Mismatch between the optimal ages for ecosystem productivity and net CO2 sequestration in Norway spruce forests

2021 ◽  
Author(s):  
Junbin Zhao ◽  
Holger Lange ◽  
Helge Meissner
Keyword(s):  
Climate Change ◽  
Primary Production ◽  
Norway Spruce ◽  
Ecosystem Respiration ◽  
Gross Primary Production ◽  
Economic Benefits ◽  
Sink Strength ◽  
Climate Change Scenarios ◽  
Ecosystem Productivity ◽  
Spruce Forests

<p>Forests have climate change mitigation potential since they sequester carbon. However, their carbon sink strength might depend on management. As a result of the balance between CO<sub>2</sub> uptake and emission, forest net ecosystem exchange (NEE) reaches optimal values (maximum sink strength) at young stand ages, followed by a gradual NEE decline over many years. Traditionally, this peak of NEE is believed to be concurrent with the peak of primary production (e.g., gross primary production, GPP); however, in theory, this concurrence may potentially vary depending on tree species, site conditions and the patterns of ecosystem respiration (R<sub>eco</sub>). In this study, we used eddy-covariance (EC)-based CO<sub>2</sub> flux measurements from 8 forest sites that are dominated by Norway spruce (Picea abies L.) and built machine learning models to find the optimal age of ecosystem productivity and that of CO<sub>2</sub> sequestration. We found that the net CO<sub>2</sub> uptake of Norway spruce forests peaked at ages of 30-40 yrs. Surprisingly, this NEE peak did not overlap with the peak of GPP, which appeared later at ages of 60-90 yrs. The mismatch between NEE and GPP was a result of the R<sub>eco</sub> increase that lagged behind the GPP increase associated with the tree growth at early age. Moreover, we also found that newly planted Norway spruce stands had a high probability (up to 90%) of being a C source in the first year, while, at an age as young as 5 yrs, they were likely to be a sink already. Further, using common climate change scenarios, our model results suggest that net CO<sub>2</sub> uptake of Norway spruce forests will increase under the future climate with young stands in the high latitude areas being more beneficial. Overall, the results suggest that forest management practices should consider NEE and forest productivity separately and harvests should be performed only after the optimal ages of both the CO<sub>2</sub> sequestration and productivity to gain full ecological and economic benefits.</p>

scholarly journals Impacts of climate change on primary production and carbon sequestration of boreal Norway spruce forests: Finland as a model

2012 ◽  
Vol 118 (2) ◽  
pp. 259-273 ◽  
Author(s):  
Zhen-Ming Ge ◽  
Seppo Kellomäki ◽  
Heli Peltola ◽  
Xiao Zhou ◽  
Hannu Väisänen ◽  
...  
Keyword(s):  
Climate Change ◽  
Carbon Sequestration ◽  
Primary Production ◽  
Norway Spruce ◽  
Spruce Forests ◽  
Impacts Of Climate Change

scholarly journals Carbon cycle uncertainty in the Alaskan Arctic

2014 ◽  
Vol 11 (15) ◽  
pp. 4271-4288 ◽  
Author(s):  
J. B. Fisher ◽  
M. Sikka ◽  
W. C. Oechel ◽  
D. N. Huntzinger ◽  
J. R. Melton ◽  
...  
Keyword(s):  
Climate Change ◽  
Carbon Cycle ◽  
Primary Production ◽  
Net Primary Production ◽  
Carbon Sink ◽  
Ecosystem Respiration ◽  
Gross Primary Production ◽  
The Arctic ◽  
Regional Patterns ◽  
Alaskan Arctic

Abstract. Climate change is leading to a disproportionately large warming in the high northern latitudes, but the magnitude and sign of the future carbon balance of the Arctic are highly uncertain. Using 40 terrestrial biosphere models for the Alaskan Arctic from four recent model intercomparison projects – NACP (North American Carbon Program) site and regional syntheses, TRENDY (Trends in net land atmosphere carbon exchanges), and WETCHIMP (Wetland and Wetland CH4 Inter-comparison of Models Project) – we provide a baseline of terrestrial carbon cycle uncertainty, defined as the multi-model standard deviation (σ) for each quantity that follows. Mean annual absolute uncertainty was largest for soil carbon (14.0 ± 9.2 kg C m−2), then gross primary production (GPP) (0.22 ± 0.50 kg C m−2 yr−1), ecosystem respiration (Re) (0.23 ± 0.38 kg C m−2 yr−1), net primary production (NPP) (0.14 ± 0.33 kg C m−2 yr−1), autotrophic respiration (Ra) (0.09 ± 0.20 kg C m−2 yr−1), heterotrophic respiration (Rh) (0.14 ± 0.20 kg C m−2 yr−1), net ecosystem exchange (NEE) (−0.01 ± 0.19 kg C m−2 yr−1), and CH4 flux (2.52 ± 4.02 g CH4 m−2 yr−1). There were no consistent spatial patterns in the larger Alaskan Arctic and boreal regional carbon stocks and fluxes, with some models showing NEE for Alaska as a strong carbon sink, others as a strong carbon source, while still others as carbon neutral. Finally, AmeriFlux data are used at two sites in the Alaskan Arctic to evaluate the regional patterns; observed seasonal NEE was captured within multi-model uncertainty. This assessment of carbon cycle uncertainties may be used as a baseline for the improvement of experimental and modeling activities, as well as a reference for future trajectories in carbon cycling with climate change in the Alaskan Arctic and larger boreal region.

scholarly journals Carbon cycle uncertainty in the Alaskan Arctic

2014 ◽  
Vol 11 (2) ◽  
pp. 2887-2932 ◽  
Author(s):  
J. B. Fisher ◽  
M. Sikka ◽  
W. C. Oechel ◽  
D. N. Huntzinger ◽  
J. R. Melton ◽  
...  
Keyword(s):  
Climate Change ◽  
Carbon Cycle ◽  
Primary Production ◽  
Net Primary Production ◽  
Carbon Sink ◽  
Ecosystem Respiration ◽  
Gross Primary Production ◽  
The Arctic ◽  
Regional Patterns ◽  
Alaskan Arctic

Abstract. Climate change is leading to a disproportionately large warming in the high northern latitudes, but the magnitude and sign of the future carbon balance of the Arctic are highly uncertain. Using 40 terrestrial biosphere models for Alaska, we provide a baseline of terrestrial carbon cycle structural and parametric uncertainty, defined as the multi-model standard deviation (σ) against the mean (x) for each quantity. Mean annual uncertainty (σ/x) was largest for net ecosystem exchange (NEE) (−0.01± 0.19 kg C m−2 yr−1), then net primary production (NPP) (0.14 ± 0.33 kg C m−2 yr−1), autotrophic respiration (Ra) (0.09 ± 0.20 kg C m−2 yr−1), gross primary production (GPP) (0.22 ± 0.50 kg C m−2 yr−1), ecosystem respiration (Re) (0.23 ± 0.38 kg C m−2 yr−1), CH4 flux (2.52 ± 4.02 g CH4 m−2 yr−1), heterotrophic respiration (Rh) (0.14 ± 0.20 kg C m−2 yr−1), and soil carbon (14.0± 9.2 kg C m−2). The spatial patterns in regional carbon stocks and fluxes varied widely with some models showing NEE for Alaska as a strong carbon sink, others as a strong carbon source, while still others as carbon neutral. Additionally, a feedback (i.e., sensitivity) analysis was conducted of 20th century NEE to CO2 fertilization (β) and climate (γ), which showed that uncertainty in γ was 2x larger than that of β, with neither indicating that the Alaskan Arctic is shifting towards a certain net carbon sink or source. Finally, AmeriFlux data are used at two sites in the Alaskan Arctic to evaluate the regional patterns; observed seasonal NEE was captured within multi-model uncertainty. This assessment of carbon cycle uncertainties may be used as a baseline for the improvement of experimental and modeling activities, as well as a reference for future trajectories in carbon cycling with climate change in the Alaskan Arctic.

Linking climate, gross primary productivity, and site index across forests of the western United States

2011 ◽  
Vol 41 (8) ◽  
pp. 1710-1721 ◽  
Author(s):  
Aaron R. Weiskittel ◽  
Nicholas L. Crookston ◽  
Philip J. Radtke
Keyword(s):  
Climate Change ◽  
Growth Models ◽  
Gross Primary Production ◽  
Site Index ◽  
Nonparametric Model ◽  
Climate Change Scenarios ◽  
Geographic Scale ◽  
Potential Productivity ◽  
The One ◽  
The Relationship

Assessing forest productivity is important for developing effective management regimes and predicting future growth. Despite some important limitations, the most common means for quantifying forest stand-level potential productivity is site index (SI). Another measure of productivity is gross primary production (GPP). In this paper, SI is compared with GPP estimates obtained from 3-PG and NASA’s MODIS satellite. Models were constructed that predict SI and both measures of GPP from climate variables. Results indicated that a nonparametric model with two climate-related predictor variables explained over 68% and 76% of the variation in SI and GPP, respectively. The relationship between GPP and SI was limited (R2 of 36%–56%), while the relationship between GPP and climate (R2 of 76%–91%) was stronger than the one between SI and climate (R2 of 68%–78%). The developed SI model was used to predict SI under varying expected climate change scenarios. The predominant trend was an increase of 0–5 m in SI, with some sites experiencing reductions of up to 10 m. The developed model can predict SI across a broad geographic scale and into the future, which statistical growth models can use to represent the expected effects of climate change more effectively.

scholarly journals Gross Primary Production and False Spring: a spatio-temporal analysis

2020 ◽  
Author(s):  
Emma Izquierdo-Verdiguier ◽  
Raúl Zurita-Milla ◽  
Álvaro Moreno-Martinez ◽  
Gustau Camps-Valls ◽  
Anja Klisch ◽  
...  
Keyword(s):  
Climate Change ◽  
Remote Sensing ◽  
Primary Production ◽  
Gross Primary Production ◽  
Damage Index ◽  
Temporal Analysis ◽  
Environmental Stressors ◽  
Impact Of Climate Change ◽  
False Spring ◽  
Spatial Temporal Analysis

<p>Phenological information can be obtained from different sources of data. For instance, from remote sensing data or products and from models driven by weather variables. The former typically allows analyzing land surface phenology whereas the latter provide plant phenological information. Analyzing relationships between both sources of data allows us to understand the impact of climate change on vegetation over space and time. For example, the onset of spring is advanced or delayed by changes in the climate. These alterations affect plant productivity and animal migrations.</p><p>Spring onset monitoring is supported by the Extended Spring Index (SI-x), which are a suite of regression-based models for key indicator plant species. These models (Schwartz et al. in 2013) are based on daily maximum and minimum temperature from the first day of the year (January 1<sup>st</sup>). The primary products of these models are the timing of first leaf and first bloom, but they also provide derivative products such as the timing of last freeze day and the risk of frost damage day (damage index) for each year. This information helps to understand if vegetation could have suffered from environmental stressors such as droughts or a late frost events. The effects of environmental stressors in vegetation could be captured by the false spring index, which relates the first leaf day and the last freeze day. Moreover, this information could be used to understand plant productivity as well as to evaluate the economic impact of climate change.</p><p>Previous works studied the relationship between remote sensing and plant level products by means of spatial-temporal analysis between Gross Primary Production (GPP) and a spring onset index. However, they did not consider the possible impact of false spring effect in these relationships. Here, we present a spatial-temporal analysis between GPP and the damage index to better understand the effect of false springs (in annual gross photosynthesis data). The analysis is done for the period 2000 to 2015 over the contiguous US and at spatial resolution of 1 km. We used the MODIS annual sum of GPP and the damage and false spring indices derived from the SI-x models.</p>

scholarly journals High Rates of Ecosytem Metabolism in Five Western Rivers

2011 ◽  
Vol 33 ◽  
pp. 115-118
Author(s):  
Robert Hall ◽  
Jennifer Tank ◽  
Michelle Baker ◽  
Emma Rosi-Marshall ◽  
Michael Grace ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Primary Production ◽  
Carbon Cycling ◽  
Carbon Balance ◽  
Carbon Fixation ◽  
Higher Plants ◽  
Ecosystem Respiration ◽  
Gross Primary Production ◽  
Total Rate ◽  
Global Carbon

Primary production and respiration are core functions of river ecosystems that in part determine the carbon balance. Gross primary production (GPP) is the total rate of carbon fixation by autotrophs such as algae and higher plants and is equivalent to photosynthesis. Ecosystem respiration (ER) measures rate at which organic carbon is mineralized to CO2 by all organisms in an ecosystem. Together these fluxes can indicate the base of the food web to support animal production (Marcarelli et al. 2011), can predict the cycling of other elements (Hall and Tank 2003), and can link ecosystems to global carbon cycling (Cole et al. 2007).

Energy and nutrients

2019 ◽  
pp. 161-176
Author(s):  
Richard T. Corlett
Keyword(s):  
Primary Production ◽  
Forest Ecology ◽  
Net Primary Production ◽  
Ecosystem Respiration ◽  
Gross Primary Production ◽  
Nitrogen And Phosphorus ◽  
New Information ◽  
Tropical Forest Ecology ◽  
Toxic Concentrations

This chapter deals with the ecology of Tropical East Asia from the perspective of water, energy, and matter flows through ecosystems, particularly forests. Data from the network of eddy flux covariance towers is revealing general patterns in gross primary production, ecosystem respiration, and net ecosystem production, and exchange. There is also new information on the patterns of net primary production and biomass within the region. In contrast, our understanding of the role of soil nutrients in tropical forest ecology still relies mostly on work done in the Neotropics, with just enough data from Asia to suggest that the major patterns may be pantropical. Nitrogen and phosphorus have received most attention regionally, followed by calcium, potassium, and magnesium, and there has been very little study of the role of micronutrients and potentially toxic concentrations of aluminium, manganese, and hydrogen ions. Animal nutrition has also been neglected.

Effects of climate change on evapotranspiration and soil water availability in Norway spruce forests in southern Finland: an ecosystem model based approach

Ecohydrology ◽  
2011 ◽  
Vol 6 (1) ◽  
pp. 51-63 ◽  
Author(s):  
Zhen-Ming Ge ◽  
Seppo Kellomäki ◽  
Xiao Zhou ◽  
Kai-Yun Wang ◽  
Heli Peltola ◽  
...  
Keyword(s):  
Climate Change ◽  
Soil Water ◽  
Norway Spruce ◽  
Water Availability ◽  
Ecosystem Model ◽  
Soil Water Availability ◽  
Spruce Forests ◽  
Model Based
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