Impact of climate uncertainty upon trends in outputs generated by an ecosystem model
Download
1 / 38

Impact of climate uncertainty upon trends in outputs generated by an ecosystem model - PowerPoint PPT Presentation


  • 77 Views
  • Uploaded on

Impact of climate uncertainty upon trends in outputs generated by an ecosystem model. Adam Butler & Glenn Marion, Biomathematics & Statistics Scotland • Ruth Doherty, Edinburgh University • Jonathan Rougier,University of Durham. Probabilistic Climate Impacts workshop, September 2006.

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'Impact of climate uncertainty upon trends in outputs generated by an ecosystem model' - fabian


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.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript

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

Adam Butler & Glenn Marion,

Biomathematics & Statistics Scotland

Ruth Doherty, Edinburgh University

Jonathan Rougier,University of Durham

Probabilistic Climate Impacts workshop, September 2006


Some background
Some background generated by an ecosystem model

  • Aims

    • To quantify uncertainties in projections of global and regional vegetation trends for the 21st century from the LPJ ecosystem model, based on future climate uncertainty

  • BIOSS

    • Public body providing quantitative consultancy & research to support biological science

    • Funded by ALARM: a 5 year EU project to assess risks of environmental change upon European biodiversity


The impacts model lpj
The Impacts model: LPJ generated by an ecosystem model

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

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


Drivers generated by an ecosystem model

Fluxes

(daily)

Vegetation

Dynamics

(annual)


Lpj vegetation model simulations
LPJ Vegetation Model simulations generated by an ecosystem model

  • Driven by climate and soils inputs LPJ simulates:

    • Daily: carbon and water fluxes

    • Annually: vegetation dynamics and competition amongst 10 Plant Functional Types (PFTs)

  • Average grid-cell basis with a 1-year time-step

  • Spin-up period of 1000 years to develop equilibrium vegetation and soil structure at start of simulation


Lpj inputs drivers
LPJ Inputs/drivers generated by an ecosystem model

  • Inputs:

    • Soils: FAO global soils dataset: 9 types inc coarse-fine range (CRU)

    • Climate: monthly temperature, precipitation, solar radiation

    • CO2: provided for 1901-1998; updated to 2002 from CDIAC

  • Model output scale determined by driving climate

  • Acknowledgements:

    • LPJ code- Ben Smith, Stephen Sitch, Sybil Schapoff

    • CRU data- David Viner (CRU), GCM data (PCMDI)


Tropical broadleaf evergreen tree fpc
Tropical Broadleaf Evergreen Tree (FPC) generated by an ecosystem model


C3 grasses fpc
C3 Grasses (FPC) generated by an ecosystem model


Sources of lpj model uncertainty
Sources of LPJ Model Uncertainty generated by an ecosystem model

  • Model inputs: future climate uncertainty

  • Representation of mechanisms driving model processes (Cramer et al. 2001; Smith et al. 2001- tests different formulations of relevant processes)- generally use most up-to date formulations from literature

  • Parameters within the model (Zaehle et al. 2005, GBC)


Lpj parameter uncertainty zaehle et al 2005
LPJ Parameter uncertainty: generated by an ecosystem modelZaehle et al. 2005

  • Latin hypercube sampling

    • Assume uniform PDF for each parameter

    • Exclude unrealistic parameter combinations

  • Simulations at sites representing major biomes (81)

  • 400 model runs (61-90 CRU climatology and HadCM2 1860-2100)

    • Identified 14 functionally important parameters

    • Differences in parameter importance in water-limited regions

    • Estimated uncertainty range of modelled results:

      61-90: NPP=43.1 –103.3 PgC/yr; cf. 44.4-66.3 Cramer et al. (2001)


Lpj parameter uncertainty zaehle et al 20051

NBP = NEE+Biob generated by an ecosystem model

Uc=full uncertainty range

C=excluding unrealistic parameters

NPP accounting

for parameter uncertainty

LPJ Parameter uncertainty:Zaehle et al (2005)


  • Increases in 2050s due to increased CO generated by an ecosystem model2 and WUE, thereafter a decline

  • Parameter uncertainty increases in the future

  • Uncertainty estimates in NBP/NPP comparable to those obtain from uncertainty amongst 6 DGVMs


Future climate uncertainty based on ipcc 4 th assessment gcm simulations

Future Climate Uncertainty based on generated by an ecosystem modelIPCC 4th Assessment GCM simulations


Ipcc ar4 simulations
IPCC-AR4 simulations generated by an ecosystem model


Gcms contributing to sres a2
GCMs contributing to SRES A2 generated by an ecosystem model


Co 2 concentrations
CO generated by an ecosystem model2 concentrations


Investigating the effect of future climate uncertainty for lpj predictions
Investigating the effect of Future Climate Uncertainty for generated by an ecosystem model LPJ predictions

  • Perform 19 separate runs of LPJ at the global scale

    • one run using CRU data for 1901-2002 at 0.5o x 0.5o

    • results from 18 simulations from 9 GCMs for the period 1850-2100 (20th Century and A2) running at the native scale of each GCM

  • GCMs with multiple ensembles

    • CCCMA-CGCM3, MPI-ECHAM5,NCAR-CCSM3

  • GCMs with single ensemble member

    • CNRM-CM3,CSIRO-MK3,GFDL-MK2,MRI-CGCM2-3,UKMO-HADCM3, UKMO-HADGEM


Global mean temperature anomaly relative to 61 90
Global mean temperature anomaly generated by an ecosystem model relative to 61-90


Lpj outputs

…we focus on globally averaged values of these variables…

LPJ Outputs

For each grid cell LPJ produces annual values for:

  • Net Primary Production

  • Net Ecosystem Production

  • Plant Functional Type

  • Heterotrophic respiration

  • Vegetation carbon

  • Soil carbon

  • Fire carbon

  • Run-off

  • Evapotranspiration

Net Primary Production

Net Ecosystem Production

Plant Functional Type

Heterotrophic respiration

Vegetation carbon

Soil carbon

Fire carbon

Run-off

Evapotranspiration


Statistical approach
Statistical approach variables…

  • Statistical post-processing of LPJ output

  • Analyse trends in global annual mean NPP based on outputs from 19 runs of the LPJ model

  • Runs forced using a total of 18 ensembles from 9 GCMs, and using gridded CRU data

  • Analysis (partially) deals with climate uncertainty, but does not deal with parameter or structural uncertainties in the LPJ model


Motivating factors
Motivating factors variables…

  • Statistical pre-processing of LPJ inputs is tough: would need to describe month-to-month trends in three climate variables for each location

  • GCMs are each run at different spatial resolutions, all of which differ from the resolution of the CRU data

  • LPJ is fairly computationally intensive to run

  • No useful observational data to validate LPJ against


Time series model
Time series model variables…

Use a hierarchical time series model to draw inferences about “true” response of LPJ model to projected climate changes based on the 19 runs

Output from past year t using CRU data:

Output for past or future year t using run i of GCM I:

Assume conditional independence in both cases


Latent trends
Latent trends variables…

Model trends in true signalt and GCM biasesYIt - t as independent random walks: e.g.

 allows process variability to change linearly over time

Can fit as a Dynamic Linear Model using the Kalman filter – easy to implement in R (sspir package)

Parameter estimation by numerical max likelihood


Results temperature
Results - temperature variables…


NPP variables…


Assumptions
Assumptions variables…

  • Observational errors are IID and unbiased

  • Inter-ensemble variabilities for a given GCM are IID

  • Random walk model can provide a good description of actual trends

  • Levels of variability do not change over the course of the runs (except for a jump at present day)



Future work methodology
Future work - methodology variables…

Explore impacts of making different assumptions about the biases in the GCM responses

Explore impacts of varying levels of inter-ensemble variability and observation error

Explore links between this and a regression-based (ASK-like) approach

Deal with uncertainty in estimation of parameters in time series model – e.g. a fully Bayesian analysis

Apply analysis to output from newer version of LPJ

Apply a similar analysis at the regional scale

Extend approach to other variables, especially PFT

Incorporate information on multiple scenarios


BUGS variables…

BUGS:free software for fitting a vast range of statistical models via Bayesian inference

Provides an environment for exploring the impacts of different assumptions

Allows for the use of informative priors

[http://www-fis.iarc.fr/bugs/wine/winbugs.jpg]

http://mathstat.helsinki.fi/openbugs

http://www.mrc-bsu.cam.ac.uk/bugs


Bayesian analogue of the dlm
Bayesian analogue of the DLM variables…

Problems:

Lack of identifiability

Bias terms are not really AR(1)


A bayesian ask like model
A Bayesian ASK-like model variables…

Problems:

Lack of fit

Unconstrained estimation leads

to weights outside range [0,1]


Open questions statistical methodology
Open questions – statistical methodology

  • What assumptions can we make about the biases in GCM responses and in the observational data?

  • How reasonable is the assumption that future variability is related to past variability, and how far can we weaken this assumption?

  • How should we best deal with small numbers of ensembles & unknown levels of “observational error”? Can we ellicit more prior information?


Future work application
Future work - application statistical methodology

Apply analysis to output from newer version of LPJ

Apply a similar analysis at the regional scale

Extend approach to other variables, especially PFT

Analyse outputs from multiple SRES scenarios


Open questions application
Open questions - application statistical methodology

Should LPJ be run at the native spatial scale of the data/GCM that is being used to force it ?

LPJ includes stochastic modules – switched off here, but how could we best deal with these…?

For a limited number of runs what experimental design would enable us to best reflect the different elements of climate and impact uncertainty?


Contact us
Contact us statistical methodology

Adam Butler

[email protected]

Ruth Doherty

[email protected]

Glenn Marion

[email protected]


ad