It has sometimes been argued that the earth’s biosphere (in large part, the terrestrial biosphere) may have the capacity to sequestor much of the increased carbon dioxide (CO₂) in the atmosphere associated with human fossil fuel burning. This effect is known as “CO₂ fertilization” because, in the envisioned scenario, higher ambient CO₂ concentrations in the atmosphere literally “fertilize” plant growth. Because plants in turn, in the process of photosynthesis, convert CO₂ into oxygen, it is thus sometimes argued that such “co2 fertilization” could potentially provide a strong negative feedback on changing CO₂ concentrations.

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A natural coupled mode of climate variability associated with both surface temperature variations tied to El Niño and atmospheric circulation changes across the equatorial Pacific (see also ‘Southern Oscillation Index’). Term was first coined by Rasmusson and Carpenter (1982). More information on ENSO can be found here.

Spatial pattern tied to a particular mode of time/space variance in a spatiotemporal data set (see also “Principal Components Analysis or “PCA”).

Simple climate model consisting of a uniform ocean and atmosphere that respond thermodynamically, but not dynamically, to changes in radiative forcing.

Forcings in the climate sense are external boundary conditions or inputs to a climate model. Obviously changes to the sun’s radiation are external, and so that is always a forcing. The same is true for changes to the Earth’s orbit (“Milankovitch cycles”). Things get a little more ambigous as you get closer to the surface. In models that do not contain a carbon cycle (and that is most of them), the level of CO₂ is set externally, and so that can be considered a forcing too. However, in models that contain a carbon cycle, changes in CO₂ concentrations will occur as a function of the climate itself and in changes in emissions from industrial activity. In that case, CO₂ levels will be a feedback, and not a forcing. Almost all of the elements that make up the atmosphere can be considered feedbacks on some timescale, and so defining the forcing is really a function of what feedbacks you allow in the model and for what purpose you are using it. A good discussion of recent forcings can be found in Hansen et al (2002) and in Schmidt et al (2004).