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Abstract We demonstrate a linking of moderately high resolution (1 km) terrestrial hydrological models to a 3‐D ocean circulation model having similar resolution in the northern Gulf of Alaska, where a distributed line source of freshwater runoff exerts strong influence over the shelf's hydrographic structure and flow dynamics. The model interfacing is accomplished via mass flux boundary conditions through the ocean model coastal wall at all land‐ocean adjoining grid cells. Despite the high runoff volume and lack of a coastal mixing estuary, the implementation maintains numerical stability by prescribing depth invariant and surface‐intensified inflows at fast and slow discharge grid cells, respectively. Based on comparisons against in situ hydrographic data, the coastal sidewall mass flux boundary condition results in more realistic hindcast surface salinity and salinity gradient fields than models that distribute coastal runoff in the form of spatially distributed precipitation. Correlations with observed thermal and haline monthly anomalies reveal statistically significant hindcast temporal variability during the freshet season when the signal‐to‐noise ratio is large. Comparisons of ocean models forced by high‐ and low‐resolution hydrological models reveal differences in salinity, surface elevation, and velocity fields, highlighting the value and importance of accurate coastal runoff fields. The model results improve our understanding of the regional influence of runoff on sea level elevations and the distribution and fate of fresh water. Our approach has potential applications to biogeochemical modeling in regions where distributed line source freshwater coastal discharges deliver heat, momentum, and chemical constituents that may influence the marine carbon pump. 

Abstract. The coastal ecosystem of the Gulf of Alaska (GOA) is especially vulnerable to the effects of ocean acidification and climate change. Detection of these long-term trends requires a good understanding of the system’s natural state. The GOA is a highly dynamic system that exhibits large inorganic carbon variability on subseasonal to interannual timescales. This variability is poorly understood due to the lack of observations in this expansive and remote region. We developed a new model setup for the GOA that couples the three-dimensional Regional Oceanic Model System (ROMS) and the Carbon, Ocean Biogeochemistry and Lower Trophic (COBALT) ecosystem model. To improve our conceptual understanding of the system, we conducted a hindcast simulation from 1980 to 2013. The model was explicitly forced with temporally and spatially varying coastal freshwater discharges from a high-resolution terrestrial hydrological model, thereby affecting salinity, alkalinity, dissolved inorganic carbon, and nutrient concentrations. This represents a substantial improvement over previous GOA modeling attempts. Here, we evaluate the model on seasonal to interannual timescales using the best available inorganic carbon observations. The model was particularly successful in reproducing observed aragonite oversaturation and undersaturation of near-bottom water in May and September, respectively. The largest deficiency in the model is its inability to adequately simulate springtime surface inorganic carbon chemistry, as it overestimates surface dissolved inorganic carbon, which translates into an underestimation of the surface aragonite saturation state at this time. We also use the model to describe the seasonal cycle and drivers of inorganic carbon parameters along the Seward Line transect in under-sampled months. Model output suggests that the majority of the near-bottom water along the Seward Line is seasonally undersaturated with respect to aragonite between June and January, as a result of upwelling and remineralization. Such an extensive period of reoccurring aragonite undersaturation may be harmful to ocean acidification-sensitive organisms. Furthermore, the influence of freshwater not only decreases the aragonite saturation state in coastal surface waters in summer and fall, but it simultaneously decreases the surface partial pressure of carbon dioxide (pCO2), thereby decoupling the aragonite saturation state from pCO2. The full seasonal cycle and geographic extent of the GOA region is under-sampled, and our model results give new and important insights for months of the year and areas that lack in situ inorganic carbon observations. 

Abstract. We present a simple method that allows snow depth measurements tobe converted to snow water equivalent (SWE) estimates. These estimates areuseful to individuals interested in water resources, ecological function,and avalanche forecasting. They can also be assimilated into models to helpimprove predictions of total water volumes over large regions. Theconversion of depth to SWE is particularly valuable since snow depthmeasurements are far more numerous than costlier and more complex SWEmeasurements. Our model regresses SWE against snow depth (h), day of wateryear (DOY) and climatological (30-year normal) values for winter (December,January, February) precipitation (PPTWT), and the difference (TD) between meantemperature of the warmest month and mean temperature of the coldest month,producing a power-law relationship. Relying on climatological normals ratherthan weather data for a given year allows our model to be applied atmeasurement sites lacking a weather station. Separate equations are obtainedfor the accumulation and the ablation phases of the snowpack. The model isvalidated against a large database of snow pillow measurements and yields abias in SWE of less than 2 mm and a root-mean-squared error (RMSE) in SWE ofless than 60 mm. The model is additionally validated against two completelyindependent sets of data: one from western North America and one from thenortheastern United States. Finally, the results are compared with three othermodels for bulk density that have varying degrees of complexity and thatwere built in multiple geographic regions. The results show that the modeldescribed in this paper has the best performance for the validation datasets.