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Abstract. The Russian drifting station North Pole 4 (NP-4) was within 5 ø latitude of the North Pole from April 1956 to April 1957. We use a wide-ranging set of snow and meteorological data collected at 3-hourly intervals on NP-4 during this period to investigate energy and mass transfer in the snow, sea ice, and atmospheric surface layer in the central Arctic. SNTHERM, a one-dimensional energy and mass balance model, synthesizes these diverse NP-4 data and thereby yields energetically consistent time series of the components of the surface heat budget. To parameterize the sensible heat flux during extremely stable stratification, we replace the usual log-linear stability function with the "Dutch" formulation and introduce a windless coefficient in the bulk parameterization. This coefficient provides sensible heat transfer at the surface, even when the mean wind speed is near zero, and thereby prevents the surface temperature from falling to unrealistically low values, a common modeling problem when the stratification is very stable. Several other modifications to SNTHERM introduce procedures for creating a realistic snowpack that has continuously variable density and is subject to erosion and wind packing. The NP-4 data provide for two distinct simulations: one on 2-year ice and one on multiyear ice. We validate our modeling by comparing simulated and observed temperatures at various depths in the snow and sea ice. Simulations for both sites show the same tendencies. During the summer, the shortwave radiation is the main term in the surface heat budget. Shortwave radiation also penetrates into the snow and causes a subsurface temperature maximum that both the data and the model capture. During the winter, the net longwave balance is the main term in the surface heat budget. The snow and sea ice cool in response to longwave losses, but the flux of sensible heat from the air to the surface mitigates these losses and is thus nearly a mirror image of the emitted longwave flux.

A 4-month deployment on Ice Station Weddell (ISW) in the western Weddell Sea yielded over 2000 h of nearly continuous surface-level meteorological data, including eddycovariance measurements of the turbulent surface fluxes of momentum, and sensible and latent heat. Those data lead to a new parameterization for the roughness length for wind speed, z 0 , for snow-covered sea ice that combines three regimes: an aerodynamically smooth regime, a high-wind saltation regime, and an intermediate regime between these two extremes where the macroscale or 'permanent' roughness of the snow and ice determines z 0 . Roughness lengths for temperature, z T , computed from this data set corroborate the theoretical model that Andreas published in 1987. Roughness lengths for humidity, z Q , do not support this model as conclusively but are all, on average, within an order of magnitude of its predictions. Only rarely are z T and z Q equal to z 0 . These parameterizations have implications for models that treat the atmosphere-ice-ocean system.

Over sea ice in winter, the clouds, the surfacelayer air temperature, and the longwave radiation are closely coupled. This report uses archived data from the Russian North Pole (NP) drifting stations and recent data from Ice Station Weddell (ISW) to investigate this coupling. Both Arctic and Antarctic distributions of total cloud amount are U-shaped: that is, observed cloud amounts are typically either 0-2 tenths or 8-10 tenths in the polar regions. These data obey beta distributions; roughly 70 station-years of observations from the NP stations yielded fitting parameters for each winter month. Although surface-layer air temperature and total cloud amount are correlated, it is not straightforward to predict one from the other, because temperature is normally distributed while cloud amount has a U-shaped distribution. Nevertheless, the report presents a statistical algorithm that can predict total cloud amount in winter from surface-layer temperature alone and, as required, produces a distribution of cloud amounts that is U-shaped. Because sea ice models usually need cloud How to get copies of CRREL technical publications:

[1] We compare daily data from the National Center for Atmospheric Research and National Centers for Environmental Prediction ''Reanalysis 1'' project with observational data obtained from the North Pole drifting stations in order to validate the atmospheric forcing data used in coupled ice-ocean models. This analysis is conducted to assess the role of errors associated with model forcing before performing model verifications against observed ocean variables. Our analysis shows an excellent agreement between observed and reanalysis sea level pressures and a relatively good correlation between observed and reanalysis surface winds. The observed temperature is in good agreement with reanalysis data only in winter. Specific air humidity and cloudiness are not reproduced well by reanalysis and are not recommended for model forcing. An example sensitivity study demonstrates that the equilibrium ice thickness obtained using NP forcing is two times thicker than using reanalysis forcing.