Lars Kaleschke

Frost flowers on sea ice as a source of sea salt and their influence on tropospheric halogen chemistry

[1] Frost flowers grow on newly-formed sea ice from a saturated water vapour layer. They provide a large effective surface area and a reservoir of sea salt ions in the liquid phase with triple the ion concentration of sea water. Recently, frost flowers have been recognised as the dominant source of sea salt aerosol in the Antarctic, and it has been speculated that they could be involved in processes causing severe tropospheric ozone depletion events during the polar sunrise. These events can be explained by heterogeneous autocatalytic reactions taking place on salt-laden ice surfaces which exponentially increase the reactive gas phase bromine (‘‘bromine explosion’’). We analyzed tropospheric bromine monoxide (BrO) and the sea ice coverage both measured from satellite sensors. Our model based interpretation shows that young ice regions potentially covered with frost flowers seem to be the source of bromine found in bromine explosion events. INDEX TERMS: 0322 Atmospheric Composition and Structure: Constituent sources and sinks; 1640 Global Change: Remote sensing; 3309 Meteorology and Atmospheric Dynamics: Climatology (1620); 3339 Meteorology and Atmospheric Dynamics: Ocean/atmosphere interactions (0312, 4504); 3360 Meteorology and Atmospheric Dynamics: Remote sensing. Citation: Kaleschke, L., et al. (2004), Frost flowers on sea ice as a source of sea salt and their influence on tropospheric halogen chemistry, Geophys. Res. Lett., 31, L16114, doi:10.1029/ 2004GL020655.

Seasonal modification of the Arctic Ocean intermediate water layer off the eastern Laptev Sea continental shelf break

up to 75% of the total variance. Our data suggest that the entire AW layer down to at least 840 m is affected by seasonal cycling, although the strength of the seasonal signal in temperature and salinity reduces from 260 m (±0.25C and ±0.025 psu) to 840 m (±0.05C and ±0.005 psu). The seasonal velocity signal is substantially weaker, strongly masked by high-frequency variability, and lags the thermohaline cycle by 45–75 days. We hypothesize that our mooring record shows a time history of the along-margin propagation of the AW seasonal signal carried downstream by the AW boundary current. Our analysis suggests that the seasonal signal in the Fram Strait Branch of AW (FSBW) at 260 m is predominantly translated from Fram Strait, while the seasonality in the Barents Sea branch of AW (BSBW) domain (at 840 m) is attributed instead to the seasonal signal input from the Barents Sea. However, the characteristic signature of the BSBW seasonal dynamics observed through the entire AW layer leads us to speculate that BSBW also plays a role in seasonally modifying the properties of the FSBW.

Intercomparison of passive microwave sea ice concentration retrievals over the high-concentration Arctic sea ice

[1] Measurements of sea ice concentration from the Special Sensor Microwave Imager (SSM/I) using seven different algorithms are compared to ship observations, sea ice divergence estimates from the Radarsat Geophysical Processor System, and ice and water surface type classification of 59 wide-swath synthetic aperture radar (SAR) scenes. The analysis is confined to the high-concentration Arctic sea ice, where the ice cover is near 100%. During winter the results indicate that the variability of the SSM/I concentration estimates is larger than the true variability of ice concentration. Results from a trusted subset of the SAR scenes across the central Arctic allow the separation of the ice concentration uncertainty due to emissivity variations and sensor noise from other error sources during the winter of 2003–2004. Depending on the algorithm, error standard deviations from 2.5 to 5.0% are found with sensor noise between 1.3 and 1.8%. This is in accord with variability estimated from analysis of SSM/I time series. Algorithms, which primarily use 85 GHz information, consistently give the best agreement with both SAR ice concentrations and ship observations. Although the 85 GHz information is more sensitive to atmospheric influences, it was found that the atmospheric contribution is secondary to the influence of the surface emissivity variability. Analysis of the entire SSM/I time series shows that there are significant differences in trend between sea ice extent and area, using different algorithms. This indicates that long-term trends in surface and atmospheric properties, unrelated to sea ice concentration, influence the computed trends.

Comprehensive isotopic composition of atmospheric nitrate in the Atlantic Ocean boundary layer from 65°S to 79°N

The comprehensive isotopic composition of atmospheric nitrate (i.e., the simultaneous measurement of all its stable isotope ratios: 15N/14N, 17O/16O and 18O/16O) has been determined for aerosol samples collected in the marine boundary layer (MBL) over the Atlantic Ocean from 65°S (Weddell Sea) to 79°N (Svalbard), along a ship-borne latitudinal transect. In nonpolar areas, the δ 15N of nitrate mostly deriving from anthropogenically emitted NO x is found to be significantly different (from 0 to 6‰) from nitrate sampled in locations influenced by natural NO x sources (−4 ± 2)‰. The effects on δ 15N(NO3 −) of different NO x sources and nitrate removal processes associated with its atmospheric transport are discussed. Measurements of the oxygen isotope anomaly (Δ17O = δ 17O − 0.52 × δ 18O) of nitrate suggest that nocturnal processes involving the nitrate radical play a major role in terms of NO x sinks. Different Δ17O between aerosol size fractions indicate different proportions between nitrate formation pathways as a function of the size and composition of the particles. Extremely low δ 15N values (down to −40‰) are found in air masses exposed to snow-covered areas, showing that snowpack emissions of NO x from upwind regions can have a significant impact on the local surface budget of reactive nitrogen, in conjunction with interactions with active halogen chemistry. The implications of the results are discussed in light of the potential use of the stable isotopic composition of nitrate to infer atmospherically relevant information from nitrate preserved in ice cores.

Exploring Arctic Transpolar Drift During Dramatic Sea Ice Retreat

The Arctic is undergoing significant environmental changes due to climate warming. The most evident signal of this warming is the shrinking and thinning of the ice cover of the Arctic Ocean. If the warming continues, as global climate models predict, the Arctic Ocean will change from a perennially ice-covered to a seasonally ice-free ocean. Estimates as to when this will occur vary from the 2030s to the end of this century. One reason for this huge uncertainty is the lack of systematic observations describing the state, variability, and changes in the Arctic Ocean.

Thin ice and storms: Sea ice deformation from buoy arrays deployed during N-ICE2015

Arctic sea ice has displayed significant thinning as well as an increase in drift speed in recent years. Taken together this suggests an associated rise in sea ice deformation rate. A winter and spring expedition to the sea ice covered region north of Svalbard–the Norwegian young sea ICE2015 expedition (N-ICE2015)—gave an opportunity to deploy extensive buoy arrays and to monitor the deformation of the first-year and second-year ice now common in the majority of the Arctic Basin. During the 5 month long expedition, the ice cover underwent several strong deformation events, including a powerful storm in early February that damaged the ice cover irreversibly. The values of total deformation measured during N-ICE2015 exceed previously measured values in the Arctic Basin at similar scales: At 100 km scale, N-ICE2015 values averaged above 0.1 d−1, compared to rates of 0.08 d−1 or less for previous buoy arrays. The exponent of the power law between the deformation length scale and total deformation developed over the season from 0.37 to 0.54 with an abrupt increase immediately after the early February storm, indicating a weakened ice cover with more free drift of the sea ice floes. Our results point to a general increase in deformation associated with the younger and thinner Arctic sea ice and to a potentially destructive role of winter storms.

Polynya Signature Simulation Method polynya area in comparison to AMSR-E 89 GHz sea-ice concentrations in the Ross Sea and off the Adelie Coast, Antarctica, for 2002-05: first results

Abstract The Polynya Signature Simulation Method (PSSM) is applied to Special Sensor Microwave/Imager observations from different Defense Meteorological Satellite Program spacecraft for 2002–05 to analyze the polynya area in the Ross Sea (Ross Ice Shelf polynya (RISP) and Terra Nova Bay polynya (TNBP)) and off the Adélie Coast (Mertz Glacier polynya (MGP)), Antarctica, on a sub-daily scale. The RISP and the MGP exhibit similar average total polynya areas. Major area changes (>10000km2; TNPB: >2000km2) occur over a range of 2–3 to 20 days in all regions. Sub-daily area changes are largest for the MGP (5800km2) and smallest for the TNBP (800km2), underlining the persistence of the forcing of the latter. ARTIST sea-ice (ASI) algorithm concentration maps obtained using 89 GHz Advanced Microwave Scanning Radiometer (AMSR-E) data are compared to PSSM maps, yielding convincing agreement in the average, similarly detailed winter polynya distribution. Average ASI algorithm ice concentrations take values of 25–40% and 65–80% for the PSSM open-water and thin-ice class, respectively. The discrepancy with expected values (0% and 100%) can be explained by the different spatial resolution and frequency used by the methods. A new land mask and a mask to flag icebergs are introduced. Comparison of PSSM maps with thermal ice thickness based on AVHRR infrared temperature and ECMWF ERA-40 data suggests an upper thickness limit for the PSSM thin-ice class of 20–25 cm.

Investigating High-Resolution AMSR2 Sea Ice Concentrations during the February 2013 Fracture Event in the Beaufort Sea

Leads with a length on the order of 1000 km occurred in the Beaufort Sea in February 2013. These leads can be observed in Moderate Resolution Imaging Spectroradiometer (MODIS) images under predominantly clear sky conditions. Sea ice concentrations (SIC) derived from the Advanced Microwave Scanning Radiometer 2 (AMSR2) using the Bootstrap (BST) algorithm fail to show the lead occurrences, as is visible in the MODIS images. In contrast, SIC derived from AMSR2 using the Arctic Radiation and Turbulence Interaction Study (ARTIST) sea ice algorithm (ASI) reveal the lead structure, due to the higher spatial resolution possible when using 89-GHz channel data. The ASI SIC are calculated from brightness temperatures interpolated on three different grids with resolutions of 3.125 km (ASI-3k), 6.25 km (ASI-6k) and 12.5 km (ASI-12k) to investigate the effect of the spatial resolution. Single-swath data is used to study the effect of temporal sampling in comparison to daily averages. For a region of interest in the Beaufort Sea, BST and ASI-3k show area-averaged SIC of 97%±0.7% and 93%±7.0%, respectively. For ASI-6k, the area-averaged SIC are similar to ASI-3k, while ASI-12k data show more agreement with BST. Visual comparison with MODIS True Color imagery exhibits good agreement with ASI-3k. In particular, ASI-3k are able to reproduce lead structure and size in the sea ice cover, which are not or are less visible in the other SIC data. The results will be valuable for selecting a SIC data product for studies of the interaction between ocean, ice, and atmosphere in the polar regions.

Surface Emissivity of Arctic Sea Ice at AMSU Window Frequencies

A method to retrieve the surface emissivity of sea ice at the window channels of the Advanced Microwave Sounding Unit (AMSU) radiometers in the polar region is presented. The instruments are on the new-generation satellites of the U.S. National Oceanic and Atmospheric Administration (NOAA-15, NOAA-16, and NOAA-17). The method assumes hypothetical surfaces with emissivities zero and one and simulates brightness temperatures at the top of the atmosphere using profiles of atmospheric parameters, e.g., from the European Centre for Medium-Range Weather Forecasts (ECMWF) model runs, as input for a radiative transfer model. The retrieval of surface emissivity is done by combining simulated brightness temperatures with the satellite-measured brightness temperature. The AMSU window channels differ in surface penetration depths and, thus, in the surface microphysical parameters that they depend on. Lowest layer air temperatures from ECMWF are used to infer temperatures of emitting layers at different frequencies of sea ice. A complete yearly cycle of monthly average emissivities in two selected regions (first- and multiyear ice) is giving insight into the variation of emissivities in various development stages of sea ice.

Comparison of different retrieval techniques for melt ponds on Arctic sea ice from Landsat and MODIS satellite data

Abstract Melt ponds are regularly observed on the surface of Arctic sea ice in late spring and summer. They strongly reduce the surface albedo and accelerate the decay of Actic sea ice. Until now, only a few studies have looked at the spatial extent of melt ponds on Arctic sea ice. Knowledge of the melt-pond distribution on the entire Arctic sea ice would provide a solid basis for the parameterization of melt ponds in existing sea-ice models. Due to the different spectral properties of snow, ice and water, a multispectral sensor such as Landsat 7 ETM+ is generally applicable for the analysis of distribution. an additional advantage of the ETM+ sensor is the very high spatial resolution (30 m). an algorithm based on a principal component analysis (PCA) of two spectral channels has been developed in order to determine the melt-pond fraction. PCA allows differentiation of melt ponds and other surface types such as snow, ice or water. Spectral bands 1 and 4 with central wavelengths at 480 and 770 nm, respectively, are used as they represent the differences in the spectral albedo of melt ponds. A Landsat 7 ETM+ scene from 19 July 2001 was analysed using PCA. the melt-pond fraction determined by the PCA method yields a different spatial distribution of the ponded areas from that developed by others. A MODIS subset from the same date and area is also analysed. the classification of MODIS data results in a higher melt-pond fraction than both Landsat classifications.