CRW Experimental Bleaching Outlook Background

2. Bleaching Thermal Stress Prediction

The SST prediction is used to estimate the thermal stress that is conducive to coral bleaching. Thermal stress indices are calculated from the predicted SST. The methodology used to derive the thermal stress indices is the same as those applied in the CRW operational near-real-time satellite coral bleaching monitoring. In the near-real-time monitoring, coral bleaching HotSpot and Degree Heating Week (DHW) are calculated as indices for identifying the appearance and intensity of bleaching thermal stress and for linking the temporally accumulated effect of the bleaching thermal stress to the distribution and severity of coral bleaching.

CRW’s coral bleaching HotSpot is a type of thermal anomaly calculated as the difference between the observed temperature at a grid point and the highest of the 12 climatological SST monthly averages at the grid point. This highest climatological average is often referred to as the maximum monthly mean SST climatology (MMMSST). The HotSpot provides the measure of the intensity of bleaching thermal stress. Since both the intensity and duration of thermal stress are important in causing coral bleaching, CRW’s Degree Heating Weeks (DHW) has been used in the near-real-time observation to track both the intensity and duration by accumulating HotSpot values that are above a certain threshold (1.0 degree Celsius in the current monitoring product) over the last 12 weeks. By measuring the accumulative effect of thermal stress, DHW is directly related to the occurrence and severity of coral bleaching. A coral reef area with DHW value of 4 degree-weeks or above is likely to experience wide-spread coral bleaching. A coral reef area with DHW value of 8 degree-weeks or above is likely to experience wide-spread severe coral bleaching.

In the bleaching thermal stress prediction, similar thermal stress indices are calculated from the predicted SST. As a result of the fact (described in the previous SST forecast model subsection) that forecasts have smaller amplitude than the original data set, a lower threshold is used for deriving predicted DHW from predicted HotSpot than that used in calculating near-real-time DHW observation values.