BRICK v0.2, a simple, accessible, and transparent model framework for climate and regional sea-level projections
- 1Earth and Environmental Systems Institute, Pennsylvania State University, University Park, PA 16802, USA
- 2Royal Netherlands Institute for Sea Research (NIOZ), Department of Estuarine & Delta Systems (EDS), and Utrecht University, Yerseke, the Netherlands
- 3Department of Geosciences, Pennsylvania State University, University Park, PA 16802, USA
- 4Department of Engineering and Public Policy, Carnegie Mellon University, Pittsburgh, PA 15289, USA
- anow at: Rijkswaterstaat, Ministry of Infrastructure and Environment, the Netherlands
- bnow at: Research Square, Durham, NC 27701, USA
- *These authors contributed equally to this work.
Abstract. Simple models can play pivotal roles in the quantification and framing of uncertainties surrounding climate change and sea-level rise. They are computationally efficient, transparent, and easy to reproduce. These qualities also make simple models useful for the characterization of risk. Simple model codes are increasingly distributed as open source, as well as actively shared and guided. Alas, computer codes used in the geosciences can often be hard to access, run, modify (e.g., with regards to assumptions and model components), and review. Here, we describe the simple model framework BRICK (Building blocks for Relevant Ice and Climate Knowledge) v0.2 and its underlying design principles. The paper adds detail to an earlier published model setup and discusses the inclusion of a land water storage component. The framework largely builds on existing models and allows for projections of global mean temperature as well as regional sea levels and coastal flood risk. BRICK is written in R and Fortran. BRICK gives special attention to the model values of transparency, accessibility, and flexibility in order to mitigate the above-mentioned issues while maintaining a high degree of computational efficiency. We demonstrate the flexibility of this framework through simple model intercomparison experiments. Furthermore, we demonstrate that BRICK is suitable for risk assessment applications by using a didactic example in local flood risk management.