Multi-year eddy covariance measurements of net ecosystem exchange in tropical dry deciduous forest of India

Rodda, Suraj Reddy; Thumaty, Kiran Chand; Praveen, M.; Jha, Chandra Shekhar; Dadhwal, V. K.

doi:10.1016/j.agrformet.2021.108351

Cited by 21 publications

(18 citation statements)

References 67 publications

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“…Surface carbon fluxes including GPP and ER were compared to upscaled Fluxnet observations from Jung et al (2011). Aboveground biomass was compared to Avitabile et al (2016), while global total biomass carbon from 1990 to 2010 was compared to Saatchi et al (2011). The performance of BCC-AVIM2 in estimating each of these variables was assessed through bias, RMSE, and Taylor diagram metrics (Taylor, 2001).…”

Section: A2 Bccmentioning

confidence: 99%

“…ILAMBv2.1 utilizes a suite of data products weighted by certainty. These included vegetation biomass (tropical: Saatchi et al, 2011;global: Kellndorfer et al, 2013;Blackard et al, 2008), burned area (Giglio et al, 2010), CO 2 concentrations, GPP (Fluxnet: Lasslop et al, 2010; Global biosphereatmosphere flux: Jung et al, 2010), LAI (AVHRR: Myneni et al, 1997;MODIS: de Kauwe et al, 2011), global net ecosystem carbon balance (GCP: Le Quéré et al, 2014;Hoffman et al, 2014), net ecosystem exchange (Fluxnet: Lasslop et al, 2010;GBAF: Jung et al, 2010), NBP, ER, NEP (equivalent to GPP-ER), soil carbon (Harmonized World Soil Database (HWSD): Todd-Brown et al, 2013;Northern Circumpolar Soil Carbon Database (NCSCDV22): Hugelius et al, 2013), and 10 functional relationships. Lawrence et al (2019) also compared the relationship between apparent soil carbon turnover times versus air temperature to observation-based estimates developed from HWSD, NCSDV22, and MODIS.…”

Section: A4 Climate and Global Dynamics Laboratory (Ncar)mentioning

confidence: 99%

“…Observation-based GPP, ER, and NEP (equivalent to GPP-ER) comparison data were obtained from Jung et al (2011Jung et al ( , 2010. Vegetation carbon was evaluated relative to observations for the tropics from Saatchi et al (2011) and the GEOCARBON and Global-Carbon datasets (Collier et al, 2018;Avitabile et al, 2016;Santoro et al, 2015). ILAMBv2.1 results from these investigations comprised a collection of statistical metrics for annual mean, bias, relative bias, RMSE, seasonal cycle phase, spatial distribution, and interannual variability, in addition to functional relationships.…”

Section: A4 Climate and Global Dynamics Laboratory (Ncar)mentioning

confidence: 99%

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Validation of terrestrial biogeochemistry in CMIP6 Earth system models: a review

Spafford

MacDougall

2021

Geosci. Model Dev.

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Abstract. The vital role of terrestrial biogeochemical cycles in influencing global climate change is explored by modelling groups internationally through land surface models (LSMs) coupled to atmospheric and oceanic components within Earth system models (ESMs). The sixth phase of the Coupled Model Intercomparison Project (CMIP6) provided an opportunity to compare ESM output by providing common forcings and experimental protocols. Despite these common experimental protocols, a variety of terrestrial biogeochemical cycle validation approaches were adopted by CMIP6 participants, leading to ambiguous model performance assessment and uncertainty attribution across ESMs. In this review we summarize current methods of terrestrial biogeochemical cycle validation utilized by CMIP6 participants and concurrent community model comparison studies. We focus on variables including the dimensions of evaluations, observation-based reference datasets, and metrics of model performance. To ensure objective and thorough validations for the seventh phase of CMIP (CMIP7), we recommend the use of a standard validation protocol employing a broad suite of certainty-weighted observation-based reference datasets, targeted model performance metrics, and comparisons across a range of spatiotemporal scales.

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Section: A2 Bccmentioning

confidence: 99%

Section: A4 Climate and Global Dynamics Laboratory (Ncar)mentioning

confidence: 99%

Section: A4 Climate and Global Dynamics Laboratory (Ncar)mentioning

confidence: 99%

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Validation of terrestrial biogeochemistry in CMIP6 Earth system models: a review

Spafford

MacDougall

2021

Geosci. Model Dev.

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“…Observation-based estimates of GPP can be obtained through satellite-derived vegetation indices such as the normalized difference vegetation index (NDVI; Phillips et al, 2008) and solar induced chlorophyll fluorescence (Zhang et al, 2020), in addition to ground-based monitoring of turbulent CO2 fluxes with the eddy covariance technique (Jung et al, 2009). Logistical challenges with eddy covariancebased techniques of estimating GPP can result in potentially extensive data gaps and systematic omission of diel cycle observations (Rodda et al, 2021;Erkkilä et al, 2017;Lasslop et al, 2010;2008;Desai et al, 2008). For example, in a study of eddy-covariance monitoring of CO2 flux, Jonsson et al (2008) report only 34% data coverage of a growing season period, of which 54% was discarded as it did not demonstrate energy balance closure.…”

Section: Reference Datasetsmentioning

confidence: 99%

Validation of Terrestrial Biogeochemistry in CMIP6 Earth System Models: A Review

Spafford¹,

MacDougall²

2021

Preprint

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Abstract. The vital role of terrestrial biogeochemical cycles in influencing global climate change is explored by modelling groups internationally through Land Surface Models (LSMs) coupled to atmospheric and oceanic components within Earth System Models (ESMs). The sixth phase of the Coupled Model Intercomparison Project (CMIP6) provided an opportunity to compare ESM output by providing common forcings and experimental protocols. Despite these common experimental protocols, a variety of terrestrial biogeochemical cycle validation approaches were adopted by CMIP6 participants, leading to ambiguous model performance assessment and uncertainty attribution across ESMs. In this review we summarize current methods of terrestrial biogeochemical cycle validation utilized by CMIP6 participants and concurrent community model comparison studies. We focus on variables including: the dimensions of evaluations, observation-based reference datasets, and metrics of model performance. To ensure objective and thorough validations for the seventh phase of CMIP (CMIP7) we recommend the use of a standard validation protocol employing a broad suite of certainty-weighted observation-based reference datasets, targeted model performance metrics, and comparisons across a range of spatiotemporal scales.

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“…Forests are the signi cant contributors to the global terrestrial carbon sink (Canadell and Raupach, 2008), absorbing approximately one-quarter to one-third of the anthropogenic CO 2 emissions. In India, Rodda et al (2021) reported the carbon balance of tropical dry deciduous forest in central India and a mean annual net ecosystem productivity shows that the forest site was a net sink of atmospheric CO 2 . In tropical deciduous teak forests of Thailand, Igarashi et al (2013) reports that the water and carbon dioxide ex-changes were strongly affected by seasonality in rainfall and moisture as well as the leaf age, a major factor causing the decrease in photosynthetic capacity.…”

Section: Introductionmentioning

confidence: 99%

Seasonal Variation of Atmospheric Carbon Dioxide Over Suburban Site of Pune, India

Patil

Dharmaraj

Waghmare

et al. 2021

Preprint

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Increase in greenhouse gases including carbon dioxide (CO2) concentrations in the atmosphere, is the responsible factor for the global warming. This increase is caused by the anthropogenic activities like combustion of petroleum products, biomass burning, etc. In recent years, measurement network of greenhouse gases has been expanded globally. In this study, we measured the CO2 and water vapor (H2O) concentrations along with wind and air temperature over suburban site of Pune, in the year of 2018-2019 comprising the four seasons : pre-monsoon (months of March, April and May (MAM)), summer monsoon (months of June, July, August and September (JJAS)), post-monsoon (months of October and November (ON)) and winter (months of December, January and February (DJF)). Using these observations, the diurnal and seasonal variations of CO2 concentrations and fluxes were analyzed. The mean CO2 concentration was observed in the range of 380.7 – 443.6 ppm. The seasonal mean of CO2 concentration was 400, 421.5, 395.4 and 384.7 ppm in pre-monsoon, monsoon, post-monsoon and winter seasons, respectively. It is found that the height of planetary boundary layer and photosynthesis process plays a major role in seasonal variation of CO2.

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Multi-year eddy covariance measurements of net ecosystem exchange in tropical dry deciduous forest of India

Cited by 21 publications

References 67 publications

Validation of terrestrial biogeochemistry in CMIP6 Earth system models: a review

Validation of terrestrial biogeochemistry in CMIP6 Earth system models: a review

Validation of Terrestrial Biogeochemistry in CMIP6 Earth System Models: A Review

Seasonal Variation of Atmospheric Carbon Dioxide Over Suburban Site of Pune, India

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