In this blog post, we will look at the fundamental reasons why long-term climate prediction is difficult, including the influence of the ocean, observation limitations, and chaos theory.

Why are long-term climate predictions so difficult?

We live in an age where we are sensitive to the weather. Weather information is closely linked to our lives, whether we are planning vacations or events, deciding when to plant crops, or preparing for disasters. In particular, seasonal weather patterns have become more irregular due to climate change in recent years, leading to increased interest in long-term climate predictions. However, in reality, long-term climate prediction remains a very difficult task. So why are long-term climate predictions less accurate than short-term forecasts? Let’s take a closer look at the reasons.

Irregular seasonal climate and limitations of prediction

Every summer, depending on the year, there are times when extreme heat waves occur, and conversely, there are times when temperatures are lower than normal, resulting in a “cool summer.” The same is true for winter. Some years are extremely cold with heavy snowfall, while other years are relatively warm with little snow. Seasonal climate varies greatly from year to year, and it is very difficult to predict such fluctuations in advance.
In fact, short-term forecasts for weather within a few days are relatively accurate, but accuracy drops sharply when the forecast period is longer than a month. Why is this? The fundamental reason is that climate systems are complex and involve various factors. In particular, the influence of the ocean is one of the most important variables in long-term climate prediction.

The influence of the ocean on climate and the difficulty of predicting sea surface temperatures

Long-term climate change is greatly influenced by changes in the temperature of the ocean, especially sea water. The ocean is a huge reservoir that stores and transfers heat energy from the Earth. Seawater has a much larger heat capacity than land and can hold about 400 times more energy than the atmosphere.
For example, Northern Europe is influenced by the North Atlantic Current, which makes the average temperature significantly higher than other regions at the same latitude, and the daily and annual temperature ranges are smaller. This is a typical example of how ocean currents greatly affect the climate.
As such, the state of the ocean plays a more decisive role in climate change than the atmosphere, and the longer the prediction period, the more essential it is to accurately understand the state of the ocean. However, the problem is that it is extremely difficult to predict sea water temperature, especially the temperature of the sea “surface.” Surface temperature is determined by ocean currents, which are extremely complex and irregular.
Various natural factors with different cycles are involved in ocean currents, and some factors have long cycles of several decades or even more than 100 years. These factors interfere with each other and cause synergistic or counteractive effects, further exacerbating the irregularity of ocean currents. As a result, the spatial distribution of sea water temperature is very difficult to predict, which leads to uncertainty in long-term climate predictions.

Technical and economic limitations of ocean observation

Ocean observation is subject to significant technical and economic constraints. Unlike the atmosphere, seawater absorbs almost all electromagnetic waves, making it difficult to detect remotely using conventional atmospheric observation equipment. Weather observation instruments such as radiosondes are useful in the atmosphere but are virtually unusable in the ocean.
Ultimately, in order to understand the state of the ocean, it is necessary to go directly to the site using ships to measure underwater temperatures and ocean currents, which is a time-consuming and costly process. Due to these limitations, there is a lack of effective underwater temperature distribution data that can be applied to actual climate prediction models. In other words, although important data exists, there are practical limitations to its full utilization.

Lack of understanding of ocean-atmosphere interactions

Another major factor contributing to the low accuracy of predictions is the lack of scientific understanding of the mechanisms of interaction between the ocean and the atmosphere. Spatial differences in sea surface temperature create winds, which in turn form ocean currents and change the distribution of sea surface temperature. This cycle is a causal interaction in which each factor influences the other.
However, it is extremely difficult to understand this mechanism in detail and model it quantitatively. For example, in the case of El Niño, which causes abnormal weather around the world, we do not yet fully understand how the interaction between ocean currents and wind causes this phenomenon. This lack of understanding ultimately makes it very difficult to predict when El Niño will occur.

Chaotic nature of climate systems

Climate systems have a chaotic nature. Chaos refers to a phenomenon in which slight differences in initial conditions expand unpredictably over time, producing completely different results. In other words, two states that appear almost identical at first can produce extremely different results over time.
Due to this chaotic nature, even the most accurate prediction models cannot completely eliminate uncertainty in long-term forecasts. The observational data used as input for climate models inevitably contain errors, and as the forecast period lengthens, these errors snowball, producing results that are completely different from reality.

Conclusion: Efforts to overcome the limitations of long-term climate prediction

As we have seen, the difficulty of long-term climate prediction cannot be explained by a single technical issue. Various factors interact in complex ways, including the heat capacity of the oceans, the irregularity of ocean currents, the limitations of sea temperature observations, the complex interactions between the oceans and the atmosphere, and the chaotic nature of the climate system itself.
However, this does not mean that we should give up on long-term climate prediction. Recently, rapid advances in satellite technology, unmanned underwater exploration equipment, big data-based climate modeling, and artificial intelligence prediction technology are gradually overcoming these limitations. However, there is still a long way to go. In order to improve prediction accuracy, we need to accumulate more observation data, develop more sophisticated models, and, above all, gain a fundamental understanding of the climate system.
Climate is not just a matter of weather; it is an important issue directly linked to the survival of humankind. Research to improve the accuracy of long-term climate predictions must continue, and scientific and technological investment and international cooperation are urgently needed to achieve this goal.