Lecture #1

1. Lecture #1 Overview of Climate Forcing Factors

2. 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

4. Climate Forcing • Defined as an energy imbalance imposed on the climate system either externally or by human activities

5. Radiative forcing: a change in energy flux at the tropopause[watts per square meter] • A non-radiative forcing is a climate forcing that creates an energy imbalance that does not immediately involve radiation • A direct forcing is a climate forcing that directly affects the radiation budget of the earth’s climate system • A climate feedback is amplification or dampening of the climate response to a specific forcing due to changes in the atmosphere, oceans, land, or continental glaciers

6. Spectrum of energy emitted by the sun resembles blackbody at 5900K • Peak at 0.470 micrometers • Absorption bands

7. Earth’s radiation • Peak between 10 and 15 micrometers • Absorption bands

8. 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.

9. Because clouds are poorly treated in General Circulation Models (GCMs) their influence on climate is a major uncertainty in climate prediction.

10. 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. ]

11. From Max Beran.

12. Atmospheric concentrations of carbon dioxide, methane and nitrous oxide over the last 10,000 years (large panels) and since 1750 (inset panels). Measurements are shown from ice cores (symbols with different colours for different studies) and atmospheric samples (red lines). The corresponding radiative forcings are shown on the right hand axes of the large panels. [From IPCC, 2007]

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

15. Summary

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 0.1 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. 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

24. 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 100 years • This leads to a large natural variability of the climate system and GCMs are unable to represent or predict this variability well

25. 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. • Predictability of volcanic activity is lacking!

27. 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!

28. 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 cloud resolving models often exhibit reduced cloudy albedo when polluted. • The indirect effect, especially through altering precipitation is a major source of uncertainty in predicting climate. In fact the treatment of clouds is a major source of uncertainty.

29. More elaboration on aerosols in later lectures

30. Natural Variability • The climate system is known to have large natural variability but the quantitative assessment of that variability is difficult. • Sources of variability include atmosphere/ocean different response times such that they are never really in equilibrium. • Others are related to unknown or not quantified forcing mechanisms. • Quantification of natural variability is essential to determine if human related forcing are greater than natural variability.

31. 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?

32. (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.

33. (3), The temperature curve of the hockey stick combined with the atmospheric curve of CO2 concentration. The figure suggests that the sharp temperature rise since 1860 was caused by CO2.

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 1980. 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(800-1200AD) or Little Ice Age( 1500-1850AD) 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?

41. 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

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

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

44. Note flattening 2004-2008

45. Akasufo argues that recent warming is a linear warming since the little ice age with natural variations

46. 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