Investigating sources of uncertainty associated with the JULES land surface model
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Date
27/06/2016Author
Slevin, Darren
Metadata
Abstract
The land surface is a key component of the climate system and exchanges
energy, water and carbon with the overlying atmosphere. It is the location
of the terrestrial carbon sink and changes in the land surface can impact
weather and climate at various time and spatial scales. It's ability to act
as a source or a sink can influence atmospheric CO2 concentrations. Both
models and observations have shown the reduced ability of the land surface
to absorb increased anthropogenic CO2 emissions with results from the
Coupled Climate{Carbon Cycle Model Intercomparison Project (C4MIP)
and phase 5 of the Coupled Model Intercomparison Project (CMIP5) have
shown that the terrestrial carbon cycle is a major source of model uncertainty.
Land surface models (LSMs) represent the interaction between the
biosphere and atmosphere in earth system models (ESMs) and are important
for simulating the terrestrial carbon cycle. In the context of land
surface modelling, uncertainty arises from an incomplete understanding of
land surface processes and the inability to model these processes correctly.
As LSMs become more advanced, there is a need to understand their accuracy.
In this thesis, the ability of the Joint UK Land Environment Simulator
(JULES), the land surface scheme of the UK Met Office Unified Model, to
simulate Gross Primary Productivity (GPP) fluxes is evaluated at various
spatial scales (point, regional and global) in order to identify and quantify
sources of uncertainty in the model. This thesis has three main objectives.
Firstly, JULES is evaluated at the point scale across a range of biomes and
climatic conditions using local (site-specific), global and satellite datasets.
It was found that JULES is biased with total annual GPP underestimated
by 16% and 30% across all sites compared to observations when using local
and global data, respectively. The model's phenology module was tested
by comparing results from simulations using the default phenology model
to those forced with leaf area index (LAI) from the MODIS sensor. Model
parameters were found to be a minor source of uncertainty compared to the
meteorological driving data at the point scale as was the default phenology
module in JULES. Secondly, in addition to evaluating simulated GPP
fluxes at the point scale, the ability of JULES to simulate GPP at the global
and regional scale for 2000{2010 was investigated with being able to simulate
interannual variability and simulated global GPP estimates were found
to be greater than the observation-based estimates, FLUXNET-MTE and
MODIS, by 8% and 25 %, respectively. At the regional scale, differences in
GPP between JULES, FLUXNET-MTE and MODIS were observed mostly
in the tropics and this was the reason for differences at the global scale.
Simulating tropical GPP was found to be a major source of uncertainty
in JULES. JULES was found to be insensitive to spatial resolution and
when driven with the PRINCETON meteorological dataset, differences between
model simulations driven using WFDEI-GPCC and PRINCETON
occurred in the tropics (at 5°N{5°S) and extratropics (at 30°N{60°N). Finally,
the response of JULES to changes in climate (surface air temperature,
precipitation, atmospheric CO2 concentrations) was explored at the global
and regional scale. Simulated GPP was found to have greater sensitivity
to changes in precipitation and CO2 concentrations than air temperature
at the global scale while LAI was sensitive only to changes in temperature
and insensitive to changes in precipitation and CO2 concentrations. It was
found that model sensitivity to climate at the global scale was determined
by its behaviour at the regional scale.