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Explore long-term and short-term processes affecting the sea ice extent minimum in 2007. Analyze data and modeling to distinguish preconditioning from triggering factors impacting interannual variability and positive feedback loops.
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Charge to the Working Group How well do we understand the 2007 sea ice extent minimum? (through modelling and data analysis including retrospective analyses of long-term observational records) To understand and explain this event it is essential to distinguish between two categories of processes: long-term processes (preconditioning) short-term processes (triggers) responsible for part of the interannual variability. They are highly non-linear and activate a number of positive feedback loops . Preconditioning The 2007 Arctic sea ice summer minimum event story started well before 2007. It is obviously strongly connected to what happened during years before, i.e., preconditioning is an intrinsically important part of the event. Atmosphere: long-term warming mostly characterized by a reduced winter cooling and a longer melting season most likely related to global GHG effect. This effect by itself imposes a reduced mean sea ice thickness of the order of 1m if we compare with the situation 20 years ago. This is equivalent to a 10W/m2 long wave radiation increase or a 20 W/m2 short wave solar radiation increase.
This effect is unequally distributed over the Arctic Ocean for two reasons: • Land (Siberia and Alaska) is warming up faster than the Ocean • The Greenland ice sheet is a permanent cold source • Ocean: water mass circulation and transformation can precondition sea ice • removal, in particular in cases when warm waters reach the surface mixed layer • and exchange heat with sea ice. • This effect is influenced by the following factors: • Fresh water inputs from river runoff and Pacific water and also to salty and • warm Atlantic waters entering through Fram Strait and the Barents Sea • The way these water masses circulate and respond to the atmospheric forcing • The asymmetry of continental shelf distribution. • In all these aspects one should consider the way sea ice (including frazil) forms • and melts (surface and bottom) and brines are rejected in deep and/or shallow • waters.
Interannual variability and related processes The long-term trends can be strongly modulated by atmospheric climatic oscillations (AO, NAM, PDO etc…) Processes involve triggering factors and amplification through non-linear, positive feedbacks. In general these processes (and related effects) are very difficult to predict in contrast with the long-term preconditioning processes. It is quite instructive to look at various previous events that occurred during the past 10 or 20 years. In particular 1997 and 2005 (although different) are quite illuminating for understanding the 2007 situation. It is also often difficult to designate the triggering factors. But in several instances we recognized the fact that abnormal atmospheric conditions in summer are capable of triggering large scale interannual variability such as the 2005 summer sea ice minimum due to persistent winds flushing away from the Arctic most of the first year ice and disrupting the MYI summer replenishment. The summer 2007 anomaly has a different origin and was mainly due to persistent atmospheric high pressure system over the Beaufort Sea during several months creating large sea ice break up under winds forcing, increasing incoming solar radiation due to clear sky conditions and heat uptake by the Ocean. In fact, in 2007, it looks like a number of factors combined to eliminate sea ice to a large extent. The 2007 large drop in sea ice extent happened in a very short time. In all cases a large sea ice minimum retreat has profound consequences during the following fall season when all the heat taken up by the Ocean must be released back to the Atmosphere delaying the onset of freezing and the amount of sea ice formed during the following winter by reducing the number of freezing degrees days by a large amount (1000°C).
What are the gaps in understanding sea-ice loss and related changes? • We need to understand better (and possibly predict) the atmospheric circulation • at the seasonal scale and in particular surface winds (SLP) and SAT. • This is very critical in summertime. We need to understand how SLP and SAT interact. • We need to improve SAT observations (by comparison, SLP is often of good quality). • We need to document the ability of winds to break up the sea ice cover enhancing • penetration of incoming solar radiation into the Ocean where it leads to increased • sea ice melting. • We need to take into account how sea ice (frazil) forms in winter, rejects brines, • enhances the shallow cold halocline and the deep thermocline protecting sea ice • from melting during summer. • There is a need to understand better sea ice drift in particular in regions acting • as main source of Sea ice (Siberian shelves) and • as a main sink: gateways like Fram Strait and the Barents Sea and • regions where sea ice is rafting and ridging (Greenland and Canada). • We need to understand what led to the observed increase in sea ice mobility. • Is it caused by sea ice mechanics (rheology), sea ice thinning, or something else? • We need to be able to better identify MYI from FYI. The MYI is the endangered species. • We need to improve our knowledge of the various modes of atmospheric circulation • and oscillation. How is the Arctic vortex sensitive to external influences (tropics • and stratosphere)?
Finally the combination of long-term and short-term (interannual) variability in the Arctic is very capable of creating extreme events like the 2007 summer sea ice minimum. Due to long-term weakening (thinning), the sea ice pack is becoming more mobile and more vulnerable and the interannual variability has much more severe impacts (and vice versa). This is also due to positive feedback loops between the two modes of variability: processes related to the long term variability becoming more active as well as short term processes becoming more active due to the preconditioning created by long term variability.