In this blog post, we will look at why long-term climate prediction is difficult, focusing on the influence of the oceans, observation limitations, and the complexity of the system.
Climate patterns vary from year to year, with some years experiencing extreme heat waves and other years experiencing cooler-than-usual summers, resulting in irregular fluctuations in summer temperatures. Winter is no exception. Some years are marked by severe cold and heavy snowfall, while other years are unusually mild with little snowfall. Seasonal climate patterns are becoming increasingly irregular in Korea and around the world, and the magnitude of these changes is growing.
These seasonal climate characteristics reduce the accuracy of weather forecasts. In particular, predicting these changes in advance is much more difficult than one might think. Thanks to advances in observation technology and numerical forecast models, short-term forecasts covering a few days maintain a fairly high level of accuracy. However, when the forecast period is longer than a month, i.e., when it enters the realm of long-term climate prediction, the situation is completely different. The accuracy of forecasts becomes significantly lower, and the results are often unreliable. This is not simply due to technical limitations, but is also an inevitable problem arising from the complex and uncertain nature of the climate system.
So why are long-term climate forecasts so difficult? There are many reasons, but the first thing to mention is the influence of the oceans. Long-term climate change is much more influenced by the oceans than by the atmosphere. Physical changes in the ocean, such as sea temperature, ocean currents, and heat exchange between the deep sea and the surface, directly affect atmospheric flows, air temperature, and precipitation. However, it is not easy to predict long-term changes in sea temperature. This is due to the unique properties of the ocean itself.
The ocean has a much larger heat capacity than land. Seawater can store heat energy from the sun for long periods of time, and its heat capacity is about 400 times that of the atmosphere. This means that the ocean is insensitive to short-term temperature changes but can have a significant impact on long-term climate change. Because of this property, the state of the ocean is a much more important factor in long-term climate prediction than short-term weather.
For example, Northern Europe is strongly influenced by warm currents, and its average temperature is much higher than other regions located at the same latitude. In addition, the daily and annual temperature ranges are small. This is because warm currents carry warm seawater northward, acting as a buffer for the local temperature.
As such, ocean currents play a decisive role in determining the climate characteristics of a particular region, and even slight changes in ocean currents can significantly alter the temperature and precipitation patterns of that region. Therefore, in order to make accurate long-term climate predictions, it is essential to accurately understand the state of the ocean, especially the temperature of the seawater and the movement of ocean currents. However, there are several obstacles to this. The biggest challenge is that ocean currents are highly irregular.
Ocean currents are not simple unidirectional flows, but complex movements formed by the intertwining of numerous factors. Various factors with different cycles are involved in ocean currents, some of which have long cycles of over 100 years. These factors interact with each other in a synergistic or counteractive manner, making ocean currents unpredictable. The irregular flow of ocean currents causes instability in the spatial distribution of sea temperatures, which in turn greatly affects the accuracy of climate predictions.
Furthermore, technical and economic constraints on measuring temperatures inside the ocean, especially underwater, cannot be ignored. In the case of the atmosphere, various atmospheric information can be collected through weather observation equipment such as radiosondes, satellites, and radars. However, the ocean absorbs electromagnetic waves well, making it difficult to collect information using electromagnetic waves from a distance as in the atmosphere. Due to this characteristic, in order to accurately measure underwater temperatures and ocean currents, observation equipment must be placed directly in the sea. This is mostly done through field observations using ships, which requires a lot of time, money, and manpower. As a result, marine observation data is often insufficient in quantity and unevenly distributed in terms of space and time.
Ultimately, despite the importance of accurate underwater temperature distribution data for climate prediction models, there are practical limitations that prevent the full utilization of such data. This further increases the uncertainty of long-term climate predictions.
Furthermore, it has been pointed out that scientific understanding of the interaction mechanisms between the ocean and the atmosphere is still insufficient. Differences in the spatial distribution of sea surface temperatures cause winds, which in turn create ocean currents that change the distribution of sea surface temperatures. In other words, sea surface temperatures, winds, and ocean currents are closely linked, and there are complex causal relationships between them.
However, these interactions are not simple linear relationships, but rather have a nonlinear and complex structure that is difficult to clearly identify. The most representative example is the El Niño phenomenon. El Niño is a phenomenon in which sea temperatures rise near the equator in the Pacific Ocean, causing abnormal weather around the world.
El Niño is caused by subtle interactions between ocean currents and winds, but many aspects of its specific mechanisms remain unclear. As a result, there are still many limitations to accurately predicting when El Niño will occur, how strong it will be, and how long it will last.
Finally, the complexity and uncertainty inherent in the climate system itself, or its chaotic nature, is one of the key factors that reduce the accuracy of long-term climate predictions. Chaos refers to the property of a system in which very small differences in initial conditions expand over time and eventually lead to completely different results. In such systems, the same state cannot be reproduced twice, and even very small changes can lead to unpredictable and significant results.
Climate prediction models simulate future climate based on data obtained by setting various factors, such as the atmosphere, oceans, land surface, and solar radiation, as initial conditions. However, these initial conditions inevitably contain errors due to limitations in observation. In particular, the longer the prediction period, the more these small errors accumulate and grow exponentially over time, eventually leading to significant differences between the prediction results and the actual climate.
The complex nature of the ocean, limitations in observation, lack of understanding of the interactions between the ocean and the atmosphere, and the inherently chaotic nature of the climate system are the main factors that make long-term climate prediction difficult. To overcome these limitations, it is necessary to develop marine observation technologies, high-resolution numerical models, and artificial intelligence technologies that can effectively process large amounts of climate data. Above all, it is necessary to deepen our scientific understanding of the climate system and develop prediction methodologies that take uncertainty into account. Long-term climate prediction is not simply a matter of scientific and technological advancement, but also an essential foundation for responding to the climate crisis facing humanity.
