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EG1204: Earth Systems: an introduction
Meteorology and Climate
Lecture 7
Climate: prediction & change
Topics we will cover
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Ancient climate prediction
Weather vs Climate
Short, Medium and Long Range prediction
Statistical forecasting
Chaos theory
Dynamical forecasting
Global climate change
Predictability is to prediction
as romance is to sex
Miyakoda, 1985
Ancient climate prediction
• The earliest attempts to predict the weather
were by farmers and the military
• The Greeks successfully used predictions
about the wind to defeat the Turkish during
sea battles
• Predicting weather could make the difference
between life and death for farmers
Weather vs Climate
• Weather forecasting is concerned with
accurate descriptions of weather type for a
short period of time
• Climate forecasting deals with how different
future conditions may be from those expected
in an average year
• Weather describes specific conditions
(raining, wind speed and direction, dew-point
etc). Climate discusses anomalies
Short, medium and long-range
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Short-range is between 3 and 72 hours
Medium-range is between 3 days an a week
Long-range is a month or more ahead
Experimental-range (X-range) includes new
seasonal forecasts up to 6 months ahead
• Global climate prediction looks at climate out
to 50 to 100 years
Short, medium and long-range
forecast accuracy
good
poor
short
Range
long
Statistical Forecasting
• The oldest form of formal weather forecasting
• A statistical model is constructed from
regression and correlation analyses
• Model is trained on past (historical) weather
observations
• Model is given data relating to patterns of
SST or other large-scale conditions prior to
the period the weather changed
Statistical Forecasting
• The model thus learns what sets of conditions
(certain SST pattern, persistence of pressure,
timing of snowmelt etc) are associated with a
particular weather regime
• To use a statistical model you enter details
about large-scale conditions and it matches
those with its historical database to give a
prediction
• Drawback - can only “see” extremes
encountered in training data
Chaos theory
• One of the most fundamental advances
in the prediction of any natural process
(climate and weather included)
occurred after the discovery of chaos
• Chaos theory is an amalgamation of
game theory, probability theory and
fluid dynamics
Chaos theory
• Edward Lorenz realised that although
the atmosphere behaved as a chaotic
and random system, there were aspects
of it which could be solved within a
phase-space
• The strange attractor (Lorenz attractor)
was his visualisation of this hyperspace
and initialised fractal theory
Dynamical forecasting
• Dynamical forecasting is the most advanced
and current method of weather/climate
prediction
• Unlike a statistical forecast, it is based on the
calculation of weather/climate conditions
from first principles (Physics)
• Calculation is undertaken for each time-step
for regularly spaced grid-points across the
earth and up through the atmosphere
Dynamical forecasting
• A modern Atmospheric Global Circulation
Model (AGCM) solves many equations for
each grid-point for the earth surface,
atmosphere and oceans
• This type of model requires extremely
powerful computers (supercomputers) and
the science of GCMs only developed after
such computers became available
Dynamical forecasting
• A single model integration provides a
deterministic solution
• A better approach (originally proposed
by Lorenz) was to use a probabilistic
ensemble approach
• Ensemble forecasting strategy allows
greater uncertainty to be sampled
Dynamical forecasting
• 1) Define an “event” (e.g. rainfall above
normal or presence/absence of high
pressure)
• 2) Run the climate model over a period
of days
• 3) Compare model output with event
criteria
Dynamical forecasting
• A single model integration would
provide only one outcome - which only
allows us to say the event will occur or
it will not
• A single integration only samples a
small proportion of the overall
probability distribution of the future
state of the atmosphere
Dynamical forecasting
• By repeating the model integration many
times we can sample more of the uncertainty
and generate a probability estimate of our
event occurring
• Models are thus initialised on separate days
and then run forward in time
• Models are initialised with actual observations
for that particular day
• Result is an ensemble of integrations referred to as members
rainfall quantity
Dynamical forecasting
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Dynamical forecasting
rainfall quantity
Probability = f/n
3
where f is number of members in a category
where n is total number of integrations
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Dynamical forecasting
rainfall quantity
Probability = f/n
3
where f is number of members in a category
where n is total number of integrations
2
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0
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Day
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Dynamical forecasting
rainfall quantity
Probability = f/n
3
Prob of Rain = 0.3 (30%)
Prob of NO rain = 0.7 (70%)
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IPCC