aos102 climate change and climate modeling p ost-midterm review *

AOS102 AOS102

Climate Change and Climate ModelingClimate Change and Climate ModelingPost-midterm ReviewPost-midterm Review**

*Yes, the final is cumulative---but weighted towards the second half

Neelin, 2011. Neelin, 2011. Climate Change and Climate Modeling, Climate Change and Climate Modeling, Cambridge Cambridge UPUP

The transition into the 1998-98 La Niña cold phase (May 1998)

Figure 4.9a

Ch. 4, contCh. 4, cont’’d. El Niño and Year-to-Year Climate Predictiond. El Niño and Year-to-Year Climate Prediction


An ensemble of forecasts duringthe onset of the 1998-99 La Niña

Figure 4.18


Jet stream and storm track changesassociated with El Niño or La Niña

Figure 4.21Neelin, 2011. Neelin, 2011. Climate Change and Climate Modeling, Climate Change and Climate Modeling, Cambridge Cambridge

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Probability distribution of precipitation and surfaceair temperature to El Niño and La Niña

Figure 4.22


Figure 4.24

Effect of ENSO on number of Atlantic “named storms” (tropical storms and hurricanes) in July-Oct. each year

Tropical storm: sustained winds > 18 m/s; hurricane: winds > 33m/s (74 mph); Category 5 > 69 m/s

• avg 8-9•Regression: La Niña ~10 El Niño ~6•But large scatter (& increases w earlier SST)


Climate ModelsClimate Models


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Topography of western North America at 0.3 and 3.0resolutions

Figure 5.3

5.1.c Resolution and computational cost 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

5.1.c Resolution and computational cost 5.1.c Resolution and computational cost

• In Fig. 5.3, 5 to 0.5 degrees factor of 10 in each horizontal direction. So even if kept vertical grid same, (# grid cells)(# 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.


Vertical column showing parameterized physics so small scale processes within a single column in a GCM

Figure 5.2


Sea surface temperature climatology - January

Sea surface temperature climatology - July

Revised Figure 2.16

Observed SST (Reynolds data set, 1982-2000)


NCAR_CCSM3 coupled simulation climatology

(20th century run, 1979-2000)

Sea surface temperature climatology - January

Sea surface temperature climatology - July


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July precipitation climatology

January precipitation climatology

Figure 2.13

mm/day


Observed (CMAP) and 5 coupled models 4 mm/day precip. contour

Figure 5.20

Coupled simulation precipitation climatology

(20th century run, 1979-2000)

June - August

December-February


Global WarmingGlobal Warming

•CO2 increases due to fossil fuel emissions.


UPUP

Figure 1.3

Global mean surface temperatures estimated since preindustrial times

•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).


Pathways of energy transfer in a global average


UPUP

Figure 6.3

Increased absorption of infrared radiation by greenhouse gases leading to surface warming

Figure 6.4

Surface temperature (C) as a function of absorptivity a

The Greenhouse EffectThe Greenhouse Effectand Climate Feedbacksand Climate Feedbacks

+ Water vapor feedback: warmer more H2O vapor, GHG (see Fig 6.5)

Ts = G


Figure 6.7

Snow/ice feedback in the global energy balance

Figure 6.8

Effects of cloud amount in the global energy balanceTend to cancel

Smal

l


Table 6.2Publication Number of

modelsMean Standard

deviationRange

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

Mean, standard deviation, and range of doubled-CO2 climate sensitivity for a number of models

6.3b Climate sensitivity6.3b Climate sensitivity

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 T2x used as a measure of climate sensitivity: doubled-CO2 climate sensitivity.


A transient response experiment where greenhouse gas emissions are suddenly stopped at time ts, so the forcing stabilizes (upper panel)

Figure 6.12

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


lag due to ocean, depends on

High sensitivity model(smaller )Low sensitivity model

Ts = G in equilibrium

A transient response experiment by climate models of different climate sensitivities to forcing

Hard to distinguish high from low initially

C + Ts = G∂Ts

∂tOcean heat storage IR to space

due to Ts

increase

Radiative forcing (GHG)

Heat storage balances GHG initially

Initially small

**to see this try Ts = in Eq. 6.15 using G = gtg(t - )

**= C


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Global average warming simulations in 11 climate models

• Global avg. sfc. air temp. change

• (ann. means rel. to 1901-1960 base

period)

• Est. observed greenhouse gas

+ aerosol forcing,

followed by • SRES A2 scenario (inset) in 21st century

• (includes both GHG and

aerosol forcing)


Figure 7.4

Observed global annual ocean heat content for 0 - 700m layer

After Bindoff et al (2007); data from Levitus et al. (2005)

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 intervalVariations: natural variability and sampling error Figure 7.18


Observed annual average anomalies of global mean sea level (mm)

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

1961 to 2003 trend in global mean sea level rise est. ~ 13 to 23 mm/decade

Figure 7.19


Annual average surface air temperature

response from an earlier version of the GFDL

climate model comparing equilibrium

response to time-dependent response

Figure 6.13

Equilibriumtemperature response

Years 60-80 of time-dependent

temperature response

Ratio of time-dependent

response to equilibrium

response

6.8b 6.8b A doubled-CO2 equilibrium A doubled-CO2 equilibrium response experimentresponse experiment

6.8c The role of oceans in slowing 6.8c The role of oceans in slowing warmingwarming


Radiative forcing as a function of time for various climate forcing scenarios

7.1.c Commonly used scenarios7.1.c Commonly used scenarios

SRES:• A1FI (fossil intensive), • A1T (green technology), • A1B (balance of these), • A2, B2 (regional economics) • B1 “greenest”• IS92a scenario used in manystudies before 2005

Top of the atmosphere radiative imbalance warming due to the net

effects of GHG and other forcings

from the Special Report on Emissions Scenarios


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Radiative forcing and global average surface temperature response

(after Mitchell & Johns 1997)

Change in radiative forcing (Wm-2)

Change in temperature (K)

7.27.2 Global-average response to greenhouse warming Global-average response to greenhouse warming scenariosscenarios


Figure 7.3

Response to the SRES A2 scenario GHG and sulfate aerosol forcing

in surface air temperature relative to

the average during 1961-90 from the

Hadley Centre climate model (HadCM3)

Figure 7.5

2010-2039

2040-2069

2070-2099

7.37.3 Spatial patterns of the Spatial patterns of the response to time-dependent response to time-dependent warming scenarioswarming scenarios


30yr. avg annual surface air temperature response

for 3 climate models centered on 2055 relative

to the average during 1961-1990

Figure 7.7

GFDL-CM2.0

NCAR-CCSM3

MPI-ECHAM5


Multi-model ensemble avg.

Figure 7.9

January and July precipitation change

for 10 model ensemble average

for 2070-2099 minus 1961-90 avg (SRES A2 scenario)


7.3.c Summary of spatial patterns of the response7.3.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)


7.3.c Summary of spatial patterns of the response (cont.)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).


Simulated ice fraction change (2070-99) minus (1961-90)as a percent of the base climatol. ice fraction

Figure 7.10

7.47.4 Ice, sea level, extreme eventsIce, sea level, extreme events7.4.a Sea ice and snow7.4.a Sea ice and snow

Echam5SRESA2

Sep. - Nov. Dec. - Feb.


7.4.b,c (Projected future) Land ice & Sea level rise7.4.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: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


7.4.d Extreme events7.4.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

Figure 7.13

Mean change

Few events above 40C (104F)

(shaded area)

Much more frequent (shaded area many

times larger)


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.


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.


Fig. 7.15 (will be expanded with supplementary figs. below)

• 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

7.57.5 Climate change observedClimate change observed to dateto date


Observed 20th C. temperature for various averaging regions with climate model simulated range: natural only vs. natural + anthropogenic forcings

Figure 7.16

Observed warming

exceeds range that can occur

by natural variability in

models

(after Hegerl et al. 2007)


SRES Multi-model mean surface warming projections

A2, A1B, B1Multi-model mean surface warming projections as a

continuation of 20th-century simulation

Warming incr with forcingPotential warming > current

Figure 7.20

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


Annual multi-model mean surface air temperature change

(relative to 1980-1999 clim.)

A2: 2080-2099

2

3

3.54

3

44.5

5

67

3

4.5 5

67

43

4

3.54

4.54

5

4

Figure 7.21


Annual multi-model mean surface air temperature change

(relative to 1980-1999 clim.)

B1: 2080-2099

2

3

4

2.5

2

3.5 3.53

45

2

2.52

2.5

2

2

2

Figure 7.21


Range for each category shown as error bar in 2050

7.87.8 The road aheadThe road aheadMitigation 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)

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).

Val

ues

cond

ense

d fr

om B

arke

r et

al.,

200

7Figure 7.22



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.

Val

ues

cond

ense

d fr

om B

arke

r et

al.,

200

7



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)



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).



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


Understanding & Understanding & predicting the predicting the climate systemclimate system

Climate modelsClimate models(complex & simple)(complex & simple)+ observations+ observations

El Niño El Niño

Global Global warmingwarming


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