EOS 365 Part II Flashcards
Average fate of anthropogenic CO2 emissions
~50% - atmosphere
~25% - biosphere
~25% - ocean
ocean CO2 absorption
in future will become less absorptive; fertilizer effect will decrease; atmospheric CO2 will become a higher absorber
Venus atmosphere
insolation: 654 W/m^2
albedo: 0.67
net solar: 216 W/m^2
97 atm
96% CO2
477ºC
Earth atmosphere
insolation: 342 W/m^2
albedo: 0.37
net solar: 216 W/m^2
1 atm
0.04% CO2
15ºC
Mars atmosphere
insolation: 147 W/m^2
albedo: 0.17
net solar: 122 W/m^2
0.006 atm
95% CO2
-63ºC
why mars is so cold even though 95% CO2
atmosphere is too thin to trap heat
proxy records
stable isotope ratios don’t change through time
- CaCO3 of plankton
- 12CO2 of stomata
- palaeosols
different elements
determined by number of protons in nucleus
different isotopes
determined by number of neutrons in nucleus (with same number of protons)
some stable isotopes
11/12B (80/20%)
12/13C (99/1%)
16/17/17O (99.8/.04/.2%)
fractionation
chemical, biological, physical processes occur differently for each isotope
water fractionation
H2(18)O, H2(16)O
takes more energy to evaporate heavier water (18)O
heavier water condenses easier
ocean sediment isotope ratio
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
photosynthesis isotope ratio
plants prefer 12CO2– become depleted in 13C relative to atmosphere
if higher CO2 in atmosphere- plant remains have less 13C
CO2 weathering thermostat
self-regulating system
slowest acting part of C cycle
most important process for stabilizing planetary climate
stable (-) feedback loop acting on million year timescales
CO2 weathering thermostat steps
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
Ca + Mg + CO2 in seawater
minerals: Calcite (CaCO3), manganite (MgCO3)
precipitate: limestone
Snowball Earth
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
> weathering
> volcanism
> CO2 drawdown
initiating snowball cycle
breakup supercontinent– weathering > volcanism– polar ice caps grow equator ward– runaway feedback– total ice-cover– loss of bioproductivity– weathering < volcanism
terminate snowball cycle
continental ice sheets– weathering «_space;volcanism– rapid loss of ice cover– hothouse– strong weathering draw down CO2– rate slows w/ sea level rise– equilib restored
Phanerozoic
541Ma - Present
age of multicellular life and fossils; proxy data for CO2, not much for T
Cenozoic
65Ma - present
ocean T proxies (δ18O)- compare CO2 and T
Paleocene-Eocene Thermal maximum
56Ma- sudden massive injection of light C into atmosphere-ocean: 3000-10,000PgC, 3000-20,000 yrs, 5-7º warming
δ13C, δ18O drop ~2%
PETM theories
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)
excess PETM C removed from atmosphere-ocean system
over 120-200 years
destabilization of methane clathrates
methane trapped in ice– exists under cold T and high P
found today in deep ocean and beneath permafrost
PETM consequences
ocean acidification- extinction of deep sea life, many corals
mammals got smaller and diversified
evolution of first primates
Quaternary
last 2.6Ma
cyclic glacial/interglcial cycles
early homosapiens lived through glaciation
earliest known fossil of Homo Sapiens
East Africa, 195,000yrs
Glacier mass balance =
snowfall - melt
snowfall - (calving + melt)
ELA
equilibrium line altitude
between net gain and net loss
snowfall = melt
net mass balance > 0
snowfall > melt
net gain of snow, accumulation
if glaciers didn’t flow
they would steepen
flow conveys mass from high–low elevations, and changes equilibrium (lower elevation)
snow –ice
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
higher mass glacier
more flowing outward
more calving
Dome Concordia
Antarctic plateaus
annual T: -51ºC
summer T: -30ºC
surface melt is negligible, only melts at edges
Dome Concordia records
CO2, CH4, ice volume, inferred Antarctic T, for 650,000yrs
all records are tightly correlated with each other
Last glacial cycle in Vostok ice core record
140,000yrs- present
roughly overall decline from ~130,000-20,000
fluctuations line up with human migrations, 4 big events
events in last glacial cycle
Out of Africa
Great Leap Forward
Domestication
Gradual extinction of Neanderthals
modern humans in proximity to neanderthals
55,000yr in Israel
Eemian interglacial
~130,000, high T anomaly due to precession, closer to sun, warmer summers, 4-6m higher seas
Dansgaard–Oeschger events
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
Domestication
~10,000yrs ago
domesticating plants and animals, farming and agriculture, able to establish ‘communities’
Great leap forward
~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?
Heinrich events
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
what happens in Heinrich event
rapid warmin– cold, heavy ice sheet– very high pressure melts (liquifies) bottom of glacier– surges forward into the ocean– extreme freshening
record of Heinrich events
ice rafted debris, further S than expected for normal calving (b/c they were much larger than normal)
Human movement out of Africa
linked with D-O oscillations, and Heinrich events
Neanderthals
tended to live further N– start to migrate S– run into ‘modern’ humans.. fight? compete? — become extinct
glacial/interglacial
glacial periods are longer
warming is much quicker
timescale btw glaciations =
~100,000 years
eccentricity
comparing orbital cycles w/ glaciation
the only one that really lines up with glaciations is eccentricity, the others are too rapid
warmer winter, colder summer, ice growth
slower snow melt– ↑α– ↓T at high altitude
cooler T at high altitude
boreal shift S– ↑α– ↓T @ alt.— soils freezes, ↑permafrost– ↓CO2, CH4 to atmos.
Reduction in CO2, CH4 sources
less GHGs– (-)radiative F– ↓T– ↓H2Og atmos.— ↓GHG— ↓T– ↑snow and ice– sea level drops
Global sea level drop
cont. shelves exposed– ↑vegetation– ↓CO2, CH4– ↓GHG– (-) rad. F– ↓T
global temperature drops
T has ↓– ↓H2Og atmos.–– ↓precip., wetlands, CH4atmos., GHGs––(-) Rad F–– ↓T global, ocean–– ↑CO2 solubility ocean–– ↑CO2 ocean uptake–– ↓CO2atmos., GHG–– (-)Rad F–– ↓T
Reduced precipitation
Aerosols travel farther–– ↑Fe rich dust in ocean–– ↑phytopl.–– ↓CO2 atmos.–– ↓GHG–– (-) Rad F–– ↓T global
feedbacks ~100,000yrs ago
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
decomposition
oxygenic- CO2
anoxygenic- CH4
global sea level drop
~120m btw depths of ice age and interglacial
feedbacks, 21,000 years ago
Last Glacial Maximum, all of those feedbacks in reverse
Greenland ice core Temperature proxy
temperature variations are chaotic, more variable, closer to source of main changes (AMOC)
AMOC
Atlantic meridional overturning circulation
T-CO2 in the past
Temperature leads CO2 in the records; not relevant now b/c GHG emissions are unnatural
physics
if ↑GHGs, positive radiative F occurs and Earth must warm until a new global radiative equilibrium is reached
CO2- weathering thermostat
long-term (-)feedback in global C-cycle
1,000,000yr timescale
end of proterozoic, Phanerozoic
glacial cycles
variation in NH summer solar radiation
100,000 yr timescale
Quaternary
last 21,000 years
coming out of last glacial maximum (LGM)
Holocene
last 11,000 years
PETM
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
LGM
21,000 years ago CO2 atmos. ~180ppm 3-5ºC cooler than pre-industrial sea level ~120m lower ~3km ice over Canada
Ice retreat, Holocene
icy till ~7kya
still experiencing isostatic rebound- Canadas coast lowering, sea level rising
Canada ice sheet, LGM
Laurentide
Isostatic rebound
ice melts, land rebounds from weight, creates ‘forebulge’ at head of glacier
some Canada rebound rates
Victoria: -1.mm/yr Richmond: -.9 mm/yr Nunavut: +6.8mm/yr Manitoba: +12 St.Johns: -1 Halifax: -1.2
Insolation curve through last 21,000years
Summer insolation was peaked in early holocene, on the down slope now; minimum at LGM
Mega Fauna extinctions
in 4 continents, extinctions followed human colonization; climate change may have aided extinction but mega fauna survived 18 previous glacial cycles
Events in the Holocene
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
Çatalhöyük
first stable city
Holocene characteristics
-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
Qin dynasty
built great wall of China
CO2 rates of change
LGM 180ppm, Pre-Indus 280ppm, ∆0.01ppm/yr
Pre-Indus 280, 2000 380, ∆0.7
1990s 350, 2015 400, ∆2
pre-industrial
1850
longest instrument measurements record
1659
measuring last ~1000 yrs
Tree cores: pick tree type restricted by T, not precipitations; tells about growing season (spring/summer)
Medieval warm period
~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
Viking settlement collapse in Greenland
Dorset culture was adapted to cold, used ice for fishing– warming gave Thule culture the ability to take over
when was the little ice age
1650-1850
Most striking climate event of the Holocene
Little ice age
outside range of internal variability of the climate system- must be change in radiative forcing
Causes of the little ice age
sun
thermohaline
volcanic activity
destruction of people
sunspots as a proxy
less sunspots = less solar output = less 14C (less bombardments)
sunspots and LIA
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
thermohaline, LIA
weakening- cooler N hemisphere
medieval warm- melting- ↑freshwater
slowdown likely made LIA worse in Europe, not much global change
Volcanic activity, LIA
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
destruction of the peoples of the Americas, LIA
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
CO2, LIA
big drop in 1650, ~10pm stating in late 1500s
~282ppm –272ppm = cooling of 0.15ºC
what caused LIA
no single hypothesis is enough to explain, combinations of hypotheses given and more are probably the best explanation
Mauna Loa
monthly measurements began 1958, Charles Keeling developed methods to measure CO2 at ppm range
Jan 2014: 397.80
Jan 2015: 399.96
δ13CO2 records
since 1980, atmosphere becoming more depleted in 13C; FFs are enriched in 12C
name of Mauna Loa CO2 record
Keeting Curve
T anomalies
1961-1990 have risen ~0.5ºC
Temperature records
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
SST records
traditionally w/ a bucket of surface water and measuring its T, obsessively by Royal Navy beginning 19th century
now w/ robotic ARGO floats
robotic ARGO floats
drift around ocean taking T measurements of surface and depths to 2000m, report data via satellite every 2 weeks
ARGO float distribution
March 2015- 3846 floats
pretty good, random coverage, a little less ~90º
change in average surface T, 1901-2012
majority is ~0.6-0.8º (over the 21yrs); fairly globally
changes in surface T, 1979-2014
more variation, shows Arctic amplification– northern latitudes ~2-3ºC, mid latitudes (NH) 0.2-1º,
‘Hiatus’
1995-2005? - decrease/stop of warming; still warmest decade in decadal averages
standard deviations from normal
% > 1σ : 31.7 % > 2σ : 4.6 % > 3σ : 0.27% % > 4σ : 0.006% % >5σ : 0.000057%
sea ice extent
1900-2000
decrease 10-12 – ~6 million km^2
measured by ships until 1970s, then satellite
minimum sea extent
2012- 3.6million sq km
sea ice coverage per month
every year since 2010 has been below 2σ of the 1981-2010 average (increases from nov.-mar)
global average upper ocean heat content
1950-2010; has increased almost 20x10^22J; estimated from T-depth profiles taken by research vessels after WWII; now estimated using ARGO floats
global average sea level
1900-2010 increased ~200mm; measured from tide gauges at sea ports, now from satellites
average sea level change 20th century
2mm/yr
average sea level change 2000-2013
3.3mm/yr
pCO2 and pH records of surface ocean, 1990-2010
pCO2 ~330-380ppm
pH ~8.12 - 8.05
acidifying
cumulative ice mass loss
1992 - 2008
glaciers: 5000Gt
greenland: 3000Gt
antarctica: 2000Gt
Cryosphere measurements
ice sheets measured using satellite gravity measurements, satellite altimetry
small glaciers measure using ablation stakes
solar irradiance measurements
measured from space starting in 1978, slight downward trend; peaked at ~1960, slight decrease now– still within 1366±1 W/m^2
population explosion
industrial agriculture and basic sanitation 1500s, skyrocket from 7 w/i the Holocene
changing populations
↓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
nation FF use
burn FF = get wealthy
China 2011 per capita C emissions below American emissions in 1900, but has overtaken US as worlds largest C emitter
emissions by fuel type
coal 43%
oil 34%
gas 18%
cement 5%
USA emissions since 1990
30% of total cumulative anthropogenic CO2, w/ only 4.5% of global population
combustion formula
fuel + oxygen = CO2 + H2Og + heat
path to decarbonization
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
‘proven’ reserves
coal: 119 years
natural gas: 63 years
oil: 46 years
but there is much more to be found
changes in CO2 coal emissions, 2008, 2010
World: increased 200-250 TgC/yr
Developed world: decreased 50-100 TgC/yr
China + India = 127% of worlds growth
FF and LUC emissions, 1960-2010
FF: 2.5 in 1960– 10 in 2010PgC/yr
LUC: ~1-2 PgC/yr
LUC emissions by region
Temperate: large spike in 1960
Tropics: large increase in 1980-2000
both declining now
heat trapped by GH effect
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
ENSO neutral
warm water off of Australia, cold off of Peru
El Nino
warm water central Pacific, warmer off of Peru; globally warmer on average, heat transferred from ocean-atmosphere
La Nina
very cold water off of Peru; La Ninas tend to follow El Nino; cooler than average, heat transfer from atmos.-ocean
Oceanic Niño Index (ONI)
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
Nino 3.4
between 5ºN and 5ºS and 120–170ºW
climate model
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
climate model hierarchy
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
climate models are composed of
components or modules which simulate a particular part of the Earth system; ex. atmosphere, ocean, land surface, ice sheets, sea ice, clouds
climate model components are represented
mathematically either as dynamics or parameterizations
model dynamics are
processes that can be fully described by laws of physics within computational limits of computer resources
parameterize
system is too complicated– mathematical relationships fitted to empirical data about the system to capture how the system behaves under varying conditions
fluid motion on a sphere
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
parameterization example
Duck- complex biological system; parameterization captures the shape of the duck and can waddle like a duck
climate parameterization example
big leaf representing land-plant photosynthesis
climate model grid
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
how climate model works
climate models break world down into grid cells
grid cells
where all of the dynamic equations, radiative transfer, parameterizations are solved for at every model time-step; grid cells exchange info. with their neighbours
grid cells assigned
land-surface/ocean/sea-ice/ice-sheet; and properties- elevation, lake cover, soil type
more grid cells =
better representation of the climate
every doubling of resolution (more grid cells)
8X the computing power
evolution of climate models, processes
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)
evolution of climate models, resolution
FAR: ~500km SAR: ~250km TAR: ~180km AR4: ~110km AR5: 88km testing: 30km
climate models have evolved
from numerical weather prediction models- originally focused on atmosphere; have become more complex w/ computing power
mid 1990s, climate models
atmospheric and ocean models coupled together to create first atmosphere ocean general circulation models
2000s, models
including interactive biology and carbon cycle to create first Earth system models
last few years, models
begun to incorporate dynamic ice-sheets
future models
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
main climate modelling groups
CCCma, Victoria; NCAR Boulder; NOAA, Princeton; Hadley centre, UK; MPI Germany; IPSL France; MIROC, Japan; MRI, Japan; CSIRO Australia
NCAR, 2012
supercomputer- Ranger
housed at Texas Advanced Computing Centre, part of Teragrid
was 30,000X faster than todays desktops, 579.4trillion operations/s (teraflops)
CCCma today
Environment Canada supercomputer in Dorval, Quebec
performs 211.7 teraflops
UVic ESCM
Earth System Climate Model
simulates C-cycle and ocean heat uptake changes on long timescales (thousands of years)- coarse resolution, simplified atmosphere
how ESCM works
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
1800 [CO2]
284ppm
10,000 model years
7 weeks on a supercomputer
community climate system model v4
CCSM4 took 2 days to run 5 model years- 11 years to run 10,000 model years!
climate model validation
by comparing model output to global climate observations; ex. surface air T or precipitation
climate model bias
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
paleoclimate models
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
detection
has some aspect of the climate system changed above what one would expect from natural climate variability?
attribution
can the observed trend be explained by and only by anthropogenic forcing?
change detection and attribution
note that models show large variation from all natural forcings
definitely have attribution, at least over last 30-40 years
changes in IPCC wording about human influence being dominant cause of warming
1996: discernible
2001: stronger evidence
2007: very likely
2013: extremely likely
all models in the world show
natural is not consistent with observed warming
no model is perfect
but multi-model mean is pretty darn good
IPCC sea ice detection and attribution
minimum summer sea-ice, 2007, reached all-time low, lower than predicted, reassess and improve models– closer representations
idealized simulations
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
isolate role of sea ice loss
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)
why do we need models
we don’t know future
see separate parts
we have 1 set of observations of the many possible (internal variability)
how do we know we can trust climate models
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
climate models over/underestimate in the past 20 years
overestimate
what is a climate model
10s of thousands of lines of code on a super computer
how do we ‘give’ a climate model
–equations of motion, radiation laws etc.- based on laws of physics
parameterizations when processes are unresolved or poorly understood
–forcing
forcing in a climate model
solar constant orbital parameters atmospheric composition (CO2, CH4, O3, aerosols, etc.)
emergent properties of the system
circulation, weather and climate
besides forcing, no observations are used- not told to have westerlies, currents, etc. these things emerge from laws of physics
satellite vs. model
both show westerlies, trade winds, cyclones, daily cycle of evaporation and rain; individual weather patterns vary
weather forecast
‘initialized’ with an analysis of observations- best estimate from various sources of what weather is now
forecasts ‘degrade’
because of small errors in initial conditions (butterfly effect) and model errors, limit to predictability is ~2weeks
analysis error
not perfect, most upper level atmos. not monitored, lots of parts of earth not monitored (don’t have i.c.’s)
butterfly effect
starting with uncertainty in i.c. (small perturbation) will lead to further uncertainty– inherent uncertainty
climate projection
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.
climate models CANNOT
and do not try to get timing of individual weather events
climate model initial conditions
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
changing boundary conditions
causes the climate to change- ‘forces’ changes
bouncing a ball down a mt. and trying to determine
–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)
knowing forcings
historical forcings are fairly well known
future forcing are totally unknown
future forcings
unknown- use a range of ‘scenarios’ or ‘pathways’
scenarios
4 Representative Concentration Pathways (RCPs)
RCP2.6, RCP4.5, RCP6.0, RCP8.5
ECPs for 2101-2300
RCP numbers
radiative forcing in 2100
to achieve RCP2.6
we actually have to REMOVE emissions, ex. artificial trees
sources of model uncertainty
scenario uncertainty
model uncertainty
internal variability
scenario uncertainty
uncertainty in future emissions pathways, spread between RCPs in the average over all model simulations; ex. who will be elected, green party?
model uncertainty
uncertainty in representations of processes; spread across simulations from different models for a given RCP; ex. clouds
internal variability
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
long term uncertainty dominated by
scenario uncertainty
local uncertainty
internal variability
ex. cold local zones (East coast)
global warming hiatus, internal variability
“pause” in warming over last 15 years; due to strong balance of warming/cooling
strong balance of warming/cooling
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
problem with this La Nina -like event
these events usually last 1-2 years, this is ~10years! the water is going to come back!, unprecedented
IPO
long term ENSO event, internal climate variability, but recent is very unusual
stronger trade winds
negative phase of Interdecadal Pacific Oscillation (IPO or PDO)
what about the climate models
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
models overestimating last 20 years
normal distribution around ~0.2ºC, but observations are ~0º
model forced by observed winds
set correct timing of IPO variability; recent hiatus occurs b/c wind-induced cooling offsets anthropogenic warming
recent wind event
highly unusual period of strong negative IPO tropical pacific trade wind trends
check ocean T’s for hiatus
no hiatus- continued warming trend; still gaining heat- storing it in the ocean
storing heat in the ocean
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
so the hiatus is really
an extended La Nina-like condition ( - IPO)
El Nino will return w/ heat from ocean– accelerated warming over anthropogenic
when is El Nino coming
its starting already- 2014 warmest year on record, very warm SSTs in E Pacific, no snow in BC
Arctic sea ice
bounced back in 2013 and 2014; interviews overestimated, IPCC models predict ice free ~2040
arctic models
spreads of observed and simulated trends agree; most spread due to internal variability; over long periods trends converge to long-term human forced trend
climate models have weather
but timing is random– weather forecasts try to get this timing of individual events right
climate models experience climate change due to
changes in forcing, or boundary conditions
uncertainty in projections arises from
inter-model spread/process uncertainty
scenario uncertainty/ future emissions pathway
internal variability/weather
specific, important examples of internal variability
hiatus in global warming
Arctic sea-ice changes
feedbacks to climate change
LW radiation, snow-ice albedo, ocean circulation, clouds, peat/permafrost, water vapour, emissions of non-CO2 GHGs, air-sea CO2 exchange, biogeophysical processes…
positive feedbacks
reinforce initial change (warming)
ex. water vapour feedback
negative feedbacks
diminish initial change
ex. LW feedback
Major + feedback
ice albedo
water vapour
major - feedbacks
longwave radiation
ocean heat uptake
major mixed feedback
cloud feedback
model uncertainty, feedbacks
models needed to estimate magnitude of climate feedbacks– variation leads to different estimates of warming from different estimates of feedback strength
LW feedback
heat up– warmer– increase radiation output– cool
RCP range
RCP2.6 - 450ppm 2100– decline to 350ppm 2300
RCP8.5 - 950ppm 2100– 2000ppm 2300
ice albedo feedback leeds to
polar amplification
old ICP scenarios
different models treated emissions differently, switched to concentrations for ease of model comparison, old models A2, B2, A1B
A1B =
RCP6 = ‘business as usual’
sources of uncertainty seen in RCP projections
from outside of uncertainty area in
human decisions/scenario spread–– process uncertainty/feedbacks/model spread–– internal climate variability/internal variability
sources of uncertainty in reality
what humans do in future
how well climate models represent climate system
internal climate variability
warming by 2100 for each scenario
RCP2.6: 0.3-1.7º
RCP4.5: 1.1-2.6º
RCP6.0: 1.4-3.1º
RCP8.5: 2.6-4.8º
climate model sensitivity
RCP2.6 = 2.6º of warming for a doubling of CO2
models with high sensitivity
take longer to reach equilibrium
we can expect more warming
on land
toward the Arctic (arctic amplification)
leeward coasts of continents
changes in precipitation
in general less certain, but: wet regions get wetter dry regions get drier intensification of Hadley cell atmospheric circulation more moisture transport to poles
changes in soil moisture
warming = increased evaporation = decrease in available water = lower P - E
P - E
precipitation - evaporation; expect it to be negative, drying– draught
soil moisture change can be used to compute
Palmer Drought Severity Index (PDSI); negative numbers = draught
- 3 = severe drought
- 4 = extreme drought
what to expect from PDSI
the highest crop yield regions have severe drought index, poorest regions
∆ in crop yield depends on
direct effect of CO2 (fertilization effect) and warming effect of CO2
fertilization effect
crops that use C3 photosynthetic pathway (wheat, rice) strong + yield w/ increased CO2
C4 synthetic pathway (maize) = little yield response
crops and warming
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
climate change and ecosystems
species ranges shifts, some ranges can’t be shifted any farther, some species can’t migrate at velocity of climate change
ocean acidification
OA tracks atmos. CO2– little intermodal variability in estimated changes in ocean pH– effect on marine organisms poorly constrained– one of big unknowns
sea level rise
all models show >0.6m by 2100; on longer timescales Greenland acetate will melt leading to ~7m sea level rise
tropical cyclones
generally: frequency decreases, intensity and precipitation rate increase; very uncertain; warming increases SST (fuel), and strength of shear-winds (disrupt cyclone formation)
carbon cycle feedbacks
w/ climate change, CO2 in atmosphere (airborne fraction) will increase
why will airborne fraction increase
CO2 less soluble in warm water
CO2 no longer limiting for plant growth (~800ppm)
decay of organics in soils faster at higher T
Irreversibility
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
irreversibility after 1000years
oceans have absorbed 80% of anthro CO2, 75% of peak warming remains; irreversible on human timescales
human temperature limits
cannot survive wet-bulb T > 35ºC; no where on Earth is currently above 31ºC
wet-bulb T
temperature of a thermometer covered in a wet sock; combines T and humidity measurements
why wet-bulb?
at high humidity, efficiency of evaporative cooling (sweating) diminishes
will we reach wet bulb >35º
> 7ºC global warming in major regions of the world
>11ºC global warming in regions that contain half human population would routinely exceed 35
Keeling Curve is from
Scripps Institution of Oceanography, NOAA Earth system research laboratory; well-mixed site, 3400m elevation
Keeling invented
precise IR analyzer for measuring CO2 atmosphere
why Mauna Loa
elevation
no anthro sources nearby
well mixed atmosphere
first time CO2 over 400ppm
May 9, 2013
now 40% higher than 1800s
CO2 is rising
exponentially (not linearly)
rate is increasing yr-yr
never had this fast of change
‘rate of change’ problem
wiggles in net growth
political/economic issues
3 main economic (-) growth changes
USSR collapse (91-92) global economic crisis (08-09) china cut coal use (2014, not just China though)
USA CO2 emissions, 08-13
decline 7.7% while economy grew 9%- moving production overseas, fracking, better fuel standards for internal combustion
why fracking decreases CO2
shift from coal to natural gas, CH4 1/2 emission of coal
average surface T, decline in 1900-1920
volcanic activity, aerosols; Mt. Pinatubo
average surface T, 97-98
‘mother of all El Ninos’
warm blob of water in NE Pacific– stratification– decreased PP
temperature changes, 45-70
increased industrialization- aerosols, ended due to Clean Air Act
NH T anomalies
1951-80: 1/3 within 1/2σ of mean
2011-13: 80% higher than 1951-80 mean
changes in glacial surface area, 1985-2005
BC -10.8±3%
AB -25.5±3.4%
implication of glacial change
no more melt, no river cooling in spring/summer– bad for fish
climate change affecting industry
Coke- disrupting company supply of sugar cane and sugar beets, citrus for fruit juices
Insurance- raising policies
threat multiplier
exacerbates many challenges dealing w/ today- infectious disease to terrorism, social unrest, mass movements
negative T anomalies in E NA
fuels deniers, rest of NH is 4-8º warmer
warming and water vapour increase
1º = 7% more H2Og
record floods
Hungary, 2013, worst of all times
Passau Germany, 2013, worst in >500yrs
Grimma Germany, 2013
Malawi, 2015, record flooding
Malawi
one of poorest nations, no resilience, nowhere to go for >300,000 ppl, and no news on this
droughts
Folsom reservoir, Sacramento July 2011-97% capacity, Jan. 2014 17% capacity, March 2016 59%
Sierra Nevada snowpack at record low
California drought implications
4th year; increased well drilling– decreased water table
some of Californias problems
residents don’t pay for water, or have meters
farmers use large scale irrigation
preferential allocations to fracking companies (not to farmers)
Israel water problems
banned fracking
manage farming w/ much stricter plans
extreme warm events, Europe
are increasing, distribution is shifting right, and flattening
last cold Europe summers
1920s
really hot Europe summers
2002-2010
Russia heat
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
Russia and wheat
Russia represents only 4% of world wheat exports, within weeks price of wheat nearly doubled- panic, demand spike, everyone bought it up
Wheat prices, last 5 years
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
Wheats affects on other food commodities
farmers can’t afford to feed to livestock, buy up other crops
Palmer Drought Severity Index for 2090-2099
≤-3 indicates severe to extreme soil-moisture deficit
S NA, N SA, W Europe, S Africa
Wheat price, 2007-2008
huge Peak, >$400
biofuels
oil price
speculation
US energy policy 2005
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
US energy policy 2007
Energy Independence and Security Act
target of 57billion L annually from corn by 2022, 80 billion from cellulose
US corn production
42% goes directly to ethanol, easiest to ferment
side effects to US energy policies
farmers rush to plant corn– fertilizer into rivers; lower corn availability– drop in stocks– poor, largest for importers– bread riots
side effects to US energy policies
farmers rush to plant corn– fertilizer into rivers; lower corn availability– drop in stocks– poor, largest for importers– bread riots
future wheat prices
expect more volatilities in future w/ more extreme climate events
water
many parts of the world desperate
Libya water
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
how much time do we have
**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
80% probability of breaking 2ºC
RCP8.5 2031
RCP2.6 2045
how to solve the dilemma
slow the rate of rise with:
pricing emissions
fuel switching (power and transportation)
pricing C emissions
carbon tax
cap and trade
initiated BCs carbon tax
mountain pine bark beetle–1990s– no more late fall cold snaps + fire supression– not dying– epidemic
why does cold snap matter
takes them time to synthesize antifreeze
consequence of bark beetle
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
consequence of bark beetle
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
bark beetle biology
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
healthy trees
survive attack by throwing out large amounts of pitch, drowning the beetle
decline in lumber production
have only a few years to harvest dead trees- large acceleration to get the dead tree before worthless
economic value (pine trees)
1m^3 = $150
lost 730M m^3 = 108 billion
2013 bark beetle infection, ha
19Mha in BC
BC Carbon tax
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
current carbon tax on fuel
6.67c/L (7.67c/L on diesel)
fossil fuels not subject to carbon tax
aviation fuel for out-of province flights, bunker fuel for ships that ply open-ocean waters
carbon tax, revenue neutral
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
since carbon tax implemented
BC FF has decreased while rest of Canada has increased, reduced more than expected- lower than money put into income taxes
petroleum coke
used for cement
%change in per capita consumption of petroleum fuels subject to BC carbon tax, 2007-2013
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
%change in per capita consumption of petroleum fuels subject to BC carbon tax, 2007-2013
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
French c tax
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
Australia C tax
was having impacts on emissions in first year, new leader– climate denier– eliminated tax– emissions back up
Mexico C tax
unlikely to succeed– no plans to increase it– public ‘get over’ initial bump- need to see it over and over again
cap and trade
economic incentive to reduce emissions
cap and trade
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
typical initial emission permits
1 credit = 1 tonne of CO2
emission permits also called
credits, allowances
total number of permits
cannot exceed cap
reducing number of permits
drives up price and increases incentive to reduce emissions
EU cap and trade, 2005
12,000 large emitters (40-45% of emissions); initial credits > actual emissions; supply/demand drove price to 0€ in 2007
why EUs cap and trade problems
emissions data estimated w/ UN definitions, ETS market used measurement-based emissions; oversupply
ETS
emissions trading system
EU cap and trade now
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%
California-Quebec cap and trade
formal 2014, Feb 2015 US$12.21; all major emitters must buy permits– raise prices of their products– gas to rise 3.5c/L
why bother w/ administrative issue of cap and trade
put certainties in place, set limits
c tax- unknown emissions, no restrictions
doesn’t have the word ‘tax’ in it- a demonized word
stefan boltzman law
F = σ T^4
hot things emit more radiation
weins displacement law
λmax = c / T
hot things emit most radiation at shorter wavelengths
radiative transfer
stefan-boltzman
weins displacement
beer-lambert law
beer-lambert law I
F1 = Fo e^-tau
Fo is incident flux
F1 is transmitted flux
tau is optical depth = absorption coefficient x number of molecules
beer-lambert in words
transmission of flux through any layer of absorbing media
beer-lambert in words
transmission of flux through any layer of absorbing media
beer lambert law II
F1/Fo decreases w/ optical depth
beer lambert law III
optical depth increases with fraction of emited thermal radiation which escapes to space
thermal emission to spaces
comes mostly from the level of tau = 1
temperature at tau = 1 is important
runaway greenhouse
failure of a planet to maintain energy balance w/ a surface T that permits liquid water (
runaway greenhouse is not
just a song version of the water vapour feedback, high climate sensitivity, etc.
when hot and moist, moist adiabatic lapse rate
tends towards saturation vapour pressure curve, atmosphere becomes optically thick
limiting fluxes
outgoing thermal radiation
planetary albedo
outgoing thermal radiation limit
asymptotes to a constant, indpendent of surface T (~280 W/m^2)
planetary albedo limit
asymptotes to a constant, indpependent of surface T, ~18%, given present S, absorbed solar radiation asymptotes to ~280W/m^2
runaway greenhouse occurs b/c
there is an upper limit on outgoing thermal radiation independent of surface T
moist columns in Earths tropics
observed to be in local runaway greenhouse; energy balance via dry columns and poleward heat transport
venus
experienced runaway greenhouse in past, earth will far in the future (close on geological timescale, not human)
anthropogenic climate change causing a runaway greenhouse
will not, can not
nuclear weapon airburst
airburst will maximize E directly at surface
airburst energy partitioned
blast wave
thermal pulse
ionizing radiation
Hiroshima
16kt TNT equiv. bomb airburst at 580m destruction area 8km^2 firestorm- fire damage area 12km^2 100,000 ppl killed
largest nuclear test
50,000 kt TNT equivelant
nuclear delivery methods
ICBM
aircraft launch
SLBM
MAD
ICBM
ground launched intercontinental ballistic missile- first strike
SLBM
submarine launched ballistic missiles
MAD
mutually assured destruction
MAD
mutually assured destruction
world nuclear forces
russia, USA, france, china, UK, Israel, pakistan, india
history of nuclear arsenals
one new state every 5 years
nuclear winter
weapons target cities– firestorm– large amount of soot to stratosphere– absorbs sunlight– global cooling– reduction of growing season– unprecedented famine
nuclear winter soot
black carbon = to Mt. Pinatubo; good sunlight absorber– Anti-greenhouse effect
scenarios of nuclear winter
regional conflict- 5Tg soot
superpower conflict- 150-180Tg
other nuclear winter implications
famine- billions of fatalities
ozone layer seriously damaged
levels of nuclear winter
regional- mild- unprecedented climate change, serious famine, billion deaths
superpower- complete- devastating climate change and famine, most of global population killed
what to do about climate change
adapt while mitigating
ex of adapting- avoid flood damage
Danube, Budapest flooding
2002 848cm
2006 865cm
2013 891cm
Danube adaptation
June 2013- installed flood wall along the Danube in Grein, Austria; over conservative building plan 1/100 yr flood event (happened 3 times)
Danube adaptation
June 2013- installed flood wall along the Danube in Grein, Austria; over conservative building plan 1/100 yr flood event (happened 3 times)
Bangladesh flooding
also have flooding rivers but no capacity to adapt
flooding facts
~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
assessing coastal flooding risk
exposure
susceptibility
resilience
exposure
people
infrastructure
agricultural production
goods
susceptibility
relative likelihood of damage
resilience
awareness
preparedness
institutional structures
flooding risk specifics
physical
social
administrative
physical (flooding)
sea level rise rate storm surge frequency # hurricanes/cyclones river discharge foreshore slope subsidence
social (flooding)
population distribution rate of growth shelter availability % disabled estimated recovery time
administrative (flooding)
flood hazard maps
% of area with uncontrolled planning
institutional strength/authority/will
weighing all factors in flood vulnerability index
Shanghai is most vulnerable of nine delta cities: 24 million ppl, Chinas largest city, major worry is storm surges
shanghai adaptation
so far $6 billion on flood infrastructure
coastline reinforcement
surge-protection barriers
Netherlands flood protection
$144 billion 2008-2100 ($2bill/year, 0.25% of GDP) for broadening coastal dunes and strengthening sea and river dikes
Delta commission
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
mitigation
via lowering emissions or direct removal from the air of already emitted CO2
global primary energy supply
40% of worlds electricity needs are provided by coal (42 in US, 12 in Canada, 66 in China)
CO2 emission changes by region
China vastly increasing- but Canadians emit 4X as much per capita
US decreasing- but cumulative emissions from US are 3X those from China
decarbonizing the world
eliminate coal from electricity generation systems- unless CO2 can be captured and stored forever
decarbonizing the world
eliminate coal from electricity generation systems- unless CO2 can be captured and stored forever
gCO2 / kWh_e
coal 1001 natural gas 469 solar PV 46 nuclear 16 wind 12 hydroelectric reservoir 4
using NG instead of coal
cuts emissions by half- IF no CH4 leakage
solar thermal power generation
concentrate heat on boiler mounted in solar reciever– capture sunlight in fluid holding tower– heats up– steams– turbine produces electricity
thermal power storage
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
cost of power for us
~8-11c/kWhr
Solana generating station
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
desert sunlight solar PV farm
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
PV module prices
down 80% since 2008, 20% in 2012 alone, 70c/W raw cell cost only
global PV install capacity
2000 - 1400
2012 - 102, 156 = 138,000 MW = 125site C dams
US employs >173,000 solar workers
wind power
supplies 4.5% of US energy Denmark- 30% capacity factor ~30% fuel is free, but intermittently supplied view shed impacts, bird mortality, sound
wind power subsidy
US 2.2c/kWh- expired 2013
denmark ~$350M/yr
Solana capacity factor
41%
Solana capacity factor
41%
Bear mt. wind farm
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
denmark abundant wind
can’t be stored- have to shut off or send to other grids– Germany or Norwegian submarine cables
norwegian submarine cables
charge to accept denmarks energy!! “Negative pricing”
wind power sound concern
infrasound– low frequency sound (can’t hear it)– feel it?– 0 connection found w/ human health concerns
canada wind capacity
installed 9,694 MW = 9 site c = 4% candies electricity
canada is second in potential only to Russia
what Canada should do about power
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!
W.A.C. Bennett Dam
Lake Williston, one of world’s largest batteries
