Stratiform and convective precipitation are known to be associated with distinct isotopic fingerprints in the tropics. Such rain type specific isotope signals are of key importance for climate proxies based on stable isotopes like for example ice cores and tree rings and can be used for climate reconstructions of convective activity. However, recently, the relation between rain type and isotope signal has been intensively discussed. While some studies point out the importance of deep convection for strongly depleted isotope signals in precipitation, other studies emphasize the role of stratiform precipitation for low concentrations of the heavy water isotopes. Uncertainties arise from observational studies as they mainly consider oceanic regions and mostly long aggregation timescales, while modelling approaches with global climate models cannot explicitly resolve convective processes and rely on parametrization. As high-resolution climate models are particularly important for studies over complex topography, we applied the isotope-enabled version of the high-resolution climate model from the Consortium for Small-Scale Modelling (COSMOiso) over the Andes of tropical south Ecuador, South America, to investigate the influence of stratiform and convective rain on the stable oxygen isotope signal of precipitation (δ18OP). Our results highlight the importance of deep convection for depleting the isotopic signal of precipitation and increasing the secondary isotope variable deuterium excess. Moreover, we found that an opposing effect of shallow and deep convection on the δ18OP signal. Based on these results, we introduce a shallow and deep convective fraction to analyze the effect of rain types on δ18OP.

Wind-driven redistribution of snow is one of the key factors leading to heterogeneous accumulation of snow at small scales. Understanding these processes is, therefore, of great importance to many glaciological and hydrological questions. High-quality information on the wind field is necessary to realistically represent drifting snow. Here, we introduce a novel, intermediate-complexity drifting and blowing snow module for the Weather Research and Forecasting (WRF) model that integrates seamlessly into the standard WRF infrastructure. The module also accounts for snow particle sublimation and considers the thermodynamic feedback on the atmospheric fields. The module was tested in an idealized model environment. The simple and computationally efficient implementation allows this module to be used for small-scale and large-scale simulations in polar and glaciated regions.