scholarly journals Identification of key parameters controlling demographically structured vegetation dynamics in a land surface model: CLM4.5(FATES)

2019 ◽  
Vol 12 (9) ◽  
pp. 4133-4164 ◽  
Author(s):  
Elias C. Massoud ◽  
Chonggang Xu ◽  
Rosie A. Fisher ◽  
Ryan G. Knox ◽  
Anthony P. Walker ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Land Surface ◽  
Canopy Structure ◽  
Surface Model ◽  
Model Parameters ◽  
Growth Strategies ◽  
Climate Conditions ◽  
Community Land Model ◽  
Vegetation Models ◽  
The Impact

Abstract. Vegetation plays an important role in regulating global carbon cycles and is a key component of the Earth system models (ESMs) that aim to project Earth's future climate. In the last decade, the vegetation component within ESMs has witnessed great progress from simple “big-leaf” approaches to demographically structured approaches, which have a better representation of plant size, canopy structure, and disturbances. These demographically structured vegetation models typically have a large number of input parameters, and sensitivity analysis is needed to quantify the impact of each parameter on the model outputs for a better understanding of model behavior. In this study, we conducted a comprehensive sensitivity analysis to diagnose the Community Land Model coupled to the Functionally Assembled Terrestrial Simulator, or CLM4.5(FATES). Specifically, we quantified the first- and second-order sensitivities of the model parameters to outputs that represent simulated growth and mortality as well as carbon fluxes and stocks for a tropical site with an extent of 1×1∘. While the photosynthetic capacity parameter (Vc,max25) is found to be important for simulated carbon stocks and fluxes, we also show the importance of carbon storage and allometry parameters, which determine survival and growth strategies within the model. The parameter sensitivity changes with different sizes of trees and climate conditions. The results of this study highlight the importance of understanding the dynamics of the next generation of demographically enabled vegetation models within ESMs to improve model parameterization and structure for better model fidelity.

scholarly journals Identification of key parameters controlling demographicallystructured vegetation dynamics in a Land Surface Model [CLM4.5(ED)]

2019 ◽  
Author(s):  
Elias C. Massoud ◽  
Chonggang Xu ◽  
Rosie Fisher ◽  
Ryan Knox ◽  
Anthony Walker ◽  
...  
Keyword(s):  
Land Surface ◽  
Canopy Structure ◽  
Land Surface Model ◽  
Carbon Stocks ◽  
Surface Model ◽  
Model Parameters ◽  
Growth Strategies ◽  
Community Land Model ◽  
Vegetation Models ◽  
The Impact

Abstract. Vegetation plays a key role in regulating global carbon cycles and is a key component of the Earth System Models (ESMs) aimed to project Earth's future climates. In the last decade, the vegetation component within ESMs has witnessed great progresses from simple 'big-leaf' approaches to demographically-structured approaches, which has a better representation of plant size, canopy structure, and disturbances. The demographically-structured vegetation models are typically controlled by a large number of parameters, and sensitivity analysis is generally needed to quantify the impact of each parameter on the model outputs for a better understanding of model behaviors. In this study, we use the Fourier Amplitude Sensitivity Test (FAST) to diagnose the Community Land Model coupled to the Ecosystem Demography Model, or CLM4.5(ED). We investigate the first and second order sensitivities of the model parameters to outputs that represent simulated growth and mortality as well as carbon fluxes and stocks. While the photosynthetic capacity parameter Vc,max25 is found to be important for simulated carbon stocks and fluxes, we also show the importance of carbon storage and allometry parameters, which are shown here to determine vegetation demography and carbon stocks through their impacts on survival and growth strategies. The results of this study highlights the importance of understanding the dynamics of the next generation of demographically-enabled vegetation models within ESMs toward improved model parameterization and model structure for better model fidelity.

scholarly journals The Impact of Error Accounting in a Bayesian Approach to Calibrating Modeled Turbulent Fluxes in an Open-Canopy Forest

2017 ◽  
Vol 18 (7) ◽  
pp. 2029-2042
Author(s):  
Tony E. Wong ◽  
William Kleiber ◽  
David C. Noone
Keyword(s):  
Markov Chain ◽  
Land Surface ◽  
Hydrological Modeling ◽  
Soil Layer ◽  
Turbulent Fluxes ◽  
Surface Model ◽  
Model Parameters ◽  
Top Soil ◽  
The Impact ◽  
Calibration Approach

Abstract Land surface models are notorious for containing many parameters that control the exchange of heat and moisture between land and atmosphere. Properly modeling the partitioning of total evapotranspiration (ET) between transpiration and evaporation is critical for accurate hydrological modeling, but depends heavily on the treatment of turbulence within and above canopies. Previous work has constrained estimates of evapotranspiration and its partitioning using statistical approaches that calibrate land surface model parameters by assimilating in situ measurements. These studies, however, are silent on the impacts of the accounting of uncertainty within the statistical calibration framework. The present study calibrates the aerodynamic, leaf boundary layer, and stomatal resistance parameters, which partially control canopy turbulent exchange and thus the evapotranspiration flux partitioning. Using an adaptive Metropolis–Hastings algorithm to construct a Markov chain of draws from the joint posterior distribution of these resistance parameters, an ensemble of model realizations is generated, in which latent and sensible heat fluxes and top soil layer temperature are optimized. A set of five calibration experiments demonstrate that model performance is sensitive to the accounting of various sources of uncertainty in the field observations and model output and that it is critical to account for model structural uncertainty. After calibration, the modeled fluxes and top soil layer temperature are largely free from bias, and this calibration approach successfully informs and characterizes uncertainty in these parameters, which is essential for model improvement and development. The key points of this paper are 1) a Markov chain Monte Carlo calibration approach successfully improves modeled turbulent fluxes; 2) ET partitioning estimates hinge on the representation of uncertainties in the model and data; and 3) despite these inherent uncertainties, constrained posterior estimates of ET partitioning emerge.

scholarly journals Simulating damage for wind storms in the land surface model ORCHIDEE-CAN (revision 4262)

2017 ◽  
Author(s):  
Yi-Ying Chen ◽  
Barry Gardiner ◽  
Ferenc Pasztor ◽  
Kristina Blennow ◽  
James Ryder ◽  
...  
Keyword(s):  
Land Surface ◽  
Canopy Structure ◽  
Land Surface Model ◽  
Model Error ◽  
Small Scale ◽  
Surface Model ◽  
Model Parameters ◽  
Storm Damage ◽  
Wind Speeds ◽  
Observed Damage

Abstract. Earth System Models (ESMs) are currently the most advanced tools with which to study the interactions between humans, ecosystem productivity and the climate. The inclusion of storm damage in ESMs has long been hampered by their big-leaf approach which ignores the canopy structure information that is required for process-based wind throw modelling. Recently the big-leaf assumptions in the large scale land surface model ORCHIDEE-CAN were replaced by a three dimensional description of the canopy structure. This opened the way to the integration of the processes from the small-scale wind damage risk model ForestGALES into ORCHIDEE-CAN. The resulting enhanced model was completed by an empirical function to convert the difference between actual and critical wind speeds into forest damage. This new version of ORCHIDEE-CAN was parametrized over Sweden. Subsequently, the performance of the model was tested against data for historical storms in Southern Sweden between 1951 and 2010, and South-western France in 2009. In years without big storms, here defined as a storm damaging less than 15 × 106 m3 of wood in Sweden, the model error is 1.62 × 106 m3 which is about 100 % of the observed damage. For years with big storms, such as Gudrun in 2005, the model error increased to 5.05 × 106 m3 which is between 10 % and 50 % of the observed damage. When the same model parameters were used over France, the model reproduced a decrease in leaf area index and an increase in albedo, in accordance with SPOT-VGT and MODIS records following the passing of Cyclone Klaus in 2009. The current version of ORCHIDEE-CAN (revision 4262) is therefore expected to have the capability to capture the dynamics of forest structure due to storm disturbance both at regional and global scales, although the empirical parameters calculating gustiness from the gridded wind fields and storm damage from critical wind speeds may benefit from regional fitting.

scholarly journals Taking off the training wheels: the properties of a dynamic vegetation model without climate envelopes

2015 ◽  
Vol 8 (4) ◽  
pp. 3293-3357 ◽  
Author(s):  
R. A. Fisher ◽  
S. Muszala ◽  
M. Verteinstein ◽  
P. Lawrence ◽  
C. Xu ◽  
...  
Keyword(s):  
Land Surface ◽  
Plant Traits ◽  
Carbon Assimilation ◽  
Life History Strategies ◽  
Costs And Benefits ◽  
Leaf Trait ◽  
Community Land Model ◽  
Plant Trait ◽  
Vegetation Models ◽  
The Impact

Abstract. We describe an implementation of the Ecosystem Demography (ED) concept in the Community Land Model. The structure of CLM(ED) and the physiological and structural modifications applied to the CLM are presented. A major motivation of this development is to allow the prediction of biome boundaries directly from plant physiological traits via their competitive interactions. Here we investigate the performance of the model for an example biome boundary in Eastern North America. We explore the sensitivity of the predicted biome boundaries and ecosystem properties to the variation of leaf properties determined by the parameter space defined by the GLOPNET global leaf trait database. Further, we investigate the impact of four sequential alterations to the structural assumptions in the model governing the relative carbon economy of deciduous and evergreen plants. The default assumption is that the costs and benefits of deciduous vs. evergreen leaf strategies, in terms of carbon assimilation and expenditure, can reproduce the geographical structure of biome boundaries and ecosystem functioning. We find some support for this assumption, but only under particular combinations of model traits and structural assumptions. Many questions remain regarding the preferred methods for deployment of plant trait information in land surface models. In some cases, plant traits might best be closely linked with each other, but we also find support for direct linkages to environmental conditions. We advocate for intensified study of the costs and benefits of plant life history strategies in different environments, and for the increased use of parametric and structural ensembles in the development and analysis of complex vegetation models.

scholarly journals Baseline Evaluation of the Impact of Updates to the MIT Earth System Model on its Model Parameter Estimates

2018 ◽  
Author(s):  
Alex G. Libardoni ◽  
Chris E. Forest ◽  
Andrei P. Sokolov ◽  
Erwan Monier
Keyword(s):  
Land Surface ◽  
Probability Distributions ◽  
Land Surface Model ◽  
Response Surfaces ◽  
Surface Model ◽  
Model Parameters ◽  
Ocean Heat Uptake ◽  
Aerosol Forcing ◽  
Baseline Evaluation ◽  
The Impact

Abstract. For over twenty years, the Massachusetts Institute of Technology Earth System Model (MESM) has been used extensively for climate change research. The model is under continuous development with components being added or updated. To provide transparency in the model development, we perform a baseline evaluation of the newest version by comparing model behavior and properties to the previous model version. In particular, the impacts resulting from updates to the land surface model component and the input forcings used in historical simulations of climate change are investigated. We run an 1800-member ensemble of MESM historical climate simulations where the model parameters that set climate sensitivity, ocean heat uptake, and the net anthropogenic aerosol forcing are systematically varied. By comparing model output to observed patterns of surface temperature changes, the linear trend in the increase in ocean heat content, and upper-air temperature changes, we derive probability distributions for the three model parameters. Furthermore, we run a 372-member ensemble of transient climate simulations where model forcings are held fixed, absent an increase in carbon dioxide concentrations at the rate of 1 % per year. From these runs, we derive a response surface for transient climate response and thermosteric sea level rise as a function of climate sensitivity and ocean heat uptake. We compare the probability distributions and response surfaces derived using the current version of MESM to the preceding version to evaluate the impact of the updated land surface model and forcing suite. We show that the probability distributions shift towards higher climate sensitivities and weaker aerosol forcing in response to the new forcing suite. The climate response surfaces are relatively unchanged between model versions, indicating that the updated land surface model has limited impact on temperature evolution in the model.

scholarly journals The impact of model and rainfall forcing errors on characterizing soil moisture uncertainty in land surface modeling

2012 ◽  
Vol 16 (10) ◽  
pp. 3499-3515 ◽  
Author(s):  
V. Maggioni ◽  
E. N. Anagnostou ◽  
R. H. Reichle
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Parameter Uncertainty ◽  
Land Surface ◽  
Accurate Estimation ◽  
Surface Model ◽  
Model Parameters ◽  
Model Parameter ◽  
Parameter Perturbations ◽  
The Impact

Abstract. The contribution of rainfall forcing errors relative to model (structural and parameter) uncertainty in the prediction of soil moisture is investigated by integrating the NASA Catchment Land Surface Model (CLSM), forced with hydro-meteorological data, in the Oklahoma region. Rainfall-forcing uncertainty is introduced using a stochastic error model that generates ensemble rainfall fields from satellite rainfall products. The ensemble satellite rain fields are propagated through CLSM to produce soil moisture ensembles. Errors in CLSM are modeled with two different approaches: either by perturbing model parameters (representing model parameter uncertainty) or by adding randomly generated noise (representing model structure and parameter uncertainty) to the model prognostic variables. Our findings highlight that the method currently used in the NASA GEOS-5 Land Data Assimilation System to perturb CLSM variables poorly describes the uncertainty in the predicted soil moisture, even when combined with rainfall model perturbations. On the other hand, by adding model parameter perturbations to rainfall forcing perturbations, a better characterization of uncertainty in soil moisture simulations is observed. Specifically, an analysis of the rank histograms shows that the most consistent ensemble of soil moisture is obtained by combining rainfall and model parameter perturbations. When rainfall forcing and model prognostic perturbations are added, the rank histogram shows a U-shape at the domain average scale, which corresponds to a lack of variability in the forecast ensemble. The more accurate estimation of the soil moisture prediction uncertainty obtained by combining rainfall and parameter perturbations is encouraging for the application of this approach in ensemble data assimilation systems.

scholarly journals Estimates of Global Surface Hydrology and Heat Fluxes from the Community Land Model (CLM4.5) with Four Atmospheric Forcing Datasets

2016 ◽  
Vol 17 (9) ◽  
pp. 2493-2510 ◽  
Author(s):  
Aihui Wang ◽  
Xubin Zeng ◽  
Donglin Guo
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Heat Fluxes ◽  
Surface Model ◽  
Atmospheric Forcing ◽  
Surface Hydrology ◽  
Community Land Model ◽  
Global Land ◽  
Ensemble Mean ◽  
The Impact

Abstract Global land surface hydrology and heat fluxes can be estimated by running a land surface model (LSM) driven by the atmospheric forcing dataset. Previous multimodel studies focused on the impact of different LSMs on model results. Here the sensitivity of the Community Land Model, version 4.5 (CLM4.5), results to the atmospheric forcing dataset is documented. Together with the model default global forcing dataset (CRU–NCEP, hereafter CRUNCEP), three newly developed, reanalysis-based, near-surface meteorological datasets (i.e., MERRA, CFSR, and ERA-Interim) with the precipitation adjusted by the Global Precipitation Climatology Project monthly product were used to drive CLM4.5. All four simulations were run at 0.5° × 0.5° grids from 1979 to 2009 with the identical initialization. The simulated monthly surface hydrology variables, fluxes, and the forcing datasets were then evaluated against various observation-based datasets (soil moisture, runoff, snow depth and water equivalent, and flux tower measurements). To partially avoid the mismatch between model gridbox values and point measurements, three approaches were taken. The model simulations based on three newly constructed forcing datasets are overall better than the simulation from CRUNCEP, in particular for soil moisture and snow quantities. The ensemble mean from the CLM4.5 simulations using the four forcing datasets is generally superior to individual simulations, and the ensemble mean latent and sensible heat fluxes over global land (60°S–90°N) are 42.8 and 40.3 W m−2, respectively. The differences in both precipitation and other atmospheric forcing variables (e.g., air temperature and downward solar radiation) contribute to the differences in simulated results. The datasets are available from the authors for further evaluation and for various applications.

scholarly journals Spatial Sensitivity Analysis of Simulated Land Surface Patterns in a Catchment Model Using a Set of Innovative Spatial Performance Metrics

2017 ◽  
Vol 18 (4) ◽  
pp. 1121-1142 ◽  
Author(s):  
Julian Koch ◽  
Gorka Mendiguren ◽  
Gregoire Mariethoz ◽  
Simon Stisen
Keyword(s):  
Sensitivity Analysis ◽  
Spatial Patterns ◽  
Land Surface ◽  
Performance Metrics ◽  
Surface Model ◽  
Model Parameters ◽  
Spatial Sensitivity ◽  
Spatial Performance ◽  
Surface Patterns ◽  
Spatial Sensitivity Analysis

Abstract Distributed hydrological models simulate states and fluxes of water and energy in the terrestrial hydrosphere at each cell. The predicted spatial patterns result from complex nonlinear relationships and feedbacks. Spatial patterns are often neglected during the modeling process, and therefore a spatial sensitivity analysis framework that highlights their importance is proposed. This study features a comprehensive analysis of spatial patterns of actual evapotranspiration (ET) and land surface temperature (LST), with the aim of quantifying the extent to which forcing data and model parameters drive these patterns. This framework is applied on a distributed model [MIKE Système Hydrologique Européen (MIKE SHE)] coupled to a land surface model [Shuttleworth and Wallace–Evapotranspiration (SW-ET)] of a catchment in Denmark. Twenty-two scenarios are defined, each having a simplified representation of a potential driver of spatial variability. A baseline model that incorporates full spatial detail is used to assess sensitivity. High sensitivity can be attested in scenarios where the simulated spatial patterns differ significantly from the baseline. The core novelty of this study is that the analysis is based on a set of innovative spatial performance metrics that enable a reliable spatial pattern comparison. Overall, LST is very sensitive to air temperature and wind speed whereas ET is rather driven by vegetation. Both are sensitive to groundwater coupling and precipitation. The conclusions may be limited to the selected catchment and to the applied modeling system, but the suggested framework is generically relevant for the modeling community. While the applied metrics focus on specific spatial information, they partly exhibit redundant information. Thus, a combination of metrics is the ideal approach to evaluate spatial patterns in models outputs.

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