Is Climate Really Predictable on 10-50 Year Time Scales?

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Is Climate Really Predictable on 10-50 Year Time
Scales?

• William R. Cotton
• Professor of Atmospheric Science
• Colorado State University

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• We continually are bomb-blasted with scientific
  articles, the news media, and talks like this
  that human-produced greenhouse gases will and is
  causing global warming
• While IPCC carefully argues that the models are
  making projections not predictions of future
  climate, there is still the implication that
  climate is inherently predictable on time scales
  of 10 to 50 years or more I ask, is it??

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Weather and Climate Prediction A Humbling
Experience

• While I have never tried to make a living
  forecasting(thank heavens) I have made forecasts
  in support of various field campaigns as well as
  soaring forecasts for our glider club on a weekly
  basis.
• It is a most humbling experience!
• Anyone who tells you that they can forecast the
  climate in the next 10-50 years simply have not
  had the opportunity to varify those forecasts and
  by really humiliated!

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• Let us begin with known climate forcing factors
  and assess their predictability

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Climate Forcing Factors

• Changes in solar luminosity and orbital
  parameters
• Greenhouse gas variabilitywater vapor, CO2,
  Methane.
• Changes in surface properties
• Differential temporal responses to external
  forcing by the atmosphere and oceans.
• Natural and human-induced changes in aerosols and
  dust--volcanoes, desert dust, pollutants

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The Greenhouse Effect

• The major gases that absorb longwave radiation
  are CO2, methane, and nitrous oxide. These are
  what are referred to as greenhouse gases.
• Water vapor is actually the dominate greenhouse
  gas. To obtain substantial greenhouse warming the
  oceans must warm and evaporate more water vapor
  in the air to cause a positive feedback.
• Clouds are also major greenhouse warming agents.
• Clouds also reflect solar radiation(cool)
• Globally clouds contribute to a net cooling as
  reflection of solar radiation dominates LW
  absorption.

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• Because clouds are poorly treated in General
  Circulation Models (GCMs) their influence on
  climate is a major uncertainty in climate
  prediction.
• For example, a 4 change in marine stratocumulus
  cloud coverage can completely negate the
  influence of greenhouse gases!

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Carbon Dioxide and climate
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The solid line depicts monthly concentrations of
atmospheric CO2 at Mauna Loa Observatory, Hawaii.
The yearly oscillation is explained mainly by
the annual cycle of photosynthesis and
respiration of plants in the northern hemisphere.
The steadily increasing concentration of
atmospheric CO2 at Mauna Loa since the 1950s is
caused primarily by the CO2 inputs from fossil
fuel combustion (dashed line). Note that CO2
concentrations have continued to increase since
1979, despite relatively constant emissions this
is because emissions have remained substantially
larger than net removal, which is primarily by
ocean uptake. From Scheraga, Joel and Irving
Mintzer, 1990 Introduction. From Policy
Options for Stabilizing Global Climate, D.A.
Lashof and D.A. Tirpak, Eds. U.S. Environmental
Protection Agency, Office of Policy, Planning and
Evaluation. Hemisphere Publishing Corp. New
York.
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• A diagram that you will rarely see is the
  following

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From Max Beran.
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• That is a really sobering figure as it suggests
  that to inhibit the growth of CO2 we must get our
  population under control. Transition to
  non-fossil fuels is a step in the right direction
  but as long as our population continues to rise,
  it is likely that CO2 will continue to rise

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• Forecasting decadal and longer climate requires a
  forecast of population as not only do more humans
  on planet mean greater changes in CO2, but also
  aerosols and land-use.
• Predictability small

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• IPCC estimates greenhouse gases contribute to
  2.32.07 to 2.5 W m-2.

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• Keep in mind that water vapor is the dominant
  greenhouse gas on earth and that clouds are
  dominant greenhouse agents

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Changes in solar luminosity and orbital parameters
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Changes in solar luminosity

• There are observed changes in solar luminosity
  which account for something like 0.12-0.4 to
  00.0 W m-2 which is small compared to the 2.3 W
  m-2 estimated for Greenhouse gases. These changes
  are related to changes in sunspot activity, solar
  diameter, and umbral penumbral ratio.
• Nonetheless there are hundreds of statistical
  studies which suggest a correlation with
  temperature and other weather parameters that is
  far stronger than the measured changes in
  luminosity imply. Is this just statistics fooling
  us or is there some unknown amplifier?
• Some studies find that these parameters correlate
  with cloud cover which would provide such an
  amplifier. But convincing physical arguments have
  not been made.

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Cosmic Ray Flux Variations

• Dozens of recent papers relate(statistically)
  variations on cosmic ray fluxes to global climate
• These studies show a positive correlation between
  cosmic ray fluxes and cloud cover(ie.
  contributing to warming)
• The argument is that high cosmic ray fluxes
  generate ions which can then serve as cloud
  condensation nuclei(CCN).

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• The problem is, CCN are large(greater than 0.1
  micrometer), soluble particles
• Ions, are several orders of magnitude smaller in
  size and are not soluble so they do not activate
  cloud droplets at real cloud supersaturations. To
  become CCN they must coalesce with solvable
  aerosols and have sulfates condense on them which
  is not all that probable
• Moreover, cloud cover is mainly controlled by
  dynamics(ascent and adiabatic cooling) and not by
  concentrations of CCN and certainly not total
  aerosol concentrations!

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(19) The variations in sun activity reflect
temperature events Dalton minimum (Dm), Maunder
minimum (Mm), Spörer minimum (Sm), Wolf minimum
(Wm), Oort minimum (Om), and Medieval Maximum
(MM).
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Changes in orbital parameters

• The earth undergoes natural oscillations in
  orbital parameters such as the eccentricity of
  the orbit, the axial tilt, and the precession of
  the equinoxes. The theory of climate change
  related to variations in these parameters is
  called the Milankovitch theory and it predicts
  the earth will be gradually moving into an ice
  age in the next 5000 years.

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The Milankovitch theory
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• Predictability of orbital-induced changes is high
  but for solar variability in general is low
  unless the statistical studies are totally
  missleading

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Changes in surface parameters

• The net albedo of Earth is determined by percent
  cover of oceans vs. land, glacial coverage,
  land-surface vegetation vs. deserts, etc. In
  addition, the latter land-surface parameters
  influence surface temperatures through changes in
  sensible vs. latent heat transfer.
• Human activity alters the land-surface parameters
  through deforestation, agriculture, and
  urbanization.
• IPCC estimates these contribute to -0.2-0.4 to
  0.0 W m-2 forcing but this does not include
  changes in sensible and latent heat fluxes

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• Prediction of land-surface changes depends on
  population forecasts as well as the global
  spatial distribution of population--moderate

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Differential temporal responses to external
forcing by the atmosphere and oceans.

• The atmosphere and the deep oceans have grossly
  different responses to changes in external
  forcing.
• The atmosphere can respond on time scales of days
  to months with lingering affects of about 1 year
• The ocean responds on time scales of 10s of
  years to even 1000 years
• This leads to a large natural variability of the
  climate system and GCMs are unable to represent
  or predict this variability well

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• Predictability of deep ocean/atmosphere remains
  quite small as ENSO, NAO, and variability of
  thermohaline circulations remains low

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Natural variations in aerosols and dust

• Volcanoes are a major contributor to upper
  tropospheric and lower stratospheric aerosols.
  These particles block sunlight contributing to
  surface cooling and can reside from a single
  volcano for several years and have even longer
  influences through cooling of the oceans.
• The period of warming during the 1930s has been
  attributed to a period of low volcanic activity.
• There is no predictability of volcanic activity
  on 10 to 50 year time scales particularly long
  clusters of volcanic activity!

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Natural variations in dust

• Deserts and Sahalian zones in particular are
  large sources of dust. These particles absorb
  solar radiation and thereby warm the air layer
  they reside in and cool the surface. Warming the
  air layer stabilizes the layer reducing
  convection. Dust also alters cloud properties
  appreciably. Human activity contributes to dust
  as well. Not predicted well!
• If greenhouse warming contributes to
  desertification, increases in surface wind
  strength, then additional dust formation counters
  the warming.
• Meteor collisions with earth also contribute to
  dust and have been blamed for the demise of
  dinosaurs. No predictability!

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Anthropogenic aerosols

• Air pollution aerosols contribute to cooling of
  the earths surface by either reflecting solar
  radiation or directly absorbing solar radiation
  which stabilizes the air layer and cools the
  surface(called the direct aerosol effect)
• They also modify cloud properties (called
  indirect effect) so that polluted clouds reflect
  more radiation (cooling effect).
• They also modify the precipitation forming
  process(called second indirect effect) which is
  treated in GCMs as enhancing cloud albedo. But
  modeling and observations suggest that there are
  many non-linear cloud dynamical responses to
  aerosol which can reduce cloud coverage, shift
  from solid stratus to open cellular convection,
  reduce cloud liquid water paths.
• Aerosol variability, especially through altering
  the hydrological cycle and precipitation, is a
  major source of uncertainty in predicting
  climate.

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Natural Variability

• How much of observed climate change in the 20th
  century is due to greenhouse forcing as opposed
  to natural forcing?
• How significant, compared to past natural
  fluctuations are the changes we now observe and
  expect in the future?

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(5), The hockey stick according to Mann, M.E.,
R.S. Bradley and M.K. Hughes (1999) (8) Blue,
Black reconstructions from tree rings, corals,
ice cores, etc. Red direct measurements from
temperature stations as from 1860.
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McIntyre and McKitrick(2003)

• They criticize the Mann et al reconstructions
  for
• Deficiencies in the data used
• Irregularities in the data
• Methodology of analysis

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(6), the hockey stick and the corrected
temperature curve (green line) by McIntyre
between 1400 and 1980. The green curve is not
intended to indicate the true temperature, but to
show the result of a correct use of data.
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• The thing that immediately struck me was the
  absence of a strong Midieval Warm
  Period(800-1200AD) or Little Ice Age(
  1500-1850AD) in Manns analysis!
• They argue these were regional not global
  phenomena
• But other studies have found the MWP in
  Europe(Lamb, 1965 Shindell et al., 2001),
  Greenland(Dahl-Jensen et al,1998),
  Africa(deMenocal et al, 2000 Holmgren et al,
  2001), North America(Campbell et al,1998 Li et
  al,2000 Petersen,1994 Shabalova and
  Weber,1999), South America(Irionda et al,1993
  Villabala,1994) and Asia(Hong et al, 2000 Liu et
  al, 1998)

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Juckes et al(2007) reconstructions

• They used other proxies other than just tree
  rings
• There results seem to confirm the Mann et al
  analysis

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Problems with reconstructions

• Proxie data such as tree rings deminish with
  time 22 extend back to AD 1400, 12 extend to AD
  1000(7 in N Hemisphere)
• Cook et al(2004) conclude reconstructions bases
  largely on tree-rings should be treated with
  caution earlier than AD 1200.
• Proxies are affected by factors other than
  temperature which are not fully understood(ie,
  Excessive Bristlecone pine growth in 20th century
  could be due to CO2 fertilization or??)

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• Can we say then that 20th century warming is
  unprecedented compared to previous natural
  periods like the Medieval Warm Period with any
  confidence?

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Loehl(2004)

• He fit time series data for inferred
  temperature from Sargasso Sea SST estimates and
  from stalagmites in a cave in South Africa to a
  simple periodic set of models
• He fit these periodic models to 3000-year
  temperature time series with minimal dating
  error.
• Tree ring data were not used because of dating
  uncertainties
• None of the models used 20th or 21st century data

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• The results clearly show the Medieval warm period
  and the Little Ice Age
• 6 out of 7 of the fit models show a warming
  trend over the 20th century similar in timing and
  magnitude to the N Hemisphere instrumental time
  series.
• One of the models passes right through the 20th
  century data

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• The results suggest that the 20th century warming
  trends are a continuation of past climatic
  cyclical patterns.
• Results are not precise enough to partition 20th
  century warming into natural vs man-made causes
• Nonetheless a major portion of the warming could
  be a result of natural causes

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Conclusion

• As far as I am concerned the jury is still out as
  to whether recent climate trends are due to human
  activity or due to natural variability associated
  with other forcing parameters or internal
  variability of the atmosphere/ocean/cryosphere.

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There is evidence that the climate is cooling in
the 21st century
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Ocean Heat Content

• This is a better measure of climate variability
• But records are of limited duration

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Note flattening 2004-2008
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Model hindcasts of climate trends
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Using NCAR coupled model Warren Washington Argues
that Natural Variations do not Explain Observed
Climatic Change

• Climate models with natural forcing (including
  volcanic and solar) do not reproduce warming
• When increase in greenhouse gases is included,
  models do reproduce warming
• Addition of increase in aerosols (cooling)
  improves agreement

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Quote for Jerry Meehl

• These simulations started from a pre-industrial
  control simulation that was hundreds of years
  long. During this control run, none of the
  forcings change, so the atmosphere and ocean come
  into balance with each other and the drifts are
  minimal, though the model is left with systematic
  errors compared to observations. Moreover cloud
  parameterizations are tweaked in order to bring
  the TOA radiation in balance. The 20th century
  runs branch from different time periods in the
  control run and the forcings then change over the
  course of the 20th century. Thus, the model
  results are anomalies from the model state,
  compared to the observations that are anomalies
  from the observed state. This is done to assess
  the relative importance of different forcings on
  the time evolution of 20th century global
  temperature anomalies.

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ECMWF 10-year Hindcasts

• ECMWF(2009) is testing their ocean coupled model
  for decade long forecasts
• They do not use techniques like anomaly
  initialization, nudging or flux corrections to
  avoid the coupled system from drifting from the
  observed state
• It includes greenhouse gases and sulphate aerosols

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• The ECMWF simulations use an initialized climate
  state (initialized with observations) based on a
  4DDA procedure. Thus, the model systematic
  errors cause the model to drift away from the
  initialized observed state towards its own state.
  

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Conclusions

• The model develops a 2-meter temperature bias of
  1C over the first 2-5 years
• The tropical and subtropical oceans exhibit
  strong cooling
• A substantial warm bias occurs over the northern
  hemisphere extra-tropical continents
• In decadal forecasts, the forecast signals are
  much smaller than model biases.

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Initial-value vs Boundary-value problem

• It is often claimed that climate is predictable
  because it is a boundary value problem(that is,
  only changes in external forcing is needed).
• But, we noted that deep ocean variability occurs
  on time scales of 100s of years
• Thus initialization of deep ocean circulations is
  needed for forecasts on decadal time scales.
• This means that decadal climate prediction is
  both an initial value problem and boundary value
  problem

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Is climate really predictable on 10 to 50 year
time scales?

• Considering the stochastic external forcing
  parameters(eg. Volcanoes), uncertainties of solar
  variability forcing, and the tendency for strong
  model biases on time scales of 2-5 years let
  alone 10 to 50 years, I see no evidence that
  climate is predictable on these time-scales nor
  will it be for dacades to come(a forecast!).