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Seasonal forecasting systems still have difficulties predicting temperature over continental regions, while their performance is better over some maritime regions. On the other hand, the land surface is a substantial source of (sub-)seasonal predictability. A crucial land surface component in focus here is the snow cover, which stores water and modulates the surface radiation balance. This paper’s goal is to attribute snow cover seasonal forecasting biases and lack of skill to either initialization or parameterization errors. For this purpose, we compare the snow representation in five seasonal forecasting systems (from DWD, ECMWF, Météo-France, CMCC, and ECCC) and their performances in predicting snow and 2-m temperature over a Siberian region against ERA5 reanalysis and station data. Although all systems use similar atmospheric and land initialization approaches and data, their snow and temperature biases differ in sign and amplitude. Too-large initial snow biases persist over the forecast period, delaying and prolonging the melting phase. The simplest snow scheme (used in DWD’s system) shows too-early and fast melting in spring. However, systems including multi-layer snow schemes (Météo-France and CMCC) do not necessarily perform better. Both initialization and parameterization are causes of snow biases, but, depending on the system, one can be more dominant.
Towards the goal to understand the role of land-surface processes over the Indian sub-continent, a series of soil-moisture sensitivity simulations have been performed using a non-hydrostatic regional climate model COSMO-CLM. The experiments were driven by the lateral boundary conditions provided by the ERA-Interim (ECMWF) reanalysis. The simulation results show that the pre-monsoonal soil moisture has a significant influence on the monsoonal precipitation. Both, positive and negative soil-moisture precipitation (S-P) feedback processes are of importance. The negative S-P feedback process is especially influential in the western and the northern parts of India.
Container-breeding Aedes spp. (Diptera: Culicidae) mosquitoes can be surveilled at low cost using ovitraps. Hence, this method is a preferred monitoring approach of dengue vectors in low-resource settings. The ovitraps consist of a cup filled with water and an oviposition substrate for female mosquitoes. The attractiveness of the substrates for female mosquitoes can greatly differ due to differences in texture, color, and smell of the materials used. We compare four oviposition substrates, which are all low priced, easy to transport, and easy to purchase, to maximize the success of Aedes egg sampling. Sampled egg material is often reared to adulthood for further taxonomic identification and transported to (international) laboratories for specialized vector research. Here we introduce a transport technique for sampled eggs. In addition, we explored the impact of international transport by means of a bilateral hatching experiment in Nepal, the country of origin, and in Germany, in a laboratory specialized in ecophysiological research. The best low-cost oviposition substrate for the dengue vectors Aedes albopictus (Skuse) and Aedes aegypti (L.) was found to be a white cotton sheet. The introduced transport technique of sampled eggs is easy to build from laboratory and household materials and ensures good transport conditions (i.e., temperature and relative humidity). Even under good temperature (17.4–31.0 °C) and humidity conditions (58.9–94.2%), hatching success of eggs was found to be reduced after international transport to Germany when compared to the hatching success of eggs in Nepal. We postulate that air pressure during international transport may have reduced the hatching success and strongly recommend pressure-regulated transport boxes for egg transport via airplane. As the proposed operation procedure is useful in assisting the monitoring of Ae. albopictus and Ae. aegypti in low-resource settings, Aedes researchers are encouraged to follow it for the sampling and transport of Aedes eggs.