On The Suitability of Air Temperature as a Predictive Tool for Lake Surface Temperature in a Changing Climate: A Case Study for Lake Tahoe, USA

The ability to predict surface water temperature is essential toward understanding how future climate scenarios will impact inland water bodies such as lakes. Numerous predictive models have been developed to perform this task although many require inputs whose future model prediction is usually associated with large uncertainties, such as e.g., precipitation, cloudiness, wind and radiative fluxes. Conversely, air temperature is one of the most widely available variables in projections from Global Climate Models (GCMs). The predictive model air2water relies solely on air temperature data to predict lake surface temperature. The objective of this study is to demonstrate that air2water can be used as a predictive tool for climate change scenarios through a case study focused on Lake Tahoe, CA/NV, USA. Lake Tahoe has been selected due to extensive historical in-situ measurements that have been collected at that location since 1967 which we utilize to calibrate and validate air2water, and evaluate its performance. For model runs, we utilize different sources of air temperature data (buoys, land-based weather stations, GCMs) to establish how robustly air2water performs. We employ air temperature data from a combination of global gridded datasets including Climate Research Unit (CRU) TS3.21 (historical), and GCM output from the Coupled Model Intercomparison Project, Phase 5 (CMIP5) Community Climate System Model, version 4 (CCSM4) model (future) with representative concentration pathways of 4.5 and 8.5. Here, we present results from air2water predictions of the relationship between air and water temperature that demonstrate how this model is able to replicate trends on seasonal and interannual timescales. This finding shows promise toward understanding the impacts of future climate change on lakes and to expanding our study to lake surface temperatures globally.