Messy Models

Hello and welcome to the continuation of my previous blog post: She's a Modeller.
If you haven't read that and you're not doing a PhD in atmospheric physics, you will need to in order to understand what I'm gonna say today.
Climat modelling sounds pretty cool, doesn't it?
The very notion of us being able to predict the weather is just mind-shattering.
BUT, there's always a "but".
Climate models are imperfect, and we will never be able to create a carbon copy of our Earth.
As a matter of fact, even if we get the perfect climate model, we still wouldn't be able predict the weather perfectly.
There are three big problems in climate modelling, two of them are kinda related and the last one is the absolute FATHER of "Why-You-Suck-At-Climate-Modelling".

I've already talked about discretisation last week (splitting the globe up into tiny boxes).
Since our computers need to calculate the atmospheric conditions in each box, one at a time, this can be extremely time-consuming.
But we are always trying to get those boxes in higher resolutions (smaller boxes) so that the Earth looks more "smooth" and less "blocky" (Figure 1).
This creates a sort of paradox: we want more (smaller) boxes, but that'll mean it takes even longer for our computers to finish calculating the whole thing.
So, as much as want to, we can't make those boxes too small.
And big boxes have big consequences!
So that's our first dilemma.

Figure 1. As supercomputer processing speeds get faster, we can afford to increase the resolution of our model. That means we can have more, smaller boxes; rather than a few big, bulky ones. This enables us to make a more accurate map of the Earth as well as areas of elevations (such as moutains).
This makes our model more realistic and Earth-like. Taken from Climate Modelling 101.

Now, on to our second problem.
If we have big boxes (more than 100 km wide), we can't represent smaller processes.
CLOUDS are an excellent example of that. We don't know whether they help reduce or induce the greenhouse effect.
But we do know they play a major role in Earth's climate.
But we don't see a full 100 km cloud do we?
That's the problem. We can't make a whole box full of clouds because that will be unrealistic and create horrible climate predictions for us.
Not only that, but we don't even know what its function is (greenhouse effect - yay or nay?).
So us scientists need to rely on (if you haven't guessed it yet) MATHS to try to represent the clouds with an mathematical equation.
This process is called parameterisation.
Don't even try pronouncing it, even I can't spell it right.
Even with parameterisation, we can't get it exactly right, simply because... we don't actually know what clouds do, exactly.
That is a major flaw. We are trying to replicate something we know very little about.
You know, normally, climate models can help advance our understanding of these clouds.
But they're just TOO.DAMN.SMALL.
And if we make our boxes small enough (around 1 km?) to realistically portray these clouds, it'll take infinity years to finish our modelling due to the sheer amount of boxes we get.
See where I'm getting at here?
Discretisation and parameterisation are both limitations to our climate model.
I hope I've made it all understandable up till this point.
The final problem will be a whole lot technical, and wayyyy more interesting.
I promise!

THE REASON YOU SUCK AT MODELLING: CHAOS

Okay, so this is our final challenge, AND our biggest one.

Unlike discretisation and parameterisation, this is not a "technical" problem.

This is all about that bass The Chaos Theory.

Sounds cool, huh?

What the Chaos Theory says is that if you put some numbers (temperature, pressure etc) into a model that's complex, non-linear and dynamic (basically, our atmosphere system), bad things happen.
The guy who came up with the Chaos Theory, Edward Lorenz was playing around with a similar model back in the 1960s.
He put a few numbers in and let the system run for a while. Came back and recorded his results.
Then, he did the test again, only this time, he rounded off his numbers before typing them into the system.
When he came back from his coffee, the results were INSANELY (much emphasis) different from his first test.
So, this is because the system is chaotic. We can't predict what goes on with it.
Not only that, we have systematic errors going on with our measurements.
Some measuring devices can only give you a certain amount of accuracy a.k.a decimal places, where as perfect modelling might need you to be really accurate and have 20 decimals places!
(28.3950358395030128394 degrees celsius, anyone?)
And what if I don't have a 20-decimal thermometer?
Well then, sucks to be you.
Your predicted weather will look nothing like the actual weather.
Therefore, modelling and predicting the climate is nigh impossible and I've just wasted your time in these two blog posts. =)
Thank you very much.

Figure 2. The Chaos Theory is also popular known as the Butterfly Effect, where a butterfly can flap its wings in Brazil and that flap will cause a cyclone to appear in Texas two years later. The butterfly's flap symbolises the small changes in measurements we input into our model. Whether it flapped its wings or not can make all the difference in a cyclone appearing (symbolising tiny changes in the beginning can result in massive differences after long periods). Taken from Mr. Lovenstein.

I'm just trolling.
The Chaos Theory only kicks in when you make the model predict the climate over a long period of time.
Right now, we can predict up to 5-7 days accurately.
10-14 days if we're really pushing it.
But after that, you get the system going haywire!
THAT is the reason why you only get weather forecasts a week in advance.
Any more ahead and the weather station can't guarantee you whether the predictions are true.
But doesn't this sound a little contradictory?
We admit that we can't predict the weather past 2 weeks tops.
And yet we claim to be able to predict how Earth surface temperature will go up by *insert number* degrees in 100 years if we keep emitting greenhouse gases.
So... what's the deal with that?

Well, you'll just have to stick around for the final part of this trilogy!
*Whoopie!*

Listening to Father Figure - Glee Cast

Still modelling,

TK

She's a Modeller

Over the course of my (short and to-be-completed) undergraduate studies, I've had the pleasure of meeting some really great scientists (No, I don't stalk my lecturers... well, actually... nevermind).

The point is, I've met quite a few climate scientists over the year.

Surprisingly (or not), they are all female. 

So, I guess there's a correlation between being female and studying the most controversial field in science (next to GM crops and vaccination).

Anyway, as a student, we are kinda expected to be a "Jack of all trades, Master of none".

That's why I have to study Earth Science, Communications, Biology, Ecology, Genetics, Statistics, Biodiversity Conservation etc in my degree. 

And this semester, it's my turn to try and tackle a unit called "The Climate System".

At the beginning, I was a little excited at the prospect of finally studying what my "scientist acquaintances" are doing/did in their PhD: Climate.

I guess I can finally understand the reasons they were interested in looking at the global climate.

My bubble sorta popped at my first lecture, when I realised the climate system, is mostly just about the atmosphere, which I do not particularly like. 

However, many students have done this and have lived to tell the tale, so I shall persevere.

For our first assignment, we had to write an essay... with some rather ambiguous instructions.

We could pick to write about one of two topics (Wow, is "two" going to be a recurring theme again in this post?):

1. If weather forecasts can only predict one week into the future, how can climate scientists predict the climate for the next 50 to 100 years!?
2. Write about the El Nino Southern Oscillation (ENSO) and bla bla bla...

People who hear me whine everyday would know how much I hate ENSO.

And therefore, I will not write about ENSO.

Which leaves with Climate Modelling *yay*.

To help me better structure my essay, I will once again blog about my assignment in order to gain a better understanding of...the structure.

So what is climate modelling?

It's (at its most basic form) human's attempt to create a virtual Earth.

We do this by applying mathematical equations that describe the laws and forces of nature.

So you'll get an equation for the conservation of energy, an equation of the laws of thermodynamics etc.

And so through the use of maths (UGH...), we can create/model the Earth's atmosphere, which is the biggest player in the climate system.

However, the atmosphere itself is highly complex. 

If we were to encompass ALL the elements and processes in the atmosphere, it would take our computers wayyyy too long to model the Earth. 

That's why we started with only a few factors. And as computer processors grew faster, we could put in even more factors (but still not all of them).

Of course, if the model was to look at the Earth as ONE big system, it would overload and explode.

Therefore, we have to split the atmosphere up into tiny boxes/grids (as shown in Figure 1). 

And then, in each of the boxes, we apply a whole set of the mathematical equations (mass, energy, thermodynamics).

At this scale, our computer can calculate everything going on in that box and come up with some answers (Oh, the temperature in Box 1 is 39°C).

It does this for every box in the model. And then the boxes communicate with each other.

Box 2 is 34°C, therefore heat will transfer from Box 1 (39°C) to Box 2.

[NOTE: I could be wrong with all the physics going on here. But hopefully my explanations will be sufficient for understanding how models work.]

With this, we have the Earth divided into boxes. 

Each box with has calculated values in them (using maths).

And then the boxes communicate their values with each other (using more maths).

Figure 1. Discretisation is the process of taking one continuous Earth and slicing it up into separate cubes/grids. This process helps simplify climate models enough to make them solvable (by computers).
Computers will take the equations in each cube and calculate them, communicating data with other cubes.
Taken from ETH Zurich.

If you're still keeping up, I am genuinely impressed.

Not because of the complex details of climate modelling, but because of my crap communicating skills.

Anyway, having the atmosphere representing the climate isn't enough.

The ocean is another major player (along with the biosphere, lithosphere and cryosphere).

Thus, scientists have made models of them too.

And what they did next is combine the atmosphere model, with the ocean model (and any other models they have).

So now, the tiny boxes in the model communicate with each other. 

And the models themselves communicate and exchange data with other models.

Because you know... the atmosphere interacts with other systems on Earth, vice versa.

By taking in more factors (the atmosphere, ocean, land, biology etc), we are getting closer to creating a 99.9% copy of the Earth.

Of course, we can't possibly hope to replicate the planet 100% (something I'll get into later).

But with more models at our hands, we can predict the climate pretty well, don't you think?

And as our computers get faster and faster, we can couple MORE models together and predict further into the future.

After the modelling run is complete, you'll get data about what your "virtual Earth" thinks might happen (which can be illustrated into Figure 2). 

With this, climate scientists publish their results, try to use this data to educate to public (but are hated for it) and inform policy makers (but are ignored by them).

Figure 2. A typical graph resulting from climate model data. Each pixel in the graph represents a box/grid that resulted from discretisation. If the cube size was smaller, we would get a more "smooth" picture and less obvious pixels. But doing so will dramatically increase processing time several folds. Taken from IMAGe.

As I promised myself to keep things light and simple, I will stop here. 

But I'll definitely get back to this topic in my next post.

The content of this blog post will form the first (of three) section of my essay.

I hope I communicated the science well enough.

And I also hope that I didn't make any mistakes with the science.

*Fingers crossed*

Listening to Chandelier - Glee Cast (Originally by Sia)

Modelling,

TK

Call Me Dr-eamer

*Deja Vu: I feel like I've blogged about this before. 0_0 But Oh Well!


When I was a kid, people (the adults) used to ask me "What do you wanna become when you grow up?".
And I'm 100% positive I'm not the only who gets asked this.
People would ask me this question.
And I would give them my answer. With great enthusiasm, no less.
Every time I get that question, the same optimism can be heard in my answer.
What was different was my answer itself.
What "I wanna become" seems to keep changing all the time.

Kids looooove blowing things wayyyy out of proportion, so my first answer was "to be an astronaut"!
Because I think space is cool (Doesn't everyone?).
But then, age and maturity caught up to me, and it was dragging me back down to Earth.
My next answer become "pilot".
Because if you can't reach for the stars, you'll just have to make due with the clouds in the sky.
Wait another year and its "artist", because drawing is easy (Hey, I was 6...) and people'll pay me for it.
How great is that!
Variations of "teaching" occupied the rest of my childhood.
Because that was all I ever was exposed to at the time: teachers.
Most times, I just alternated between "English teacher" and "Piano teacher".
Then, the idea of "teacher" just up-and-disappeared when I entered my teenage years.
Being an avid "player" of Neopets gave me some HTML coding skills, something many of my peers didn't have (unless you blog!).
And with that, my career goals at the time shifted into the IT field.
I thought "IT's the only thing I can do".
Journalism (and copywriting, in particular) was also something I've looked into, because my English was decent, but I never looked too deep into it.
So off I went, telling people I was interested in IT.
All this culminated at an Education Fair, where a lady representing a local A-levels college said to me,
"You know IT is a serious field, right? Its not about making computer games or stuff like that."
Wow. Just wow.
I was a little offended, to be honest.
My interest in all-things-computer started from Neopets.
HTML coding was fun for me.
I have never lived with the notion that IT = making games.
Yeah, that wasn't much of a culmination, was it?
Just some trivia.

I didn't really think about "my ambition" until my first trip to Perth, back in 2010.
My parents wanted me to attend UWA, so we paid the uni a visit, and managed to snag a course guide from the Future Students office.
That night, I had a look at all the various courses available.
Two things made me go "wow":

1. So many courses to pick from
2. So many pretty pictures

Mostly the second reason, really.

So, with computing in mind, I browsed through the entire guide and narrowed it down to two courses:

1. Applied Computing
2. Computer Science

"Two" seems to be the recurring theme in this post.

Anyway, Computer Science looked promising. 

But I just wasn't "feeling" it.

So I kept a more open mind and the natural sciences looked interesting!

Again, because I was so sick of Pure Biology, Chemistry and Physics.

"Environmental Science? What a "noble" degree."

And that's how I ended up with Environmental Science.

Also, the person representing the Conservation Biology major in the guide was Asian.

So I guess that helped too!

A badly taken photo of my lab results. Tiny spots on the agar plates are actually colonies of Escherichia coli.
Some plates contain ampicillin, which kills E. coli (up).
Some E. coli have been genetically modified, ie: fluoresce under UV light (down, right).
A simple but fun experiment for my first time in the lab!

*Present Day*
I guess relying on my gut instinct back then worked!
I... can't really imagine myself doing a computing degree.
And I do find the topics in the natural sciences interesting.
Not only that, I do suppose I can see myself working in this field when I grow up (because 21 year olds aren't adults yet).
And I look forward to it.
Oh gosh, this post was due last week. I've procrastinated so much, which means I'm normal and doing what every uni student is doing.


Listening to All Out of Love - Glee Cast

Uni work abound,

TK