The dynamic global vegetation model LPJmL4 is a process-based
model that simulates climate and land use change impacts on the terrestrial
biosphere, agricultural production, and the water and carbon cycle. Different
versions of the model have been developed and applied to evaluate the role of
natural and managed ecosystems in the Earth system and the potential impacts of
global environmental change. A comprehensive model description of the new
model version, LPJmL4, is provided in a companion paper
The terrestrial biosphere is a central element in the Earth system
supporting ecosystem functioning and also providing food to human societies.
Dynamic global vegetation models (DGVMs) have been developed and used to
study biosphere dynamics under climate and land use change. LPJmL4 is a
DGVM with managed land that has been developed to investigate the potential
impacts of climate change on the terrestrial biosphere, including natural and
managed ecosystems, and is now described in full detail in the companion
paper
LPJmL4 spans a wide range of processes (from biogeochemical to ecological
aspects, from leaf-level photosynthesis to biome composition) and combines
natural ecosystems, terrestrial water cycling, and managed ecosystems in one
consistent framework. As such, it is increasingly applied for cross-sectoral
studies, such as the quantification of planetary boundaries
In the following we describe in detail the model benchmarking scheme employed here, which allows for a consistent evaluation of processes simulated by LPJmL4 at seasonal and annual resolution and at spatial scales from site level (using e.g. eddy flux measurements for comparison) to global level (using e.g. remote sensing products). The evaluation spans the time period from 1901 to 2011. The benchmarking analysis also considers results from different model set-ups and previous model versions in order to demonstrate advancements achieved with the current LPJmL4 version and the sensitivity of results to individual new modules.
As described in LPJmL4-GSI-GlobFIRM is a simulation with all standard model features enabled
as used in LPJmL4-GSI-GlobFIRE-PNV is the same, but for potential natural vegetation (PNV)
to evaluate the role of managed land in global pattern and processes. This model
experiment mimics the original LPJ model (i.e. without agriculture) but with improved phenology. LPJmL4-NOGSI-GlobFIRM is a simulation with land use, permafrost dynamics, and
the simplified fire model, but without the GSI phenology for testing the sole
effect of the GSI phenology. Instead of the GSI phenology, here we use the original
phenology model LPJmL4-NOGSI-NOPERM-GlobFIRM is a simulation with land use and the simplified
fire model but without permafrost and without the GSI phenology. This model experiment
mimics the original LPJmL 3.0 model with the LPJ core LPJmL4-GSI-SPITFIRE is a simulation set-up as LPJmL4-GSI-GlobFIRM but with the
process-based fire model (SPITFIRE;
Following
We compare simulated vegetation cover to the ISLSCP II vegetation continuous
fields of
To evaluate the model's capacity to capture global-scale, intra- and
inter-annual fluctuations of atmospheric CO
Model-independent reference data for carbon stocks and fluxes are available
from
River discharge measurements are taken from theArcticNET
(
Evapotranspiration fluxes are taken from the FLUXNET database
(
The irrigation withdrawal and consumption data that we compare to are from other modelling approaches. Nonetheless, human water use for irrigation is an important component in the terrestrial water cycle and we discuss modelled LPJmL4 estimates in comparison to other model-based estimates, acknowledging the limitation of this comparison and addressing different sources of uncertainty.
For the evaluation of simulated permafrost dynamics, we use the measured thaw
depth data from 131 stations of the Circumpolar Active Layer Monitoring
(CALM) station dataset (
For the evaluation of simulated fire dynamics, we employ data on fractional
area burnt from the Global Fire Emissions Database GFED4 version 4 (GFED4;
Data on the fraction of absorbed photosynthetically active radiation (FAPAR)
are derived from three different satellite datasets to account for
differences between datasets for model evaluation (see
Table
Detailed data on crop growth and productivity are available for individual
sentinel sites
To evaluate the accuracy of the simulated rain-fed sowing dates, we use the
global dataset of growing areas and growing periods, MIRCA2000
We employ Taylor diagrams
For global gridded reference datasets, such as for carbon stocks, we show
spatial patterns in maps and aggregations as latitudinal means and quantify
overall differences as a spatial correlation analysis over all grid cells
(see Table
Evaluation metrics used in this study.
Note:
Overview of variables and measures used for the evaluation of LPJmL4 local scale.
Centred root mean square error (CRMSE).
To envisage the degree of agreement between simulated (LPJmL4) and observed
(MIRCA2000) sowing dates, we follow
In the following we compare the standard version LPJmL4, which refers to the
experiment LPJmL4-GSI-GlobFIRM. In the case of the other experiments we refer to
the names defined in Sect.
LPJmL4 reproduces the observed vegetation distribution better than the random
model (Table
Comparison metric scores for LPJmL4 simulations against observations
of fractional vegetation cover data from International Satellite Land-Surface
Climatology Project (ISLSCP) II vegetation continuous field (VCF)
MM suggested by
Comparison of the atmospheric CO
Comparison of the atmospheric CO
LPJmL4 reproduces the observed long-term and seasonal dynamics of
atmospheric CO
Further analysis shows that the standard set-up (LPJmL4-GSI-GlobFIRM) can best
produce the mean seasonal cycle in MLO, whereas the version that omits land
use (LPJmL4-GSI-GlobFIRM-PNV) performs slightly better than this in BRW
(Fig.
Net ecosystem exchange rate measured at eddy flux towers:
Overview of variables evaluating LPJmL4 showing measures and references at the global scale.
Normalized mean error (NME) and normalized mean square error
(NMSE) as suggested by
We evaluate the model performance of simulated NEE from LPJmL4 for temporal and
spatial variation in NEE data from eddy flux measurements using Taylor
diagrams
The spatial correlation between simulated and observation-based estimates of
SOC by
Evaluation of vegetation carbon
The comparison of simulated and observation-based assessments of vegetation
carbon show a good spatial correlation (
The maps
Figure
The global estimation of 123.7
The site data comparison to
Comparison of satellite-derived ecosystem respiration with that simulated by
LPJmL4 reveals similar spatial patterns (Figs.
Ecosystem respiration (
The spatial distribution of evapotranspiration in LPJmL4 shows a very similar
pattern to that estimated by
Evaporation rate measured at eddy flux towers:
Comparison of simulated discharge with 287 gauges provided by
ArcticNET (
Discharge simulated by earlier LPJmL versions was previously evaluated in several
studies, also in comparison with other global hydrological and land surface
models
The evaluation at the global aggregation (computed for all stations and then
averaged) shows very high agreement between observed and modelled discharge
(see Table
Global estimates of irrigation water withdrawal (
Simulated irrigation efficiencies are difficult to compare with observations
due to inhomogeneous definitions and field measurement problems. Yet, in
Table S1 in the Supplement we relate our results to comparable literature.
Our simulations meet the indicative estimates of
The current permafrost distribution and the active layer thickness
(Fig.
Observed and simulated permafrost distribution and active layer
thickness.
Simulated fractional area burnt is largest in the seasonal dry tropics and
temperate regions in all model versions and smallest in cold or wet
environments (Fig. S72). However, maximum fractional burnt area does not
exceed 0.0625 in tropical and subtropical savanna and shrubland areas when
the GlobFIRM model is applied. It is comparable to GFED4 and CCI estimates
only in South America, while in other tropical regions GFED4
Both fire model approaches simulate a comparable latitudinal distribution of
biomass starting from the wet tropics towards dry and colder areas in the
north and south. Both model versions simulate comparable values in the wet
tropics around the Equator and capture the gradient to seasonal dry tropics
in the north (until 10
The modelling errors in fractional area burnt compensate in different ways in
each fire model. SPITFIRE simulates global biomass burning values of
2.7
Evaluations against multiple satellite datasets of FAPAR have already shown
that LPJmL-GSI can reproduce the seasonality of FAPAR and the
inter-annual variability and trends well at the start and end of the growing season
within observational uncertainties
Evaluation of FAPAR for different data sources: MODIS
LPJmL4 reproduces the global patterns of annual peak FAPAR
(Fig.
FAPAR mean annual peak comparison with three different remote sensing products.
LPJmL4 overestimates albedo in all regions (Fig. S74). The temporal dynamic of
snow-free albedo was reproduced well in cold steppes (climate region BSk) and
in boreal regions (climate regions D
The evaluation of simulated crop growth and yield can be assessed at
individual sites if the model is used as a point model as in different model
intercomparison simulations
Evaluation of simulated yield variability for wheat
Map of simulated biomass yields by LPJmL4 from rain-fed
herbaceous
The agreement between simulated and observed yields is not only dependent on
model performance, but also on the aggregation mask used
For the purpose of this evaluation, irrigated and rain-fed biomass plants were
simulated to grow globally wherever biophysical conditions allow for sustained
growth. The averaged simulated yields for the 16-year period (1994–2009)
were compared to reported biomass yields of switchgrass,
Indices of agreement between simulated (LPJmL4) and observed (MIRCA2000) sowing dates.
Mean absolute error (ME) and the Willmott coefficient of agreement (W).
Evaluation of sowing dates for wheat. From
The average mean error (ME) for all crops globally is smaller than
2 months, with the exception of pulses (Table
There are several reasons for these disagreements between sowing dates
simulated solely using climate data and the global crop calendar; please see
The comparison to the global crop calendar, however, shows that close agreement between simulated and observed sowing dates can be achieved with purely climate-driven rules for large parts of the Earth for wheat, rice, maize, millet, soybean, and sunflower, as well as for pulses and groundnut in temperate regions. For about 75 % of the global cropping area the difference between simulated and observed sowing dates is 2 months; with the exception of cassava and rapeseed, 80 % of the crop area displays a difference of only 1 month, which is the minimum possible difference as the crop calendar reports monthly sowing dates.
This article provides a comprehensive evaluation of the now launched version
4.0 of the LPJmL DGVM that includes an operational representation of
agriculture. Unique in its combination of features, the LPJmL4 model enables
the simulation of carbon and water fluxes linked to the dynamics of both natural
and agricultural vegetation in a single, internally consistent framework. We
show that the model has great strength in reproducing carbon fluxes,
especially for NBP on the global scale and NEE on the local scale. But we are
also able to show that water fluxes match well with other estimates. Both
carbon and water fluxes are the link to many ecosystem processes that the
model represents and therefore are very important for the understanding of
its interrelation. In the agriculture sector we conclude that in regions
with a strong weather signal the model is able to match annual yield
variability. Nevertheless, in highly managed countries yield variability is not
well reproduced by the LPJmL4 model. This can be explained by the absence of a
management module in the model. By following suggestions for objective
intercomparative benchmarking systems of multiple models with dedicated
software
Pending major model improvements – anticipated as part of forthcoming LPJmL
versions – are the incorporation of a scheme for calculating groundwater
recharge and storage, the representation of nitrogen cycling for both natural
and agricultural landscapes, consideration of ozone effects on plants
The model code of LPJmL4 is publicly available through
PIK's gitlab server at
The authors declare that they have no conflict of interest.
This study was supported by the German Federal Ministry of Education and
Research (BMBF) project “PalMod 2.3 Methankreislauf, Teilprojekt 2 Modellierung
der Methanemissionen von Feucht- und Permafrostgebieten mit Hilfe von
LPJmL” (FKZ 01LP1507C). Matthias Forkel was funded by the TU Wien
Wissenschaftspreis 2015 awarded to Wouter Dorigo. This work used eddy
covariance data acquired and shared by the