Around fifteen years of continuous data is sufficient to detect changes in the ocean that are a direct response to increases in atmospheric CO2, such as sea surface temperature and ocean acidity. However, changes that are less directly related to increasing CO2 levels are harder to pick out from the 'noise' of natural variability. These include populations of tiny marine plants, known as phytoplankton, which are the base of the marine food web and help the ocean absorb CO2 from the atmosphere.

The lead author, Dr Stephanie Henson from the NOC, said that "Picking out some trends from natural variability is like listening for a soft sound in a noisy room. This research really highlights the importance of continuing long-term observations at a range of sites across the ocean, so we can better detect the way it is changing. Without long-term datasets, it's difficult to understand how ocean biology may respond to global climate change. "

Stephanie estimates the natural variability in phytoplankton populations is so great that spotting any trends relating to climate change will require thirty to forty years of continuous data. This is a problem, as currently the longest available continuous global data set is twenty years.

The data for this research was taken from the publically available models used in the IPCC assessments. The project team used the models to find out whether the current network of ocean observatories is adequate to spot a climate change trend in ocean biology. The OceanSITES network is an international network of fixed point ocean observatories collecting long-term data. This network includes the NOC in addition to thirty-three other research organisations.

This research received Natural Environment Research Council (NERC) funding through the National Capability programme. It forms part of the NOC's ongoing research into the relationship between Earth's climate and ocean biology.

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The above post is reprinted from materials provided by National Oceanography Centre. Note: Materials may be edited for content and length.