Scientific methods to study how forests affect climate are distinguished as environmental monitoring, experimental manipulation, or modeling. Meteorological measurements of air temperature and wind speed in forests and adjacent clearings characterize microclimates. More complex measurements of energy, water, and carbon dioxide fluxes obtained using principles of eddy covariance required sophisticated instruments on tall towers extending above the forest canopy. In situ measurements of leaves and individual trees reveal physiological functioning and can be extrapolated to an entire forest. Whole-ecosystem manipulations that warm the soil or enrich the air with carbon dioxide provide insight to ecosystem responses to environmental change. Ecosystem studies monitor carbon and elemental stocks and fluxes, and watershed studies monitor water flows. Remote sensing instruments that acquire radiative signatures of the land provide an indicator of vegetation type, health, and productivity. Numerical models of terrestrial ecosystems and climate provide a means to test theories and develop understanding of the biosphere-atmosphere system.

The processes by which forests influence large-scale climate are well known, but the specific response to changes in forest cover varies with background climate (e.g., tropical, temperate, boreal), the extent of forest change and type of conversion, and time of year, and differs between day and night. Forests remove carbon dioxide from the atmosphere, thereby lessening planetary warming. Forests also affect climate through exchanges of energy, water, and momentum with the atmosphere, which can warm or cool the climate depending on geographic location, time of year, and time of day. There is a distinct latitudinal pattern from tropical to temperate to boreal forests, with different influences on temperature and different underlying mechanisms. In general, forests cool the daytime surface climate during the growing season through evapotranspiration and other non-radiative processes, and they warm the nighttime climate. Outside of the growing season, forests are generally warmer than open fields, especially in locations where snow is present. Further climate influences occur through chemical emissions that produce aerosols. Much of this understanding comes from models of Earth’s climate.

Climate computer models are irreplaceable scientific tools to study the climate system and to allow projections of future climate change. They play a major role in IPCC reports, underpinning paleoclimate reconstructions, attribution studies, scenarios of future climate change, and concepts such as climate sensitivity and carbon budgets. While models have greatly contributed to the construction of climate change as a global problem, they are also influenced by political expectations. Models have their limits, they never escape uncertainties, and they receive criticisms, in particular for their hegemonic role in climate science. And yet climate models and their simulations of past, present and future climates, coordinated via an efficient model intercomparison project, have greatly contributed to the IPCC’s epistemic credibility and authority.

Climate modeling developed further at the National Center for Atmospheric Research (NCAR) in Boulder, Colorado. The laws of physics that form the foundation of weather and climate models imply strict conservation of properties like mass, momentum, and energy. A household budget analogy can be used to explain these conservation requirements, which are stricter for climate models as opposed to weather models. A mismatch in the energy transfer between atmospheric and oceanic models that were part of a climate model led to a correction technique developed in the 1980s known as flux adjustment, which violated energy conservation. Subsequent improvements in climate models obviated the need for these artificial flux adjustments. Now we have more complex models, known as Earth System Models, that include biological and chemical processes such as the carbon cycle. The concept of the constraining power of models is introduced.