A multidecadal (50-80 year timescale) pattern of North Atlantic ocean-atmosphere variability whose existence has been argued for based on statistical analyses of observational and proxy climate data, and coupled Atmosphere-Ocean General Circulation Model (“AOGCM”) simulations. This pattern is believed to describe some of the observed early 20th century (1920s-1930s) high-latitude Northern Hemisphere warming and some, but not all, of the high-latitude warming observed in the late 20th century. The term was introduced in a summary by Kerr (2000) of a study by Delworth and Mann (2000).
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Fully coupled atmosphere-ocean model of the three-dimensional global climate. See also ‘General Circulation Model (GCM)’.
Approach to reconstructing a target large-scale climate field from predictors employing multivariate regression methods. CFR methods have been applied both to filling spatial gaps in early instrumental climate data sets, and to the problem of reconstructing past climate patterns from ‘climate proxy’ data.
Climate ‘proxies’ are sources of climate information from natural archives such as tree rings, ice cores, corals, lake and ocean sediments, tree pollen, or human archives such as historical records or diaries, which can be used to estimate climate conditions prior to the modern period (e.g. mid 19th century to date) during which widespread instrumental measurements are available. Proxy indicators typically must be calibrated against modern instrumental information to yield a quantitative reconstruction of past climate.
Climate sensitivity is a measure of the equilibrium global surface air temperature change for a particular forcing. It is usually given as a °C change per W/m2 forcing. A standard experiment to determine this value in a climate model is to look at the doubled CO2 climate, and so equivalently, the climate sensitivity is sometimes given as the warming for doubled CO2 (i.e. from 280 ppm to 560 ppm). The forcing from doubled CO2 is around 4 W/m2 and so a sensitivity of 3°C for a doubling, is equivalent to a sensitivity of 0.75 °C/W/m2. The principal idea is that if you know the sum of the forcings, you can estimate what the eventual temperature change will be.
We should underscore that the concepts of radiative forcing and climate sensitivity are simply an empirical shorthand that climatologists find useful for estimating how different changes to the planet’s radiative balance will lead to eventual temperature changes. There are however some subtleties which rarely get mentioned. Firstly, there are a number of ways to define the forcings. The easiest is the ‘instantaneous forcing’ – the change is made and the difference in the net radiation at the tropopause is estimated. But it turns out that other definitions such as the ‘adjusted forcing’ actually give a better estimate of the eventual temperature change. These other forcings progressively allow more ‘fast’ feedbacks to operate (stratospheric temperatures are allowed to adjust for instance), but the calculations get progressively more involved.
Secondly, not all forcings are equal. Because of differences in vertical or horizontal distribution of forcings, some changes can have a more than proportional effect on temperatures. This can be described using a relative ‘efficacy’ factor that depends on the individual forcing. For instance, the effect of soot making snow and sea ice darker has a higher efficacy than an equivalent change in CO2 with the same forcing, mainly because there is a more important ice-albedo feedback in the soot case. The ideal metric of course would be a forcing that can be calculated easily and where every perturbation to the radiative balance had an relative efficacy of 1. Unfortunately, that metric has not yet been found!
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.
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).