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 ...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.展开更多
基金This work has received funding from the European Union’s Horizon 2020 research and innovation programme under the BlueBRIDGE project[grant agreement no 675680].
文摘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.
