// lecture 23 Flashcards
why is CO2 highly correlated to ice volume?
incomplete answer: colder oceans can dissolve more atmospheric CO2.
- but possibly more plankton active taking CO2 out of atmosphere and/or seawater exchange between surface and deep was greater
Younger Dryas (YD)
- example of rapid climate change
- 14700 kbp, the warming trend reversed
- relatively cold period lasted about 2,000 years
- warmed very abruptly about 12,000 years ago, and has been relatively stable since
cause of YD
- probably by ice sheet breakup and flooding in the northern N. Atlantic
- meltwater pulse could cause the thermohaline circulation to shutdown
- reducing heat transport into northern n. atlantic
numerical weather prediction (NWP)
- improvement in weather prediction data over the last 60 years is among the most impressive accomplishments of society; 3, 5, and 7 day forecasts have improved in both NH and SH
Lewis Fry Richardson
- made the first numerical weather prediction in 1922
Richardson’s Dream: The Forecast Factory
- filled with employees (“Computers”) doing calculations; estimated 64,000 people would be necessary to forecast over the global
Richardson’s Experiment
- used data from - May 20, 1910 and made Leipzig charts for surface pressure and temp.
- data was taken when haley’s comet was passing through; all values were tabulated by hand.
Richardson’s calculations
- took 10,000 hours of work to perform calculations
- book lost during a battle, but eventually recovered and published in 1922
Richardson… failure or success?
- first prediction was for pressure to change by 145 mbar in 6 hrs… that would be record setting
- he realized that noisy wind data was likely the problem and suggested 5 diff. filtering methods to fix this
- but he couldn’t try this experiment again, need computer’s of today
computer forecast w/ richardson’s proposed fix
- becomes a good forecast with his filtering methods used
first weather prediction on computers
- 1950: Charney, Fjortoft and von Neumann paper
- May 1955: Joint numerical weather prediction unit, maryland: first operational computer forecasts in US
- global coverage since 1973
- computers surpassed human forecasts: 1980’s?
ENIAC computer (1943-56)
- 17,468 vacuum tubes, was 1000 times faster than that of previous computing machines
- original computer weather forecast done in 1950 can now be done on a cell phone
weather vs climate forecasting
- similar bc use similar mathematical equations
- but weather is short term; climate is long term
Chaos Theory
- Ed Lorenz was running a computer model and put in slightly different inputs; found the predictions were similar for a while but then wildly diverged to diff. solutions
Butterfly effect
- Ed Lorenz; weather forecasts depend on initial observations but climate models don’t
Climate forecasts
examples
- summer is hotter than winter, after a strong volcano erupts, the earth will cool, the earth will be hotter with more GHG’s, shifts in weather patterns when El nino is present
Suki Manabe: father of climate modeling
- gradually builds up more sophisticated climate models:
- radiation only model (LW and SW): M. and Moller (1961)
- above plus convection: M. and Strickler (1964)
- model with atmospheric motions (but no ocean yet): Smagorinsky, M. and Holloway (1965)
first coupled climate model
Manabe and Bryan (1969)
first global warming forecast
Manabe and Wetherald (1975)
other early manabe studies
- effect of ocean circulation on climate: turn off ocean model
- effect of moisture: don’t allow condensation to occur
- effect of mountains: bulldoze all topography
- effect of changing soalr radiation, doubling CO2, ice sheets, clouds, soil moisture, etc.
GCM (global climate model) Components
- equations of fluid motion on a rotating sphere
- both the atmosphere and ocean are just fluids
- equations put simple physics principles in mathematical form
parts of climate model - 1968
- uses laws of physics
- momentum equation
- heat equation
- water equation
- for both atmosphere and surface and later ocean too
change in resolution overtime
FAR -> TAR -> SAR -> AR4
within each grid cell
there are things that are not explicitly modeled (clouds) that must be approx. or parameterized
