deke arndt october 2015 noaa’s national climatic data center
DESCRIPTION
3 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 OUTPUTTRANSCRIPT
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”