1 / 17

Chris Forest, Andrei Sokolov, Peter Stone: MIT Myles Allen: University of Oxford

Chris Forest, Andrei Sokolov, Peter Stone: MIT Myles Allen: University of Oxford. Limitations of historical data records for constraining the properties of the climate system that are relevant for decadal and longer predictions. VTT Workshop, Asheville, NC October 27, 2003.

alagan
Download Presentation

Chris Forest, Andrei Sokolov, Peter Stone: MIT Myles Allen: University of Oxford

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Chris Forest, Andrei Sokolov, Peter Stone: MIT Myles Allen: University of Oxford Limitations of historical data records for constraining the properties of the climate system that are relevant for decadal and longer predictions VTT Workshop, Asheville, NC October 27, 2003

  2. Limitations of historical records • Short? • Start dates: 1979, 1958, 1948, 1860(?) • Accurate? • Spatial and Temporal sampling • Instrumental changes • Limitations outside of climate records? • Forcings, incompletely known or missing • Natural variability estimates (d.o.f.) • Relevance to 21st century changes? • Distinguish btwn response characteristics of different earth system components

  3. Major Climate Projection Uncertainties Climate System Properties • Future forcings • Pathways of climate relevant emissions and concentrations (GHGs, aerosols, ... ) (IPCC: SRES?) • How much can pollutants reflect sunlight? • (Net Aerosol forcing, Faer(IPCC: ??) • Climate System Response Uncertainty • Equilibrium temperature change • How much will global-mean temperature change after oceans, ice, or ecosystems adjust? • (Climate Sensitivity to 2x[CO2], S) (IPCC: 1.5-4.5 K) • Transient climate change • How fast can oceans (and ice) take up excess heat? • (Rate of heat uptake by the deep ocean, Kv) (IPCC: ??)

  4. MIT Integrated Global System Model (IGSM)

  5. Estimating Uncertainty in Climate System Properties: p(S,Kv,FaerTobs) • Simulate 20th century climate using anthropogenic and natural forcings while systematically varying the choices of climate system properties: S, Kv, and Faer • Compare each model response against observed T as in optimal fingerprint detection algorithm • Compare goodness-of-fit statistics to estimate p(S,Kv,FaerTobs) for individual T diagnostics • Estimate p(S,Kv,FaerTobs) for multiple diagnostics and combine results using Bayes’ Theorem see Forest et al. (2002), Science

  6. Est. p(S,Kv,FaerTobs) (cont.) • Compare 20th c. model simulation with observations: T = T(S,Kv,Faer) - Tobs • use optimal fingerprint detection algorithm to yield goodness-of-fit statistics, r2: r2 = TT [CN]-1 T =  CN = noise estimate from AOGCM control run r2 ~ mFm, to provide hypothesis test From Forest et al. (2001)

  7. Updated Climate Forcings • Anthropogenic • Greenhouse gases (eqCO2 vs CO2, CH4, CFCs, N2O) • Sulfate Aerosols (emissions updated to 2001) • Ozone: Stratospheric and Tropospheric (GISS SI2002) • Vegetation Land-use changes • Natural • Volcanic aerosols (Sato et al.) • Solar forcing (Lean et al.) • GSO (old work) vs. GSOVSV (new work) • Both use sulfate aerosols for uncertainty in net forcing

  8. Climate-change diagnostics (DTi) • Upper-air temperature changes, latitude-height pattern, [1986-1995] - [1961-1980] (Parker et al. 1997) (M=36x8) • Deep-ocean temperature trend, global, 0-3km (1952-1995) (Levitus et al. 2000) (M=1) • Surface temperature change, latitude-time pattern, (1946-1995 decadal means, 1906-1995 climatology, 4 zonal bands) (updated from Jones, 1994) (M=4 x 5)

  9. Summary of Changes from GSO  GSOVSV • Updated Forcings for 1860-2001 • Updated Greenhouse Gas concentrations • Updated Sulfur emissions from 1990-2001 • Updated Ozone concentrations • Added Land-use Vegetation Changes • Added Volcanic and Solar forcings • Updated climate model to 4o resolution and included new sea-ice model • DT Diagnostics updated to 2001(future work)

  10. GSO Marginal 2D p(S,Kv|DT) 1% 10% 20% Cluster of AOGCMs (Sokolov et al., 2003) 20% significance level 10% 1% Slow Sea level rise Fast

  11. Individual r2 distributions Increasing Aerosol Forcing Upper air Surface Deep ocean

  12. Marginal PDFs for S, KV, Faer with Uniform Prior

  13. GSO Marginal 2D p(S,Kv|DT) 1% 10% 20% Cluster of AOGCMs (Sokolov et al., 2003) 20% significance level 10% 1% Slow Sea level rise Fast

  14. GSOVSV Marginal 1% 10% 20% Cluster of AOGCMs (Sokolov et al., 2003) 20% significance level 10% 1% Slow Sea level rise Fast

  15. Major difference is response to volcanic aerosol forcing. Two Simulations with Anthropogenic + Natural Forcings No Volcanoes Volcanoes (CS=3.5 K, KV=9. cm2/s,FA=-0.5w/m2)

  16. Results • Quantified p(S,Kv,Faer | DT, CN) for two forcing scenarios: GSO (anthropogenic only) and GSOVSV (anthropogenic plus natural). • Main changes from GSO  GSOVSV • Poorer constraint on high S • Stronger constraint on high Kv • Net aerosol forcing estimate is weaker due to volcanic forcing • Clear dependence on prior for Expert S

  17. Next steps • Updating diagnostics • Ocean temperatures • Tropospheric temperatures • Sea ice trends • Multiple AOGCM estimates of CN • HadCM3, CCSM, GFDL, … • Exploring statistical method

More Related