Week 5- The Perfect Storm

Sorry about the late post; the last two weeks were quite busy but everything is fine now.

In week 5, we learned about the mathematical and computational models that model the climate around the earth. The earth is split into separate grids, and each grid has its conditions (humidity, temperature, precipitation) modeled.

Below is a temperature map from NOAA (based on measurements, not models). The model that we learned about, the GCM, can make predictions into both the future and the past.

While New Zealand had around average temperatures for July (i personally found June-July this year quite cold), much of the world had above average temperatures and even record warmest temperatures. It seems like the world is tending towards not just a warm trend but also instability. This is not great because the weather extremes are harder to prepare for.

Typhoon Haiyan- an extreme weather event

In the West pacific basin, storms form much more frequently than cyclones form near South pacific (where NZ is). Since summer in the Northern Hemisphere is opposite to summer here, ocean temperatures are high between July-September. Warm ocean water increases the rate of convection and is great for forming powerful cyclones.

In November 2013, Typhoon Haiyan got to pass a strip of sea surface exceptionally warmer than normal temperatures, especially for november, which is essentially the start of a winter season.

All credits to scienceblogs for the diagram below.

So how strong was this storm? Here is a satellite photo of the storm at peak intensity (from wikipedia):

The country in the photo, the Phillipines, is approximately the same size as NZ.

This storm caused the deaths of at least 6,340 people, and the cost of $2.86 Billion USD. Incredibly scary..

Modelling for extreme weather events and climate change

So, you might wonder if there is a way to predict when, where and how such a storm could form. However, Climate models are currently trend-based, meaning there could be predictions as to how likely a region will be in severe drought or be flooded, but we can’t know for sure. This is similar to modelling climate change. While we will be able to estimate what the future will look like, a change in one factor could easily change everything. Thus, sometimes there exists a need for modelling based on different possibilities, the most known being the worst case and best case scenarios.

During the session, I was astonished how much computational power there needs to be to predict and model global climate systems, and even then, the confidence in each predicted outcome is not very high.

This all makes me wonder about the claims media make about the polar sea caps melting and the rise in sea levels due to this. Are they just taking the worst-case scenarios, adding some pictures of polar bears on melting ice rafts (although they can just swim away) to trigger some sort of alarm response in people?

Although climate change is, and will continue to be a huge issue, painting it in its worst colour will only trigger fear in more people and let others start to deny it (as it seems so extreme).

The problem with this issue is there is no ‘lab rat’ of the earth for us to experiment with, and we end up with the results of whatever we do to the one planet we have. I hope that if we can’t reverse what’s been done, then we don’t do any more harm. Like we blame the industrial revolution for causing climate change, perhaps future generations will blame us for not doing enough to stop it.

Thank you for reading!


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