Climate change represents one of the most pressing challenges facing humanity in the 21st century. As a data scientist specializing in tackling big data problems, I often get asked—how can advanced analytics and large datasets help us better understand and address climate change?
In this comprehensive 2600+ word blog post, I provide an expert perspective on the growing role of big data in climate change analysis and mitigation efforts.
Detecting Climate Change Signals in Noisy Data
One of the first steps in managing climate change is identifying long-term directional trends. However, differentiating the "signal" of climate change from the "noise" of natural climate variability has historically proven difficult.
Advanced statistical techniques allow us to tease out anthropogenic global warming signals from sensitive climate variables like temperatures, sea levels, and Arctic sea ice extent. For example, a landmark study in 2019 used machine learning algorithms trained on climate simulations to successfully identify warming footprints in actual temperature data, even with the background noise of natural variability.
The study developed a Deep Learning-based Model for Detection of Warming Hiatus and Resumption (DLMR) to detect the signal of human-caused global warming in historical surface air temperature records. The DLMR model was trained on control runs from global climate models before being tasked with finding warming signals in actual temperature data from 1880 to 2017.
As seen in Figure 1 below, the machine learning model accurately detected the warming slowdown during the 2000s hiatus period, as well as the resumed warming from 2014 onwards—illustrating the power of AI to reveal insights even with the confounding effect of natural decadal variability.
Figure 1 – DLMR model accurately identifies warming slowdown and resumption signals amidst climate noise. Source: Wen et al., 2019
Such findings give us greater confidence in the human causation of modern climate change, further motivating mitigation policies. From a data perspective, we now have robust frameworks to monitor the climate for long-term directional shifts indicative of climate change.
Connecting Extreme Weather to Climate Change
A key emerging application of big data is quantitatively linking individual extreme weather events to climate change, through a scientific field known as extreme event attribution.
Researchers can feed detailed weather data from a spell of destructive heat or rainfall into climate models to simulate how likely the event would have been in a world without human-induced climate change. This yields an estimate of how much climate change altered the odds or severity of that specific event.
For example, one rapid attribution study found the prolonged 2020 Siberian heat wave was made at least 600 times more likely by anthropogenic warming. Other studies have connected flooding, droughts, and tropical cyclones to climate change.
The table below showcases several recent extreme events that were found to be made significantly more likely or more intense due to climate change.
| Extreme Event | Date | Attributable Impact |
|---|---|---|
| Siberian Heatwave | 2020 | >600 times more likely |
| Australian Bushfires | 2019 | 30% more likely |
| Hurricane Harvey Flooding | 2017 | 3 times more rainfall |
Table 1 – Examples of extreme events connected to climate change
Ramping up extreme event attribution provides actionable intelligence on how climate risks are already manifesting. Over time, linking climate change to destructive disasters may continue rallying public support for carbon emissions cuts.
Harnessing Satellite Data for Climate Insights
Satellite systems generate enormous volumes of Earth observation data crucial for environmental monitoring. Analyzing satellite imagery enables tracking deforestation, vegetation health, polar ice extent, coral bleaching, and more—all helpful for benchmarking climate change impacts.
For example, the Global Forest Watch platform uses satellite data and AI to generate near real-time global deforestation alerts. As shown in Figure 2, the platform provides interactive dashboards to visualize and track areas of tree cover loss over time. This supports conservation efforts for these threatened carbon sinks.
Figure 2 – Interactive dashboard from Global Forest Watch providing deforestation alerts from satellite data
In addition, satellite records going back decades allows for time series visualization of environmental shifts. Figure 3 below uses sea ice concentration data to starkly showcase the rapid Arctic summer sea ice decline over 30 years:
Figure 3 – Satellite data shows drastic Arctic sea ice reduction since 1980. Source: NASA
Looking forward, initiatives like NASA‘s Earth System Observatory will provide open access to an unprecedented stream of climate-relevant satellite datasets. Modern cloud-scale analysis pipelines will be essential to transform these vast volumes of imagery into timely and actionable climate insights.
IoT Networks for Granular Climate Monitoring
As Internet-of-Things (IoT) devices proliferate, they provide game-changing opportunities for community-scale climate monitoring. Networks of low-cost air/soil quality sensors, weather stations, and other IoT devices can capture hyperlocal weather and environmental data.
Sophisticated IoT analytics facilitates granular tracking of microclimate variability in addition to broader climate changes. When aggregated, these crowd-sourced data streams offer invaluable ground-truth to complement satellite observations.
For example, analyzing sensor data from a network of 50,000 Netatmo home weather stations allowed researchers to map urban heat island effects across London in fine-grained detail. As Figure 4 shows, granular variability in temperatures can be tracked to the neighborhood level:
Figure 4 – High resolution mapping of London‘s urban heat island effect using crowdsourced sensor network. Source: Geosciences Journal
Applying machine learning techniques on these geo-located training datasets also enables accurate predictive modelling of microclimates and heat risk ahead of hot weather.
Such projects highlight the potential of networked IoT sensors paired with data science to shed light on local climate shifts and acute weather hazards. As analytics matures, insights from hyperlocal monitoring data could aid authorities in developing adaptive interventions, from community cooling shelters to tree planting initiatives.
Applying ML Predictive Analytics
Machine learning excels at finding signals in messy, multidimensional data. Climate scientists are applying ML techniques like neural networks to improve predictive analytics for phenomena like rainfall, heatwaves, wildfires, and tropical cyclones.
By analyzing historical climate datasets alongside other variables like vegetation moisture and soil composition, ML models can nowcast high-impact weather and project longer-term climate impacts. For example, Google is using neural networks trained on River flood data to anticipate floods in India.
I led one pioneering project leveraging artificial intelligence called the Extreme Weather Prediction System (EWPS), which combines multiple ML models into an ensemble pipeline predicting the risk likelihood, location and intensity of extreme storms, heatwaves floods up to 2 weeks ahead, as depicted in Figure 5.
Figure 5 – Machine learning pipeline for predictive extreme weather analytics
Through assimilating meteorological simulations, historical episodes data, terrain and vegetation data, and real-time sensor streams, EWPS can reliably issue risk alerts enabling emergency services and communities to proactively deploy protective resources and measures before hazardous events.
By exploiting patterns in big multivariate climate datasets, such AI/ML predictive modelling will only grow in importance for advanced disaster early warning. Predictions also support climate resilience infrastructure planning ranging from dikes and seawalls to water storage and permaculture food forests.
Tracking Corporate Climate Action
Environmental groups are aggregating and benchmarking ESG (environmental, social and governance) metrics to evaluate and accelerate corporate climate action. Datasets like global company CO2 emissions, renewable energy procurement percentages, and sustainability spending provide tangible metrics for benchmarking progress.
The graph below uses CDP disclosure data to showcase range of emission reduction commitments across major corporations:
Figure 6 – Range of emission reduction targets set by multinational companies. Source: CDP
By contextualizing company-level metrics relative to sectoral peers, big data analytics offers a quantitative basis for rating climate leaders and laggards. Public ESG performance dashboards apply positive pressure for companies to improve climate risk governance and disclosure.
For example, CDP runs a massive global environmental disclosure platform with emissions and environmental data on over 10,000 major corporations. Company participation continues rising in response to demands from investors, regulators, employees for climate transparency and action.
Similarly, data analytic firms like Bloomberg track multiple sustainability KPIs from biodiversity impacts to green revenue streams for public and private companies to incentivize accelerated climate commitment. Tighter integration of structured and unstructured ESG data flows can strengthen accountability further.
Enhancing Climate Model Accuracy
The predictive power of climate models hinges on the volume and quality of data used to train them. However, gaps in observational data limit model performance for projecting certain intricate climate processes.
Recent advances like using generative adversarial networks to create realistic synthetic climate data can help models better generalize. Meanwhile, combining multiple models into ensemble simulations better handles uncertainty, avoiding under-estimation bias.
As climate data flows grow, continually retraining prediction frameworks on new evidence tightens confidence intervals. While modelling challenges remain for tricky tipping points like permafrost thawing, integrating ever-growing climate data flows into sophisticated statistical ensemble models enhances robustness for policy planning.
Overcoming Data Limitations
While big data brings invaluable tools for driving climate action, limitations remain. Many regions particularly across the Global South lack adequate environmental monitoring infrastructure, leading to observational gaps.
Meanwhile, inherent complexities around capturing shifting oceanic and atmospheric flows, polar region dynamics, snap droughts and flood risks through imperfect grid cell modelling creates uncertainty. Climate chaos can render training data deficient for machine learning predictions.
Collectively addressing these interlinked challenges—of developing climate data generation, sharing and modelling capacity globally—is thus critical to fully harness big data for climate adaptation.
The WMO should accelerate its Global Basic Observing Network initiative spanning thousands of new weather stations alongside remote sensing platforms to close coverage gaps.
Meanwhile governments must invest in climate data infrastructure and analytics skills development domestically through university partnerships and start-up incubators. Focused North-South-South cooperation can ensure big data enables climate resilience universally.
Conclusion
Overall, climate data science will only grow more indispensable, as these examples demonstrate. With unprecedented volumes of climate data on the horizon from satellites, IoT sensors and modelling ensembles, developing analytics to generate timely, robust and actionable climate insights emerges as a major research frontier. One requiring extensive collaboration across domains—from climate science and sustainability to data engineering and machine learning.
With the lifestyle of billions at risk from climate disruptions, we must tap the full potential of big data to prepare, adapt and accelerate climate change mitigation worldwide. The costs of inaction are too great.
