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Abstract.A state-of-the-art regional model, the Weather Research and Forecasting (WRF) model (Skamarock et al., 2008) coupled with a chemistry component (Chem) (Grell et al., 2005), is coupled with the snow, ice, and aerosol radiative (SNICAR) model that includes the most sophisticated representation of snow metamorphism processes available for climate study. The coupled model is used to simulate black carbon (BC) and dust concentrations and their radiative forcing in seasonal snow over North China in January-February of 2010, with extensive field measurements used to evaluate the model performance. In general, the model simulated spatial variability of BC and dust mass concentrations in the top snow layer (hereafter BCS and DSTS, respectively) are consistent with observations. The model generally moderately underestimates BCS in the clean regions but significantly overestimates BCS in some polluted regions. Most model results fall within the uncertainty ranges of observations. The simulated BCS and DSTS are highest with > 5000 ng g −1 and up to 5 mg g −1 , respectively, over the source regions and reduce to < 50 ng g −1 and < 1 µg g −1 , respectively, in the remote regions. BCS and DSTS introduce a similar magnitude of radiative warming (∼ 10 W m −2 ) in the snowpack, which is comparable to the magnitude of surface radiative cooling due to BC and dust in the atmosphere. This study represents an effort in using a regional modeling framework to simulate BC and dust and their direct radiative forcing in snowpack.Although a variety of observational data sets have been used to attribute model biases, some uncertainties in the results remain, which highlights the need for more observations, particularly concurrent measurements of atmospheric and snow aerosols and the deposition fluxes of aerosols, in future campaigns.

We investigate the sensitivity of precipitation characteristics (mean, extreme, and diurnal cycle) to a set of uncertain parameters that influence the qualitative and quantitative behavior of cloud and aerosol processes in the Community Atmosphere Model (CAM5). We adopt both the Latin hypercube and QuasiMonte Carlo sampling approaches to effectively explore the high-dimensional parameter space and then conduct two large sets of simulations. One set consists of 1100 simulations (cloud ensemble) perturbing 22 parameters related to cloud physics and convection, and the other set consists of 256 simulations (aerosol ensemble) focusing on 16 parameters related to aerosols and cloud microphysics. In the cloud ensemble, six parameters having the greatest influences on the global mean precipitation are identified, three of which (related to the deep convection scheme) are the primary contributors to the total variance of the phase and amplitude of the precipitation diurnal cycle over land. The extreme precipitation characteristics are sensitive to a fewer number of parameters. Precipitation does not always respond monotonically to parameter change. The influence of individual parameters does not depend on the sampling approaches or concomitant parameters selected. Generally, the Generalized Linear Model is able to explain more of the parametric sensitivity of global precipitation than local or regional features. The total explained variance for precipitation is primarily due to contributions from the individual parameters (75-90% in total). The total variance shows a significant seasonal variability in midlatitude continental regions, but very small in tropical continental regions.