Graduate Thesis Or Dissertation
 

Evaluating a Zonal-mean Hydrologic Model Against a Water Tag-enabled Global Simulation

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https://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/5m60r107z

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  • The hydrologic cycle on Earth comprises the transitions among the solid, liquid, and gaseous phases of water. Understanding the hydrologic cycle is of course important for climate science, but also for agricultural, drinking water, and disaster preparedness purposes. Improvements in satellite observations and general circulation models (GCMs) have led to advancements in understanding Earth's hydrologic cycle. GCMs are often computationally expensive, creating a niche for intermediate-complexity models which can simplify the Earth system. In this study, we test a simplified zonal-mean model previously developed by \cite{siler_large-scale_2021} against a Community Earth System Model (CESM2) isotope-enabled simulation that includes scalar tags for atmospheric water. We are able to make a one-to-one assessment of a zonal-mean hydrologic attenuation model to a global simulation due to the latter having novel water-tagging capability. We find that the zonal-mean model (1D model) exhibits high correlation to CESM2, but overestimates attenuation by precipitation and underestimates transport distance, particularly at high latitudes. We test two additional methods for mitigating these differences. First, we account for bidirectional northward and southward vapor transport separately rather than the net northward value, to account for eddies and other forms of cyclonic transport that are more frequent in the extratropical latitudes. Second, we manually scale the attenuation coefficient to reduce high-latitude overestimation of attenuation and increase the underestimated high-latitude transport distance. Although this scaling improves high latitudes, it causes the model to perform worse in subtropical and mid-latitude regions. Accounting for aspects of bidirectional transport seems important for the next iteration of the model, likely through a parameterization. Still, in its current state the 1D model has value in its ease of use and modular nature to recreate the spatial and seasonal patterns of watertags in the zonal-mean.
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