Mark Wensnahan

Evaluation of Seven Different Atmospheric Reanalysis Products in the Arctic

AbstractAtmospheric reanalyses depend on a mix of observations and model forecasts. In data-sparse regions such as the Arctic, the reanalysis solution is more dependent on the model structure, assumptions, and data assimilation methods than in data-rich regions. Applications such as the forcing of ice–ocean models are sensitive to the errors in reanalyses. Seven reanalysis datasets for the Arctic region are compared over the 30-yr period 1981–2010: National Centers for Environmental Prediction (NCEP)–National Center for Atmospheric Research Reanalysis 1 (NCEP-R1) and NCEP–U.S. Department of Energy Reanalysis 2 (NCEP-R2), Climate Forecast System Reanalysis (CFSR), Twentieth-Century Reanalysis (20CR), Modern-Era Retrospective Analysis for Research and Applications (MERRA), ECMWF Interim Re-Analysis (ERA-Interim), and Japanese 25-year Reanalysis Project (JRA-25). Emphasis is placed on variables not observed directly including surface fluxes and precipitation and their trends. The monthly averaged surface tem...

Analysis of the Arctic System for Freshwater Cycle Intensification: Observations and Expectations

Abstract Hydrologic cycle intensification is an expected manifestation of a warming climate. Although positive trends in several global average quantities have been reported, no previous studies have documented broad intensification across elements of the Arctic freshwater cycle (FWC). In this study, the authors examine the character and quantitative significance of changes in annual precipitation, evapotranspiration, and river discharge across the terrestrial pan-Arctic over the past several decades from observations and a suite of coupled general circulation models (GCMs). Trends in freshwater flux and storage derived from observations across the Arctic Ocean and surrounding seas are also described. With few exceptions, precipitation, evapotranspiration, and river discharge fluxes from observations and the GCMs exhibit positive trends. Significant positive trends above the 90% confidence level, however, are not present for all of the observations. Greater confidence in the GCM trends arises through lowe...

Passive microwave remote sensing of thin sea ice using principal component analysis

Time sequences of surface based measurements of passive microwave emission from growing saline ice reported by Wensnahan et al. (1993) are used to explore the possibility of developing a satellite based sea ice concentration algorithm which solves for the presence of thinner ice. It is shown that two classes of thinner ice can be distinguished from mixtures of open water (OW), first-year (FY) ice, and multiyear (MY) ice. The two classes do not necessarily correspond to specific World Meteorological Organization ice types; rather, newly formed ice represents a brief transition spectrum between OW and thin ice. Newly formed ice appears to be optically thick at 37 and 90 GHz and has a relatively dry surface. The thin ice spectrum occurs when the ice is greater than 4 cm thick and appears to result from the accumulation of brine at the surface of the ice. Thin ice has a relatively stable spectrum characterized by high brightness temperatures, a near-zero spectral gradient at vertical polarization, and a large difference between vertical and horizontal polarizations. Supervised principal component analysis (PCA) was done of laboratory data using 10 channels of passive data: vertical and horizontal polarization at 6.7, 10, 19, 37, and 90 GHz. Analyses were also done on subsets of the laboratory data at 6.7 to 37 GHz as well as 19 to 90 GHz, representing the scanning multichannel microwave radiometer (SMMR) and special sensor microwave imager (SSM/I) satellite frequencies, respectively. Using all of the channels or the SMMR subset makes it possible to solve for mixtures of OW and FY, MY, newly formed and thin ice but with large errors. However, any four of these scene types can be distinguished with reasonable accuracy. The SSM/I frequencies allow determination of at most four of these scene types but with moderate errors. PCA was used in a case study of SSM/I data from the Bering Sea for April 2, 1988. Winds from the north formed thin ice areas which the NASA Team algorithm interprets as large amounts of OW and MY ice. With PCA, these same areas are interpreted as 20–30% OW near the lee shores but otherwise as consisting almost entirely of thin ice. We conclude that thin ice can be detected using satellite data. However, questions remain as to how the thin ice spectrum varies with environmental conditions, how it evolves to that of FY, and how this evolution affects the predicted concentrations of thin ice.

The Accuracy of Sea Ice Drafts Measured from U.S. Navy Submarines

Abstract Navy submarines in the Arctic Ocean routinely obtain observations from an upward-looking sonar of the draft of the sea ice cover overhead. Draft data are now publicly available from some 40 cruises from 1975 to 2000 covering over 120 000 km of track in roughly the central half of the Arctic Ocean. To apply these observations to geophysics, error estimates are needed. This paper assesses how well the correction of the data during normal processing accounts for the major sources of error in the draft data from U.S. Navy submarines and what errors remain in the data. The error treated is the error for the average draft over tens of kilometers. The following sources of error are considered: measurement precision error; errors in identifying open water (as ice of zero draft); sound speed error; errors caused by variable sonar footprint size, by uncontrolled gain and thresholds, and by ship’s trim; and differences between data from analog charts and digitally recorded data. The bias with respect to the...

Observations and theoretical studies of microwave emission from thin saline ice

Observations of time-dependent changes in microwave emission from thin (0–9 cm) saline ice were made at frequencies of 6.7, 10, 19, 37, and 90 GHz for both vertical and horizontal polarizations. Experiments were carried out on artificial sea ice in the laboratory and on natural ice in the Greenland Sea. Coincident data were also collected on ice thickness, temperature, and salinity together with a variety of basic meteorological data. Several phenomena were found to be common to all the laboratory measurements, with some indication that similar features were also present in the field data. These include (1) a sharp rise in surface temperature when the ice was between 1 and 2 cm thick, apparently unrelated to any environmental changes, (2) a decrease in brightness temperature (Tb) at 37 GHz during or just after the surface temperature rise, and (3) an initial increase in Tb with increasing ice thickness, followed by substantial decreases in Tb at the higher (19, 37, and 90 GHz) frequencies. The maximum Tb values observed were higher than those previously reported for young ice. Tb was also found to be much more sensitive to variations in ice properties at horizontal polarization than at vertical polarization. Numerical modeling results indicate that the high-frequency decreases in Tb were not due to bulk changes in either ice temperature or salinity. The most likely explanation for the observed rise in surface temperature and decrease in Tb was the formation of a salinity-enhanced ice or brine surface layer caused by the upward transport of brine as the ice grows.

New Arctic sea ice draft data from submarines

Arctic sea ice thickness data from 17 newly processed submarine cruises covering over 49,000 kilometers of cruise track have now been added to the archive at the National Snow and Ice Data Center (NSIDC). This addition increases the archive by 68%, to a total of over 120,000 kilometers of track from 37 U.S. Navy and 2 Royal Navy submarine cruises. The data are actually of ice draft, the submerged portion of floating sea ice, which is about 89% of ice thickness. Declines in Arctic sea ice extent and thickness in recent decades make this an invaluable data set for research into Arctic climate variations [e.g., Tucker et al., 2001], for testing sea ice models [e.g., Rothrock et al., 2003; Miller et al., 2005], and for intercomparison with measurements of thickness by other methods [e.g., Laxon et al., 2003].

Measurements of microwave emission from new and young saline ice during the 1993 CRREL pond experiment

The goals of this study were to observe multifrequency microwave brightness temperatures and emissivities in new and young saline ice, and to assess the effects of ice growth and structural changes on the microwave signatures. The authors were particularly interested in investigating the later stages of young ice growth for comparison with surface-based field observations of first-year (FY) and multiyear (MY) sea ice. Two particular points of interest were: (i) to compare certain roughened surfaces with an undisturbed surface evolving under the same growth conditions, and (ii) to isolate the changes in physical properties which accompany the transition of the microwave signature from young ice to FY ice. Young ice typically has a high polarization ratio (PRe) of about 0.08 as compared with values near 0.018 for FY and MY ice (Grenfell et al., 1992), where polarization ratio is defined as PR/sub e/(v)=[e(v, V)-e(v,H)]/[e(V, V)+e(v, H)], e is the ice emissivity as defined below, v is the observation frequency, and V and H refer to vertical and horizontal polarization respectively.<<ETX>>

Passive Microwave Signatures of Simulated Pancake Ice and Young Pressure Ridges

Abstract This paper describes surface based passive microwave observations of simulated pancake ice and of a first year pressure ridge. The observed emissivity spectra are quite similar to those observed on several field experiments in the polar regions. Certain spectral features such as a maximum emissivity at 37 GHz for pancake ice and a polarization ratio very close to zero for ridged ice are distinct from those of new, young, and first‐year ice. They suggest possible signatures which may help to characterize these ice types using passive microwave techniques.