# Presentation: 794167

## Limitations of historical data records for constraining the properties of the climate system that are relevant for decadal and longer predictions

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

**VTT Workshop, Asheville, NC**
- October 27, 2003

## 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

## 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: ??)

- 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: ??)

- _Major Climate Projection Uncertainties_

## MIT Integrated Global System Model (IGSM)

**MIT Integrated Global System Model (IGSM)**

## 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

## MIT 2D Climate Model Description

- 2D statistical-dynamical atmospheric model derived from 3D GISS II AGCM (GSO, 7.86o latitude x 11 vertical layers, GSOVSV, 4o x11 layers with 2.5D ocean and sea-ice) [Sokolov and Stone, 1998]
- Q-flux mixed layer ocean model where temperature anomalies are mixed into deep-ocean. Q-flux held fixed in transient simulations.
- Adjustable model properties: climate sensitivity (S, via cloud feedback strength), rate of deep-ocean heat uptake (via Effective Kv), and net aerosol forcing (Faer, via optical depth)

## Est. p(S,Kv,FaerTobs) (cont.)

- 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)

## 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

## Climate-change diagnostics (Ti)

- 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)

## 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
-  T Diagnostics updated to 2001(future work)

## Marginal 2D p(S,Kv|T)

- 20%

- 1%

- 10%

- 1%

- 10%

- 20% significance level

- Cluster of AOGCMs
- (Sokolov et al., 2003)

- Slow                   Sea level rise                   Fast

**GSO**

## Individual r2 distributions

- Upper air

- Deep ocean

- Surface

- Increasing
- Aerosol
- Forcing

- Individual r2 distributions

## Marginal PDFs for S, KV, Faer with Uniform Prior

- Marginal PDFs for S, KV, Faer with Uniform Prior

## Marginal 2D p(S,Kv|T)

- 20%

- 1%

- 10%

- 1%

- 10%

- 20% significance level

- Cluster of AOGCMs
- (Sokolov et al., 2003)

- Slow                   Sea level rise                   Fast

**GSO**

## GSOVSV

- 20%

- 1%

- 10%

- 1%

- 10%

- 20% significance level

- Cluster of AOGCMs
- (Sokolov et al., 2003)

- Slow                   Sea level rise                   Fast

- Marginal

**GSOVSV**

## No Volcanoes

- Volcanoes

- Two Simulations with Anthropogenic + Natural Forcings

- Major difference is response to volcanic aerosol forcing.

- (CS=3.5 K, KV=9. cm2/s,FA=-0.5w/m2)

## Results 

- Quantified p(S,Kv,Faer | T, 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

## Next steps

- Updating diagnostics
- Ocean temperatures
- Tropospheric temperatures
- Sea ice trends
- Multiple AOGCM estimates of CN
- HadCM3, CCSM, GFDL, ...
- Exploring statistical method