Tett, A comparison of surface air temperature variability in three 1000-year coupled oceanatmosphere model integrations, Clim. Dyn
This study compares the variability of surface air temperature in three long coupled ocean–atmosphere general circulation model integrations. It is shown that the annual mean climatology of the surface air temperatures (SAT) in all three models is realistic and the linear trends over the 1000-yr int...
| Main Authors: | , |
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| Format: | Text |
| Language: | English |
| Published: |
1999
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| Subjects: | |
| Online Access: | http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.143.6620 http://www.gfdl.noaa.gov/reference/bibliography/2000/rjs0001.pdf |
| Summary: | This study compares the variability of surface air temperature in three long coupled ocean–atmosphere general circulation model integrations. It is shown that the annual mean climatology of the surface air temperatures (SAT) in all three models is realistic and the linear trends over the 1000-yr integrations are small over most areas of the globe. Second, although there are notable differences among the models, the models ’ SAT variability is fairly realistic on annual to decadal timescales, both in terms of the geographical distribution and of the global mean values. A notable exception is the poor simulation of observed tropical Pacific variability. In the HadCM2 model, the tropical variability is overestimated, while in the GFDL and HAM3L models, it is underestimated. Also, the ENSO-related spectral peak in the globally averaged observed SAT differs from that in any of the models. The relatively low resolution required to integrate models for long time periods inhibits the successful simulation of the variability in this region. On timescales longer than a few decades, the largest variance in the models is generally located near sea ice margins in high latitudes, which are also regions of deep oceanic convection and variability related to variations in the thermohaline circulation. However, the exact geographical location of these maxima varies from model to model. The preferred patterns of interdecadal variability that are common to all three coupled models can be isolated by computing empirical orthogonal functions (EOFs) of |
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