1. -- of a small rock in space Weather and Climate Jeffrey W. Stehr, Ph.D. University of Maryland Atmospheric & Oceanic Science With contributions from John S. Perry, Ph.D. May 24, 2011
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4. 2010: Global Temperatures Global Top 10 Warmest Years (Jan-Dec) Anomaly °C Anomaly °F 2010 0.62 1.12 2005 0.62 1.12 1998 0.60 1.08 2003 0.58 1.04 2002 0.58 1.04 2009 0.56 1.01 2006 0.56 1.01 2007 0.55 0.99 2004 0.54 0.97 2001 0.52 0.94 The 1901-2000 average combined land and ocean annual temperature is 13.9°C (56.9°F), the annually averaged land temperature for the same period is 8.5°C (47.3°F), and the long-term annually averaged sea surface temperature is 16.1°C (60.9°F).
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8. A Rock in Space (Moon) Max: 123°C Min: - 233°C (*or -243°C?) Not a nice place!
47. Arrhenius, 1896 (equilibrium) Present day best estimate: 2.5-4.0°C with a best estimate of 3.0°C for 2100 from doubling CO 2 from the IPCC 4 th assessment, 2007
This illustrates one of the biggest problems with GEOENGINEERING, or altering/managing the climate using artificial means. Most geoengineering approaches affect the incoming visible light. The excess greenhouse effect reflects problems with the outgoing infrared. So every geoengineering approach addresses the problem in a way that is fundamentally different from the way it originated. There are also moral and societal issues with how much you want to geoengineer—if you can pick your climate, which one do you choose? Is it different if you’re poor or wealthy? If you live in the Arctic or the tropics?
So the greenhouse effect is actually a *good* thing. It’s just an excessive greenhouse that’s a problem.
Those in red are GCOS, Global Climate Observing System, sites
Every 10 years, we improve by about a day’s warning for large scale features: storm system tracks, hurricane tracks, but not features dictated by small-scale processes (e.g. hurricane strength).
S. Arrhenius from Philosophical Magazine and Journal of Science, “On the Influence of Carbonic Acid in the Air upon the Temperature of the Ground”, Phil. Mag. S 4. Vol. 41. No. 251. April 1896.
FAQ 2.1, Figure 1. Atmospheric concentrations of important long-lived greenhouse gases over the last 2,000 years. Increases since about 1750 are attributed to human activities in the industrial era. Concentration units are parts per million (ppm) or parts per billion (ppb), indicating the number of molecules of the greenhouse gas per million or billion air molecules, respectively, in an atmospheric sample. (Data combined and simplified from Chapters 6 and 2 of this report.)
Figure 3.1. Annual anomalies of global land-surface air temperature (°C), 1850 to 2005, relative to the 1961 to 1990 mean for CRUTEM3 updated from Brohan et al. (2006). The smooth curves show decadal variations (see Appendix 3.A). The black curve from CRUTEM3 is compared with those from NCDC (Smithand Reynolds, 2005; blue), GISS (Hansen et al., 2001; red) and Lugina et al. (2005; green).
Figure 5.14. Variations in global mean sea level (difference to the mean 1993 to mid-2001) computed from satellite altimetry from January 1993 to October 2005, averaged over 65°S to 65°N. Dots are 10-day estimates (from the TOPEX/Poseidon satellite in red and from the Jason satellite in green). The blue solid curve corresponds to 60-day smoothing. Updated from Cazenave and Nerem (2004) and Leuliette et al. (2004).
Source: IPCC, 2007
Dark shaded: range of average response Light shaded: range of response not allowing for feedbacks associated with land ice changes Black lines: range of response considering land ice changes (note: West Antarctic ice sheet highly uncertain) March 24, 2006 Science Overpeck and Otto-Bliesner Conditions in 2100 may be as warm as those 130,000 years ago, when sea level rose ~6 m. Melting of Greenland ice sheet raised sea level by ~2.2 to 3.4 m.
Greenland glaciers—moulins are meltwater plunging to the core.
1m sea level rise
6 m sea level rise: Losing roughly west Antarctic ice sheet + Greenland
FAQ 1.2, Figure 1. Schematic view of the components of the climate system, their processes and interactions.
Figure 10.4. Multi-model means of surface warming (relative to 1980–1999) for the scenarios A2, A1B and B1, shown as continuations of the 20th-century simulation. Values beyond 2100 are for the stabilisation scenarios (see Section 10.7). Linear trends from the corresponding control runs have been removed from these time series. Lines show the multi-model means, shading denotes the ±1 standard deviation range of individual model annual means. Discontinuities between different periods have no physical meaning and are caused by the fact that the number of models that have run a given scenario is different for each period and scenario, as indicated by the coloured numbers given for each period and scenario at the bottom of the panel. For the same reason, uncertainty across scenarios should not be interpreted from this figure (see Section 10.5.4.6 for uncertainty estimates).
Figure TS.5. (a) Global mean radiative forcings (RF) and their 90% confidence intervals in 2005 for various agents and mechanisms. Columns on the right-hand side specify best estimates and confidence intervals (RF values); typical geographical extent of the forcing (Spatial scale); and level of scientific understanding (LOSU) indicating the scientific confidence level as explained in Section 2.9. Errors for CH 4 , N 2 O and halocarbons have been combined. The net anthropogenic radiative forcing and its range are also shown. Best estimates and uncertainty ranges can not be obtained by direct addition of individual terms due to the asymmetric uncertainty ranges for some factors; the values given here were obtained from a Monte Carlo technique as discussed in Section 2.9. Additional forcing factors not included here are considered to have a very low LOSU. Volcanic aerosols contribute an additional form of natural forcing but are not included due to their episodic nature. The range for linear contrails does not include other possible effects of aviation on cloudiness. (b) Probability distribution of the global mean combined radiative forcing from all anthropogenic agents shown in (a). The distribution is calculated by combining the best estimates and uncertainties of each component. The spread in the distribution is increased significantly by the negative forcing terms, which have larger uncertainties than the positive terms. {2.9.1, 2.9.2; Figure 2.20}
SPM.6. Projected surface temperature changes for the late 21st century (2090-2099). The map shows the multi-AOGCM average projection for the A1B SRES scenario. Temperatures are relative to the period 1980-1999. {Figure 3.2}
Box 11.1, Figure 2. Robust findings on regional climate change for mean and extreme precipitation, drought, and snow. This regional assessment is based upon AOGCM based studies, Regional Climate Models, statistical downscaling and process understanding. More detail on these findings may be found in the notes below, and their full description, including sources is given in the text. The background map indicates the degree of consistency between AR4 AOGCM simulations (21 simulations used) in the direction of simulated precipitation change. (1) Very likely annual mean increase in most of northern Europe and the Arctic (largest in cold season), Canada, and the North-East USA; and winter (DJF) mean increase in Northern Asia and the Tibetan Plateau. (2) Very likely annual mean decrease in most of the Mediterranean area, and winter (JJA) decrease in southwestern Australia. (3) Likely annual mean increase in tropical and East Africa, Northern Pacific, the northern Indian Ocean, the South Pacific (slight, mainly equatorial regions), the west of the South Island of New Zealand, Antarctica and winter (JJA) increase in Tierra del Fuego. (4) Likely annual mean decrease in and along the southern Andes, summer (DJF) decrease in eastern French Polynesia, winter (JJA) decrease for Southern Africa and in the vicinity of Mauritius, and winter and spring decrease in southern Australia. (5) Likely annual mean decrease in North Africa, northern Sahara, Central America (and in the vicinity of the Greater Antilles in JJA) and in South-West USA. (6) Likely summer (JJA) mean increase in Northern Asia, East Asia, South Asia and most of Southeast Asia, and likely winter (DJF) increase in East Asia. (7) Likely summer (DJF) mean increase in southern Southeast Asia and southeastern South America (8) Likely summer (JJA) mean decrease in Central Asia, Central Europe and Southern Canada. (9) Likely winter (DJF) mean increase in central Europe, and southern Canada (10) Likely increase in extremes of daily precipitation in northern Europe, South Asia, East Asia, Australia and New Zealand. (11) Likely increase in risk of drought in Australia and eastern New Zealand; the Mediterranean, central Europe (summer drought); in Central America (boreal spring and dry periods of the annual cycle). (12) Very likely decrease in snow season length and likely to very likely decrease in snow depth in most of Europe and North America.
Snow: (nobody has them up to date yet) http://www.ncdc.noaa.gov/ussc/USSCAppController?action=options&state=44
Figure 11.12. Temperature and precipitation changes over North America from the MMD-A1B simulations. Top row: Annual mean, DJF and JJA temperature change between 1980 to 1999 and 2080 to 2099, averaged over 21 models. Middle row: same as top, but for fractional change in precipitation. Bottom row: number of models out of 21 that project increases in precipitation.
Milankovitch, M. 1920. Theorie Mathematique des Phenomenes Thermiques produits par la Radiation Solaire. Gauthier-Villars Paris.Milankovitch, M. 1930. Mathematische Klimalehre und Astronomische Theorie der Klimaschwankungen, Handbuch der Klimalogie Band 1 Teil A Borntrager Berlin.Milankovitch, M. 1941 Kanon der Erdbestrahlungen und seine Anwendung auf das Eiszeitenproblem Belgrade. (New English Translation, 1998, Canon of Insolation and the Ice Age Problem. With introduction and biographical essay by Nikola Pantic. 636 pp. $79.00 Hardbound. Alven Global. ISBN 86-17-06619-9.)
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