To improve surface mass balance (SMB) estimates for the Greenland Ice Sheet (GrIS), we develop a spatially and temporally high resolution polar regional climate model combining the Japan Meteorological Agency Non-Hydrostatic atmospheric Model and the Snow Metamorphism and Albedo Process model (NHM–SMAP). At present, the horizontal resolution of the model is set to be 5 km with the temporal interval of 1-hour. The model forced by the Japanese 55year Reanalysis (JRA-55) has been evaluated in the GrIS during the 2011–2014 mass balance years using in-situ surface meteorological and stake measurement data [1]. Results show that the model reproduced the measured features of the GrIS climate, diurnal variations, and even a meso-scale strong wind event. In particular, it reproduces the GrIS surface melt area extent well. Recently, near real-time calculations with the model are conducted operationally. Here we introduce the concept and framework of the model, then present performance of the model from various aspects. In addition, some new results from the operational near-real time calculations will be shown and discussed. Reference [1] Niwano et al. (2018) NHM-SMAP: Spatially and temporally high resolution non-hydrostatic atmospheric model coupled with detailed snow process model for Greenland Ice Sheet. The Cryosphere, 12, 635-655, doi:10.5194/tc-12-635-2018.

Clouds have been recognized to enhance surface melt on the Greenland Ice Sheet (GrIS). However, quantitative estimates of the effects of clouds on the GrIS melt area and ice-sheet-wide surface mass balance are still lacking. Here we assess the effects of clouds with the state-of-the-art regional climate model NHM-SMAP forced by the JRA-55 reanalysis [1], conducting a numerical sensitivity test in which adiabatic atmospheric conditions as well as zero cloud water/ice amounts are assumed (i.e., clear-sky conditions), although the precipitation rate is the same as in the control all-sky simulation. By including or excluding clouds, we quantify time-integrated feedbacks for the first time. We find that clouds were responsible for a 3.1%, 0.3%, and 0.7% increase in surface melt extent (of the total GrIS area) in 2012, 2013, and 2014, respectively. During the same periods, clouds reduced solar heating and thus daily runoff by 1.6, 0.8, and 1.0 Gt day–1, respectively: clouds did not enhance surface mass loss. In the ablation areas, the presence of clouds results in a reduction of downward latent heat flux at the snow/ice surface so that much less energy is available for the surface melt, which highlights the importance of indirect time-integrated feedbacks of cloud radiative effects. Reference [1] Niwano et al. (2018) NHM-SMAP: Spatially and temporally high resolution non-hydrostatic atmospheric model coupled with detailed snow process model for Greenland Ice Sheet. The Cryosphere, 12, 635-655, doi:10.5194/tc-12-635-2018.