• Energy Research

Abstract. Accurate assessment of the size and distribution of carbon dioxide (CO2) sources and sinks is important for efforts to understand the carbon cycle and support policy decisions regarding climate mitigation actions. Satellite retrievals of the column-averaged dry-air mole fractions of CO2 (XCO2) have been widely used to infer spatial and temporal variations of carbon fluxes through atmospheric inversion techniques. In this study, we present a global spatially resolved terrestrial and ocean carbon flux dataset for 2015–2022. The dataset was generated by the Global ObservatioN-based system for monitoring Greenhouse GAses (GONGGA) atmospheric inversion system through the assimilation of Orbiting Carbon Observatory 2 (OCO-2) XCO2 retrievals. We describe the carbon budget, interannual variability, and seasonal cycle for the global scale and a set of TransCom regions. The 8-year mean net biosphere exchange and ocean carbon fluxes were −2.22 ± 0.75 PgC yr−1 and –2.32 ± 0.18 PgC yr−1, absorbing approximately 23 % and 24 % of contemporary fossil fuel CO2 emissions, respectively. The annual mean global atmospheric CO2 growth rate was 5.17 ± 0.68 PgC yr−1, which is consistent with the National Oceanic and Atmospheric Administration (NOAA) measurement (5.24 ± 0.59 PgC yr−1). Europe has the largest terrestrial sink among the 11 TransCom land regions, followed by Boreal Asia and Temperate Asia. The dataset was evaluated by comparing posterior CO2 simulations with the observations from Total Carbon Column Observing Network (TCCON) and Observation Package (ObsPack). Compared with CO2 simulations using the unoptimized fluxes, the bias and root mean square error of posterior CO2 simulations were largely reduced across the full range of locations, confirming that the GONGGA system improves the estimates of spatial and temporal variations in carbon fluxes by assimilating OCO-2 XCO2 data. This dataset will improve the broader understanding of global carbon cycle dynamics and their response to climate change. The dataset can be accessed at https://doi.org/10.5281/zenodo.8368846 (Jin et al., 2023a).

Abstract. Accurate assessment of the size and distribution of carbon dioxide (CO2) sources and sinks is important for efforts to understand the carbon cycle and support policy decisions regarding climate mitigation actions. Satellite retrievals of the column-averaged dry-air mole fractions of CO2 (XCO2) have been widely used to infer spatial and temporal variations of carbon fluxes through atmospheric inversion techniques. In this study, we present a global spatially resolved terrestrial and ocean carbon flux dataset for 2015–2022. The dataset was generated by the Global ObservatioN-based system for monitoring Greenhouse GAses (GONGGA) atmospheric inversion system through the assimilation of Orbiting Carbon Observatory 2 (OCO-2) XCO2 retrievals. We describe the carbon budget, interannual variability, and seasonal cycle for the global scale and a set of TransCom regions. The 8-year mean net biosphere exchange and ocean carbon fluxes were −2.22 ± 0.75 PgC yr−1 and –2.32 ± 0.18 PgC yr−1, absorbing approximately 23 % and 24 % of contemporary fossil fuel CO2 emissions, respectively. The annual mean global atmospheric CO2 growth rate was 5.17 ± 0.68 PgC yr−1, which is consistent with the National Oceanic and Atmospheric Administration (NOAA) measurement (5.24 ± 0.59 PgC yr−1). Europe has the largest terrestrial sink among the 11 TransCom land regions, followed by Boreal Asia and Temperate Asia. The dataset was evaluated by comparing posterior CO2 simulations with the observations from Total Carbon Column Observing Network (TCCON) and Observation Package (ObsPack). Compared with CO2 simulations using the unoptimized fluxes, the bias and root mean square error of posterior CO2 simulations were largely reduced across the full range of locations, confirming that the GONGGA system improves the estimates of spatial and temporal variations in carbon fluxes by assimilating OCO-2 XCO2 data. This dataset will improve the broader understanding of global carbon cycle dynamics and their response to climate change. The dataset can be accessed at https://doi.org/10.5281/zenodo.8368846 (Jin et al., 2023a).

Carbon Monitor est un ensemble de données en temps quasi réel pour les émissions quotidiennes de CO2 avec une couverture mondiale de 6 secteurs principaux (énergie, industrie, transport terrestre, aviation, transport résidentiel et international).Ici, nous fournissons les données annuelles de 2019 et 2020.Visitez notre site Web pour plus d'informations : https://carbonmonitor.org Carbon Monitor es un conjunto de datos casi en tiempo real para las emisiones diarias de CO2 con cobertura global de 6 sectores principales (energía, industria, transporte terrestre, aviación, transporte residencial e internacional).Aquí, proporcionamos los datos de todo el año de 2019 y 2020.Visite nuestro sitio web para obtener más información: https://carbonmonitor.org Carbon Monitor is a near real-time dataset for daily CO2 emissions with global coverage from 6 main sectors (power, industry, ground transportation, aviation, residential and international shipping).Here, we provide the whole year data of 2019 and 2020.Visit our website for more information: https://carbonmonitor.org كربون مونيتور هي مجموعة بيانات شبه آنية لانبعاثات ثاني أكسيد الكربون اليومية مع تغطية عالمية من 6 قطاعات رئيسية (الطاقة والصناعة والنقل البري والطيران والشحن السكني والدولي). نقدم هنا بيانات العام بأكمله لعامي 2019 و 2020.قم بزيارة موقعنا الإلكتروني لمزيد من المعلومات: https://carbonmonitor.org

Carbon Monitor est un ensemble de données en temps quasi réel pour les émissions quotidiennes de CO2 avec une couverture mondiale de 6 secteurs principaux (énergie, industrie, transport terrestre, aviation, transport résidentiel et international).Ici, nous fournissons les données annuelles de 2019 et 2020.Visitez notre site Web pour plus d'informations : https://carbonmonitor.org Carbon Monitor es un conjunto de datos casi en tiempo real para las emisiones diarias de CO2 con cobertura global de 6 sectores principales (energía, industria, transporte terrestre, aviación, transporte residencial e internacional).Aquí, proporcionamos los datos de todo el año de 2019 y 2020.Visite nuestro sitio web para obtener más información: https://carbonmonitor.org Carbon Monitor is a near real-time dataset for daily CO2 emissions with global coverage from 6 main sectors (power, industry, ground transportation, aviation, residential and international shipping).Here, we provide the whole year data of 2019 and 2020.Visit our website for more information: https://carbonmonitor.org كربون مونيتور هي مجموعة بيانات شبه آنية لانبعاثات ثاني أكسيد الكربون اليومية مع تغطية عالمية من 6 قطاعات رئيسية (الطاقة والصناعة والنقل البري والطيران والشحن السكني والدولي). نقدم هنا بيانات العام بأكمله لعامي 2019 و 2020.قم بزيارة موقعنا الإلكتروني لمزيد من المعلومات: https://carbonmonitor.org

AbstractInterannual variations of photosynthesis in tropical seasonally dry vegetation are one of the dominant drivers to interannual variations of atmosphericCO2growth rate. Yet, the seasonal differences in the response of photosynthesis to climate variations in these ecosystems remain poorly understood. Here using Normalized Difference Vegetation Index (NDVI), we explored the response of photosynthesis of seasonally dry tropical vegetation to climatic variations in the dry and the wet seasons during the past three decades. We found significant (p < 0.01) differences between dry and wet seasons in the interannual response of photosynthesis to temperature (γint) and to precipitation (δint).γintis ~1% °C−1more negative andδintis ~8% 100 mm−1more positive in the dry season than in the wet season. Further analyses show that the seasonal difference inγintcan be explained by background moisture and temperature conditions. Positiveγintoccurred in wet season where mean temperature is lower than 27°C and precipitation is at least 60 mm larger than potential evapotranspiration. Two widely used Gross Primary Productivity (GPP) estimates (empirical modeling by machine‐learning algorithm applied to flux tower measurements, and nine process‐based carbon cycle models) were examined for theGPP–climate relationship over wet and dry seasons. TheGPPderived from empirical modeling can partly reproduce the divergence ofγint, while most process models cannot. The overestimate by process models on negative impacts by warmer temperature during the wet season highlights the shortcomings of current carbon cycle models in representing interactive impacts of temperature and moisture on photosynthesis. Improving representations on soil water uptake, leaf temperature, nitrogen cycling, and soil moisture may help improve modeling skills in reproducing seasonal differences of photosynthesis–climate relationship and thus the projection for impacts of climate change on tropical carbon cycle.

AbstractInterannual variations of photosynthesis in tropical seasonally dry vegetation are one of the dominant drivers to interannual variations of atmosphericCO2growth rate. Yet, the seasonal differences in the response of photosynthesis to climate variations in these ecosystems remain poorly understood. Here using Normalized Difference Vegetation Index (NDVI), we explored the response of photosynthesis of seasonally dry tropical vegetation to climatic variations in the dry and the wet seasons during the past three decades. We found significant (p < 0.01) differences between dry and wet seasons in the interannual response of photosynthesis to temperature (γint) and to precipitation (δint).γintis ~1% °C−1more negative andδintis ~8% 100 mm−1more positive in the dry season than in the wet season. Further analyses show that the seasonal difference inγintcan be explained by background moisture and temperature conditions. Positiveγintoccurred in wet season where mean temperature is lower than 27°C and precipitation is at least 60 mm larger than potential evapotranspiration. Two widely used Gross Primary Productivity (GPP) estimates (empirical modeling by machine‐learning algorithm applied to flux tower measurements, and nine process‐based carbon cycle models) were examined for theGPP–climate relationship over wet and dry seasons. TheGPPderived from empirical modeling can partly reproduce the divergence ofγint, while most process models cannot. The overestimate by process models on negative impacts by warmer temperature during the wet season highlights the shortcomings of current carbon cycle models in representing interactive impacts of temperature and moisture on photosynthesis. Improving representations on soil water uptake, leaf temperature, nitrogen cycling, and soil moisture may help improve modeling skills in reproducing seasonal differences of photosynthesis–climate relationship and thus the projection for impacts of climate change on tropical carbon cycle.

AbstractDay-to-day changes in CO2emissions from human activities, in particular fossil-fuel combustion and cement production, reflect a complex balance of influences from seasonality, working days, weather and, most recently, the COVID-19 pandemic. Here, we provide a daily CO2emissions dataset for the whole year of 2020, calculated from inventory and near-real-time activity data. We find a global reduction of 6.3% (2,232 MtCO2) in CO2emissions compared with 2019. The drop in daily emissions during the first part of the year resulted from reduced global economic activity due to the pandemic lockdowns, including a large decrease in emissions from the transportation sector. However, daily CO2emissions gradually recovered towards 2019 levels from late April with the partial reopening of economic activity. Subsequent waves of lockdowns in late 2020 continued to cause smaller CO2reductions, primarily in western countries. The extraordinary fall in emissions during 2020 is similar in magnitude to the sustained annual emissions reductions necessary to limit global warming at 1.5 °C. This underscores the magnitude and speed at which the energy transition needs to advance.