The improvement of a regional climate model by coupling a land surface model with eco-physiological processes: A case study in 1998

Li, Dan; Cao, Fang; Rong, Gao

doi:10.1007/s10584-013-0997-8

Cited by 22 publications

(17 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Within the LUE models, Global Production Efficiency Model (GloPEM) introduced the largest uncertainty, as it had two extremely large values (Figure ), which might have arisen from the biased driving data from remote sensing [ Dan et al ., ]. When the driving climate data were corrected further, the GloPEM's output was close to the average of these models [ Dan et al ., ]. For the NBP from the MsTMIP data, the majority of the overall uncertainty was caused by the dramatic difference between TRIPLEX‐GHG and other models (1.10 versus 0.15 Pg C yr −1 ; Figure ), which mainly came from the much lower heterotrophic respiration (Rh) in TRIPLEX‐GHG (1.83 versus 3.07 Pg C yr −1 ).…”

Section: Discussionmentioning

confidence: 99%

Uncertainty analysis of terrestrial net primary productivity and net biome productivity in China during 1901–2005

et al. 2016

Self Cite

View full text Add to dashboard Cite

Despite the importance of net primary productivity (NPP) and net biome productivity (NBP), estimates of NPP and NBP for China are highly uncertain. To investigate the main sources of uncertainty, we synthesized model estimates of NPP and NBP for China from published literature and the Multi-scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP). The literature-based results showed that total NPP and NBP in China were 3.35 ± 1.25 and 0.14 ± 0.094 Pg C yr À1 , respectively. Classification and regression tree analysis based on literature data showed that model type was the primary source of the uncertainty, explaining 36% and 64% of the variance in NPP and NBP, respectively. Spatiotemporal scales, land cover conditions, inclusion of the N cycle, and effects of N addition also contributed to the overall uncertainty. Results based on the MsTMIP data suggested that model structures were overwhelmingly important (>90%) for the overall uncertainty compared to simulations with different combinations of time-varying global change factors. The interannual pattern of NPP was similar among diverse studies and increased by 0.012 Pg C yr À1 during 1981-2000. In addition, high uncertainty in China's NPP occurred in areas with high productivity, whereas NBP showed the opposite pattern. Our results suggest that to significantly reduce uncertainty in estimated NPP and NBP, model structures should be substantially tested on the basis of empirical results. To this end, coordinated distributed experiments with multiple global change factors might be a practical approach that can validate specific structures of different models.SHAO ET AL.

show abstract

Section: Discussionmentioning

confidence: 99%

Uncertainty analysis of terrestrial net primary productivity and net biome productivity in China during 1901–2005

et al. 2016

Self Cite

View full text Add to dashboard Cite

show abstract

“…The details of these processes and the related parameters are given in the aforementioned references (Dan et al, , ; Lu & Ji, ). The model's ability to simulate the heat, water, and carbon fluxes in different ecosystems has been studied comprehensively (Dan et al, , , ). This model was part of the Ecosystem Model Data Intercomparison, and simulations agree well with the observed data (Dan et al, ).…”

Section: Model Descriptionsmentioning

confidence: 99%

The Improved Freeze–Thaw Process of a Climate‐Vegetation Model: Calibration and Validation Tests in the Source Region of the Yellow River

Yang

et al. 2018

JGR Atmospheres

View full text Add to dashboard Cite

The freeze-thaw process significantly impacts land surface processes in permafrost and influences the regional climate. In this study, the freeze-thaw process in the atmosphere-vegetation interaction model (AVIM) was improved by utilizing the universal soil hydrothermal coupling equations, and by introducing the freezing depression point-based freeze-thaw parameterization scheme to form a model known as the AVIM frozen soil model (AVIM_FSM). Then, the seasonal frozen soil observational data at Maqu station, located in the Yellow River source region, were used to calibrate the freeze-thaw-related parameters with an artificial intelligence particle swarm optimization method, and the model was validated. The results indicated that by improving the freeze-thaw process, the temporal variations in soil temperature and liquid water content simulated by the AVIM_FSM model agreed well with the observations. By calibrating the parameters, the deviations between the observations and corresponding simulations were reduced compared with those in the AVIM. Based on the AVIM_FSM, the physical mechanism of the freeze-thaw process was discussed, the different freeze-thaw parameterization schemes were compared, the related freeze-thaw process parameters were quantitatively evaluated, and the following was indicated: (1) the freeze-thaw process was mainly determined by radiation, sensible heat flux, and ice change in frozen soil; (2) the freezing depression point-based freeze-thaw parameterization scheme was superior to the empirical scheme, which can reasonably describe the freezing and thawing start dates with lower deviations; and (3) the particle swarm optimization algorithm can efficiently calibrate the freeze-thaw-related parameters and improve the simulation accuracy. Special Section:Water-soil-air-plant-human nexus: Modeling and observing complex land-surface systems at river basin scale Key Points:• The freeze-thaw process in the vegetation-climate model AVIM was improved to form a new model known as AVIM_FSM • The freeze-thaw-related parameters were calibrated with the artificial intelligence particle swarm optimization (PSO) method • The AVIM_FSM model was validated and compared with the AVIM simulations using seasonal frozen soil observational dataSupporting Information:• Supporting information S1• Data Set S1

show abstract

“…The carbon cycle is source of uncertainty in climate simulation and projection models (IPCC 2013). It is therefore important to quantify accurately the magnitude and variation of carbon fluxes in regions with a complex climate and topography, such as China, which is affected by strong interactions between ecosystems and the monsoon climate (Fu et al 2002;Dan, Cao, and Gao 2015). However, it is difficult to reproduce accurately the flux of carbon and nitrogen in China as a result of a lack of global-and regional-scale observations of the nitrogen cycle and the complex climate-vegetation system.…”

Section: Introductionmentioning

confidence: 99%

Integration of nitrogen dynamics into the land surface model AVIM. Part 2: baseline data and variation of carbon and nitrogen fluxes in China

Yang

et al. 2020

Atmospheric and Oceanic Science Letters

Self Cite

View full text Add to dashboard Cite

The spatiotemporal features of carbon and nitrogen fluxes over China between 1979 and 2015 were simulated by the Atmosphere-Vegetation Interaction Model (AVIM). The carbon fluxes of gross primary production and net primary production captured the distribution pattern in China better than MODIS and TRENDY data. The results for nitrogen deposition and biological nitrogen fixation show the good performance of the AVIM simulation compared with the CMIP6 and CABLE data, with a deposition rate >4 g N m −2 yr −1 in south China. The variation in the gross primary production and net primary production can be up to 300 and 200 g C m −2 yr −1 in south and southeast China, respectively, and there is a discrepancy between the AVIM and the data from MODIS and TRENDY. This shows the difficulty in simulating the carbon flux in a monsoon climate region and the importance of coupling the nitrogen-carbon fluxes. The standard deviation of nitrogen deposition and biological nitrogen fixation is simulated well by the AVIM and there is a large range in nitrogen deposition of 0.8-1.2 g N m −2 yr −1 in south China. The climatological mean of the fluxes performs better than the variation in the standard deviation and anomaly and this variation in the carbon-nitrogen flux is the key to decreasing bias in future modeling studies.

show abstract

The improvement of a regional climate model by coupling a land surface model with eco-physiological processes: A case study in 1998

Cited by 22 publications

References 27 publications

Uncertainty analysis of terrestrial net primary productivity and net biome productivity in China during 1901–2005

Uncertainty analysis of terrestrial net primary productivity and net biome productivity in China during 1901–2005

The Improved Freeze–Thaw Process of a Climate‐Vegetation Model: Calibration and Validation Tests in the Source Region of the Yellow River

Integration of nitrogen dynamics into the land surface model AVIM. Part 2: baseline data and variation of carbon and nitrogen fluxes in China

Contact Info

Product

Resources

About