Science is not responsible for climate change – at least not directly – but it does take the responsibility for reversing or slowing down this dangerous trend. It has become one of the primary topics among academia members and beyond because everyone can already see the effects of climate change.

But the change won’t come alone. On the contrary, it takes some serious data science effort to understand, predict, and fight climate change. This post is going to explain how this process functions.

The Role of Data Science in Climate Action

The ultimate purpose of data science is to equip us with the right tools for coping with climate change. For instance, some predictive models successfully forecast extreme weather events like floods or droughts – that help prepare and drastically mitigate damage.

Advanced analytics also identifies the primary sources of greenhouse gas emissions in different sectors. For example, NASA’s Carbon Monitoring System relies on satellite information as well as superior algorithms to monitor CO2 levels globally – this allows the organization to detect where emissions are highest as well as which mitigation strategies are working.

Key Climate Data and Its Challenges

Though the fight against this global issue depends on accurate information, the process of collecting and interpreting data isn’t without its unique obstacles. Before we get to that, allow us to remind you that key climate data includes the following:

– Atmospheric greenhouse gas concentrations

– Global temperature records

– Sea-level rise measurements

– Biodiversity monitoring

Datasets from the likes of the National Oceanic and Atmospheric Administration (NOAA) or the European Space Agency (ESA) are critical in tracking changes over time. On the downside, however, collecting such information is everything but simple.

For example, tracking atmospheric changes requires sophisticated satellite networks paired with ground-based sensors. The same goes for ocean temperatures – this type of monitoring demands special buoys as well as autonomous vehicles. But that’s not even the only obstacle when it comes to data science processes.

Once collected, everything must be standardized and cleaned: This is tough because information often comes from totally disparate sources. After that, it also becomes difficult to actually interpret extremely complex interactions within the overall climate system.

For instance, we can quickly observe a correlation between deforestation and regional weather patterns, but that doesn’t make it any easier to distinguish causation. The latter requires advanced modeling tools based on interdisciplinary expertise. At the same time, public concern over these changes is growing, as evidenced by climate marches across the globe – in the USA, Europe, and Canada – led by initiatives like Actionclimat march, which unite communities in the fight against climate change.

Sources of Climate Data

We could write a whole article about information sources, but it’s enough to briefly point out that different tools provide unique insights into the Earth’s systems. For instance, satellites allow us to see global-scale observations of atmospheric composition or sea-level rise. On the other hand, ground-based sensors serve as credible sources of high-resolution and highly localized data. Here’s what it looks like in a nutshell:

SOURCE	FEATURES	EXAMPLES
Satellites	Global coverage Remote sensing capabilities	NASA’s Terra ESA’s Sentinel-5P
Ground sensors	High-res Local information	Weather stations Ocean buoys
Climate models	Scenario analysis	IPCC climate projections NOAA’s GFDL CM4

Common Data Challenges

A whole lot of issues appear in this process, but some are more obvious than others. One of them is the sheer volume of data being generated on a daily – just think how much information comes from satellites and all other tools every single minute. In such circumstances, only organizations with huge storage and processing capabilities can actually make use of the info collected.

At the same time, inconsistencies across datasets appear all over the place. For instance, differences in collection methods or geographic coverage often make it difficult – if not impossible – to integrate data from multiple points.

Case Applications

The point of data science isn’t just to observe or analyze. As a matter of fact, the best thing is that we use data science to really do something practical and react to the effects of climate change.

For instance, we can often predict extreme weather conditions (think of hurricanes or heatwaves) through accurate forecasts. This makes it possible to issue early warnings that save lives whilst minimizing financial losses. That is exactly what happens with NOAA’s prediction models as these help entire communities prepare for storms.

The same logic applies to carbon emission tracking. A good example comes from ESA’s Sentinel-5P which monitors CO2 and methane levels across the globe so as to pinpoint hotspots of industrial pollution and deforestation. These insights inform policies like carbon pricing and emission reduction targets.

Challenges and Opportunities

Our goal isn’t to emphasize problems, but allow us to mention that different organizations or governments own datasets – this sometimes makes collaboration quite difficult.

However, challenges also open doors to interesting innovation. Take open-access platforms like Copernicus Open Access Hub as an example – such innovations democratize data availability so as to encourage global cooperation. This trend goes hand in hand with improvements in artificial intelligence and cloud computing, as both create fresh opportunities to process data faster.

Future outlook

The bottom line is that data science is going to help us understand and better prepare for the influences that come with climate change. It’s an expensive toy that demands careful calibration as well as international cooperation, but it’s also the only one that guarantees results short-term and long-term. Given that it saves people and money, it is clear that data science makes for a worthy investment on a global scale.