A gentle reflexion on the influence of big data in climate change: how the availability of a vast amount of data recorded permanently by satellites and terrestrial-based arrays allows understanding and to some extends forecasting at an unprecedent scale complex phenomena around Earth – can ultimately big data create the same success story in Earth sciences as we witness in other fields (i.e. electronic & electrical engineering, advertisement,...)? (further reading 10.1089/big.2014.0026, 10.1073/pnas.1614023113)
Information collected and processed in Big Data database systems from climate change
In my opinion, information obtained from various research centers, meteorological centers, satellites, etc., then collected and processed in Big Data database systems should help in more and more precise prediction of new, unfavorable weather phenomena, including climatic cataclysms and others. In this way, earlier and in a more planned way, crisis management systems can be organized in the situation when the predetermined flood disaster becomes real and will happen. Gradually increasing computing power of powerful computer servers managing platforms of Big Data database systems, implemented artificial intelligence, increasing number of verified historical data on the overall climate phenomena on Earth will allow in the future more precisely, more accurately determine the level of threats, risk value, predict time, place and scale adverse weather events, i.e. also cataclysms that threaten people's lives.
Therefore, the question is still valid:
Will the information collected and processed in the Big Data database systems in future be able to accurately check future climate disasters?
Please, answer, comments. I invite you to the discussion.
Information collected and processed in Big Data database systems from climate change
In my opinion, information obtained from various research centers, meteorological centers, satellites, etc., then collected and processed in Big Data database systems should help in more and more precise prediction of new, unfavorable weather phenomena, including climatic cataclysms and others. In this way, earlier and in a more planned way, crisis management systems can be organized in the situation when the predetermined flood disaster becomes real and will happen. Gradually increasing computing power of powerful computer servers managing platforms of Big Data database systems, implemented artificial intelligence, increasing number of verified historical data on the overall climate phenomena on Earth will allow in the future more precisely, more accurately determine the level of threats, risk value, predict time, place and scale adverse weather events, i.e. also cataclysms that threaten people's lives.
I invite you to the discussion.
Dear Dariusz,
Thank you for your reply. I agree with you about how the vast amount of data collected by various missions is helping to improve models, simulations and sometimes forecasting the evolution of those phenomena. From a scientific point-of-view, I would call it a success.
However, judging the impact on the society of the scientific results in climate change using those big database, is still in the "learning" phase. We witness the past years around the world that politics are afraid of taking big steps for necessary changes in current policies in order to establish a sustainable future (see https://pdfs.semanticscholar.org/3325/7e5e0527033da982421faadd6367c34057ac.pdf). Scientists spend a lot of time developing and improving models using newly available data, but there is a certain resistance within the society which has opened a debate on the scientific truth. On the other hand, the success story of Big Data in other fields is generally driven by the consumer market that we are creating.
Thus, Big data in climate change is still a long way of the success story from a societal view.
I think currently some of our major challenges in the domains you mentioned tied with data fusion. Some works:
Conference Paper Towards hard+ soft data fusion: Processing architecture and ...
Article Methodologies for Cross-Domain Data Fusion: An Overview
https://ieeexplore.ieee.org/document/7527926/
Thank you, Kenneth!
I notice that paper cites Hansen et al. 2008. Open Atmos. Sci. J. (2008), vol. 2, pp. 217-231 DOI: 10.2174/1874282300802010217
Abstract: Paleoclimate data show that climate sensitivity is ~3°C for doubled CO2, including only fast feedback processes. Equilibrium sensitivity, including slower surface albedo feedbacks, is ~6°C for doubled CO2 for the range of climate states between glacial conditions and ice-free Antarctica. Decreasing CO2 was the main cause of a cooling trend that began 50 million years ago, the planet being nearly ice-free until CO2 fell to 450 ± 100 ppm; barring prompt policy changes, that critical level will be passed, in the opposite direction, within decades. If humanity wishes to preserve a planet similar to that on which civilization developed and to which life on Earth is adapted, paleoclimate evidence and ongoing climate change suggest that CO2 will need to be reduced from its current 385 ppm to at most 350 ppm, but likely less than that. The largest uncertainty in the target arises from possible changes of non-CO2 forcings. An initial 350 ppm CO2 target may be achievable by phasing out coal use except where CO2 is captured and adopting agricultural and forestry practices that sequester carbon. If the present overshoot of this target CO2 is not brief, there is a possibility of seeding irreversible catastrophic effects.
Obviously the CO2 mentioned is a little outdated, but don't think that civilization as we know it and life on Earth today was around during the period of time you are considering.
~Tom
Dear all,
Thank you for your answers. Reading the last posts, they emphasise the relationship between big data and sustainability. Beyond the use of all sources of data to study and “try” to forecast climate change, there are also emerging models on the establishment of a more sustainable future (Assessing environmental risks, optimizing resource usage, … see https://www.sustainablebrands.com/news_and_views/ict_big_data/saurabh_tyagi/what_does_big_data_mean_sustainability). Thus, if we could achieve such sustainable future with the necessary modifications in our society, “big data” may then become a success in Earth Sciences…. hopefully?
Information collected and processed in Big Data database systems from climate change
In my opinion, information obtained from various research centers, meteorological centers, satellites, etc., then collected and processed in Big Data database systems should help in more and more precise prediction of new, unfavorable weather phenomena, including climatic cataclysms and others. In this way, earlier and in a more planned way, crisis management systems can be organized in the situation when the predetermined flood disaster becomes real and will happen. Gradually increasing computing power of powerful computer servers managing platforms of Big Data database systems, implemented artificial intelligence, increasing number of verified historical data on the overall climate phenomena on Earth will allow in the future more precisely, more accurately determine the level of threats, risk value, predict time, place and scale adverse weather events, i.e. also cataclysms that threaten people's lives.
Therefore, the question is still valid:
Will the information collected and processed in the Big Data database systems in future be able to accurately check future climate disasters?
Please, answer, comments. I invite you to the discussion.
In my opinion, supported with empirical data and real-life examples, we are using big data with success to make predictions and save lives. There will always be some who disagree using arguments inconsistent with their shown beliefs, but that should not prevent positive discussion to build momentum and improve the life-saving tools that big data has and will develop.
Tom, I am supporting your view. Empirical models coupled with robust statistics can make successful predictions in many areas beyond Earth sciences. However, Kenneth has a point with the limits of the predictions. New events or extreme events are generally hard to predict, because they are outliers. The question is : can AI long-term evolution be able to include those extreme events? Due to the randomness of those events, big/long database may not be enough ...
Thank you, Jean-Philippe, for the support. I hear your question about stochastic events and the probability of predicting them. This will sound simple, but from my understanding, there are limits to the certainty with which we can make predictions, hence such predictions will always have a probability attached to them. This is true beyond Earth sciences as you made clear. In addition, there will always be the chance occurrence of an event never before observed. However, among known events that occur randomly but repeatedly, AI and quantum computers hold the promise of prediction with useful probability, in my humble opinion. Big/long databases by themselves have limited usefulness in respect to predicting with a high degree of probability stochastic events.
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