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

Abstract

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.

Agricultural Big Data Architectures in the Context of Climate Change: A Systematic Literature Review

“…Various data analysis tools are described in the papers. For example, Coro et al, used the DataMiner tool to analyze ocean data [35]. Delgado et al, used the DNDC, Comment-Farm, CEEOT, and NTT tools to analyze agricultural data [48].…”

“…They also use environmental parameters processed through a computation platform in the cloud to generate intermediate scenarios and new informative parameters to show climate change patterns in 10 marine areas. The authors conclude that the Mediterranean Sea may have an independent response to climate change compared to other areas, affecting fishing and other inputs [35]. Senay et al use Landsat and meteorological Big Data to quantify and understand the relative importance of water management and climate variability on crops.…”

The Applicability of Big Data in Climate Change Research: The Importance of System of Systems Thinking

“…Identifying patterns in time-series data (Ise et al, 2020) helps with research in many other areas, whether it is agricultural water management (Ismail et al, 2020) or marine habitat protection (Coro et al, 2020). It allows (Kubo et al, 2020) forecasting and a better understanding of coastal traffic and increases the reliability of disaster resilience estimation (Sasaki et al, 2020).…”

“…And comparable to other approaches (Jimenez et al, 2019), which strengthens confidence in the models (Qin and Chi, 2020). Better resolution data supports marine habitat protection planning (Coro et al, 2020), risk assessment input (Fenu and Malloci, 2019), but can also be used (Gouveia and Palma, 2019) to analyze building consumption data. The efficiency of downscaling techniques can be increased with the Internet of Things (Lambrinos, 2019) toolbar.…”

“…The Climate Action goal (SDG13) tackles most data gaps, so research such as linking satellite images to Semlali and El Amrani (2021) with air quality, preprocessing them (Meraner et al, 2020;Qin and Chi, 2020;Semlali et al, 2020), the analysis of time series data (Ise et al, 2020) and its exploration (Joshi et al, 2019), downscaling (Wang Q. et al, 2020) techniques, enrichment of precipitation and temperature data (Jimenez et al, 2019), tracking the movement of clouds (Xie Y. et al, 2019), or just using IoT sensors (Mabrouki et al, 2021) are all key in creating a strategy to support the achievement of the climate goal. For the sustainability of life below water (SDG14), marine life prediction models (Coro et al, 2020) and human coastal activity (Kubo et al, 2020) can be integrated. Of course, the goal of life on land (SDG15) also requires new research, where a satellite-based study of agriculture and forestry (Majidi et al, 2021), deployment of IoT sensors (Lambrinos, 2019), analysis of climatic factors of potato damage (Fenu and Malloci, 2019), studying the morphology of plants (Meineke et al, 2020), or social media based illustration of palm oil consumption (Teng et al, 2020) are promising.…”