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Global Warming: What do we really know?. Inez Fung University of California, Berkeley MSRI Climate Change Summer School July 14 2008. 1. Power Source: Blackbody Radiation. 620 K. 380 K. Planck’s Law: - PowerPoint PPT Presentation
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Global Warming: What do we really know?
Inez FungUniversity of California, BerkeleyMSRI Climate Change Summer School July 14 2008
Planck’s Law:The amount and spectrum of radiation emitted by a blackbody is uniquely determined by its temperature
Max Planck (1858 – 1947) Max Planck (1858 – 1947) Nobel Prize 1918Nobel Prize 1918
Emission from warm bodies peak at short wavelengths
wavelength
620 K
380 K
1. Power Source: Blackbody Radiation
Sun: ~6000K :0.5m (shortwave)Earth: ~300K :10m (longwave)
What is a greenhouse gas?
C OO
CO O
O OC
symmetric
bending 15 m
asymmetric 4.3 m
Greenhouse effect: Radiation at specific wavelengths excite CO2 into higher energy states: energy is “absorbed” by the CO2 molecules
Earth’s Energy Balance: without GHG
Shortwave Longwave
3070
NN22
70
50 absorbed by sfc
100
20 absorbed by atm
20
Sensible heat
Latent heat
Earth’s Energy Balance: with GHG
COCO22, H, H22O, GHGO, GHG
Longwave
70
95114
23
7
50 absorbed by sfc
Shortwave
30
20 absorbed by atm
100
Incoming from Sun:High energy,
short wavelength
Outgoing from EarthLow energy
Long wavelength
0.5 m
10 m20 m
Earth Spectrum
What do we really know?
• Climate Forcing• Climate Feedback• Climate Response
– Equilibrium (?5000 years)– Transient (<500 years)
• Climate Projections
Changing Composition of Earth’s Atmosphere
Ancient air bubbles trapped in ice contains info about past atm composition
The Last
500,000 yearsand the
last 200
years
Climate Forcing: expressed as a change in radiative heating (W/m2) at surface for a given change in trace gas composition or other change external to the climate system
Hansen PNAS 2001
Cumulative climate forcing since 1800
Ship Tracks:- more cloud condensation nuclei- smaller drops- more drops- more reflective- energy balance
What do we really know?
• Climate Forcing• Climate Feedback• Climate Response
– Equilibrium (?5000 years)– Transient (<500 years)
• Climate Projections
Climate Feedback
Given a climate forcing (e.g. CO2 increase) initial warming
• Amplifying loops (positive feedback) magnify the warming
• Diminishing loops (negative feedback)
Climate Feedbacks
Warming
Evaporation from ocean,Increase water vapor in atmEnhance greenhouse effect
Increase cloud cover;Decrease absorption of solar energy
Decrease snow cover;Decrease reflectivity of surfaceIncrease absorption of solar energy
What do we really know?
• Climate Forcing• Climate Feedback• Climate Response
– Equilibrium (?5000 years)– Transient (<500 years)
• Climate Projections
At equilibrium (thousands of years):
High CO2 --> warm; Low CO2 --> cold
J. Hansen
Warmest 7: 1998, 2002, 2003, 2004, 2005, 2006,
2007Amplification of warming due to decrease of albedo (melting of snow and ice)
Warming greatest at high latitudes
Melting glaciers on Greenland--> feedback
--> accelerating warming
Oceans: Bottleneck to warminglong memory of climate system
• 4000 meters of water, heated from above
• Stably stratified • Very slow diffusion of
chemicals and heat to deep ocean
• Fossil fuel CO2: • 200 years emission,• penetrated to upper 500-1000
m
Slow warming of oceans --> continue evaporation, continue warming
What do we really know?
• Climate Forcing• Climate Feedback• Climate Response
– Equilibrium (?5000 years)– Transient (<500 years)
• Climate Projections
Weather Prediction by Numerical Process
Lewis Fry Richardson 1922
Weather Prediction by Numerical Process
Lewis Fry Richardson 1922
• Grid over domain • Predict pressure,
temperature, wind
Temperature -->density Pressure
Pressure gradient Wind temperature
Weather Prediction by Numerical Process
Lewis Fry Richardson 1922
• Predicted: 145 mb/ 6 hrs
• Observed: -1.0 mb / 6 hs€
∂ps
∂t
First Successful Numerical Weather Forecast: March
1950•Grid over US
•24 hour, 48 hour forecast
•33 days to debug code and do the forecast
•Led by J. Charney (far left) who figured out the quasi-geostrophic equations
ENIAC: <10 words of read/write
memory
Function tables(read memory)
16 operations in each time step
Platzman, Bull. Am Meteorol. Soc. 1979
Reasons for success in 1950
• More & better observations after WWII--> initial conditions + assessment
• Faster computers & correct computational math (24 hour forecast in 24 hours)
• Improved physics - – Atm flow is quasi 2-D (Ro<<1) – quasi-geostrophic vorticity equations– filtered out gravity waves– Initial C: pressure (no need for u,v) t ~30 minutes (instead of 5-10
minutes)
Continued Success Since 1950
• More & better observations
• Faster computers and advanced computational mathematics
• Improved physics
mass
energy
water vapor
momentum
)(
...),,,(
,...),(
)(
),(;
0)(
)(ˆ12
2
qonCondensatiEvapqutq
GHGCOqTfLW
aerosolscloudsfSW
TLHSHLWSWTutT
qTfRTp
ut
uFkgpuuutu
ℑ+−=∇•+∂∂
==
ℑ++++=∇•+∂∂
==
=•∇+∂∂
ℑ+++∇−=×Ω+∇•+∂∂
r
bbr
r
rrrrrr
ρρ
ρρ
ρ
Atmosphere
ℑ convective mixing
Modern climate models
• Forcing: solar irradiance, volanic aerosols, greenhouse gases, …
• Predict: T, p, wind, clouds, water vapor, soil moisture, ocean current, salinity, sea ice, …
• Very high spatial resolution:<1 deg lat/lon resolution~50 atm layers~30 ocn layers~10 soil layers
==> 6.5 million grid boxes
• Very small time steps (~minutes)
• Ensemble runs (multiple experiments)
Model experiments (e.g. 1800-2100) take weeks to months on supercomputers
Processes in Climate Models
• Radiative transfer: solar & terrestrial
• phase transition of water
• Convective mixing
• cloud microphysics
• Evapotranspirat’n• Movement of heat
and water in soils
A B + water vapor + greenhouse Warming
A C + water vapor + cloud cover + greenhouse - absorption of sunlight
C
275 280 285 290 295 3000
5
10
15
20
25
30
35
40
1 2 3 4 5 6
Temperature (K)
Sat
ura
tio
n V
apo
r P
ress
ure
(m
b)
A
B
liquid
vapor
Ice Liquid + absorption of sunlight
100% relative humidity
C
Climate Dial: Three phases of water
AttributionAttribution
• are observed changes consistent with
expected responses to forcings
inconsistent with alternative explanations
Observations
Climate model: All forcing
Climate model: Solar+volcanic only
IPCC AR4
21stC warming depends on rate of CO2
increase
20thC stabilizn:CO2 constant at 380 ppmv for the 21stC
21thC “Business as usual”:CO2 increasing 380 to 680 ppmv
Meehl et al. (Science 2005)
greatest over land & at most high N latitudes
and least over the South. Ocean & parts of the N Atlantic Ocean
Projections of Climate Change
IPCC AR4
9oF
7oF
3oF
Multi-model Projections of Climate Change
IPCC AR4
Uncertainties in global projections:2020: concurrence2050: depend on CO2 increase2100: depend on CO2 increase and ocean response time
Inter-model range
Stern Review 2006
Stern Review 2006
PROBLEM: The Elusive Carbon Sink
• Only half of the CO2 produced by human activities is remaining in the atmosphere
• Where are the sinks that are absorbing over 40% of the CO2 that we emit?
– Land or ocean?– Eurasia/North America?
• Why does CO2 buildup vary dramatically with nearly uniform emissions?
• How will CO2 sinks respond to climate change?
Cumulative Ocean Carbon Sink of FF
CO2
(Cumulative)Sabine et al 2004
• Thermocline: barrier to transport of perturbations to depth
• Thermohaline circulation: lateral transport of perturbation
Warm-wet
Warm-dry
T, Soil Moisture Index}
Regression ofNPP vs T
NPP decreases with carbon-climate coupling
Fung et al. Evolution of carbon sinks in a changing climate. PNAS 2005
21st Century Correlations & Regressions: FF= SRES A2 ; = Coupled minus
Uncoupled
With SRES A2 (fast FF emission): as CO2 increases• Capacity of land and ocean to store carbon
decreases (slowing of photosyn; reduce soil C turnover time; slower thermocline mixing …)
• Airborne fraction increases --> accelerate global warming
Fung et al. Evolution of carbon sinks in a changing climate. PNAS 2005
Airborne fraction=atm increase /Fossil fuel emission
Changing Carbon Sink Capacity
Ocean
momentum
mass
energy
salinity
€
∂r
u 2∂t+
r u 2 • ∇
r u 2 + 2Ω×
r u 2 = −
1
ρ 0∇p +
r F +
r τ 0
wind stress{
∇ •r u 2 +
∂w
∂z= 0
0 = −∂p
∂z+ ρg; ρ = f (T, s )
∂T
∂t+
r u 3 • ∇T = Q
0
surface heating{
+ ℑ (T )
∂s
∂t+
r u 3 • ∇s =
s0ρ 0Δz
(E − P )0
freshwater flux1 2 4 4 3 4 4
+ ℑ (s)
Numerical Weather Prediction
( ~ days)
Initial Conditions
t = 0 hr
Prediction t = 6 hr 12 18 24
•Predict evolution of state of atmosphere (t)
•Error grows w time --> limit to weather prediction
Seasonal Climate Prediction ( El – Nino Southern Oscillation )
{ Initial Conditions}
Atm + Ocn t = 0
{Prediction}
t = 1 month 2 3
• Coupled atmosphere-ocean instability• Require obs of initial states of both atm & ocean, esp. Equatorial Pacific• {Ensemble} of forecasts • Forecast statistics (mean & variance) – probability• Now – experimental forecasts (model testing in ~months)