Critical needs to close monitoring gaps in pan-tropical wetland CH 4 emissions
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Qing Zhu↗
Engineering topics
Hanqin Tian
Publications and source records attributed to Hanqin Tian.
The optimization of model ensemble composition and size can enhance the robustness of crop yield projections
Linked climate and crop simulation models are widely used to assess the impact of climate change on agriculture. However, it is unclear how ensemble configurations (model composition and size) influence crop yield projections and uncertainty. Here, we investigate the influences of ensemble configurations on crop yield projections and modeling uncertainty from Global Gridded Crop Models and Global Climate Models under future climate change. We performed a cluster analysis to identify distinct groups of ensemble members based on their projected outcomes, revealing unique patterns in crop yield projections and corresponding uncertainty levels, particularly for wheat and soybean. Furthermore, our findings suggest that approximately six Global Gridded Crop Models and 10 Global Climate Models are sufficient to capture modeling uncertainty, while a cluster-based selection of 3-4 Global Gridded Crop Models effectively represents the full ensemble. The contribution of individual Global Gridded Crop Models to overall uncertainty varies depending on region and crop type, emphasizing the importance of considering the impact of specific models when selecting models for local-scale applications. Our results emphasize the importance of model composition and ensemble size in identifying the primary sources of uncertainty in crop yield projections, offering valuable guidance for optimizing ensemble configurations in climate-crop modeling studies tailored to specific applications.
Agriculture↗
Observational Constraints Reduce Model Spread but Not Uncertainty in Global Wetland Methane Emission Estimates
The recent rise in atmospheric methane (CH 4 ) concentrations accelerates climate change and offsets mitigation efforts. Although wetlands are the largest natural CH 4 source, estimates of global wetland CH 4 emissions vary widely among approaches taken by bottom-up (BU) process-based biogeochemical models and top-down (TD) atmospheric inversion methods. Here, we integrate in situ measurements, multi-model ensembles, and a machine learning upscaling product into the International Land Model Benchmarking system to examine the relationship between wetland CH 4 emission estimates and model performance. We find that using better-performing models identified by observational constraints reduces the spread of wetland CH 4 emission estimates by 62% and 39% for BU- and TD-based approaches, respectively. However, global BU and TD CH 4 emission estimate discrepancies increased by about 15% (from 31 to 36 TgCH 4 year −1 ) when the top 20% models were used, although we consider this result moderately uncertain given the unevenly distributed global observations. Our analyses demonstrate that model performance ranking is subject to benchmark selection due to large inter-site variability, highlighting the importance of expanding coverage of benchmark sites to diverse environmental conditions. We encourage future development of wetland CH 4 models to move beyond static benchmarking and focus on evaluating site-specific and ecosystem-specific variabilities inferred from observations.
Kuang-Yu Chang↗
Inland Water Greenhouse Gas Budgets for RECCAP2: 2. Regionalization and Homogenization of Estimates
Inland waters are important emitters of the greenhouse gasses (GHGs) carbon dioxide (CO 2 ), methane (CH 4 ), and nitrous oxide (N 2 O) to the atmosphere. In the framework of the 2 nd phase of the REgional Carbon Cycle Assessment and Processes (RECCAP-2) initiative, we synthesize existing estimates of GHG emissions from streams, rivers, lakes and reservoirs, and homogenize them with regard to underlying global maps of water surface area distribution and the effects of seasonal ice cover. We then produce regionalized estimates of GHG emissions over 10 extensive land regions. According to our synthesis, inland water GHG emissions have a global warming potential of an equivalent emission of 13.5 (9.9-20.1) and 8.3 (5.7-12.7) Pg CO 2 -eq. yr -1 at a 20 and 100 year horizon (GWP 20 and GWP 100 ), respectively. Contributions of CO 2 dominate GWP100, with rivers being the largest emitter. For GWP 20 , lakes and rivers are equally important emitters, and the warming potential of CH4 is more important than that of CO2. Contributions from N 2 O are about two orders of magnitude lower. Normalized to the area of RECCAP-2 regions, S-America and SE-Asia show the highest emission rates, dominated by riverine CO 2 emissions.
Ronny Lauerwald↗
Soil Respiration–Driven CO 2 Pulses Dominate Australia’s Flux Variability
The Australian continent contributes substantially to the year-to-year variability of the global terrestrial carbon dioxide (CO 2 ) sink. However, the scarcity of in situ observations in remote areas prevents the deciphering of processes that force the CO 2 flux variability. In this study, by examining atmospheric CO 2 measurements from satellites in the period 2009–2018, we find recurrent end-of-dry-season CO 2 pulses over the Australian continent. These pulses largely control the year-to-year variability of Australia’s CO 2 balance. They cause two to three times larger seasonal variations compared with previous top-down inversions and bottom-up estimates. The pulses occur shortly after the onset of rainfall and are driven by enhanced soil respiration preceding photosynthetic uptake in Australia’s semiarid regions. The suggested continental-scale relevance of soil-rewetting processes has substantial implications for our understanding and modeling of global climate–carbon cycle feedbacks.
Eva-Marie Metz↗
Harmonising the Land-Use Flux Estimates of Global Models and National Inventories for 2000–2020
As the focus of climate policy shifts from pledges to implementation, there is a growing need to track progress on climate change mitigation at the country level, particularly for the land-use sector. Despite new tools and models providing unprecedented monitoring opportunities, striking differences remain in estimations of anthropogenic land-use CO 2 fluxes between, on the one hand, the national greenhouse gas inventories (NGHGIs) used to assess compliance with national climate targets under the Paris Agreement and, on the other hand, the Global Carbon Budget and Intergovernmental Panel on Climate Change (IPCC) assessment reports, both based on global bookkeeping models (BMs). Recent studies have shown that these differences are mainly due to inconsistent definitions of anthropogenic CO 2 fluxes in managed forests. Countries assume larger areas of forest to be managed than BMs do, due to a broader definition of managed land in NGHGIs. Additionally, the fraction of the land sink caused by indirect effects of human-induced environmental change (e.g. fertilisation effect on vegetation growth due to increased atmospheric CO 2 concentration) on managed lands is treated as non-anthropogenic by BMs but as anthropogenic in most NGHGIs. We implement an approach that adds the CO 2 sink caused by environmental change in countries' managed forests (estimated by 16 dynamic global vegetation models, DGVMs) to the land-use fluxes from three BMs. This sum is conceptually more comparable to NGHGIs and is thus expected to be quantitatively more similar. Our analysis uses updated and more comprehensive data from NGHGIs than previous studies and provides model results at a greater level of disaggregation in terms of regions, countries and land categories (i.e. forest land, deforestation, organic soils, other land uses). Our results confirm a large difference (6.7 GtCO 2 yr −1 ) in global land-use CO 2 fluxes between the ensemble mean of the BMs, which estimate a source of 4.8 GtCO 2 yr −1 for the period 2000–2020, and NGHGIs, which estimate a sink of −1.9 GtCO 2 yr −1 in the same period. Most of the gap is found on forest land (3.5 GtCO 2 yr −1 ), with differences also for deforestation (2.4 GtCO 2 yr −1 ), for fluxes from other land uses (1.0 GtCO 2 yr −1 ) and to a lesser extent for fluxes from organic soils (0.2 GtCO2 yr−1). By adding the DGVM ensemble mean sink arising from environmental change in managed forests (−6.4 GtCO 2 yr −1 ) to BM estimates, the gap between BMs and NGHGIs becomes substantially smaller both globally (residual gap: 0.3 GtCO 2 yr −1 ) and in most regions and countries. However, some discrepancies remain and deserve further investigation. For example, the BMs generally provide higher emissions from deforestation than NGHGIs and, when adjusted with the sink in managed forests estimated by DGVMs, yield a sink that is often greater than NGHGIs. In summary, this study provides a blueprint for harmonising the estimations of anthropogenic land-use fluxes, allowing for detailed comparisons between global models and national inventories at global, regional and country levels. This is crucial to increase confidence in land-use emissions estimates, support investments in land-based mitigation strategies and assess the countries' collective progress under the Global Stocktake of the Paris Agreement.
Giacomo Grassi↗
A Process-Model Perspective on Recent Changes in the Carbon Cycle of North America
Continental North America has been found to be a carbon (C) sink over recent decades by multiple studies employing a variety of estimation approaches. However, several key questions and uncertainties remain with these assessments. Here we used results from an ensemble of 19 state-of-the-art dynamic global vegetation models from the TRENDYv9 project to improve these estimates and study the drivers of its interannual variability. Our results show that North America has been a C sink with a magnitude of 0.37 ± 0.38 (mean and one standard deviation) PgC year−1 for the period 2000–2019 (0.31 and 0.44 PgC year−1 in each decade); split into 0.18 ± 0.12 PgC year−1 in Canada (0.15 and 0.20), 0.16 ± 0.17 in the United States (0.14 and 0.17), 0.02 ± 0.05 PgC year−1 in Mexico (0.02 and 0.02) and 0.01 ± 0.02 in Central America and the Caribbean (0.01 and 0.01). About 57% of the new C assimilated by terrestrial ecosystems is allocated into vegetation, 30% into soils, and 13% into litter. Losses of C due to fire account for 41% of the interannual variability of the mean net biome productivity for all North America in the model ensemble. Finally, we show that drought years (e.g., 2002) have the potential to shift the region to a small net C source in the simulations (−0.02 ± 0.46 PgC year−1). Our results highlight the importance of identifying the major drivers of the interannual variability of the continental-scale land C cycle along with the spatial distribution of local sink-source dynamics.
Guillermo Murray-Tortarolo↗
Process-Oriented Analysis of Dominant Sources of Uncertainty in the Land Carbon Sink
The observed global net land carbon sink is captured by current land models. All models agree that atmospheric CO 2 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 CO 2 (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.
Attribution↗
A Century‐Long Trajectory of Phosphorus Loading and Export From Mississippi River Basin to the Gulf of Mexico: Contributions of Multiple Environmental Changes
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Zihao Bian↗
Response to Comments on “Recent global decline of CO2 fertilization effects on vegetation photosynthesis”
Our study suggests that the global CO2 fertilization effect (CFE) on vegetation photosynthesis has declined during the past four decades. The Comments suggest that the temporal inconsistency in AVHRR data and the attribution method undermine the results’ robustness. Here, we provide additional evidence that these arguments did not affect our finding and that the global decline in CFE is robust.
evapotranspiration↗
Regional trends and drivers of the global methane budget
The ongoing development of the Global Carbon Project (GCP) global methane (CH4) budget shows a continuation of increasing CH4 emissions and CH4 accumulation in the atmosphere during 2000–2017. Here, we decompose the global budget into 19 regions (18 land and 1 oceanic) and five key source sectors to spatially attribute the observed global trends. A comparison of top-down (TD) (atmospheric and transport model-based) and bottom-up (BU) (inventory- and process model-based) CH4 emission estimates demonstrates robust temporal trends with CH4 emissions increasing in 16 of the 19 regions. Five regions—China, Southeast Asia, USA, South Asia, and Brazil—account for >40% of the global total emissions (their anthropogenic and natural sources together totaling >270 Tg CH4 yr−1 in 2008–2017). Two of these regions, China and South Asia, emit predominantly anthropogenic emissions (>75%) and together emit more than 25% of global anthropogenic emissions. China and the Middle East show the largest increases in total emission rates over the 2000 to 2017 period with regional emissions increasing by >20%. In contrast, Europe and Korea and Japan show a steady decline in CH4 emission rates, with total emissions decreasing by ~10% between 2000 and 2017. Coal mining, waste (predominantly solid waste disposal) and livestock (especially enteric fermentation) are dominant drivers of observed emissions increases while declines appear driven by a combination of waste and fossil emission reductions. As such, together these sectors present the greatest risks of further increasing the atmospheric CH4 burden and the greatest opportunities for greenhouse gas abatement.
Global Carbon Project (GCP)↗
Modelled land use and land cover change emissions – a spatio-temporal comparison of different approaches
Quantifying the net carbon flux from land use and land cover changes (fLULCC) is critical for understanding the global carbon cycle and, hence, to support climate change mitigation. However, large-scale fLULCC is not directly measurable and has to be inferred from models instead, such as semi-empirical bookkeeping models and process-based dynamic global vegetation models (DGVMs). By definition, fLULCC estimates are not directly comparable between these two different model types. As an important example, DGVM-based fLULCC in the annual global carbon budgets is estimated under transient environmental forcing and includes the so-called loss of additional sink capacity (LASC). The LASC results from the impact of environmental changes on land carbon storage potential of managed land compared to potential vegetation and accumulates over time, which is not captured in bookkeeping models. The fLULCC from transient DGVM simulations, thus, strongly depends on the timing of land use and land cover changes mainly because LASC accumulation is cut off at the end of the simulated period. To estimate the LASC, the fLULCC from pre-industrial DGVM simulations, which is independent of changing environmental conditions, can be used. Additionally, DGVMs using constant present-day environmental forcing enable an approximation of bookkeeping estimates. Here, we analyse these three DGVM-derived fLULCC estimations (under transient, pre-industrial, and present-day forcing) for 12 models within 18 regions and quantify their differences as well as climate- and CO2-induced components and compare them to bookkeeping estimates. Averaged across the models, we find a global fLULCC (under transient conditions) of 2.0±0.6 PgC/yr for 2009–2018, of which ∼40 % are attributable to the LASC (0.8±0.3 PgC/yr). From 1850 onward, the fLULCC accumulated to 189±56 PgC with 40±15 PgC from the LASC. Around 1960, the accumulating nature of the LASC causes global transient fLULCC estimates to exceed estimates under present-day conditions, despite generally increased carbon stocks in the latter. Regional hotspots of high cumulative and annual LASC values are found in the USA, China, Brazil, equatorial Africa, and Southeast Asia, mainly due to deforestation for cropland. Distinct negative LASC estimates in Europe (early reforestation) and from 2000 onward in the Ukraine (recultivation of post-Soviet abandoned agricultural land), indicate that fLULCC estimates in these regions are lower in transient DGVM compared to bookkeeping approaches. Our study unravels the strong dependence of fLULCC estimates on the time a certain land use and land cover change event happened to occur and on the chosen time period for the forcing of environmental conditions in the underlying simulations. We argue for an approach that provides an accounting of the fLULCC that is more robust against these choices, for example by estimating a mean DGVM ensemble fLULCC and LASC for a defined reference period and homogeneous environmental changes (CO2 only).
land use and land cover change↗
The Global Methane Budget 2000–2017
Understanding and quantifying the global methane (CH4) budget is important for assessing realistic pathways to mitigate climate change. Atmospheric emissions and concentrations of CH4 continue to increase, making CH4 the second most important human-influenced greenhouse gas in terms of climate forcing, after carbon dioxide (CO2). The relative importance of CH4 compared to CO2 depends on its shorter atmospheric lifetime, stronger warming potential, and variations in atmospheric growth rate over the past decade, the causes of which are still debated. Two major challenges in reducing uncertainties in the atmospheric growth rate arise from the variety of geographically overlapping CH4 sources and from the destruction of CH4 by short-lived hydroxyl radicals (OH). To address these challenges, we have established a consortium of multidisciplinary scientists under the umbrella of the Global Carbon Project to synthesize and stimulate new research aimed at improving and regularly updating the global methane budget. Following Saunois et al. (2016), we present here the second version of the living review paper dedicated to the decadal methane budget, integrating results of top-down studies (atmospheric observations within an atmospheric inverse-modelling framework) and bottom-up estimates (including process-based models for estimating land surface emissions and atmospheric chemistry, inventories of anthropogenic emissions, and data-driven extrapolations). For the 2008–2017 decade, global methane emissions are estimated by atmospheric inversions (a top-down approach) to be 576 Tg CH4/yr (range 550–594, corresponding to the minimum and maximum estimates of the model ensemble). Of this total, 359 Tg CH4/yr or ∼ 60 % is attributed to anthropogenic sources, that is emissions caused by direct human activity (i.e. anthropogenic emissions; range 336–376 Tg CH4/yr or 50 %–65 %). The mean annual total emission for the new decade (2008–2017) is 29 Tg CH4/yr larger than our estimate for the previous decade (2000–2009), and 24 Tg CH4/yr larger than the one reported in the previous budget for 2003–2012 (Saunois et al., 2016). Since 2012, global CH4 emissions have been tracking the warmest scenarios assessed by the Intergovernmental Panel on Climate Change. Bottom-up methods suggest almost 30 % larger global emissions (737 Tg CH4/yr, range 594–881) than top-down inversion methods. Indeed, bottom-up estimates for natural sources such as natural wetlands, other inland water systems, and geological sources are higher than top-down estimates. The atmospheric constraints on the top-down budget suggest that at least some of these bottom-up emissions are overestimated. The latitudinal distribution of atmospheric observation-based emissions indicates a predominance of tropical emissions (∼ 65 % of the global budget, < 30° N) compared to mid-latitudes (∼ 30 %, 30–60° N) and high northern latitudes (∼ 4 %, 60–90° N). The most important source of uncertainty in the methane budget is attributable to natural emissions, especially those from wetlands and other inland waters. Some of our global source estimates are smaller than those in previously published budgets (Saunois et al., 2016; Kirschke et al., 2013). In particular wetland emissions are about 35 Tg CH4/yr lower due to improved partition wetlands and other inland waters. Emissions from geological sources and wild animals are also found to be smaller by 7 Tg CH4/yr by 8 Tg CH4/yr, respectively. However, the overall discrepancy between bottom-up and top-down estimates has been reduced by only 5 % compared to Saunois et al. (2016), due to a higher estimate of emissions from inland waters, highlighting the need for more detailed research on emissions factors. Priorities for improving the methane budget include (i) a global, high-resolution map of water-saturated soils and inundated areas emitting methane based on a robust classification of different types of emitting habitats; (ii) further development of process-based models for inland-water emissions; (iii) intensification of methane observations at local scales (e.g., FLUXNET-CH4 measurements) and urban-scale monitoring to constrain bottom-up land surface models, and at regional scales (surface networks and satellites) to constrain atmospheric inversions; (iv) improvements of transport models and the representation of photochemical sinks in top-down inversions; and (v) development of a 3D variational inversion system using isotopic and/or co-emitted species such as ethane to improve source partitioning.
methane budget↗
Evaluation of global terrestrial evapotranspiration using state-of-the-art approaches in remote sensing, machine learning and land surface modeling
Evapotranspiration (ET) is critical in linking global water, carbon and energy cycles. However, direct measurement of global terrestrial ET is not feasible. Here, we first reviewed the basic theory and state-of-the-art approaches for estimating global terrestrial ET, including remote-sensing-based physical models, machine-learning algorithms and land surface models (LSMs). We then utilized 4 remote-sensing-based physical models, 2 machine-learning algorithms and 14 LSMs to analyze the spatial and temporal variations in global terrestrial ET. The results showed that the ensemble means of annual global terrestrial ET estimated by these three categories of approaches agreed well, with values ranging from 589.6 mm/yr (6.56×10^4 cu.km/yr) to 617.1 mm/yr (6.87×10^4 cu.km/yr). For the period from 1982 to 2011, both the ensembles of remote-sensing-based physical models and machine-learning algorithms suggested increasing trends in global terrestrial ET (0.62 mm/sq.yr with a significance level of p<0.05 and 0.38 mm yr−2 with a significance level of p<0.05, respectively). In contrast, the ensemble mean of the LSMs showed no statistically significant change (0.23 mm/sq.yr, p>0.05), although many of the individual LSMs reproduced an increasing trend. Nevertheless, all 20 models used in this study showed that anthropogenic Earth greening had a positive role in increasing terrestrial ET. The concurrent small interannual variability, i.e., relative stability, found in all estimates of global terrestrial ET, suggests that a potential planetary boundary exists in regulating global terrestrial ET, with the value of this boundary being around 600 mm/yr. Uncertainties among approaches were identified in specific regions, particularly in the Amazon Basin and arid/semiarid regions. Improvements in parameterizing water stress and canopy dynamics, the utilization of new available satellite retrievals and deep-learning methods, and model–data fusion will advance our predictive understanding of global terrestrial ET.
surface modeling↗
Impacts of land use change and elevated CO2 on the interannual variations and seasonal cycles of gross primary productivity in China
Climate change, rising CO2 concentration, and land use and land cover change (LULCC) are primary driving forces for terrestrial gross primary productivity (GPP), but their impacts on the temporal changes in GPP are uncertain. In this study, the effects of the three main factors on the interannual variation (IAV) and seasonal cycle amplitude (SCA) of GPP in China were investigated using 12 terrestrial biosphere models from the Multi-scale Synthesis and Terrestrial Model Intercomparison Project. The simulated ensemble mean value of China's GPP between 1981 and 2010, driven by common climate forcing, LULCC and CO2 data, was found to be 7.4±1.8 Pg C/yr. In general, climate was the dominant control factor of the annual trends, IAV and seasonality of China's GPP. The overall rising CO2 led to enhanced plant photosynthesis, thus increasing annual mean and IAV of China's total GPP, especially in northeastern and southern China, where vegetation is dense. LULCC decreased the IAV of China's total GPP by ∼7 %, whereas rising CO2 induced an increase of 8 %. Compared to climate change and elevated CO2, LULCC showed less contributions to GPP's temporal variation, and its impact acted locally, mainly in southwestern China. Furthermore, this study also examined subregional contributions to the temporal changes in China's total GPP. Southern and southeastern China showed higher contributions to China's annual GPP, whereas southwestern and central parts of China explained larger fractions of the IAV in China's GPP.
land use change↗
State of the science in reconciling top‐down and bottom‐up approaches for terrestrial CO2 budget
Robust estimates of CO2 budget, CO2 exchanged between the atmosphere and terrestrial biosphere, are necessary to better understand the role of the terrestrial biosphere in mitigating anthropogenic CO2 emissions. Over the past decade, this field of research has advanced through understanding of the differences and similarities of two fundamentally different approaches: “top‐down” atmospheric inversions and “bottom‐up” biosphere models. Since the first studies were undertaken, these approaches have shown an increasing level of agreement, but disagreements in some regions still persist, in part because they do not estimate the same quantity of atmosphere–biosphere CO2 exchange. Here, we conducted a thorough comparison of CO2 budgets at multiple scales and from multiple methods to assess the current state of the science in estimating CO2 budgets. Our set of atmospheric inversions and biosphere models, which were adjusted for a consistent flux definition, showed a high level of agreement for global and hemispheric CO2 budgets in the 2000s. Regionally, improved agreement in CO2 budgets was notable for North America and Southeast Asia. However, large gaps between the two methods remained in East Asia and South America. In other regions, Europe, boreal Asia, Africa, South Asia, and Oceania, it was difficult to determine whether those regions act as a net sink or source because of the large spread in estimates from atmospheric inversions. These results highlight two research directions to improve the robustness of CO2 budgets: (a) to increase representation of processes in biosphere models that could contribute to fill the budget gaps, such as forest regrowth and forest degradation; and (b) to reduce sink–source compensation between regions (dipoles) in atmospheric inversion so that their estimates become more comparable. Advancements on both research areas will increase the level of agreement between the top‐down and bottom‐up approaches and yield more robust knowledge of regional CO2 budgets.
atmospheric inversion↗
Global Carbon Budget 2019
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).
global carbon budget 2019↗
Negative extreme events in gross primary productivity and their drivers in China during the past three decades
Climate extremes have remarkable impacts on ecosystems and are expected to increase with future global warming. However, only few studies have focused on the ecological extreme events and their drivers in China. In this study, we carried out an analysis of negative extreme events in gross primary productivity (GPP) in China and the sub-regions during 1982–2015, using monthly GPP simulated by 12 process-based models (TRENDYv6) and an observation-based model (Yao-GPP). Extremes were defined as the negative 5th percentile of GPP anomalies, which were further merged into individual extreme events using a three-dimensional contiguous algorithm. Spatio-temporal patterns of negative GPP anomalies were analyzed by taking the 1000 largest extreme events into consideration. Results showed that the effects of extreme events decreased annual GPP by 2.8% (i.e. 208 TgC/year) in TRENDY models and 2.3% (i.e. 151 TgC/year) in Yao-GPP. Hotspots of extreme GPP deficits were mainly observed in North China (−53 gC/sq.m year) in TRENDY models and Northeast China (−42 gC/sq.m year) in Yao-GPP. For China as a whole, attribution analyses suggested that extreme low precipitation was associated with 40%–50% of extreme negative GPP events. Most events in northern and western China could be explained by meteorological droughts (i.e. low precipitation) while GPP extreme events in southern China were more associated with temperature extremes, in particular with cold spells. GPP was revealed to be much more sensitive to heat/drought than to cold/wet extreme events. Combined with projected changes in climate extremes in China, GPP negative anomalies caused by drought events in northern China and by temperature extremes in southern China might be more prominent in the future.
Climate change↗