Development of a global high-resolution marine dynamic environmental forecasting system

A project entitled‘Development of a Global High-resolution Marine Dynamic Environmental Forecasting System’has been funded by‘The Program on Marine Environmental Safety Guarantee’of The National Key Research and Development Program of China.This project will accomplish its objectives through basic theoretical research,model development and expansion,and system establishment and application,with a focus on four key issues separated into nine tasks.A series of research achievements have already been obtained,including datasets,observations,theories,and model results.

A New model to forecast fishing ground of Scomber japonicus in the Yellow Sea and East China Sea

The pelagic species is closely related to the marine environmental factors, and establishment of forecasting model of fishing ground with high accuracy is an important content for pelagic fishery. The chub mackerel（Scomber japonicus） in the Yellow Sea and East China Sea is an important fishing target for Chinese lighting purse seine fishery. Based on the fishery data from China＇s mainland large-type lighting purse seine fishery for chub mackerel during the period of 2003 to 2010 and the environmental data including sea surface temperature（SST）, gradient of the sea surface temperature（GSST）, sea surface height（SSH） and geostrophic velocity（GV）, we attempt to establish one new forecasting model of fishing ground based on boosted regression trees. In this study, the fishing areas with fishing effort is considered as one fishing ground, and the areas with no fishing ground are randomly selected from a background field, in which the fishing areas have no records in the logbooks. The performance of the forecasting model of fishing ground is evaluated with the testing data from the actual fishing data in 2011. The results show that the forecasting model of fishing ground has a high prediction performance, and the area under receiver operating curve（AUC） attains 0.897. The predicted fishing grounds are coincided with the actual fishing locations in 2011, and the movement route is also the same as the shift of fishing vessels, which indicates that this forecasting model based on the boosted regression trees can be used to effectively forecast the fishing ground of chub mackerel in the Yellow Sea and East China Sea.

Detecting patterns of climate change in long-term forecasts of marine environmental parameters

Forecasting environmental parameters in the distant future requires complex modelling and large computational resources.Due to the sensitivity and complexity of forecast models,long-term parameter forecasts(e.g.up to 2100)are uncommon and only produced by a few organisations,in heterogeneous formats and based on different assumptions of greenhouse gases emissions.However,data mining techniques can be used to coerce the data to a uniform time and spatial representation,which facilitates their use in many applications.In this paper,streams of big data coming from AquaMaps and NASA collections of 126 long-term forecasts of nine types of environmental parameters are processed through a cloud computing platform in order to(i)standardise and harmonise the data representations,(ii)produce intermediate scenarios and new informative parameters,and(iii)align all sets on a common time and spatial resolution.Time series crosscorrelation applied to these aligned datasets reveals patterns of climate change and similarities between parameter trends in 10 marine areas.Our results highlight that(i)the Mediterranean Sea may have a standalone‘response’to climate change with respect to other areas,(ii)the Poles are most representative of global forecasted change,and(iii)the trends are generally alarming for most oceans.