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Teaching Computational Thinking: Examples from Weather and Climate Modeling “Essentially, all models are wrong, but some models are useful.” - George E. P. Box (1951) Teresa Eastburn & Randy Russell National Center for Atmospheric Research University Corporation for Atmospheric Research NSTA Denver, December 12, 2013

Teaching Computational Thinking: Examples from Weather and Climate Modeling

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Teaching Computational Thinking: Examples from Weather and Climate Modeling. “Essentially, all models are wrong, but some models are useful.” - George E. P. Box (1951). Teresa Eastburn & Randy Russell National Center for Atmospheric Research University Corporation for Atmospheric Research. - PowerPoint PPT Presentation

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Page 1: Teaching Computational Thinking: Examples from Weather and Climate Modeling

Teaching Computational Thinking: Examples from Weather and Climate

Modeling“Essentially, all models are wrong,but some models are useful.”

- George E. P. Box (1951)Teresa Eastburn & Randy RussellNational Center for Atmospheric ResearchUniversity Corporation for Atmospheric Research

NSTA Denver, December 12, 2013

Page 2: Teaching Computational Thinking: Examples from Weather and Climate Modeling

Computational Thinking Solving problems, designing systems, and

understanding human behavior by drawing on the concepts fundamental to computer science.

~ Jeannette Wing, Carnegie Mellon

Integrating the power of human thinking with the capabilities of computers. ~CSTA

Steven GilbertNSTA Press

Page 3: Teaching Computational Thinking: Examples from Weather and Climate Modeling

1. What is a climate model, why are supercomputers needed, and what do they do and not do?2. The Systems Game – Why systems thinking matters3. What’s the difference between a weather model vs a climate model (initial value problem vs. a boundary value problem)?4. Chaos Theory5. Climate simulations for your you and your students to explore

Here’s What We’ll Be Covering

Page 4: Teaching Computational Thinking: Examples from Weather and Climate Modeling

Spark – science education at NCAR

National Center for Atmospheric Researchin Boulder

Page 5: Teaching Computational Thinking: Examples from Weather and Climate Modeling

NCAR Mesa Lab in Boulder

Public and School Group Visits

spark.ucar.edu/visit

Page 6: Teaching Computational Thinking: Examples from Weather and Climate Modeling

spark.ucar.edu/workshops

Page 7: Teaching Computational Thinking: Examples from Weather and Climate Modeling

spark.ucar.edu/events/workshop-computational-thinking-nsta-regional-2013

Page 8: Teaching Computational Thinking: Examples from Weather and Climate Modeling

Evolution of Climate Models

Credit: Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4): Working Group 1: Chapter 1, page 99, Fig. 1.2

Page 9: Teaching Computational Thinking: Examples from Weather and Climate Modeling
Page 10: Teaching Computational Thinking: Examples from Weather and Climate Modeling

Climate Model Components

Credit: UCAR (Paul Grabhorn)

Page 11: Teaching Computational Thinking: Examples from Weather and Climate Modeling

Climate Model Components

Credit: UCAR

Page 12: Teaching Computational Thinking: Examples from Weather and Climate Modeling

• Observations• Theory• Numerical Modeling

Progress in climate models occurs as a result of:

Like a sturdy 3-legged stool

OB

SE

RVA

TIO

N

THE

OR

Y MO

DE

LING

“Science presumes that things and events

in the Universe occur in consistent patterns

that are comprehensible through careful,

systematic study.” ~ AAAS

Page 13: Teaching Computational Thinking: Examples from Weather and Climate Modeling

Models are today’s tech test tube for the Earth system.

Image sourceadaption:NOAA

Images adapted from K. Dickson, NOAA

Page 14: Teaching Computational Thinking: Examples from Weather and Climate Modeling

Climate Models = Virtual Earth• Now we can model various components

(parts or subsystems) in the Earth system (atmosphere, ocean, sea ice, land physics…) and how they will interact and respond over time to a natural or human-made forcing agent.

Atmosphere Circulation & Radiation

Sea Ice

Ocean Circulation

Land Physics

Page 15: Teaching Computational Thinking: Examples from Weather and Climate Modeling

Resolution: What Does It Mean?

Page 16: Teaching Computational Thinking: Examples from Weather and Climate Modeling

Improving Resolution of Climate Models

Credit: Warren Washington, NCAR

Grid Cell Sizes• 1990s (T42)

• 200 x 300 km• 120 x 180 miles

• 2000s (T85)• 100 x 150 km• 60 x 90 miles

Page 17: Teaching Computational Thinking: Examples from Weather and Climate Modeling

Improving Resolution of Climate Models

Credit: Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4): Working Group 1: Chapter 1, page 113, Fig. 1.4

Page 18: Teaching Computational Thinking: Examples from Weather and Climate Modeling

Vertical Resolution of Climate Models

Vertical Layers• 1990s

• 10 layer atmosphere• 1 layer “slab” ocean

• 2000s• 30 layer atmosphere• 30 layer ocean

Credit: UCAR

Page 19: Teaching Computational Thinking: Examples from Weather and Climate Modeling

Horizontal and Vertical Grid

Page 20: Teaching Computational Thinking: Examples from Weather and Climate Modeling

Horizontal and Vertical Grid

Page 21: Teaching Computational Thinking: Examples from Weather and Climate Modeling

Hexagonal Grid and Sub-grids

Credit: UCAR (Lisa Gardiner)

Page 22: Teaching Computational Thinking: Examples from Weather and Climate Modeling

spark.ucar.edu/sites/default/files/SystemInMotionMaster.pdf

Page 23: Teaching Computational Thinking: Examples from Weather and Climate Modeling

Using Models in Education

“Essentially, all models are wrong,but some models are useful.”

- George E. P. Box (1951)

Page 24: Teaching Computational Thinking: Examples from Weather and Climate Modeling

Weather vs Climate ProjectionsPhysics is Physics, Right?

Why do we think we can make meaningful 100 year climate projections when we can’t forecast the day-to-day weather a month from now? Initial Value Problem vs Boundary Value Problem

Page 25: Teaching Computational Thinking: Examples from Weather and Climate Modeling

Weather Model vs Climate ModelCompare and Contrast

Differences (and similarities) betweenWeather vs. Climate Models

• Area Covered (scale)• Resolution – distance (spatial) and time (temporal)

• Timespan covered by model runs• Impacts on computing resources needed, time required to run models

Page 26: Teaching Computational Thinking: Examples from Weather and Climate Modeling

Weather Model vs Climate ModelArea Covered

Weather Model – up to about continental size scale Climate Model – global size

scale

Larger area requires either more computing power/time or lower resolution (spatial and/or temporal)

Page 27: Teaching Computational Thinking: Examples from Weather and Climate Modeling

Weather Model vs Climate ModelResolution and Precision

Weather Model• resolution typically about 3-10 km• timesteps of hourly to 6 hours, forecast for next 3-4 days

Climate Models• resolutions from about 25-30 km up to 100 (or a couple

hundred) km• running computer models can take days or weeks, which

would be impractical for weather models

Precision – why Wx forecast for Christmas is suspect, but temperature next July is reliable (relationship to chaos)

Page 28: Teaching Computational Thinking: Examples from Weather and Climate Modeling

Weather Model vs Climate Model

Timeframe

Weather Forecast – hours to days(up to about 10 days)

Climate Projection – decades to centuries or longer(climate is usually defined as at least 30 years of observations)

Page 29: Teaching Computational Thinking: Examples from Weather and Climate Modeling

Resolution: Spatial & Temporal (Time)• Timesteps can be a few minutes to 12 hours or

more• Durations can be hours to centuries

Page 30: Teaching Computational Thinking: Examples from Weather and Climate Modeling

Resolution and Computing Power Double resolution – increase number of nodes – more

calculations! One Dimension

Two Dimensions

2 times as many nodes

4 times as many nodes

Page 31: Teaching Computational Thinking: Examples from Weather and Climate Modeling

Resolution and Computing PowerWhat if we increase model to three dimensions (space) plus time?

Page 32: Teaching Computational Thinking: Examples from Weather and Climate Modeling

Resolution and Computing PowerWhat if we increase model to three dimensions (space) plus time?

16 times as many nodes – 16x computing power required!

This is why we need supercomputers!

Page 33: Teaching Computational Thinking: Examples from Weather and Climate Modeling

Chaos• Chaos – 10-day forecast reliability limit• Ensemble runs of models – tipping points –

arctic ice melt – sea ice and open water albedo images

• Why Wx forecast for Xmas is suspect, but temperature next July is reliable (relationship to chaos)

Page 34: Teaching Computational Thinking: Examples from Weather and Climate Modeling

Climate Forcings

Page 35: Teaching Computational Thinking: Examples from Weather and Climate Modeling

Source: Meehl et al NCAR

Page 36: Teaching Computational Thinking: Examples from Weather and Climate Modeling

Which of the following cannot be addressed by a physical climate model?

1. How would Earth’s average surface temperature be expected to change if carbon dioxide doubled?

2. How much carbon dioxide and methane will humans add to the atmosphere during each of the next five decades?

3. Can cosmic rays from the sun affect clouds and hence play an important role in climate variability and change?

4. Is it possible to learn about past climate variations by gathering data from holes drilled deep into the Earth’s crust?

5. All above can be addressed by physical climate science.

Page 37: Teaching Computational Thinking: Examples from Weather and Climate Modeling

F = P x g x e x f x d• F = total GHG emission rate• P = population size (global and/or national)• g = per capita gross world/domestic capital• e = energy use per $ of gross world/national

product• f = GHG emissions per unit energy use• d = deforestation effects

How will GHG vary?

Page 38: Teaching Computational Thinking: Examples from Weather and Climate Modeling

Ensemble Projections of Global Temperature for Various Emission

Scenarios

Source: UCAR/NCAR

FutureProjections

VersesForecasts

Page 39: Teaching Computational Thinking: Examples from Weather and Climate Modeling

Climate Models help with…

DETECTION - Is the planet’s climate changing significantly?

ATTRIBUTION – If so, what is causing the change?Nature? Human Actions? Both?

PROJECTION – What does the future hold for Earth’s climate?

Page 40: Teaching Computational Thinking: Examples from Weather and Climate Modeling

Models in the Standards

Next Generation Science Standards

Page 41: Teaching Computational Thinking: Examples from Weather and Climate Modeling

Greenhouse Effect Review

CO2 absorbs heat in the atmosphere

When heat accumulates in the Earth system, the average global temperature rises

Page 42: Teaching Computational Thinking: Examples from Weather and Climate Modeling

Increased CO2 & the Greenhouse Effect When the amount of carbon dioxide in the atmosphere

increases, average global temperature rises. Longwave radiation emitted by CO2 is absorbed by the

surface, so average global temperature rises

Page 43: Teaching Computational Thinking: Examples from Weather and Climate Modeling

Emissions -> More CO2 in Air -> Higher Temperature

15°

18°

Page 44: Teaching Computational Thinking: Examples from Weather and Climate Modeling

Climate Sensitivity - definitionWhenever the amount of carbon dioxide in the

atmosphere doubles, average global temperature rises by 3 degrees Celsius.

15°

18°

15°

18°

Page 45: Teaching Computational Thinking: Examples from Weather and Climate Modeling

Learning from the Past (ice cores)

Ice ageIce ageIce ageIce age

Page 46: Teaching Computational Thinking: Examples from Weather and Climate Modeling

CO2 Emissions – Where are we now?

In 2013, CO2 emissions are around 10 gigatons (GtC) per year (10,000 million tons in units used on this graph)

Page 47: Teaching Computational Thinking: Examples from Weather and Climate Modeling

CO2 in Atmosphere – Where are we now?

iceage

iceage ice

ageiceage

396 ppm in 2013 For hundreds of thousands of

years, CO2 varied between 180 and 280 parts per million, beating in time with ice ages

Since the Industrial Revolution, CO2 has risen very rapidly to about 400 ppm today

Page 48: Teaching Computational Thinking: Examples from Weather and Climate Modeling

Math of Climate SensitivityWhen the CO2 concentration in the atmosphere doubles,temperature rises by 3°Celsius (about 5.4°F)

Examples: If CO2 rises from 200 ppmv to 400 ppmv,

temperature rises 3°C If CO2 rises from 400 ppmv to 800

ppmv, temperature rises 3°C Note: as CO2 rises from 200 to 800

ppmv (800 = 4 x 200), temperature rises 6°C ( = 2 x 3 degrees, not 4 x 3 degrees)

Page 49: Teaching Computational Thinking: Examples from Weather and Climate Modeling

Climate Sensitivity Calculator demo

spark.ucar.edu/climate-sensitivity-calculator

Page 50: Teaching Computational Thinking: Examples from Weather and Climate Modeling

Climate Sensitivity Calculator Activity

Use the calculator (previous slide) to determine the expected temperature for the various CO2 concentrations listed in column 1 of the table above (students fill in column 2); then have them graph.

Page 51: Teaching Computational Thinking: Examples from Weather and Climate Modeling

Advanced Climate Sensitivity Math

T = T0 + S log2 (C / C0)T : new/current temperatureT0 : reference temperature (e.g. 13.7 degrees C in 1820)S : climate Sensitivity (3 degrees C)C : new/current atmospheric CO2 concentrationC0 : reference atmospheric CO2 concentration (e.g. 280 ppmv in 1820)Example:What is new temperature if CO2 rises to 400 ppmv (from 280 ppmv)?T = T0 + S log2 (C / C0) = 13.7 + 3 log2 (400/280) = 13.7 + 3 log2 1.43 = 13.7 + 1.54 = 15.2 degrees C

Page 52: Teaching Computational Thinking: Examples from Weather and Climate Modeling

Dry air mass of atmosphere = 5.135 x 1018 kg = 5,135,000 GigatonsCO2 currently about 599 ppm by mass (395 ppmv) = 0.0599%CO2 current mass = 0.0599% x 5,135,000 Gt = 3,076 GtCO2 current emissions = 9.5 GtC/yearAtmospheric fraction = 45%

M = M0 + [0.45 x (3.67 x m)] = 3,076 GtCO2 + [0.45 x (3.67 x 9.5 GtC/yr)] = 3,076 + 15.7 GtCO2 = 3,092 GtCO2

CO2 concentration = 3,092/5,135,000 = 602 ppm by massCO2 concentration = (602/599) x 395 ppmv = 397 ppmv

Math of CO2 Emissions andAtmospheric Concentration

(16 + 12 + 16) / 12

= 44/12 = 3.67

GtC vs GtCO2

Page 53: Teaching Computational Thinking: Examples from Weather and Climate Modeling

Poll: Rising Emissions

B

A

C

?

?

?

Page 54: Teaching Computational Thinking: Examples from Weather and Climate Modeling

Poll: Rising Emissions

B

A

C

?

?

?

Page 55: Teaching Computational Thinking: Examples from Weather and Climate Modeling

B

A

C

?

?

?

Poll: Emissions rise then steady

Page 56: Teaching Computational Thinking: Examples from Weather and Climate Modeling

B

A

C

?

?

?

Poll: Emissions rise then fall

Page 57: Teaching Computational Thinking: Examples from Weather and Climate Modeling

Very Simple Climate Model demo

spark.ucar.edu/simple-climate-model

Page 58: Teaching Computational Thinking: Examples from Weather and Climate Modeling

Why does temperature continue to rise as emission rate declines?

Atmosphere

CO2 in Atmosphere

CO2

Emissions

CO2 Removal byOceans & Plants

spark.ucar.edu/climate-bathtub-model-animations-flow-rate-rises-fallsspark.ucar.edu/imagecontent/carbon-cycle-diagram-doe