Written by Andrea Ballor
Sceptics of manmade climate change offer
various natural causes to explain why the Earth has warmed 0.8 degrees Celsius
since 1880. In this piece, our aim will be not to argue about men’s faults.
We’ll just assume the fact that climate is changing, which is a fact, beyond
any criticism.
The term ‘climate change’, can cover many
things, some natural and some manmade, including global warming, as well as
loss of wildlife habitat. Each of these brings its own challenges but,
increasingly, big data and analytics are being put to use to come up with new
solutions and research methods. Climate scientists have been gathering a great
deal of data for a long time, but analytics technology’s catching up is
comparatively recent. Now that massive amounts of processing power are
affordable for almost everyone, those data sets are being put to use. On top of
that, the growing number of Internet of Things devices we are carrying around bring huge
amounts of data to the collectors. The rise of social media means more and more
people are reporting environmental data and uploading photos and videos of
their environment, which also can be analysed for clues.
One of the most ambitious projects
that employ big data to study the environment is Microsoft’s Madingley, said to be the first ‘General Ecosystem
Model’, or GEM. The project already provides a working simulation of the global
carbon cycle, and it is hoped that, eventually, everything from deforestation
to animal migration, pollution, and overfishing will be modelled in a real-time
“virtual biosphere”. It offers decision-makers a tool to explore the potential
effects of their decisions on the environment, in a computer, before decisions
are rolled out in the real world. The model has been released as open source
code, allowing anyone to inspect the current version of the model or develop it
further. They hope this project will encourage other scientists to become
involved in developing this, or analogous global models of life on planet
Earth. Just a few years ago, this idea would have seemed ridiculous. Today, one
of the world’s biggest companies is pouring serious money in the project, sign
that they believe that analytical technology has finally caught up with the
ability to collect and store data.
Adding evidence to the trend, last
year the UN, launched the Big Data Climate Challenge, a competition aimed to
promote innovative data-driven climate change projects. Among the first to
receive recognition under the program is Global Forest
Watch, which
combines satellite imagery, crowd-sourced witness accounts, and public datasets
to track deforestation around the world. The project has been promoted as a way
for ethical businesses to ensure that their supply chain is not complicit in
deforestation. Other initiatives are targeted at a more personal level, for
example by analysing transit routes that could be used for individual
journeys, using Google Maps, and making recommendations based on carbon
emissions for each route. Others more specific, such as Weathersafe, which
helps coffee growers adapt to changing weather patterns and soil conditions.
Analytics can as well help smart
cities to grow. The Internet of Things, the idea that everyday objects and
tools are becoming increasingly connected, interactive, and intelligent, and
capable of communicating with each other independently of humans, is becoming
more and more central, and provides amount of significant data we never even
imagined before. Smart metering allows utility companies to increase or
restrict the flow of electricity, gas, or water to reduce waste and ensure
adequate supply at peak periods. IBM has recently entered this sector. Back in
2014 they’ve been helping Beijing combat its air pollution crisis using a data
analysis platform called Green Horizons. This software uses machine
learning to analyse previous weather forecasts, crunching data to
determine how good those predictions were in different scenarios, and then
build better forecasting models over time. It’s been developed
because it has been discovered that weather conditions have a direct effect upon how city residents experience the effects
of air pollution. Depending on the forecasts provided, the software is able to
reduce pollution emissions in the areas where they could hit worst. "Knowing
where pollution is coming from and how much is in the air will drive action to
reduce it," told Bob Perciasepe, president of the Centre for Climate
and Energy Solutions "Experience shows that when measurement happens,
pollution levels go down and public health is improved. This near-term action
improves the liveability of communities and the wellbeing of citizens”. Using
data analysis to more accurately source, model and mitigate air pollution is a
key strategy for combating climate change in urban environments. Knowing more
about the problems drives more cities towards solving them.
It’s apparent that data – big or
small – can tell us if, how, and why climate change is happening. Of course,
this is only really valuable to us if it also can tell us what we can do about
it. All these projects are built around the principle of predictive modelling.
Once a working simulation of a climate change system – deforestation,
overfishing, ice cap melt, or carbon emissions – has been built based on real,
observed data, then by adjusting variables we can see how it might be possible
to halt or even, in some cases, reverse the damage that is being done. After
all, the whole point of big data analysis, in climate science or otherwise, is
to generate actionable insights that can drive growth or change.
Thanks to the growth of big data
analysis, it is becoming apparent that the actions of individuals can make a
difference, a measurable difference, when they are able to make decisions based
on sophisticated analysis of accurate data.