Climate research provides insights about our planet’s natural and human systems. It aims to understand how the Earth’s complex systems interact and helps us predict future climatic conditions. It incorporates observations from a wide range of sources and makes extensive use of theoretical knowledge and mathematical modeling.
For example, in order to construct global temperature datasets that are useful for climate analysis, tens of thousands of station records must be merged, subjected to quality control procedures, homogenized and transformed to a grid. This is known as data assimilation. The resulting assimilated data can then be used to generate projections of how climate might change under various greenhouse gas emission scenarios. Such predictions are based on an ensemble of climate models, or sets of mathematical representations of the climate system that vary in their complexity and the degree to which particular components or processes are represented.
While it is important to recognize the limitations of scientific evidence (see Odenbaugh 2012), a consensus among experts can provide indirect evidence for a claim—though only if the consensus is produced in the right way. This is especially true in the case of the consensus on the reality and causes of global warming, which is supported by a broad spectrum of scientific evidence, including observations of climate changes, results from historical climate simulations using computer models, and insights from physical, chemical, biological, geographical, geological, and social science data. As such, the evidence supporting a global warming consensus is strong, regardless of whether one accepts the conclusions of the Intergovernmental Panel on Climate Change (IPCC) or not.