Toward a Deeper Understanding of Our Climate System Through Data Science | Emily Gordon

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Transcripts

[Music]

next up in this block we'll hear about

one of the critical areas of

sustainability and that's climate change

and for that we have Stanford data

science postto Emily Gordon Emily please

join

us so um I'm really excited to share

some of the um challenges that we face

um understanding climate change and sort

of bringing data science um into the

process so here I'm showing a plot of

annual mean Global sea surface

temperatures and I've plotted this as a

departure from the 1850 to about 1900

mean and you can see from about 1950 uh

1950 onwards we have our uh Global mean

uh global

warming so we might ask uh you know how

much uh you know warming is attributable

to greenhouse gas emissions and it's

actually a really hard problem to figure

out what the amount of observations that

we have because the time scales that

we're interested on are about the same

as the amount of data we have from our

observational methods so we turn to

climate models so this black line I'm

showing here is the same as the Green

Line in the other figure it's our

observed Trend in Sea surface

temperatures um but in the Gray Line I'm

showing the uh from a climate modeling

experiment what we would actually expect

how much warming we expect from

greenhouse gas emissions alone so we see

we have slightly more warming than we

actually have currently observed and

that's because of this blue line which

is aerosol emissions so this plot to me

at the sort of takehome here is that we

don't have enough observations to

understand completely what's going on in

our climate system and so we have to

supplement this with uh climate model

data to really pick about pick out what

our what is going on in our

system so climate models are an

incredibly powerful tool for

understanding our climate we can think

of them as all these different models as

sort of different ways of uh uh uh

thinking about the Earth system we can

make different assumptions we can

include different processes we can

parameterize them in different ways so

we can get all these different

realizations of our Earth system and we

can perform experiments on them we can

do things like warm up the planet we can

cool it down we can hit it with big

shocks like volcanic eruptions we can

remove components like the land and see

uh how the fluid flows without you know

uh topography or we can do smaller scale

things like brighten clouds and see how

that affects um the climate

system but another part of the power of

these tools is that because we have all

of these different models with these

different assumptions

we can perform the same experiment on

all of our models and see how they

respond and and and sort of say

something about our our own planets

response to that forcing so for example

if we were to double the amount of

carbon dioxide in the atmosphere we

might want to look at how much warming

we would get across all of our different

climate models and then build a

distribution from these models and we

can put a sort of an uncertainty

estimation on how much warming we might

expect um in the

future and in fact this is a pretty

common experiment to perform so this was

first performed in about

1967 um and then I've taken this quote

from the Chie report in 1979 who looked

at um using simple climate models they

found that we would expect between 2 to

3.5 degrees warming under a doubling of

carbon

dioxide and this experiment has still is

still being run to this day it was um

included in the most recent ipcc

International uh intergovernmental panel

on climate change report and this is the

result from the most like from the most

recent report where they find the likely

range of warming is between 2.5 and 4°

so actually we were doing really well

with our climate models back um in

1979 so we can take all of our climate

models and we can rank them by how

sensitive they are to carbon

dioxide and then we can project them

forward under different scenarios of

climate change so uh for you know more

High sensitivity uh climate models we

would have expected to have passed this

1.5 degree threshold which is in the

sort of dark this orangey color um you

know a couple years ago so maybe we're

not quite so sensitive to to carbon

dioxide as these really high sensitivity

models but we may be looking at Crossing

1.5 in the next sort of 10 years or

so and then because these climate models

are are run you know globally we can

then link the sort of warming that that

we get from these different models to

the surface impacts on Regional scales

so this top uh figure here is showing

the increases in heat events under

different levels of warming and then the

bottom is showing the increases in uh

extreme rainfall events under the

different level of

warming and I also want to just

reinforce from just these three figures

that I've shown the sheer amount of data

that's gone into making them so there um

from that probability distribution

there'd be about at least 50 climate

models run between 200 to 1,000 years of

data from Those runs and then on top of

that those scenarios there were five

scenarios with three members each so

that's another 300 years or so uh so

there's just so so much data that we're

churning out from our climate models

now so how do we build a climate model

so we take our Earth system and we need

to divide it up into our grid boxes

where we're going to solve our equations

of motion and sort of pass the

information between the boxes at

different time

steps but we have things that are going

on on smaller than our grid Box level uh

we have things like clouds we have to

understand our bottom topography so

whether we're over land over the ocean

you know over mountains and we also have

to get the radiation scheme correct and

so we do this uh by a process called

parameterization so we uh make sort of

empirical functions to solve what's

going on in these um subg good scale

levels and this has to be done for all

of the different processes so I'm

showing us the algorithm for one type of

cloud process within a within a grid

box so from our most recent round of

modeling experiments we have about 20

pedabytes of data available we're not

just interested in our response to risk

forcing which is what I've shown so far

we're also interested in um variability

and predictability so understanding

actually our internal climate processes

how they project onto clim climate

change and then um cause some of these

more extreme events that we've seen and

another really important part is the

systematic biases and our climate models

making sure we understand the processes

that we are realizing and the processes

that we are not so if we're providing

information to stakeholders that we know

that we have good calibrated

uncertainty and then this is a figure

that projects forward the amount of data

that we're going to be having in the

next uh sort of 10 years or so and and

we just this this data challenge is just

going to become uh more and more St

um and and there's so much room for for

bringing in good data science methods

for analyzing all of this

data so what are we doing um in our

future so first of all I've already

shown this using our existing data

sources is uh making projections of

future climate change uh using the

existing modeling runs to uh sort of

pull out these different um impacts of

climate

change this uh this one's slightly

different this is looking at using all

of the data we have to train uh

emulators to train AI so that you can

quickly and and efficiently query you

know um you might be interested in in a

carbon dioxide forcing a certain year a

certain variable and pull out what the

regional impact of climate change um

that you're interested

in another thing is a process called

downscaling so I mentioned that climate

models are run at pretty CSE resolution

but you know um we uh we we we feel the

climate impacts on our on our small grid

boxes you know um in in our day-to-day

lives and so there's a lot of uh effort

now in in bringing our big large scale

climate information to the smaller

scales and this is a really exciting

area because we are linking our observed

relationships between the large scale we

can go out and do field campaigns we can

understand how the large scale

variability um impacts the smaller

scales to sort of bring this large scale

understanding of climate models to um

the smaller scale impact

analysis and then finally um sort of

ethical responsible trustworthy Ai and

data driven methods if all of our

information is coming from or most of

our information is coming from

datadriven analysis on climate models we

want to make sure that any information

that we're providing is absolutely

trustworthy and

robust the other thing I think that's

actually really exciting here is this

actually has allowed for scientific

discovery so if we're using our data

driven methods to understand um to uh to

a certain response to climate change and

we find something that's not expected

well there could be two reasons for this

one you've done something wrong oh no

that's bad but you you do all these

checks and you go okay actually know you

know everything is still meshing with my

physical understanding of the system so

so this is an opportunity to discover

new

science the other thing that I think

that I really want to un underscore here

is that not only is is this a data

science effort but this is a physical

science eff and this is um I think

bringing together sort of this

interdisciplinary theme of of of this

meeting that we that we need to be

working together with our data science

understanding and with our domain um

knowledge and then I wanted to finish

with this final thing that's um uh

become sort of a really a really great

Avenue for future Improvement is not

only like thinking of our climate models

as you know these things that are over

here existing tuning out data but how

can we improve the models themselves so

I talked before about how we have these

sub grid scale

parameterizations what about if we start

replacing them with with neural networks

with AI we can use again our

observational data of the relationships

between um between you know different

processes and and and really quickly

build um empirical relationships to make

um to make uh our climate models better

and and run more quickly and and and I

saw a talk about this a couple of days

ago and it's just so so impressive what

what people are doing with

this so I'm finishing here with a

satellite Loop of an atmospheric River

Event that was over California on the

4th of February if people remember it um

and and I I guess I want to also

reinforce that uh you know not only do

we have these beautiful pictures but

this data is just so so important to us

and our understanding of climate

change thank

[Applause]

you