Data-Justified Analysis and Climate Change

Storm Bert hit our lovely Welsh town of Pontypridd for the last few days. Emergency services have evacuated families from a street where I almost bought a house in 2021. I am safe. My home is on a street with sufficient elevation from the Taff. The Taff is the river that runs to Cardiff, the fortress (caer in Welsh) at the Taff's mouth. This flood is all the more heartbreaking because Storm Dennis also caused even more devastating floods in 2020, right when the pandemic lockdowns started. It took years for the valley to recover, and some businesses barely have. Our town proudly hosted the National Eisteddfod, a festival celebrating Welsh culture. It was, for many, the town affirming that the damage caused by Storm Dennis is behind us. Storm Bert may cause less damage, but it is still raining as I write this. Many blame climate change, of course. Climate change is a fascinating test of our trust in data.

Climate change is claimed to have occurred because average temperatures have slowly increased since the Industrial Revolution, which began in the late 18th century. The United Kingdom and many other countries started extracting coal to power steam engines. Many power sources supplemented coal, most of all oil and gas, as well as uranium and increasingly renewable sources. Coal and oil produce heat and significant volumes of carbon dioxide. As temperatures increase, more water evaporates, resulting in more rainfall. To reduce destructive floods like the ones hitting my town, or Valencia in Spain a few weeks ago, or the French village where I grew up and where my parents still live, we should drastically reduce our consumption of carbon dioxide-producing energy sources.

The trouble with renewable traffic sources is that many rely on the weather. Several European countries invested massively in wind farms, but Mother Nature spared them from windy weather in 2021 and 2022. After Storm Uri in 2021, Texas experienced weather so cold that its wind turbines froze. Solar energy is not without challenges either. Solar panels require silver. Although historically more plentiful than gold to the ratio of 15 to 1, silver recycling is far less frequent than gold recycling to the point that some claim the ratio could be 1 to 1. We consume most industrial silver but recycle gold. Solar panels also require batteries with a lifetime of 10 to 15 years. Tidal and geothermal energy may prove more reliable. Iceland has been depending on the latter for decades.

While you may think that having the weather increase yearly by 0.02oC is insignificant, some sources fear that melting polar ice could change how salty seawater will become. Such a change could slow or even stop sea currents. Iceland enjoys mild winters because of the Gulf Stream despite sitting just below the Arctic Circle. The British Isles also enjoy milder winter weather than Newfoundland. The Newfies occasionally see icebergs despite a latitude comparable to the United Kingdom or France. Climate experts believe there is a threshold for the concentration of salt in seawater, which could lead to losing the Gulf Stream current and seeing Northern Europe much colder.

I am not reading much about how colder Northern Europe leads to more snowfall, especially in the Arctic. Perhaps nature has a built-in, self-regulating mechanism where seawater gets less salty when the weather heats up because polar ice is melting. This impacts sea currents and produces colder weather and polar ice reforming. Let's hope that the Gulf Stream and other currents keep working.

Carbon dioxide may be a greenhouse gas, causing the planet to heat up. However, it is also an essential gas for plants and trees. They take carbon dioxide, break it apart to keep carbon to grow, and release the rest, which is oxygen. It seems that the more carbon dioxide we produce, the more the plants have to absorb to grow, creating more oxygen. However, throttling carbon dioxide production will also limit photosynthesis, i.e., oxygen production. There is a sweet spot: 150 parts per million (ppm) and 1,500 ppm with an optimal concentration between 350 and 1,000 ppm.

Looking at historical trends older than the Industrial Revolution, we also find anomalies in the weather. Some years were hotter than expected, and others were colder despite a lack of large-scale industry. Immediately before the Industrial Revolution came a period stretching back to the 1300s, some called the Little Ice Age. Have you ever wondered why Greenland is called green despite being mostly ice-covered? When the Norse navigators settled in Greenland around the year 1000, the weather was milder, and Greenland was green until the Little Ice Age kicked in.

Volcanic eruptions also emit carbon dioxide and sulphur dioxide, disrupting weather patterns and resulting in harsh winters. Our planet also does not sit idly in space; it rotates around the sun, and the solar system travels through the galaxy. As it does, the sun may be a little dimmer than usual, sometimes for centuries, as in the case of the Little Ice Age. The sun also goes through an 11-year cycle. The sun becomes more intense as it waxes, producing solar flares and higher Earth temperatures. The sun is now about to enter its waning phase. The Earth also wobbles around its axis in a so-called Milankovitch Cycle, which impacts the climate.

When discussing global warming, we should compare apples to apples, i.e., normalise the temperature data to eliminate external causes outside our control first. Some of the predictions Al Gore presented in his 2006 documentary An Inconvenient Truth have failed to materialise. Although sea levels have increased, no major coastal cities were flooded within a decade. The twentieth anniversary of this documentary will be in two short years, and the probability of seeing it happen is low.

These predictions will take longer than expected. Perhaps, perhaps we are dealing with cognitive dissonance. Cognitive dissonance happens when we believe in one thing, but reality fails to match that belief. The ensuing gap causes psychological discomfort, so we seek a compromise that does not compel us to abandon our beliefs. The endowment effect makes us hold on to our beliefs. We formed these beliefs with some effort, and we value anything we gain through effort: the more strenuous the effort, the greater the value. A mechanism in our psyche will defend our beliefs, expressing belief persistence.

Michael Mann, Raymond Bradley, and Malcolm Hughes published their temperature model in 1998, which predicted a hockey stick rise and covered a whole millennium. Naturally, formidable claims attract criticism, especially when they blame our behaviour. Mark Steyn called the claims fraudulent, and Mann sued Steyn for defamation in 2012. Mann won.

Steven McIntyre, a Canadian statistician, and Ross McKittrick, a Canadian economist, claimed Mann cherrypicked the data to build the model. Mann et al. used Principal Components Analysis (PCA). Although a valid technique, PCA allows for choosing some data, such as tree rings, while ignoring the rest. With that freedom, one can build a model coming to any conclusion, especially one you picked before building the model: data-justified analysis.

McIntyre and McKittrick formally asked Mann to share his team's data and code with them. Mann refused, at least at first, despite his team's work being publicly funded. McIntyre and McKittrick resorted to reverse-engineering Mann's work. In 2009, a hacking scandal, the Climategate scandal, broke out. Emails about Mann's work leaked, revealing some questionable data practices.

Meanwhile, there is always a risk when robust methodologies let you pick the data and come up with any conclusion that will provide a defence for claims different parties with a predefined agenda have made. It gives the analysis a varnish of scientific rigour that the general public cannot debunk. An independent and neutral team must analyse the data without passion or prejudice so that the involved parties can trust the data. Such a team must accept requests for the raw data and the code that produced that actionable insight so that others can reproduce the findings. We need to centralise the data to avoid the creation of data silos and decentralise the analysis, i.e. empower the departments to analyse their data.

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