The Climate Clock project began with an autocorrelation. An autocorrelation, which did not behave as expected. It had a steep slope which told us that we can only calculate one year ahead, in the darkness of the future. Then I see a small bump at the end of the autocorrelation. It cannot be possible. I had expected a bell-shaped course. The autocorrelation told me that Norwegian Arctic recruits in tact, in periods of 6 years. Could it really be possible?

The fishing industry has always been the mainstay of economic development and settlement along the Norwegian coast. At the same time, it has been a capricious industry, where good years are followed by bad years. After some good years in the 1950s and 1960s, there were bad years in the 1970s and 1980s. A period that created major challenges in the fishing industry and the maritime industrial cluster in North-West Norway. Then came the 1990s with new growth in the cod population. The new growth created new optimism. The time had come to build a new modern fishing fleet.

In the autumn of 1994, I participated in a life cycle analysis of a new modern trawler. The bank had created an income model since the biomass of Norwegian Arctic cod will increase over the next five years. It made me ask a critical question. “How can the bank know that growth in biomass will continue to grow the next five years?” I found the Norwegian Marine Research Institute’s report in the library, read the data for Norwegian Arctic cod briefly, wrote a computer program and calculated the autocorrelation. It was all done in just an hour.

What the survey reveals

The autocorrelation showed that we know nothing about the future of the cod stock, beyond a period of just one year. This means that we know nothing about expected future quotas and future income. We can only calculate expected costs. The calculation of the trawler’s life cycle was based on a large uncertainty. My first thought was that if I tell this, it will ruin the whole project. At the same time, the autocorrelation had an unexpected peak at 6-7 years. This created an uncertainty about the result. At the same time, it created an opportunity. It revealed that the cod stock may have a periodic recruitment. Investments in the fishing industry can be compared to investing on the stock exchange. The trick is to find the time, when you are faced with boom times. If the timing of the period’s growth is known, income will be created during an upturn. If you arrive too late, you face a down time. A 6-year cycle in the cod population was a very important discovery. One can predict upswings and downswings.

The autocorrelation

When we study a data series, it is inadvisable to see upcoming upswings or downswings. We therefore need some methods to be able to understand the characteristics of the variation. A data series y(t) can be composed of three properties: y(t) = a*y(t-1) + n(t) + cos(wt). In this simple model, a*y(t-1) represents a form of inertia in change from one year to the next, n(t) represents a random change from an unknown source and cos(wt) represents periodic change. Autocorrelation is defined as: Ryy(m) = E[y(t)y(t+m)]. So the mean value of the product y(t)y(t+m), where m represents the distance between two points in time. If the autocorrelation falls quickly, it is a sign that there are large random variations from one year to the next. We cannot therefore rely on a forecast of future development. If the data series has a period cos(wt), the period time will give peaks in the autocorrelation in accordance with the period time.

The result

The autocorrelation revealed that there is something in nature that causes the entire population to recruit more in periods of 6 years. This has taken place since the measurements began in 1946. This means that there is a stationary 6-year cycle in the cod stock, which will continue for the foreseeable future. The scope of a stationary 6-year cycle is that a good cohort will reinforce recruitment in 6 years. Biomass will then be able to grow in predictable periods of [1, 2, 3,…]6 = [6, 12, 18,…] years. This means that we can expect to find longer periodic changes in the cod population. This may explain why there are good years and bad years in the fishing industry.

A 6-year cycle in the cod stock was unknown to the trawler’s owners. The cycle was also unknown to the Marine Research Institute. It is not something that is constant in the ecosystem. Nor can the biomass decide to recruit for periods of 6 years. There must therefore be something in nature, outside the biomass, which causes periodic changes in the cod population. An unknown source, which no one has heard of. An unknown source, which affects the fishing industry in good years and bad years. This source must have a first cause. I decided to find out what this could be. Fortunately, I had no idea what a formidable subject I would reveal.

30 years later

It’s been 30 years. The fishing industry has gained a new generation of skippers. Some of them have started reading my blog and newspaper articles. This leads to me being invited to give a lecture at Skipper-conference 2023. My lecture is:

Cycles in marine eco systems

After the lecture, I get questions about whether this is controversial. I answer that a researcher’s task is not to be popular, but to tell the facts in data. The facts in the data show that it is nature that rules the climate. The fishing industry can now expect a colder climate period, with reduced growth in the Barents Sea.