FORECASTS, MODELS AND PROJECTIONS

Deke ArndtOctober 2015

N O A A’ S N AT I O N A L C L I M AT I C D ATA C E N T E R

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Topic Goals

Learners will be able to:• understand and articulate basic differences between

climate projection models and weather forecast models

• articulate basic differences between deterministic and probabilistic tools

• identify credible sources of climate projection information

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Three categories of prediction

Weather FORECAST• Now through 7-10 days from now

Climate Seasonal OUTLOOK• Next month through Next Year at this time

Climate Change PROJECTIONS• Decades to Many Decades into the future

All of these are built on or heavily influenced by MODEL OUTPUT

Remember This!

“All models are wrong, but some are useful.”• George Box, English

statistician “Weather models break

down after about a week, then chaos rules.”• Any operational weather

forecaster, anywhere

WEATHER FORECASTING

How Daily Wx Forecasts Are Made Wx forecasts

ultimately issued by humans, but heavily rely on weather models

Computer algorithms track many weather variables and use the laws of physics to calculate future states (in 3-D!)“There’s an equation for that!”

How Daily Wx Forecasts Are Made

Output: specific numbers for time, date and place

Forecast for Asheville, NC at noon today:• 59°F temperature• 58°F dewpoint• South wind, 6 mph• 95% sky in cloud• 65% chance of precip• 0.21” rain today

(slightly different than today’s forecast for the same time)

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How will you judge?

In your eyes (which are the eyes that matter), is the forecast a “Bust” if …• … the temperature missed by two degrees?• … the wind is 9 mph instead of 6 mph?• … it does not rain?

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Comparing PredictionsPrediction Type Weather

FORECASTSSeasonal OUTLOOKS

Climate PROJECTIONS

Basic Drivers PhysicsElements Tracked State variables,

ProcessesType of surfaces Grid cells, 3DEnsembles? SometimesWhat causes uncertainty (besides imperfect science)?

Imperfect initial conditions

More resolution or computing power desirable?

Yes

Prediction values are actually

Specific values at specific places and times

Uncertainty handled how?

Mostly outside the forecast itself

SEASONALOUTLOOKS

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Here’s an example

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A new Challenge If Wx models break down after ~10 days, how do we

forecast Nov-Dec-Jan?• We won’t, but we will make a seasonal outlook

Still use wx models, but new things become important• Changes in Sea surface temps, sea ice extent, El Nino, etc.

change slowly enough for weather models to ignore for 7 days, but over three months, they become important

Months and seasons last long enough for outcome patterns to emerge (or be preferred)• Outlooks won’t forecast wx at Asheville at 1pm on Jan. 17, but

they will say we are likely to see more warm ridges and fewer cold troughs than normal over the season

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So there’s a trade-off

We know several external factors that shape weekly to seasonal outcomes• Arctic Oscillation, ENSO, etc.• We also know weather just

happens too Deterministic models become

less useful, so predictions take different shapes• They take the shape of

probabilities and categories

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Ensemble method: one example

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So there’s a trade-off Because these are not

deterministic values• Map contours are not

how much warmer or cooler

• Contours represent expressions of confidence in a certain categories of outcomes

• How much would a forecaster bet on that outcome category?

This Winter (as of now)

17Prediction Type Weather FORECASTS

Seasonal OUTLOOKS

Climate PROJECTIONS

Basic Drivers Physics PhysicsElements Tracked State variables,

ProcessesState variables, Processes

Type of surfaces Grid cells, 3D Grid Cells, 3DEnsembles? Sometimes YesWhat causes uncertainty (besides imperfect science)?

Imperfect initial conditions

Climate Variability (ENSO, AO, etc.); statistical power

More resolution or computing power desirable?

Yes Yes

Prediction values are actually

Specific values at specific places and times

Expressions of confidence in categories of outcome

Uncertainty handled how?

Mostly outside the forecast itself

They are built into the proabilities

Comparing Predictions

CLIMATE PROJECTIONS

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The First Assumptions

Abandon all hope of predicting specific outcomes for specific seasons in the future, much less specific days and times.• “Lose the battles; win the war” approach

Run a bunch of models over time, use their aggregated statistics, not their specific output

Do not worry about initial conditions• In fact, bump them around a little bit every time to make

sure you explore all the possible outcomes Understand that we can’t do as fine a resolution as

weather models

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More Important Factors Emerge

Foremost: human behavior. How will GHGs change over time? • Factors: Economy, Societal will to regulate GHGs

Little things that add up over time become very important!• Example: exacting cloud reflectance is pretty much

irrelevant to weather forecasts and seasonal outlooks, but it becomes important on long timescales

• So does evolution of sea ice, land use changes, the type and height of cloud formation, etc.

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Making sure the model is robust

20th Century (and smaller chunks of it) is the proving ground for GCMs• Not every run needs to nail the 20th century, but the

ensemble should map to 20th century outcomes to build confidence

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GHG scenarios drive projections

There are four major “RCPs” in the IPCC • RCP2.6 (most optimistic: GHGs peak now and level off)• RCP4.5• RCP6.0• RCP8.5 (“business as usual” – unrestrained GHG growth)

The numbers represent the additional “forcing” (additional energy) in Watts per square meter of GHGs by century’s end.• They came from their very own modeling exercises – by

economists and social scientists

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Projections Look Like This

24Prediction Type

Weather FORECASTS

Seasonal OUTLOOKS

Climate PROJECTIONS

Basic Drivers Physics Physics PhysicsElements Tracked

State variables, Processes

State variables, Processes

State variables, Processes

Type of surfaces Grid cells, 3D Grid Cells, 3D Grid Cells, 3DEnsembles? Sometimes Yes YES - it’s the only

wayWhat causes uncertainty (besides imperfect science)?

Imperfect initial conditions

Climate Variability (ENSO, AO, etc.); statistical power

Uncertainty in human behavior over time (GHGs); microphysics

More resolution or computing power desirable?

Yes Yes Yes

Prediction values are actually

Specific values at specific places and times

Expressions of confidence in categories of outcome

Averages for periods (decades, etc.), with uncertainty envelopes

Uncertainty handled how?

Mostly outside the forecast itself

They are built into the probabilities

Explicit part of the projection

Comparing Predictions

SOME PROJECTIONSFrom:

IPCC: http://www.ipcc.ch/report/ar5/wg1/ National Climate Assessment:

http://nca2014.globalchange.gov/downloads

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Global Surface Temperature (IPCC)

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Global Surface Temp (IPCC)

Lt. Column: mid 21st centuryRt. Column: late 21st century

Top Row: RCP2.6 (today)2nd Row: RCP4.53rd Row: RCP6.0Bot Row: RCP 8.5

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US Surface Temperature (NCA)

Changes as of late 21st Century (2071-99),shown relative to late 20th Century (1970-99)

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Change in Avg # of Consecutive Dry Days

• Changes as of late 21st Century (2071-99),

• shown relative to late 20th Century (1970-99)

• Stippling shows agreement in 80% of models

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N. AmericanPrecip Changes

• Changes as of late 21st Century (2071-99),

• shown relative to late 20th Century (1970-99)

• Hatching shows significant and consistent among models

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Changes in Southeast US Temperature

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Vulnerability to Sea Level Rise

Based on:tidal range, wave height, coastal slope, shoreline change, landform and processes, and historical rate of relative sea level rise

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Summary: Similarities Wx models and Cx models compute conditions at grid

cells Both start with initial conditions and run for a specified

time into the future The physics are the same Both use ensemble / composite strategies to help

characterize uncertainties Both benefit from higher time/space resolution Both benefit from improvements in parameterizations,

etc. Both are constrained by computational costs

Summary: Differences

WEATHER MODELS

Purpose is to construct explicit solutions (forecasts) for specific places (grids) at specific times

Deterministic: The temperature at a specific place, date and time is what you’re after

We call the resulting prognostic a “forecast”

CLIMATE MODELS

Purpose: construct an understanding of most likely outcomes in terms of average and variability

Statistical: The temp at a specific place, date and time matters as an item within a statistical portfolio

We call the resulting prognostic a “projection”

THANK YOU!QUESTIONS?

Derek.Arndt@noaa.gov

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