This paper describes and evaluates the assimilation component of a seamless sea ice prediction system, which is developed based on the fully coupled Alfred Wegener Institute, Helmholtz Center for Polar and Marine Research Climate Model (AWI-CM, v1.1). Its ocean/ice component with unstructured-mesh discretization and smoothly varying spatial resolution enables seamless sea ice prediction across a wide range of space and time scales. The model is complemented with the Parallel Data Assimilation Framework to assimilate observations in the ocean/ice component with an Ensemble Kalman Filter. The focus here is on the data assimilation of the prediction system. First, the performance of the system is tested in a perfect-model setting with synthetic observations. The system exhibits no drift for multivariate assimilation, which is a prerequisite for the robustness of the system. Second, real observational data for sea ice concentration, thickness, drift, and sea surface temperature are assimilated. The analysis results are evaluated against independent in situ observations and reanalysis data. Further experiments that assimilate different combinations of variables are conducted to understand their individual impacts on the model state. In particular, assimilating sea ice drift improves the sea ice thickness estimate, and assimilating sea surface temperature is able to avert a circulation bias of the free-running model in the Arctic Ocean at middepth. Finally, we present preliminary results obtained with an extended system where the atmosphere is constrained by nudging toward reanalysis data, revealing challenges that still need to be overcome to adapt the ocean/ice assimilation. We consider this system a prototype on the way toward strongly coupled data assimilation across all model components.Plain Language Summary Sea ice prediction over seasonal time scale has attracted the focus from both the scientific and socioeconomic communities recently. We develop a system aiming for seamless sea ice prediction using a coupled model that is equipped with an unstructured ocean/sea ice component. The high-resolution mesh over the polar regions allows us to explore its possible benefits on the prediction across a wide range of time scales. To this end, the sea surface temperature, sea ice concentration, sea ice thickness, and sea ice drift observations are assimilated in the current system to diagnose the performance of the model initialization for future forecasts.
Livestock, and particularly cattle production, represents one of the main sources of revenue for Upper Egypt rural communities. An understanding of the socio-cultural factors influencing rural communities' traditional livestock production systems is essential for the formulation and implementation of any intervention strategies willing to preserve and manage animal genetic resources at community-based level. The objective of this study was to identify and understand the socio-cultural factors responsible for the division of roles and responsibilities within the Upper Egypt rural society in relation to cattle production related activities. A structured survey undertaken within selected households in the governorates of Sohag and Assiut, showed that adult women play the most important role in cattle farming, nevertheless they participate very rarely in the decision making processes, which is typically an adult men responsibility. This fact is most probably due to Upper Egypt rural women's little access to information, as a consequence of their limited interaction outside their family unit, and the economical nature of the decision which is mainly the responsibility of adult men.
With the increasing scientific and socioeconomic demands for long-term sea ice prediction (Jung et al., 2016), dynamical and statistical models are following different strategies to enhance prediction skill. When it comes to sea-ice forecasting with dynamical models, it is now common practice to assimilate remotely sensed sea ice concentration (SIC), ensuring a basic level of sea-ice forecast skill. With the advent of sea ice thickness (SIT) observations from satellites such as CryoSat-2 (Ricker et al., 2014), Soil Moisture and Ocean Salinity (SMOS, Tian-Kunze et al., 2014) and Ice, Cloud and land Elevation Satellite (ICESat-2, Petty et al., 2020) and their assimilation into forecast systems in recent years, increased sea ice forecast skill with lead times ranging from synoptic to seasonal time scale has been reported as a result of better SIT initialization (
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.