Frank Kauker

One more step toward a warmer Arctic

This study was motivated by a strong warming signal seen in mooring-based and oceanographic survey data collected in 2004 in the Eurasian Basin of the Arctic Ocean. The source of this and earlier Arctic Ocean changes lies in interactions between polar and sub-polar basins. Evidence suggests such changes are abrupt, or pulse-like, taking the form of propagating anomalies that can be traced to higher-latitudes. For example, an anomaly found in 2004 in the eastern Eurasian Basin took ∼1.5 years to propagate from the Norwegian Sea to the Fram Strait region, and additional ∼4.5–5 years to reach the Laptev Sea slope. While the causes of the observed changes will require further investigation, our conclusions are consistent with prevailing ideas suggesting the Arctic Ocean is in transition towards a new, warmer state.

Twenty-first-century warming of a large Antarctic ice-shelf cavity by a redirected coastal current

The Antarctic ice sheet loses mass at its fringes bordering the Southern Ocean. At this boundary, warm circumpolar water can override the continental slope front, reaching the grounding line through submarine glacial troughs and causing high rates of melting at the deep ice-shelf bases. The interplay between ocean currents and continental bathymetry is therefore likely to influence future rates of ice-mass loss. Here we show that a redirection of the coastal current into the Filchner Trough and underneath the Filchner–Ronne Ice Shelf during the second half of the twenty-first century would lead to increased movement of warm waters into the deep southern ice-shelf cavity. Water temperatures in the cavity would increase by more than 2 degrees Celsius and boost average basal melting from 0.2 metres, or 82 billion tonnes, per year to almost 4 metres, or 1,600 billion tonnes, per year. Our results, which are based on the output of a coupled ice–ocean model forced by a range of atmospheric outputs from the HadCM3 climate model, suggest that the changes would be caused primarily by an increase in ocean surface stress in the southeastern Weddell Sea due to thinning of the formerly consolidated sea-ice cover. The projected ice loss at the base of the Filchner–Ronne Ice Shelf represents 80 per cent of the present Antarctic surface mass balance. Thus, the quantification of basal mass loss under changing climate conditions is important for projections regarding the dynamics of Antarctic ice streams and ice shelves, and global sea level rise.

Variability of Arctic and North Atlantic sea ice: A combined analysis of model results and observations from 1978 to 2001

Ice cover data simulated by a coupled sea ice-oceanmodel of the North Atlantic and the Arctic Ocean are compared withsatellite observations for the period 1978 to 2001. The capability ofthe model in reproducing the long-term mean state and the inter-seasonalvariability is demonstrated. The main modes of variability of thesatellite data and the simulation in the summer and winter half yearsare highly similar.Using NCEP/NCAR reanalysis data and the results from the sea ice-oceanmodel, we describe the relationship with atmospheric and oceanicvariables for the first two modes of sea-ice concentration variabilityin winter and in summer. The first winter mode shows a time delayedresponse to the Arctic Oscillation due to advection of heatanomalies in the ocean. The second winter mode is dominated by anevent in the late 1990s that is characterized by anomalously highpressure over the eastern Arctic. The first summer mode isstrongly influenced by the Arctic Oscillation of the previouswinter. The second summer mode is caused by anomalous air temperaturein the Arctic. This mode shows a distinctive trend and is related to anice extent reduction of about 4 10^5 km^2 over the 23 years ofanalysis.

Arctic Ocean basin liquid freshwater storage trend 1992–2012

Freshwater in the Arctic Ocean plays an important role in the regional ocean circulation, sea ice, and global climate. From salinity observed by a variety of platforms, we are able, for the first time, to estimate a statistically reliable liquid freshwater trend from monthly gridded fields over all upper Arctic Ocean basins. From 1992 to 2012 this trend was 600±300 km3 yr−1. A numerical model agrees very well with the observed freshwater changes. A decrease in salinity made up about two thirds of the freshwater trend and a thickening of the upper layer up to one third. The Arctic Ocean Oscillation index, a measure for the regional wind stress curl, correlated well with our freshwater time series. No clear relation to Arctic Oscillation or Arctic Dipole indices could be found. Following other observational studies, an increased Bering Strait freshwater import to the Arctic Ocean, a decreased Davis Strait export, and enhanced net sea ice melt could have played an important role in the freshwater trend we observed.

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.

Modeling decadal variability of the Baltic Sea: 1. Reconstructing atmospheric surface data for the period 1902–1998

[1] A statistical model is developed to reconstruct atmospheric surface data for the period 1902–1998 to force a coupled sea ice-ocean model of the Baltic Sea. As the response timescale of the Baltic Sea on freshwater inflow is of the order of 30–40 years, climate relevant model studies should cover at least century-long simulations. Such an observational atmospheric data set is not available yet. We devised a statistical model using a ‘‘redundancy analysis’’ to reconstruct daily sea level pressure (SLP) and monthly surface air temperature (SAT), dew-point temperature, precipitation, and cloud cover of the Baltic. The predictor fields are daily SLP at 19 stations and monthly coarse gridded SAT and precipitation available for the period 1902 to 1998. The second input is a gridded atmospheric data set, with high resolution in space and time, based on synoptic stations, which is available for the period 1970–2001. Spatial patterns are selected by maximizing predictand variance during the ‘‘learning’’ period 1980–1998. The remainder period 1970–1979 is used for validation. We found the highest skill of the statistical model for SLP and the lowest skill for cloud cover. For wintertime the dominant modes of variability on the interannual to interdecadal timescales of the reconstruction are discussed. It is shown that the wintertime variability of SLP, SAT, and precipitation is related to well-known atmospheric patterns of the Northern Hemisphere: the North Atlantic Oscillation, the Scandinavia pattern, the East Atlantic/West Russia pattern, and the Barents Sea Oscillation. INDEX TERMS: 4215 Oceanography: General: Climate and interannual variability (3309); 4235 Oceanography: General: Estuarine processes; 4243 Oceanography: General: Marginal and semienclosed seas; 4255 Oceanography: General: Numerical modeling; KEYWORDS: Baltic Sea, climate reconstruction, redundancy analysis, sea iceocean modeling, major Baltic inflows Citation: Kauker, F., and H. E. M. Meier, Modeling decadal variability of the Baltic Sea: 1. Reconstructing atmospheric surface data for the period 1902 – 1998, J. Geophys. Res., 108(C8), 3267, doi:10.1029/2003JC001797, 2003.

Arctic Ocean freshwater: How robust are model simulations?

The Arctic freshwater (FW) has been the focus of many modeling studies, due to the potential impact of Arctic FW on the deep water formation in the North Atlantic. A comparison of the hindcasts from ten ocean-sea ice models shows that the simulation of the Arctic FW budget is quite different in the investigated models. While they agree on the general sink and source terms of the Arctic FW budget, the long-term means as well as the variability of the FW export vary among models. The best model-to-model agreement is found for the interannual and seasonal variability of the solid FW export and the solid FW storage, which also agree well with observations. For the interannual and seasonal variability of the liquid FW export, the agreement among models is better for the Canadian Arctic Archipelago (CAA) than for Fram Strait. The reason for this is that models are more consistent in simulating volume flux anomalies than salinity anomalies and volume-flux anomalies dominate the liquid FW export variability in the CAA but not in Fram Strait. The seasonal cycle of the liquid FW export generally shows a better agreement among models than the interannual variability, and compared to observations the models capture the seasonality of the liquid FW export rather well. In order to improve future simulations of the Arctic FW budget, the simulation of the salinity field needs to be improved, so that model results on the variability of the liquid FW export and storage become more robust.

Sensitivity of the Baltic Sea salinity to the freshwater supply

The sensitivity of the Baltic Sea salinity to the freshwater supply is investigated using a 3-dimensional (3D) coupled sea-ice-ocean model. Todays climate is characterized by an average salinity of about 7.4‰ and a freshwater supply, including river runoff and net precipitation, of about 16 000 m 3 s -1 . As recent results of some regional climate models have suggested a significant increase in precipitation in the Baltic catchment area due to anthropogenic climate change, in this study the response of salinity in the Baltic Sea to changing freshwater inflow is investigated. Of special interest is the possibility of the Baltic Sea becoming a freshwater sea with 0 ‰ salinity in the future. There- fore, model simulations with modified river runoff and precipitation for 1902-1998 were performed. The model is forced with daily sea-level observations in the Kattegat, monthly basin-wide discharge data, and reconstructed atmospheric surface data. The reconstruction utilizes a statistical model to calculate daily sea-level pressure, and monthly surface-air temperature, dew-point temperature, pre- cipitation, and cloud-cover fields. It is assumed that the Kattegat deepwater salinity of about 33 ‰ will not change regardless of the changed freshwater supply. In most of the experiments the final stratifi- cation is almost in a steady state after 100 yr. We found that even for a freshwater supply increased by 100% compared to 1902-1998 the Baltic Sea cannot be classified as a freshwater sea. A pro- nounced halocline still separates the upper and lower layers in the Baltic Proper, limiting the impact of direct wind mixing to the surface layer. A calculated phase diagram suggests that the relationship between freshwater supply and average salinity of the final steady state is non-linear. The results of the 3D model are in agreement with an analytical steady-state model assumed to work for freshwa- ter changes smaller than 30%. The latter model was applied in scenarios for the average salinity of the Baltic Sea.

Comparing modeled streamfunction, heat and freshwater content in the Arctic Ocean

Within the framework of the Arctic Ocean Model Intercomparison Project results from several coupled sea ice–ocean models are compared in order to investigate vertically integrated properties of the Arctic Ocean. Annual means and seasonal ranges of streamfunction, freshwater and heat content are shown. For streamfunction the entire water column is integrated. For heat and freshwater content integration is over the upper 1000 m. The study represents a step toward identifying differences among model approaches and will serve as a base for upcoming studies where all models will be executed with common forcing. In this first stage only readily available outputs are compared, while forcing as well as numerical parameterizations differ. The intercomparison shows streamfunctions differing in pattern and by several Sverdrups in magnitude. Differences occur as well for the seasonal range, where streamfunction is subject to large variability.

An intercomparison of Arctic ice drift products to deduce uncertainty estimates

An intercomparison of four low-resolution remotely sensed ice-drift products in the Arctic Ocean is presented. The purpose of the study is to examine the uncertainty in space and time of these different drift products. The comparison is based on monthly mean ice drifts from October 2002 to December 2006. The ice drifts were also compared with available buoy data. The result shows that the differences of the drift vectors are not spatially uniform, but are covariant with ice concentration and thickness. In high (low) ice-concentration areas, the differences are small (large), and in thick (thin) ice-thickness areas, the differences are small (large). A comparison with the drift deduced from buoys reveals that the error of the drift speed depends on the magnitude of the drift speed: larger drift speeds have larger errors. Based on the intercomparison of the products and comparison with buoy data, uncertainties of the monthly mean drift are estimated. The estimated uncertainty maps reasonably reflect the difference between the products in relation to ice concentration and the bias from the buoy drift in relation to drift speed. Examinations of distinctive features of Arctic sea ice motion demonstrate that the transpolar drift speed differs among the products by 13% (0.32 cm s−1) on average, and ice drift curl in the Amerasian Basin differs by up to 24% (3.3 × 104 m2 s−1). These uncertainties should be taken into account if these products are used, particularly for model validation and data assimilation within the Arctic.