Global Carbon Budget 2021 Friedlingstein, Pierre; Jones, Matthew W; O'Sullivan, Michael ...
Earth system science data,
04/2022, Volume:
14, Issue:
4
Journal Article
Peer reviewed
Open access
Accurate assessment of anthropogenic carbon dioxide (CO.sub.2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere in a changing climate is critical to better ...understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe and synthesize datasets and methodology to quantify the five major components of the global carbon budget and their uncertainties. Fossil CO.sub.2 emissions (E.sub.FOS) are based on energy statistics and cement production data, while emissions from land-use change (E.sub.LUC ), mainly deforestation, are based on land use and land-use change data and bookkeeping models. Atmospheric CO.sub.2 concentration is measured directly, and its growth rate (G.sub.ATM) is computed from the annual changes in concentration. The ocean CO.sub.2 sink (S.sub.OCEAN) is estimated with global ocean biogeochemistry models and observation-based data products. The terrestrial CO.sub.2 sink (S.sub.LAND) is estimated with dynamic global vegetation models. The resulting carbon budget imbalance (B.sub.IM ), the difference between the estimated total emissions and the estimated changes in the atmosphere, ocean, and terrestrial biosphere, is a measure of imperfect data and understanding of the contemporary carbon cycle. All uncertainties are reported as ±1Ï. For the first time, an approach is shown to reconcile the difference in our E.sub.LUC estimate with the one from national greenhouse gas inventories, supporting the assessment of collective countries' climate progress.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
The terrestrial gross primary productivity (GPP) plays a crucial role in regional or global ecological environment monitoring and carbon cycle research. Many previous studies have produced multiple ...products using different models, but there are still significant differences between these products. This study generated a global GPP dataset (NI-LUE GPP) with 0.05° spatial resolution and at 8 day-intervals from 2001 to 2018 based on an improved light use efficiency (LUE) model that simultaneously considered temperature, water, atmospheric CO2 concentrations, radiation components, and nitrogen (N) index. To simulate the global GPP, we mapped the global optimal ecosystem temperatures (Topteco) using satellite-retrieved solar-induced chlorophyll fluorescence (SIF) and applied it to calculate temperature stress. In addition, green chlorophyll index (CIgreen), which had a strong correlation with the measured canopy N concentrations (r = 0.82), was selected as the vegetation index to characterize the canopy N concentrations to calculate the spatiotemporal dynamic maximum light use efficiency (εmax). Multiple existing global GPP datasets were used for comparison. Verified by FLUXNET GPP, our product performed well on daily and yearly scales. NI-LUE GPP indicated that the mean global annual GPP is 129.69 ± 3.11 Pg C with an increasing trend of 0.53 Pg C/yr from 2001 to 2018. By calculating the SPAtial Efficiency (SPAEF) with other products, we found that NI-LUE GPP has good spatial consistency, which indicated that our product has a reasonable spatial pattern. This product provides a reliable and alternative dataset for large-scale carbon cycle research and monitoring long-term GPP variations.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
3.
Global Carbon Budget 2019 Friedlingstein, Pierre; Jones, Matthew W.; O'Sullivan, Michael ...
Earth system science data,
12/2019, Volume:
11, Issue:
4
Journal Article
Peer reviewed
Open access
Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere – the “global carbon budget” – is important to ...better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe data sets and methodology to quantify the five major components of the global carbon budget and their uncertainties. Fossil CO2 emissions (E(FF)) are based on energy statistics and cement production data, while emissions from land use change (E(LUC)), mainly deforestation, are based on land use and land use change data and bookkeeping models. Atmospheric CO2 concentration is measured directly and its growth rate (G(ATM)) is computed from the annual changes in concentration. The ocean CO2 sink (S(OCEAN)) and terrestrial CO2 sink (S(LAND)) are estimated with global process models constrained by observations. The resulting carbon budget imbalance (B(IM)), the difference between the estimated total emissions and the estimated changes in the atmosphere, ocean, and terrestrial biosphere, is a measure of imperfect data and understanding of the contemporary carbon cycle. All uncertainties are reported as ±1σ. For the last decade available (2009–2018), E(FF) was 9.5±0.5 GtC/yr, E(LUC) 1.5±0.7 GtC/yr, G(ATM) 4.9±0.02 GtC/yr (2.3±0.01 ppm/yr), S(OCEAN) 2.5±0.6 GtC/yr, and S(LAND) 3.2±0.6 GtC/yr, with a budget imbalance B(IM) of 0.4 GtC/yr indicating overestimated emissions and/or underestimated sinks. For the year 2018 alone, the growth in E(FF) was about 2.1 % and fossil emissions increased to 10.0±0.5 GtC/yr, reaching 10 GtC/yr for the first time in history, E(LUC) was 1.5±0.7 GtC/yr, for total anthropogenic CO2 emissions of 11.5±0.9 GtC/yr (42.5±3.3 GtCO2). Also for 2018, G(ATM) was 5.1±0.2 GtC/yr (2.4±0.1 ppm/yr), S(OCEAN) was 2.6±0.6 GtC/yr, and S(LAND) was 3.5±0.7 GtC/yr, with a B(IM) of 0.3 GtC. The global atmospheric CO2 concentration reached 407.38±0.1 ppm averaged over 2018. For 2019, preliminary data for the first 6–10 months indicate a reduced growth in E(FF) of +0.6 % (range of −0.2 % to 1.5 %) based on national emissions projections for China, the USA, the EU, and India and projections of gross domestic product corrected for recent changes in the carbon intensity of the economy for the rest of the world. Overall, the mean and trend in the five components of the global carbon budget are consistently estimated over the period 1959–2018, but discrepancies of up to 1 GtC/yr persist for the representation of semi-decadal variability in CO2 fluxes. A detailed comparison among individual estimates and the introduction of a broad range of observations shows (1) no consensus in the mean and trend in land use change emissions over the last decade, (2) a persistent low agreement between the different methods on the magnitude of the land CO2 flux in the northern extra-tropics, and (3) an apparent underestimation of the CO2 variability by ocean models outside the tropics. This living data update documents changes in the methods and data sets used in this new global carbon budget and the progress in understanding of the global carbon cycle compared with previous publications of this data set (Le Quéré et al., 2018a, b, 2016, 2015a, b, 2014, 2013).
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The observed global net land carbon sink is captured by current land models. All models agree that atmospheric CO2 and nitrogen deposition driven gains in carbon stocks are partially offset by ...climate and land-use and land-cover change (LULCC) losses. However, there is a lack of consensus in the partitioning of the sink between vegetation and soil, where models do not even agree on the direction of change in carbon stocks over the past 60 years. This uncertainty is driven by plant productivity, allocation, and turnover response to atmospheric CO2 (and to a smaller extent to LULCC), and the response of soil to LULCC (and to a lesser extent climate). Overall, differences in turnover explain ~70% of model spread in both vegetation and soil carbon changes. Further analysis of internal plant and soil (individual pools) cycling is needed to reduce uncertainty in the controlling processes behind the global land carbon sink.
Global net land carbon uptake or net biome production (NBP) has increased during recent decades
. Whether its temporal variability and autocorrelation have changed during this period, however, ...remains elusive, even though an increase in both could indicate an increased potential for a destabilized carbon sink
. Here, we investigate the trends and controls of net terrestrial carbon uptake and its temporal variability and autocorrelation from 1981 to 2018 using two atmospheric-inversion models, the amplitude of the seasonal cycle of atmospheric CO
concentration derived from nine monitoring stations distributed across the Pacific Ocean and dynamic global vegetation models. We find that annual NBP and its interdecadal variability increased globally whereas temporal autocorrelation decreased. We observe a separation of regions characterized by increasingly variable NBP, associated with warm regions and increasingly variable temperatures, lower and weaker positive trends in NBP and regions where NBP became stronger and less variable. Plant species richness presented a concave-down parabolic spatial relationship with NBP and its variability at the global scale whereas nitrogen deposition generally increased NBP. Increasing temperature and its increasing variability appear as the most important drivers of declining and increasingly variable NBP. Our results show increasing variability of NBP regionally that can be mostly attributed to climate change and that may point to destabilization of the coupled carbon-climate system.
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GEOZS, IJS, IMTLJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBMB, UL, UM, UPUK, ZAGLJ
Ensemble runs of high‐resolution (~10 km; N1280) global climate simulations (2005–2010) with the Met Office HadGEM3 model are analysed over large urban areas in the south‐east UK (London) and ...south‐east China (Shanghai, Hangzhou, Nanjing region). With a focus on urban areas, we compare meteorological observations to study the response of modelled surface heat fluxes and screen‐level temperatures to urbanization. HadGEM3 has a simple urban slab scheme with prescribed, globally fixed bulk parameters. Misrepresenting the magnitude or the extent of urban land cover can result in land‐surface model bias. As urban land‐cover fractions are severely under‐estimated in China, this impacts surface heat‐flux partitioning and quintessential features, such as the urban heat island. Combined with the neglect of anthropogenic heat emissions, this can result in misrepresentation of heat‐wave intensities (or cold spells) in cities. The model performance in urban areas could be improved if bulk parameters are modelled instead of prescribed, but this necessitates the availability of local morphology data on a global level. Improving land‐cover information and providing more flexible ways to account for differences between cities (e.g., anthropogenic emission; morphology) is essential for realistic future projections of city climates, especially if model output is intended for urban climate services.
Misrepresenting the magnitude or the extent of urban land cover can lead to land‐surface model bias in high‐resolution (~10 km; N1280) HadGEM3 climate simulations. This affects surface heat‐flux partitioning and quintessential features of city climates such as the urban heat island. Combined with the neglect of anthropogenic heat emissions, this can result in misrepresentation of heat‐wave intensities, with implications for future projections of city climates and their use to inform urban climate services.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
Abstract
Most biological rates depend on the rate of respiration. Temperature variation is typically considered the main driver of daily plant respiration rates, assuming a constant daily respiration ...rate at a set temperature. Here, we show empirical data from 31 species from temperate and tropical biomes to demonstrate that the rate of plant respiration at a constant temperature decreases monotonically with time through the night, on average by 25% after 8 h of darkness. Temperature controls less than half of the total nocturnal variation in respiration. A new universal formulation is developed to model and understand nocturnal plant respiration, combining the nocturnal decrease in the rate of plant respiration at constant temperature with the decrease in plant respiration according to the temperature sensitivity. Application of the new formulation shows a global reduction of 4.5 −6 % in plant respiration and an increase of 7-10% in net primary production for the present-day.
The loss of biodiversity is threatening ecosystem productivity and services worldwide, spurring efforts to quantify its effects on the functioning of natural ecosystems. Previous research has focused ...on the positive role of biodiversity on resource acquisition (i.e., niche complementarity), but a lack of study on resource utilization efficiency, a link between resource and productivity, has rendered it difficult to quantify the biodiversity–ecosystem functioning relationship. Here we demonstrate that biodiversity loss reduces plant productivity, other things held constant, through theory, empirical evidence, and simulations under gradually relaxed assumptions. We developed a theoretical model named niche–efficiency to integrate niche complementarity and a heretofore-ignored mechanism of diminishing marginal productivity in quantifying the effects of biodiversity loss on plant productivity. Based on niche–efficiency, we created a relative productivity metric and a productivity impact index (PII) to assist in biological conservation and resource management. Relative productivity provides a standardized measure of the influence of biodiversity on individual productivity, and PII is a functionally based taxonomic index to assess individual species’ inherent value in maintaining current ecosystem productivity. Empirical evidence from the Alaska boreal forest suggests that every 1% reduction in overall plant diversity could render an average of 0.23% decline in individual tree productivity. Out of the 283 plant species of the region, we found that large woody plants generally have greater PII values than other species. This theoretical model would facilitate the integration of biological conservation in the international campaign against several pressing global issues involving energy use, climate change, and poverty.
Significance International society has made a commitment to mainstreaming biodiversity conservation into broader socioeconomic development, but an incomplete theoretical basis translates into a lack of practical applications, especially regarding how individual plant productivity changes in response to the overall species loss. In this study, we developed niche–efficiency theory to address two mechanisms behind the effects of biodiversity on individual plant productivity. Supported by empirical evidence at the individual plant level, our theory facilitates adaptive biodiversity management implementation, and the straightforward mathematical formula facilitates the integration of biological conservation in our campaign against pressing global issues. Based on this theory, we developed relative productivity and productivity impact index to provide function-based tools for prioritizing biological conservation efforts.
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BFBNIB, NMLJ, NUK, PNG, SAZU, UL, UM, UPUK
There is high confidence that global warming intensifies all components of the global water cycle. This work investigates the possible effects of global warming on river flows worldwide in the coming ...decades. We conducted 18 global hydrological simulations to assess how river flows are projected to change in the near future (2015–2050) compared to the recent past (1950–2014). The simulations are forced by runoff from the High Resolution Model Intercomparison Project (HighResMIP) CMIP6 global climate models (GCMs), which assume a high-emission scenario for the projections. The assessment includes estimating the signal-to-noise (S/N) ratio and the time of emergence (ToE) of all the rivers in the world. Consistently with the water cycle intensification, the hydrological simulations project a clear positive global river discharge trend from ∼2000 that emerges beyond the levels of natural variability and becomes “unfamiliar” by 2017 and “unusual” by 2033. Simulations agree that the climate change signal is dominated by strong increases in the flows of rivers originating in central Africa and South Asia and those discharging into the Arctic Ocean, partially compensated for by the reduced flow projected for Patagonian rivers. The potential implications of such changes may include more frequent floods in central African and South Asian rivers, driven by the projected magnification of the annual cycles with unprecedented peaks, a freshening of the Arctic Ocean from extra freshwater release, and limited water availability in Patagonia given the projected drier conditions of its rivers. This underscores the critical need for a paradigm shift in prioritizing water-related concerns amidst the challenges of global warming.
Most studies of adaptive immunity to SARS-CoV-2 infection focus on peripheral blood, which may not fully reflect immune responses at the site of infection. Using samples from 110 children undergoing ...tonsillectomy and adenoidectomy during the COVID-19 pandemic, we identified 24 samples with evidence of previous SARS-CoV-2 infection, including neutralizing antibodies in serum and SARS-CoV-2-specific germinal center and memory B cells in the tonsils and adenoids. Single-cell B cell receptor (BCR) sequencing indicated virus-specific BCRs were class-switched and somatically hypermutated, with overlapping clones in the two tissues. Expanded T cell clonotypes were found in tonsils, adenoids and blood post-COVID-19, some with CDR3 sequences identical to previously reported SARS-CoV-2-reactive T cell receptors (TCRs). Pharyngeal tissues from COVID-19-convalescent children showed persistent expansion of germinal center and antiviral lymphocyte populations associated with interferon (IFN)-γ-type responses, particularly in the adenoids, and viral RNA in both tissues. Our results provide evidence for persistent tissue-specific immunity to SARS-CoV-2 in the upper respiratory tract of children after infection.
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GEOZS, IJS, IMTLJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBMB, UL, UM, UPUK, ZAGLJ
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