Impact of climate uncertainty upon Impact of climate uncertainty upon trends in outputs generated by an trends in outputs generated by an

ecosystem modelecosystem model

Ruth Doherty, Edinburgh UniversityRuth Doherty, Edinburgh University

Adam Butler & Glenn Marion, BioSSAdam Butler & Glenn Marion, BioSS

ALARM meeting, Athens, January 2007ALARM meeting, Athens, January 2007

AcknowledgementsAcknowledgements

LPJ code: Ben Smith, Stephen Sitch, Sybil Schapoff

CRU data: David Viner

GCM data: PCMDI

Statistical methods: Jonathan Rougier, Chris Glasbey

Uncertainty analysis: Bjoern Reineking, Stijn Bierman

AimAim

Quantify uncertainties in projections of global &

regional vegetation trends for the 21st century

from the LPJ ecosystem model, based on future

climate uncertainty within the SRES A2 scenario

The LPJ modelThe LPJ model

http://www.pik-potsdam.de/lpj/

“…the Lund-Potsdam-Jena Dynamic Global Vegetation Model combines process-based, large-scale representations of terrestrial vegetation dynamics and land-atmosphere carbon and water exchanges in a modular framework”

Fluxes(daily)

VegetationDynamics (annual)

Drivers

Sources of uncertainty in LPJSources of uncertainty in LPJZaehle et al. (2005) analysed parameter uncertainty: used Latin hypercube sampling to sample uniformly over values of 14 functionally important parameters

Cramer et al. (2001) and Smith et al. (2001) analysed structural uncertainty, by looking at alternative parameterisations of processes within LPJ

Estimated uncertainty range (NPP, 1961-1990):43 –103 PgC/yr in Zaehle et al., 200544 – 66 PgC/yr in Cramer et al., 2001

Zaehle et al.: uncertainty range increases in future

Climate uncertaintyClimate uncertainty

LPJ is driven by climate, CO2 and soils data.

Future climate inputs are uncertain, due to:

- Future emissions: choice of SRES scenario

- Choice of GCM (climate model)

- Intra-model uncertainty for each GCM

Climate model runsClimate model runs

GCM simulations from the IPCC 4th Assessment

We consider only SRES scenario A2

We use 17 ensemble runs, from a total of 9 GCMs

GCMs with multiple ensemblesCCCMA-CGCM3, MPI-ECHAM5, NCAR-CCSM3

GCMs with a single runCNRM-CM3, CSIRO-MK3, GFDL-MK2,

MRI-CGCM2-3, UKMO-HADCM3, UKMO-HADGEM

LPJ model runsLPJ model runs

We run LPJ 18 times at a global scale

Soil inputs: FAO global soils dataset, with 9 types

CO2 inputs

Climate inputs:- monthly temperature, precipitation, solar radiation- control run: gridded 0.5o x 0.5o CRU data for 1900-2001- other runs: GCM model runs for 1900-2098, with LPJ run at

native spatial scale of the GCM

Spin-up period of 1000 years at start of each run

Run on average grid-cell basis with 1-year time-step

Daily: carbon and water fluxes

Annual: vegetation dynamics and competition amongst 10 Plant Functional Types

Spatial scale of outputs varies, depending on scale of the climate data / model used to provide the inputs

We analyse trends from 2002 to 2098 in global annual values of vegetation carbon, soil carbon & NPP

LPJ OutputsLPJ Outputs

Systematic biasesSystematic biases

• LPJ runs using GCMs exhibit systematic biases – presumably related to coarse spatial scale

• By calibrating against the LPJ control run we can use a statistical model to describe the statistical properties of these biases over the period 1900-2001

• This model can then, along with the LPJ runs under scenario A2, be used to predict the response of the LPJ model to climate over the 21st century

Statistical methodologyStatistical methodology

Past

t = years 1900,…,2001k = GCM run 1,…,17

We have data on: xt = LPJ control run ykt = LPJ run using GCM run k bkt = xt - ykt (bias in run k)

Assume bkt = k + ekt, where:

ekt is AR(1): ekt ~ N(k ek,t-1 ,k2)

vague priors on k, k ,k,ek,1899

Future

t = years 2002,…,2098k = GCM run 1,…,17

We have data on: ykt = LPJ run with GCM k

Predict Xt = BKt + yKt

K is randomly chosen GCM run: K = k with probability 1/17

BKt is predicted using the fitted AR(1) model for {bkt}

Statistical assumptionsStatistical assumptions

• Historical biases between the control & GCM-forced runs can be described by a simple time series model

• Future biases have the same distributional properties as historical biases

• The future LPJ runs provide equal information about year-to-year variations in vegetation characteristics

• The control run of LPJ rovides an error-free and unbiased representation of current vegetation

ResultsResults

Fit using LinBUGS (http://mathstat.helsinki.fi/openbugs): free software for fitting a vast range of statistical models via Bayesian inference

Can obtain similar results using ARIMA() function in R: but this does not account for estimation uncertainty

Simulated data

Annual global NPP

Annual global soil carbon

Annual global vegetation carbon

DiagnosticsDiagnostics

• Does the AR model describe historical biases well?

• Model checking:- plot of residuals from model, sample

autocorrelations, estimates for k

- sensitivity of predictions to value of K

• Possible extensions:- long-term linear or quadratic trends- higher-order terms in an ARIMA model- model responses y1t,…,y17,t as covariates

Annual global NPP

Future workFuture work

• Improve time series model for bias terms

• Investigate possible reasons for systematic bias

• Apply a similar analysis at the regional scale

• Analyse outputs from the other SRES scenarios

• Incorporate global satellite data on NPP…?

Open questionsOpen questions• How reasonable is the assumption that future biases

are related to past biases?

• Should we assign equal weights to model runs?

• Should we run LPJ at the native spatial scale of the climate model that is being used to force it?

• We use statistical post-processing – could we use statistical methods to generate climate inputs for LPJ?

• LPJ can be run with stochastic modules – how could we incorporate uncertainty from these?

Contact usContact us

Adam Butler adam@bioss.ac.uk

Ruth Dohertyruth.doherty@ed.ac.uk

Glenn Marionglenn@bioss.ac.uk

File: created 11 December, last modified 13 December, author Adam Butler