Sirpa Häkkinen

Atmospheric Blocking and Atlantic Multidecadal Ocean Variability

Changing ocean circulation patterns and sea surface temperatures affect atmospheric flow in the North Atlantic region. Atmospheric blocking over the northern North Atlantic, which involves isolation of large regions of air from the westerly circulation for 5 days or more, influences fundamentally the ocean circulation and upper ocean properties by affecting wind patterns. Winters with clusters of more frequent blocking between Greenland and western Europe correspond to a warmer, more saline subpolar ocean. The correspondence between blocked westerly winds and warm ocean holds in recent decadal episodes (especially 1996 to 2010). It also describes much longer time scale Atlantic multidecadal ocean variability (AMV), including the extreme pre–greenhouse-gas northern warming of the 1930s to 1960s. The space-time structure of the wind forcing associated with a blocked regime leads to weaker ocean gyres and weaker heat exchange, both of which contribute to the warm phase of AMV.

An Arctic source for the Great Salinity Anomaly - A simulation of the Arctic ice-ocean system for 1955-1975

A fully prognostic Arctic ice-ocean model is used to study the interannual variability of the sea ice during the period 1955–1975 and to explain the large variability of the ice extent in the Greenland and Iceland seas during the late 1960s. In particular, the model is used to test the conjecture of Aagaard and Carmack (1989) that the Great Salinity Anomaly (GSA) was a consequence of the anomalously large ice export in 1968. The objective here is to explore the high-latitude ice-ocean circulation changes due to wind field changes. In the simulations the ice extent in the Greenland Sea increased during the 1960s, reaching a maximum in 1968, as observed, and maxima in ice extent were always preceded by large pulses of ice export through the Fram Strait. The ice export event of 1968 was the largest in the simulation, being about twice as large as the average and corresponding to 1600 km3 of excess fresh water. The simulated upper water column in the Greenland Sea has a salinity minimum in the fall of 1968, followed by very low winter salinities. The simulations suggest that, besides the above average ice export to the Greenland Sea, there was also fresh water export to support the larger than average ice cover. Three low-salinity anomalies, which are created by the variability in ice production/melt, exited through the Fram Strait in 1963–1965, 1966, and 1967–1969, the later two events being associated with a net freshwater export of about 900 km3.The total simulated freshwater input of 2500 km3 to the Greenland Sea compares well with the estimated total freshwater excess of the GSA of about 2200 km3 as it passed through the Labrador Sea (Dickson et al., 1988). Considering the uncertainties in the model, it is possible that the ice export could account for even larger portion of the freshwater excess. However, the main conclusion is that these model results show the origin of the GSA to be in the Arctic, as suggested by Aagaard and Carmack (1989) and support the view that the Arctic may play an active role in climate change.

Variability of the simulated meridional heat transport in the North Atlantic for the period 1951–1993

A 43-year ocean model simulation for the period 1951–1993 is analyzed, with a focus on the meridional heat transport (MHT) as a proxy for the strength of meridional overturning cell (MOC) at 25°N. The surface heat flux forcing associated with the North Atlantic Oscillation (NAO) pattern is related to the variability in MHT both on interannual and longer timescales. The manifestation of the interannual and decadal oceanic response to NAO is nonlocal, as evidenced by the concentrated heat content and sea level variability at the Gulf Stream and North Atlantic Current regions and shown by the comparison of the model sea level variability to the altimeter data between low MHT years in mid-1980s and high MHT years in early 1990s. The same area is singled out by empirical orthogonal function analysis in the leading mode of the sea level. MHT and time series of the leading sea level mode are highly correlated, which reflects the importance of the MOC in determining the variability of the heat content of the whole basin. Also, the model suggests that the MHT/MOC has entered a very strong phase since the mid-1980s and this trend has continued up to the end of 1993; this behavior follows the general trend in the NAO index.

A Generalization of a Sigma Coordinate Ocean Model and an Intercomparison of Model Vertical Grids

Numerical ocean models increasingly make use of σ — coordinate systems. A paper by Gerdes (1993) shows that these coordinate systems can be more general; he termed the generalized form an “s — coordinate” system. The main advantage of the σ or s — system is that, when cast in a finite difference form, a smooth representation of the bottom topography is obtained; one can also easily incorporate a bottom boundary layer as well as a surface boundary layer in those coordinate systems. This is intuitively appealing and Gerdes has shown that superior numerical results are obtained relative to a z — level system. However, in regions of steep topography and crude resolution — a limiting case would be a seamount represented by a single grid point surrounded by a flat bottom — the so-called sigma coordinate pressure gradient error exists (Haney 1991, Mellor et al. 1994, 1998) and at least locally a z — level coordinate system might be preferred. On the other hand, in a recent study, Bell (1997) has shown that the step structure of z — level models lead to vorticity errors and consequent errors in the barotropic component of the flow which, he reports, cause rather large temperature errors (3 to 4° C) on a 1° x 1° grid of an Atlantic Ocean model after 3 months of integration. And it is difficult to model bottom boundary layers in a z — level model (Winton et al. 1998).

Modeling the seasonal variability of a coupled Arctic ice-ocean system

Results from modeling studies of the ice-ocean system in the Arctic Basin and in the Norwegian-Greenland-Barents seas are presented. We used a three-dimensional coupled ice-ocean model developed at Princeton University. The ocean model applies the primitive equations and a second moment turbulence closure for turbulent mixing. The snow-ice model uses a three-level thermodynamic scheme which resembles Semtners (1976a) model. Our conclusions based on the seasonal simulations are as follows. (1) Using monthly climatological surface heat flux and wind stress, the seasonal variability of the ice cover is quite realistic in that the thickest ice is located north of Greenland and the average ice thickness is about 3 m. The largest deviation between the simulated and observed ice cover is in the Greenland Sea where oceanic conditions determine the ice edge. Basically, the monthly climatological forcing does not result in strong enough mixing to bring sufficient heat from the deep ocean to keep the central Greenland Sea gyre ice free. The results improve for both the ice cover and ocean by invoking daily wind forcing for which we first chose year 1987. In the ocean model, the large mixing events associated with storm passages are resolved, and as a result, the overall oceanic structure in the Greenland Sea appears to be more realistic. However, no deep convection takes place in the model during 1987 which is likely the result of diminished storm activity in the northern part of the Greenland Sea. The ice thickness field appears to be very anomalous 1987, so an experiment with 1986 daily wind forcing was also done, which resulted in an ice thickness field similar to some reported from other ice models. (2) Both monthly and daily surface forcing result in a similar behavior of the Atlantic waters in the Arctic Basin. The Atlantic waters circulate at about the observed level, between 400 and 600 m. The survival of the Atlantic waters in the basin depends strongly on the heat loss through the ice cover, and it appears that too much heat is lost on the Eurasian side through the ice because the simulated Atlantic waters are too cool by about 0.2–0.5°C. (3) For the monthly climatology case, a large amount of cold and salty water enters the Eurasia Basin from the Kara and Laptev seas area and finds its way toward the Canada Basin. This water mass appears to result from ice formation in the Kara and Laptev seas. When applying the daily forcing, this deep salinity maximum disappears due to increased mixing on the shelves. Nevertheless, this suggests a mechanism within the Arctic Ocean as to why the deep Canada Basin is much saltier than the Eurasia Basin.

Influence of sea ice on the thermohaline circulation in the Arctic-North Atlantic Ocean

A fully prognostic coupled ocean-ice model is used to study the sensitivity of the overturning cell of the Arctic-North-Atlantic system to sea ice forcing. The strength of the thermohaline cell will be shown to depend on the amount of sea ice transported from the Arctic to the Greenland Sea and further to the subpolar gyre. The model produces a 2–3 Sv increase of the meridional circulation cell at 25N (at the simulation year 15) corresponding to a decrease of 800 km³ in the sea ice export from the Arctic. Previous modeling studies suggest that interannual and decadal variability in sea ice export of this magnitude is realistic, implying that sea ice induced variability in the overturning cell can reach 5–6 Sv from peak to peak.

Evaluation of Arctic sea ice thickness simulated by Arctic Ocean Model Intercomparison Project models

Six AOMIP model simulations are compared with estimates of sea ice thickness derived from pan-arctic satellite freeboard measurements (2004-2008), airborne electromagnetic measurements (2001-2009), ice-draft data from moored instruments in Fram Strait, the Greenland Sea and the Beaufort Sea (1992- 2008) and from submarines (1975-2000), drill hole data from the Arctic basin, Laptev and East Siberian marginal seas (1982-1986) and coastal stations (1998-2009). Despite an assessment of six models that differ in numerical methods, resolution, domain, forcing, and boundary conditions, the models generally overestimate the thickness of measured ice thinner than ~2 m and underestimate the thickness of ice measured thicker than about ~2 m. In the regions of flat immobile land-fast ice (shallow Siberian Seas with depths less than 25-30 m), the models generally overestimate both the total observed sea ice thickness and rates of September and October ice growth from observations by more than four times and more than one standard deviation, respectively. The models do not reproduce conditions of fast-ice formation and growth. Instead, the modeled fast-ice is replaced with pack ice which drifts, generates ridges of increasing ice thickness, in addition to thermodynamic ice growth. Considering all observational data sets, the better correlations and smaller differences from observations are from the ECCO2 and UW models.

Arctic Ocean Study: Synthesis of Model Results and Observations

Model development and simulations represent a comprehensive synthesis of observations with advances in numerous disciplines (physics; mathematics; and atmospheric, oceanic, cryospheric, and related sciences), enabling hypothesis testing via numerical experiments. For the Arctic Ocean, modeling has become one of the major instruments for understanding past conditions and explaining recently observed changes. In this context, the international Arctic Ocean Model Intercomparison Project (AOMiphttp://fish.cims.nyu.edu/project_aomip/overview. html) has investigated various aspects of ocean and sea ice changes for the time period 1948 to present. Among the major AOMIP themes are investigations of the origin and variability of Atlantic water (AW) circulation, mechanisms of accumulation and release of fresh water (FW), causes of sea level rise, and the role of tides in shaping climate.

Secular sea level change in the Russian sector of the Arctic Ocean

[1] Sea level is a natural integral indicator of climate variability. It reflects changes in practically all dynamic and thermodynamic processes of terrestrial, oceanic, atmospheric, and cryospheric origin. The use of estimates of sea level rise as an indicator of climate change therefore incurs the difficulty that the inferred sea level change is the net result of many individual effects of environmental forcing. Since some of these effects may offset others, the cause of the sea level response to climate change remains somewhat uncertain. This paper is focused on an attempt to provide first-order answers to two questions, namely, what is the rate of sea level change in the Arctic Ocean, and furthermore, what is the role of each of the individual contributing factors to observed Arctic Ocean sea level change? In seeking answers to these questions we have discovered that during the period 1954–1989 the observed sea level over the Russian sector of the Arctic Ocean is rising at a rate of approximately 0.123 cm yr � 1 and that after correction for the process of glacial isostatic adjustment this rate is approximately 0.185 cm yr � 1 . There are two major causes of this rise. The first is associated with the steric effect of ocean expansion. This effect is responsible for a contribution of approximately 0.064 cm yr � 1 to the total rate of rise (35%). The second most important factor is related to the ongoing decrease of sea level atmospheric pressure over the Arctic Ocean, which contributes 0.056 cm yr � 1 , or approximately 30% of the net positive sea level trend. A third contribution to the sea level increase involves wind action and the increase of cyclonic winds over the Arctic Ocean, which leads to sea level rise at a rate of 0.018 cm yr � 1 or approximately 10% of the total. The combined effect of the sea level rise due to an increase of river runoff and the sea level fall due to a negative trend in precipitation minus evaporation over the ocean is close to 0. For the Russian sector of the Arctic Ocean it therefore appears that approximately 25% of the trend of 0.185 cm yr � 1 , a contribution of 0.048 cm yr � 1 , may be due to the effect of increasing Arctic Ocean mass. INDEX TERMS: 3309 Meteorology and Atmospheric Dynamics: Climatology (1620); 4207 Oceanography: General: Arctic and Antarctic oceanography; 4255 Oceanography: General: Numerical modeling; 4532 Oceanography: Physical: General circulation; 4556 Oceanography: Physical: Sea level variations; KEYWORDS: Arctic, sea level rise, decadal variability, steric effects, inverted barometer effect, glacial isostatic adjustment

Interannual Variability in the Tropical Atlantic and Linkages to the Pacific

Abstract The variability of sea surface temperature anomalies (SSTAs) in the tropical Atlantic is examined using data from 1900 to the present. SSTAs are filtered to focus on the interannual band with fluctuations less than 60 months. Both SSTAs over the northern tropical Atlantic (NTA) and the southern tropical Atlantic (STA) are associated with the El Nino–Southern Oscillation (ENSO) variability in the tropical Pacific. SSTAs over the STA are associated with the quasi-biennial component of ENSO with a timescale of 22–32 months, and SSTAs over the NTA are influenced by the low-frequency part of the ENSO signal with a timescale of 36–48 months. The ENSO influence is seasonally dependent. The strongest linkages occur in the spring of each hemisphere. In addition to ENSO, SSTAs in the north equatorial Atlantic are also modulated by the circulation and net heat flux anomalies associated with the North Atlantic oscillation (NAO). The atmospheric impact on the ocean is different in the STA and NTA regions. Whe...