Intercomparison of Global Scale
Ecological Models and Field Data
OLSON, R.J1, HIBBARD, K.2, SCURLOCK, J.M.O.1, and JOHNSON, K.R.1
1Oak
Ridge National Laboratory, Oak Ridge, TN, 37831-6407, USA
and 2University of New Hampshire, Durham, NH, 03824-3525, USA
ABSTRACT
Objective
Understanding global-scale ecosystem responses to changing environmental conditions is important
both as a scientific question and as the basis for making policy decisions. The confidence in
regional models of ecosystem processes depends on how well the field data used to develop the
model represent the region of interest, how well the environmental driving variables represent the
region of interest, and how well regional model predictions agree with observed data for the region.
To assess how much confidence to place in global model forecasts of terrestrial carbon cycling, the
first Ecosystem Model-Data Intercomparison (EMDI) Workshop was held in December 1999. Initial
results showed general agreement between model predictions of net primary productivity (NPP) and
NPP field measurements but with obvious differences that indicated areas for potential data and
model improvement. The EMDI Workshop included 12 biogeochemical, satellite-driven, detailed
process, and DVGM model types Extensive worldwide NPP data were assembled; model driver data
including vegetation, climate, and soils were associated with each site; model simulations were
performed; and the model-data differences analyzed. NPP and model driver data were compiled for
1200 study sites and for over 2100 0.5-degree grid cells. Comparing the field measurements with
an ensemble NPP (average of predictions from several models) provided a unique tool to improve
NPP data, model driver data, and model processes. The Workshop demonstrated that model-data
intercomparison is an important new direction in model evaluation; however, it is an extraordinarily
complex task..
To assess how much confidence to place in global model forecasts of
terrestrial carbon cycling. Specific questions:
 How does the simulated NPP compare with the field data?
 How sensitive are models to site-specific climate?
 Does additional mechanistic detail in models result in a better match
with field measurements?
 How useful are the measures of NPP for evaluating model predictions?
 How well do models represent regional patterns of NPP?
Our Approach
1,
2.
3.
4.
5.
6.
Compile and evaluated NPP data for Class A, B, and C sites
Associate model driver data with NPP sites
Conduct outlier assessment of NPP and model driver data
Provide model driver data for sites to modelers
Run global/regional models for sites without fine tuning models
Compare model results with field measurements
1. Compile and evaluated NPP data for Class A, B, and C sites
Criteria for selecting NPP data from extant sources:




use of an accepted method to estimate above or below net
primary production
geographical reference to the study site
definition of biome or vegetation type
a citable reference to peer-reviewed publication, symposium
or workshop proceedings, book chapter, or technical
memorandum
Class A sites - intensively-studied or well-documented study
sites
Humid Savanna NPP site - Lamto, Côte d’Ivoire
photograph from the ORNL DAAC collection
Contributors of NPP field data
Class B Sites - “extensive” sites with less documentation and site-specific
Jonathan Scurlock, Dick Olson, Keri Johnson - Oak Ridge National Lab
Steve Prince, Daolan Zheng - University of Maryland
Bill Parton, Steve Del Grossa - Colorado State University, NREL
Tom Gower, Drew Feldkirchner - University of Wisconsin-Madison
Jian Ni - University of Beijing, China
Larry Tieszen - USGS EROS Data Center
Jen Jenkins - U.S. Forest Service
information
Class C Cells - regional collections of 0.5 grid cells, central and eastern U.S.
2. Associate model driver data with NPP sites
NPP by biome
Xilingol grassland site (Inner Mongolia Grassland
Ecosystem Research Station), China.
Acknowledgements: Funding and support has been provided by the following groups:



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International Geosphere-Biosphere Programme – Data and Information System (IGBP-DIS)
IGBP-Global Analysis, Interpretation and Modelling Task Force (GAIM)
National Center for Ecological Analysis and Synthesis (NCEAS)
National Aeronautics and Space Administration (NASA) Terrestrial Ecosystem Program
GAIM and NCEAS provided funding for the EMDI workshop; NCEAS hosted an FTP Server for data exchange. Kathy Hibbard, GAIM, led the workshop.
The organizing committee included Wolfgang Cramer, PIK; Steve Prince, UMD; Bill Parton, CSU; Dick Olson, ORNL; Jonathan Scurlock, ORNL; and others.
Olson and Scurlock, with assistance from ORNL Intern Keri Johnson, organized and distributed the model driver data, NPP data, and model results with
funding from both the NASA and NCEAS. The workshop was possible because of the continued support from the NASA Terrestrial Ecosystem Program for
the Global Primary Production Data Initiative through Oak Ridge National Laboratory and the University of Maryland to compile the NPP measurement data.
Oak Ridge National Laboratory is managed by UT-Battelle, LLC for the U.S. Dept. of Energy under contract DE-AC05-00OR22725.
Variable
Individual
Institution
Elevation: global DEM
Rob Braswell
University of New Hampshire
Soil Properties: texture, pH, carbon and
nitrogen density, water capacity
Dick Olson, Keri
Johnson
Oak Ridge National
Laboratory
Land cover: 14 cover classes
Monthly NDVI: 1986, 1987, and 1990
Steve Prince, Robb University of Maryland
Wright
Rob Braswell
University of New Hampshire
Atmospheric CO2 (1901-1995)
Bob Cushman
30-year Average Monthly Climate (1960- Wolfgang Cramer,
1990): temperature, precipitation, sunshine Stephen Sitch
Actual Monthly Climates (1901-1995) for
Class A sites only: temperature,
precipitation, sunshine
Wolfgang Cramer,
Stephen Sitch
ORNL Carbon Dioxide
Information and Analysis
Center
Potsdam Institute for Climate
Impact Research
Potsdam Institute for Climate
Impact Research
NPP Data: go to the ORNL Distributed Active Archive for
Biogeochemical Dynamics Data: http://daac.ornl.gov/NPP/