Use of ISLSCP II data to intercompare and validate the terrestrial net primary production in a land surface model coupled to a general circulation model

2007 ◽  
Vol 112 (D2) ◽  
Author(s):  
Li Dan ◽  
Jinjun Ji ◽  
Yong He
Keyword(s):  
Primary Production ◽  
General Circulation Model ◽  
Land Surface ◽  
General Circulation ◽  
Net Primary Production ◽  
Land Surface Model ◽  
Circulation Model ◽  
Surface Model

scholarly journals Effect of Human-Induced Land Disturbance on Subseasonal Predictability of Near-Surface Variables Using an Atmospheric General Circulation Model

Atmosphere ◽  
2019 ◽  
Vol 10 (11) ◽  
pp. 725
Author(s):  
Tomohito J. Yamada ◽  
Yadu Pokhrel
Keyword(s):  
General Circulation Model ◽  
Land Surface ◽  
General Circulation ◽  
Atmospheric General Circulation Model ◽  
Circulation Model ◽  
Weather Forecast ◽  
Surface Model ◽  
Near Surface ◽  
Land Disturbance ◽  
Atmospheric General Circulation

Irrigation can affect climate and weather patterns from regional to global scales through the alteration of surface water and energy balances. Here, we couple a land-surface model (LSM) that includes various human land-water management activities including irrigation with an atmospheric general circulation model (AGCM) to examine the impacts of irrigation-induced land disturbance on the subseasonal predictability of near-surface variables. Results indicate that the simulated global irrigation and groundwater withdrawals (circa 2000) are ~3600 and ~370 km3/year, respectively, which are in good agreement with previous estimates from country statistics and offline–LSMs. Subseasonal predictions for boreal summers during the 1986–1995 period suggest that the spread among ensemble simulations of air temperature can be substantially reduced by using realistic land initializations considering irrigation-induced changes in soil moisture. Additionally, it is found that the subseasonal forecast skill for near-surface temperature and sea level pressure significantly improves when human-induced land disturbance is accounted for in the AGCM. These results underscore the need to incorporate irrigation into weather forecast models, such as the global forecast system.

scholarly journals A comprehensive set of benchmark tests for a land surface model of simultaneous fluxes of water and carbon at both the global and seasonal scale

2011 ◽  
Vol 4 (2) ◽  
pp. 255-269 ◽  
Author(s):  
E. Blyth ◽  
D. B. Clark ◽  
R. Ellis ◽  
C. Huntingford ◽  
S. Los ◽  
...  
Keyword(s):  
Carbon Dioxide ◽  
Land Surface ◽  
General Circulation ◽  
River Flow ◽  
Land Surface Model ◽  
Circulation Model ◽  
Global Network ◽  
Surface Model ◽  
Model Errors ◽  
Benchmark Tests

Abstract. Evaluating the models we use in prediction is important as it allows us to identify uncertainties in prediction as well as guiding the priorities for model development. This paper describes a set of benchmark tests that is designed to quantify the performance of the land surface model that is used in the UK Hadley Centre General Circulation Model (JULES: Joint UK Land Environment Simulator). The tests are designed to assess the ability of the model to reproduce the observed fluxes of water and carbon at the global and regional spatial scale, and on a seasonal basis. Five datasets are used to test the model: water and carbon dioxide fluxes from ten FLUXNET sites covering the major global biomes, atmospheric carbon dioxide concentrations at four representative stations from the global network, river flow from seven catchments, the seasonal mean NDVI over the seven catchments and the potential land cover of the globe (after the estimated anthropogenic changes have been removed). The model is run in various configurations and results are compared with the data. A few examples are chosen to demonstrate the importance of using combined use of observations of carbon and water fluxes in essential in order to understand the causes of model errors. The benchmarking approach is suitable for application to other global models.

scholarly journals A comprehensive set of benchmark tests for a land surface model of simultaneous fluxes of water and carbon at both the global and seasonal scale

2010 ◽  
Vol 3 (4) ◽  
pp. 1829-1859 ◽  
Author(s):  
E. Blyth ◽  
D. B. Clark ◽  
R. Ellis ◽  
C. Huntingford ◽  
S. Los ◽  
...  
Keyword(s):  
Carbon Dioxide ◽  
Land Surface ◽  
General Circulation ◽  
River Flow ◽  
Land Surface Model ◽  
Circulation Model ◽  
Global Network ◽  
Surface Model ◽  
Model Errors ◽  
Benchmark Tests

Abstract. This paper describes a set of benchmark tests that is designed to quantify the performance of the land surface model that is used in the UK Hadley Centre General Circulation Model (JULES: Joint UK Land Environment Simulator). The tests are designed to assess the ability of the model to reproduce the observed fluxes of water and carbon at the global and regional spatial scale, and on a seasonal basis. Five datasets are used to test the model: water and carbon dioxide fluxes from ten FLUXNET sites covering the major global biomes, atmospheric carbon dioxide concentrations at four representative stations from the global network, river flow from seven catchments, the seasonal mean NDVI over the seven catchments and the potential land cover of the globe (after the estimated anthropogenic changes have been removed). The model is run in various configurations and results are compared with the data. The results show that combined use of observations of carbon and water fluxes is essential in order to understand the causes of model errors. The benchmarking approach is suitable for application to other global models.

scholarly journals Biases in Reanalysis Snowfall Found by Comparing the JULES Land Surface Model to GlobSnow

2014 ◽  
Vol 27 (2) ◽  
pp. 624-632 ◽  
Author(s):  
Steven Hancock ◽  
Brian Huntley ◽  
Richard Ellis ◽  
Robert Baxter
Keyword(s):  
Land Surface ◽  
General Circulation ◽  
Snow Water Equivalent ◽  
European Space Agency ◽  
Land Surface Model ◽  
Circulation Model ◽  
Reanalysis Data ◽  
Climatic Research Unit ◽  
Surface Model ◽  
Gauge Data

Abstract Snow exerts a strong influence on weather and climate. Accurate representation of snow processes within models is needed to ensure accurate predictions. Snow processes are known to be a weakness of land surface models (LSMs), and studies suggest that more complex snow physics is needed to avoid early melt. In this study the European Space Agency (ESA)’s Global Snow Monitoring for Climate Research (GlobSnow) snow water equivalent and NASA’s “MOD10C1” snow cover products are used to assess the accuracy of snow processes within the Joint U.K. Land Environment Simulator (JULES). JULES is run “offline” from a general circulation model and so is driven by meteorological reanalysis datasets: “Princeton,” Water and Global Change–Global Precipitation Climatology Centre (WATCH–GPCC), and WATCH–Climatic Research Unit (CRU). This reveals that when the model achieves the correct peak accumulation, snow does not melt early. However, generally snow does melt early because peak accumulation is too low. Examination of the meteorological reanalysis data shows that not enough snow falls to achieve observed peak accumulations. Thus, the earlier studies’ conclusions may be as a result of weaknesses in the driving data, rather than in model snow processes. These reanalysis products “bias correct” precipitation using observed gauge data with an undercatch correction, overriding the benefit of any other datasets used in their creation. This paper argues that using gauge data to bias-correct reanalysis data is not appropriate for snow-affected regions during winter and can lead to confusion when evaluating model processes.

scholarly journals AGCM Biases in Evaporation Regime: Impacts on Soil Moisture Memory and Land–Atmosphere Feedback

2005 ◽  
Vol 6 (5) ◽  
pp. 656-669 ◽  
Author(s):  
Sarith P. P. Mahanama ◽  
Randal D. Koster
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
General Circulation ◽  
Land Surface Model ◽  
Circulation Model ◽  
Seasonal Prediction ◽  
Surface Model ◽  
Negative Effects ◽  
Memory Time ◽  
Soil Moisture Initialization

Abstract Because precipitation and net radiation in an atmospheric general circulation model (AGCM) are typically biased relative to observations, the simulated evaporative regime of a region may be biased, with consequent negative effects on the AGCM’s ability to translate an initialized soil moisture anomaly into an improved seasonal prediction. These potential problems are investigated through extensive offline analyses with the Mosaic land surface model (LSM). The LSM was first forced globally with a 15-yr observation-based dataset. The simulation was then repeated after imposing a representative set of GCM climate biases onto the forcings—the observational forcings were scaled so that their mean seasonal cycles matched those simulated by the NASA Seasonal-to-Interannual Prediction Project (NSIPP-1; NASA Global Modeling and Assimilation Office) AGCM over the same period. The AGCM’s climate biases do indeed lead to significant biases in evaporative regime in certain regions, with the expected impacts on soil moisture memory time scales. Furthermore, the offline simulations suggest that the biased forcing in the AGCM should contribute to overestimated feedback in certain parts of North America—parts already identified in previous studies as having excessive feedback. The present study thus supports the notion that the reduction of climate biases in the AGCM will lead to more appropriate translations of soil moisture initialization into seasonal prediction skill.

scholarly journals Modelling northern peatlands area and carbon dynamics since the Holocene with the ORCHIDEE-PEAT land surface model (SVN r5488)

2018 ◽  
Author(s):  
Chunjing Qiu ◽  
Dan Zhu ◽  
Philippe Ciais ◽  
Bertrand Guenet ◽  
Shushi Peng ◽  
...  
Keyword(s):  
Carbon Storage ◽  
Land Surface ◽  
Net Primary Production ◽  
Soil Column ◽  
Land Surface Model ◽  
Surface Model ◽  
Past Century ◽  
Positive Trend ◽  
Peat Core ◽  
Northern Peatlands

Abstract. The importance of northern peatlands in the global carbon cycle has recently been recognized, especially for long-term changes. Yet, the complex interactions between climate and peatland hydrology, carbon storage and area dynamics make it challenging to represent these systems in land surface models. This study describes how peatland are included as an independent sub-grid hydrological soil unit (HSU) into the ORCHIDEE-MICT land surface model. The peatland soil column in this tile is characterized by multi-layered vertical water and carbon transport, and peat-specific hydrological properties. A cost-efficient TOPMODEL approach is implemented to simulate the dynamics of peatland area, calibrated by present-day wetland areas that are regularly inundated or subject to shallow water tables. The model is tested across a range of northern peatland sites and for gridded simulations over the Northern Hemisphere (> 30° N). Simulated northern peatland area (3.9 million km2), peat carbon stock (463 PgC) and peat depth are generally consistent with observed estimates of peatland area (3.4–4.0 million km2), peat carbon (270–540 PgC) and data compilations of peat core depths. Our results show that both net primary production (NPP) and heterotrophic respiration (HR) of northern peatlands increased over the past century in response to CO2 and climate change. NPP increased more rapidly than HR, and thus net ecosystem production (NEP) exhibited a positive trend, contributing a cumulative carbon storage of 11.13 Pg C since 1901, most of it being realized after the 1950s.

scholarly journals A system of conservative regridding for ice–atmosphere coupling in a General Circulation Model (GCM)

2014 ◽  
Vol 7 (3) ◽  
pp. 883-907 ◽  
Author(s):  
R. Fischer ◽  
S. Nowicki ◽  
M. Kelley ◽  
G. A. Schmidt
Keyword(s):  
General Circulation Model ◽  
General Circulation ◽  
Circulation Model ◽  
Grid Cell ◽  
Surface Model ◽  
Ice Flow ◽  
Surface Mass ◽  
Flow Models ◽  
Ice Surface ◽  
Ice Model

Abstract. The method of elevation classes, in which the ice surface model is run at multiple elevations within each grid cell, has proven to be a useful way for a low-resolution atmosphere inside a general circulation model (GCM) to produce high-resolution downscaled surface mass balance fields for use in one-way studies coupling atmospheres and ice flow models. Past uses of elevation classes have failed to conserve mass and energy because the transformation used to regrid to the atmosphere was inconsistent with the transformation used to downscale to the ice model. This would cause problems for two-way coupling. A strategy that resolves this conservation issue has been designed and is presented here. The approach identifies three grids between which data must be regridded and five transformations between those grids required by a typical coupled atmosphere–ice flow model. This paper develops a theoretical framework for the problem and shows how each of these transformations may be achieved in a consistent, conservative manner. These transformations are implemented in Glint2, a library used to couple atmosphere models with ice models. Source code and documentation are available for download. Confounding real-world issues are discussed, including the use of projections for ice modeling, how to handle dynamically changing ice geometry, and modifications required for finite element ice models.

scholarly journals Interannual variability in Australia's terrestrial carbon cycle constrained by multiple observation types

2016 ◽  
Vol 13 (23) ◽  
pp. 6363-6383 ◽  
Author(s):  
Cathy M. Trudinger ◽  
Vanessa Haverd ◽  
Peter R. Briggs ◽  
Josep G. Canadell
Keyword(s):  
Interannual Variability ◽  
Land Surface ◽  
Atmospheric Carbon Dioxide ◽  
Net Primary Production ◽  
Land Surface Model ◽  
Heterotrophic Respiration ◽  
Arid Ecosystems ◽  
Surface Model ◽  
Calibration Data ◽  
Terrestrial Carbon

Abstract. Recent studies have shown that semi-arid ecosystems in Australia may be responsible for a significant part of the interannual variability in the global concentration of atmospheric carbon dioxide. Here we use a multiple constraints approach to calibrate a land surface model of Australian terrestrial carbon and water cycles, with a focus on interannual variability. We use observations of carbon and water fluxes at 14 OzFlux sites, as well as data on carbon pools, litterfall and streamflow. We include calibration of the function describing the response of heterotrophic respiration to soil moisture. We also explore the effect on modelled interannual variability of parameter equifinality, whereby multiple combinations of parameters can give an equally acceptable fit to the calibration data. We estimate interannual variability of Australian net ecosystem production (NEP) of 0.12–0.21 PgC yr−1 (1σ) over 1982–2013, with a high anomaly of 0.43–0.67 PgC yr−1 in 2011 relative to this period associated with exceptionally wet conditions following a prolonged drought. The ranges are due to the effect on calculated NEP anomaly of parameter equifinality, with similar contributions from equifinality in parameters associated with net primary production (NPP) and heterotrophic respiration. Our range of results due to parameter equifinality demonstrates how errors can be underestimated when a single parameter set is used.

scholarly journals Evaluating the effect of alternative carbon allocation schemes in a land surface model (CLM4.5) on carbon fluxes, pools and turnover in temperate forests

2017 ◽  
Author(s):  
Francesc Montané ◽  
Andrew M. Fox ◽  
Avelino F. Arellano ◽  
Natasha MacBean ◽  
M. Ross Alexander ◽  
...  
Keyword(s):  
Primary Production ◽  
Aboveground Biomass ◽  
Land Surface ◽  
Turnover Rate ◽  
Land Surface Model ◽  
Biomass Accumulation ◽  
Deciduous Forests ◽  
Surface Model ◽  
Allocation Scheme

Abstract. How carbon (C) is allocated to different plant tissues (leaves, stem and roots) determines C residence time and thus remains a central challenge for understanding the global C cycle. We used a diverse set of observations (AmeriFlux eddy covariance tower observations, biomass estimates from tree-ring data, and Leaf Area Index (LAI) measurements) to compare C fluxes, pools, and LAI data with those predicted by a Land Surface Model (LSM), the Community Land Model (CLM4.5). We ran CLM for nine temperate (including evergreen and deciduous) forests in North America between 1980 and 2013 using four different C allocation schemes: i) Dynamic C allocation scheme (named "D-CLM") with one dynamic allometric parameter, which allocates C to the stem and leaves to vary in time as a function of annual Net Primary Production (NPP). ii) An alternative dynamic C allocation scheme (named "D-Litton"), where, similar to (i) C allocation is a dynamic function of annual NPP, but unlike (i) includes two dynamic allometric parameters involving allocation to leaves, stem and coarse roots iii–iv) Two fixed C allocation schemes, one representative of observations in evergreen (named "F-Evergreen") and the other of observations in deciduous forests (named "F-Deciduous"). D-CLM generally overestimated Gross Primary Production (GPP) and ecosystem respiration, and underestimated Net Ecosystem Exchange (NEE). In D-CLM, initial aboveground biomass in 1980 was largely overestimated (between 10527 and 12897 g Cm-2) for deciduous forests, whereas aboveground biomass accumulation through time (between 1980 and 2011) was highly underestimated (between 1222 and 7557 g Cm-2) for both evergreen and deciduous sites due to a lower stem turnover rate in the sites than the one used in the model. D-CLM overestimated LAI in both evergreen and deciduous sites because the leaf C-LAI relationship in the model did not match the observed leaf C-LAI relationship at our sites. Although the four C allocation schemes gave similar results for aggregated C fluxes, they translated to important differences in long-term aboveground biomass accumulation and aboveground NPP. For deciduous forests, D-Litton gave more realistic Cstem/Cleaf ratios and strongly reduced the overestimation of initial aboveground biomass, and aboveground NPP for deciduous forests by D-CLM. We identified key structural and parameterization deficits that need refinement to improve the accuracy of LSMs in the near future. That could be done by addressing some of the current model assumptions about C allocation and the associated parameter uncertainty. Our results highlight the importance of using aboveground biomass data to evaluate and constrain the C allocation scheme in the model, and in particular, the sensitivity to the stem turnover rate. Revising these will be critical to improving long-term C processes in LSMs, and improve their projections of biomass accumulation in forests.

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