Research paper on Role of Big Data in the Climate Change

Introduction

Climate change refers to a change in the climate resulting from human activities that alter the composition of the atmosphere (UNFCCC 2014). It has widespread impact on the world, and it is important to proactively manage its effects (Thompson 2010). Climate change is dynamic, and it involves complex interactions and changing likelihoods of diverse impacts (IPCC 2014). However, there are gaps in our knowledge about global climate change, its causes and its impacts. This gap can be filled by analysing large datasets on climate science. However, though the potential use of big data in the field is acknowledged, its power has not been adequately leveraged so far (Faghmous and Kumar 2014). Big data analytics can be particularly beneficial in climate change studies because, by definition, big data refers to datasets that are too large for traditional data processing systems (Provost and Fawcett 2013). Big data analytics can offer vital insights into climate change (Boyd and Crawford 2012).

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Literature survey

Profound impact of climate change

The past 15 years have seen the 12 hottest years on our planet, and diseases such as asthma have increased due to air pollution (Freedman 2013). In several part of the world, changes in precipitation, and melting of snow and glaciers is impacting the hydrological balance. There has been an impact on the ecosystem, geographic range, migration pattern etc. of several land and sea animals. Extinction of some species is considered a result of climate change. The crop yields have been negatively impacted. Changes in temperature and rainfall have also altered the distribution of some waterborne diseases and vectors. Extreme climate-related conditions are more common, and we see abnormal incidence of heat waves, droughts, floods, cyclones, and wildfires. Weaker sections of the society are particularly vulnerable to the adverse impact of climate change, and poverty, health and human security are some major concerns. A failure to adequately anticipate the consequences of climate change can result in maladaptation to the phenomena (IPCC 2014).

 

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Need for urgent response

Our response to global warming can be proactive mitigation and reactive adaptation (Thompson 2010).  The adaptation and mitigation related decisions being made now are likely to impact the risks associated with climate change over the next several decades. Active decision making is required for effective adaptation. Monitoring and learning are vital elements in the complex equation. However, the ability to adapt is limited by practical issues. Availability of funds is a concern, especially in resource challenged nations (IPCC 2014). The impact of global warming is evident from analysis of data, and the response also has to be based on hard facts (Thompson 2010). Therefore, there is an urgent need for an efficient and effective response to climate change based on analysis of information. Big data can play a critical role in building that response.

 

Importance of big data in responding to climate change

Increase in velocity, variety and volume of data, and greater analytical complexity has created a 4th paradigm called data-driven science. This is above and beyond the traditional 3 paradigms, theory, experiments and simulations (Department of Energy 2014). Data intensive sciences, such as climate science, depend on collection, analysis and management of big data. Furthermore, climate science is no longer restricted to atmospherics alone. It includes other perspectives such as changes in vegetation and melting of glaciers (Department of Energy 2014). Also, climate science data is usually n-dimensional. Consequently, it requires special tools that are compatible with different data types and primitives for proper storage, access, analysis and visualisation (Aloisio et al. 2013). Big data about climate is gathered through sensors (satellites and on ground) and new parameters are discovered. Climate change studies incorporate modelling and simulation to forecast weather of the entire world over the long term. This analysis requires every ounce of the power of big data analytics. Also, analysis leads to more insights about subjectivity of the analysis through data assimilation. This assimilation helps in a dynamic model to improve our understanding of climate (Borne 2014). Climate science brings in new challenges for big data, and theory guided data science can play a synergistic role to augment the power of big data analytics (Faghmous and Kumar 2014).

 

Issues related to use of big data in climate science

Analysis of the humungous amount of climate data requires collaboration, and, therefore, it is important to have standardized protocols. Exascale computing (performance in excess of 1018 floating point operations per second) is vital for analysis and transformation of data into practical insights to support decision making. Consequently, investments in infrastructure and human resources, of both data-intensive research and exascale computing, are imperative for successful research. It is also important to standardize activities, Application Programmer’s Interfaces (APIs), protocols, and architecture for different climate activities. An end-user orientation is a must for optimal use of resources collaboratively (Department of Energy 2012).

Integration of various datasets also helps discern correlations to augment the understanding of various phenomenon (Ularu 2012). Existing On-Line Analytical Processing (OLAP) systems are inadequate for such tasks due to scalability and relevance related issues. Inter-alia, more efficient and effective scientific workflows, storage, and data management and analysis ability are required for tackling data science issues (Aloisio et al. 2013). Furthermore, analysis of weather and climate data is highly compute-intensive, and exploiting massive parallelisation is difficult due to weak scalability, large ensemble size and increase in the complexity of models (Vidale et al. 2013). There are issues at each stage of the data life-cycle which relate to data retention, preservation, sharing, provenance, metadata, and security. Software capabilities and integration of resources (physical infrastructure, networking of computers and human resources) are major issues which need to be addressed (Department of Energy 2012). It is important to analyse climate big data within the constraints of inherent uncertainties. Hence, there is a need to involve specialist statisticians and analysts. Attracting and retaining these specialists is also vital for proper analysis of the enormous expanse of data (ASA 2014).

 

Steps being taken

Various governments and other organisations are responding to the climate change challenge by using big data analytics. For example, the NASA Center for Climate Simulation (NCCS) analyses big data on climate and weather helping researchers gain valuable insights about climate change. Data is continuously assimilated, analysed, and worked through climate-model simulations (Mangelsdorf 2012). The White House recently released some data and tools for developers, planners and the general public. The data and tools can help visualize the impact of climate change. Basically, data, which is available with different agencies and in different formats may be accessed through this channel. Private corporations and non-profit organizations have been roped in to make apps related to climate change, and the organizations have already committed resources. For example, Google has committed 100 terra bytes of cloud storage space for observations and models (Kahn 2014). Private organizations are also using big data to help farmers adapt to climate change. Precision agriculture, which uses real time forecasts based on climate data, can help farmers optimize the inputs used in farming. Monsanto has launched apps and tools for farmers to gain access to this data. Monsanto also stands to benefit by being able to gain data about farmers and their farming practices when they use the app (McDonnell 2014). New concepts such as Climate-as-a-Service (CAaaS) are coming up. CAaaS is a specialization and an extension of the SaaS based on cloud (NASA 2014).

The United Nations’ Big Data Climate Challenge initiative was also aimed to use big data to generate factual evidence on the economic impact of climate change. Smart cities, management of natural resources, food systems, resilience, architecture and design and climate finance are some dimensions of response to climate change (Borne 2014). Importantly, effective governance for adaptation also requires a scientific and community level understanding of the impact of climate change. Fostering dialogue on web based platforms and social media can lead to a more inclusive process of adaptation. Internet-based platforms which are supported by big-data analysis can play a role in supporting scientific discussion (Muir 2013) and may increase the credibility of the information.

Based on the above discussion, it is evident that the phenomena of climate change requires an urgent response for adaptation and mitigation of its impacts. It is also clear that an efficient and effective response can be built using gathering and analysis of big data. However, there are issues and concerns which need to be understood in more detail to gain more insights into how the power of big data can be leveraged in climate science studies. With this goal in mind, research questions, aim and objects for further research on the issue are proposed.

 

Research question: What role can big data play in understanding climate change and building a response to the phenomena?

 

Aim: The aim of this project is to understand how big data can be used to understand climate change, mitigate its impacts, and help adapt to the phenomena.

 

Objectives of this project are to:

  1. Evaluate the present status of the use of big data in the climate change field.
  2. Examine the specific areas of climate change research where big data can be particularly useful.
  3. Analyse the factors hindering the use of big data in climate change studies.
  4. Examine the initiatives taken by governments and other organizations to facilitate the use of big data in climate change studies.
  5. Identify specific steps which can be taken to support and enhance the use of big data in climate change studies.

 

References

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ASA, 2014. Discovery with Data: Leveraging Statistics with Computer Science to Transform Science and Society [online]. Virginia: American Statistical Association (ASA) [viewed 3 December 2014]. Available from: www.amstat.org/policy/pdfs/BigDataStatisticsJune2014.pdf

BORNE, K., 2014. 3 Ways That Big Data Are Used to Study Climate Change – Monitoring, Modeling, and Assimilation [online]. California: MAPR [viewed on 3 December 2014]. Available from: https://www.mapr.com/blog/3-ways-big-data-are-used-study-climate-change-%E2%80%93-monitoring-modeling-and-assimilation#.VH7uEVJxldg

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