# User:William M. Connolley/Climate change feedbacks

A vortex street occurs when cloud formations over the ocean are disturbed by wind passing over land or another obstacle. Marine boundary layer clouds have a large effect on Earth's energy balance, and understanding them is important to predicting the response of climate to human activities.

Climate change feedbacks are processes in the climate system that either augment or diminish the systems response to a climate forcing. The increase in atmospheric greenhouse gases is an anthropogenic forcing that increases the temperature of the surface. This recent increase in surface temperatures and its projected continuation is called global warming. The International Panel on Climate Change in its fourth assessment reported a climate sensitivity to be likely in the range of 2 to 4.5 °C for a doubling of atmospheric carbon dioxide. The uncertainty is largely due to differences in the how different global climate models incorporate and parameterize each of the climate feedbacks.

Feedbacks can be positive or negative, but it is important to remember that a feedback subsystem never acts in isolation, but is always embedded within the overall climate system. The climate system is always subject to one very powerful negative feedback, the temperature-radiation feedback: emitted radiation rises with the fourth power of temperature. Hence, on earth the long-term gain of the overall system is always less than one, i.e. there can be no unlimited runaway effects.

## Climate sensitivity

Main article: Climate sensitivity

Equilibrium climate sensitivity refers to the equilibrium change in global mean near-surface air temperature that would result from a sustained change in radiative forcing. This can be expressed mathematically as $\Delta T_{eq} = \lambda \Delta Q$, where ΔTeq is the equilibrium temperature resultant from a change in radiative forcing ΔQ. The proportionality constant relating the two, λ, is called the climate sensitivity parameter.

The fundamental climate feedback is how radiative emission changes with temperature. $Q = \epsilon \sigma T^4$, where ε is the emissivity and σ is the Stefan–Boltzmann constant. The resultant temperature change for only this feedback can be expressed as $\Delta T_0 = \lambda_0 \Delta Q$. The climate sensitivity parameter from this feedback can be found by differentiation:$\lambda_0 = (4\epsilon\sigma{T_e}^3)^{-1}$.

This strong negative feedback is central to the changing temperature of the climate system. Therefore, when diagnosing other climate feedbacks, it is useful to express them in terms of a gain factor that accounts for this. The gain factor, g, is the change in equilibrium temperature associated with a particular feedback mechanism accounting for the fundamental blackbody feedback.[1]

$g = \frac{\Delta T_{eq} - \Delta T_0}{\Delta T_{eq}} = \frac{\Delta T_{feedbacks}}{\Delta T_{eq}}$

When all the feedbacks are taken into account, the total equilibrium temperature change can be found by the temperature change with respect to the blackbody feedback and the cumulative gain.

$\Delta T_{eq} = \frac{\Delta T_0}{1-\sum g}$

## Effects on magnitude of climate response

There are a variety of feedbacks that affect the climate system.[1][2]

### Cloud feedbacks

Warming is expected to change the distribution and type of clouds. Seen from below, clouds emit infrared radiation back to the surface, and so exert a warming effect; seen from above, clouds reflect sunlight and emit infrared radiation to space, and so exert a cooling effect.[3] Cloud representations vary among global climate models, and small changes in cloud cover have a large impact on the climate.[4][5] Differences in boundary layer cloud modeling schemes can lead to large differences in derived values of climate sensitivity. A model that decreases boundary layer clouds in response to global warming has a climate sensitivity twice that of a model that does not include this feedback.[1] However, satellite data show that cloud optical thickness actually increases with increasing temperature.[6] Whether the net effect is warming or cooling depends on details such as the type and altitude of the cloud, details that are difficult to represent in climate models.

In addition to how clouds themselves will respond to increased temperatures, there exist other feedbacks that will affect clouds properties and formation. The amount and vertical distribution of water vapor is closely links to the formation of clouds. Ice crystals have been shown to largely influence the amount of water vapor.[7] Water vapor in the subtropical upper troposphere has been linked to the convection of water vapor and ice. Changes in subtropical humidity could provide a negative feedback that decreases the amount of water vapor which would act to mediate global climate transitions.[8]

Changes in cloud cover are closely coupled with other feedback, including the water vapor feedback and ice albedo feedback. Changing climate is expected to alter the relationship between cloud ice and supercooled cloud water, which in turn would influence the microphysics of the cloud which would result in changes in the radiative properties of the cloud. Climate models suggest that a warming will increase fractional cloudiness. More clouds cools the climate, resulting in a negative feedback.[9] Increasing temperatures in the polar regions is expected in increase the amount of low-level clouds, whose stratification prevents the convection of moisture to upper levels. This feedback would partically cancel the increased surface warming due to the cloudiness.[10]

### Water vapor feedback

From the Clausius–Clapeyron relation, it is known that the saturation vapor pressure over a flat surface of water increases exponentially with temperature. An increase in temperature of 3°C (5.4°F) above present conditions would lead to an increase of 20% in the saturation vapor pressure. Since water vapor is a greenhouse gas, the changes in atmospheric water vapor content are important for projecting future temperatures. Climate sensitivity is doubled in the presence of a water vapor feedback if the relative humidity is held constant. Climate models and observations agree that the relative humidity is constant at interannual time scales.[11][12] Recent modeling results suggests that although this feedback process causes an increase in the absolute moisture content of the air, the relative humidity may actually decreases slightly because the warmer air.[13]

It is not known if relative humidity will stay constant with the changing mean climate. Hypotheses suggesting that the relative humidity will be altered have been suggested.[14][15] Using data from the Earth Radiation Budget Experiment, a comparison was made between the surface temperature and the outgoing longwave radiation at the top of the atmosphere, and found to be consistent with the hypothesis of fixed relative humidity.[16]

The lack of reliable instrumentation to measure the amount of water vapor and its spatial variation is the leading reason why there is uncertainty in the water vapor feedback.[17] Efforts have been made to use the current radiosonde network,[18] but it is unable to produce accurate and precise measurements of water vapor in the upper troposphere needed to diagnose the water vapor feedback. [citation needed] An observing network with an accuracy to measure decadal trends, and to test whether the increase in water vapor content is consistent with models has been proposed, but not implemented.[1]

### Lapse rate feedback

The atmospheric lapse rate refers to the decrease in temperature above the surface of the Earth. The numerical value is a function of convective, radiative, and large-scale dynamical processes. Convection and dynamics generally transport heat from the surface upwards to space, while radiation warms the surface and cools the atmosphere.[1] The lapse rate in the tropics is observed to be near the moist adiabatic lapse rate due to the high moisture content. This is the rate that saturated parcels will cool when lifted adiabatically. Models and observations show that with an increased surface temperature, the moist adiabatic lapse rate will decrease. By itself, theory says the lapse rate feedback is negative in the tropics.[19][20]

It has been shown that if the relative humidity remains constant in a warming atmosphere, then the lapse rate feedback and the water vapor feedback partially cancel each other.[21] The changes in vertical temperature structure are one of the tools that climate scientists use to attribute climate change to greenhouse forcing or natural variability.[22] Climate models which model convection and radiation generally produce the observed lapse rate changes. Temperatures in the upper atmosphere are difficult to accurately measure, and there have been minor inconsistencies between observations and the models.[23] The radiosonde data has been shown to contain biases that are difficult to remove.[24][25]

The United States National Research Council has recommended that radiosonde observations must be sustained and improved with the objective to remove biases that will allow them to be used for the monitoring of long-term climate.[26]

### Sea ice feedbacks

Aerial photograph showing a section of sea ice. The lighter blue areas are melt ponds and the darkest areas are open water, both have a lower albedo than the white sea ice. The melting ice contributes to the ice-albedo feedback.

At high latitudes, temperatures get low enough that sea water freezes into ice. Sea ice strongly impacts the climate through two different positive feedbacks. First is the sea ice-albedo feedback (or ice-albedo feedback). As temperatures increase, the sea ice melts, resulting in a lower surface albedo, and increasing the amount of solar radiation absorbed, which reinforces the initial warming.[27] Reviews of various models show that the magnitude of this feedback is uncertain due to differences in ice dynamics, and in their treatment of clouds.[28] The albedo has been shown to change in two ways: changes in the areal extent of the ice, and changes in the multi-year ice such as ice thickness, and melt pond characteristics. Sea ice models that incorporate the multi-year ice feedback are more sensitive to initial changes in temperature.[27]

Sea ice also acts as an insulator between the ocean and the atmosphere. As the ice melts, it allows more liquid water to evaporate into the air. This latent heat flux from the ocean to the atmosphere results in additional warming of the boundary layer. Melting of sea ice also results in an increase in the poleward moisture flux from the tropical oceans.[29]

Several climate models suggest that there may be a negative feedback between sea ice and the meridional overturning circulation. The meridional overturning in the Southern Hemisphere westerlies may delay the response to the radiative forcing from greenhouse gases by upwelling deep, cold, unmodified water.[30] In another experiment, the higher sea surface temperatures resulting from greenhouse warming lead to more evaporation than precipitation, resulting in a higher ocean salinity. The changes in salinity cancel the effects of changes in sea surface temperatures so that there is no net change in the circulation.[31] The Antarctic subpolar gyre is an important process in determining the thickness of Antarctic sea ice. Changes in the meridional overturning circulation will feedback into changing the deep ocean heat content and sea ice thickness.[32]

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## References

1. National Research Council (2004). Understanding Climate Change Feedbacks. Panel on Climate Change Feedbacks, Climate Research Committee. National Academies Press. ISBN 0309090725.
2. ^ Randall, D.A., R.A. Wood, S. Bony, R. Colman, T. Fichefet, J. Fyfe, V. Kattsov, A. Pitman, J. Shukla, J. Srinivasan, R.J. Stouffer, A. Sumi and K.E. Taylor, 2007. "Climate Models and Their Evaluation". In Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M.Tignor and H.L. Miller. Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, United Kingdom and New York, NY, USA: Cambridge University Press.
3. ^ Hartmann, D.L., M.E. Ockert-Bell, and M.L. Michelsen (1992). "The Effect of Cloud Type on Earth's Energy Balance: Global Analysis". J. Climate 5: 1281–1304. doi:10.1175/1520-0442(1992)005<1281:TEOCTO>2.0.CO;2.
4. ^ Cess, R. D. et al. (1990). "Intercomparison and Interpretation of Climate Feedback Processes in 19 Atmospheric General Circulation Models". J. Geophys. Res. 95 (D10): 16,601–16,615.
5. ^ Stocker, T.F. et al. (2001). "Physical climate processes and feedbacks". In J.T. Houghton et al. Climate Change 2001: The Scientific Basis, Contributions of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, U.K.: Cambridge University Press.
6. ^ Tselioudis, G., W.B. Rossow, and D. Rind (1992). "Global Patterns of Cloud Optical Thickness Variation with Temperature". J. Climate 5: 1484–1495. doi:10.1175/1520-0442(1992)005<1484:GPOCOT>2.0.CO;2.
7. ^ Donner, L. J., C. J. Seman, B. J. Soden, R. S. Hemler, J. C. Warren, J. Ström, and K.-N. Liou (1997). "Large-scale ice clouds in the GFDL SKYHI general circulation model". J. Geophys. Res. 102 (D18)): 21,745–21,768. doi:10.1029/97JD01488.
8. ^ Pierrehumbert, R. T., and R. Roca (1998). "Evidence for Control of Atlantic Subtropical Humidity by Large Scale Advection" (PDF). Geophys. Res. Lett. 25 (24): 4537–4540. doi:10.1029/1998GL900203.
9. ^ Fowler, L.D., and D.A. Randall (1996). "Liquid and Ice Cloud Microphysics in the CSU General Circulation Model. Part III: Sensitivity to Modeling Assumptions". J. Climate 9: 561–586. doi:10.1175/1520-0442(1996)009<0561:LAICMI>2.0.CO;2.
10. ^ Wetherald, R., and S. Manabe (1988). "Cloud Feedback Processes in a General Circulation Model". J. Atmos. Sci. 45: 1397–1416. doi:10.1175/1520-0469(1988)045<1397:CFPIAG>2.0.CO;2.
11. ^ Soden, B.J., R.T. Wetherald, G.L. Stenchikov, and A. Robock (2002). "Global Cooling After the Eruption of Mount Pinatubo: A Test of Climate Feedback by Water Vapor". Science 296 (5568): 727. doi:10.1126/science.296.5568.727.
12. ^ Wentz, F.J. and M. Schabel (2000). "Precise climate monitoring using complementary satellite data sets". Nature 403: 414–416. doi:10.1038/35000184.
13. ^ Soden B.J. and I.M. Held (2005). "An Assessment of Climate Feedbacks in Coupled Ocean–Atmosphere Models". Journal of Climate 19 (14): 3354–3360. doi:10.1175/JCLI3799.1. Interestingly, the true feedback is consistently weaker than the constant relative humidity value, implying a small but robust reduction in relative humidity in all models on average
14. ^ Lindzen, R.S., M.D. Chou, and A.Y. Hou (2001). "Does the Earth Have an Adaptive Infrared Iris?". Bull. Amer. Meteor. Soc. 82: 417–432. doi:10.1175/1520-0477(2001)082<0417:DTEHAA>2.3.CO;2.
15. ^ Pierrehumbert, R. (1995). "Thermostats, Radiator Fins, and the Local Runaway Greenhouse". J. Atmos. Sci. 52: 1784–1806. doi:10.1175/1520-0469(1995)052<1784:TRFATL>2.0.CO;2.
16. ^ Inamdar, A., and V. Ramanathan (1998). "Tropical and global scale interactions among water vapor, atmospheric greenhouse effect, and surface temperature". J. Geophys. Res. 103: 32177–32194.
17. ^ Kley, D., J.M. Russell III, and C. Phillips, eds. (2000). SPARC Assessment of Upper Tropospheric and Stratospheric Water Vapour. Number WCRP-113 in World Climate Research Programme Reports. Geneva, Switzerland: World Climate Research Programme.
18. ^ Randal, D.L., T.H. Von der Haar, M.A. Ringerud, G.L. Stephens, T.J. Greenwald, and C.L. Combs (1996). "A new global water vapor dataset". Bull. Am. Meteorol. Soc. 77: 1233–1246. doi:10.1175/1520-0477(1996)077<1233:ANGWVD>2.0.CO;2.
19. ^ Hansen, J., A. Lacis, D. Rind, G. Russell, P. Stone, I. Fung, J. Lerner, and R. Ruedy (1984). J.E. Hansen and T. Takahashi, ed. Climate sensitivity: Analysis of feedback mechanisms. Washington D.C.: American Geophysical Union. pp. 130–163.
20. ^ Weatherald, R.T. and S. Manabe (1986). "An investigation of cloud cover change in response to thermal forcing". Clim. Change 8: 5–23. doi:10.1007/BF00158967.
21. ^ Cess, R.D. (1975). "Global climate change: An investigation of atmospheric feedback mechanisms". Tellus 27: 193–109.
22. ^ Tett, S.F.B., G.S. Jones, P.A. Stott, D.C. Hill, J.F.B. Mitchell, M.R. Allen, W.J. Ingram, T.C. Johns, C.E. Johnson, A. Jones, D.L. Roberts, D.M.H. Sexton, and M.J. Woodage (2002). "Estimation of natural and anthropogenic contributions to twentieth century temperature change". J. Geophys. Res. 107 (D16): 4306. doi:10.1029/2000JD000028.
23. ^ Santer, B.D., T.M.L. Wigley, D.J. Gaffen, L. Bengtsson, C. Doutriaux, J.S. Boyle, M. Esch, J.J Hnilo, P.D. Jones, G.A. Meehl, E. Roeckner, K.E. Taylor, and M. Wehner (2000). "Interpreting differential temperature trends at the surface and in the lower troposphere". Science 287 (5456): 1227–1232. doi:10.1126/science.287.5456.1227.
24. ^ Lanzante, J.R., S.A. Klein, and D.J. Seidel (2003). "Temporal Homogenization of Monthly Radiosonde Temperature Data. Part II: Trends, Sensitivities, and MSU Comparison". J. Climate 16: 241–262. doi:10.1175/1520-0442(2003)016<0241:THOMRT>2.0.CO;2.
25. ^ Gettelman, A., D.J. Seidel, M.C. Wheeler, and R.J. Ross (2002). "Multidecadal trends in tropical convective available potential energy". J. Geophys. Res. 107 (D21): 4606. doi:10.1029/2001JD001082.
26. ^ National Research Council (2000). Improving Atmospheric Temperature Monitoring Capabilities. Washington, D.C.: National Academy Press.
27. ^ a b Curry, J.A., J.L. Schramm, and E.E. Ebert (1995). "Sea Ice-Albedo Climate Feedback Mechanism". J. Climate 8: 240–247. doi:10.1175/1520-0442(1995)008<0240:SIACFM>2.0.CO;2.
28. ^ Ingram, W. J., C. A. Wilson, and J. F. B. Mitchell (1989). "Modeling Climate Change:An Assessment of Sea Ice and Surface Albedo Feedbacks". J. Geophys. Res. 94 (D6): 8609–8622.
29. ^ Manabe, S., and R. J. Stouffer (1980). "Sensitivity of a global climate model to an increase of CO2 concentration in the atmosphere". J. Geophys. Res. 85 (C10): 5529–5554.
30. ^ Bryan, K., S. Manabe, and M. Spelman (1988). "Interhemispheric Asymmetry in the Transient Response of a Coupled Ocean–Atmosphere Model to a CO2 Forcing". J. Phys. Oceanogr. 18: 851–867. doi:10.1175/1520-0485(1988)018<0851:IAITTR>2.0.CO;2.
31. ^ Gent, P. R. (2001). "Will the North Atlantic Ocean Thermohaline Circulation Weaken during the 21st Century?". Geophys. Res. Lett. 28 (6): 1023–1026.
32. ^ Martinson, D. G. (1990). "Evolution of the Southern Ocean Winter Mixed Layer and Sea Ice: Open Ocean Deepwater Formation and Ventilation". J. Geophys. Res. 95 (C7): 11,641–11,654.
33. ^ Seinfeld, J.H. And S.N. Pandis (1998). Atmospheric Chemistry and Physics: From Air Pollution to Climate Change. John Wiley and Sons, Inc. ISBN 0471178160.
34. ^ Vaghjiani, G.L. And A.R. Ravishankara (1991). "New measurement of the rate coefficient for the reaction of OH with methane". Nature 350: 406–409. doi:10.1038/350406a0.
35. ^ Twomey, S (1991). "Aerosols, clouds, and radiation". Atmospheric Environment 25A (11): 2435-2442.
36. ^ Charlson, R.J., J.E. Lovelock, M.O. Andreae, and S.G. Warren (1987). "Oceanic phytoplankton, atmospheric sulphur, cloud albedo and climate". Nature 326: 655–661. doi:10.1038/326655a0.
37. ^ Shaw, G. (1983). "Bio-controlled thermostasis involving the sulfur cycle". Climatic Change 5 (3): 297–303. doi:10.1007/BF02423524.
38. ^ Ayers, G.P., J.P. Wey, and R.W. Gillett (1991). "Coherence between seasonal cycles of dimethyl sulphide, methanesulphonate and sulphate in marine air". Nature 349: 404–406. doi:10.1038/349404a0.
39. ^ Boers, R., G.P. Ayers, and J.L. Gras (1994). "Coherence between seasonal variation in satellite-derived cloud optical depth and boundary layer CCN concentrations at a mid-latitude Southern Hemisphere station". Tellus 46B (2): 123–131. doi:10.1034/j.1600-0889.1994.t01-1-00004.x.
40. ^ Bates, T.S., and P.K. Quinn (1997). "Dimethylsulfide (DMS) in the Equatorial Pacific Ocean (1982 to 1996): Evidence of a Climate Feedback?". Geophys. Res. Lett. 24 (8). doi:10.1029/97GL00784. Text "pages-861–864 " ignored (help)
41. ^ Davis, D. et al. (1999). "Dimethyl sulfide oxidation in the equatorial Pacific: Comparison of model simulations with field observations for DMS, SO2, H2SO4(g), MSA(g), MS and NSS". J. Geophys. Res. 104 (D5): 5765–5784.
42. ^ Lohmann, U., J. Feichter, C. C. Chuang, and J. E. Penner (1999). "Prediction of the number of cloud droplets in the ECHAM GCM". J. Geophys. Res. 104 (D8): 9169–9198. doi:10.1029/1999JD900046.
43. ^ Hartmann, D.L., J.M. Wallace, V. Limpasuvan, D.W.J. Thompson, and J.R. Holton (2000). "Can ozone depletion and global warming interact to produce rapid climate change?". Proceedings of the National Academy of Sciences of the United States of America 97 (4): 1412–1417.
44. ^ Shindell, D.T., J.L. Grenfell, D. Rind, V. Grewe, and C. Price (2001). "Chemistry-climate interactions in the Goddard Institute for Space Studies general circulation model: 1. Tropospheric chemistry model description and evaluation". J. Geophys. Res. 106: 8047–8076. doi:10.1029/2000JD900704.
45. ^ Thompson, D.W.J. and S. Solomon (2002). "Interpretation of recent Southern Hemisphere climate change". Science 296 (5569): 895–899. doi:10.1126/science.1069270.