Basic definitions, terms, e.g., Climate, climatology, anomaly, teleconnection,

1
Mid-course overview(NB overview a guide but not
necessarily everything)
Chapter 1. Intro. Review elements include

• Basic definitions, terms, e.g., Climate,
  climatology, anomaly, teleconnection,
• Trace gases, anthropogenic increase
• Perspective from paleoclimate on where fossil
  fuels come from, some idea of time scales,
  natural variability, are recent changes
  unusual,..
• ENSO material, mostly as comes back in chpt 4.

Neelin, 2011. Climate Change and Climate
Modeling, Cambridge UP
2
1.1 Climate dynamics, climate change and climate
prediction

• Climate average condition of the atmosphere,
  ocean, land surfaces and the ecosystems in them
  (includes average measures of weather-related
  variability, e.g. storm frequency)
• Weather state of atmosphere and ocean at given
  moment.
• Average taken over January of many different
  years to obtain a climatological value for
  January, etc.

Neelin, 2011. Climate Change and Climate
Modeling, Cambridge UP
3

• Climate change
• occurring on many time scales, including those
  that affect human activities.
• time period used in the average will affect the
  climate that one defines.
• e.g., 1950-1970 will differ from the average from
  1980-2000.
• Climate variability
• essentially all the variability that is not just
  weather.
• e.g., ice ages, warm climate at the time of
  dinosaurs, drought in African Sahel region, and
  El Niño.

Neelin, 2011. Climate Change and Climate
Modeling, Cambridge UP
4

• Global warming predicted warming, associated
  changes in the climate system in response to
  increases in "greenhouse gases" emitted into
  atmosphere by human activities.
• Greenhouse gases e.g., carbon dioxide, methane
  and chlorofluorocarbons trace gases that absorb
  infrared radiation, affect the Earth's energy
  budget.
• warming tendency, known as the greenhouse effect
  
• Climate prediction endeavor to predict not only
  human-induced changes but the natural variations.
  e.g., El Niño
• Climate models
• Mathematical representations
• of the climate system
• typically equations for temperature,
• winds, ocean currents and other climate
• variables solved numerically on computers.

Neelin, 2011. Climate Change and Climate
Modeling, Cambridge UP
5

• El Niño
• largest interannual (year-to-year) climate
  variation interaction between the tropical
  Pacific ocean and the atmosphere above it.
• a prime example of natural climate variability.
• first phenomenon for which the essential role of
  dynamical interaction between atmosphere and
  ocean was demonstrated.

Neelin, 2011. Climate Change and Climate
Modeling, Cambridge UP
6

• Teleconnections remote effects of El Niño (or
  other regional climate variations).
• Anomaly departure from normal climatological
  conditions.
• calculated by difference between value of a
  variable at a given time, e.g., pressure or
  temperature for a particular month, and
  subtracting the climatology of that variable.
• Climatology includes the normal seasonal cycle.
• e.g., anomaly of summer rainfall for June, July
  and August 1997, average of rainfall over that
  period minus averages of all June, July and
  August values over a much longer period, such as
  1950-1998.
• To be precise, the averaging time period for the
  anomaly and the averaging time period for the
  climatology should be specified.
• e.g., monthly averaged SST anomalies relative to
  1950-2000 mean.

Neelin, 2011. Climate Change and Climate
Modeling, Cambridge UP
7
Table 1.1
1.4 Global change in recent history
Air main constituents Formula Concentration
Nitrogen N2 78.08
Oxygen O2 20.95
Water H2O 0.1 to 2
Trace gas name Concentration (in 2004)
Carbon dioxide CO2 377 (parts per million) ppm, 0.038
Methane CH4 1.75 ppm
Nitrous oxide N2O 0.32 ppm
Ozone O3 0.000251 ppm (atm. average) (10 ppm max in stratosphere)
Neelin, 2011. Climate Change and Climate
Modeling, Cambridge UP
8
Carbon dioxide concentrations since
1958,measured at Mauna Loa, Hawaii.
Figure 1.1

• Annual, interannual variations
• biological impacts on carbon cycle
• Trend due to fossil fuel emissions.

Neelin, 2011. Climate Change and Climate
Modeling, Cambridge UP
9
Figure 1.2
Concentration of various trace gasesestimated
since 1850

• Methane Cattle, sheep, rice paddies, fossil fuel
  by-product wetlands, termites (parts per
  billion).
• Nitrous Oxide biomass burning, fertilizers?
• Chlorofluorocarbons man-made, zero before 1950
  (parts per trillion).

Neelin, 2011. Climate Change and Climate
Modeling, Cambridge UP
10
Figure 1.3
Global mean surface temperatures estimated since
preindustrial times

• Anomalies relative to 1961-1990 mean
• Annual average values of combined near-surface
  air temperature over continents and sea surface
  temperature over ocean.
• Curve smoothing similar to a decadal running
  average.
• From University of East Anglia Climatic Research
  Unit, Brohan et al. (2006).

Neelin, 2011. Climate Change and Climate
Modeling, Cambridge UP
11
1.5 El Niño An example of natural climate
variability

• ENSO El Niño/Southern Oscillation.
• Contd in Chpt 4
• El Niño is associated with warm phase of a
  phenomenon that is largely cyclic.
• La Niña for the cold phase.
• El Niño arises in tropical Pacific along the
  equator.
• Changes in sea surface temperature, ocean
  subsurface temperatures down to a few hundred
  meters depth, rainfall, and winds ocean-atmos.
  interaction!
• Variations in the Pacific basin within about
  10-15 degrees latitude of the equator are the
  primary variables.

Neelin, 2011. Climate Change and Climate
Modeling, Cambridge UP
12
Paleoclimate A few climate notes with Geological
time scale
Figure 1.12

• Very distant past--- Myrmillions of years
• Key points
• Climate can vary substantially, on all timescales
• Long periods in deep past with warmer climate
  than present ( higher est. CO2 )
• deposition over 100s of millions of years
  sequesters carbon dioxide as fossil fuels (oil,
  coal, natural gas)
• return of this CO2 to atmosphere occurring over
  very short period.

Neelin, 2011. Climate Change and Climate
Modeling, Cambridge UP
13
Antarctic ice core records of CO2, deuterium
isotope ratio variations (dD), and Antarctic air
temperature inferred from dD
Figure 1.13
Neelin, 2011. Climate Change and Climate
Modeling, Cambridge UP
14
Chapter 2 Basics of Global Climate

• Review elements include
• Basic concepts and terminology that tend to recur
  later
• E.g., albedo, moist convection, easterly winds

2.1 Components and phenomena in the climate system

• Climate processes
• solar radiation tends to get through the
  atmosphere ocean heated from above Þ stable to
  vertical motions.
• warm surface layer, colder deep waters
• mixing near ocean surface Þ upper mixed layer
  50 m
• mixing carries surface warming down as far as
  thermocline, layer of rapid transition of
  temperature to the colder abyssal waters below

Neelin, 2011. Climate Change and Climate
Modeling, Cambridge UP
15
Satellite image based on visible light with 5x5
grid overlay
Figure 2.2
Courtesy of NASA
Neelin, 2011. Climate Change and Climate
Modeling, Cambridge UP
16
The parameterization problem

• For each grid box in a climate model, only the
  average across the grid box of wind, temperature,
  etc. is represented.
• The average of smaller scale effects has
  important impacts on large-scale climate.
• e.g., clouds primarily occur at small scales, yet
  the average amount of sunlight reflected by
  clouds affects the average solar heating of a
  whole grid box.
• Parameterization representing average effects of
  scales smaller than the grid scale (e.g., clouds)
  as a function of the grid scale variables (e.g.
  temperature and moisture) in a climate model.

Neelin, 2011. Climate Change and Climate
Modeling, Cambridge UP
17
2.2 Basics of radiative forcing

• Solar radiation input Infrared radiation (IR) is
  the only way this heat input can be balanced by
  heat loss to space
• Since IR emissions depend on the Earth's
  temperature, the planet tends to adjust to a
  temperature where IR energy loss balances solar
  input
• Blackbody radiation approximation for how
  radiation depends on temperature

• Total energy flux integrated across all
  wavelengths of light
• R sT4
• Full climate models do detailed computation as a
  function of wavelength, for every level in the
  atmosphere,
• Trace gases absorb IR at wavelengths where O2,
  N2 ineffective.

Neelin, 2011. Climate Change and Climate
Modeling, Cambridge UP
18
Pathways of energy transfer in a global average
2.3 Globally averaged energy budget
Figure 2.8
Neelin, 2011. Climate Change and Climate
Modeling, Cambridge UP
19

• Greenhouse effect
• The upward IR from the surface is mostly trapped
  in the atmosphere, rather than escaping directly
  to space, so it tends to heat the atmosphere.
• The atmosphere warms to a temperature where it
  emits sufficient radiation to balance the heat
  budget, but it emits both upward and downward, so
  part of the energy is returned back down to the
  surface where it is absorbed.
• This results in additional warming of the
  surface, compared to a case with no atmospheric
  absorption of IR.

• Atmosphere emits IR downward Þ absorbed at
  surface.

• Both gases and clouds contribute to absorption of
  IR and thus to the greenhouse effect.

Neelin, 2011. Climate Change and Climate
Modeling, Cambridge UP
20
Pathways of energy transfer in a global average
(cont.)

• At the top of the atmosphere, in the global
  average and for a steady climate
• IR emitted balances incoming solar.
• Global warming involves a slight imbalance
• a change in the greenhouse effect Þ slightly less
  IR emitted from the top (chap. 6).
• small imbalance Þ slow warming.
• Three roles for clouds and convection
• heating of the atmosphere (through a deep layer)
• reflection of solar radiation (contributing to
  albedo)
• trapping of infrared radiation (contributing to
  the greenhouse effect)

Neelin, 2011. Climate Change and Climate
Modeling, Cambridge UP
21
2.4 Gradients of rad. forcing and energy
transport by atm.

• Differences in input of solar energy between
  latitudes Þ temperature gradients.
• These gradients would be huge if it were not for
  heat transport in ocean atmosphere and heat
  storage in ocean.

Figure 2.9
Neelin, 2011. Climate Change and Climate
Modeling, Cambridge UP
22
Latitude structure of the circulation (cont.)

• Hadley cell thermally driven, overturning
  circulation, rising in the tropics and sinking at
  slightly higher latitudes (the subtropics).
• Explanations of this in chpt. 3
• Relation to observations
• rising branch assoc. with convective heating and
  heavy rainfall subtropical descent regions,
  little rain.

Neelin, 2011. Climate Change and Climate
Modeling, Cambridge UP
23
Figure 2.13

• Intertropical convergence zones (ITZCs) or
  tropical convection zones heavy precipitation
  features deep in the tropics, (convergence refers
  to the low level winds that converge into these
  regions).
• Monsoons tropical convection zones move
  northward in northern summer, southward in
  southern summer, especially over continents.
• Not just a function of latitude, e.g., strong
  convection over tropical western Pacific, little
  over cold eastern Pacific Walker circulation
  along equator

Neelin, 2011. Climate Change and Climate
Modeling, Cambridge UP
24
Figure 2.16

• Equatorial cold tongue along the equator in the
  Pacific.
• maintained by upwelling of cold water from below.

Neelin, 2011. Climate Change and Climate
Modeling, Cambridge UP
25
Equatorial Walker circulation
Figure 2.14
Neelin, 2011. Climate Change and Climate
Modeling, Cambridge UP
26
Ocean vertical structure
Figure 2.18

• Ocean surface is warmed from above Þ lighter
  water over denser water (stable
  stratification).
• Deep waters tend to remain cold
• on long time scales, import of cold waters from a
  few sinking regions near the poles maintains cold
  temperatures.

Neelin, 2011. Climate Change and Climate
Modeling, Cambridge UP
27
The thermohaline circulation
Figure 2.19

• Salinity (concentration of salt) affects ocean
  density in addition to temperature.
• Waters dense enough to sink cold and salty
• Thermohaline circulation deep overturning
  circulation is termed the (thermal for the
  temperature, haline from the greek word for salt,
  hals).

Neelin, 2011. Climate Change and Climate
Modeling, Cambridge UP
28
The carbon cycle
Figure 2.21b
Values from Denman et al. , IPCC (2007) format
follows Sarmiento and Gruber , Physics Today
(2002).

• Fossil fuels 6.4 PgC/yr (1990s) (incl. 0.1 PgC/yr
  cement production)
• 40 coal, 40 from oil and derivatives such as
  gasoline, 20 from natural gas
• 1.6 PgC/yr land-use change e.g., deforestation
  to agricultural (smaller C storage)
• 1990s anthropogenic emissions 8 PgC/yr (6.4
  fossil fuels 1.6 land use change)
• Fortunately, less than half remains in the
  atmosphere
• 2.2 PgC/yr increased flux into ocean 2.5
  PgC/yr taken up by land vegetation

Neelin, 2011. Climate Change and Climate
Modeling, Cambridge UP
29
Fossil fuel emissions and increases in
atmospheric CO2 concentrations
Figure 2.22
Values are from Denman et al., IPCC (2007)
format follows Sarmiento and Gruber , Physics
Today (2002).

• Fossil fuel emissions converted to CO2
  concentration change if all remained in the
  atmosphere (1 ppm for each 2.1 PgC) rising
• Actual rate of accumulation (change in
  concentration each year all positive rising
  concentration, but variable rate of increase)
• Variations primarily due to land biosphere, e.g.,
  droughts assoc with ENSO
• Accumulation rate 55 of fossil fuel emissions
  on average

Neelin, 2011. Climate Change and Climate
Modeling, Cambridge UP
30
Chapter 3 Physical Processes in the Climate
System
Review elements include

• Where do we get the equations in climate models
  (conservation of momentum, energy, mass,eq of
  state)
• Main balances from these equations especially as
  we apply them to explain important climate
  features
• PGF vs Coriolis
• Thermal circulation, thermal expansion
• Upwelling,

Neelin, 2011. Climate Change and Climate
Modeling, Cambridge UP
31
3.1 Conservation of Momentum
Only in vertical

• Coriolis force due to rotation of earth
  (apparent force).
• PGF pressure gradient force. Tends to move air
  from high to low pressure.
• Fdrag friction-like forces due to turbulent or
  surface drag.

Use force per unit mass for atm/oc.
Neelin, 2011. Climate Change and Climate
Modeling, Cambridge UP
32
Coriolis force (cont.)

• Turns a body or air/water parcel to the right in
  the northern hemisphere to the left in the
  southern hemisphere.
• Exactly on the equator, the horizontal component
  of the Coriolis force is zero
• Acts only for bodies moving relative to the
  surface of the Earths equator and is
  proportional to velocity.

Neelin, 2011. Climate Change and Climate
Modeling, Cambridge UP
33
Schematic of geostrophic wind and wind with
frictional effects
Figure 3.4
Geostrophic balance At large scales at
mid-latitudes and approaching the tropics the
Coriolis force and the pressure gradient force
are the dominant forces (for horizontal motions)
Neelin, 2011. Climate Change and Climate
Modeling, Cambridge UP
34
Section 3.1 Overview

• An approximate balance between the Coriolis force
  and the pressure gradient force holds for winds
  and currents in many applications (geostrophic
  balance) (Fig. 3.4).
• The Coriolis force tends to turn a flow to the
  right of its motion in the Northern Hemisphere
  (left in the Southern Hemisphere) the pressure
  gradient force acts from high toward low
  pressure.
• The Coriolis parameter f varies with latitude
  (zero at the equator, increasing to the north,
  negative to the south) this is called the
  beta-effect (?? rate of change of f with
  latitude).
• In the vertical direction, the pressure gradient
  force balances gravity (hydrostatic balance).
  This allows us to use pressure as a vertical
  coordinate. Pressure is proportional to the mass
  above in the atmospheric or oceanic column.

Neelin, 2011. Climate Change and Climate
Modeling, Cambridge UP
35
Figure 3.5
Application thermal circulation

• Tropics (Hadley circ) subtropics
• West Pacific (Walker circ.) East Pacific
• relatively low pressure (at given height) at
  low levels in warm region PGF toward warm region
  (near surface)

e.g.
Neelin, 2011. Climate Change and Climate
Modeling, Cambridge UP
36
Section 3.2 Overview

• Atmos relationship of density to pressure and
  temperature from ideal gas law
• Ocean density depends on temperature (warmer
  less dense, e.g. sea level rise by warming)
  salinity (saltier more dense).
• Thermal circulations (Fig. 3.5) warm atmospheric
  column has low pressure near the surface and
  high pressure aloft relative to pressure at same
  height in a neighboring cold region. Reason see
  Fig. 3.5
• PGF near surface toward warm region Coriolis
  force may affect circulation but warm region
  tends to have convergence rising. e.g. Walker,
  Hadley circulations
• sea level rise by thermal expansion density
  changes by given fraction (thermal expansion
  coefficient) so sea level rise proportional to
  depth of column that warmsdT e.g., deep vs
  upper ocean

Neelin, 2011. Climate Change and Climate
Modeling, Cambridge UP
37
Section 3.3 Overview

• Ocean time rate of change of temperature of
  water parcel given by heating
• for a surface layer net surface heat flux from
  the atm. minus the flux out the bottom by mixing
• Atmosphere Temperature eqn. similar to ocean
  but
• when an air parcel rises, temperature decreases
  as parcel expands towards lower pressure.
• Quickly rising air parcel (e.g. in thermals)
  little heat is exchanged
• temperature decreases at 10 C/km (the dry
  adiabatic lapse rate).

Neelin, 2011. Climate Change and Climate
Modeling, Cambridge UP
38
Section 3.3 Overview (cont.)

• Time derivatives following parcel hide
  complexity of the system the parcels themselves
  tend to deform in complex ways if followed for a
  long time.
• Results in the loss of predictability for
  weather.
• The time derivative for temperature at a fixed
  point is obtained by expanding the time
  derivative for the parcel in terms of velocity
  times the gradients of temperature (advection).
• Similar procedure applies in other equations.

Neelin, 2011. Climate Change and Climate
Modeling, Cambridge UP
39
Coastal upwelling e.g., Peru northward wind
component along a north-south coast
Figure 3.9

• Drag of wind stress tends to accelerate currents
  northward
• Coriolis force turns current to left in S. Hem
• momentum eqn.
• u away from coast horizontal divergence
  upwelling from below thru bottom of surface
  layer 50m
• Continuity eqn.

Neelin, 2011. Climate Change and Climate
Modeling, Cambridge UP
40
Processes leading to equatorial upwelling
Figure 1.7

• Wind stress accelerates currents westward
• wind speed fast relative to currents, so
  frictional drag at surface slows the wind but
  accelerates the currents

• Just north of Equator small Coriolis force turns
  current slightly to right (south of Equator to
  the left) divergence in surface layer
  balanced by upwelling from below

Neelin, 2011. Climate Change and Climate
Modeling, Cambridge UP
41
Figure 3.11
3.4e Conservation of warm water mass in idealized
layer above thermocline
warm less dense
cold dense

• Warm light water above thermocline at depth h
• Horizontal divergence/convergence in upper layer
  movement of thermocline

approx. H mean
thermocline depth,
D vertical avg. thru layer

Neelin, 2011. Climate Change and Climate
Modeling, Cambridge UP
42
Section 3.5 Overview

• Conservation of mass gives equations for water
  vapor (atmosphere) and salinity (ocean) and
  other things, e.g. ice/snow
• water vapor main sinks moist convection
  precipitation source surface evaporation
  (transport in between)
• Salinity at the ocean surface is increased by
  evaporation and decreased by precipitation.
• latent heat of condensation water vapor sink
  gives heating in clouds (connecting mass and
  energy equations)
• Latent heat of melting important to surface mass
  balance of ice (or snow), e.g., application to
  time needed to melt an ice sheet by surface heat
  flux imbalance W/m2 vs kg/m2 J/kg

Neelin, 2011. Climate Change and Climate
Modeling, Cambridge UP
43
Section 3.6 Overview

• Saturation of moist air depends on temperature
  according to Figure 3.12. Relative humidity gives
  the water vapor relative to the saturation value.
• A rising parcel in moist convection decreases in
  temperate according to the dry adiabatic lapse
  rate until it saturates, then has a smaller moist
  adiabatic lapse rate. The temperature curve in
  Figure 3.13 (the moist adiabat) depends on only
  the surface temperature and humidity where the
  parcel started.
• If this curve is warmer than the temperature at
  upper levels, convection typically occurs.

Neelin, 2011. Climate Change and Climate
Modeling, Cambridge UP
44
3.7 Wave Processes in the Atmosphere and Ocean
Overview
Skim for qualitative background for chpt 4.

• Waves play an important role in communicating
  effects from one part of the atmosphere to
  another.
• Rossby waves depend on the beta-effect change
  of coriolis force with latitude. Their inherent
  phase speed is westward. In a westerly mean flow,
  stationary Rossby waves can occur in which the
  eastward motion of the flow balances the westward
  propagation. Stationary perturbations, such as
  convective heating anomalies during El Nino, tend
  to excite wavetrains of stationary Rossby waves.

Neelin, 2011. Climate Change and Climate
Modeling, Cambridge UP
45
Chapter 4El Nino
Review elements include

• Tropical Pacific climatology as it sets the stage
  for ENSO
• Schematics of ENSO (but with sense of how these
  connect to observations seen earlier)
• Feedbacks that strengthen El Nino/La Nina
  (Bjerknes hypothesis feedbacks)
• Linkage between sea surface height, thermocline
  depth, and pressure gradients in the upper ocean

Neelin, 2011. Climate Change and Climate
Modeling, Cambridge UP
46
December 1997 Anomalies of sea surface
temperatureduring the fully developed warm phase
of ENSO
Figure 1.7
Neelin, 2011. Climate Change and Climate
Modeling, Cambridge UP
47
Figure 4.2 (Chapter 4 preview)
Neelin, 2011. Climate Change and Climate
Modeling, Cambridge UP
48
The Bjerknes feedbacks (warm phase)
Figure 4.3

• Positive feedback loop reinforces initial anomaly

Neelin, 2011. Climate Change and Climate
Modeling, Cambridge UP
49
Commonly used index regions for ENSO SST anomalies
Figure 1.5

• When SST in the Niño-3 region is warm during El
  Niño, the SOI tends to be negative, i.e.,
  pressure is low in the eastern Pacific relative
  to the west.
• Pressure gradient tends to produce anomalous
  winds blowing from west to east along the
  equator.
• Reverse during periods of cold equatorial Pacific
  SST (La Niña).

Neelin, 2011. Climate Change and Climate
Modeling, Cambridge UP
50
Nino-3 index of equatorial Pacific sea surface
temperature anomalies and the Southern
Oscillation Index of atmospheric pressure
anomalies
Figure 1.6
Neelin, 2011. Climate Change and Climate
Modeling, Cambridge UP
51
December 1997 Anomalies of sea level
heightduring the fully developed warm phase of
ENSO
Figure 1.10
Neelin, 2011. Climate Change and Climate
Modeling, Cambridge UP
52
Idealized upper ocean layer
4.4 Pressure gradients in an idealized upper layer
Figure 4.4

• Sea surface height ? thermocline depth cm vs.
  m
• PGF from regions of deep thermocline (high sea
  surfacehigh pressure above thermocline) toward
  regions of shallower thermocline

Neelin, 2011. Climate Change and Climate
Modeling, Cambridge UP
53
Response of the ocean to a westerly wind anomaly
Re Onset and demise of El Niño warm phase
Figure 4.13
Wind anoms currents

• To east of the wind anomalies, equatorial jet
  (Kelvin wave) extends east, deepening thermocline
  (H)
• (recall warms SST)
• To west, inflow of water to jet (in oc. upper
  layer) comes from off the equator (but little
  effect on SST)
• shallow thermocline in west extends westward
  (Rossby wave), as mass transferred to east by jet
• when reaches western boundary, can no longer
  supply mass by extending shallow region
• Weakening of jet extends eastward, ending warm
  phase
• As wind anomalies weaken, shallow thermocline
  extends eastward transition to cold phase

Deep Thermocline anoms
Neelin, 2011. Climate Change and Climate
Modeling, Cambridge UP
54
Two positions of the thermocline,
indicatingregion of thermocline anomalies
Figure 4.5
Neelin, 2011. Climate Change and Climate
Modeling, Cambridge UP
55
The transition into the 1997-98 El Niño warm
phase (Apr. 1997)
Figure 4.8b
Slowly evolving thermocline depth anomalies (
subsurface temperature anoms) affect eastern
Pacific SST later. Key to predictability.
Neelin, 2011. Climate Change and Climate
Modeling, Cambridge UP
56
The transition into the 1997-98 El Niño warm
phase (Sep. 1997)
Figure 4.8c
Neelin, 2011. Climate Change and Climate
Modeling, Cambridge UP
57
The transition into the 1997-98 El Niño warm
phase (Jan. 1998)
Figure 4.8d
Neelin, 2011. Climate Change and Climate
Modeling, Cambridge UP
58
Basis for ENSO forecasts and Limits to skill

• weather unpredictable beyond two weeks
• But interaction with the slowly evolving ocean
  (both in Bjerknes feedbacks and delayed effects
  of Western Pacific thermocline anomalies)
• permits prediction of ENSO Eastern Pacific
  SST anomalies (and associated atmospheric
  anomalies) up to 9 months ahead
• Forecast skill tends to degrade with longer lead
  time for forecast
• Loss of skill in ENSO forecasts
• Imperfections in the forecast system
• -e.g., model errors, scarcity of input data (can
  be improved, if )
• Fundamental limits to predictability
• - weather noise acts like a random forcing
  on slow ocean-atmosphere interaction
• e.g., SST gradient determines average strength
  of Tradewinds. But in a particular month,
  transient weather events can cause equatorial
  Easterlies to differ from this

Neelin, 2011. Climate Change and Climate
Modeling, Cambridge UP
59
An ensemble of forecasts duringthe onset of the
1998-99 La Niña

• Start coupled model from different ocean initial
  conditions (leading also to changes in atm. )
• Initial differences grow Þ ensemble of prediction
  runs
• Ensemble spread gives estimate of uncertainty
• Spread tends to grow with time (due to weather
  noise coupled feedbacks)
• Ensemble mean gives best estimate

Figure 4.18
Courtesy of the European Centre for Medium-range
Weather Forecasting.
Neelin, 2011. Climate Change and Climate
Modeling, Cambridge UP