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Chapter 7 Climate Model Scenarios for Global Warming

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  1. Chapter 7Climate Model Scenarios for Global Warming 7.1Greenhouse gases, aerosols and other climate forcings 7.2Global-average response to greenhouse warming scenarios 7.3Spatial patterns of the response to time-dependent warming scenarios 7.4Ice, sea level, extreme events 7.5Summary: the best-estimate prognosis 7.6Climate change observed to date 7.7Emissions paths and their impacts Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

  2. 7.1Greenhouse gases, aerosols and other climate forcings 7.1.a Scenarios, forcings and feedbacks • Climate model predictions for global warming respond to a forcing that is continuously applied (e.g., radiative effects of greenhouse gases (GHG)) as prescribed by a specified emissions scenario (section 7.1.c) • Predictable: if forcing occurs, then response will occur—with range of uncertainty (error bars) • Natural variability unpredictable at long lead times • Aerosols: particles (notably sulfate aerosols) • Net cooling tendency by reflection of sunlight • short residence times comp. to long-lived GHG • [· aerosol indirect effects via cloud condensation nuclei may have similar magnitude of cooling but big error bars] Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

  3. 7.1.b Forcing by sulfate aerosols Spatial patterns of estimates of radiative forcingdue to effects of human activity Well mixed greenhouse gases Direct sulfate Shine & Forster, Global and Planetary Change, 1999 Figure 7.1 Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

  4. 7.1.c Commonly used scenarios Radiative forcing as a function of time for various climate forcing scenarios Top of the atmosphere radiative imbalance Þwarming due to the net effects of GHG and other forcings from the Special Report on Emissions Scenarios • SRES: • A1FI (fossil intensive), • A1T (green technology), • A1B (balance of these), • A2,B2(regional economics) • B1 “greenest” • IS92a scenario used in many • studies before 2005 Figure 7.2 Adapted from Meehl et al., 2007 in in IPCC Fourth Assessment Report Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

  5. SRES emissions scenarios, cont’d A1 scenario family: assumes low population growth, rapid economic growth, reduction in regional income differences A1FI : Fossil fuel Intensive A1B: energy mix, incl. non-fossil fuel A2: uneven regional economic growth, high income toward non-fossil, population 15 billion in 2100 B1: like A1 but switch to information and service economy, introduction of resource-efficient technology. Emphasis on global solutions to economic, social, and environmental sustainability, including improved equity. • No explicit consideration of treaties • Natural forcings e.g., volcanoes set to avg. from 20th C. Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

  6. 7.2Global-average response to greenhouse warming scenarios Radiative forcing and global average surface temperature response Change in radiative forcing (Wm-2) Change in temperature (K) Figure 7.3 Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP Mitchell and Johns, 1997, J. Climate

  7. CCMA_CGCM3.1, Canadian Community Climate Model • CNRM_CM3, Meteo-France, Centre National de Recherches Meteorologiques • CSIRO_MK3.0, CSIRO Atmospheric Research, Australia • GFDL_CM2.0, NOAA Geophysical Fluid Dynamics Laboratory • GFDL_CM2.1, NOAA Geophysical Fluid Dynamics Laboratory • GISS_ER, NASA Goddard Institute for Space Studies, ModelE20/Russell • MIROC3.2_medres, CCSR/NIES/FRCGC, medium resolution • MPI_ECHAM5, Max Planck Institute for Meteorology, Germany • MRI_CGCM2.3.2a, Meteorological Research Institute, Japan • NCAR_CCSM3.0, NCAR Community Climate System Model • NCAR_PCM1, NCAR Parallel Climate Model (Version 1) • UKMO_HADCM3, Hadley Centre for Climate Prediction, Met Office, UK Model names (a sample) Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

  8. Fig. 7.4 Global average warming simulations in 11 climate models • Global avg. sfc. air temp. change • (ann. means rel. to 1901-1960 base period) • Est. observed greenhouse gas + aerosol forcing, followed by • SRES A2 scenario (inset) in 21st century • (includes both GHG and aerosol forcing) Data from the Program for Model Diagnosis and Intercomparison (PCMDI) archive. Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

  9. 7.3Spatial patterns of the response to time-dependent warming scenarios Response to the SRES A2 scenario GHG and sulfate aerosol forcing in surface air temperature relative to the average during 1961-90 from the Hadley Centre climate model (HadCM3)[choosing one model simulation through the 21st century as an example; later compare models or average results from several models] 2010-2039 2040-2069 2070-2099 Figure 7.5 Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

  10. Supplementary Figure Response to the SRES A2 scenario GHG and sulfate aerosol forcing in surface air temperature relative to the average during 1961-90 from the National Center for Atmospheric Research Community Climate Simulation Model (NCAR_CCSM3) 2010-2039 2040-2069 2070-2099 Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

  11. January January and July surface temperature from HadCM3 averaged 2040-2069 (SRES A2 scenario) July Figure 7.6 Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

  12. Supplementary Figure January January and July surface temperature from NCAR_CCSM3 averaged 2040-2069 (SRES A2 scenario) July Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

  13. Poleward amplification of warming • 1. The snow/ice feedback as described in chapter 6, operates in these regions. The impacts are even larger regionally than they are in the global average. • 2. The lapse rate feedback. The lapse rate (rate of temperature decrease with height) is larger at high latitudes than in the tropics. This affects the greenhouse feedback between the atmospheric temperature in the upper troposphere and the surface temperature • Also: • Thinner sea ice, so greater heat transfer from ocean in winter • Changes in very cold stable layer near surface in winter Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

  14. Comparing projections of different climate models 30yr. avg annual surface air temperature response for 3 climate models centered on 2055 relative to the average during 1961-1990 GFDL- CM2.0 NCAR- CCSM3 MPI- ECHAM5 Figure 7.7 Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

  15. Comparing projections of different climate models • Provides estimate of uncertainty • Differences often occur with physical processes e.g., shift of jet stream, reduction of soil moisture, … • At regional scales (~size of country or state) more disagreement • Precip challenging at regional scales Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

  16. Comparing projections of different climate models Figure 7.8 GFDL- CM2.0 Precipitation from 3 models for Jun.-Aug. 2070-2099 average minus 1961-90 avg(SRES A2 scenario) NCAR- CCSM3 MPI- ECHAM5 (mm/day) Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

  17. Comparing projections of different climate models Precipitation from 3 models for Dec.-Feb. 2070-2099 average minus 1961-90 avg (SRES A2 scenario) GFDL- CM2.0 Supplementary Figures NCAR- CCSM3 MPI- ECHAM5 (mm/day) Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

  18. Precipitation from HadCM3 for Dec.-Feb. 2070-2099 avg. (SRES A2) Supplementary figure Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

  19. Precipitation from HadCM3 for Jun.-Aug. 2070-2099 avg. (SRES A2) Supplementary Figure Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

  20. North American West Coast Precipitation change under global warming SRES A2 scenario 2070-2099 rel. to 1979-2000 Dec.-Feb. (DJF) Prec. Anom. Multi-model ensemble average Compare to individual models Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

  21. December – February Multi-model ensemble avg. Figure 7.9 January and July precipitation change for 10 model ensemble average for 2070-2099 minus 1961-90 avg(SRES A2 scenario) June – August Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

  22. 7.3.c Summary of spatial patterns of the response • Poleward amplification of the warming is a robust feature. It is partly due to the snow/ice feedback and partly to effects involving the difference in lapse rate between high latitudes and the tropics. • In time-dependent runs polar amplification is seen first in the northern hemisphere. In the North Atlantic and Southern Ocean effect of circulation to the deep ocean slows the warming. • Continents generally tend to warm before the oceans. • There is a seasonal dependence to the response. For instance, winter warming in high latitudes is greater than in summer. • The models tend to agree on continental scale and larger, but there are many differences at the regional scale. Regionalscale predictions (e.g. for California) tend to have higher levels of uncertainty, esp. for some aspects (e.g., precipitation) Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

  23. 7.3.c Summary of spatial patterns of the response (cont.) • Natural variability will tend to cause variations about the forced response, especially at the regional scale. • Precipitation increase (about 5%-15%) on a global average; high latitudes and tropical areas with high precipitation tend to have precipitation increase but subtropical areas that currently have low precipitation tend to decrease. However, regional aspects can be quite variable between models, so there is uncertainty in which areas will have the largest impacts. There is reason to believe that regional changes are likely. Mid/high latitude wintertime precipitation tends to increase. • Summer soil moisture tends to decrease in some regions. This is an example of an effect that would have implications for agriculture. But soil moisture models depend on such things as vegetation response, which are crudely modeled and have much regional dependence (hence higher uncertainty). Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

  24. 7.4Ice, sea level, extreme events Simulated ice fraction change (2070-99) minus (1961-90)as a percent of the base climatol. ice fraction 7.4.a Sea ice and snow Dec. - Feb. Sep. - Nov. Echam5 SRESA2 Figure 7.10 Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

  25. Simulated change in ice fraction (% coverage) Sep.-Nov. (2040-69) minus (1961-90) HadCM3 SRESA2 Supplementary Figure Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

  26. Simulated snow fraction change (2070-99) minus (1961-90) as a percentof the base climatological snow amount (where base exceeds 1Kg/m3) Echam5 SRESA2 Sep. - Nov. Dec. - Feb. Figure 7.11 Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

  27. 7.4.b,c Land ice & Sea level rise • Sea level rise due to thermal expansion in GCMs ~0.13 to 0.32 m in 21st Cent. (1980-99 to 2090-99; A1B , similar for A2)(~13±7 mm/decade to 2020) • Deep ocean warming continues, e.g., 1-4 m rise if stabilize at 4xCO2 • Warming impact on Greenland and Antarctic ice sheets poorly constrained • [NOT relevant: all melt = mean sea level rise > 75 meters] • Greenland eventual melting ~7m over millennial time scale • Most of Antarctica cold enough to remain below freezing • Ice sheet dynamics complicated: “calving” of icebergs affect pressure on inland parts of ice sheet, flow rate • Surprises: Larsen B ice shelf broke up in a period of months • small but ice shelf retreats since 1974 ~ 13,000 km2 • Radar monitoring of ice thickness in coming decades Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

  28. Breakup of the Larsen B Ice Shelf in Antarctica Late austral summer: melt ponds on shelf. Source: National Snow and Ice Data Center, University of Colorado, Boulder. Images from the MODIS (Moderate Resolution Imaging Spectrometer) instrument on NASA's Terra satellite. Jan. 31, 2002 MODIS images from NASA's Terra satellite, National Snow and Ice Data Center Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

  29. Breakup of the Larsen B Ice Shelf in Antarctica Minor retreat takes place Feb. 17, 2002 MODIS images from NASA's Terra satellite, National Snow and Ice Data Center Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

  30. Breakup of the Larsen B Ice Shelf in Antarctica Retreat continues (800 km2) Feb. 23, 2002 MODIS images from NASA's Terra satellite, National Snow and Ice Data Center Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

  31. Breakup of the Larsen B Ice Shelf in Antarctica Figure 7.12 Main collapse (~2600 km2), leaving thousands of icebergs Mar. 5, 2002 MODIS images from NASA's Terra satellite, National Snow and Ice Data Center Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

  32. 7.4.d Extreme events • If standard deviation of daily temperatures remains similar as mean temperature rises Þ more frequent occurrence of events currently considered extreme • e.g., heat waves Figure 7.13 Few events above 40C (104F) (shaded area) Much more frequent (shaded area many times larger) Mean change Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

  33. 7.4.d Extreme events (cont.) • Also applies to frost days (on low side), mid-winter thaws • Precipitation events with higher mean moisture may act similarly • e.g., hurricane models for ~2xCO2 avg. increase ~ ½ a category on the 1-5 Saffir Simpson scale • Tendency for increase in heavy rainfall events • High natural variability in precip, implies precip effects of warming will rise above natural variability more slowly than temperature Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

  34. Summary of predicted climate change Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

  35. Summary of predicted climate change Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

  36. Schematic summary of best-estimate climate changes due to greenhouse warming Adapted from IPCC, Third Assessment Report, 2001. Figure 7.14 Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

  37. 7.5Climate change observed to date Figure 7.15 (will be expanded with supplementary figs. below) • Amplitude of natural variations depends on the spatial and time averages considered. • much of weather/climate T variability due to heat transport anomalies; but these tend to cancel in large regional averages • anthropogenic trend in temperature expected to have large spatial scales; i.e. clearer relative to noise in large-scale avgs Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

  38. Surface air temperature CRU* 5 x 5 degree grid(with selected averaging regions) *CRU= Climate Research Unit, U. of East Anglia

  39. Annual and Decadal CRU 2m Tanom Area Avg.(relative to 1961-1990 clim.) Global N. Hem. S. Hem. Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

  40. Annual and Decadal CRU 2m Tanom Area Avg. (relative to 1961-1990 clim.) N. America Note axis scale chg. United States Europe Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

  41. Annual and Decadal CRU 2m Tanom (relative to 1961-1990 clim., 5x5 degree avgs.) Note axis scale chg. Germany ~Beijing ~Washington D.C Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

  42. January CRU 2m Tanom (relative to 1961-1990 clim.) Note axis scale chg. Germany ~Beijing ~Washington D.C Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

  43. From observed time series, don’t have multiple examples of 50 or 100 year trends to establish range for decadal and centennial scale natural variability Thus, compare to range from models Can do this for model runs with natural forcing only versus runs that also have the observed 20th-century anthropogenic forcing(GHG+aerosol) [Next slide] The range in the natural forcing runs comes both from specified forcings (volcanoes, changes in solar input,…) and climate variability (like El Niño or variations in the thermohaline circulation) that occurs even for constant radiative forcing [More sophisticated “fingerprinting” techniques: use weighted spatial averages associated with the spatial pattern predicted for the warming rather than with spatial patterns of natural variability] 7.6.b. Is the observed warming trend consistent with natural variability or anthropogenic forcing? Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

  44. Observed 20th C. temperature for various averaging regions with climate model simulated range: natural only vs. natural + anthropogenic forcings Observed warming exceeds range that can occur by natural variability in models Figure 7.16 After Hegerl et al., 2007, in IPCC Fourth Assessment Report Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

  45. (a) Arctic sea ice extent anomalies(area with greater than 15% sea ice coverage). Bars= yearly values; line= decadal average. (b) Global glacier mass balance. Bars=yearly mass balance. Red line = cumulative global glacier mass balance (right axis) 7.6.c. Sea ice, land ice, ocean heat storage and sea level rise Figure 7.17 After Lemke et al., 2007, in IPCC Fourth Assessment Report Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

  46. After Bindoff et al., 2007, in IPCC Fourth Assessment Report; data from Levitus et al., 2005 Observed global annual ocean heat content for 0 - 700m layer Ocean heat content anomaly rel . to 1961-90 (black curve) i.e. global upper ocean heat storage in response to accumulated heat flux imbalance (surface + exchange with lower layers) Figure 7.18 [Heat content anom. = (temperature anom x heat capacity x density), integrated surface to 700m depth over global ocean area] [For refc: 1 Wm-2 surface heat flux anom. = 1.1x1022 J/yr over 3.6x1014m2 ocean] Shaded area = 90% confidence interval Variations: natural variability and sampling error Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

  47. Red reconstructed sea level fields rel. to 1961-90 [tide gauges avgd using spatial patterns from recent satellite data; Church & White, 2006] Blue curve coastal tide gauge measurements [rel. to 1961-90; alt method; Holgate & Woodworth, 2004] Black curve satellite altimetry rel. to 1993-2001 (After Bindoff et al 2007) Error bars denote 90% confidence interval Observed annual average anomalies of global mean sea level (mm) 1961 to 2003 trend in global mean sea level rise est. ~ 13 to 23 mm/decade Figure 7.19 After Bindoff et al., 2007, in IPCC Fourth Assessment Report, 2007 Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

  48. 7.7Emissions paths and their impacts Radiative forcing as a function of time for various climate forcing scenarios Recall: emissions scenarios • SRES: • A1FI (fossil intensive), • A1T (green technology), • A1B (balance of these), • A2,B2(regional economics) • B1 “greenest” • IS92a scenario used in many • studies before 2005 Figure 7.2 Adapted from Meehl et al., 2007 in in IPCC Fourth Assessment Report Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

  49. Recall: emissions scenarios Radiative forcing as a function of time for various climate forcing scenarios Focus on A2, A1B, B1 • SRES: • A1FI (fossil intensive), • A1T (green technology), • A1B (balance of these), • A2,B2 (regional economics) • B1 “greenest” • IS92a scenario used in many studies before 2005 Adapted from Meehl et al., 2007 in in IPCC Fourth Assessment Report Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

  50. Recall: Other emissions scenarios A1 low population growth, rapid economic growth, reduction in regional income differences A1B: energy mix, incl. non-fossil fuel A2: uneven regional economic growth, high income toward non-fossil, population 15 billion in 2100 B1: like A1 but resource-efficient technology. Emphasis on global economic, social, and environmental sustainability, equity. Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP