1. Is Climate Really Predictable on 10-50 Year Time Scales?
William R. Cotton
Professor of Atmospheric Science
Colorado State University

2. 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??

3. 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 years simply have not had the opportunity to varify those forecasts and by really humiliated!

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

5. Climate Forcing Factors
Changes in solar luminosity and orbital parameters
Greenhouse gas variability—water 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

6. 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.

7. 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!

8. Carbon Dioxide and climate

9. 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. ]

10. A diagram that you will rarely see is the following:

11. From Max Beran.

12. 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

13. 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

14. IPCC estimates greenhouse gases contribute to 2.3[2.07 to 2.5] W m-2.

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

16. Changes in solar luminosity and orbital parameters

17. 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.

18. 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).

19. The problem is, CCN are large(greater than 0
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!

20. (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).

21. 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.

22. The Milankovitch theory

23. Predictability of orbital-induced changes is high but for solar variability in general is low unless the statistical studies are totally missleading

24. 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

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

26. 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 10’s 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

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

28. 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 1930’s 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!

30. 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!

31. Anthropogenic aerosols
Air pollution aerosols contribute to cooling of the earth’s 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.

32. 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?

33. (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.

34. McIntyre and McKitrick(2003)
They criticize the Mann et al reconstructions for:
Deficiencies in the data used
Irregularities in the data
Methodology of analysis

35. (6), the hockey stick and the corrected temperature curve (green line) by McIntyre between 1400 and The green curve is not intended to indicate the true temperature, but to show the result of a correct use of data.

36. The thing that immediately struck me was the absence of a strong Midieval Warm Period( AD) or Little Ice Age( AD) in Mann’s 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)

37. Juckes et al(2007) reconstructions
They used other proxies other than just tree rings
There results seem to confirm the Mann et al analysis

39. 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??)

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

41. 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

44. 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

45. 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

46. 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.

47. There is evidence that the climate is cooling in the 21st century

48. Ocean Heat Content: This is a better measure of climate variability
But records are of limited duration

49. Note flattening

50. Model hindcasts of climate trends

51. 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

52. 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.

53. 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

55. 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.

56. 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.

57. 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 100’s 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

58. 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!).