Climate Change: The Move to Action
(AOSS 480 // NRE 480)
Richard B. Rood
Cell: 301-526-8572
2525 Space Research Building (North Campus)
rbrood@umich.edu
http://aoss.engin.umich.edu/people/rbrood
Winter 2012
February 14, 2012
Class News
• Ctools site: AOSS_SNRE_480_001_W12
• 2008 and 2010 Class On Line:
– http://climateknowledge.org/classes/index.php
/Climate_Change:_The_Move_to_Action
Some Uncertainty References
• Climate Change Science Program, Synthesis
Assessment Report, Uncertainty Best Practices
Communicating, 2009
• Climate Change Science Program, Synthesis
Assessment Report, Transportation Gulf Coast, 2008
• Moss and Schneider, Uncertainty Reporting, 2000
• Pidgeon and Fischhoff, Communicating Uncertainty,
2011
• Lemos and Rood, Uncertainty Fallacy, 2010
The Current Climate (Released Monthly)
• Climate Monitoring at National Climatic
Data Center.
– http://www.ncdc.noaa.gov/oa/ncdc.html
• State of the Climate: Global
• Plant Hardiness - 2012
Today
• Observations of Ecosystems
• Attribution (3 ways?)
Question
• Madden and Ramanathan Predicted in 1980
would be discernable in 2000.
• What would you do to evaluate the theory and
predictions of global warming?
– Surface of planet will warm
– Sea level will rise
– Weather will change
• Think about
•
•
•
•
Measurements
Feedbacks
Correlative behavior
Impacts
Signal to Noise
From Leeds X-ray Imaging
Signal to Noise (perhaps more like climate)
From social research methods .net
Signal to Noise (another example)
Noise
Signal
Signal / Noise Ratio
From astronomy and astrophysics .org
Some signal to noise issues
• We have many sources of variability
– Sun, volcanoes, etc.
– El Nino, La Nina, etc.
– Ice ages, Little Ice Age, Warm Periods, etc.
– Land use changes, natural carbon dioxide
variability, etc.
– How do we detect a trend in temperature and
attribute it to human released carbon dioxide?
Let’s go back to the physical climate
Correlated behavior of different parameters
Fig. 2.5. (State of Climate 2009) Time
series from a range of indicators that
would be expected to correlate
strongly with the surface record.
Note that stratospheric cooling is an
expected consequence of greenhouse
gas increases. A version of this figure
with full references is available at
www.ncdc.noaa.gov/bams-state-of-climate/ .
Today
• Observations of Ecosystems
• Attribution (3 ways?)
Edges
• “Edges” are places where we really might
be able to see things definitively. What
are the edges?
– Ice (Phase transition)
– Deserts
– Seasons
There is an accumulation of observations
• Physical and biological impacts correlated
with temperature increase and dryer
conditions.
– What is the relationship between warming and
surface dryness?
– Strongly correlated with population and where
we have looked.
Project Budburst
• A community science activity collect
observations of the onset of spring
– Project Budburst
• How to observe the onset of spring
– National Phenology Network
Project of Trees
• A community science activity to collect
observations on types of trees
– Canadian Plant Hardiness Site
• Paper (including yours truly) on how
foresters think about climate change
• McKenney et al. (2011)
Interestingly significant news story
Hardiness Map
• Arbor Day Foundation Maps of Hardiness
Zones
• Plant Hardiness - 2012
Can we get a global perspective from satellites?
• NDVI: Normalized Difference Vegetation
Index
– Looks at radiative budgets, measurements,
and the absorption of photosynthetically
active radiation, relative to the rest of the
radiation.
How would these changes be revealed?
 Changes in vegetation activity can be characterized through
1. changes in growing season
2. changes in “productivity”
Increases in growing season
delayed
fall
earlier
spring
Jan
Jul Aug
NDVI
Dec
Increases in Productiviy
Increase
Jan
Jul Aug
NDVI
From Compton J. Tucker, NASA Goddard
Dec
From Kirsten de Beures
Length of
Growing
Season
From Ranga B. Myneni, Boston University
Changes in the Amplitude of the Keeling Curve
(Keeling et al, 1996)
Amplitude has increased
40% in Alaska, Canada
Amplitude has increased
20% in Hawaii
The phase, start of the
decrease, start of the
growing season, has
moved forward 7 days.
Geographical extent of warming
Osborn Spatial Extent of Warming
Coherent and Convergent?
• There is evidence in both the physical climate
system and ecosystems of systematic global
warming.
• This evidence shows correlated behavior
through many systems.
• Taken independently each piece could be
challenged.
• Taken together the evidence converges.
– Consistent with human-related forcing
Coherent and Convergent?
• Taken independently each piece could
be challenged.
• Taken together the evidence converges.
– Consistent with human-related forcing
• Consistent with human-related forcing
– Really?
Today
• Observations of Ecosystems
• Attribution (3 ways?)
– Fingerprinting
– Joint Attribution (end-to-end method)
– Event attribution
Attribution
• The physical climate and ecological
observations in the previous are consistent
with the planet is warming.
• How do we decide that this is consistent
with human-induced warming?
Natural mechanisms influence climate
Natural mechanisms
• Changes in the Sun
• Changes in the amount of
volcanic dust in the
atmosphere
• Internal variability of the
coupled atmosphere and
ocean
Thanks to Ben Santer for Content!
Thanks to Ben Santer for Content!
Human factors also influence climate
Non-natural mechanisms
•
Changes in atmospheric
concentrations of greenhouse gases
•
Changes in aerosol particles from
burning fossil fuels and biomass
•
Changes in the reflectivity (albedo) of
the Earth’s surface
Smoke from fires in Guatemala and Mexico (May 14, 1998)
Recent changes in carbon dioxide are largely
human-induced
Carbon
dioxide
is
the
most
important
greenhouse gas produced by human activities
Atmospheric CO2 has increased from a preindustrial value of about 280 parts per million
(ppm) to 379 390 ppm in 2005 2010
The atmospheric concentration of CO2 in 2010
exceeds by far the natural range (180 to 300
ppm) over the last 650,000 years
Fossil fuel use is the primary source of the
increased concentration of CO2 since the preindustrial period
Thanks to Ben Santer for Content!
Source: IPCC AR4 (2007)
Thanks to Ben Santer for Content!
Multiple lines of evidence on which “discernible human
influence” conclusions are based
1. “Basic physics” evidence
– Physical understanding of the climate system and the heat-trapping
properties of greenhouse gases
2. Circumstantial evidence
– Qualitative agreement between observed climate changes and
model predictions of human-caused climate changes (warming of
oceans, land surface, and troposphere, stratospheric cooling, water
vapor increases, etc.)
3. Paleoclimate evidence
– Temperature reconstructions enable us to place the warming of the
20th century in a longer-term context
4. Fingerprint evidence
– Rigorous statistical comparisons between modeled and observed
patterns of climate change
Average surface temperature change (°C)
Models can perform the “control experiment” that we
can’t do in the real world
Meehl et al., J. Climate (2004)
What is “climate fingerprinting”?
•
Strategy:
Search for a computer model-predicted pattern of
climate change (the “fingerprint”) in observed climate records
•
Assumption: Each factor that influences climate has a different
characteristic signature in climate records
•
Method:
•
Advantage: Fingerprinting allows researchers to make rigorous
tests of competing hypotheses regarding the causes of recent
climate change
Standard signal processing techniques
Thanks to Ben Santer for Content!
IPCC
Temperature
Observations
Note: It gets smoother
away from the surface.
Different factors that influence climate
have different “fingerprints”
10
24
20
50
20
10 0
16
10 0
16
20 0
12
20 0
12
8
50 0
70 0
10 00
90 N
4
60 N
- 1 .2
Pressure (hPa)
30 N
-1
10
Eq
- 0 .6
- 0 .8
- 0 .2
30 S
0. 2
- 0 .4
0
60 S
0. 6
0. 4
90 S
1
0. 8
50
10 0
20 0
30 0
60 N
30 N
-1
- 1 .2
- 0 .6
- 0 .8
Eq
- 0 .2
- 0 .4
30 S
0. 2
0
60 S
0. 6
0. 4
Pressure (hPa)
- 0 .4
30 S
0. 2
0
60 S
0. 6
0. 4
0. 8
1. 2
28
24
20
16
12
20 0
12
30 0
8
50 0
70 0
10 0 0
90 N
4
60 N
-1
- 1 .2
28
30 N
Eq
- 0 .6
- 0 .8
- 0 .2
30 S
0. 2
- 0 .4
0
60 S
0. 6
0. 4
90 S
1
0. 8
1. 2
28
24
25
50
20
50
20
10 0
16
10 0
16
20 0
12
20 0
12
8
30 0
30 0
50 0
70 0
10 00
90 N
4
60 N
-1
- 1 .2
30 N
- 0 .6
- 0 .8
Eq
- 0 .2
- 0 .4
30 S
0. 2
0
60 S
0. 6
0. 4
90 S
24
8
50 0
70 0
10 00
90 N
4
60 N
1
0. 8
-1
1. 2
6. 1st five
factors
combined
90 S
1
25
10
1. 2
- 0 .8
Eq
- 0 .2
10 0
90 S
25
Santer et al., CCSP, 2007
- 1 .2
30 N
- 0 .6
16
1
0. 8
60 N
-1
20
4
4. Ozone
4
50
8
2. Volcanoes
8
50 0
70 0
10 00
90 N
28
24
10
30 0
10
1. 2
25
50 0
70 0
10 0 0
90 N
5. Sulfate
aerosol
particles
24
50
30 0
3. Well-mixed
greenhouse
gases
28
25
Height (km)
25
Height (km)
1. Solar
Pressure (hPa)
28
Height (km)
10
- 1 .2
30 N
- 0 .6
- 0 .8
Eq
- 0 .2
- 0 .4
30 S
0. 2
0
60 S
0. 6
0. 4
90 S
1
0. 8
1. 2
°C/century
“Fingerprinting” with temperature
changes in Earth’s atmosphere
Model Changes: CO2 + Sulfate Aerosols + Stratospheric Ozone
18
100
14
200
10
300
Height (km)
Pressure (hPa)
50
6
500
2
850
60N
45N
30N
15N
0
15S
30S
45S
60S
Observed Changes
50
100
14
200
10
300
6
500
2
850
60N
45N
30N
-1. 5
-1. 8
Santer et al., Nature (1996)
Height (km)
Pressure (hPa)
18
15N
-0. 9
-1. 2
0
-0. 3
-0. 6
15S
0. 3
0
30S
45S
0. 9
0. 6
60S
1. 5
1. 2
1. 8
Temperature
changes in oC
Searching for fingerprints of human
activities in the world’s oceans
•
Initial work by Syd Levitus and
colleagues showed an increase
in the heat content of the oceans
over the second half of the 20th
century (Levitus et al., 2001,
Science)
•
Subsequent research by Tim
Barnett and colleagues identified
a human fingerprint in the
observed ocean heat content
changes (Barnett et al., 2001,
Science)
Thanks to Ben Santer for Content!
“Fingerprinting” in the ocean: Warming
of the North Atlantic over 1955-99
Thanks to Ben Santer for Content!
Barnett et al., Science (2005)
“Fingerprinting” in the ocean: Warming
of the world’s oceans over 1955-99
Thanks to Ben Santer for Content!
Barnett et al., Science (2005)
Fingerprint detection explained pictorially….
Thanks to Ben Santer for Content!
Time-varying observed patterns
MO D E L : P C M E O F : 1 E X P T :control
B 0 0 4 . 1 0 - c n t r l Erun
V : 27.40%
N T : 3 0 0 MO N T H S
Time-varying
patterns
D i m e n s 2i o9 n/ 0l e3s/ 0s 4 1 6 : 3 2 : 2 6
MO D E L : P C M E O F : 1
E X P T : B 0 0 4 . 1 0 - c n t r l E V : 2 7 . 4 0 % N T : 3 0 0 MO N T H S
D i m e n s 2i o9 n/ 0l e3s/ 0s 4 1 6 : 3 2 : 2 6
E X P T : B 0 0 4 . 1 0 - c n t r l E V : 2 7 . 4 0 % N T : 3 0 0 MO N T H S
D i m e n s 2i o9 n/ 0l e3s/ 0s 4 1 6 : 3 2 : 2 6
MO D E L : P C M E O F : 1 E X P T : B 0 0 4 . 1 0 - c n t r l E V : 2 7 . 4 0 % N T : 3 0 0 MO N T H S
D i m e n s 2i o9 n/ 0l e3s/ 0s 4 1 6 : 3 2 : 2 6
MO D E L : P C M E O F : 1 E X P T : B 0 0 4 . 1 0 - c n t r l E V : 2 7 . 4 0 % N T : 3 0 0 MO N T H S
D i m e n s 2i o9 n/ 0l e3s/ 0s 4 1 6 : 3 2 : 2 6
MO D E L : P C M E O F : 1
t=1
t=2
t=3
t=4
t=n
-1
-0 . 6
- 1- 0 . 8
-1 . 2
-0 . 2
- 0 .-60 . 4
- 1- 0 . 8
-1 . 2
- 0 .-60 . 4
- 1- 0 . 8
-1 . 2
-1 . 2
0.6
0 . 20 . 4
- 0 . 20
- 0 .-60 . 4
- 1- 0 . 8
-1 . 2
0.2
- 0 . 20
-0 . 8
0 . 20 . 4
Projection onto model
fingerprint
0 . 60 . 8
11.2
0 . 60 . 8
0 . 20 . 4
0
Model fingerprint
-1 . 5
11.2
0 . 20 . 4
- 0 . 20
-0 . 4
1
0 . 60 . 8
- 0 . 20
- 0 .-60 . 4
t=1
t=2
t=3
t=4
t=n
-1 . 2 5
0 . 60 . 8
0.4
0.8
-1 . 5
1.2
Signal and noise time series
1 . 215. 5
0 . 7 51
0 . 205. 5
- 0 . 2 05
-0 . 5
1.25
0 . 7 51
0 . 205. 5
- 0 . 2 05
- 0 . 7- 05. 5
-1
0.75
0 . 205. 5
- 0 . 2 05
- 0 . 7- 05. 5
- 1 . 2 -51
-1 . 5
0.25
- 0 . 2 05
- 0 . 7- 05. 5
- 1 . 2 -51
-1 . 5
11.2
-0 . 2 5
- 0 . 7- 05. 5
- 1 . 2 -51
-1 . 5
11.2
-0 . 7 5
- 1 . 2 -51
0 . 205. 5
0
1 . 215. 5
0 . 7 51
1 . 215. 5
0 . 7 51
0.5
1 . 215. 5
1
.5
Projection 1onto
model
fingerprint
Signal-to-noise ratios
-1
-1 . 2
-0 . 6
-0 . 8
-0 . 2
-0 . 4
0.2
0
0.6
0.4
1
0.8
1.2
Human-caused fingerprints have been identified in
many different aspects of the climate system
Thanks to Ben Santer for Content!
Surface specific humidity
Water vapor over oceans
Tropospheric temperatures
-0 . 5
-0 . 6
Ocean temperatures
-0 . 3
-0 . 4
-0 . 1
-0 . 2
0.1
0
0.3
0.2
Stratospheric temperatures
0.5
0.4
-1
-0 . 6
-0 . 2
Tropopause height
0.6
-1 . 2
-0 . 8
-0 . 4
0.2
0
0.6
0.4
Sea-level pressure
Atmospheric temperature
50
18
100
14
200
Zonal-mean rainfall
Near-surface temperature
10
300
6
500
2
850
60N
45N
30N
-1. 5
-1. 8
15N
-0. 9
-1. 2
0
-0. 3
-0. 6
15S
0. 3
0
30S
45S
0. 9
0. 6
Continental runoff
60S
1. 5
1. 2
1. 8
1
0.8
1.2
Key results of IPCC AR4: We are now able to
identify human influences on climate at
continental scales
Continental
warming
likely shows a
significant
anthropogenic
contribution
over the past 50
years
Thanks to Ben Santer for Content!
Today
• Observations of Ecosystems
• Attribution (3 ways?)
– Fingerprinting
– Joint Attribution (end-to-end method)
• Rosenzweig et al., Nature, 2008
– Event attribution
Global
distribution of
changes
sensitive to
temperature
IPCC Technical
Summary WG2
Summary from Rosenzweig et al., Nature, 2008
•
•
•
•
•
shrinking glaciers in every
continent
melting permafrost
shifts in the spring peak of river
discharge associated with earlier
snowmelt
lake and river warming with effects
on thermal stratification, chemistry
and freshwater organisms
increases in coastal erosion
•
•
shifts in spring events (for
example, leaf unfolding, blooming
date, migration and time of
reproduction), species
distributions and community
structure
demonstrated changes in marineecosystem functioning and
productivity, including shifts from
cold-adapted to warm adapted
communities, phenological
changes and alterations in species
interactions
Joint attribution
• What would you do to evaluate the theory and
predictions of global warming?
– Surface of planet will warm
– Sea level will rise
– Weather will change
• Think about
•
•
•
•
Measurements
Feedbacks
Correlative behavior
Impacts
• Joint Attribution
• Documented statistical analysis
• Process-level understanding
Joint attribution
• Joint Attribution
– Documented statistical analysis
– Process-level understanding
• Look to see for biological systems
– Unlikely due entirely to natural variability
– Consistent with estimated responses of physical or biological
variables
– Non consistent with alternative, plausible explanations
• That are in regions where physical variables, esp.
temperature, can also be attributed to climate change
• Consist with behavior of models run with and without
carbon dioxide increase
Rosenzweig et al., Nature, 2008
Today
• Observations of Ecosystems
• Attribution (3 ways?)
– Fingerprinting
– Joint Attribution (end-to-end method)
– Event attribution
Event Attribution
• Barriopedro et al., Russian Heat Wave,
Science, 2011
• Dole et al., Russian Heat Wave, GRL,
2011
• Rahmstorf, Increase of Extreme Events,
PNAS, 2011
• Shearer and Rood, Earthzine, 2011