BRICK v0.1, a simple, accessible, and transparent model framework for climate and regional sea-level projections

Abstract

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 easier to reproduce. These qualities make simple models useful for uncertainty quantification and risk characterization. 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 introduce a simple model framework for projections of global mean temperatures as well as regional sea levels and coastal flood risk (BRICK: Building blocks for Relevant Ice and Climate Knowledge). The BRICK model framework is written in R and Fortran and aims to help mitigate these 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.

Impacts of Antarctic fast dynamics on sea-level projections and coastal flood defense

“…We employ and expand upon a model framework that has been previously applied for probabilistic projections of sea-level rise [4]. This model has recently been made available as the Building blocks for Relevant Ice and Climate Knowledge (BRICK) model v0.1 to simulate global mean surface temperature, ocean heat uptake, global mean sea level and its contributions from the Antarctic ice sheet, Greenland ice sheet, thermal expansion and glaciers and small ice caps [51]. BRICK uses a semi-empirical modeling approach, combining a platform of previously published models.…”

“…BRICK uses a semi-empirical modeling approach, combining a platform of previously published models. The model is described in greater detail by Wong et al [51], so we only provide an overview here.…”

“…where τ T E is the e-folding time-scale of the thermal expansion response, and the quantity 1/τ T E is estimated as a model parameter [51]. The Antarctic ice sheet is represented by the Danish Center for Earth System Science Antarctic Ice Sheet model, or DAIS [44].…”

Synthesizing long-term sea level rise projections – the MAGICC sea level model v2.0

“…Other studies have used an extended suite of methods, analyzing paleoclimatic archives, modeling parts of the SLR response with a reduced complexity model, and deriving future projections for land-ice contribution-based semi-empirical considerations (Clark et al, 2016). The growing efforts in the sea level modeling community to provide fully transparent and freely available model code are reflected by the recent introduction of a transparent, simple model framework to estimate regional sea levels (Wong et al, 2017). Previous MAGICC versions also provided SLR estimates based on simplified parameterizations for selected components (Wigley and Raper, 1987, 2005Wigley, 1995).…”

Epistemic and ethical trade-offs in decision analytical modelling

“…The reproducibility and versatility of DPM are illustrated by the publication of the model's simple source code (Applegate et al 2016;Wong et al 2017b) and the range of other contexts in which it has been applied. Eijgenraam, for example, considers the base model of DPM (i.e., "the Van Dantzig model") under condition of an improved flood defence system, changing economic growth, and sea-level rise (Eijgenraam 2006).…”