Transcript Final review - UCLA: Atmospheric and Oceanic Sciences

AOS102
Climate Change and Climate Modeling
Post-midterm Review*
*Yes, the final is cumulative---but weighted towards the second half
Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP
Ch. 4, cont’d. El Niño and Year-to-Year Climate Prediction
The transition into the 1998-98 La Niña cold phase (May 1998)
Figure 4.9a
Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP
An ensemble of forecasts during
the onset of the 1998-99 La Niña
Figure 4.18
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Jet stream and storm track changes
associated with El Niño or La Niña
Figure 4.21
Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP
Probability distribution of precipitation and surface
air temperature to El Niño and La Niña
Figure 4.22
Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP
Effect of ENSO on number of Atlantic “named storms”
(tropical storms and hurricanes) in July-Oct. each year
Figure 4.24
• avg 8-9
•Regression:
La Niña ~10
El Niño ~6
•But large
scatter (&
increases w
earlier SST)
Tropical storm: sustained winds > 18 m/s; hurricane: winds > 33m/s (74 mph); Category 5 > 69 m/s
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Climate Models
Figure 5.1
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5.1.c Resolution and computational cost
Topography of western North America at 0.3 and 3.0 resolutions
Figure 5.3
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5.1.c Resolution and computational cost
• Computational time = (computer time per operation)
(operations per equation)(No. equations per grid-box)
(number of grid boxes)(number of time steps per simulation)
• Increasing resolution: # grid boxes increases & time step decreases
• Half horizontal grid size  half time step (why? See below)
 twice as many time steps to simulate same number of years
• Doubling resolution in x, y & z (# grid cells)
(# of time steps)
 cost increases by factor of 24 =16
• In Fig. 5.3, 5 to 0.5 degrees  factor of 10 in each horizontal direction. So
even if kept vertical grid same, 1010(# grid cells)10(# of t steps)= 103
• Suppose also double vertical res.  2000 times the computational time
i.e. costs same to run low-res. model for 40 years as high res. for 1 week
• To model clouds, say 50m res.  10000 times res. in horizontal, if same in
vertical and time  1016 times the computational time … and will still have
to parameterize raindrop, ice crystal coalescence etc.
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Vertical column showing parameterized physics so small scale
processes within a single column in a GCM
Figure 5.2
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Observed SST
(Reynolds data set, 1982-2000)
Sea surface temperature
climatology - January
Sea surface temperature
climatology - July
Revised Figure 2.16
Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP
NCAR_CCSM3 coupled
simulation climatology
(20th century run, 1979-2000)
Sea surface temperature
climatology - January
Sea surface temperature
climatology - July
Figure 5.21
Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP
January
precipitation
climatology
July
precipitation
climatology
mm/day
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Figure 2.13
Observed (CMAP) and 5 coupled models 4 mm/day precip. contour
December-February
Coupled simulation
precipitation climatology
(20th century run, 1979-2000)
June - August
Figure 5.20
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Global Warming
•CO2 increases due to fossil fuel emissions.
Figure 1.1
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Global mean surface temperatures estimated since preindustrial times
Figure 1.3
•Anomalies relative to 1961-1990 mean
•Annual average values of combined near-surface air temperature over
continents and sea surface temperature over ocean.
•Curve: smoothing similar to a decadal running average.
•From University of East Anglia Climatic Research Unit, following Jones
and Moberg (2003).
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Pathways of energy transfer in a global average
Figure 2.8
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The Greenhouse Effect
and Climate Feedbacks
Surface temperature (C)
as a function of
absorptivity ea
Increased absorption of infrared radiation by
greenhouse gases leading to surface warming
aDTs = G
+ Water vapor feedback: warmer
 more H2O vapor, GHG (see Fig 6.5)
Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP
Figure 6.3
Figure 6.4
Snow/ice feedback in the global energy balance
Figure 6.7
Effects of cloud amount in the global energy balance
Figure 6.8
Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP
Small
Tend to cancel
6.3b Climate sensitivity
Mean, standard deviation, and range of doubled-CO2
climate sensitivity for a number of models
Table 6.2
Publication
Number of
models
Mean
Standard
deviation
Range
IPCC (1996)
IPCC (2001)
IPCC (2007)
17
15
18
3.8
3.5
3.2
0.8 C
0.9 C
0.7 C
1.9 to 5.2 C
2.0 to 5.1 C
2.1 to 4.4 C
Double CO2 & run the simulation to new equilibrium climate state.
Change in the long term average defines doubled-CO2 response.
Global-average surface temperature response DT2x used as a
measure of climate sensitivity: doubled-CO2 climate sensitivity.
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A transient response experiment where greenhouse gas emissions are
suddenly stopped at time ts, so the forcing stabilizes (upper panel)
Idealized case: cap GHG at given level (i.e., stop emissions suddenly!)
Temperature was less than equilibrium due to lag so continues to rise
for several decades
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Figure 6.12
A transient response experiment by climate models of different
climate sensitivities to forcing
Initially small
∂DTs
C
+ aDTs = G
∂t
Ocean heat storage IR to space Radiative forcing (GHG)
due to Ts
increase
*to see this try DT = g(t - ) in Eq. 6.15 using G = gt
s
a
C lag due to ocean,
a depends on a
Hard to distinguish
high a from low
ainitially
*=
G
DTs = a in equilibrium
High
sensitivity
model
(smaller a)
Low
sensitivity
model
Heat storage balances GHG initially
Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP
Figure 6.14
Global average warming simulations in 11 climate models
• Global avg.
sfc. air temp.
change
• (ann. means
rel. to 19011960 base
period)
• Est. observed
greenhouse gas
+ aerosol
forcing,
followed by
• SRES A2
scenario (inset)
in 21st century
• (includes both
GHG and
aerosol
forcing)
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Figure 7.4
Observed global annual ocean heat content for 0 - 700m layer
Ocean heat content anomaly rel . to 1961-90 (black curve)
i.e. global upper ocean heat storage in response to accumulated heat flux
imbalance (surface + exchange with lower layers)
[Heat content anom. =
(temperature anom x
heat capacity x density),
integrated surface to
700m depth over global
ocean area]
[For refc: 1 Wm-2
surface heat flux anom. =
1.1x1022 J/yr over
3.6x1014m2 ocean]
Shaded area = 90%
confidence interval
Variations: natural
variability and sampling
error
Figure 7.18
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After Bindoff et al (2007); data from Levitus et al. (2005)
Observed annual average anomalies of global mean sea level (mm)
1961 to 2003 trend in global mean sea level rise est. ~ 13 to 23 mm/decade
Red reconstructed sea level
fields rel. to 1961-90
[tide gauges avgd using spatial
patterns from recent satellite
data; Church & White, 2006]
Blue curve coastal tide
gauge measurements [rel. to
1961-90; alt method; Holgate &
Woodworth, 2004]
Black curve satellite
altimetry rel. to 1993-2001
(After Bindoff et al 2007)
Error bars denote 90%
confidence interval
Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP
Figure 7.19
6.8b A doubled-CO2 equilibrium
response experiment
6.8c The role of oceans in slowing
warming
Equilibrium
temperature response
Years 60-80 of
Annual average surface time-dependent
temperature
air temperature
response
response from an earlier
version of the GFDL
climate model
comparing equilibrium
response to timedependent response
Ratio of timedependent
response to
equilibrium
response
Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP
Figure 6.13
7.1.c Commonly used scenarios
Radiative forcing as a function of time for various climate forcing scenarios
Top of the atmosphere
radiative imbalance 
warming due to the net
effects of GHG and other
forcings
SRES:
• A1FI (fossil intensive),
• A1T (green technology),
• A1B (balance of these),
• A2, B2 (regional economics)
• B1 “greenest”
from the Special Report
on Emissions Scenarios
• IS92a scenario used in many
studies before 2005
Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP
Figure 7.2
7.2 Global-average response to greenhouse warming scenarios
Radiative forcing and global average
surface temperature response
Change in radiative forcing (Wm-2)
Change in temperature (K)
(after Mitchell & Johns 1997)
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Figure 7.3
7.3 Spatial patterns of the
response to time-dependent
warming scenarios
2010-2039
Response to the SRES
A2 scenario GHG and
sulfate aerosol forcing
in surface air
temperature relative to 2040-2069
the average during
1961-90 from the
Hadley Centre climate
model (HadCM3)
2070-2099
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Figure 7.5
GFDLCM2.0
30yr. avg annual surface
air temperature response
for 3 climate models
centered on 2055 relative
to the average during
1961-1990
NCARCCSM3
MPIECHAM5
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Figure 7.7
Multi-model ensemble avg.
January and July
precipitation change
for 10 model
ensemble average
for 2070-2099
minus 1961-90 avg
(SRES A2 scenario)
Figure 7.9
Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP
7.3.c Summary of spatial patterns of the response
• Poleward amplification of the warming is a robust feature. It is
partly due to the snow/ice feedback and partly to effects involving
the difference in lapse rate between high latitudes and the tropics.
• In time-dependent runs polar amplification is seen first in the
northern hemisphere, while the North Atlantic and Southern
Ocean effects of circulation to the deep ocean slow the warming.
• Continents generally tend to warm before the oceans.
• There is a seasonal dependence to the response. For instance,
winter warming in high latitudes is greater than in summer.
• The models tend to agree on continental scale and larger, but
there are many differences at the regional scale. Regional scale
predictions (e.g. for California) tend to have higher levels of
uncertainty, esp. for some aspects (e.g. precipitation)
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7.3.c Summary of spatial patterns of the response (cont.)
• Natural variability will tend to cause variations about the forced
response, especially at the regional scale.
• Precipitation is increased (about 5%-15%) on a global average,
but regional aspects can be quite variable between models. There
is reason to believe that regional changes are likely. Wintertime
precipitation tends to increase.
• Summer soil moisture tends to decrease. This is an example of an
effect that would have implications for agriculture. But soil
moisture models depend on such things as vegetation response,
which are crudely modeled and have much regional dependence
(hence higher uncertainty).
Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP
7.4 Ice, sea level, extreme events
7.4.a Sea ice and snow
Simulated ice fraction change (2070-99) minus (1961-90)
as a percent of the base climatol. ice fraction
Sep. - Nov.
Dec. - Feb.
Echam5
SRESA2
Figure 7.10
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7.4.b,c (Projected future) Land ice & Sea level rise
•Sea level rise due to thermal expansion in GCMs ~0.13 to 0.32 m in 21st
Cent. (1980-99 to 2090-99; A1B , similar for A2) (~13±7 mm/decade to 2020)
•Deep ocean warming continues, e.g., 1-4 m rise if stabilize at 4xCO2
•Warming impact on Greenland and Antarctic ice sheets poorly constrained
•Greenland eventual melting ~7m over millennial time scale
•Most of Antarctica cold enough to remain below freezing
•Ice sheet dynamics complicated: “calving” of icebergs, … flow rate;
Surprises, e.g. Larsen B ice shelf; monitoring, ….
7.6.c. Observed Sea ice, land ice, ocean heat storage and sea level rise:
Trends:
decrease; decrease;
increase;
increase;
~Consistent with predicted.
1961 to 2003 trend in global mean sea level rise ~ 13 to 23 mm/decade
Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP
7.4.d Extreme events
• If standard deviation of daily temperatures remains similar as mean
temperature rises  more frequent occurrence of events currently
considered extreme
• e.g., heat waves
Few events above
40C (104F)
(shaded area)
Mean change
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Much more frequent
(shaded area many
times larger)
Figure 7.13
Summary of predicted climate change
Temperature • The lower atmosphere and Earth's surface warm (the
stratosphere cools).
• The surface warming at high latitudes is greater than the global
average in winter but smaller in summer. (In time dependent
simulations with a full ocean, there is less warming over the high
latitude southern ocean).
• surface warming smaller in the tropics, but can be large rel to
natural variability
• For equilibrium response to doubled CO2, global average
surface warming likely lies between +2C and +4.5C, with a most
likely value of 3C, based on models and fits to past variations.
• "Best-estimate” (IPCC 2007) temperature increase in 2090-99
relative to 1980-99 depends on future emissions. For A2 scenario
3.4C; B1 1.8C; A1B 2.8C,;A1FI 4.0C. Likely ranges est at 60% to
160% of these values (actual model ensemble ranges are smaller)
• Due to the thermal inertia of the ocean, the temperature would
increase for decades beyond whatever time stabilization of
greenhouse gases might be achieved.
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Summary of predicted climate change
Precipitation
• The global average increases (as does average evaporation);
the larger the warming, the larger the increase.
• Precipitation increases at high latitudes throughout the year;
for equilibrium response to doubled CO2, the average increase
is 3 to 15%.
• The zonal mean value increases in the tropics although there
are areas of decrease. Shifts in the main tropical rain bands
differ from model to model, so there is little consistency between
models in simulated regional changes.
Soil Moisture
• Increases in high latitudes in winter.
• Decreases over northern mid-latitude continents in summer
(growing season).
Snow and
Sea-Ice
• The area of sea-ice and seasonal snow-cover diminish.
Sea Level
• Sea level increases excluding rapid changes in ice flow for
2090-99 relative to 1980-99: for A2 0.23-0.51m, B1 0.18-0.38;
even if greenhouse gases are stabilized deep ocean warming
creates ongoing sea level rise for centuries.
Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP
7.5 Climate change observed
to date
• Amplitude of natural variations
depends on the spatial and time
averages considered.
• much of weather/climate T
variability due to heat transport
anomalies; but these tend to
cancel in large regional averages
• anthropogenic trend in
temperature expected to have
large spatial scales; i.e. clearer
relative to noise in large-scale
avgs
Fig. 7.15 (will be expanded with
supplementary figs. below)
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Observed 20th C. temperature for various averaging regions with climate
model simulated range: natural only vs. natural + anthropogenic forcings
Observed
warming
exceeds range
that can occur
by natural
variability in
models
Figure 7.16
(after Hegerl et al. 2007)
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SRES Multi-model mean surface warming projections
A2, A1B, B1
Multi-model mean surface
warming projections as a
continuation of 20thcentury simulation
Constant composition (2000
values) simulation, forcing
kept at year 2000 level
(gives global warming
commitment)
+ Constant composition
commitment simulations
from A1B and B1 2100
values
Warming incr with forcing
Potential warming > current
Figure 7.20
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Annual multi-model mean surface air temperature change
(relative to 1980-1999 clim.)
A2: 2080-2099
7
6
7
5
4.5
4
5
4
4.5
4
3.5
3
2
4.5
4
3.5
6
5
3
4
4
3
4
3
Figure 7.21
Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP
Annual multi-model mean surface air temperature change
(relative to 1980-1999 clim.)
B1: 2080-2099
4
5
3.5
3
2
3.5
3
2.5
2
2.5
4
2.5
2
2
2
2
Figure 7.21
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2
7.8 The road ahead
Range for
each
category
shown as
error bar in
2050
Values condensed from Barker et al., 2007
Mitigation scenarios estimating greenhouse gas emissions as a
function of time (emissions pathways) that would lead to stabilization
of greenhouse gases, i.e., eventually bring emissions to low levels so
concentration stop increasing
(Climate change mitigation: actions aimed at limiting the size of the
climate change; Adaptation, actions that attempt to minimize the
impact of the climate change)
Figure 7.22
Mitigation scenarios shown as center of a range of emissions for six
categories (CO2 emissions shown as a function of time; other
greenhouse gases follow a similar paths).
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Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP
Values condensed from Barker et al., 2007
Categories IV-VI emissions continue to increase over the first decades ~
recent trends, modest societal action
Recall for long-lived gas,
• Constant emissions ongoing increase of concentration;
• Increasing emissions concentration increases at ever faster rate;
• Decreasing emissions  concentration increases but less quickly
• Stabilization occurs for very low emissions.
• If emissions are not brought down quickly enough, CO2 overshoots
stabilization target negative emissions are required, i.e. methods for
actively removing CO2 (categories I-II). Alternative: bring down emissions
sooner.
Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP
Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP
One way of visualizing contributions to the change in energy supply:
a “wedge” in which a low emission technology grows from small contribution
today to displace 1 PgC/yr of fossil fuel emissions 50 years from now (Pacala &
Socolow, 2004)
(25 PgC of emissions prevented overall)
Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP
Examples of scale-up required to give this (Pacala & Socolow, 2004)
(each to displace 1 PgC/yr of fossil fuel emissions 50 years from now )
1. Doubling the fuel efficiency of cars
2. Cutting in half the average mileage each car travels
3. Energy-efficient buildings (reduce emissions by 25% including in developing world).
4. Increase efficiency of coal-based electricity generation from 32% to 60%
5. Wind power substituted for coal power (50 times current capacity).
6. Photovoltaic power increased to about 700 times the current capacity to substitute for
coal
7.
Nuclear power substituted for 700 GW of coal power (a doubling of current
capacity).
8.
Biomass fuel production scaled to ~100 times current Brazil or US ethanol
production
9.
Carbon capture and storage a factor of 100 times today’s injection rates or the
equivalent of 3500 times the injection by Norway’s Sleipner project in the North
Sea.
10. Decrease tropical deforestation completely plus double current rate of tree
plantation
11. Conservation tillage applied to all cropland (10 times current).
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Roughly how many of these contributions are required to move from category VI
emissions path to a lower emissions path?
Category VI emissions increase by between 7 and 8 PgC/year over 1st 50 years Þ
7-8 of the above required just to keep emissions rates close to present values (in
face of increasingly energy intensive economies and population growth)
Category I requires emissions to decrease ~ 4 to 5 PgC/year in 50 years (~12
PgC/year relative to category VI) roughly 12 of the above items if started in 2000
(11 shown)
Which approach?
All of the above plus more.
The 2C warming target is already
challenging
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Understanding &
predicting the
climate system
El Niño
Global
warming
Climate models
(complex & simple)
+ observations
Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP