• Journal of the Korean earth science society
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• v.35 no.4
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• pp.249-258
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• 2014

Despite the greenhouse gases like carbon dioxide have steadily increased in atmosphere, the overall trend of the global average surface air temperature has stalled during the last decade (2002-present). This phenomenon is often called hiatus or warming pause, which is challenging the prevailing view that anthropogenic forcing causes warming environment. Our study characterized the hiatus by analyzing the HadGEM2-AO (95 yrs) simulation data based on RCP8.5 scenario. The PC2 time series from the EOF of the zonal mean vertical ocean temperature has been defined as the index that represents the warming pause. The relationship between the hiatus, ENSO and the changes in climate system are identified by utilizing the newly defined PC2. Since the La Nina index (defined as the negative of NINO3 index) leads PC2 by about 11 months, it may be possible that the La Nina causes the warming to be interrupted. We also show that the cooling of the climate system closed tied to the heat penetration into the deep ocean, indicating the weakening the warming rate is due to the oceanic heat uptake. Finally, the global warming hiatus is characterized by the anomalous warming in Arctic region as well as the intensification of the trade wind in the equatorial Pacific.

• Ocean and Polar Research
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• v.27 no.2
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• pp.189-195
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• 2005

Several models predict large and potentially abrupt ocean circulation changes due to anthropogenic greenhouse-gas emissions. These circulation changes drive-in the models-considerable oceanic oxygen trend. A sound estimate of the observed oxygen trends can hence be a powerful tool to constrain predictions of future changes in oceanic deepwater formation, heat and carbon dioxide uptake. Estimating decadal scale oxygen trends is, however, a nontrivial task and previous studies have come to contradicting conclusions. One key potential problem is that changes in the historical observation network might introduce considerable errors. Here we estimate the likely magnitude of these errors for a subset of the available observations in the Southern Ocean. We test three common data analysis methods south of Australia and focus on the decadal-scale trends between the 1970's and the 1990's. Specifically, we estimate errors due to sparsely sampled observations using a known signal (the time invariant, temporally averaged, World Ocean Atlas 2001) as a negative control. The crossover analysis and the objective analysis methods are for less prone to spatial sampling location biases than the area averaging method. Subject to numerous caveats, we find that errors due to sparse sampling for the area averaging method are on the order of several micro-moles $kg^{-1}$. for the crossover and the objective analysis method, these errors are much smaller. For the analyzed example, the biases due to changes in the spatial design of the historical observation network are relatively small compared to the tends predicted by many model simulations. This raises the possibility to use historic oxygen trends to constrain model simulations, even in sparsely sampled ocean basins.