Transcript PowerPoint - Biomathematics and Statistics Scotland

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
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 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
Fluxes
(daily)
Vegetation
Dynamics
(annual)
LPJ Vegetation Model simulations
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
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)
C3 Grasses (FPC)
Sources of LPJ Model Uncertainty
Model inputs: future climate uncertainty
Representation of mechanisms driving model
processes (Cramer et al. 2001; Smith et al. 2001tests 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
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 (2005)
NPP accounting
for parameter uncertainty
NBP = NEE+Biob
Uc=full uncertainty range
C=excluding unrealistic
parameters
Increases in 2050s due
to increased CO2 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 4th Assessment GCM
simulations
IPCC-AR4 simulations
GCMs contributing to SRES A2
CO2 concentrations
Investigating the effect of Future
Climate Uncertainty for
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
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
…we focus on globally
averaged values of these
variables…
Statistical approach
• 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
• 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
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:
xt ~ N(t , vt )
Output for past or future year t using run i of GCM I:
yit ~ N(zIt , )
Assume conditional independence in both cases
Latent trends
Model trends in true signal t and GCM biases YIt - t
as independent random walks: e.g.
t ~ N(t 1 , s  t )
 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
NPP
Assumptions
• 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)
Inter-ensemble variability
Future work - methodology
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
BUGS
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
z It  t  bIt
t  2t 1  t 2 ~ N (0, )
Problems:
Lack of identifiability
Bias terms are not really AR(1)
bI ,t   I bI ,t 1 ~ N (0,  I )
A Bayesian ASK-like model
M
t   wI z It  bt
I 1
zIt  2zI ,t 1  zI ,t 2 ~ N (0,  I )
Problems:
Lack of fit
Unconstrained estimation leads
to weights outside range [0,1]
bt  bt 1 ~ N (0, )
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
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
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
Adam Butler
[email protected]
Ruth Doherty
[email protected]
Glenn Marion
[email protected]