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Abstract:
Land models, which have been developed by the
modeling community in the past few decades to predict future
states of ecosystems and climate, have to be critically
evaluated for their performance skills of simulating ecosystem
responses and feedback to climate change. Benchmarking
is an emerging procedure to measure performance of
models against a set of defined standards. This paper proposes
a benchmarking framework for evaluation of land
model performances and, meanwhile, highlights major challenges
at this infant stage of benchmark analysis. The framework
includes (1) targeted aspects of model performance
to be evaluated, (2) a set of benchmarks as defined references
to test model performance, (3) metrics to measure and
compare performance skills among models so as to identify
model strengths and deficiencies, and (4) model improvement.
Land models are required to simulate exchange of water,
energy, carbon and sometimes other trace gases between
the atmosphere and land surface, and should be evaluated
for their simulations of biophysical processes, biogeochemical
cycles, and vegetation dynamics in response to climate
hange across broad temporal and spatial scales. Thus, one major challenge is to select and define a limited number of benchmarks to effectively evaluate land model performance.
The second challenge is to develop metrics of measuring mismatches
between models and benchmarks. The metrics may
include (1) a priori thresholds of acceptable model performance
and (2) a scoring system to combine data–model mismatches
for various processes at different temporal and spatial
scales. The benchmark analyses should identify clues of
weak model performance to guide future development, thus
enabling improved predictions of future states of ecosystems
and climate. The near-future research effort should be on development
of a set of widely acceptable benchmarks that can
be used to objectively, effectively, and reliably evaluate fundamental
properties of land models to improve their prediction performance skills.
