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Impacts of Climate Extremes on Terre...

Wei, Suhua.

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Impacts of Climate Extremes on Terrestrial Productivity.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Impacts of Climate Extremes on Terrestrial Productivity./
作者:	Wei, Suhua.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
面頁冊數:	138 p.
附註:	Source: Dissertation Abstracts International, Volume: 80-02(E), Section: B.
Contained By:	Dissertation Abstracts International80-02B(E).
標題:	Environmental science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10936429
ISBN:	9780438461666

Impacts of Climate Extremes on Terrestrial Productivity.
Wei, Suhua.

Impacts of Climate Extremes on Terrestrial Productivity. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 138 p.

Source: Dissertation Abstracts International, Volume: 80-02(E), Section: B.

Thesis (Ph.D.)--City University of New York, 2018.

Terrestrial biosphere absorbs approximately 28% of anthropogenic CO 2 emissions. This terrestrial carbon sink might become saturated in a future climate regime. To explore the issues associated with this topic, an accurate estimate of gross primary production (GPP) of global terrestrial ecosystems is needed. A major uncertainty in modeling global terrestrial GPP is the parameter of light use efficiency (LUE). Most LUE estimates in global models are satellite-based and coarsely measured with emphasis on environmental variables. Others are from eddy covariance towers with much greater spatial and temporal data quality and emphasis on mechanistic processes, but in a limited number of sites. In this study, we conducted a comprehensive global study of tower-based LUE from 237 FLUXNET towers, and scaled up LUEs from in-situ tower level to global biome level. We integrated the tower-based LUE estimates with key environmental and biological variables at 0.5º x 0.5º grid-cell resolutions, using a random forest regression (RFR) approach. Then we developed a RFR-LUE-GPP model using the grid-cell LUE data. In order to calibrate the LUE model, we developed a data-driven RFR-GPP model using random forest regression method only. Our results showed LUE varies largely with latitude. We estimated a global area-weighted average of LUE at 1.23+/-0.03 gC m-2 MJ-1 APAR, which led to an estimate of global gross primary production (GPP) of 107.5+/-2.5 Gt C /year from 2001 to 2005. Large uncertainties existed in GPP estimations over sparsely vegetated areas covered by savannas and woody savannas at middle to low latitude (i.e. 20ºS to 40ºS and 5ºN to 40ºN) due to the lack of available data. Model results were improved by incorporating Koppen climate types to represent climate/meteorological information in machine learning modeling. This brought a new understanding to the recognized problem of climate-dependence of spring onset of photosynthesis and the challenges in accurately modeling the biome GPP of evergreen broad leaf forests (EBF). The divergent responses of GPP to temperature and precipitation at mid-high latitudes and at mid-low latitudes echo the necessity of modeling GPP separately by latitudes.

ISBN: 9780438461666Subjects--Topical Terms:

677245
Environmental science.

Impacts of Climate Extremes on Terrestrial Productivity.
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520 $a Terrestrial biosphere absorbs approximately 28% of anthropogenic CO 2 emissions. This terrestrial carbon sink might become saturated in a future climate regime. To explore the issues associated with this topic, an accurate estimate of gross primary production (GPP) of global terrestrial ecosystems is needed. A major uncertainty in modeling global terrestrial GPP is the parameter of light use efficiency (LUE). Most LUE estimates in global models are satellite-based and coarsely measured with emphasis on environmental variables. Others are from eddy covariance towers with much greater spatial and temporal data quality and emphasis on mechanistic processes, but in a limited number of sites. In this study, we conducted a comprehensive global study of tower-based LUE from 237 FLUXNET towers, and scaled up LUEs from in-situ tower level to global biome level. We integrated the tower-based LUE estimates with key environmental and biological variables at 0.5º x 0.5º grid-cell resolutions, using a random forest regression (RFR) approach. Then we developed a RFR-LUE-GPP model using the grid-cell LUE data. In order to calibrate the LUE model, we developed a data-driven RFR-GPP model using random forest regression method only. Our results showed LUE varies largely with latitude. We estimated a global area-weighted average of LUE at 1.23+/-0.03 gC m-2 MJ-1 APAR, which led to an estimate of global gross primary production (GPP) of 107.5+/-2.5 Gt C /year from 2001 to 2005. Large uncertainties existed in GPP estimations over sparsely vegetated areas covered by savannas and woody savannas at middle to low latitude (i.e. 20ºS to 40ºS and 5ºN to 40ºN) due to the lack of available data. Model results were improved by incorporating Koppen climate types to represent climate/meteorological information in machine learning modeling. This brought a new understanding to the recognized problem of climate-dependence of spring onset of photosynthesis and the challenges in accurately modeling the biome GPP of evergreen broad leaf forests (EBF). The divergent responses of GPP to temperature and precipitation at mid-high latitudes and at mid-low latitudes echo the necessity of modeling GPP separately by latitudes.
520 $a We also used a perfect-deficit approach to identify forest canopy photosynthetic capacity (CPC) deficits and analyze how they correlate to climate extremes, based on observational data measured by the eddy covariance method at 27 forest sites over 146 site-years. We found that droughts severely affect the carbon assimilation capacities of evergreen broadleaf forest and deciduous broadleaf forest. The carbon assimilation capacities of Mediterranean forests were highly sensitive to climate extremes, while marine forest climates tended to be insensitive to climate extremes. Our estimates suggest an average global reduction of forest canopy photosynthetic capacity due to unfavorable climate extremes of 6.3 Pg C (&sim;5.2% of global gross primary production) per growing season over 2001-2010, with evergreen broadleaf forests contributing 52% of the total reduction.
520 $a At biome-scale, terrestrial carbon uptake is controlled mainly by weather variability. Observational data from a global monitoring network indicate that the sensitivity of terrestrial carbon sequestration to mean annual temperature (T) breaks down at a threshold value of 16°C, above which terrestrial CO2 fluxes are controlled by dryness rather than temperature. Here we show that since 1948 warming climate has moved the 16°C T latitudinal belt poleward. Land surface area with T >16°C and now subject to dryness control rather than temperature as the regulator of carbon uptake has increased by 6% and is expected to increase by at least another 8% by 2050.
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