Axel Schweiger

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...

Sea ice motion from satellite passive microwave imagery assessed with ERS SAR and buoy motions

Observing the motion of sea ice from space is analogous to observing wind stress over the wet oceans; both provide surface forcing for modeling ocean dynamics. Ice motion also directly provides the advective component of the equations governing the mass balance of the sea ice cover. Thus its routine observation from space would be of great value to understanding ice and ocean behavior. To demonstrate the feasibility of creating a global multidecadal ice motion record from satellite passive microwave imagery and to quantitatively assess the errors in the estimated ice motions, we have tracked ice every 3 days in the Arctic Ocean and daily in the Fram Strait and Baffin Bay during the 8 winter months from October 1992 to May 1993 and daily in the Weddell Sea during the 8 winter months from March to October 1992. The method, which has been well used previously, involves finding the spatial offset that maximizes the cross correlation of the brightness temperature fields over 100-km patches in two images separated in time by from 1 to 3 days. The resulting ice motions are compared with contemporaneous buoyand SAR-derived ice motions. The uncertainties in the displacement vectors, between 5 and 12 km, are better than the spatial resolution of the data. Both 85-GHz data with 12-km spatial resolution and 37-GHz data with 25-km resolution are tracked. These trials with the 37-GHz data are new and show quite surprisingly that the error is only about 1 km larger with these data than with the 12-km 85-GHz data. Errors are typically larger than average in areas of lower ice concentration; in the most dynamic regions, particularly near the ice edge in the Barents and Greenland Seas; and in zones of high shear. These passive microwave ice motions show a large increase in spatial detail over motion fields optimally interpolated from buoy and wind observations, especially where buoy data are virtually absent such as near coasts and in some passages between the Arctic Ocean and its peripheral seas. The feasibility of obtaining ice motion from the 37-GHz data in addition to the 85-GHz data should allow an important record of ice motion to be established for the duration of the scanning multichannel microwave radiometer (SMMR), special sensor microwave/imager (SSM/I), and future microwave sensors, that is, from 1978 into the next millenium.

Arctic Sea Ice Retreat in 2007 Follows Thinning Trend

The minimum of Arctic sea ice extent in the summer of 2007 was unprecedented in the historical record. A coupled ice–ocean model is used to determine the state of the ice and ocean over the past 29 yr to investigate the causes of this ice extent minimum within a historical perspective. It is found that even though the 2007 ice extent was strongly anomalous, the loss in total ice mass was not. Rather, the 2007 ice mass loss is largely consistent with a steady decrease in ice thickness that began in 1987. Since then, the simulated mean September ice thickness within the Arctic Ocean has declined from 3.7 to 2.6 m at a rate of 0.57 m decade 1 . Both the area coverage of thin ice at the beginning of the melt season and the total volume of ice lost in the summer have been steadily increasing. The combined impact of these two trends caused a large reduction in the September mean ice concentration in the Arctic Ocean. This created conditions during the summer of 2007 that allowed persistent winds to push the remaining ice from the Pacific side to the Atlantic side of the basin and more than usual into the Greenland Sea. This exposed large areas of open water, resulting in the record ice extent anomaly.

Relationships between Arctic Sea Ice and Clouds during Autumn

The connection between sea ice variability and cloud cover over the Arctic seas during autumn is investigated by analyzing the 40-yr ECMWF Re-Analysis (ERA-40) products and the Television and Infrared Observation Satellite (TIROS) Operational Vertical Sounder (TOVS) Polar Pathfinder satellite datasets. It is found that cloud cover variability near the sea ice margins is strongly linked to sea ice variability. Sea ice retreat is linked to a decrease in low-level cloud amount and a simultaneous increase in midlevel clouds. This pattern is apparent in both data sources. Changes in cloud cover can be explained by changes in the atmospheric temperature structure and an increase in near-surface temperatures resulting from the removal of sea ice. The subsequent decrease in static stability and deepening of the atmospheric boundary layer apparently contribute to the rise in cloud level. The radiative effect of this change is relatively small, as the direct radiative effects of cloud cover changes are compensated for by changes in the temperature and humidity profiles associated with varying ice conditions.

Seasonal evolution and interannual variability of the local solar energy absorbed by the Arctic sea ice–ocean system

The melt season of the Arctic sea ice cover is greatly affected by the partitioning of the incident solar radiation between reflection to the atmosphere and absorption in the ice and ocean. This partitioning exhibits a strong seasonal cycle and significant interannual variability. Data in the period 1998, 2000-2004 were analyzed in this study. Observations made during the 1997-1998 SHEBA (Surface HEat Budget of the Arctic Ocean) field experiment showed a strong seasonal dependence of the partitioning, dominated by a five-phase albedo evolution. QuikSCAT scatterometer data from the SHEBA region in 1999-2004 were used to further investigate solar partitioning in summer. The time series of scatterometer data were used to determine the onset of melt and the beginning of freezeup. This information was combined with SSM/I-derived ice concentration, TOVS-based estimates of incident solar irradiance, and SHEBA results to estimate the amount of solar energy absorbed in the ice-ocean system for these years. The average total solar energy absorbed in the ice-ocean system from April through September was 900 MJ m(sup -2). There was considerable interannual variability, with a range of 826 to 1044 MJ m(sup -2). The total amount of solar energy absorbed by the ice and ocean was strongly related to the date of melt onset, but only weakly related to the total duration of the melt season or the onset of freezeup. The timing of melt onset is significant because the incident solar energy is large and a change at this time propagates through the entire melt season, affecting the albedo every day throughout melt and freezeup.

NASA team algorithm for sea ice concentration retrieval from Defense Meteorological Satellite Program special sensor microwave imager: Comparison with Landsat satellite imagery

Validation of the NASA team algorithm for the determination of sea ice concentrations from the Defense Meteorological Satellite Program special sensor microwave imager (SSM/I) is described. A total of 28 cloud-free Landsat scenes were selected in order to permit validation of the passive microwave ice concentration algorithm for a range of ice concentrations and ice types. The sensitivity of the NASA team algorithm to the selection of locally and seasonally adjusted algorithm parameters is discussed in detail. Mean absolute differences between SSM/I and Landsat ice concentrations are within 1% during fall using local and global tie points. Standard deviations of the difference are ±3.1% and ±6.2% respectively. The overall accuracy of the NASA team algorithm is lower in spring than in fall. In areas with greater amounts of nilas and young ice, we found that the NASA team algorithm underestimates ice concentrations by as much as 9%. The Landsat and SSM/I ice concentrations have a correlation of 0.968 for all spring and fall case studies with a standard deviation of ±6.6% using global tie points and a correlation of 0.982 with a standard deviation of ±4.5% using local tie points. The NASA team algorithm tends to underestimate ice concentration in areas of close pack ice and to overestimate ice concentrations in areas of open pack ice. In summer, mean differences between SSM/I and Landsat ice concentrations are 3.8% for local tie points and 11.0% for global tie points for Arctic areas and 7.2% for local tie points and 11.7% for global tie points for Antarctic areas. These large differences are attributable to surface melt during summer and comparison problems arising from a time lag of up to 8 hours between the DMSP and Landsat satellites. It appears that seasonally and regionally adjusted tie points (local tie points) will improve the overall performance of the NASA team algorithm. Our work suggests that the standard deviation between SSM/I and Landsat ice concentrations decreases from ±7% to ±5% with local tie points compared to global ones for spring and fall.

Arctic cloudiness - Comparison of ISCCP-C2 and Nimbus-7 satellite-derived cloud products with a surface-based cloud climatology

Abstract One surface-based and two satellite arctic cloud climatologies are compared in terms of the annual cycle and spatial patterns of total monthly cloud amounts. Additionally, amounts and spatial patterns of low, middle, and high cloud type are compared. The surface-based dataset is for the years 1951–81, while the satellite-based data are for 1979–85 and 1983–86. The satellite cloud amounts are generally 5%−35% less than the surface observations over the entire Arctic. However, regional differences may be as high as 45%. During July the surface-based cloud amounts for the central Arctic are about 40% greater than the satellite-based, but only 10% greater in the Norwegian Sea area. Surprisingly, (ISCCP) cloud climatology and surface observations agree better during winter than during summer. Possible reasons for these differences are discussed, though it is not possible to determine which cloud climatology is the “correct” one.

Arctic Clouds in Multiyear Satellite Data Sets

Cloud amounts from the International Satellite Cloud Climatology Project (ISCCP) D-series data set and the TOVS Polar Pathfinder data set are compared to surface observations of cloud amount from the North Polar (NP) drifting station data set for the central Arctic. The results show that cloud observations in the ISCCP data set differ significantly from surface observations and the annual cycle of cloudiness in the Arctic is reversed from what is observed from the surface. In contrast, cloud amounts provided by the TOVS Polar Pathfinder data set represent the annual cycle of cloudiness well. Path-P cloud retrievals also correspond well with surface observations (NP) at timescales greater than 4 days.

Expected uncertainty in satellite‐derived estimates of the surface radiation budget at high latitudes

An analysis of spatial and temporal variations of the polar radiation budget will undoubtedly require the use of multispectral satellite data. How well we can estimate the radiation balance depends on how well we can estimate the physical and microphysical properties of the surface and atmosphere that directly affect it, e.g., surface temperature and albedo, cloud droplet effective radius, cloud optical depth, cloud thickness, and cloud height. Here we examine our current ability to estimate the high-latitude surface radiation budget using visible and thermal satellite data. The method for estimating radiative fluxes incorporates estimates of surface and atmospheric parameters, so the accuracy with which these can be retrieved from satellite data is first assessed. The effects of errors in the estimates of these parameters on the surface net radiation during summer and winter are quantified, and the relative sensitivity of the net radiation budget to errors in individual parameters is assessed. The combined uncertainty is then determined and examined in light of validation data in the Arctic. The results show upper and lower bounds for the uncertainties between 7.9 and 41 W m -2 for instantaneous retrievals of net radiation. By far, the largest portion of the uncertainty in net radiation is associated with errors in the retrieval of surface temperature and surface albedo. Although improvements in retrievals are desirable, currently available methods can provide surface net radiation in the Arctic with uncertainties similar to those of surface-based climatologies.

Sea ice floe size distribution in the marginal ice zone: Theory and numerical experiments

To better describe the state of sea ice in the marginal ice zone (MIZ) with floes of varying thicknesses and sizes, both an ice thickness distribution (ITD) and a floe size distribution (FSD) are needed. In this work, we have developed a FSD theory that is coupled to the ITD theory of Thorndike et al. (1975) in order to explicitly simulate the evolution of FSD and ITD jointly. The FSD theory includes a FSD function and a FSD conservation equation in parallel with the ITD equation. The FSD equation takes into account changes in FSD due to ice advection, thermodynamic growth, and lateral melting. It also includes changes in FSD because of mechanical redistribution of floe size due to ice ridging and, particularly, ice fragmentation induced by stochastic ocean surface waves. The floe size redistribution due to ice fragmentation is based on the assumption that wave-induced breakup is a random process such that when an ice floe is broken, floes of any smaller sizes have an equal opportunity to form, without being either favored or excluded. To focus only on the properties of mechanical floe size redistribution, the FSD theory is implemented in a simplified ITD and FSD sea ice model for idealized numerical experiments. Model results show that the simulated cumulative floe number distribution (CFND) follows a power law as observed by satellites and airborne surveys. The simulated values of the exponent of the power law, with varying levels of ice breakups, are also in the range of the observations. It is found that floe size redistribution and the resulting FSD and mean floe size do not depend on how floe size categories are partitioned over a given floe size range. The ability to explicitly simulate multicategory FSD and ITD together may help to incorporate additional model physics, such as FSD-dependent ice mechanics, surface exchange of heat, mass, and momentum, and wave-ice interactions.