The impact of melt ponds on summertime microwave brightness temperatures and sea-ice concentrations

Sea-ice concentrations derived from satellite microwave brightness temperatures are less accurate during summer. In the Arctic Ocean the lack of accuracy is primarily caused by melt ponds, but also by changes in the properties of snow and the sea-ice surface itself. We investigate the sensitivity of eight sea-ice concentration retrieval algorithms to melt ponds by comparing sea-ice concentration with the melt-pond fraction. We derive gridded daily sea-ice concentrations from microwave brightness temperatures of summer 2009. We derive the daily fraction of melt ponds, open water between ice floes, and the ice-surface fraction from contemporary Moderate Resolution Spectroradiometer (MODIS) reflectance data. We only use grid cells where the MODIS sea ice concentration, which is the melt-pond fraction plus the ice-surface fraction, exceeds 90 %. For one group of algorithms, e.g., Bristol and Comiso bootstrap frequency mode (Bootstrap_f), sea-ice concentrations are linearly related to the MODIS melt-pond fraction quite clearly after June. For other algorithms, e.g., Near9OGHz and Comiso bootstrap polarization mode (Bootstrap_p), this relationship is weaker and develops later in summer. We attribute the variation of the sensitivity to the melt-pond fraction across the algorithms to a different sensitivity of the brightness temperatures to snow-property variations. We find an underestimation of the sea-ice concentration by between 14 % (Bootstrap_f) and 26 % (Bootstrap_p) for 100 % sea ice with a melt-pond fraction of 40 %. The underestimation reduces to 0 % for a melt pond fraction of 20 %. In presence of real open water between ice floes, the sea-ice concentration is overestimated by between 26 % (Bootstrap_f) and 14 % (Bootstrap_p) at 60 % sea-ice concentration and by 20 % across all algorithms at 80 % sea-ice concentration. None of the algorithms investigated performs best based on our investigation of data from summer 2009. We suggest that those algorithms which are more sensitive to melt ponds could be optimized more easily because the influence of unknown snow and sea-ice surface property variations is less pronounced

Explicitly determined sea ice emissivity and emission temperature over the Arctic for surface‐sensitive microwave channels

Data assimilation of satellite microwave measurements is one of the importantkeys to improving weather forecasting over the Arctic region. However, the useofsurface-sensitivemicrowave-soundingchannelmeasurementsfordataassim-ilation or retrieval has been limited, especially during winter, due to the poorlyconstrained sea ice emissivity. In this study, aiming at more use of those channelmeasurements in the data assimilation, we propose an explicit method for speci-fying the surface radiative boundary conditions (namely emissivity and emittinglayer temperature of snow and ice). These were explicitly determined with aradiativetransfermodelforsnowandiceandwithsnow/icephysicalparameters(i.e. snow/ice depths and vertical distributions of temperature, density, salinity,and grain size) simulated from the thermodynamically driven snow/ice growthmodel. We conducted 1D-Var experiments in order to examine whether thisapproach can help to use the surface-sensitive microwave temperature channelmeasurements over the Arctic sea ice region for data assimilation. Results showthat (1) the surface-sensitive microwave channels can be used in the 1D-Varretrieval, and (2) the specification of the radiative boundary condition at thesurface using the snow/sea ice emission model can significantly improve theatmospheric temperature retrieval, especially in the lower troposphere (500hPato surface). The successful retrieval suggests that useful information can beextracted from surface-sensitive microwave-sounding channel radiances oversea ice surfaces through the explicit determination of snow/ice emissivity andemitting layer temperature

Estimating sea ice parameters

International audienceMapping sea ice concentration (SIC) and understanding sea ice properties and variability is important, especially today with the recent Arctic sea ice decline. Moreover , accurate estimation of the sea ice effective temperature (T eff) at 50 GHz is needed for atmospheric sounding applications over sea ice and for noise reduction in SIC estimates. At low microwave frequencies, the sensitivity to the atmosphere is low, and it is possible to derive sea ice parameters due to the penetration of microwaves in the snow and ice layers. In this study, we propose simple algorithms to derive the snow depth, the snow-ice interface temperature (T Snow−Ice) and the T eff of Arctic sea ice from microwave brightness temperatures (TBs). This is achieved using the Round Robin Data Package of the ESA sea ice CCI project, which contains TBs from the Advanced Microwave Scanning Radiometer 2 (AMSR2) collocated with measurements from ice mass balance buoys (IMBs) and the NASA Operation Ice Bridge (OIB) airborne campaigns over the Arctic sea ice. The snow depth over sea ice is estimated with an error of 5.1 cm, using a multilinear regression with the TBs at 6, 18, and 36 V. The T Snow−Ice is retrieved using a linear regression as a function of the snow depth and the TBs at 10 or 6 V. The root mean square errors (RMSEs) obtained are 2.87 and 2.90 K respectively, with 10 and 6 V TBs. The T eff at microwave frequencies between 6 and 89 GHz is expressed as a function of T Snow−Ice using data from a thermodynam-ical model combined with the Microwave Emission Model of Layered Snowpacks. T eff is estimated from the T Snow−Ice with a RMSE of less than 1 K

Modeling Snow and Ice Microwave Emissions in the Arctic for a Multi-Parameter Retrieval of Surface and Atmospheric Variables From Microwave Radiometer Satellite Data

Monitoring surface and atmospheric parameters—like water vapor—is challenging in the Arctic, despite the daily Arctic-wide coverage of spaceborne microwave radiometer data. This is mainly due to the difficulties in characterizing the sea ice surface emission: sea ice and snow microwave emission is high and highly variable. There are very few data sets combining relevant in situ measurements with co-located remote sensing data, which further complicates the development of accurate retrieval algorithms. Here, we present a multi-parameter retrieval based on the inversion of a forward model for both, atmosphere and surface, for non-melting conditions. The model consists of a layered microwave emission model of snow and ice. Since snow scattering and emission effects, as well as temperature gradients, are taken into account, a high variability in brightness temperatures can be simulated. For ocean regions and the atmosphere existing parameterized forward models are used. By using optimal estimation, the forward model can be inverted allowing for the simultaneous and consistent retrieval of nine variables: integrated water vapor, liquid water path, sea ice concentration, multi-year ice fraction, snow depth, snow-ice interface temperature and snow-air interface temperature as well as sea-surface temperature and wind speed (over open ocean). In addition, the method provides retrieval uncertainty estimates for each retrieved parameter. To evaluate the forward model as well as the retrieval, we use the extensive data sets acquired during the year-long Arctic expedition Multidisciplinary drifting Observatory for the Study of Arctic Climate (2019–2020) as a reference

Overview of the MOSAiC expedition: Snow and sea ice

Year-round observations of the physical snow and ice properties and processes that govern the ice pack evolution and its interaction with the atmosphere and the ocean were conducted during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition of the research vessel Polarstern in the Arctic Ocean from October 2019 to September 2020. This work was embedded into the interdisciplinary design of the 5 MOSAiC teams, studying the atmosphere, the sea ice, the ocean, the ecosystem, and biogeochemical processes.The overall aim of the snow and sea ice observations during MOSAiC was to characterize the physical properties of the snow and ice cover comprehensively in the central Arctic over an entire annual cycle. This objective was achieved by detailed observations of physical properties and of energy and mass balance of snow and ice. By studying snow and sea ice dynamics over nested spatial scales from centimeters to tens of kilometers, the variability across scales can be considered. On-ice observations of in situ and remote sensing properties of the different surface types over all seasons will help to improve numerical process and climate models and to establish and validate novel satellite remote sensing methods; the linkages to accompanying airborne measurements, satellite observations, and results of numerical models are discussed. We found large spatial variabilities of snow metamorphism and thermal regimes impacting sea ice growth. We conclude that the highly variable snow cover needs to be considered in more detail (in observations, remote sensing, and models) to better understand snow-related feedback processes.The ice pack revealed rapid transformations and motions along the drift in all seasons. The number of coupled ice–ocean interface processes observed in detail are expected to guide upcoming research with respect to the changing Arctic sea ice