by Paulina Cwik
“All models are wrong, but some are useful” (George E. P. Box). Scientific models predict the behavior of a certain process or a mechanism under investigation. Inevitably, models are too simplified to capture the real state of a system. For example, scientists use climate models, idealized mathematical representations of climate system components such a as atmosphere, land, surface, sea-ice, etc. (see example), to simulate how the Earth’s climate might change due to natural variability, human activity, or a combination of both.
Although climate models are based on real, fundamental rules of thermodynamics and conservation laws (that reflect how energy and matter interact across land, atmosphere, cryosphere, and ocean), they also involve some degree of simplification, distortion, and assumptions and can only ever approximate the behavior of the climate system. Specifically, to capture planetary-scale circulation flow, various climate models require not only mathematical equations but specification of:
- Initial conditions, which can be defined as the starting state of variables, such as surface temperature, surface pressure, wind, etc., at a particular time, denoted as the initial time (t=0) and are constructed using observational data or idealized probability distributions
- Boundary conditions, which can include values such as solar radiation, composition of the atmosphere, topography etc., and are prescribed by a modeler
- Resolution, which can be both spatial (measured in degrees of longitude/latitude or miles/km), or temporal – the duration of the time steps for the model
Only a slight change to any of these characteristics can result in a very different final state of climate simulation. See the discussion on the ‘butterfly effect’. This is because of climate feedbacks, such as for example ice-albedo feedback, that act to either enlarge (positive feedback) or suppress (negative feedback) introduced disturbance. However, there is no rule that gives precise or ‘right’ amount of detail or elements that are required in any particular model, and sometimes the straightforward combination of the ingredients in a model can result in particularly complex and unusual climate outcomes. Nevertheless, climate models typically capture essential elements or processes that are useful to build a general understanding of the climate system.
To project a future climatic state, each model can be set according to a possible future scenario or a pathway that captures various relationships between human carbon emissions and corresponding change in temperature. These scenarios are known as Representative Concentration Pathways or RCPs and represent the change in radiative forcing (+2.6, +4.5, +6.0 and +8.5 watts per square meter) resulting from greenhouse gasses at the tropopause by 2100 relative to preindustrial levels. The RCPs are used by climate modelers around the world and result in range of projections that, although they differ in projected yearly values, indicate consistent change and trends in temperature and precipitation. For example, climate models reliably show that adding more greenhouse gases to the atmosphere by continuous burning of fossil fuels will cause rise in the average global temperatures (especially enhanced warming in the Arctic), rise in sea level, and enhancement of the hydrologic cycle with dryer places getting drier and wet areas wetter. In an already changing climate, many of these factors can be amplified by affecting each other and resulting in drastic alteration in the future. Climate models play an essential role in understanding these interactions, and in the predictability of climate behavior on different time scales.
Finally, there are multiple reasons for climate modeling that are different from prediction or investigating the climate simulations. Some of them include testing various theories, illuminating some uncertainties, raising new questions, explaining concepts, educational purposes, and encouraging sensible and critical thinking. Climate modeling efforts also inform new policies and rules designed toward adaptation to and mitigation of climate impacts. For example, global climate model simulations are presented in the Intergovernmental Panel on Climate Change (IPCC) assessment reports and the U.S. National Climate Assessments (NCAs) guiding the process of assessment of the science of climate change, its impacts, and future risks across the U.S. and the world.