Warmer spring alleviated the impacts of 2018 European summer heatwave and drought on vegetation photosynthesis

Wang, Songhan; Zhang, Yongguang; Ju, Weimin; Porcar‐Castell, Albert; Ye, Shanshan; Zhang, Zhaoying; Brümmer, Christian; Urbaniak, Marek; Mammarella, Ivan; Juszczak, Radosław; Boersma, K. Folkert

doi:10.1016/j.agrformet.2020.108195

Cited by 65 publications

(24 citation statements)

References 91 publications

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“…Generally, the IAVs of annual NEE and seasonal cycles are related to large-scale climate anomalies and regional extreme climate events like droughts, heat waves, and precipitation, which have been widely studied around the world (e.g., Ciais et al, 2005;Betts et al, 2020;Koren et al, 2018;Reichstein et al, 2013;Frank et al, 2015;Zhao and Running, 2010). Evidence has shown that severe drought events that occurred in the Amazon in 2010 (Potter et al, 2011;Doughty et al, 2015), Europe in 2010 (Bastos et al, 2020a), 2012 (He et al, 2019), and 2018 (Bastos et al, 2020b;Graf et al, 2020;Wang et al, 2020), the United States in 2011-2012 (He et al, 2018;Wolf et al, 2016;Byrne et al, 2020) and 2018 (Li et al, 2020), and Australia in 2019 (Byrne et al, 2021) caused significant reductions of terrestrial carbon uptakes. Accordingly, as shown in Fig.…”

Section: Interannual Variations and Seasonal Cyclesmentioning

confidence: 99%

A 10-year global monthly averaged terrestrial net ecosystem exchange dataset inferred from the ACOS GOSAT v9 XCO₂ retrievals (GCAS2021)

Jiang

et al. 2022

Earth Syst. Sci. Data

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Abstract. A global gridded net ecosystem exchange (NEE) of CO2 dataset is vital in global and regional carbon cycle studies. Top-down atmospheric inversion is one of the major methods to estimate the global NEE; however, the existing global NEE datasets generated through inversion from conventional CO2 observations have large uncertainties in places where observational data are sparse. Here, by assimilating the GOSAT ACOS v9 XCO2 product, we generate a 10-year (2010–2019) global monthly terrestrial NEE dataset using the Global Carbon Assimilation System, version 2 (GCASv2), which is named GCAS2021. It includes gridded (1∘×1∘), globally, latitudinally, and regionally aggregated prior and posterior NEE and ocean (OCN) fluxes and prescribed wildfire (FIRE) and fossil fuel and cement (FFC) carbon emissions. Globally, the decadal mean NEE is -3.73±0.52 PgC yr−1, with an interannual amplitude of 2.73 PgC yr−1. Combining the OCN flux and FIRE and FFC emissions, the net biosphere flux (NBE) and atmospheric growth rate (AGR) as well as their inter-annual variabilities (IAVs) agree well with the estimates of the Global Carbon Budget 2020. Regionally, our dataset shows that eastern North America, the Amazon, the Congo Basin, Europe, boreal forests, southern China, and Southeast Asia are carbon sinks, while the western United States, African grasslands, Brazilian plateaus, and parts of South Asia are carbon sources. In the TRANSCOM land regions, the NBEs of temperate N. America, northern Africa, and boreal Asia are between the estimates of CMS-Flux NBE 2020 and CT2019B, and those in temperate Asia, Europe, and Southeast Asia are consistent with CMS-Flux NBE 2020 but significantly different from CT2019B. In the RECCAP2 regions, except for Africa and South Asia, the NBEs are comparable with the latest bottom-up estimate of Ciais et al. (2021). Compared with previous studies, the IAVs and seasonal cycles of NEE of this dataset could clearly reflect the impacts of extreme climates and large-scale climate anomalies on the carbon flux. The evaluations also show that the posterior CO2 concentrations at remote sites and on a regional scale, as well as on vertical CO2 profiles in the Asia-Pacific region, are all consistent with independent CO2 measurements from surface flask and aircraft CO2 observations, indicating that this dataset captures surface carbon fluxes well. We believe that this dataset can contribute to regional- or national-scale carbon cycle and carbon neutrality assessment and carbon dynamics research. The dataset can be accessed at https://doi.org/10.5281/zenodo.5829774 (Jiang, 2022).

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Section: Interannual Variations and Seasonal Cyclesmentioning

confidence: 99%

A 10-year global monthly averaged terrestrial net ecosystem exchange dataset inferred from the ACOS GOSAT v9 XCO₂ retrievals (GCAS2021)

Jiang

et al. 2022

Earth Syst. Sci. Data

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“…For example, in tropical Asia, the carbon sources from January to April in 360 Generally, the IAVs of annual NEE and seasonal cycles are related to large-scale climate anomalies and regional extreme climate events like droughts, heatwaves and precipitation, which have been widely studied around the world (e.g., Ciais et al, 2005;Betts et al, 2020;Bastos et al, 2018;Koren et al, 2018;Reichstein et al, 2013;Frank et al, 2015;Zhao and Running, 365 2010). Evidences have shown that severe drought events occurred in Amazon in 2010 (Potter et al, 2011;Doughty et al, 2015), Europe in 2010 (Bastos et al, 2020a), 2012 (He et al, 2019) and 2018 (Bastos et al, 2020b;Graf et al, 2020;Wang et al, 2020), the Unite States in 2011-2012 (He et al, 2018;Wolf et al, 2016) and 2018 (Li et al, 2020), and Australia in 2019 (Byrne et al, 2021) had caused significant reductions of terrestrial carbon uptakes. Accordingly, as shown in Figure 8, the NEE in this dataset are also much smaller in those years and regions compared with the normal year.…”

Section: Interannual Variations and Seasonal Cyclesmentioning

confidence: 99%

A ten-year global monthly averaged terrestrial NEE inferred from the ACOS GOSAT v9 XCO<sub>2</sub> retrievals (GCAS2021)

Jiang

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

Abstract. A global gridded Net Ecosystem Exchange (NEE) of CO2 dataset is vital in global and regional carbon cycle studies. Top-down atmospheric inversion is one of the major methods to estimate the global NEE, however, the existing global NEE datasets generated through inversion from conventional CO2 observations have large uncertainties in places where observational data are sparse. Here, by assimilating the GOSAT ACOS v9 XCO2 product, we generate a ten-year (2010–2019) global monthly terrestrial NEE dataset using the Global Carbon Assimilation System, version 2 (GCASv2), which is named as GCAS2021. It includes gridded (1° × 1°), globally, latitudinally, and regionally aggregated prior and posterior NEE and ocean (OCN) fluxes, and prescribed wildfire (FIRE) and fossil fuel and cement (FFC) carbon emissions. Globally, the decadal mean NEE is −3.73 ± 0.52 PgC yr−1, with interannual amplitude of 2.73 PgC yr−1. Combining the OCN flux, and FIRE and FFC emissions, the net biosphere flux (NBE) and atmospheric growth rate (AGR) as well as their inter-annual variabilities (IAVs) agree well with the estimates of Global Carbon Budget 2020. Regionally, our dateset shows that eastern North America, Amazon, Congo Basin, Europe, boreal forests, southern China and Southeast Asia are carbon sinks, while western US, African grasslands, Brazilian plateaus and parts of South Asia are carbon sources. In the TRANSCOM land regions, the NBEs of temperate N. America, northern Africa and boreal Asia are between the estimates of CMS-Flux NBE 2020 and CT2019B, and those in temperate Asia, Europe, and Southeast Asia are consistent with CMS-Flux NBE 2020 but significantly different from CT2019B. In the RECCAP2 regions, except for Africa and South Asia, the NBEs are comparable with the latest bottom-up estimate of Ciais et al. (2021). Compared with previous studies, the IAVs and seasonal cycles of NEE of this dataset could clearly reflect the impacts of extreme climates and large-scale climate anomalies on the carbon flux. The evaluations also show that the posterior CO2 concentrations at remote sites and in regional scale, as well as on vertical CO2 profiles in the Asia-Pacific region and the Amazon basin, are all consistent with independent CO2 measurements from surface flask and aircraft CO2 observations, indicating that this dataset captures surface carbon fluxes well. We believe that this dataset will contribute to regional or national-scale carbon cycle and carbon neutrality assessment, and carbon dynamics research. The dataset can be accessed at https://doi.org/10.5281/zenodo.5829774 (Jiang, 2022).

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“…For the 90day application, we concentrate on the extended summer growing season (April-October) to examine the capacities of the methodology in detecting large-scale terrestrial biosphere anomalies. We focus in particular on the summer of 2018, which saw a spate of intense droughts and heat waves across Central and Northern Europe that altered continent-wide GPP and CO2 storage and flux patterns (Lindroth et al, 2020;Ramonet et al, 2020;Rinne et al, 2020;Wang et al, 2020). Table 2 summarizes the results of the SSA extraction for the ten sites for the period 1 November 2015 to 31 March 2020.…”

Section: Synoptic and Seasonal Anomaly Detectionmentioning

confidence: 99%

An algorithm to detect non-background signals in greenhouse gas time series from European tall tower and mountain stations

Resovsky

Ramonet

Rivier

et al. 2021

Preprint

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Abstract. We present a statistical framework for near real-time signal processing to identify regional signals in CO2 time series recorded at stations which are normally uninfluenced by local processes. A curve-fitting function is first applied to the detrended time series to derive a harmonic describing the annual CO2 cycle. We then combine a polynomial fit to the data with a short-term residual filter to estimate the smoothed cycle and define a seasonally-adjusted noise component, equal to two standard deviations of the smoothed cycle about the annual cycle. Spikes in the smoothed daily data which rise above this 2σ threshold are classified as anomalies. Examining patterns of anomalous behavior across multiple sites allows us to quantify the impacts of synoptic-scale weather events and better understand the regional carbon cycling implications of extreme seasonal occurrences such as droughts.

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Warmer spring alleviated the impacts of 2018 European summer heatwave and drought on vegetation photosynthesis

Cited by 65 publications

References 91 publications

A 10-year global monthly averaged terrestrial net ecosystem exchange dataset inferred from the ACOS GOSAT v9 XCO₂ retrievals (GCAS2021)

A 10-year global monthly averaged terrestrial net ecosystem exchange dataset inferred from the ACOS GOSAT v9 XCO₂ retrievals (GCAS2021)

A ten-year global monthly averaged terrestrial NEE inferred from the ACOS GOSAT v9 XCO<sub>2</sub> retrievals (GCAS2021)

An algorithm to detect non-background signals in greenhouse gas time series from European tall tower and mountain stations

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Warmer spring alleviated the impacts of 2018 European summer heatwave and drought on vegetation photosynthesis

Cited by 65 publications

References 91 publications

A 10-year global monthly averaged terrestrial net ecosystem exchange dataset inferred from the ACOS GOSAT v9 XCO2 retrievals (GCAS2021)

A 10-year global monthly averaged terrestrial net ecosystem exchange dataset inferred from the ACOS GOSAT v9 XCO2 retrievals (GCAS2021)

A ten-year global monthly averaged terrestrial NEE inferred from the ACOS GOSAT v9 XCO&lt;sub&gt;2&lt;/sub&gt; retrievals (GCAS2021)

An algorithm to detect non-background signals in greenhouse gas time series from European tall tower and mountain stations

Contact Info

Product

Resources

About

A 10-year global monthly averaged terrestrial net ecosystem exchange dataset inferred from the ACOS GOSAT v9 XCO₂ retrievals (GCAS2021)

A 10-year global monthly averaged terrestrial net ecosystem exchange dataset inferred from the ACOS GOSAT v9 XCO₂ retrievals (GCAS2021)

A ten-year global monthly averaged terrestrial NEE inferred from the ACOS GOSAT v9 XCO<sub>2</sub> retrievals (GCAS2021)