Four-Month Coral Bleaching Thermal Stress Outlook
(Experimental, Version 3.0, CFSv2-based, updated weekly, 0.5x0.5-degree spatial resolution)

   Current Maps: Global | East | West | Pacific | Caribbean | Coral Triangle     Archive 

Outlook of Coral Bleaching Thermal Stress:            (Click on the images below to see them in full-size.)

90% Chance:
(Stress level predicted by 90% of ensemble members)  
Current Seasonal Bleaching Outlook (CFS-based, V3.0) Weekly Outlook
60% Chance:
(Stress level predicted by 60% of ensemble members)  
Current Seasonal Bleaching Outlook (CFS-based, V3.0) Weekly Outlook

Probabilistic Outlook of Bleaching Thermal Stress Reaching and Exceeding Specified Levels:           

Alert Level 2:
Current Bleaching Outlook Alert level 2 Probability (CFS-based, V3.0) Weekly Outlook
Alert Level 1 & higher:
Current Seasonal Bleaching Outlook Alertlevel 1 Probability (CFS-based, V3.0) Weekly Outlook
Warning & higher:
Current Seasonal Bleaching Outlook Warning Probability (CFS-based, V3.0) Weekly Outlook
Watch & higher:
(stress predicted)
Current Bleaching Outlook Watch Probability (CFS-based, V3.0 Weekly Outlook

 Current Maps: Global | East | West | Pacific | Caribbean | Coral Triangle       Archive 

 Data Files (netCDF4 format): FTP
 Product Metadata: Web-format Metadata  |  XML Metadata  |  Plain Text Metadata

Product Description

The NOAA Coral Reef Watch (CRW) experimental weekly Four-Month Coral Bleaching Thermal Stress Outlook product at 0.5°x0.5° spatial resolution presented here is the newest version (3.0), released February 2015. The Outlook is updated weekly, usually on late Tuesday morning (U.S. Eastern Time), and is based on the daily sea surface temperature (SST) forecast from the NOAA/National Weather Service National Centers for Environmental Prediction's (NCEP) Climate Forecast System Version 2 (CFSv2). CFS is an operational, dynamical, fully coupled ocean-land-atmosphere seasonal climate forecast model system. A detailed description of CRW's CFS-based Bleaching Outlook product is given in Eakin et al. (2012).

CRW's CFS-based Outlook product predicts the likelihood of coral bleaching thermal stress up to four months in the future (typical length of a bleaching season). Four-month composite outlook maps of six variables are displayed on this page. Outlooks of the six variables that predict potential thermal stress conditions for each week during the target four-month period, and that the four-month composite outlooks are derived from, are accessible through the "Weekly Outlook" links provided to the right of the corresponding composite maps. With CFSv2's four forecast runs per day, CRW constructs 28 ensemble members at a weekly time scale to produce probabilistic outlooks. The details of the six maps are described below. The relationship between the predicted thermal stress levels and potential bleaching severity is based on CRW's pre-defined stress levels for its satellite coral bleaching thermal stress monitoring, as follows:

Stress Level
       Potential Bleaching Intensity
No Stress
Bleaching Watch
Bleaching Warning
Bleaching Alert Level 1
Bleaching Alert Level 2
       No Bleaching
Possible Bleaching
Bleaching Likely
Mortality Likely

Note that CRW's Four-Month Coral Bleaching Thermal Stress Outlook was formerly known as the Seasonal Coral Bleaching Thermal Stress Outlook. With the release of the Version 3.0 Outlook, we felt a name change was in order. Although the product does provide the maximum composite outlook for the entire coverage period of four months, it also provides an outlook for each individual week within a four-month period. Furthermore, the product updates weekly, not seasonally as the previous name might suggest.

In a normal year, the Outlook forecasts no potential for bleaching. When the forecasted SST exceeds bleaching thresholds over a long enough period to cause bleaching, the Outlook maps display the bleaching potential. Actual conditions may vary due to model uncertainty, subsequent changes in climatic conditions, extreme localized variability, or weather patterns.

The 90% and 60% Chance global maps at the top of the page show the thermal stress level predicted by 90% and 60%, respectively, of the 28 ensemble members. Taking the 90% Chance map as an example, at any given data grid, the individual stress levels predicted by the 28 ensemble members are ranked based on the severity of predicted stress, from the lowest level to the highest. The highest ranking 90% of the members are then selected. The lowest stress level predicted by the highest ranking ensemble members is the thermal stress level displayed in the 90% Chance map. In other words, at least one of the ensemble members in the highest ranking 90% predicted the stress level shown; others may have predicted the same or higher stress, if any. Ensemble members of the remaining 10% predicted either the displayed stress level or lower, if any. Note that at any data grid, the chance for thermal stress that is higher than what is indicated in the map is less than 90% and can even be zero. Users are referred to the probabilistic maps (the bottom four maps on this page) for the chance of any particular stress level occurring across the globe.

The probability of each specified thermal stress level (Bleaching Watch, Bleaching Warning, Alert Level 1, and Alert Level 2) occurring across the globe over the next four-months is presented in the bottom four maps on this page. Each map displays the percentage of the 28 ensemble members that predicted thermal stress within the range specified. For instance, if a data grid shows 70% for the map of Warning & higher, it indicates that 70% of the 28 ensemble members predicted Warning, Alert Level 1, and/or Alert Level 2 and any two of these three levels may not have been predicted at all.

Due to limitations in model physical processes, numerical calculations, initializations, and inherent unpredictability of the climate system, the accuracy of forecasts depends significantly on geographic location and forecast lead-time. For the same geophysical location, the forecast accuracy decreases with increasing lead-time.

In general, a model performs better for regions where the processes are controlled by large scale variations; for example, over the central-eastern tropical Pacific Ocean, the central-eastern tropical Indian Ocean and the Caribbean. The skill is relatively high even for longer lead-times in these regions when large-scale climate signals, such as ENSO, prevail. Ensembles (repeated runs of the model using slightly different initial conditions) used in probabilistic forecasts help to increase the chance of capturing the reality among a set of possible future climate patterns generated.

As noted above, CRW's Coral Bleaching Thermal Stress Outlook products are based on NOAA's Climate Forecast System (CFS) Version 2. Assessment of the CFS SST forecast skill is available here.

While acknowledging these limitations, longer lead-time predictions are helpful in preparing coral reef stakeholders to understand potential future thermal stress; we recommend using them to support effective reef management and conservation decisions and to communicate with the public and local decision makers. NOAA CRW suggests caution in the use and analysis of outlooks, especially with lead-times longer than 20 weeks.

Weekly Outlooks, incorporating model runs from Weeks 2 to 15 and often up to Week 20, were used in deriving the Four-Month Bleaching Thermal Stress Outlook. Outlook.

CRW's very first (pioneer) weekly Four-Month Coral Bleaching Thermal Stress Outlook product was at 2°x2° spatial resolution and was released to the public in July 2008 during the 11th International Coral Reef Symposium. It was based on the SST forecast from an experimental, statistical Linear Inverse Modeling (LIM) system (Liu et al. 2009). The LIM-based Outlook was discontinued in February 2015 due to issues discovered with the LIM model's forecast for specific coral reef areas around the globe.

The first version of CRW's CFS-based weekly Four-Month Coral Bleaching Thermal Stress Outlook product, based on the SST forecast from CFS Version 1 (CFSv1, Saha et a. 2006), was released to the public in July 2012 during the 12th International Coral Reef Symposium. The availability of the CFS-based Outlook product has significantly enhanced CRW's capability for predicting the likelihood of coral bleaching thermal stress. With CFS's multiple forecast runs per day, CRW is able to produce a probabilistic outlook that was not possible with CRW's older, LIM-based deterministic Outlook. In December 2012, the second version of the CFS-based Thermal Stress Outlook was released by upgrading the system to use the SST forecast from the operational CFS Version 2 (CFSv2) (Saha et al. 2012; Saha et al. 2010) that replaced CFSv1. In February 2015, the third version of the CFS-based Thermal Stress Outlook was launched with a finer spatial resoltion of 0.5x0.5-degrees, increased from the previous 1x1-degree resolution. Furthermore, the daily 0.25-degree Optimum Interpolation SST (OISST) Version 2 analysis now provides the observations that feed the Version 3.0 Outlook product, instead of the weekly 1x1-degree OISST Version 2. As a result, the Version 3.0 Outlook is produced one week earlier than the Version 2 Outlook product.

This effort is made possible through collaboration between NOAA's NCEP and CRW, with funding support from NOAA's NCEP, Climate Program Office, and Coral Reef Conservation Program.

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Eakin CM, Liu G, Chen M, Kumar A (2012) Ghost of bleaching future: Seasonal Outlooks from NOAA's Operational Climate Forecast System. Proc 12th Int Coral Reef Sym. ICRS2012_10A_1.

Liu G, Matrosova LE, Penland C, Gledhill1 DK, Eakin CM, Webb RS, Christensen1 TRL, Heron SF, Morgan JA, Skirving WJ, Strong AE (2009) NOAA Coral Reef Watch Coral Bleaching Outlook System. Proc 11th Int Coral Reef Sym. 951-955.

Saha S, Moorthi S, Wu X, Wang J, Nadiga S, Patrick T, Pan H, Behringer D, Hou Y, Chuang H, Iredell M, Ek M, Meng J, Yang R, van den Dool H, Zhang Q, Wang W, Chen M (2012) The NCEP Climate Forecast System Version 2. Submitted to the Journal of Climate.

Saha S, Moorthi S, Pan H, Wu X, Wang J, Nadiga S, Tripp P, Kistler R, Woollen J, Behringer D, Liu H, Stokes D, Grumbine R, Gayno G, Wang J, Hou Y, Chuang H, Juang H, Sela J, Iredell M, Treadon R, Kleist D, Van Delst P, Keyser D, Derber J, Ek M, Meng J, Wei H, Yang R, Lord S, van den Dool H, Kumar A, Wang W, Long C, Chelliah M, Xue Y, Huang B, Schemm J, Wesl ey Ebisuzaki, Lin R, Xie P, Chen M, Zhou S, Higgins W, Zou C, Liu Q, Chen Y, Han Y, Cucurull L, Reynolds RW, Rutledge G, Goldberg M (2010) The NCEP Climate Forecast System Reanalysis. Bull. Amer. Meteor. Soc., 1015-1057. doi: 10.1175/2010BAMS3001.1.

Saha S, Nadiga S, Thiaw C, Wang J, Wang W, et al. (2006) The NCEP Climate Forecast System. J. Climate 19: 3483-3517.

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