Welcome to Near Real-time Carbon Budget Project
A WAKE UP CALL TO THE CARBON BUDGET? OUR SCIENTIFIC PUBLICATION
It is crucial to monitor how human caused CO2 emissions change with time and how the land and ocean carbon sinks help us mitigating climate warming by capturing CO2. The current assessment provides an update of the budget one year ago. Here we accelerate the time and provide each quarter a synthesis of emissions and natural CO2 sinks based on the latest atmospheric data and a suite of peer-reviewed modeling approaches .
Two independent and complementary approaches are used to estimate the global CO2 budget and the regional distribution of emissions and natural CO2 sinks. The two approaches are very consistent globally and they show similar anomalies at the regional level, but we see more contrasted flux anomalies in the inversion.
Global Carbon Budget
Here we see the global growth rate of atmospheric CO2 seen from surface marine stations from NOAA and from the Mauna Loa observatory from NOAA and SCRIPPS, the longest atmospheric record. The year 2003 shows a record high growth rate at Mauna Loa and a very high growth rate at the marine stations.
Here we see the global budget, showing the fate of carbon from fossil CO2 emissions which stays in the atmosphere and makes the atmospheric growth rate (blue), or is absorbed by the land (green) or the ocean reservoirs (blue-green). The bottom up budget is the large bar. The top-down inversion budget is he inside bar.
Land Carbon Regional Fluxes
The 2003 anomaly from the low latency Dynamic vegetation models is shown in red. The color bars show the range and density of the 15 Dynamic vegetation models used by the Global Carbon Project up to 2022 (darker green colors mean more models predict this value). Red bars are the median from the 15 models of the Global Carbon Project. Black dots show the average of the 3 models used for low latency estimates.
Ocean Carbon Regional Fluxes
The 2003 anomaly. Red bars are the median from the 15 models of the Global Carbon Project. Black dots show the average of the 3 models used for low latency estimates.
