Abstract

Climate change is a variation in the long-term average temperature and weather patterns of the planet. Human activities (e.g., electricity generation, transportation, or food production) release large amounts of greenhouse gases into the atmosphere, which trap heat and enhance the average temperature of the planet. The Intergovernmental Panel on Climate Change (IPCC) has warmed us against exceeding an increase of 1.5 °C on the planet temperature in relation to the preindustrial era average, otherwise we will witness devastating and irreversible consequences to our social, economic, and natural systems. We have, at most, 30 years before passing that threshold (IPCC, 2018), and staying within that safe limit requires everyone’s commitment to support and adopt mitigation strategies. People are more likely support and adopt such strategies when they possess knowledge about climate change (Sewell et al., 2017). Unfortunately, climate change is not a phenomenon easy to understand. Its planetary scale makes it difficult for a single person to experience all of its consequences. Its complexity requires the person to deal with cognitively demanding actions such as conceiving multiple interrelated variables (interconnectedness), identifying causality loops between variables (feedback), and dealing with non-proportional cause and effect between variables (nonlinearity). Mathematical modeling can be used to address those cognitive obstacles and promote climate change education (Barwell, 2013a, 2013b; Renert, 2011). Mathematics teachers have a crucial role to play in this endeavor. A promising approach involves applying quantitative covariational reasoning to make sense of and model climate change. Quantitative covariational reasoning is central for mathematical modeling because it encompasses “the very operations that enable one to see invariant relationship between quantities in dynamic situations” (Thompson, 2011, p. 46). There are two constructs considered as central to understand climate change: the Earth’s energy balance and the link between carbon dioxide (CO2) pollution and global warming (Lambert & Bleicher, 2013). I propose that, by studying these constructs as dynamic situations and from a quantitative covariation perspective, mathematics teachers and their students can effectively understand climate change. However, studying climate change from a quantitative covariation perspective rarely forms part of teacher education courses, and thus mathematics teacher may not be prepared to do it or teach it. This chapter discusses how preservice mathematics teachers (PSTs) can make sense of the energy balance and the link between CO2 and global warming from a quantitative covariation perspective. In particular, I discuss ways in which PSTs’ quantitative covariational reasoning can constrain/support their ability to: (i) conceive interconnectedness in terms of several quantities changing simultaneously and interdependently, (ii) identify feedbacks or causality loops between two covarying quantities, and (iii) deal with nonlinearity through linearization (considering a quantity’s rate of change to be proportional to another quantity’s magnitude). The chapter illustrates these points with cases from my previous research on PSTs’ understanding of simple climate change models. The cases contrast the quantitative covariational reasoning of PSTs in relation to several original mathematical tasks. The chapter also discusses how using climate change as a context for applying quantitative covariational reasoning can attend to curriculum requirements (Common Core State Standards Initiative [CCSSI], 2010; National Research Council [NRC], 2013) and the implications for mathematics teacher education.