whatever happened to palaeoqump?

Whatever happened to PalaeoQUMP?Multi-palaeo archive constraints on climate sensitivity

Tamsin Edwards, Open UniversityJonty Rougier, University of BristolMat Collins, University of Exeter

Multi-palaeo archive constraints on climate sensitivity

Workshop questions

• How strongly can observations (including palaeo) constrain sensitivities?

• How consistent are different constraints and what can be inferred from multiple constraints?

- Consistency across models

- Proxy types

- Palaeoclimate eras

PalaeoQUMP 2006-2010• Palaeoclimate version of QUMP

- QUMP: Quantifying Uncertainty in Model Projections (UKCP09; David Sexton, Mat Collins and others)

- PI Sandy Harrison; Mat Collins, Michel Crucifix, and others

• Bayesian update of perturbed parameter ensemble (PPE) with palaeoclimate reconstructions to constrain climate sensitivity

- HadCM3

• Palaeoclimates

- Mid-Holocene (MH: 6000 cal yr BP)

- Last Glacial Maximum (LGM: 21,000 cal yr BP)

• Magnitude of change in tropical SST

MMEs and PPEs at the LGM

Other PPEs and MMEUpdated from Edwards et al. (2007) PiPG

MIROC: Annan et al. (2005); Hargreaves et al. (2006)CLIMBER: Schneider von Deimling (2006)MME: Crucifix (2006)

HadCM3PPE

Clim

ate

sens

itivity

(deg

C)

Magnitude of Last Glacial Maximum tropical SST anomaly (degC)

• Magnitude of change in tropical SST

MMEs and PPEs at the LGM

Other PPEs and MMEUpdated from Edwards et al. (2007) PiPG

MIROC: Annan et al. (2005); Hargreaves et al. (2006)CLIMBER: Schneider von Deimling (2006)MME: Crucifix (2006)

HadCM3PPE

Clim

ate

sens

itivity

(deg

C)

Magnitude of Last Glacial Maximum tropical SST anomaly (degC)

MARGO reconstruction

(a)!

Temperature anomaly (degrees C)!(b)!

(c)!

State-dependent feedback parameter

(Wm-2K-1)!

(Wm-2K-1 )!

MIROC: Hargreaves et al. (2006) MME: Crucifix (2006)

HadCM3PPE

Mid-Holocene Green Sahara

pollen reconstruction

HadCM3 PPE

Mea

n pr

ecip

itatio

n ch

ange

(m

m/y

r)

New statistical framework• Rougier, Goldstein, and House (2013), “Second-order

exchangeability analysis for multi-model ensembles”, Journal of the American Statistical Association

- See also Williamson et al. (2013) Climate Dyn.

• Update MME mean and variance with observations

• Exchangeability of

- ensemble members

- reality with ensemble

• Simplest possible judgements about structural uncertainty: how much does MME under-sample?

1. inflation factor for variance: e.g. 1.5x

2. minimum variance: e.g. (0.5 degC)2

Palaeoclimate application• Joint update across MH and LGM

• MME:

- Palaeoclimate Model Intercomparison Project (PMIP)

- PMIP3/PMIP2V/PMIP2/most similar other model

• Reconstructions

- MH and LGM pollen-based growing degree days (GDD5) Bartlein et al. (2010)

- LGM multi-proxy sea surface temperatures (SST) MARGO et al. (2009); updates by Schmittner et al. (2011)

• Use for:

- critiquing judgements about structural uncertainty of MME mean

- Bayes Linear update of MME mean and estimated variance

Very preliminary update• mean = 3.0; > 95% credibility interval = [ 1.6 , 5.4 ]

• smaller if assume Gaussian distribution for reconstructions <->

Clim

ate

Sens

itivi

ty X

0

5

10

15

0

5

10

15MH GDD5

Initi

alU

pdat

ed

●3.2

● 6.1

LGM GDD5

Initi

alU

pdat

ed

●3.2● 2.9

LGM SST

Initi

alU

pdat

ed

●3.2 ● 3.1

All GDD5

Initi

alU

pdat

ed

●3.2 ● 3.0

All LGM

Initi

alU

pdat

ed

●3.2● 2.9

All

Initi

alU

pdat

ed

●3.2 ● 3.0●●●

●

●

●

●

●●

●●

●

●

Ense

mbl

e

PRIOR

Ensemble: 1.5-4.7



Clim

ate

Sens

itivi

ty X

0

5

10

15

0

5

10

15MH GDD5

Initi

alU

pdat

ed

●3.2

● 6.1

LGM GDD5

Initi

alU

pdat

ed

●3.2● 2.9

LGM SST

Initi

alU

pdat

ed

●3.2 ● 3.1

All GDD5

Initi

alU

pdat

ed

●3.2 ● 3.0

All LGM

Initi

alU

pdat

ed

●3.2● 2.9

All

Initi

alU

pdat

ed

●3.2 ● 3.0●●●

●

●

●

●

●●

●●

●

●

Ense

mbl

e

PRIOR

Update with each field

Ensemble: 1.5-4.7



Clim

ate

Sens

itivi

ty X

0

5

10

15

0

5

10

15MH GDD5

Initi

alU

pdat

ed

●3.2

● 6.1

LGM GDD5

Initi

alU

pdat

ed

●3.2● 2.9

LGM SST

Initi

alU

pdat

ed

●3.2 ● 3.1

All GDD5

Initi

alU

pdat

ed

●3.2 ● 3.0

All LGM

Initi

alU

pdat

ed

●3.2● 2.9

All

Initi

alU

pdat

ed

●3.2 ● 3.0●●●

●

●

●

●

●●

●●

●

●

Ense

mbl

e

LGM SST change mostly < 3sigma from zero (grey)

PRIOR


Ensemble: 1.5-4.7



Clim

ate

Sens

itivi

ty X

0

5

10

15

0

5

10

15MH GDD5

Initi

alU

pdat

ed

●3.2

● 6.1

LGM GDD5

Initi

alU

pdat

ed

●3.2● 2.9

LGM SST

Initi

alU

pdat

ed

●3.2 ● 3.1

All GDD5

Initi

alU

pdat

ed

●3.2 ● 3.0

All LGM

Initi

alU

pdat

ed

●3.2● 2.9

All

Initi

alU

pdat

ed

●3.2 ● 3.0●●●

●

●

●

●

●●

●●

●

●

Ense

mbl

e> 95% credibility interval

[1.6 , 5.4]Gaussian assumption:

approx. [2, 4.5]!

PRIOR


Ensemble: 1.5-4.7

MTWA MH anomalies in AR5

“Terrestrial MH summer-season temperatures were higher than modern in the mid-to-high latitudes of the NH, consistent with...PMIP2 and PMIP3/CMIP5 simulated responses to orbital forcing”

“Pollen-based records indicate positive MH temperature anomalies in southern South Africa that are not reproduced in the PMIP3/CMIP5 simulations.”

Tentative summary• Importance of using multiple datasets for robustness• State-dependent lambda, structural uncertainty & constraint• Is it coherent to combine different eras?- LGM temps (except E Antarctic) support lower ECS - MH land temp & Sahara precip support higher

• What would narrow the range in this study?- Better agreement between MH reconstructions and MME mean- Smaller LGM SST uncertainties- Smaller MME spread if better agreement of mean- New constraints e.g. historical?

• Why do GCMs systematically overestimate NH summer warming (underestimate monsoon response), esp low sens.?- What implications does this have for future precip projections?

whatever happened to palaeoqump?

Documents