1. UWBRAD Modeling Summary KCJ Version 1 Please update and improve

2. Cloud Model Assumptions Inputs Firn layering ignored Seasonal temperature ignored Freq and temp independent  ’ Various thermodynamic constants - depth independent Temperature Model Ice/rock, ice/water, ice air boundary reflectivity Rayleigh Scattering Absorption Model Ice thickness Vertical Incidence Tb sensitive to temperature at depth Scattering marginally important Basal water extinguishes upwelling DMRT Constant layer thickness in firn Incoherent (energy) model Freq and temp independent  ’ Oblique incidence Firn layer reflectivity from density Basal Reflectivity Scattering (???) Seasonal surface temperarture Firn layering required to match in situ data (is solution unique) Statistical firn parameters can be deduced from in situ data Number of firn layers influences the final Tb Layers important to about 50 m depth Surface temp, ice thickness and model can be used to explain SMOS data Simple Coherent Model Uniform layer thickness Fresnel Reflection No attenuation Normal incidence UW Scattering Model UW Coherent Model + + Uniform layer thickness Fresnel Reflection No attenuation Reverberation is important at least under the assumptions UWBRAD Modelling Summary

3. Some Questions Given that observed Tb is an integrated effect, how unique are the model predictions? Layering and reverberation seem to be important. How do these properties influence Tb as absorption, scattering and non uniform layer thickness are introduced into the models? Are Fresnel reflection coefficients appropriate? Is an improved scattering model required? Is interface roughness important?

4. UWBRAD Modeling Paper Concept Initial Tb model was limited by assumptions Further analysis has indicated that assumptions must be further explored Based on a progression of models and model intercomparisons, arrive at an optimal model for UWBRAD and for comparing modeled Tb with SMOS, Aquarius and SMAP. Illustrate whether UWBRAD parameters need to be modified. Illustrate how UWBRAD data can be processed in the context of the optimized modeling results to achieve desired temperature profiles