Transcript Document

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 LPJ 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
Fluxes
(daily)
Vegetation
Dynamics
(annual)
LPJ-Lund Potsdam Jena
Vegetation Model
Based on climate and soils inputs LPJ
simulates:
Vegetation dynamics and competition amongst 10
Plant Functional Types (PFTs)
Vegetation and soil carbon and water fluxes
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-Lund Potsdam Jena
Vegetation 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)
C3 Grasses (FPC)
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)
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)
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 1900-2002 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…