scholarly journals Interactive Impacts of Fire and Vegetation Dynamics on Global Carbon and Water Budgets using Community Land Model version 4.5

2018 ◽  
Author(s):  
Hocheol Seo ◽  
Yeonjoo Kim
Keyword(s):  
Land Surface ◽  
Vegetation Dynamics ◽  
Carbon Sink ◽  
Vegetation Type ◽  
Water Fluxes ◽  
Carbon Sinks ◽  
Terrestrial Carbon ◽  
Moisture Profile ◽  
Community Land Model ◽  
Model Version

Abstract. Fire plays an important role in terrestrial ecosystems. The burning of biomass affects carbon and water fluxes and the distribution of vegetation. To understand the effect of the interactive processes of fire and ecological succession on land surface carbon and water fluxes, this study utilized the Community Land Model version 4.5 to conduct a series of experiments that included and excluded fire and dynamic vegetation processes. Results of the experiments that excluded dynamic vegetation showed a global increase in net ecosystem production (NEP) in post-fire regions, which has been shown in previous studies with the similar modeling practices. However, inclusion of dynamic vegetation revealed a fire-induced decrease in NEP in some regions. Additionally, the carbon sink in post-fire regions reduced when the dominant vegetation type was changed from trees to grasses. This study shows that inclusion of dynamic vegetation enhances carbon emissions from fire by reducing terrestrial carbon sinks; however, this effect is somewhat mitigated by the increase in terrestrial carbon sinks when dynamic vegetation is not used. Results also show that fire-induced changes in vegetation modify the soil moisture profile because grasslands are more dominant in post-fire regions; this results in less moisture within top soil layers compared to non-burned regions, even though transpiration is reduced overall. These findings are different from those of previous fire model evaluations, that ignore vegetation dynamics, and thus highlight the importance of interactive processes between fire and vegetation dynamics, particularly when evaluating recent model developments with respect to fire and vegetation dynamics.

scholarly journals Interactive impacts of fire and vegetation dynamics on global carbon and water budget using Community Land Model version 4.5

2019 ◽  
Vol 12 (1) ◽  
pp. 457-472 ◽  
Author(s):  
Hocheol Seo ◽  
Yeonjoo Kim
Keyword(s):  
Vegetation Dynamics ◽  
Vegetation Type ◽  
Soil Layer ◽  
Terrestrial Ecosystems ◽  
Water Fluxes ◽  
Moisture Profile ◽  
Community Land Model ◽  
Model Version ◽  
Global Increase ◽  
Induced Changes

Abstract. Fire plays an important role in terrestrial ecosystems. The burning of biomass affects carbon and water fluxes and vegetation distribution. To understand the effect of interactive processes of fire and ecological succession on surface carbon and water fluxes, this study employed the Community Land Model version 4.5 to conduct a series of experiments that included and excluded fire and dynamic vegetation processes. Results of the experiments that excluded the vegetation dynamics showed a global increase in net ecosystem production (NEP) in post-fire regions, whereas the inclusion of vegetation dynamics revealed a fire-induced decrease in NEP in some regions, which was depicted when the dominant vegetation type was changed from trees to grass. Carbon emissions from fires are enhanced by reduction in NEP when vegetation dynamics are considered; however, this effect is somewhat mitigated by the increase in NEP when vegetation dynamics are not considered. Fire-induced changes in vegetation modify the soil moisture profile because grasslands are more dominant in post-fire regions. This results in less moisture within the top soil layer than that in unburned regions, even though transpiration is reduced overall. These findings are different from those of previous fire model evaluations that ignored vegetation dynamics and thus highlight the importance of interactive processes between fires and vegetation dynamics in evaluating recent model developments.

scholarly journals Sensitivity Study of Four Land Surface Schemes in the WRF Model

2010 ◽  
Vol 2010 ◽  
pp. 1-11 ◽  
Author(s):  
Jiming Jin ◽  
Norman L. Miller ◽  
Nicole Schlegel
Keyword(s):  
Land Surface ◽  
Wrf Model ◽  
Sensitivity Study ◽  
Surface Processes ◽  
Maximum Temperature ◽  
Weather Research And Forecasting ◽  
Land Surface Processes ◽  
Community Land Model ◽  
Model Version ◽  
Close Relationship

The Weather Research and Forecasting (WRF) model version 3.0 developed by the National Center for Atmospheric Research (NCAR) includes three land surface schemes: the simple soil thermal diffusion (STD) scheme, the Noah scheme, and the Rapid Update Cycle (RUC) scheme. We have recently coupled the sophisticated NCAR Community Land Model version 3 (CLM3) into WRF to better characterize land surface processes. Among these four land surface schemes, the STD scheme is the simplest in both structure and process physics. The Noah and RUC schemes are at the intermediate level of complexity. CLM3 includes the most sophisticated snow, soil, and vegetation physics among these land surface schemes. WRF simulations with all four land surface schemes over the western United States (WUS) were carried out for the 1 October 1995 through 30 September 1996. The results show that land surface processes strongly affect temperature simulations over the (WUS). As compared to observations, WRF-CLM3 with the highest complexity level significantly improves temperature simulations, except for the wintertime maximum temperature. Precipitation is dramatically overestimated by WRF with all four land surface schemes over the (WUS) analyzed in this study and does not show a close relationship with land surface processes.

scholarly journals Sub-grid scale representation of vegetation in global land surface schemes: implications for estimation of the terrestrial carbon sink

2013 ◽  
Vol 10 (10) ◽  
pp. 16003-16041 ◽  
Author(s):  
J. R. Melton ◽  
V. K. Arora
Keyword(s):  
Water Balance ◽  
Environmental Conditions ◽  
Land Surface ◽  
Carbon Balance ◽  
Spatial Representation ◽  
Carbon Sink ◽  
Terrestrial Ecosystem ◽  
Terrestrial Carbon ◽  
Global Land ◽  
Terrestrial Carbon Sink

Abstract. Terrestrial ecosystem models commonly represent vegetation in terms of plant functional types (PFTs) and use their vegetation attributes in calculations of the energy and water balance and to investigate the terrestrial carbon cycle. To accomplish these tasks, two approaches for PFT spatial representation are widely used: "composite" and "mosaic". The impact of these two approaches on the global carbon balance has been investigated with the Canadian Terrestrial Ecosystem Model (CTEM v 1.2) coupled to the Canadian Land Surface Scheme (CLASS v 3.6). In the composite (single-tile) approach, the vegetation attributes of different PFTs present in a grid cell are aggregated and used in calculations to determine the resulting physical environmental conditions (soil moisture, soil temperature, etc.) that are common to all PFTs. In the mosaic (multi-tile) approach, energy and water balance calculations are performed separately for each PFT tile and each tile's physical land surface environmental conditions evolve independently. Pre-industrial equilibrium CLASS-CTEM simulations yield global totals of vegetation biomass, net primary productivity, and soil carbon that compare reasonably well with observation-based estimates and differ by less than 5% between the mosaic and composite configurations. However, on a regional scale the two approaches can differ by > 30%, especially in areas with high heterogeneity in land cover. Simulations over the historical period (1959–2005) show different responses to evolving climate and carbon dioxide concentrations from the two approaches. The cumulative global terrestrial carbon sink estimated over the 1959–2005 period (excluding land use change (LUC) effects) differs by around 5% between the two approaches (96.3 and 101.3 Pg, for the mosaic and composite approaches, respectively) and compares well with the observation-based estimate of 82.2 ± 35 Pg C over the same period. Inclusion of LUC causes the estimates of the terrestrial C sink to differ by 15.2 Pg C (16%) with values of 95.1 and 79.9 Pg C for the mosaic and composite approaches, respectively. Spatial differences in simulated vegetation and soil carbon and the manner in which terrestrial carbon balance evolves in response to LUC, in the two approaches, yields a substantially different estimate of the global land carbon sink. These results demonstrate that the spatial representation of vegetation has an important impact on the model response to changing climate, atmospheric CO2 concentrations, and land cover.

scholarly journals A machine learning approach to emulation and biophysical parameter estimation with the Community Land Model, version 5

2020 ◽  
Vol 6 (2) ◽  
pp. 223-244
Author(s):  
Katherine Dagon ◽  
Benjamin M. Sanderson ◽  
Rosie A. Fisher ◽  
David M. Lawrence
Keyword(s):  
Machine Learning ◽  
Optimal Parameter ◽  
Learning Approach ◽  
Water Fluxes ◽  
Biophysical Parameters ◽  
Biophysical Parameter ◽  
Community Land Model ◽  
Model Version ◽  
Machine Learning Approach ◽  
Parameter Values

Abstract. Land models are essential tools for understanding and predicting terrestrial processes and climate–carbon feedbacks in the Earth system, but uncertainties in their future projections are poorly understood. Improvements in physical process realism and the representation of human influence arguably make models more comparable to reality but also increase the degrees of freedom in model configuration, leading to increased parametric uncertainty in projections. In this work we design and implement a machine learning approach to globally calibrate a subset of the parameters of the Community Land Model, version 5 (CLM5) to observations of carbon and water fluxes. We focus on parameters controlling biophysical features such as surface energy balance, hydrology, and carbon uptake. We first use parameter sensitivity simulations and a combination of objective metrics including ranked global mean sensitivity to multiple output variables and non-overlapping spatial pattern responses between parameters to narrow the parameter space and determine a subset of important CLM5 biophysical parameters for further analysis. Using a perturbed parameter ensemble, we then train a series of artificial feed-forward neural networks to emulate CLM5 output given parameter values as input. We use annual mean globally aggregated spatial variability in carbon and water fluxes as our emulation and calibration targets. Validation and out-of-sample tests are used to assess the predictive skill of the networks, and we utilize permutation feature importance and partial dependence methods to better interpret the results. The trained networks are then used to estimate global optimal parameter values with greater computational efficiency than achieved by hand tuning efforts and increased spatial scale relative to previous studies optimizing at a single site. By developing this methodology, our framework can help quantify the contribution of parameter uncertainty to overall uncertainty in land model projections.

scholarly journals Coupling the Community Land Model version 5.0 to the parallel data assimilation framework PDAF: Description and applications

2021 ◽  
Author(s):  
Lukas Strebel ◽  
Heye Bogena ◽  
Harry Vereecken ◽  
Harrie-Jan Hendricks Franssen
Keyword(s):  
Data Assimilation ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Land Surface ◽  
Land Surface Models ◽  
Community Land Model ◽  
Model Version ◽  
Parallel Data ◽  
Surface Models

Abstract. Land surface models are important for improving our understanding of the earth system. They are continuously improving and becoming more accurate in describing the varied surface processes, e.g. the Community Land Model version 5 (CLM5). Similarly, observational networks and remote sensing operations are increasingly providing more and higher quality data. For the optimal combination of land surface models and observation data, data assimilation techniques have been developed in the past decades that incorporate observations to update modeled states and parameters. The Parallel Data Assimilation Framework (PDAF) is a software environment that enables ensemble data assimilation and simplifies the implementation of data assimilation systems in numerical models. In this paper, we present the further development of the PDAF to enable its application in combination with CLM5. This novel coupling adapts the optional CLM5 ensemble mode to enable integration of PDAF filter routines while keeping changes to the pre-existing parallel communication infrastructure to a minimum. Soil water content observations from an extensive in-situ measurement network in the Wüstebach catchment in Germany are used to illustrate the application of the coupled CLM5+PDAF system. The results show overall reductions in root mean square error of soil water content from 7 % up to 35 % compared to simulations without data assimilation. We expect the coupled CLM5+PDAF system to provide a basis for improved regional to global land surface modelling by enabling the assimilation of globally available observational data.

Impact of ozone on the carbon, heat and water fluxes in a land-surface model

2021 ◽  
Author(s):  
Noel Clancy ◽  
William Collins ◽  
Pier Luigi Vidale ◽  
Gerd Folberth
Keyword(s):  
Land Surface ◽  
Hydrological Cycle ◽  
Carbon Sink ◽  
Land Surface Model ◽  
Surface Model ◽  
Water Fluxes ◽  
Ozone Sensitivity ◽  
Energy Cycle ◽  
Earth’S Climate ◽  
Quantitative Impact

<p>Carbon uptake by land ecosystems is a hugely important carbon sink for the Earth's climate. Plants uptake carbon dioxide from the atmosphere via pores on the surface of their leaves called stomata. However, ozone can also be taken up by plants in this way leading to damage to the plant, a decrease in its growth rate and an impact on the carbon cycle. Ozone damage to plants also modifies other processes within the ecosystem such as transpiration and respiration rates, thereby effecting the hydrological cycle and energy cycle. The Joint UK Land and Environment Simulator (JULES) land-surface model includes ozone sensitivity parameters for all its vegetation cover (plant functional types). Our recent results from JULES experiments at FLUXNET sites show that ozone reduces photosynthesis and suppresses transpiration, thereby impacting the carbon, heat and water fluxes in JULES. Furthermore, we identify differences in a quantitative impact on leaf phenology.</p>

scholarly journals The Use of Remote Sensing-Based ET Estimates to Improve Global Hydrological Simulations in the Community Land Model Version 5.0

2021 ◽  
Vol 13 (21) ◽  
pp. 4460
Author(s):  
Dayang Wang ◽  
Dagang Wang ◽  
Chongxun Mo
Keyword(s):  
Remote Sensing ◽  
Water Supply ◽  
Bias Correction ◽  
Land Surface ◽  
Land Surface Models ◽  
Community Land Model ◽  
Model Version ◽  
Correction Effect ◽  
Surface Models ◽  
Global Land

Terrestrial evapotranspiration (ET) is a critical component of water and energy cycles, and improving global land evapotranspiration is one of the challenging works in the development of land surface models (LSMs). In this study, we apply a bias correction approach into the Community Land Model version 5.0 (CLM5) globally by utilizing the remote sensing-based ET dataset. Results reveal that the correction approach can alleviate both overestimation and underestimation of ET by CLM5 over the globe. The adjustment to overestimation is generally effective, whereas the effectiveness for underestimation is determined by the ET regime, namely water-limited or energy-limited. In the areas with abundant precipitation, the underestimation is effectively corrected by increasing ET without the water supply limit. In areas with rare precipitation, however, increasing ET is limited by water supply, which leads to an undesirable correction effect. Compared with the ET simulated by CLM5, the bias correction approach can reduce the global-averaged relative bias (RB) and the root mean square error (RMSE) by 51.8% and 65.9% against Global Land Evaporation Amsterdam Model (GLEAM) ET data, respectively. Meanwhile, the correlation coefficient (CC) can also be improved from 0.93 to 0.98. Continentally, the most substantial ET improvement occurs in Asia, with the RB and RMSE decreased by 69.7% (from 7.04% to 2.14%) and 70.2% (from 0.312 mm day−1 to 0.093 mm day−1, equivalent to from 114 mm year−1 to 34 mm year−1), and the CC increased from 0.92 to 0.99, respectively. Consequently, benefiting from the improvement of ET, the simulations of runoff and soil moisture are also improved over the globe and each of the six continents, and the improvement varies with region. This study demonstrates that the use of satellite-based ET products is beneficial to hydrological simulations in land surface models over the globe.

scholarly journals Nested atmospheric inversion for the terrestrial carbon sources and sinks in China

2013 ◽  
Vol 10 (1) ◽  
pp. 1177-1205
Author(s):  
F. Jiang ◽  
H. Wang ◽  
J. M. Chen ◽  
W. Ju ◽  
A. Ding
Keyword(s):  
Atmospheric Transport ◽  
Southern Oscillation ◽  
Carbon Sink ◽  
Carbon Sources ◽  
Measurement Data ◽  
Strong Relationship ◽  
Horizontal Resolution ◽  
Carbon Sinks ◽  
Terrestrial Carbon ◽  
Atmospheric Inversion

Abstract. In this study, we establish a~nested atmospheric inversion system with a focus on China using the Bayes theory. The global surface is separated into 43 regions based on the 22 TransCom large regions, with 13 small regions in China. Monthly CO2 concentrations from 130 GlobalView sites and a Hong Kong site are used in this system. The core component of this system is atmospheric transport matrix, which is created using the TM5 model with a horizontal resolution of 3° × 2°. The net carbon fluxes over the 43 global land and ocean regions are inverted for the period from 2002 to 2009. The inverted global terrestrial carbon sinks mainly occur in Boreal Asia, South and Southeast Asia, eastern US and southern South America (SA). Most China areas appear to be carbon sinks, with strongest carbon sinks located in Northeast China. From 2002 to 2009, the global terrestrial carbon sink has an increasing trend, with the lowest carbon sink in 2002. The inter-annual variation (IAV) of the land sinks shows remarkable correlation with the El Niño Southern Oscillation (ENSO). However, no obvious trend is found for the terrestrial carbon sinks in China. The IAVs of carbon sinks in China show strong relationship with drought and temperature. The mean global and China terrestrial carbon sinks over the period 2002–2009 are −3.15 ± 1.48 and −0.21 ± 0.23 Pg C yr−1, respectively. The uncertainties in the posterior carbon flux of China are still very large, mostly due to the lack of CO2 measurement data in China.

Fire-vegetation feedbacks in JULES-INFERNO

2020 ◽  
Author(s):  
Chantelle Burton ◽  
Richard Betts ◽  
Chris Jones ◽  
Douglas Kelley
Keyword(s):  
Land Use ◽  
Land Surface ◽  
Carbon Sink ◽  
Land Surface Model ◽  
Burned Area ◽  
Surface Model ◽  
Terrestrial Carbon ◽  
Terrestrial Carbon Sink ◽  
The Tropics ◽  
Carbon Stores

<p>Fire has an important impact on the terrestrial carbon cycle, affecting the growth and distribution of vegetation, and altering carbon stores in vegetation and soils. This is further complicated by the interaction with people, through land-use change, ignitions and fire management. This work presents the latest results from the recently coupled JULES-INFERNO fire enabled land surface model, and the interaction of fire, dynamic vegetation and varying land use. The results of historical and present-day global simulations are evaluated using observations of burned area and emissions, and through use of tools such as ilamb. The model performs well globally compared to observations, and improves the simulation of vegetation especially in the tropics. The model is also used to address how fire may change under different climate scenarios, including El Niño events, and future simulations of climate change. Results show that burned area increases in some areas with El Niño conditions such as those of 2015/16, especially in South America where a 13% increase in burned area and emitted carbon is simulated. This negatively impacts carbon uptake in this region, and reduces the terrestrial carbon sink.</p>

scholarly journals Incorporating remote sensing ET into Community Land Model version 4.5

2017 ◽  
Author(s):  
Dagang Wang ◽  
Guiling Wang ◽  
Dana T. Parr ◽  
Weilin Liao ◽  
Youlong Xia ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Correction Method ◽  
Energy Budgets ◽  
Land Surface Models ◽  
Community Land Model ◽  
Model Version ◽  
Surface Models ◽  
Time Invariant ◽  
The Relationship

Abstract. Land surface models bear substantial biases in simulating surface water and energy budgets despite of the continuous development and improvement of model parameterizations. To reduce model biases, Parr et al. (2015) proposed a method incorporating satellite-based evapotranspiration (ET) products into land surface models. Here we apply this method to the Community Land Model version 4.5 (CLM4.5) and test its performance over the conterminous US (CONUS). We first calibrate a relationship between the observational ET from the Global Land Evaporation Amsterdam Model (GLEAM) product and the model ET from CLM4.5, and assume that this relationship holds beyond the calibration period. During the validation or application period, a simulation using the default CLM4.5 (CLM) is conducted first, and its output is combined with the calibrated observational-vs-model ET relationship to derive a corrected ET; an experiment (CLMET) is then conducted in which the model-generated ET is overwritten using the corrected ET. Using the observations of ET, runoff, and soil moisture content as benchmarks, we demonstrate that CLMET greatly reduces the biases existing in CLM. The improvement differs with region, being more significant in eastern CONUS than western CONUS, with the most striking improvement over the southeast CONUS. This regional dependence reflects primarily the regional dependence in the degree to which the relationship between observational and model ET remains time-invariant (a fundamental hypothesis of the Parr et al. method). The bias correction method provides an alternative way to improve the performance of land surface models, which could lead to more realistic drought evaluations with improved ET and soil moisture estimates.

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