Organisms are influenced by microclimates, and high spatial and temporal resolution environmental data is necessary to understand how past and future environmental conditions influence the physiology, performance, and fitness of organisms. Frameworks are lacking to efficiently subset, process, analyze, and disseminate the data. How can a data solution be optimized for uses emphasizing either spatial or temporal resolution?
Results/Conclusions
We used dynamic downscaling to develop a dataset describing current (1976-2005) and future (2076-2105) climates for North America at a resolution of 36 km and 1h (Levy et al. 2016). The dataset includes 17 microclimate variables at different heights and shade levels relevant to the energy balance of organisms. We have released a beta version of our online user interface (microclim.org) to disseminate our data to the public and ultimately incorporate it in educational initiatives. The interface is implemented in amazon web services (AWS) and relies on the NCAR Command Language (NCL) to subset NetCDFs and process the data. Furthermore, we are developing a workflow in AWS for integrating heterogeneous environmental data, processing it using energy balance models, and disseminating the output through APIs and a web application. The initiatives will improve estimating how the environment influences organisms and forecasting impacts of climate change.