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EOS > EOS 365 Part II > Flashcards

EOS 365 Part II Flashcards

1
Q

Average fate of anthropogenic CO2 emissions

A

~50% - atmosphere
~25% - biosphere
~25% - ocean

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2
Q

ocean CO2 absorption

A

in future will become less absorptive; fertilizer effect will decrease; atmospheric CO2 will become a higher absorber

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3
Q

Venus atmosphere

A

insolation: 654 W/m^2
albedo: 0.67
net solar: 216 W/m^2
97 atm
96% CO2
477ºC

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4
Q

Earth atmosphere

A

insolation: 342 W/m^2
albedo: 0.37
net solar: 216 W/m^2
1 atm
0.04% CO2
15ºC

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5
Q

Mars atmosphere

A

insolation: 147 W/m^2
albedo: 0.17
net solar: 122 W/m^2
0.006 atm
95% CO2
-63ºC

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6
Q

why mars is so cold even though 95% CO2

A

atmosphere is too thin to trap heat

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7
Q

proxy records

A

stable isotope ratios don’t change through time

  • CaCO3 of plankton
  • 12CO2 of stomata
  • palaeosols
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8
Q

different elements

A

determined by number of protons in nucleus

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9
Q

different isotopes

A

determined by number of neutrons in nucleus (with same number of protons)

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10
Q

some stable isotopes

A

11/12B (80/20%)
12/13C (99/1%)
16/17/17O (99.8/.04/.2%)

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11
Q

fractionation

A

chemical, biological, physical processes occur differently for each isotope

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12
Q

water fractionation

A

H2(18)O, H2(16)O
takes more energy to evaporate heavier water (18)O
heavier water condenses easier

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13
Q

ocean sediment isotope ratio

A

cold climate– build up ice on land– ocean enriched in H2(18)O– shells have larger 18O/16O– proxy for volume of land ice and deep ocean T

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14
Q

photosynthesis isotope ratio

A

plants prefer 12CO2– become depleted in 13C relative to atmosphere
if higher CO2 in atmosphere- plant remains have less 13C

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15
Q

CO2 weathering thermostat

A

self-regulating system
slowest acting part of C cycle
most important process for stabilizing planetary climate
stable (-) feedback loop acting on million year timescales

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16
Q

CO2 weathering thermostat steps

A

CO2 emitted from volcano– atmosphere build up– dissolves in rain water– creates carbonic acid– acidic rain water– warms climate, increases rainfall– increased chemical weathering of mafics– release Ca, Mg ions into ocean– ions react w/ CO2 in seawater to produce minerals, precipitate, remove CO2– removal cools, reduces acidity of rain, slows chemical weathering

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17
Q

Ca + Mg + CO2 in seawater

A

minerals: Calcite (CaCO3), manganite (MgCO3)
precipitate: limestone

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18
Q

Snowball Earth

A 
750Ma- Marinoan
635Ma- Sturtian
0.94S
breakup of supercontinent, Rodinia-- more shoreline-- more evaporation near land-- enhanced weathering and CO2 drawdown
continents at mid latitudes
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19
Q

> weathering

A

> volcanism

> CO2 drawdown

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20
Q

initiating snowball cycle

A

breakup supercontinent– weathering > volcanism– polar ice caps grow equator ward– runaway feedback– total ice-cover– loss of bioproductivity– weathering < volcanism

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21
Q

terminate snowball cycle

A

continental ice sheets– weathering &laquo_space;volcanism– rapid loss of ice cover– hothouse– strong weathering draw down CO2– rate slows w/ sea level rise– equilib restored

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22
Q

Phanerozoic

A

541Ma - Present

age of multicellular life and fossils; proxy data for CO2, not much for T

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23
Q

Cenozoic

A

65Ma - present

ocean T proxies (δ18O)- compare CO2 and T

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24
Q

Paleocene-Eocene Thermal maximum

A

56Ma- sudden massive injection of light C into atmosphere-ocean: 3000-10,000PgC, 3000-20,000 yrs, 5-7º warming
δ13C, δ18O drop ~2%

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25
Q

PETM theories

A

destabilization of methane clathrate
injection of magma into organic C reservoir (FF reservoir)
degradation of permafrost C reservoir in Antarctica (no Ant. ice sheet at this time)

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26
Q

excess PETM C removed from atmosphere-ocean system

A

over 120-200 years

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27
Q

destabilization of methane clathrates

A

methane trapped in ice– exists under cold T and high P

found today in deep ocean and beneath permafrost

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28
Q

PETM consequences

A

ocean acidification- extinction of deep sea life, many corals
mammals got smaller and diversified
evolution of first primates

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29
Q

Quaternary

A

last 2.6Ma
cyclic glacial/interglcial cycles
early homosapiens lived through glaciation

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30
Q

earliest known fossil of Homo Sapiens

A

East Africa, 195,000yrs

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31
Q

Glacier mass balance =

A

snowfall - melt

snowfall - (calving + melt)

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32
Q

ELA

A

equilibrium line altitude
between net gain and net loss
snowfall = melt

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33
Q

net mass balance > 0

A

snowfall > melt

net gain of snow, accumulation

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34
Q

if glaciers didn’t flow

A

they would steepen

flow conveys mass from high–low elevations, and changes equilibrium (lower elevation)

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35
Q

snow –ice

A

snow: 90% air, aged crystals become rounder and fuse together
granular ice: 50% air, air bubbles start to seal off and snow forms ice
firn: 20-30% air
glacial ice: 20% air- trapping atmosphere at time of ice formation

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36
Q

higher mass glacier

A

more flowing outward

more calving

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37
Q

Dome Concordia

A

Antarctic plateaus
annual T: -51ºC
summer T: -30ºC
surface melt is negligible, only melts at edges

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38
Q

Dome Concordia records

A

CO2, CH4, ice volume, inferred Antarctic T, for 650,000yrs

all records are tightly correlated with each other

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39
Q

Last glacial cycle in Vostok ice core record

A

140,000yrs- present
roughly overall decline from ~130,000-20,000
fluctuations line up with human migrations, 4 big events

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40
Q

events in last glacial cycle

A

Out of Africa
Great Leap Forward
Domestication
Gradual extinction of Neanderthals

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41
Q

modern humans in proximity to neanderthals

A

55,000yr in Israel

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42
Q

Eemian interglacial

A

~130,000, high T anomaly due to precession, closer to sun, warmer summers, 4-6m higher seas

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43
Q

Dansgaard–Oeschger events

A

D-O events, 20-50yrs
glacial melt– fresher sea– less density difference– slow down AMOC– cools NH– too cool for evaporation– can’t grow ice sheet– less calving– less freshening– increase AMOC; (-) feedback

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44
Q

Domestication

A

~10,000yrs ago

domesticating plants and animals, farming and agriculture, able to establish ‘communities’

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45
Q

Great leap forward

A

~50,000 yrs ago
Behavioural modernity, beginning of modern human like thinking, artwork, bone tools, jewelry, human ingenuity - to increase survival in extreme climate change?

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46
Q

Heinrich events

A

150-250years
natural phenomenon in which large icebergs broke off glaciers and traverse the North Atlantic; occurred during past glacial periods; particularly well documented for the last glacial period

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47
Q

what happens in Heinrich event

A

rapid warmin– cold, heavy ice sheet– very high pressure melts (liquifies) bottom of glacier– surges forward into the ocean– extreme freshening

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48
Q

record of Heinrich events

A

ice rafted debris, further S than expected for normal calving (b/c they were much larger than normal)

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49
Q

Human movement out of Africa

A

linked with D-O oscillations, and Heinrich events

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50
Q

Neanderthals

A

tended to live further N– start to migrate S– run into ‘modern’ humans.. fight? compete? — become extinct

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51
Q

glacial/interglacial

A

glacial periods are longer

warming is much quicker

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52
Q

timescale btw glaciations =

A

~100,000 years

eccentricity

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53
Q

comparing orbital cycles w/ glaciation

A

the only one that really lines up with glaciations is eccentricity, the others are too rapid

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54
Q

warmer winter, colder summer, ice growth

A

slower snow melt– ↑α– ↓T at high altitude

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55
Q

cooler T at high altitude

A

boreal shift S– ↑α– ↓T @ alt.— soils freezes, ↑permafrost– ↓CO2, CH4 to atmos.

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56
Q

Reduction in CO2, CH4 sources

A

less GHGs– (-)radiative F– ↓T– ↓H2Og atmos.— ↓GHG— ↓T– ↑snow and ice– sea level drops

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57
Q

Global sea level drop

A

cont. shelves exposed– ↑vegetation– ↓CO2, CH4– ↓GHG– (-) rad. F– ↓T

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58
Q

global temperature drops

A

T has ↓– ↓H2Og atmos.–– ↓precip., wetlands, CH4atmos., GHGs––(-) Rad F–– ↓T global, ocean–– ↑CO2 solubility ocean–– ↑CO2 ocean uptake–– ↓CO2atmos., GHG–– (-)Rad F–– ↓T

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59
Q

Reduced precipitation

A

Aerosols travel farther–– ↑Fe rich dust in ocean–– ↑phytopl.–– ↓CO2 atmos.–– ↓GHG–– (-) Rad F–– ↓T global

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60
Q

feedbacks ~100,000yrs ago

A

continue until quasi-equilibrium; small change in radiation received in winter vs. summer is amplified by many feedbacks, ∆ice occurs due to changes in seasonal distribution of E, not change in total E

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61
Q

decomposition

A

oxygenic- CO2

anoxygenic- CH4

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62
Q

global sea level drop

A

~120m btw depths of ice age and interglacial

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63
Q

feedbacks, 21,000 years ago

A

Last Glacial Maximum, all of those feedbacks in reverse

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64
Q

Greenland ice core Temperature proxy

A

temperature variations are chaotic, more variable, closer to source of main changes (AMOC)

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65
Q

AMOC

A

Atlantic meridional overturning circulation

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66
Q

T-CO2 in the past

A

Temperature leads CO2 in the records; not relevant now b/c GHG emissions are unnatural

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67
Q

physics

A

if ↑GHGs, positive radiative F occurs and Earth must warm until a new global radiative equilibrium is reached

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68
Q

CO2- weathering thermostat

A

long-term (-)feedback in global C-cycle
1,000,000yr timescale
end of proterozoic, Phanerozoic

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69
Q

glacial cycles

A

variation in NH summer solar radiation
100,000 yr timescale
Quaternary

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70
Q

last 21,000 years

A

coming out of last glacial maximum (LGM)

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71
Q

Holocene

A

last 11,000 years

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72
Q

PETM

A

Paleocene-Eocene thermal maximum
injection of light C into atoms-ocean for 3-20,000 years
CO2 removed over 120-220,000 years
extinctions of deeps sea life, corals

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73
Q

LGM

A 
21,000 years ago
CO2 atmos. ~180ppm 
3-5ºC cooler than pre-industrial
sea level ~120m lower
~3km ice over Canada
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74
Q

Ice retreat, Holocene

A

icy till ~7kya

still experiencing isostatic rebound- Canadas coast lowering, sea level rising

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75
Q

Canada ice sheet, LGM

A

Laurentide

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76
Q

Isostatic rebound

A

ice melts, land rebounds from weight, creates ‘forebulge’ at head of glacier

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77
Q

some Canada rebound rates

A 
Victoria: -1.mm/yr
Richmond: -.9 mm/yr
Nunavut: +6.8mm/yr
Manitoba: +12
St.Johns: -1
Halifax: -1.2
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78
Q

Insolation curve through last 21,000years

A

Summer insolation was peaked in early holocene, on the down slope now; minimum at LGM

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79
Q

Mega Fauna extinctions

A

in 4 continents, extinctions followed human colonization; climate change may have aided extinction but mega fauna survived 18 previous glacial cycles

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80
Q

Events in the Holocene

A 
stable climate, stable CO2, less than 1º T anomaly 
Catalhoyuk- 10,000 bp
first writing- 5,000bp
Pyramids of Giza- 4,500bp
Qin dynasty- 2,000bp
medieval warm period-1,000bp
little ice age- 500bp
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81
Q

Çatalhöyük

A

first stable city

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82
Q

Holocene characteristics

A

-9000 - 2000
no wild climate fluctuations
CO2 atmos 260-280ppm
onset of agriculture, domestication, modern civilization; in last 20,000yrs only period w/ ~no T anomaly

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83
Q

Qin dynasty

A

built great wall of China

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84
Q

CO2 rates of change

A

LGM 180ppm, Pre-Indus 280ppm, ∆0.01ppm/yr
Pre-Indus 280, 2000 380, ∆0.7
1990s 350, 2015 400, ∆2

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85
Q

pre-industrial

A

1850

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86
Q

longest instrument measurements record

A

1659

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87
Q

measuring last ~1000 yrs

A

Tree cores: pick tree type restricted by T, not precipitations; tells about growing season (spring/summer)

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88
Q

Medieval warm period

A

~1000yrs ago
900-1100 AD
warmest period prior to 20th century, cooler than 1961-1990 mean
coincident w/ 1st viking settlement in Greenland- which collapsed ~300 years later

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89
Q

Viking settlement collapse in Greenland

A

Dorset culture was adapted to cold, used ice for fishing– warming gave Thule culture the ability to take over

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90
Q

when was the little ice age

A

1650-1850

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91
Q

Most striking climate event of the Holocene

A

Little ice age

outside range of internal variability of the climate system- must be change in radiative forcing

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92
Q

Causes of the little ice age

A

sun
thermohaline
volcanic activity
destruction of people

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93
Q

sunspots as a proxy

A

less sunspots = less solar output = less 14C (less bombardments)

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94
Q

sunspots and LIA

A

low # 1650-1700
14C record shows minimum during this period
could explain part of cooling
deepest part of cooling occurred after recovery of solar activity

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95
Q

thermohaline, LIA

A

weakening- cooler N hemisphere
medieval warm- melting- ↑freshwater
slowdown likely made LIA worse in Europe, not much global change

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96
Q

Volcanic activity, LIA

A

increased (aerosols); high volcanic activity from 1600-1800; large eruptions injected S into stratosphere (last longer); also high output in 12-1300 w/o cooling

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97
Q

destruction of the peoples of the Americas, LIA

A

Europe–America contact in 16th century–– diseases endemic to Eurasia/Africa spread to America––decimate indigenous populations––collapse of farmin–– uptake of CO2 by reforestation––cooling

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98
Q

CO2, LIA

A

big drop in 1650, ~10pm stating in late 1500s

~282ppm –272ppm = cooling of 0.15ºC

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99
Q

what caused LIA

A

no single hypothesis is enough to explain, combinations of hypotheses given and more are probably the best explanation

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100
Q

Mauna Loa

A

monthly measurements began 1958, Charles Keeling developed methods to measure CO2 at ppm range
Jan 2014: 397.80
Jan 2015: 399.96

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101
Q

δ13CO2 records

A

since 1980, atmosphere becoming more depleted in 13C; FFs are enriched in 12C

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102
Q

name of Mauna Loa CO2 record

A

Keeting Curve

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103
Q

T anomalies

A

1961-1990 have risen ~0.5ºC

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104
Q

Temperature records

A

begin in 1700s, global in 1850s
traditionally 2 thermometers to measure daily high and low- manually, daily
now w/ automatic weather stations- every 30s, uploaded hourly

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105
Q

SST records

A

traditionally w/ a bucket of surface water and measuring its T, obsessively by Royal Navy beginning 19th century
now w/ robotic ARGO floats

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106
Q

robotic ARGO floats

A

drift around ocean taking T measurements of surface and depths to 2000m, report data via satellite every 2 weeks

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107
Q

ARGO float distribution

A

March 2015- 3846 floats

pretty good, random coverage, a little less ~90º

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108
Q

change in average surface T, 1901-2012

A

majority is ~0.6-0.8º (over the 21yrs); fairly globally

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109
Q

changes in surface T, 1979-2014

A

more variation, shows Arctic amplification– northern latitudes ~2-3ºC, mid latitudes (NH) 0.2-1º,

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110
Q

‘Hiatus’

A

1995-2005? - decrease/stop of warming; still warmest decade in decadal averages

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111
Q

standard deviations from normal

A 
% > 1σ : 31.7
% > 2σ : 4.6
% > 3σ : 0.27%
% > 4σ : 0.006%
% >5σ : 0.000057%
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112
Q

sea ice extent

A

1900-2000
decrease 10-12 – ~6 million km^2
measured by ships until 1970s, then satellite

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113
Q

minimum sea extent

A

2012- 3.6million sq km

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114
Q

sea ice coverage per month

A

every year since 2010 has been below 2σ of the 1981-2010 average (increases from nov.-mar)

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115
Q

global average upper ocean heat content

A

1950-2010; has increased almost 20x10^22J; estimated from T-depth profiles taken by research vessels after WWII; now estimated using ARGO floats

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116
Q

global average sea level

A

1900-2010 increased ~200mm; measured from tide gauges at sea ports, now from satellites

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117
Q

average sea level change 20th century

A

2mm/yr

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118
Q

average sea level change 2000-2013

A

3.3mm/yr

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119
Q

pCO2 and pH records of surface ocean, 1990-2010

A

pCO2 ~330-380ppm
pH ~8.12 - 8.05
acidifying

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120
Q

cumulative ice mass loss

A

1992 - 2008

glaciers: 5000Gt
greenland: 3000Gt
antarctica: 2000Gt

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121
Q

Cryosphere measurements

A

ice sheets measured using satellite gravity measurements, satellite altimetry
small glaciers measure using ablation stakes

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122
Q

solar irradiance measurements

A

measured from space starting in 1978, slight downward trend; peaked at ~1960, slight decrease now– still within 1366±1 W/m^2

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123
Q

population explosion

A

industrial agriculture and basic sanitation 1500s, skyrocket from 7 w/i the Holocene

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124
Q

changing populations

A

↓fertility rate, 2012- 2.35children/woman– population set to stabilize
expanding life expectancy, inertia from past high birthrates population will continue to grow to 9bill. by 2050

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125
Q

nation FF use

A

burn FF = get wealthy

China 2011 per capita C emissions below American emissions in 1900, but has overtaken US as worlds largest C emitter

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126
Q

emissions by fuel type

A

coal 43%
oil 34%
gas 18%
cement 5%

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127
Q

USA emissions since 1990

A

30% of total cumulative anthropogenic CO2, w/ only 4.5% of global population

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128
Q

combustion formula

A

fuel + oxygen = CO2 + H2Og + heat

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129
Q

path to decarbonization

A 
coal C/H = 2.0
Oil C/H = 0.5
Propane C/H = 0.375
Methane = 0.25
Hydrogen = 0
less CO2, less C, more energy
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130
Q

‘proven’ reserves

A

coal: 119 years
natural gas: 63 years
oil: 46 years
but there is much more to be found

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131
Q

changes in CO2 coal emissions, 2008, 2010

A

World: increased 200-250 TgC/yr
Developed world: decreased 50-100 TgC/yr
China + India = 127% of worlds growth

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132
Q

FF and LUC emissions, 1960-2010

A

FF: 2.5 in 1960– 10 in 2010PgC/yr
LUC: ~1-2 PgC/yr

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133
Q

LUC emissions by region

A

Temperate: large spike in 1960
Tropics: large increase in 1980-2000
both declining now

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134
Q

heat trapped by GH effect

A

mostly goes into ocean; small changes in ocean-atoms. heat partitioning have big impacts on yearly global average air T
land + atmos + ice < 50x10^21J
ocean 250x10^21J in 2010

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135
Q

ENSO neutral

A

warm water off of Australia, cold off of Peru

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136
Q

El Nino

A

warm water central Pacific, warmer off of Peru; globally warmer on average, heat transferred from ocean-atmosphere

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137
Q

La Nina

A

very cold water off of Peru; La Ninas tend to follow El Nino; cooler than average, heat transfer from atmos.-ocean

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138
Q

Oceanic Niño Index (ONI)

A

characterized by 5 consecutive 3-month running mean SST anomalies in the Niño 3.4 region that is above the threshold of +0.5°C

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139
Q

Nino 3.4

A

between 5ºN and 5ºS and 120–170ºW

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140
Q

climate model

A

numerical representation of the climate system based on physical, chemical, biological properties of its components; their interactions and feedback processes, and accounting for some of its known properties

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141
Q

climate model hierarchy

A

the climate system can be represented by models of varying complexity; differing by # of spatial dimensions, extent to which processes are explicitly represented, or level at which empirical parameterizations are involved

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142
Q

climate models are composed of

A

components or modules which simulate a particular part of the Earth system; ex. atmosphere, ocean, land surface, ice sheets, sea ice, clouds

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143
Q

climate model components are represented

A

mathematically either as dynamics or parameterizations

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144
Q

model dynamics are

A

processes that can be fully described by laws of physics within computational limits of computer resources

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145
Q

parameterize

A

system is too complicated– mathematical relationships fitted to empirical data about the system to capture how the system behaves under varying conditions

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146
Q

fluid motion on a sphere

A

Navier-Stokes equations
cannot be solved using analytical pen and paper mathematics, can be solved using numerical methods (computers); simulate motion of atmosphere and oceans

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147
Q

parameterization example

A

Duck- complex biological system; parameterization captures the shape of the duck and can waddle like a duck

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148
Q

climate parameterization example

A

big leaf representing land-plant photosynthesis

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149
Q

climate model grid

A

horizontal grid- latitude-longitude
vertical grid- height/pressure
physical processes in a model- in each ‘square’ of the grid
like the world is made of lego bricks

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150
Q

how climate model works

A

climate models break world down into grid cells

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151
Q

grid cells

A

where all of the dynamic equations, radiative transfer, parameterizations are solved for at every model time-step; grid cells exchange info. with their neighbours

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152
Q

grid cells assigned

A

land-surface/ocean/sea-ice/ice-sheet; and properties- elevation, lake cover, soil type

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153
Q

more grid cells =

A

better representation of the climate

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154
Q

every doubling of resolution (more grid cells)

A

8X the computing power

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155
Q

evolution of climate models, processes

A

1970: rain, CO2, sun
1980: land surface, clouds, prescribed ice
1990: ‘swamp’ ocean (FAR)
1995: ocean, suphates, volcanic activity (SAR)
2001: carbon cycle, aerosols, overturning circulation, rivers (TAR)
2007: chemistry, interactive vegetation (AR4)

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156
Q

evolution of climate models, resolution

A 
FAR: ~500km
SAR: ~250km
TAR: ~180km
AR4: ~110km
AR5: 88km
testing: 30km
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157
Q

climate models have evolved

A

from numerical weather prediction models- originally focused on atmosphere; have become more complex w/ computing power

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158
Q

mid 1990s, climate models

A

atmospheric and ocean models coupled together to create first atmosphere ocean general circulation models

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159
Q

2000s, models

A

including interactive biology and carbon cycle to create first Earth system models

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160
Q

last few years, models

A

begun to incorporate dynamic ice-sheets

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161
Q

future models

A

clouds still poorly represented in models, create largest uncertainty in model projections; developing super-parameterizations of clouds; embed a cloud resolving model within each grid cell

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162
Q

main climate modelling groups

A

CCCma, Victoria; NCAR Boulder; NOAA, Princeton; Hadley centre, UK; MPI Germany; IPSL France; MIROC, Japan; MRI, Japan; CSIRO Australia

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163
Q

NCAR, 2012

A

supercomputer- Ranger
housed at Texas Advanced Computing Centre, part of Teragrid
was 30,000X faster than todays desktops, 579.4trillion operations/s (teraflops)

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164
Q

CCCma today

A

Environment Canada supercomputer in Dorval, Quebec

performs 211.7 teraflops

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165
Q

UVic ESCM

A

Earth System Climate Model
simulates C-cycle and ocean heat uptake changes on long timescales (thousands of years)- coarse resolution, simplified atmosphere

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166
Q

how ESCM works

A

intialize w/ 1800 conditions and keep constant for simulation; run for 10,000 model years to get equilibrated year 1800 climate; simulate from 1800-2000 by giving transient radiative forcing from 1800-present including natural and anthropogenic radiation F

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167
Q

1800 [CO2]

A

284ppm

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168
Q

10,000 model years

A

7 weeks on a supercomputer

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169
Q

community climate system model v4

A

CCSM4 took 2 days to run 5 model years- 11 years to run 10,000 model years!

170
Q

climate model validation

A

by comparing model output to global climate observations; ex. surface air T or precipitation

171
Q

climate model bias

A

locations where the simulated climate does not match observed climate; reduces as models become more complex; largest in distribution of precipitation; good at surface air T

172
Q

paleoclimate models

A

data is sparse so not as easy to validate as modern (only if proxy present); simulations allow us to show models are valid outside range of modern climate conditions and that the model has not been over tuned to 20th century climate

173
Q

detection

A

has some aspect of the climate system changed above what one would expect from natural climate variability?

174
Q

attribution

A

can the observed trend be explained by and only by anthropogenic forcing?

175
Q

change detection and attribution

A

note that models show large variation from all natural forcings
definitely have attribution, at least over last 30-40 years

176
Q

changes in IPCC wording about human influence being dominant cause of warming

A

1996: discernible
2001: stronger evidence
2007: very likely
2013: extremely likely

177
Q

all models in the world show

A

natural is not consistent with observed warming

178
Q

no model is perfect

A

but multi-model mean is pretty darn good

179
Q

IPCC sea ice detection and attribution

A

minimum summer sea-ice, 2007, reached all-time low, lower than predicted, reassess and improve models– closer representations

180
Q

idealized simulations

A

use ∆sea-ice as a boundary condition to atmosphere-only model to see how sea ice loss in isolation affects the atmosphere (no GHG change)– find up to 3ºC local warming in Arctic– warming up to 50ºN JUST due to sea ice loss– statistically significant

181
Q

isolate role of sea ice loss

A

warming due to ALL forcings – warming due to JUST sea ice = warming due to everything else
find that most of polar amplification is due to sea ice loss (feedback to rising GHGs and T)

182
Q

why do we need models

A

we don’t know future
see separate parts
we have 1 set of observations of the many possible (internal variability)

183
Q

how do we know we can trust climate models

A

based on laws of physics and them
successfully reproduce present-day average climate (climatology)
successfully reproduce observed climate changes over instrumental record
simulate realistic climate for past time periods based on paleoproxy records
multiple modelling centres build models independently and produce similar results

184
Q

climate models over/underestimate in the past 20 years

A

overestimate

185
Q

what is a climate model

A

10s of thousands of lines of code on a super computer

186
Q

how do we ‘give’ a climate model

A

–equations of motion, radiation laws etc.- based on laws of physics
parameterizations when processes are unresolved or poorly understood
–forcing

187
Q

forcing in a climate model

A 
solar constant
orbital parameters
atmospheric composition (CO2, CH4, O3, aerosols, etc.)
188
Q

emergent properties of the system

A

circulation, weather and climate
besides forcing, no observations are used- not told to have westerlies, currents, etc. these things emerge from laws of physics

189
Q

satellite vs. model

A

both show westerlies, trade winds, cyclones, daily cycle of evaporation and rain; individual weather patterns vary

190
Q

weather forecast

A

‘initialized’ with an analysis of observations- best estimate from various sources of what weather is now

191
Q

forecasts ‘degrade’

A

because of small errors in initial conditions (butterfly effect) and model errors, limit to predictability is ~2weeks

192
Q

analysis error

A

not perfect, most upper level atmos. not monitored, lots of parts of earth not monitored (don’t have i.c.’s)

193
Q

butterfly effect

A

starting with uncertainty in i.c. (small perturbation) will lead to further uncertainty– inherent uncertainty

194
Q

climate projection

A

climate models are forced by constant boundary conditions (CO2, F, etc) which determine the climate (statistics of weather); much longer time period; much less concerned w/ i.c.

195
Q

climate models CANNOT

A

and do not try to get timing of individual weather events

196
Q

climate model initial conditions

A

are set randomly, infinitely many possible ‘realizations’ which represent random internal climate variability (also known as weather); observations are NOT used to initialize; start from lots of different places- take average

197
Q

changing boundary conditions

A

causes the climate to change- ‘forces’ changes

198
Q

bouncing a ball down a mt. and trying to determine

A

–if it would be bouncing up or down at any given point in time (weather)
–if it will be trending up or down in a given time, obvious due to laws of physics (climate)

199
Q

knowing forcings

A

historical forcings are fairly well known

future forcing are totally unknown

200
Q

future forcings

A

unknown- use a range of ‘scenarios’ or ‘pathways’

201
Q

scenarios

A

4 Representative Concentration Pathways (RCPs)
RCP2.6, RCP4.5, RCP6.0, RCP8.5
ECPs for 2101-2300

202
Q

RCP numbers

A

radiative forcing in 2100

203
Q

to achieve RCP2.6

A

we actually have to REMOVE emissions, ex. artificial trees

204
Q

sources of model uncertainty

A

scenario uncertainty
model uncertainty
internal variability

205
Q

scenario uncertainty

A

uncertainty in future emissions pathways, spread between RCPs in the average over all model simulations; ex. who will be elected, green party?

206
Q

model uncertainty

A

uncertainty in representations of processes; spread across simulations from different models for a given RCP; ex. clouds

207
Q

internal variability

A

uncertainty due to random internal climate variability; it is the spread across multiple realizations from the same climate model for a given RCP; weather, ex. ball goes up sometimes even though overall is down

208
Q

long term uncertainty dominated by

A

scenario uncertainty

209
Q

local uncertainty

A

internal variability

ex. cold local zones (East coast)

210
Q

global warming hiatus, internal variability

A

“pause” in warming over last 15 years; due to strong balance of warming/cooling

211
Q

strong balance of warming/cooling

A

regional cooling in E Pacific due to ENSO, La Nina-like event: stronger trade winds upwell cool water– sea surface height higher on W side of Pacific

212
Q

problem with this La Nina -like event

A

these events usually last 1-2 years, this is ~10years! the water is going to come back!, unprecedented

213
Q

IPO

A

long term ENSO event, internal climate variability, but recent is very unusual

214
Q

stronger trade winds

A

negative phase of Interdecadal Pacific Oscillation (IPO or PDO)

215
Q

what about the climate models

A

if chances of IPO were 1/100 we’d expect to see that in the models; similar hiatus events can occur but are fairly rare; timing doesn’t match well and never will (not simulating weather); considering all 15 year trends instead of 1, model does good job

216
Q

models overestimating last 20 years

A

normal distribution around ~0.2ºC, but observations are ~0º

217
Q

model forced by observed winds

A

set correct timing of IPO variability; recent hiatus occurs b/c wind-induced cooling offsets anthropogenic warming

218
Q

recent wind event

A

highly unusual period of strong negative IPO tropical pacific trade wind trends

219
Q

check ocean T’s for hiatus

A

no hiatus- continued warming trend; still gaining heat- storing it in the ocean

220
Q

storing heat in the ocean

A

ocean takes up 90% of heat due to increased GHGs, better climate change indicator than surface air Ts (4X specific heat capacity); small changes in ocean heat storage from year-year can have large influences on surface Ts

221
Q

so the hiatus is really

A

an extended La Nina-like condition ( - IPO)

El Nino will return w/ heat from ocean– accelerated warming over anthropogenic

222
Q

when is El Nino coming

A

its starting already- 2014 warmest year on record, very warm SSTs in E Pacific, no snow in BC

223
Q

Arctic sea ice

A

bounced back in 2013 and 2014; interviews overestimated, IPCC models predict ice free ~2040

224
Q

arctic models

A

spreads of observed and simulated trends agree; most spread due to internal variability; over long periods trends converge to long-term human forced trend

225
Q

climate models have weather

A

but timing is random– weather forecasts try to get this timing of individual events right

226
Q

climate models experience climate change due to

A

changes in forcing, or boundary conditions

227
Q

uncertainty in projections arises from

A

inter-model spread/process uncertainty
scenario uncertainty/ future emissions pathway
internal variability/weather

228
Q

specific, important examples of internal variability

A

hiatus in global warming

Arctic sea-ice changes

229
Q

feedbacks to climate change

A

LW radiation, snow-ice albedo, ocean circulation, clouds, peat/permafrost, water vapour, emissions of non-CO2 GHGs, air-sea CO2 exchange, biogeophysical processes…

230
Q

positive feedbacks

A

reinforce initial change (warming)

ex. water vapour feedback

231
Q

negative feedbacks

A

diminish initial change

ex. LW feedback

232
Q

Major + feedback

A

ice albedo

water vapour

233
Q

major - feedbacks

A

longwave radiation

ocean heat uptake

234
Q

major mixed feedback

A

cloud feedback

235
Q

model uncertainty, feedbacks

A

models needed to estimate magnitude of climate feedbacks– variation leads to different estimates of warming from different estimates of feedback strength

236
Q

LW feedback

A

heat up– warmer– increase radiation output– cool

237
Q

RCP range

A

RCP2.6 - 450ppm 2100– decline to 350ppm 2300

RCP8.5 - 950ppm 2100– 2000ppm 2300

238
Q

ice albedo feedback leeds to

A

polar amplification

239
Q

old ICP scenarios

A

different models treated emissions differently, switched to concentrations for ease of model comparison, old models A2, B2, A1B

240
Q

A1B =

A

RCP6 = ‘business as usual’

241
Q

sources of uncertainty seen in RCP projections

A

from outside of uncertainty area in
human decisions/scenario spread–– process uncertainty/feedbacks/model spread–– internal climate variability/internal variability

242
Q

sources of uncertainty in reality

A

what humans do in future
how well climate models represent climate system
internal climate variability

243
Q

warming by 2100 for each scenario

A

RCP2.6: 0.3-1.7º
RCP4.5: 1.1-2.6º
RCP6.0: 1.4-3.1º
RCP8.5: 2.6-4.8º

244
Q

climate model sensitivity

A

RCP2.6 = 2.6º of warming for a doubling of CO2

245
Q

models with high sensitivity

A

take longer to reach equilibrium

246
Q

we can expect more warming

A

on land
toward the Arctic (arctic amplification)
leeward coasts of continents

247
Q

changes in precipitation

A 
in general less certain, but:
wet regions get wetter
dry regions get drier
intensification of Hadley cell atmospheric circulation
more moisture transport to poles
248
Q

changes in soil moisture

A

warming = increased evaporation = decrease in available water = lower P - E

249
Q

P - E

A

precipitation - evaporation; expect it to be negative, drying– draught

250
Q

soil moisture change can be used to compute

A

Palmer Drought Severity Index (PDSI); negative numbers = draught

  • 3 = severe drought
  • 4 = extreme drought
251
Q

what to expect from PDSI

A

the highest crop yield regions have severe drought index, poorest regions

252
Q

∆ in crop yield depends on

A

direct effect of CO2 (fertilization effect) and warming effect of CO2

253
Q

fertilization effect

A

crops that use C3 photosynthetic pathway (wheat, rice) strong + yield w/ increased CO2
C4 synthetic pathway (maize) = little yield response

254
Q

crops and warming

A

all crops have - yield w/ increased T in already warm climate, longer growing season in higher latitudes; sustained T>35ºC catastrophic damage to photosyn. and reproductive organs

255
Q

climate change and ecosystems

A

species ranges shifts, some ranges can’t be shifted any farther, some species can’t migrate at velocity of climate change

256
Q

ocean acidification

A

OA tracks atmos. CO2– little intermodal variability in estimated changes in ocean pH– effect on marine organisms poorly constrained– one of big unknowns

257
Q

sea level rise

A

all models show >0.6m by 2100; on longer timescales Greenland acetate will melt leading to ~7m sea level rise

258
Q

tropical cyclones

A

generally: frequency decreases, intensity and precipitation rate increase; very uncertain; warming increases SST (fuel), and strength of shear-winds (disrupt cyclone formation)

259
Q

carbon cycle feedbacks

A

w/ climate change, CO2 in atmosphere (airborne fraction) will increase

260
Q

why will airborne fraction increase

A

CO2 less soluble in warm water
CO2 no longer limiting for plant growth (~800ppm)
decay of organics in soils faster at higher T

261
Q

Irreversibility

A

following cessation of emissions, removal of atmos. CO2 decreases radiative forcing, but is largely compensated by slower loss of heat to the ocean so that atmos T do not drop significantly for at least 1,000 years

262
Q

irreversibility after 1000years

A

oceans have absorbed 80% of anthro CO2, 75% of peak warming remains; irreversible on human timescales

263
Q

human temperature limits

A

cannot survive wet-bulb T > 35ºC; no where on Earth is currently above 31ºC

264
Q

wet-bulb T

A

temperature of a thermometer covered in a wet sock; combines T and humidity measurements

265
Q

why wet-bulb?

A

at high humidity, efficiency of evaporative cooling (sweating) diminishes

266
Q

will we reach wet bulb >35º

A

> 7ºC global warming in major regions of the world

>11ºC global warming in regions that contain half human population would routinely exceed 35

267
Q

Keeling Curve is from

A

Scripps Institution of Oceanography, NOAA Earth system research laboratory; well-mixed site, 3400m elevation

268
Q

Keeling invented

A

precise IR analyzer for measuring CO2 atmosphere

269
Q

why Mauna Loa

A

elevation
no anthro sources nearby
well mixed atmosphere

270
Q

first time CO2 over 400ppm

A

May 9, 2013

now 40% higher than 1800s

271
Q

CO2 is rising

A

exponentially (not linearly)
rate is increasing yr-yr
never had this fast of change
‘rate of change’ problem

272
Q

wiggles in net growth

A

political/economic issues

273
Q

3 main economic (-) growth changes

A 
USSR collapse (91-92)
global economic crisis (08-09)
china cut coal use (2014, not just China though)
274
Q

USA CO2 emissions, 08-13

A

decline 7.7% while economy grew 9%- moving production overseas, fracking, better fuel standards for internal combustion

275
Q

why fracking decreases CO2

A

shift from coal to natural gas, CH4 1/2 emission of coal

276
Q

average surface T, decline in 1900-1920

A

volcanic activity, aerosols; Mt. Pinatubo

277
Q

average surface T, 97-98

A

‘mother of all El Ninos’

warm blob of water in NE Pacific– stratification– decreased PP

278
Q

temperature changes, 45-70

A

increased industrialization- aerosols, ended due to Clean Air Act

279
Q

NH T anomalies

A

1951-80: 1/3 within 1/2σ of mean

2011-13: 80% higher than 1951-80 mean

280
Q

changes in glacial surface area, 1985-2005

A

BC -10.8±3%

AB -25.5±3.4%

281
Q

implication of glacial change

A

no more melt, no river cooling in spring/summer– bad for fish

282
Q

climate change affecting industry

A

Coke- disrupting company supply of sugar cane and sugar beets, citrus for fruit juices
Insurance- raising policies

283
Q

threat multiplier

A

exacerbates many challenges dealing w/ today- infectious disease to terrorism, social unrest, mass movements

284
Q

negative T anomalies in E NA

A

fuels deniers, rest of NH is 4-8º warmer

285
Q

warming and water vapour increase

A

1º = 7% more H2Og

286
Q

record floods

A

Hungary, 2013, worst of all times
Passau Germany, 2013, worst in >500yrs
Grimma Germany, 2013
Malawi, 2015, record flooding

287
Q

Malawi

A

one of poorest nations, no resilience, nowhere to go for >300,000 ppl, and no news on this

288
Q

droughts

A

Folsom reservoir, Sacramento July 2011-97% capacity, Jan. 2014 17% capacity, March 2016 59%
Sierra Nevada snowpack at record low

289
Q

California drought implications

A

4th year; increased well drilling– decreased water table

290
Q

some of Californias problems

A

residents don’t pay for water, or have meters
farmers use large scale irrigation
preferential allocations to fracking companies (not to farmers)

291
Q

Israel water problems

A

banned fracking

manage farming w/ much stricter plans

292
Q

extreme warm events, Europe

A

are increasing, distribution is shifting right, and flattening

293
Q

last cold Europe summers

A

1920s

294
Q

really hot Europe summers

A

2002-2010

295
Q

Russia heat

A

heat wave, 2010, Ts never seen before; 10,935 ‘excess’ deaths in Moscow in July, August attributed to heat, smog, smoke; 25% of crops lost; >$15billion economic loss; affects on wheat prices

296
Q

Russia and wheat

A

Russia represents only 4% of world wheat exports, within weeks price of wheat nearly doubled- panic, demand spike, everyone bought it up

297
Q

Wheat prices, last 5 years

A

2 major increases
2010 Russian heat wave, record rains on canadian prairies, and Indus Valley
2012 midwest US drought
now $233/tonne, will raise with next big crisis

298
Q

Wheats affects on other food commodities

A

farmers can’t afford to feed to livestock, buy up other crops

299
Q

Palmer Drought Severity Index for 2090-2099

A

≤-3 indicates severe to extreme soil-moisture deficit

S NA, N SA, W Europe, S Africa

300
Q

Wheat price, 2007-2008

A

huge Peak, >$400
biofuels
oil price
speculation

301
Q

US energy policy 2005

A

attempt to deal w/ global warming, Ethanol for vehicle combustion;
Energy Policy Act
set renewable fuels standard, require 28 billion L of EtOH by 2012

302
Q

US energy policy 2007

A

Energy Independence and Security Act

target of 57billion L annually from corn by 2022, 80 billion from cellulose

303
Q

US corn production

A

42% goes directly to ethanol, easiest to ferment

304
Q

side effects to US energy policies

A

farmers rush to plant corn– fertilizer into rivers; lower corn availability– drop in stocks– poor, largest for importers– bread riots

305
Q

side effects to US energy policies

A

farmers rush to plant corn– fertilizer into rivers; lower corn availability– drop in stocks– poor, largest for importers– bread riots

306
Q

future wheat prices

A

expect more volatilities in future w/ more extreme climate events

307
Q

water

A

many parts of the world desperate

308
Q

Libya water

A

4m wide pipe carrying water from 8000 wells in Sahara desert all the way across libya, aquifer is also under egypt and chile, 100yr supply t current pumping rate

309
Q

how much time do we have

A 
**VIP IPCC figure
track now- RCP8.5
2ºC = tipping point- major ecosystem collapse
we have ~300PgC to go
we've emitted ~600PgC
globally 10bill t C/yr-- 30yrs
BC 62million t C/yr
310
Q

80% probability of breaking 2ºC

A

RCP8.5 2031

RCP2.6 2045

311
Q

how to solve the dilemma

A

slow the rate of rise with:
pricing emissions
fuel switching (power and transportation)

312
Q

pricing C emissions

A

carbon tax

cap and trade

313
Q

initiated BCs carbon tax

A

mountain pine bark beetle–1990s– no more late fall cold snaps + fire supression– not dying– epidemic

314
Q

why does cold snap matter

A

takes them time to synthesize antifreeze

315
Q

consequence of bark beetle

A

blue-stain fungus- streaks in wood ‘denim pine’
extending to Sask., yukon, NWT, jumping to new trees, decline in lumber production, 24 closed mills in BC, allowable cuts will shrink 70-41M m^3/yr

316
Q

consequence of bark beetle

A

blue-stain fungus- streaks in wood ‘denim pine’
extending to Sask., yukon, NWT, jumping to new trees, decline in lumber production, 24 closed mills in BC, allowable cuts will shrink 70-41M m^3/yr

317
Q

bark beetle biology

A

1-2 yr life cycle, female burrows, builds egg gallery, summer/autumn lays eggs, 10-14days eggs hatch, larvae feed on trees phloem 10months, after pupa-adult bore exit holes, fly to new tree and restart cycle, blue stain fungus on their bodies spreads through tree, interrupts flow of nutrients

318
Q

healthy trees

A

survive attack by throwing out large amounts of pitch, drowning the beetle

319
Q

decline in lumber production

A

have only a few years to harvest dead trees- large acceleration to get the dead tree before worthless

320
Q

economic value (pine trees)

A

1m^3 = $150

lost 730M m^3 = 108 billion

321
Q

2013 bark beetle infection, ha

A

19Mha in BC

322
Q

BC Carbon tax

A

Gordon Cambell, 2008; July $10/t, 2.25c/L, rose $5/t/yr from July 09-12, frozen June 2013 by Premier Clark at $30/t until 2018

323
Q

current carbon tax on fuel

A

6.67c/L (7.67c/L on diesel)

324
Q

fossil fuels not subject to carbon tax

A

aviation fuel for out-of province flights, bunker fuel for ships that ply open-ocean waters

325
Q

carbon tax, revenue neutral

A

by law– every penny used to reduce other taxes; lowest personal income tax in Canada, northern/rural homeowner benefit, low income climate action tax credit

326
Q

since carbon tax implemented

A

BC FF has decreased while rest of Canada has increased, reduced more than expected- lower than money put into income taxes

327
Q

petroleum coke

A

used for cement

328
Q

%change in per capita consumption of petroleum fuels subject to BC carbon tax, 2007-2013

A

BC -16.1
rest of canada 3.0
difference -19.1
and our economy grew at least as fast as Canadian average for this period

329
Q

%change in per capita consumption of petroleum fuels subject to BC carbon tax, 2007-2013

A

BC -16.1
rest of canada 3.0
difference -19.1
and our economy grew at least as fast as Canadian average for this period

330
Q

French c tax

A

started higher than BCs is now-set at 7€/t of C (25€/ tCO2); rising to €22 by 2016
major exemptions- transport, fishing sectors exempt, less impact
expect €4billion revenue in 2016

331
Q

Australia C tax

A

was having impacts on emissions in first year, new leader– climate denier– eliminated tax– emissions back up

332
Q

Mexico C tax

A

unlikely to succeed– no plans to increase it– public ‘get over’ initial bump- need to see it over and over again

333
Q

cap and trade

A

economic incentive to reduce emissions

334
Q

cap and trade

A

economic incentive to reduce emissions; cap is set on amount of CO2 that may be emitted; emission permits allocated/sold; cap reduced over time; buyer pays to pollute; seller is rewarded for reducing emissions

335
Q

typical initial emission permits

A

1 credit = 1 tonne of CO2

336
Q

emission permits also called

A

credits, allowances

337
Q

total number of permits

A

cannot exceed cap

338
Q

reducing number of permits

A

drives up price and increases incentive to reduce emissions

339
Q

EU cap and trade, 2005

A

12,000 large emitters (40-45% of emissions); initial credits > actual emissions; supply/demand drove price to 0€ in 2007

340
Q

why EUs cap and trade problems

A

emissions data estimated w/ UN definitions, ETS market used measurement-based emissions; oversupply

341
Q

ETS

A

emissions trading system

342
Q

EU cap and trade now

A

starting to recover, allowances capped and 40$ sold by auction, trading price up to ~6.3€/t
target is ~30€/tonne
emissions still fell 18%, GDP grew 45%

343
Q

California-Quebec cap and trade

A

formal 2014, Feb 2015 US$12.21; all major emitters must buy permits– raise prices of their products– gas to rise 3.5c/L

344
Q

why bother w/ administrative issue of cap and trade

A

put certainties in place, set limits
c tax- unknown emissions, no restrictions
doesn’t have the word ‘tax’ in it- a demonized word

345
Q

stefan boltzman law

A

F = σ T^4

hot things emit more radiation

346
Q

weins displacement law

A

λmax = c / T

hot things emit most radiation at shorter wavelengths

347
Q

radiative transfer

A

stefan-boltzman
weins displacement
beer-lambert law

348
Q

beer-lambert law I

A

F1 = Fo e^-tau
Fo is incident flux
F1 is transmitted flux
tau is optical depth = absorption coefficient x number of molecules

349
Q

beer-lambert in words

A

transmission of flux through any layer of absorbing media

350
Q

beer-lambert in words

A

transmission of flux through any layer of absorbing media

351
Q

beer lambert law II

A

F1/Fo decreases w/ optical depth

352
Q

beer lambert law III

A

optical depth increases with fraction of emited thermal radiation which escapes to space

353
Q

thermal emission to spaces

A

comes mostly from the level of tau = 1

temperature at tau = 1 is important

354
Q

runaway greenhouse

A

failure of a planet to maintain energy balance w/ a surface T that permits liquid water (

355
Q

runaway greenhouse is not

A

just a song version of the water vapour feedback, high climate sensitivity, etc.

356
Q

when hot and moist, moist adiabatic lapse rate

A

tends towards saturation vapour pressure curve, atmosphere becomes optically thick

357
Q

limiting fluxes

A

outgoing thermal radiation

planetary albedo

358
Q

outgoing thermal radiation limit

A

asymptotes to a constant, indpendent of surface T (~280 W/m^2)

359
Q

planetary albedo limit

A

asymptotes to a constant, indpependent of surface T, ~18%, given present S, absorbed solar radiation asymptotes to ~280W/m^2

360
Q

runaway greenhouse occurs b/c

A

there is an upper limit on outgoing thermal radiation independent of surface T

361
Q

moist columns in Earths tropics

A

observed to be in local runaway greenhouse; energy balance via dry columns and poleward heat transport

362
Q

venus

A

experienced runaway greenhouse in past, earth will far in the future (close on geological timescale, not human)

363
Q

anthropogenic climate change causing a runaway greenhouse

A

will not, can not

364
Q

nuclear weapon airburst

A

airburst will maximize E directly at surface

365
Q

airburst energy partitioned

A

blast wave
thermal pulse
ionizing radiation

366
Q

Hiroshima

A 
16kt TNT equiv. bomb
airburst at 580m
destruction area 8km^2
firestorm- fire damage area 12km^2
100,000 ppl killed
367
Q

largest nuclear test

A

50,000 kt TNT equivelant

368
Q

nuclear delivery methods

A

ICBM
aircraft launch
SLBM
MAD

369
Q

ICBM

A

ground launched intercontinental ballistic missile- first strike

370
Q

SLBM

A

submarine launched ballistic missiles

371
Q

MAD

A

mutually assured destruction

372
Q

MAD

A

mutually assured destruction

373
Q

world nuclear forces

A

russia, USA, france, china, UK, Israel, pakistan, india

374
Q

history of nuclear arsenals

A

one new state every 5 years

375
Q

nuclear winter

A

weapons target cities– firestorm– large amount of soot to stratosphere– absorbs sunlight– global cooling– reduction of growing season– unprecedented famine

376
Q

nuclear winter soot

A

black carbon = to Mt. Pinatubo; good sunlight absorber– Anti-greenhouse effect

377
Q

scenarios of nuclear winter

A

regional conflict- 5Tg soot

superpower conflict- 150-180Tg

378
Q

other nuclear winter implications

A

famine- billions of fatalities

ozone layer seriously damaged

379
Q

levels of nuclear winter

A

regional- mild- unprecedented climate change, serious famine, billion deaths
superpower- complete- devastating climate change and famine, most of global population killed

380
Q

what to do about climate change

A

adapt while mitigating

ex of adapting- avoid flood damage

381
Q

Danube, Budapest flooding

A

2002 848cm
2006 865cm
2013 891cm

382
Q

Danube adaptation

A

June 2013- installed flood wall along the Danube in Grein, Austria; over conservative building plan 1/100 yr flood event (happened 3 times)

383
Q

Danube adaptation

A

June 2013- installed flood wall along the Danube in Grein, Austria; over conservative building plan 1/100 yr flood event (happened 3 times)

384
Q

Bangladesh flooding

A

also have flooding rivers but no capacity to adapt

385
Q

flooding facts

A

~1.5billion ppl live in coastal zones, many large low-lying cities on deltas
~200 million below historical 1/100 yr storm-surge level
global sea level will be 40-80cm higher by 2100

386
Q

assessing coastal flooding risk

A

exposure
susceptibility
resilience

387
Q

exposure

A

people
infrastructure
agricultural production
goods

388
Q

susceptibility

A

relative likelihood of damage

389
Q

resilience

A

awareness
preparedness
institutional structures

390
Q

flooding risk specifics

A

physical
social
administrative

391
Q

physical (flooding)

A 
sea level rise rate
storm surge frequency
# hurricanes/cyclones
river discharge
foreshore slope
subsidence
392
Q

social (flooding)

A 
population distribution 
rate of growth
shelter availability
% disabled
estimated recovery time
393
Q

administrative (flooding)

A

flood hazard maps
% of area with uncontrolled planning
institutional strength/authority/will

394
Q

weighing all factors in flood vulnerability index

A

Shanghai is most vulnerable of nine delta cities: 24 million ppl, Chinas largest city, major worry is storm surges

395
Q

shanghai adaptation

A

so far $6 billion on flood infrastructure
coastline reinforcement
surge-protection barriers

396
Q

Netherlands flood protection

A

$144 billion 2008-2100 ($2bill/year, 0.25% of GDP) for broadening coastal dunes and strengthening sea and river dikes

397
Q

Delta commission

A

said netherlands must plan for a rise in the north sea of 1.3m by 2100, 4m by 2200; building for higher than these levels– committed, capacity

398
Q

mitigation

A

via lowering emissions or direct removal from the air of already emitted CO2

399
Q

global primary energy supply

A

40% of worlds electricity needs are provided by coal (42 in US, 12 in Canada, 66 in China)

400
Q

CO2 emission changes by region

A

China vastly increasing- but Canadians emit 4X as much per capita
US decreasing- but cumulative emissions from US are 3X those from China

401
Q

decarbonizing the world

A

eliminate coal from electricity generation systems- unless CO2 can be captured and stored forever

402
Q

decarbonizing the world

A

eliminate coal from electricity generation systems- unless CO2 can be captured and stored forever

403
Q

gCO2 / kWh_e

A 
coal 1001
natural gas 469
solar PV 46
nuclear 16
wind 12
hydroelectric reservoir 4
404
Q

using NG instead of coal

A

cuts emissions by half- IF no CH4 leakage

405
Q

solar thermal power generation

A

concentrate heat on boiler mounted in solar reciever– capture sunlight in fluid holding tower– heats up– steams– turbine produces electricity

406
Q

thermal power storage

A

tanks of nitrate salts (~28,000t)- use 1/2 E on sunny days to melt salts– when not sunny the heat is directed to turbines- can run 24/7
~2X what we pay for power

407
Q

cost of power for us

A

~8-11c/kWhr

408
Q

Solana generating station

A

Arizona, 2013; 100% owned by spain, 3,232 parabolic troughs focus light into synthetic oil, outlet T = 380ºC– 280MW; heat stored in tanks of molten salts; 6 hour storage; $2B capital cost

409
Q

desert sunlight solar PV farm

A

Riverside country, California, 2015
worlds largest, 14.5km^2, 550MW output, 1/3size of site C, 7c wholesale, 1/2 power of site C, will offset daylight energy use in Cali

410
Q

PV module prices

A

down 80% since 2008, 20% in 2012 alone, 70c/W raw cell cost only

411
Q

global PV install capacity

A

2000 - 1400
2012 - 102, 156 = 138,000 MW = 125site C dams
US employs >173,000 solar workers

412
Q

wind power

A 
supplies 4.5% of US energy
Denmark- 30% 
capacity factor ~30%
fuel is free, but intermittently supplied
view shed impacts, bird mortality, sound
413
Q

wind power subsidy

A

US 2.2c/kWh- expired 2013

denmark ~$350M/yr

414
Q

Solana capacity factor

A

41%

415
Q

Solana capacity factor

A

41%

416
Q

Bear mt. wind farm

A

Dawson creek, 2009, 34x3 MW turbines, 78m, 102 MW, capacity factor ~29%, 6X energy need by dawson creek, had to put close to existing transmission line- very expensive

417
Q

denmark abundant wind

A

can’t be stored- have to shut off or send to other grids– Germany or Norwegian submarine cables

418
Q

norwegian submarine cables

A

charge to accept denmarks energy!! “Negative pricing”

419
Q

wind power sound concern

A

infrasound– low frequency sound (can’t hear it)– feel it?– 0 connection found w/ human health concerns

420
Q

canada wind capacity

A

installed 9,694 MW = 9 site c = 4% candies electricity

canada is second in potential only to Russia

421
Q

what Canada should do about power

A

connect power grids between BC and AB– share hydro and wind power (wind can be the battery recharger), get rid of coal use, stop sharing/relying on US!

422
Q

W.A.C. Bennett Dam

A

Lake Williston, one of world’s largest batteries

EOS (7 decks)

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