SAGES
Scottish Alliance for Geoscience, Environment & Society
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Why Monitor Climate?
An extended version of the
2007 Margary Lecture
Prof. Simon Tett, Chair of Earth System Dynamics &
Modelling: The University of Edinburgh
Margary
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•Margary died in 1976 and
his obituary was written by
Manley.
•Published several papers
in the QJ but his major
work was running the
Phenological network.
•One of the last gentlemen
amateurs. He wrote serious
books on Roman roads…
•Lecture established for
“Broader interest which he
and many others found in
the manifestation of the
British weather”.
From Sparks et al, 2000
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“Reconstructions of climate data for the last 1000
years ... indicate this warming was unusual and
is unlikely to be entirely natural in origin”
Reconstructions of past temperatures from several different
investigators. Graphic supplied by Tim Osborn, UEA
3
Global temperatures
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Outline
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• Climate models and why they are
uncertain
• Case for monitoring climate.
– Ozone depletion as example of how nature
surprised us.
• How and issues with past climate records
• How?
• Example model/data comparision
Models are not the real world.
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• Despite the increasing complexity of Earth
System Modelling models they are not the
real world.
• Choices are made about how and what to
model
• These choices lead to different outcomes
– We care about the “emergent properties” of
the models not their detailed evolution.
Modelling the Climate System
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Main
Message:
Lots of
things
going on!
Karl and Trenberth 2003
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Meteorology is (roughly) fluid
dynamics on rotating sphere.
DV
1
 2Ω  V   p  g a  Ff
Dt

D

  V 
Dt t

 ( V )  0
t
Navier-stokes on
rotating sphere
Continuity
Continuity
+ thermodynamics + moisture +
radiation…+ some simplifications to
remove sound and other fast waves
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Representing the fields: Gridpoint
models
Represent
space as a
grid of
regular (in
long/latt coords)
Sub-grid.
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• Recall equations of motion
• Split into large scale average and
residual. Reynolds averaging
V  V  (V  V)  (V  V)


 V  V  V  V  V  V  V  V
 V  V  V  V
Get large-scale terms that result
from sub-grid scale motions…
Parameterisation
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• Like the closure problem for fluid dynamics.
• Key processes:
– Convection (which involves latent heat release from water vapour
condensing)
– Clouds in general.
– Boundary layers.
– Need to simplify radiation calculations into relatively small number of
broad bands and assume radiation only goes up and down. Can verify
calculations through comparison with line-by-line calculations.
– Friction…
• Many specialists work in each area. An atmospheric model
(Weather) is a complex piece of software. Numerical methods for
dynamics are complex as are parameterisations.
• Parameterisations also contain many empirically defined constants
which need to be “tuned”. Model tuning quite time consuming and
aims to get a reasonable simulation of current climate.
Parameterized Processes
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Model’s do not have enough resolution to resolve
these processes. So they are represented in terms of
the large-scale flow (what gets simulated). Many of
these processes act at scales of 1-10km.
Slingo
From Kevin E. Trenberth, NCAR
“Mass-flux” parameterization
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Detrainment
Environmental
subsidence
Entrainment
into cloud
Uplift
Rain (&
snow)
What are we trying to parameterize?
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What is there…
How we
parameterise
Future modelling
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• Since the 1960’s super-computer
performance has doubled every 18
months (or so)
• Implies can double the resolution of
models every 10 years.
• Still would take many decades to get to 110km global modelling.
• Bottom line will need to parameterize
processes for many decades to come.
Chaos
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• Numerical models of atmosphere (and
ocean) show sensitivity to initial conditions
• For atmosphere practical limit of
deterministic forecasts is 10 days.
• Small uncertainties amplify and affect
evolution of large-scale state
• For climate purposes this means that
future forecasts are probabilistic and
detailed evolution of system unknowable.
Predicting the Future
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Results based on multi-model archive.
Typically show average across all model
simulations with uncertainties from range
Scenarios used to drive models. Selfconsistent atmospheric concentrations of
CO2 and other greenhouse gases. Based
on different human development paths
Projections of Future Changes in Climate
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Best estimate for
low scenario (B1)
is 1.8°C (likely
range is 1.1°C to
2.9°C), and for
high scenario
(A1FI) is 4.0°C
(likely range is
2.4°C to 6.4°C).
Projections of Future Changes in Climate
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Projected warming
in 21st century
expected to be
greatest over land
and at most high
northern latitudes
and least over the
Southern Ocean
and parts of the
North Atlantic
Ocean
Projections of Future Changes in Climate
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Precipitation increases very likely in high latitudes
Decreases likely in most subtropical land regions
Models are not the real world.
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• Despite the increasing complexity of Earth
System Modelling models they are not the real
world.
• Choices are made about how and what to model
• These choices lead to different outcomes
– We care about the “emergent properties” of the
models not their detailed evolution. (as we have learnt
that models are chaotic and thus their detailed
evolution is un-predictable.)
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Ozone Depletion as an example of a
failure of environmental modelling.
• In the early 1980’s theory (and models)
suggested that CFC’s would only cause
moderate stratospheric O3 depletion.
– “… United States National Research Council
report projected that continued use of CFCs
at then-current rates would … depletion of the
total global ozone layer by only about three
percent in about a century. ...”
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Ozone Depletion – Observations
Halley Bay, Farmen et al, Nature 1985
57-73
Ozone depletion
over Antarctica much
larger than expected.
Oct ‘84
Reason: models only
used gas-phase
chemistry. But ozone
depletion occurring
on polar
stratospheric clouds
80-84
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The Discovery of the Ozone Hole
• 1985: British Antarctic Survey
balloon measurements show much
less ozone than normal at 10-20 km
altitude in spring.
• 1999: ozone at 15-20 km, where it
normally peaks, was almost
completely depleted.
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2007 Sea-ice (its ½ what is should
be)
Is this unexpected? Are we missing
something fundamental in our understanding
of the Earth system? Is this the “ozone”
moment?
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Observations are direct evidence of
change.
• The public believe that observations of climate change
are very direct evidence of change. Seem to be using
them as view as to what is to come.
• Drives need for monitoring as wants answers soon after
“interesting” events.
• Apparently more trustworthy than models. (Though in
some cases models are more reliable than
observations!)
• Communicating uncertainties (which general public are
unaware of).
– I.e. need to escape from sterile debate on what warmest year is.
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What is the problem with
observations?
• Observing system not stable
• Climate changes slowly compared to
obs. system.
• Examples:
Bias corrections
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• As observing practice or location changes this introduces
biases.
– For example a move of a temperature sensor can cause a
change in average temperature recorded due to sensor being in
a different micro-climate
• Key are systematic biases – lots of small random
changes will just introduce a small amount of uncertainty
when averaged over a large number of observations
• Estimate biases in a variety of ways. Thus they are
uncertain. Relatively small uncertainty for SST; very
large for changes in tropospheric temperature
Examples:
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• Change from buckets to engine intakes as
way of measuring Sea Surface
Temperature. Affected many sensors.
• Increasingly large number of buoy SST
measurements
• Orbit drift in polar orbitors.
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Uncertainties – incomplete coverage.
SST example
Uncertainties in observations
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• Sampling
– Depends on the variable (annual-mean
temperature anomalies vs daily rainfall)
– Where they are and their correlation scales.
– Temperature with long correlation scales is
less uncertain than extreme daily rainfall with
very short correlation scales.
Communicating Uncertainties
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John Kennedy, Met Office Hadley Centre
Extreme events have consequences
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Tewkesbury 2007Photograph: Daniel
Berehulak/GettyImages
Met Office provisional
figures show that May to
July in the England and
Wales Precipitation is the
wettest in a record that
began in 1766.
We must learn from the events of recent days. These rains
were unprecedented, but it would be wrong to suppose that
such an event could never happen again…. (Hazel Blears,
House of Commons, July 2007)
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Models can generally reproduce what
has happened…
SPM-4
likely shows a
significant
anthropogenic
contribution
over the past
50 years
Observations
All forcing
natural forcing
So Why Observe?
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• The rate of climate change is unprecedented and so past
climate conditions will no longer be a guide to future
climate conditions. Need combination of models and
observations to provide data for decadal infrastructure
planning.
• How to determine which modelling choices are right (or
best)?
– Depends on the purpose of model.
– Test models ability to simulated observed change as that is
directly relevant to what is to come.
• Provide evidence of change to support policy action.
• Allow Detection & Attribution of climate change (to
support policy action..) [It’s the sun wot did it and other
sillyness]
How (GCOS monitoring principles)?
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Effective monitoring systems for climate should adhere to the following
principles:
1. The impact of new systems or changes to existing systems should
be assessed prior to implementation. So we know what the change did
2. A suitable period of overlap for new and old observing systems is
required. ditto
3. The details and history of local conditions, instruments, operating
procedures, data processing algorithms and other factors pertinent
to interpreting data (i.e., metadata) should be documented and
treated with the same care as the data themselves. So we can figure
out when changes happened rather then looking for break points.
4. The quality and homogeneity of data should be regularly assessed
as a part of routine operations. So the data is homogeneous
5. Consideration of the needs for environmental and climate-monitoring
products and assessments, such as IPCC assessments, should be
integrated into national, regional and global observing priorities. The
observing system is not just to estimate the mean climate or for
weather forecasting but to look for relatively small changes early,.
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How (GCOS monitoring principles) -cont?
6. Operation of historically-uninterrupted stations and observing
systems should be maintained. Long homogenious records are
valuable
7. High priority for additional observations should be focused on datapoor regions, poorly-observed parameters, regions sensitive to
change, and key measurements with inadequate temporal
resolution. Observed where we don’t have data and where new data
would help most.
8. Long-term requirements should be specified to network designers,
operators and instrument engineers at the outset of system design
and implementation. Don’t spec; Don’t get!
9. The conversion of research observing systems to long-term
operations in a carefully-planned manner should be promoted.
Research data has a short lifetime relative to climate change.
10. Data management systems that facilitate access, use and
interpretation of data and products should be included as essential
elements of climate monitoring systems. No point collecting the data
unless people can use it.
Digitisation as a source of new data
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• Available observed weather
data are limited before 1950 and
almost non-existent before 1850.
• Many more observations exist,
in logbooks, reports and other
paper records (mostly in the UK).
If we digitised them we could
improve the climate record and
extend it back to 1800.
• Hadley Centre digitised
observations from Royal Navy
Ships logbooks for WW2. These
give a much-improved picture of
1940s climate.
Example of model-data comparison
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N-Euro
W-Euro
Changes in
European
Precipitation and
Temperature.
Source data:
Med
1. CRUTEMP land-sfc
temperatures
2. GPCC precipitation
data
3. Multi-model archive
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Expected change in annual cycle from
multi-model ensemble
Dashed lines
are precip;
Solid
temperature
Split into
warm
(May-Oct)
and Cold
(Nov-Apr)
Expected changes with time
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Do Models agree with Observations?
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20th
century
– warm
season
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20th
century
– cold
season
Late 20th century – warm season
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Late 20th century – cold season
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Do Models agree with Observations?
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Not
natural
Problems
with
capturing
change in
Nov-Apr
precip
Summary
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• Hope I convinced you that climate models are
uncertain
• Ozone hole shows possibility of surprise
• Is Arctic sea-ice changes a surprise too?
• Need observations so we know what the climate
system is doing.
• Example comparison between observations and
models showed some issues with simulations of
winter precipitation change.