Sheets ◽  
The North ◽  
The North Atlantic

scholarly journals Identification of the Mechanism of Interdecadal Variability in the North Atlantic Ocean

2004 ◽  
Vol 34 (12) ◽  
pp. 2792-2807 ◽  
Author(s):  
Lianke te Raa ◽  
Jeroen Gerrits ◽  
Henk A. Dijkstra
Keyword(s):  
Atlantic Ocean ◽  
Ocean Circulation ◽  
North Atlantic ◽  
Physical Mechanism ◽  
North Atlantic Ocean ◽  
Bottom Topography ◽  
Interdecadal Variability ◽  
Geophysical Fluid Dynamics Laboratory ◽  
The North ◽  
The North Atlantic

Abstract The aim of this paper is to identify the physical mechanism of interdecadal variability in simulations of the North Atlantic Ocean circulation with the Modular Ocean Model of the Geophysical Fluid Dynamics Laboratory. To that end, a hierarchy of increasingly complex model configurations is used. The variability in the simplest case, that of viscous, purely thermally driven flows in a flat-bottom ocean basin with a box-shaped geometry, is shown to be caused by an internal interdecadal mode. The westward propagation of temperature anomalies and the phase difference between the anomalous zonal and meridional overturning that characterize the interdecadal mode are used as “fingerprints” of the physical mechanism of the variability. In this way, the variability can be followed toward a less viscous regime in which the effects of continental geometry and bottom topography are also included. It is shown that, although quantitative aspects of the variability like period and spatial pattern are changing, the physical mechanism of the interdecadal variability in the more complex simulations can be attributed to the same processes as in the simplest model configuration.

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