3.5 Climate Change Prediction
3.5 Climate Change Prediction
(i) Climate Modeling
(ii) Detection and Attribution of Climate
Change
(iii) Climate Change Predictions
(i) Climate Modeling: The Need for Climate Models
•
•
•
•
3
Test of understanding
Evaluation of response
Prediction of climate change
Attribution of causes of climate change
Elements of a Climate Model
•
Atmospheric and oceanic circulation
–
–
•
Atmospheric radiation budget
–
–
•
Critical in determining the surface temperature and magnitude of the OLR
Sensitive to the nature of the soil and the soil moisture, which is strongly varying in
time and space
Biosphere
–
4
Affect composition, which feeds back on the radiation balance and the biosphere.
Energy flow in rocks and soils
–
–
•
Cloud processes on scales of 10's to 1000's of km
Sea ice and snow cover
Chemical reactions in the atmosphere and ocean
–
•
Radiation absorbed, transmitted, reflected and scattered by each level of the
atmosphere, in each wavelength band
Sensitive to the composition of the atmosphere, which varies in time and position
Hydrology, and water phase changes
–
–
•
Equations of motion for a fluid (air or water). These represent Newton's laws, mass
conservation for the fluid and some thermodynamic relationships
They take the form of nonlinear partial differential equations.
Responses of plant growth and ocean plankton development to climatic changes and
changes in CO2
A ‘Hierarchy’ of Climate Models
• AGCM
– Atmospheric General Circulation Model
– Simulates atmosphere but prescribes the oceans and land
surface
• OGCM
– Ocean General Circulation
– Simulates the ocean circulation, but with a simple
atmosphere sufficient to provide surface wind stress and
heat supply
• AOGCM
– Coupled Atmosphere-Ocean General Circulation Model
– Used extensively in climate change experiments
5
Example Model Processes
Relative humidity
change
Clouds
Cloud formation
Number of cloud
drops
Horizontal motion
of air
Cooling of air
Meteorology
Onset of rainfall
Ascent of air
Rainfall rate
Buoyancy change
of air
Radiation
Start
Change in solar
radiation reaching
surface
Rate of absorption
of solar radiation
Change in surface
moisture
6
Change in
temperature of
surface air
Evaporation rate
Surface/vegetation
Response of
vegetation
Model Structure
• Discretisation
– Splitting continuous quantities up
into discrete units that can be acted
on by the driving processes
– Necessary because a model can
carry information only at a fixed
number of points
– Averaging over large ranges
• Examples
– Spatial (lat,lon,altitude)
– Aerosol and cloud particles (usually
just mass)
– Wavelengths (wavelength bands)
7
Typical model
resolution is 2o x 2o x
20 altitude levels,
equivalent to 250 x
250 km x 1 km
Example of Discretisation
• Simulation of a wind-blown cloud of pollution
Discretised
concentration
Real
distance
Effect of wind
Pollution blown by the wind
Pollution blown by the wind
as represented on the grid
Discretisation causes reduction
in ‘resolution’ (detail)
Changes of the discretised
quantity are not the same as
those of the real quantity
8
Parameterisation
• Simplification of processes in terms of simpler
equations with physically or empirically derived
parameters (which can be ‘tunable’)
• Example for clouds:
– Rainfall assumed to occur when the liquid water content of
the cloud reaches a prescribed value
– Reality is a highly complex interaction of different sized
droplets, ice crystals, hail, etc.
• Parameterisations capture the essence of real
processes but they can be inaccurate and unreliable
when used to make predictions under new conditions
• Almost all processes are parameterised in climate
models
9
Cloud Parameterisation
RH Scheme: Assumes
clouds form wherever the
Relative Humidity is above a
certain value
CW Scheme: Treats Cloud
Water as a ‘prognostic’
model variable and
distinguishes ice and water
clouds, and the different
precipitation from them
All schemes have adjustable parameters that can
be tuned to reproduce climatological cloud cover
However, in a double-CO2 experiment, the RH
scheme produced a 5 K warming, CW produced
3 K and CWRP produced 2 K. This result shows
the problem with key climate model
parameterisations!
10
CWRP Scheme: As the CW
scheme, but accounts for
the change in cloud
reflectivity with water
content
Climate Models vs. Weather Forecast Models
• Can be the same model
– The UK ‘Unified Model’ is used for Met Office weather
forecasts and climate prediction
• Climate models are mostly ‘free running’
– Day-to-day weather patterns not used, but average ‘climatic’
state should be OK
• Weather forecast models
are ‘nudged’ to match
observations as much as
possible
–‘Data assimilation’
12
Evolution of climate models (IPCC 2007)
Grid size resolution
13
Evolution of climate models (IPCC 2007)
How has model performance improved?
14
IPCC (2007)
Two Major Problems with Early Climate Simulations
• Ocean heat ‘flux adjustments’
– A non-physical ‘adjustment’ to ocean heat content to account for
incomplete ocean physics (failure to resolve narrow ocean currents,
such as found in the N Atlantic)
• Cloud responses
– Clouds remain one of the largest uncertainties in climate response
simulations
– Cloud feedbacks still responsible for a large part of inter-model
differences – IPCC 2007
15
Model Comparisons With Observations
• Models do not simulate the current weather,
but only a climatological state consistent with
the prescribed forcings (greenhouse gas
content of the atmosphere, aerosols, etc.)
• Need to evaluate models against average
climate over, say, 1 year
• Can also look at ‘typical’ seasonal cycle or
typical El Nino variations, but not for any
particular year
16
Climatological Temperature
• Absolute error
generally < 2oC
• Slight general cold bias
Labelled contours: climatological SST and surface air temp
Colours: mean model error from several models
17
IPCC (2007)
Climatological Precipitation
General pattern very good
Obs
Model
Dry bias: problems
modelling monsoon
Errors in Indo-Pacific
warm pool  affects
ability of model to
capture teleconnections
(El Nino)
IPCC (2007)
18
Summary of Climatological Experiments from AR4
• Confidence in model simulations has
improved since previous IPCC (2001).
• Increased confidence from models no longer
needing ‘flux adjustments’
– These models are able to maintain stable climates
over centuries
– Some biases and long-term trends remain
– Tropical precipitation a problem
– Clouds remain a key uncertainty in models
19
20th Century Climate Variability
58 models driven by changes in natural and anthropogenic forcings
Obs
Mean of models
IPCC (2007)
20
Simulation of ENSO
• Climate models have substantially improved spatial
representation of pattern of SST anomalies in S Pacific
- Better physics
- Increased resolution
• Some even used to forecast ENSO
• SST gradients in equatorial Pacific still not well captured
- Thermoclines too diffuse
• Most models produce ENSO variability on timescales faster than
observed
• Helped by further increases in model resolution?
21
Extreme Weather
• Climate models are not weather forecast models, so
they can’t simulate individual events during a long
simulation (of perhaps 100 years)
• We need to test the models’ variability
•
•
•
•
22
Temperature: Simulation of hot and cold extremes has improved, with
large regional discrepancies.
Rainfall: Frequency of intense events and amount of precipitation
during them are underestimated.
Extra-tropical storms: These are storms affecting mid-latitude
regions, such as northern Europe. These are well captured by models –
improved since 2001.
Tropical cyclones: Frequency and distribution captured well by some
models – improvement since IPCC 2001
(ii) Detection and Attribution of Climate Change
• Anthropogenic climate change occurs against a
backdrop of natural climate variability
• Internal variability
– Climate variability not forced by external agents
– All time-scales (weeks to centuries)
• Externally forced variability
– Natural (volcanic, solar)
– Anthropogenic (greenhouse gases, aerosols)
– not forgetting...Changes in natural variability
• Detection of anthropogenic climate change within all
this other climate variability is a statistical “signal-tonoise” problem
23
Definitions
• Detection
– Demonstrating that an observed change is
significantly different (in a statistical sense) from
that which can be explained by natural internal
climate variability
– Detection does not imply an understanding of the
causes
• Attribution
– The isolation of cause and effect
24
Problems with Attribution
• Reality
– Statistical analysis of observational record
– Demonstrate that observed changes are:
• unlikely to be due entirely to internal variability
• consistent with estimated/anticipated responses (models)
• inconsistent with alternative explanations (models)
• Limited data and imperfect model
– Proof of cause and effect (100% agreement) impossible
– Relies on rejecting alternatives
– Incomplete knowledge means that “new alternatives” are still
emerging
25
Measures of Confidence Used by IPCC 2007
26
Requirements for Successful Detection and Attribution
• Good data
– Sufficient coverage to identify main features of
natural variability
– So far, surface and upper air temperatures have
been used
– Other climate variables used for ‘qualitative’
assessment (changes broadly consistent)
27
climate quantity
climate quantity
Example of Need for Quality Data
time
time
28
climate quantity
The Need for Long Data Records
time
29
Beware of Correlations!
30
Beware of Correlations!
• Temperatures have increased since 1700 to present
• The number of pirates has decreased since 1700 to
present
Does this mean lack of pirates is causing climate
change???
The existence of a correlation does not indicate a causal
mechanism
31
Quantifying Internal Climate Variability
• From the instrumental record
– Relatively short (compared to 30-50 year period of interest)
– Coverage incomplete, and varies with time
• Paleoclimatic data
– Reconstructions of climate before anthropogenic
perturbations
– Poor resolution and global coverage
– Contains unknown external forcings
• GCM ‘control’ runs over long periods (1000 years)
32
The Magnitude of Modelled Natural Variability
3 climate models run with no external forcings. All variability is due to internal climate
processes. These simulations are compared with observations in the right-hand panels.
No evidence for model ‘natural variability’ anything like recent changes
33
A reminder of what we are dealing with:
Estimated Forcings since pre-industrial times (IPCC 2007)
34
Radiative Forcing
• Definition: A change in the net radiation at the
top of the atmosphere due to some external
factor.
Net Radiation
Net radiation = Incoming - Outgoing
Positive net radiation
 Incoming > Outgoing
Negative net radiation
 Outgoing > Incoming
Positive & Negative Forcing
• Positive forcing  warming
• Negative forcing  cooling
Forcing and Feedbacks
Radiative forcing
(external)
Internal response
(including
feedbacks)
Climate
system
Forcing and Feedbacks
• “Forcing” is produced by an external process,
e.g.
– Changes in solar flux
– Volcanic eruptions
– Human actions
• A feedback is a response to temperature
changes
– Example: Increased water vapor due to warming
More
Anthropogenic increases in greenhouse
gases are considered forcings
Increases in greenhouse gases that are
caused by temperature changes are
feedbacks
• The same gas can be involved in forcings
and feedbacks, e.g., CO2
• Forcing:
– CO2 increase from burning of fossil fuels
• Feedback
– temp  decay  CO2
Comparing Causes of Temperature Change
• Assumption: Larger radiative forcing  larger
effect on temperature
• Comparisons follow
Source: Intergovernmental Panel on Climate
Change (IPCC)
Positive Radiative Forcings
• Largest – by far: increased greenhouse
gases
– Increase is almost entirely anthropogenic
Long-Lived Greenhouse Gases
Gas
Forcing (Wm-2)
CO2
1.66
CH4(methane)
0.48
N2O (nitrous
oxide)
Halocarbons
0.16
Total
2.64
0.34
More About Greenhouse Gases
Radiative transfer model
Adding greenhouse gas reduces outgoing
longwave radiation (OLR) at top of
atmosphere
Initial Equilibrium
Top of
atmosphere
Absorbed
Shortwave
OLR
Now, add greenhouse gas
Keep temperatures fixed
Reduced Upward Flux
Top of
atmosphere
Absorbed
Shortwave
OLR
Net Downward Flux
Top of
atmosphere
Net Flux
Result: A positive radiative forcing
Negative Radiative Forcings
Largest: Increase in sulfate aerosols
 Mostly anthropogenic
Anthropogenic Sulfate Aerosols
• Coal and diesel fuel contain sulfur
• Burning of these fuels produces sulfur dioxide
(a gas)
• In the atmosphere, this gas is converted into
particles
Effect of Anthropogenic Sulfate Aerosols on Temperature
• Direct effect
– The aerosols themselves reflect sunlight
– This is similar to the effect of volcanic aerosols
• Indirect effect
– Sulfate aerosols act as condensation nuclei
– This increases the droplet concentration in clouds
– Result: Increased cloud albedo
• Both effects tend to increase the Earth’s albedo
Evidence for Indirect Effect
Bright streaks
are areas of
enhanced albedo
Cause: Emissions
from ships
Streaks called
“ship tracks”
Cause of Ship Tracks
• Ship
exhaust
contains
aerosols
• The
aerosols
cause more
droplets to
form
• Cloud
albedo is
increased
Total Anthropogenic Effect on Climate
• Total Anthropogenic Climate Forcing =
sum of all anthropogenic forcings
• Mainly, greenhouse gases (+)
+
sulfate aerosols (-)
Net Anthropogenic Radiative Forcing (1750 – 2005)
Best Estimate:1.6
Positive.
2
W/m
Carbon Dioxide
•
From fossil fuel burning
•
~60% contribution to total radiative forcing
•
Atmospheric concentration increased from 280 ppm in 1750 to 380
ppm in 2005 (36%)
•
1999 – 2005 CO2 fossil fuel / cement emissions increased by ~3% / yr
•
Today’s CO2 concentration has not been exceeded during the past
420,000 years and likely not during the past 20 million years.
•
The rate of increase over the past century is unprecedented, at least
during the past 20,000 years
58
Methane Trends
IPCC (2007)
Factor 2.5
levelling off of upward
trend not understood
59
Trends in Halocarbons
Radiative forcing
peaked in 2003 – now
beginning to decline
60
Using Forcing-Response Relationships for Detection and
Attribution
• Use the temporal and spatial variation of the
different forcings
• Can separate natural and anthropogenic
influences only if spatial and temporal
responses are known
– Climate record: Different responses are
superimposed – impossible to separate
– Climate model: Study responses to individual
forcings
61
Example of model Forcing-Response Patterns
Solar
Temp change 18901999 (oC / century)
66
Volcanic
Well mixed GHGs
Ozone
Direct sulfate
Total
Can natural forcings explain Global Warming?
• A climate model including only natural forcings (solar + volcanic aerosol)
does not explain the temporal change in global mean temperature
IPCC (2007)
67
Can natural forcings explain Global Warming?
Models with both natural and anthropogenic forcings do far better
IPCC (2007)
68
Regional response to natural and anthropogenic forcings
69
Detection of natural and anthropogenic signals
Contribution from GHGs, other anthropogenic and natural foircings to
temperature changes between 1990s and 1900s.
70
Conclusions
• It is extremely unlikely (<5%) that the global pattern
of warming during last 50 years can be explained
without external forcing.
• Greenhouse gas forcing has very likely caused most
of warming over last 50 years.
• It is likely that there has been a substantial
anthropogenic contribution to surface temperature
increases in every continent except Antarctica since
the middle of the 20th century.
• Recommend read summary and conclusions to Chapter 9
IPCC AR4.
71
(iii) Climate Change Projections (IPCC 2013)
• Climate model experiments
• Projections of future climate (IPCC AR5)
and
• ‘Geo-engineering’
IPCC AR5 - Chapter 12
72
Radiative Forcing
~2.3 W/m2
Radiative Forcing: aerosols
• See notes
74
Climate Projections
• It is not possible to make deterministic projections of future
climate change.
• It is not even possible to make projections of all possible
outcomes (as is “done” in medium range weather forecasting).
• Projections are uncertain because:
(i) They are primarily dependent on scenarios of future
anthropogenic and natural forcings that are uncertain.
(ii) incomplete understanding and inprecise models of
climate system.
(iii) Presence of internal variability.
Climate Projections
• The word “projection” is used to reflect the uncertainties and
dependencies.
• Nevertheless as greenhouse gas concentations continue to rise
we expect changes in the climate system to be greater than
those already observed.
• It is possible to understand future climate change using models
to characterise likely outcomes and uncertainties under specific
assumptions about future forcing scenarios.
Description of Scenarios
• Previous IPCC reports based projected emissions on socioeconomic scenarios – from storylines based on future
demographics and economic development, regionalisation,
energy production and use, technology, agriculture, forestry,and
land-use. Models were then forced with an appropriate level of
GHGs and aerosols.
• A new set of scenarios were created for AR5 – so-called
“Representative Concentration Pathways” – these are focused
on the net radiative forcing rather than on the atmospheric
constituents reflecting the multiple “pathways” that can result in
he same radiative forcing.
• They are defined by their net radiative forcing by 2100.
Description of Scenarios
• Four scenarios are used:
RCP2.6 is the lowest of the four peaks at 3.0Wm-2 and declines
to 2.6Wm-2 by 2100.
RCP4.5 (medium-low) stabilizes at 4.2Wm-2 by 2100.
RCP6.0 (medium-high) stabilizes at 6.0Wm-2 by 2100.
RCP8.5 (high) reaches 8.3Wm-2 by 2100, on a rising trajectory.
A1B - Rapid economic growth, population
peak mid-century, balance across sources
A2 – Heterogeneous world, continuously
increasing population, self-reliance
B1 – Convergent world, population peak
mid-century, clean technologies
IS92a – “business as usual” (as
understood in 1996), 1% growth in CO2
concentration per year
80
Geo-engineering
• Implementation of man-made or artificial enhancement of natural negative
radiative forcing to counteract increase in GHGs
• Could be alternative to mitigation or adaptation?
Some proposed methods:
- Artificial enhancement of the sulfur cycle through e.g. addition of iron to the
oceans
- Enhancemnet of marine cloud reflectance through increased sea-salt fluxes
– i.e. ‘man-made sea spray’
- ‘Space mirrors’
- Injection of sulphate aerosol into the stratosphere – compare with volcanic
eruptions
93
Geo-engineering
Ocean-going sea-spray
producers proposed by
Latham and Salter
94
Geo-engineering
For:
“An opportunity to ‘buy time’ while technologies are improved
to enable effective emissions reductions and development
of non-fossil fuel energy sources”
“Not an alternative to mitigation”
Against:
“At best, a last resort to preserve habitability when all else
fails; at worst, dangerous interference with the Earth
system with catastrophic consequences”
“Insurance policies can encourage risky behaviour”
95
CO2
Net RF
Radiative forcing
Radiative forcing
Mitigation and geo-engineering
CO2
Net RF
Time
Albedo modification
Time
Albedo modification
Geoengineering instead of mitigation
96
Geoengineering to ‘take edge off the heat’
Geo-engineering – open questions
Only tackles radiative GHG impacts – e.g. ocean acidity would remain problem
Who decides that we go ahead with it?
Are the unknowns presented by uncertainty in the future climate state any
more dangerous than those introduced by deliberate man-made interference?
If we have the ability to act, should we do it sooner rather than later?
Public/political perceptions – would the existence of these tools be seen as a
carte-blanche for uncontrolled emissions?
Unforeseen impacts – ‘perpetrator’ and ‘victim’ determinable?
 international conflicts?
The case for Geoengineering by Dr. David Keith
http://www.ted.com/talks/view/id/192
97