The effects of spatiotemporal patterns of atmospheric CO2 concentration on terrestrial gross primary productivity estimation

2020 ◽  
Vol 163 (2) ◽  
pp. 913-930
Author(s):  
Zhongyi Sun ◽  
Xiufeng Wang ◽  
Haruhiko Yamamoto ◽  
Hiroshi Tani ◽  
Tangzhe Nie
Keyword(s):  
Primary Productivity ◽  
Co2 Concentration ◽  
Gross Primary Productivity ◽  
Spatiotemporal Patterns ◽  
Atmospheric Co2 ◽  
Atmospheric Co2 Concentration ◽  
Productivity Estimation

scholarly journals CO2 Concentration, A Critical Factor Influencing the Relationship between Solar-induced Chlorophyll Fluorescence and Gross Primary Productivity

2020 ◽  
Vol 12 (9) ◽  
pp. 1377 ◽  
Author(s):  
Ruonan Qiu ◽  
Ge Han ◽  
Xin Ma ◽  
Zongyao Sha ◽  
Tianqi Shi ◽  
...  
Keyword(s):  
Chlorophyll Fluorescence ◽  
Linear Model ◽  
Primary Productivity ◽  
Terrestrial Ecosystem ◽  
Co2 Concentration ◽  
Gross Primary Productivity ◽  
Atmospheric Co2 ◽  
Atmospheric Co2 Concentration ◽  
Non Linear ◽  
The Relationship

The uncertainty of carbon fluxes of the terrestrial ecosystem is the highest among all flux components, calling for more accurate and efficient means to monitor land sinks. Gross primary productivity (GPP) is a key index to estimate the terrestrial ecosystem carbon flux, which describes the total amount of organic carbon fixed by green plants through photosynthesis. In recent years, the solar-induced chlorophyll fluorescence (SIF), which is a probe for vegetation photosynthesis and can quickly reflect the state of vegetation growth, emerges as a novel and promising proxy to estimate GPP. The launch of Orbiting Carbon Observatory 2 (OCO-2) further makes it possible to estimate GPP at a finer spatial resolution compared with Greenhouse Gases Observing Satellite (GOSAT), Global Ozone Monitoring Experiment-2 (GOME-2) and SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY). However, whether the relationship between GPP and SIF is linear or non-linear has always been controversial. In this research, we proposed a new model to estimate GPP using SIF and the atmospheric CO2 concentration from OCO-2 as critical driven factors simultaneously (SIF-CO2-GPP model). Evidences from all sites show that the introduction of the atmospheric CO2 concentration improves accuracies of estimated GPP. Compared with the SIF-CO2-GPP linear model, we found the SIF-GPP model overestimated GPP in summer and autumn but underestimated it in spring and winter. A series of simulation experiments based on SCOPE (Soil-Canopy Observation of Photosynthesis and Energy) was carried out to figure out the possible mechanism of improved estimates of GPP due to the introduction of atmospheric CO2 concentrations. These experiments also demonstrate that there could be a non-linear relationship between SIF and GPP at half an hour timescale. Moreover, such relationships vary with CO2 concentration. As OCO-2 is capable of providing SIF and XCO2 products with identical spatial and temporal scales, the SIF-CO2-GPP linear model would be implemented conveniently to monitor GPP using remotely sensed data. With the help of OCO-3 and its successors, the proposed SIF-CO2-GPP linear model would play a significant role in monitoring GPP accurately in large geographical extents.

scholarly journals Response of simulated burned area to historical changes in environmental and anthropogenic factors: a comparison of seven fire models

2019 ◽  
Vol 16 (19) ◽  
pp. 3883-3910 ◽  
Author(s):  
Lina Teckentrup ◽  
Sandy P. Harrison ◽  
Stijn Hantson ◽  
Angelika Heil ◽  
Joe R. Melton ◽  
...  
Keyword(s):  
Land Use ◽  
Co2 Concentration ◽  
Fire Regimes ◽  
Atmospheric Co2 ◽  
Anthropogenic Factors ◽  
Burned Area ◽  
Earth System ◽  
Atmospheric Co2 Concentration ◽  
Fire Models ◽  
The Impact

Abstract. Understanding how fire regimes change over time is of major importance for understanding their future impact on the Earth system, including society. Large differences in simulated burned area between fire models show that there is substantial uncertainty associated with modelling global change impacts on fire regimes. We draw here on sensitivity simulations made by seven global dynamic vegetation models participating in the Fire Model Intercomparison Project (FireMIP) to understand how differences in models translate into differences in fire regime projections. The sensitivity experiments isolate the impact of the individual drivers on simulated burned area, which are prescribed in the simulations. Specifically these drivers are atmospheric CO2 concentration, population density, land-use change, lightning and climate. The seven models capture spatial patterns in burned area. However, they show considerable differences in the burned area trends since 1921. We analyse the trajectories of differences between the sensitivity and reference simulation to improve our understanding of what drives the global trends in burned area. Where it is possible, we link the inter-model differences to model assumptions. Overall, these analyses reveal that the largest uncertainties in simulating global historical burned area are related to the representation of anthropogenic ignitions and suppression and effects of land use on vegetation and fire. In line with previous studies this highlights the need to improve our understanding and model representation of the relationship between human activities and fire to improve our abilities to model fire within Earth system model applications. Only two models show a strong response to atmospheric CO2 concentration. The effects of changes in atmospheric CO2 concentration on fire are complex and quantitative information of how fuel loads and how flammability changes due to this factor is missing. The response to lightning on global scale is low. The response of burned area to climate is spatially heterogeneous and has a strong inter-annual variation. Climate is therefore likely more important than the other factors for short-term variations and extremes in burned area. This study provides a basis to understand the uncertainties in global fire modelling. Both improvements in process understanding and observational constraints reduce uncertainties in modelling burned area trends.

scholarly journals Investigating the evolution of major Northern Hemisphere ice sheets during the last glacial-interglacial cycle

2009 ◽  
Vol 5 (3) ◽  
pp. 329-345 ◽  
Author(s):  
S. Bonelli ◽  
S. Charbit ◽  
M. Kageyama ◽  
M.-N. Woillez ◽  
G. Ramstein ◽  
...  
Keyword(s):  
Northern Hemisphere ◽  
Climate Model ◽  
Ice Sheets ◽  
Ice Sheet ◽  
Co2 Concentration ◽  
Atmospheric Co2 ◽  
Glacial Cycle ◽  
Last Glacial ◽  
Atmospheric Co2 Concentration ◽  
The Last Glacial

Abstract. A 2.5-dimensional climate model of intermediate complexity, CLIMBER-2, fully coupled with the GREMLINS 3-D thermo-mechanical ice sheet model is used to simulate the evolution of major Northern Hemisphere ice sheets during the last glacial-interglacial cycle and to investigate the ice sheets responses to both insolation and atmospheric CO2 concentration. This model reproduces the main phases of advance and retreat of Northern Hemisphere ice sheets during the last glacial cycle, although the amplitude of these variations is less pronounced than those based on sea level reconstructions. At the last glacial maximum, the simulated ice volume is 52.5×1015 m3 and the spatial distribution of both the American and Eurasian ice complexes is in reasonable agreement with observations, with the exception of the marine parts of these former ice sheets. A set of sensitivity studies has also been performed to assess the sensitivity of the Northern Hemisphere ice sheets to both insolation and atmospheric CO2. Our results suggest that the decrease of summer insolation is the main factor responsible for the early build up of the North American ice sheet around 120 kyr BP, in agreement with benthic foraminifera δ18O signals. In contrast, low insolation and low atmospheric CO2 concentration are both necessary to trigger a long-lasting glaciation over Eurasia.

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