Detection and attribution of climate change in satellite records of ocean productivity

Abstract

Phytoplankton make up approximately half of the global biosphere production. Climate change is predicted to affect phytoplankton productivity. Detecting the climate change signal in satellite records of productivity would imply that ocean primary production has been affected by anthropogenic influences. Long-term trends in chlorophyll (chl) concentration in the ocean have been observed by several studies. However, the effect of internal variability in chl was not taken into account in these observed trends. This thesis aims to perform a formal detection and attribution analysis on observed chl concentration using the optimal fingerprint (OF) method. The methodology has been applied to detect and attribute greenhouse gas induced climate change in sea-surface temperature records, ocean heat content, atmospheric air temperature etc., but this is the first attempt to apply it to ocean productivity records.

The OF method was applied to monthly observations of chl data (1999-2005) from NASA’s Ocean Biogeochemical Model (NOBM) which assimilates satellite-derived chl. Control run and forced simulations from four Earth System Models were used to derive the internal variability of chl and response of chl to climate forcings (anthropogenic and natural), respectively. Three metrics were defined to describe the climate change signal in chl - spatial linear trend of chl; linear trend of zonal average; and time series of the size of the oligotrophic gyres. The OF technique of detection and attribution was implemented on the observational datasets for each of the three metrics. The amplitude of the responses provide an indication of whether a climate forcing signal is present in the observations.

Out of the three metrics, the study demonstrated that the second metric (linear trend of zonal average in chl) is the best, and the third metric (size of the oligotrophic gyres) is the worst, 'direction' to look for a climate change signal in chl. Thus, metrics should be defined such that they capture the relevant change in chl and at the same time do not contain too much small scale variability which leads to noise. It was also illustrated that climate models do not necessarily simulate the internal variability of chl well, or the response of chl to climate forcings, indicating the need to improve the performance of climate models.

A greenhouse gas signal was detected in observations in some regions of the ocean indicating that chl concentration is likely being affected by climate change. The canonical model of chl response to global warming, i.e. decrease in chl in lower latitudes and increase in chl in higher latitudes, was not consistently observed in all the regions of the ocean. This signifies that changing climate is affecting chl in a way which is not yet completely understood and in future the effects of climate change on chl may be surprisingly different from our current conceptual model.

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