Global positive gross primary productivity extremes and climate contributions during 1982–2016
Graphical abstract
Introduction
Climate change is and will continue to impact the natural environment and human well-being (Parry et al., 2007; Frank et al., 2015). Current projections, based upon contrasted emission scenarios, suggest somewhere between 0.3 and 4.8 °C warmings by the end of this century (IPCC, 2013). Moreover, the frequency and intensity of climate extremes are projected to further increase in the mid-to-late 21st century due to the ongoing global warming (Niu et al., 2018; Sui et al., 2018). Climate change, especially climate extreme events, may have a potential impact on the terrestrial carbon cycle (Du et al., 2018; von Buttlar et al., 2018). Terrestrial ecosystems are more sensitive to climate extremes than to gradual climate change because they typically show greater response strengths during shorter response times to these extremes (Frank et al., 2015; Hanson et al., 2006). Global climate extremes could affect the composition, structure, and function of the terrestrial ecosystem (Frank et al., 2015). Therefore, studying the impact of climate extremes on the global carbon cycle is crucially important (Ciais et al., 2005; Schwalm et al., 2012; Seneviratne et al., 2012).
Gross primary production (GPP) quantifies the amount of carbon dioxide fixed into organic compounds through photosynthesis by land plants (Campbell et al., 2017), and it is the basis of the global carbon cycle. GPP is the largest carbon flux in the global terrestrial ecosystem, driving a variety of ecological functions, such as vegetation respiration, growth, etc. Changes in GPP may significantly affect the global carbon cycle (W.Z. Chen et al., 2019).
Extreme events are generally defined as statistically unusual episodes or occurrences, which are beyond the bounds of typical or normal variability (Reichstein et al., 2013). Positive extremes represent a statistically unusual episode in a positive direction, which opposite to negative extremes. The most widely definition of extremes is the anomaly departing one or more standard deviations from the average value (Liu et al., 2013; Linthicum et al., 1999; Xu et al., 2012). In this study, after detrending the GPP time series, positive GPP extremes are defined as GPP at least 1.5 times the standard deviation higher than the mean value.
Positive GPP extremes represent the potential capacity of the terrestrial ecosystem to uptake carbon dioxide. Studying negative GPP extremes and their control mechanisms is an important step for developing adaptation strategies and risk reduction in the context of future climate change (W.Z. Chen et al., 2019). Comparing to negative GPP extremes, studying positive GPP extremes and their control mechanisms are useful for finding optimal climate conditions for vegetation growth, which are beneficial for developing planting strategies. Moreover, studying the positive GPP extreme is vital for the global carbon cycle and mitigation of global warming.
There are massive studies focusing on the negative impact of climate extremes on the global carbon cycle, such as extreme droughts (Lewis et al., 2011; Phillips et al., 2009), heat waves (Ciais et al., 2005; Lesk et al., 2016; Reichstein et al., 2007), wind storms (Sun et al., 2012), ice storm (Sun et al., 2012; Stone, 2008), and other climate extremes (Hong et al., 2011; Poulter et al., 2014). These climate extremes exerted huge impacts on the terrestrial ecosystem. For example, a European heatwave in 2003 caused an amount of CO2 loss in Western European equivalent to that had been absorbed from the atmosphere during the previous three to five years under normal weather conditions (Ciais et al., 2005; Vetter et al., 2008). During 2000–2004, drought results in carbon sink reduction of 30–298 TgC yr−1 in the western North America (Schwalm et al., 2012). In the 1999 storm, Lothar wind storms reduced 30% of Europe's carbon sink (Lindroth et al., 2009). But not all climate extremes can cause extreme impacts in terrestrial ecosystems (Frank et al., 2015).
There are also some studies that focus on negative GPP extremes and their control mechanisms. For example, W.Z. Chen et al. (2019) used monthly GPP simulated by ecological models to analyze negative GPP extremes in China during 1982–2015, and found that climate extremes decreased China's annual GPP by 2.8%. Furthermore, they reported that GPP negative anomalies can be explainable by drought in northern China and by temperature extremes in southern China. Reichstein et al. (2013) explored the mechanisms and impacts of climate extremes on the terrestrial carbon cycle. They investigated the control mechanisms of the hundred largest negative GPP extreme events during 1982–2011, and indicated that there are 56% of negative events caused by water scarcity and 14% of events by extremely high temperatures. Zscheischler et al. (2014) pointed out that GPP extremes are associated with climate anomalies; and more specifically, negative GPP extremes are mostly associated with droughts.
However, the effects of climate extremes on the global carbon cycle are diverse; not all climate extreme events will adversely affect the terrestrial ecosystem. Wang et al. (2018) used terrestrial biosphere models to simulate China's terrestrial ecosystem GPP during 1982–2015 and found that favorable climate extreme events contributed to positive GPP extremes in the terrestrial ecosystem in China. But there is still a lack of study on positive GPP extremes and their drivers on a global scale. Most previous studies mainly focus on negative climate extremes and their impacts, but do not analyze whether climate extremes are beneficial to global carbon uptake.
In this study, we used the BEPS (Boreal Ecosystem Productivity Simulator) and TRENDY models to detect positive GPP extremes, and investigated the relationship between GPP extremes and climate extreme events on a global scale during the period from 1982 to 2016. The specific objectives are: (i) to explore the spatial patterns of global positive GPP extremes and the contribution of Plant Function Types (PFTs) during 1982–2016; (ii) to investigate the effects of positive extremes of temperature, precipitation, and solar radiation on the carbon uptake of the global terrestrial ecosystem. The outcome of this study may provide new knowledge on the impact of positive climate extremes on the productivity of global terrestrial ecosystems.
Section snippets
The BEPS model
BEPS is a process-based model, which is driven by leaf area index (LAI), meteorological data, land cover types, soil data, and CO2 concentrations to simulate daily carbon flux from the terrestrial ecosystem (Liu et al., 1997; J.M. Chen et al., 2019). The BEPS model stratifies canopies into sunlit and shaded leaves and calculates canopy-level GPP by the sum of sunlit and shaded leaves GPP (Chen et al., 1999).where Asun
The spatial patterns of the positive GPP extremes
In order to map the spatial pattern of the global positive GPP extremes, for a specific location, all anomalies of positive GPP extremes were summed and then divided by 35 years. According to the BEPS simulation results (Fig. 2a), the anomalies of positive GPP extremes in Europe, Southeast Asia, Eastern North America, and most areas of South America were relatively large, with anomalies larger than 20.0 gC/m2/year, but in these areas, the relative extremes (GPP anomalies/annual GPP) were
Model performance
The TRENDY models showed quite distinct spatial patterns of positive GPP anomalies among models from 1982 to 2016; this is due to different assumptions and parameter settings among these models (Smith et al., 2016). J.M. Chen et al. (2019) indicated that the biggest uncertainty of the TRENDY models is in the simulation of changes in vegetation structural parameters with time. The TRENDY models were prone to large uncertainties in the simulation of vegetation structural parameters such as LAI (
Conclusions
This study makes the first attempt to investigate the spatial patterns of positive GPP extremes and their relationship with climate extremes in the global terrestrial ecosystem. We used the BEPS and TRENDY models to detect positive GPP extremes at the pixel level and explored the role of climate extremes in the global terrestrial ecosystem from 1982 to 2016. We found that the TRENDY showed a large difference among models in some areas of the global, and the results of the BEPS model were close
CRediT authorship contribution statement
Miaomiao Wang: Conceptualization, Methodology, Formal analysis, Investigation, Writing – original draft. Shaoqiang Wang: Conceptualization, Methodology, Writing – review & editing, Supervision. Jian Zhao: Resources, Writing – review & editing, Supervision. Weimin Ju: Validation, Resources, Data curation. Zhuo Hao: Methodology, Resources.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgments
This work was supported by the National Key Research and Development Program of China [grant number 2017YFC0503803 and 2017YFC1200601-2] and the project of the Central Government Guides Local Science and Technology Development [grant number 2020L3020]. We would like to acknowledge all data providers. We greatly appreciate Dr. Yang Liu of the Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, for providing the GLOBMAP LAI data to drive the BPES model.
Funding
This work was supported by the National Key Research and Development Program of China [grant number 2017YFC0503803 and 2017YFC1200601-2] and the project of the Central Government Guides Local Science and Technology Development [grant number 2020L3020].
References (72)
- et al.
Daily canopy photosynthesis model through temporal and spatial scaling for remote sensing applications
Ecol. Model.
(1999) - et al.
Negative extreme events in gross primary productivity and their drivers in China during the past three decades
Agricultural and Forest Meteorology
(2019) - et al.
Global patterns of extreme drought-induced loss in land primary production: Identifying ecological extremes from rain-use efficiency
Sci. Total Environ.
(2018) - et al.
Net primary productivity of China’s terrestrial ecosystems from a process model driven by remote sensing
J. Environ. Manag.
(2007) - et al.
A process-based boreal ecosystem productivity simulator using remote sensing inputs
Remote Sens. Environ.
(1997) - et al.
Integrating remotely sensed data with an ecosystem model to estimate net primary productivity in East Asia
Remote Sens. Environ.
(2002) - et al.
Leaf phenology sensitivity to temperature in European trees: do within-species populations exhibit similar responses?
Agric. For. Meteorol.
(2009) - et al.
Terrestrial Gross Carbon Dioxide Uptake: Global Distribution and Covariation with Climate
Science
(2010) - et al.
Seasonal and interannual variability of climate and vegetation indices across the Amazon
Proc. Natl. Acad. Sci. U. S. A.
(2010) - et al.
Impacts of droughts and extreme-temperature events on gross primary production and ecosystem respiration: a systematic assessment across ecosystems and climate zones
Biogeosciences
(2018)
Large historical growth in global terrestrial gross primary production
Nature
Effects of foliage clumping on the estimation of global terrestrial gross primary productivity
Glob. Biogeochem. Cycles
Vegetation structural change since 1981 significantly enhanced the terrestrial carbon sink
Nat. Commun.
Europe-wide reduction in primary productivity caused by the heat and drought in 2003
Nature
Joint UK Land Environment Simulator (JULES) Version 3.0 User Manual
Effects of climate extremes on the terrestrial carbon cycle: concepts, processes and potential future impacts
Global Change Biology
Mapping conservation priorities and connectivity pathways under climate change for tropical ecosystems
Climatic Change
Response of temperate grasslands at different altitudes to simulated summer drought differed but scaled with annual precipitation
Biogeosciences
Interannual variations in atmospheric mass over liquid water oceans, continents, and sea-icecovered arctic regions and their possible impacts on the boreal winter climate
J. Geophys. Res. Atmos.
ORCHIDEEMICT (v8.4.1), a land surface model for the high latitudes: model description and validation
Geosci. Model. Dev. Discuss
Bridging the gap between science and the stakeholder: the case of climate change research
Clim. Res.
A new version of the CABLE land surface model (Subversion revision r4601) incorporating land use and land cover change, woody vegetation demography, and a novel optimisation-based approach to plant coordination of photosynthesis
Geosci. Model. Dev. Discuss
Climate extremes in the Kobresia meadow area of the Qinghai-Tibetan Plateau, 1961–2008
Environ. Earth Sci.
Terrestrial ecosystem carbon dynamics and climate feedbacks
Nature
Roles of European blocking and tropical-extratropical interaction in the 2010 Pakistan flooding
Geophys. Res. Lett.
Evaluation of spatially explicit emission scenario of land-use change and biomass burning using a processbased biogeochemical model
l. J. Land Use Sci.
20th century changes in carbon isotopes and water-use efficiency: tree-ring-based evaluation of the CLM4.5 and LPX-Bern models
Biogeosciences
A dynamic global vegetation model for studies of the coupled atmosphere-biosphere system
Glob. Biogeochem. Cycles
Global Carbon Budget 2017
Earth Syst. Sci. Data
Influence of extreme weather disasters on global crop production
Nature
The 2010 Amazon drought
Science
Storms can cause Europe-wide reduction in forest carbon sink
Glob. Chang. Biol.
Climate and satellite indicators to forecast rift valley fever epidemics in Kenya
Science
Retrospective retrieval of long-term consistent global leaf area index (1981–2011) from combined AVHRR and MODIS data
J. Geophys. Res. Biogeosci.
Changes of net primary productivity in China during recent 11 years detected using an ecological model driven by MODIS data
Front. Earth Sci.
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