Improvement/optimization of a sea ice model is of great significance for understanding the sea ice physics and for understanding the Arctic climate system and its linkage to the global climate. For better representation of modeled sea ice properties, we develop a parameter optimization system for a couped ocean-sea ice model. Since the sensitivities of dynamic and thermodynamic parameters of sea ice models are interrelated, the system handles both sets of parameters simultaneously. The system also handles a long assimilation window of 33 years. Such a long time window has never been tested by other algorithms (e.g., adjoint method, EnKF). Since the cost function defined by the model - data misfit may have an ill-shaped structure (multiple local minima), we apply an algorithm, which can find the global minimum of an ill-shaped function. A micro-Genetic Algorithm is one of the possible solutions to optimize sea ice model parameters.

Remote sensing data show a continuous decrease of sea ice in the past 30 years. Climate models predict a further decreasing for the future. Therefore, a closer analysis of the production processes, the trend development and the regional variability is necessary. The Laptev Sea plays an important role for the Arctic sea ice budget due to a high polynya activity at the Siberian coast. The coupled ocean-sea ice model NAOSIM (North Atlantic/Arctic Ocean-Sea Ice Model) is used for the study of thermodynamic and dynamic ice production processes for the whole Arctic for the period 1990-2008. The simulation is driven by daily NCEP/NCAR data, and the horizontal resolution of the model is about 9km. Sea ice concentration from satellite data is used for the verification of model results. The model is able to reproduce the mean annual cycle and the negative trend realistically. A detailed analysis of the thermodynamic sea ice production/melt and the dynamic redistribution for different regions of the Arctic shows that the mean sea ice production of the Laptev Sea area exceeds the sea ice melt rate by 740 km3/a. That sea ice volume is transported into the central Arctic. The net ice production in the Laptev Sea is as large as the net ice production in the central Arctic north of 80° N. The Laptev Sea is found to be the largest ice producer compared to other Arctic shelf areas. In addition, the interannual variability of sea ice production in the Laptev Sea is small compared to other regions. A negative trend of sea ice in the Laptev-Sea is not found. For the entire Arctic sea ice volume decrease amounts to the average of -450 km3/a from 1990 to 2008. Studies for years with extreme sea ice anomalies show no direct connections between the sea ice production of the Laptev Sea and the sea ice volume of the entire Arctic.

Fast ice covers large areas of the Laptev Sea from late November until June. It forms an immobile lid over the inner shelf and it efficiently decouples the atmosphere from the ocean. The aim of this paper is to study the impact of fast ice on the ocean circulation on the Laptev Sea shelf and in the adjacent Arctic Ocean. We use a sea ice ocean coupled model in an experiment with and without fast ice parameterization. The comparative study of model runs with partly idealized configuration can not give absolute estimates, but can point out the relative importance of the fast ice cover for the salinity distribution in the Laptev Sea and its vicinity. Our results show that on the shelf covered exclusively by drift ice, the surface layer is on average saltier by 0.7 and the bottom layer is fresher by 1 compared to the case when the inner shelf is covered by fast ice. This difference in salinity is caused by the difference in the Ekman pumping location, enhanced import of more saline water from the outer shelf and weaker vertical redistribution of the river water between the depth layers in the experiment with the fast ice. Thus, the Laptev Sea fast ice has an important role in the winter river water redistribution.

● We found no simple relationship between the phytoplankton bloom and the sea ice concentration. ● Results of correlating CHL time series with that of 6 physical parameters show the parameter mostly correlated with CHL is the Mixed Layer Depth. The use of two different MLD definitions did not significantly change the results. ● Mixed Layer Depth is negatively correlated with the chlorophyll-a in the open ocean part of region, i.e. shallow MLD corresponds to higher phytoplankton concentrations. We suggest the reason for this lies in the more stratified waters triggering the bloom start. ● In the coastal part we observed hardly any correlation, which can be explained either by different mechanisms guiding phytoplankton growth on the coast or by poor data quality in the coastal area 1Alfred-Wegener-Institute for Polar and Marine Research, Bremerhaven, Germany, 2Institute of Environmental Physics, University of Bremen, Bremen, Germany, 3Max Planck Institute for Marine Microbiology, Bremen, Germany

The marginal seas of the Arctic Ocean are well recognized as strong ice producers and might gain special attention regarding ice volume changes in the Arctic Ocean. Hence, the monitoring of ice production taking place inside leads, polynyas and over extensive thin ice areas is one of the major challenges of current polar research. In this study we compare different satellite-based methodologies with respect to their applicability for an operational investigations of shelf sea ice. First we provide an overview of the feasibility and comparability of the existing methods in describing distinct polynya/lead features. Second, we cross-validate satellite-derived polynya/lead characteristics and compare approaches to helicopter-borne electromagnetic (EM) ice thickness measurements acquired during field campaigns. We further assess the ability of the newly launched Soil Moisture and Ocean Salinity (SMOS) satellite for ice monitoring. The MIRAS instrument (1.4 GHz) on the SMOS satellite provides daily coverage of the complete polar seas with a resolution of about 35 km in nadir view. The resolution is of course too low to observe leads or polynyas, but could provide measurements and monitoring of extensive thin ice areas.

Fernerkundungsdaten haben eine kontinuierliche Abnahme des Meereises in den vergangenen 30 Jahren gezeigt, Klimamodelle prognostizieren eine anhaltende Abnahme fur die Zukunft. Dies erfordert eine genauere Analyse der verursachenden Prozesse, der Trendentwicklung und der regionalen Variabilitat. Dabei spielt die Laptev-See in der sibirischen Arktis eine bedeutende Rolle, da es hier, bedingt durch eine grose Polynja-Aktivitat, zur vermehrten Eisproduktion kommt. Zur naheren Untersuchung der verursachenden thermodynamischen und dynamischen Prozesse nutzen wir eine mit taglichen NCEP/NCAR-Daten angetriebene Simulation mit dem gekoppelten Ozean-Meereismodell NAOSIM (North Atlantic/Arctic Ocean-Sea Ice Model) von 1990-2008 mit 0.08° Auflosung. Aufgrund seiner realitatsnahen Wiedergabe des mittleren Jahresgangs und des negativen Trends der Eisbedeckung ist dieses Modell fur die Auswertung gut geeignet. Die getrennte Analyse der thermodynamischen Eisproduktion bzw. Eisschmelze und der dynamischen Umverteilung fur die gesamte Arktis bestatigt, dass im Bereich der Laptev-See die Eisproduktion im Mittel 850km3/a groser ist als die Eisschmelze. Dieses Eis wird von der Laptev-See in die zentrale Arktis exportiert. In der gesamten Arktis nimmt das Eisvolumen im Mittel um -450km3/a von 1990-2008 ab. usammenhange zwischen der Eisproduktion der Laptev-See und des Eisvolumens der Arktis werden mittels einer Zeitreihenananlyse untersucht. Die Entstehungsgrunde fur Extremjahre (Bsp.: Minimum 2007, Maximum 1996) werden aufgezeigt und ihre regionalen Folgen in der Arktis diskutiert.

The fresh water export from the Arctic has not been measured yet. The major problem lies in the transport over the shallow East Greenland shelf that is not easily accessible for oceanographic vessels and so far has been off-limits for moored instrumentation. Even if we would be able to start measurements now, we would have no statistics to evaluate trends and natural variability of the transport. For long time series and for predictions of future changes, there is no other means than numerical models of the oceanic circulation and the water mass distribution. For past times, models can perhaps be combined with observations of different variables to yield better reconstructions of long-term variability in fresh water fluxes between the Arctic and the sub-polar North Atlantic. The liquid fresh water export from the Arctic Ocean through the passages of the Canadian Archipelago, Fram Strait and the Barents Sea is constrained by the fresh water fluxes entering the Arctic Ocean and by changes in the fresh water contents in the Arctic halocline. If one knew the fluxes entering the Arctic Ocean and the changes in the salinity very precisely, the export rates could be determined as a residual. (We use this technique to derive export rates in a coupled climate model in Section 17.5.) Different components of the Arctic Ocean fresh water balance exhibit very different long-term variability. Serreze et al. (2006) provide a recent compilation of estimates of the interannual variability of river discharge, net precipitation, Bering Strait inflow, and Fram Strait ice flux. Fram Strait ice transport shows by far the largest standard deviation of these fresh water fluxes. River run-off into the Arctic Ocean has increased over the last 50 years by approximately 5% (Peterson et al. 2002). Interannual variability as shown by Peterson et al. is of similar or smaller magnitude. Compared to fluctuations in other components of the fresh water balance, this is a small variability. The variability in river discharge is also indicative of the variability of the total atmospheric moisture convergence at