Source: towardsdatascience.com
In recent media reports, the threat of climate change has supposedly become so great that it is said to be capable of wiping out all of humanity, a “climapocalypse” — the data do not support this claim. As a practicing climatologist, I have personally handled and analyzed datasets with large time spans (over 100 years) and spatial extents (global scale), and while there are signals of change within the data the possibility of a species or even nation-ending event is far from reality. Climate change is guaranteed to place new burdens upon humanity, our infrastructure, and our socio-economic status quo, but we need to be realistic when we communicate its impacts with individuals not familiar with the science. Of course, the only meaningful way of communicating this information is to use the data we have available and even this must be done with the utmost caution.
To understand where I am coming from with this article, go to Google and search “climate apocalypse”, then take a gander at how many results are returned from this query. At the time of writing over 9 million results were returned, and the highlighted articles can vary depending on the current “hot topic” trending that is related to weather or climate. If you navigate to Google trends, you can see that the term “climate change” peaks in searches whenever there is an extreme weather event. From a non-political standpoint, one could say that this is natural. A cluster of tornadoes moves through an area and wreaks havoc causing millions in damages — people want to know how it happened, where it happened, and when will it happen again. The number of articles pertaining to the “climate apocalypse” also skyrockets around these times as well, with media outlets publishing dreary reports of a warming world in which millions more will suffer from increased frequency of these types of events.
Presenting the information in this way not only has blatant political motivations that skew the truth, but it also omits the data and its message as well as an abundance of scientific research surrounding the frequency of these events. For tornadoes, droughts, flood inducing precipitation events, and tropical cyclones, research has shown that under a warming world these events will become less frequent but more intense. While many hear this and recall the overdramatized storm from the movie The Day After Tomorrow, such an event is unlikely to happen and even it if did humanity would survive it. This isn’t just the wishful thinking of an overly optimistic scientist, it can be observed in the data. In 1900 the worst hurricane in United States history made landfall in Galveston, Texas. Fatalities were high, property damage was high, but humans weren’t completely annihilated from the earth by this event. Relative to today, the building codes were much less strict and unsafe and the Galveston hurricane is reported to have been close to the strength of Hurricane Harvey. Both were category 4 hurricanes. The difference is that the Galveston hurricane killed 6,000 to 12,000 people, while Harvey killed 68. The loss of lives is most definitely tragic, but the sharp decline between these two events is evidence that our understanding of the impacts of extreme weather have yielded improved infrastructure, hazard management, and emergency response. Nevertheless, there were hundreds of articles claiming Harvey as the beginning of the end of humanity by climate change induced weather, which was farthest from the truth.
From a data science perspective advances in feature engineering, artificial intelligence, and computational methods are providing us with new ways of analyzing climate data. Feature engineering techniques such as outlier analysis, binning, k-means, correlation matrices, and even linear discriminant analysis, allow analysts and researchers to understand which variables within a dataset contribute the most to a phenomena of interest. This information can then be used to refine inferential models that forecast into the future. For artificial intelligence algorithms, it is crucial to use the best features from a dataset. Providing a simple sigmoid function based classifier temperature data from a weather station installed in a paved urban environment, or a shaded field, can lead to inaccurate and imprecise output from an algorithm. For other methods such as convolutional and recurrent neural networks, this problem will still persist, as these methods can only provide results based on the data being provided to it. One technique that is on the rise and will likely see more widespread use within the climate community is reservoir computing. It combines the best of big data techniques and machine learning to create forecasted products depending on the information provided to it. All of these methods are only as good as the data provided to it, and it’s true that the majority of our data support global warming they do not support a climate apocalypse, even if the planet continues to warm at its current rate.
Being in climatology or any natural science these days is interesting. The majority of the instruction or advice we receive from seniors within the field supports using data and previous literature to develop new insight regarding the field. However, if you are in a field that uses a lot of data from instrumentation, you are likely also developing yourself as a data scientist — or you should be. Certain aspects of Climatology will require more data science skills than ever before, and those who are unaware of these techniques will likely make poor models that will improperly inform the public. Understanding data and how to manipulate it is a necessary component of creating useful information, advancing the field, and assisting communities in building more sustainable and environmentally rigid infrastructure. It will also be necessary to debunk exaggerated events such as the “climapocalypse” in the near future.