Monitoring snow cover to infer climate change impacts is now feasible using Earth observation data together with reanalysis products derived from Earth system models and data assimilation. Temporal stability becomes essential when these products are used to monitor snow cover changes over time. While the temporal stability of satellite products can be altered when multiple sensors are combined and due to the degradation and orbital drifts in each sensor, the stability of reanalysis datasets can be compromised when new observations are assimilated into the model. This study evaluates the stability of some of the longest satellite-based and reanalysis products (ERA5, 1950–2020, ERA5-Land, 1950–2020, and the National Oceanic and Atmospheric Administration Climate Data Record (NOAA CDR), 1966–2020) by using 527 ground stations as reference data (1950–2020). Stability is assessed with the time series of the annual bias in snow depth and snow cover duration of the products at the different stations. Reanalysis datasets face a trade-off between accuracy and stability when assimilating new data to improve their estimations. The assimilation of new observations in ERA5 improved its accuracy significantly during the recent years (2005–2020) but introduced three negative step discontinuities in 1977–1980, 1991–1992, and 2003–2004. By contrast, ERA5-Land is more stable because it does not assimilate snow observations directly, but this leads to worse accuracy despite having a finer spatial resolution. The NOAA CDR showed a positive artificial trend from around 1992 to 2015 during fall and winter that could be related to changes to the availability of satellite data. The magnitude of most of these artificial trends and/or discontinuities is larger than actual snow cover trends and the stability requirements of the Global Climate Observing System (GCOS). The use of these products in seasons and regions where artificial trends and discontinuities appear should be avoided. The study also updates snow trends (1955–2015) over local sites in the Northern Hemisphere (NH), corroborating the retreat of snow cover, driven mainly by an earlier melt and recently by a later snow onset. In warmer regions such as Europe, snow cover decrease is coincident with a decreasing snow depth due to less snowfall, while in drier regions such as Russia, earlier snowmelt occurs despite increased maximum seasonal snow depth.

Assessing the world’s collective progress towards the Paris Agreement’s long-term goals is a global priority. Local authorities (LAs), in particular, play an important role in a just transition. This paper evaluates the real achievements of local climate action plans developed in Europe from 2008 to 2020 under the Covenant of Mayors initiative. On average, 85.6% of the GHG reduction targets were achieved way before the year 2020; however, our assessment shows different reduction patterns, with several leading LAs exceeding by 2–4 times their targets and 12% of LAs increasing their baseline emissions. This paper weighs the factors which have a determinant impact on these patterns, investigating the key drivers and barriers towards a clean energy transition under a new population-driven approach. While, for large LAs, the climate experience and the engagement of stakeholders is an asset for increasing their achievements, small LAs are much more conditioned by the political mandate and support from regional governments or external actors. The key factor for climate action planning appears to be the joint partnership between several government levels from a national perspective.

Gridded bottom-up inventories of CO₂ emissions are needed in global CO₂ inversion schemes as priors to initialize transport models and as a complement to top-down estimates to identify the anthropogenic sources. Global inversions require gridded datasets almost in near-real time that are spatially and methodologically consistent at a global scale. This may result in a loss of more detailed information that can be assessed by using regional inventories because they are built with a greater level of detail including country-specific information and finer resolution data. With this aim, a global mosaic of regional, gridded CO₂ emission inventories, hereafter referred to as CoCO2-MOSAIC 1.0, has been built in the framework of the CoCO2 project. CoCO2-MOSAIC 1.0 provides gridded (0.1^∘ × 0.1^∘) monthly emissions fluxes of CO₂ fossil fuel (CO₂ff, long cycle) and CO₂ biofuel (CO₂bf, short cycle) for the years 2015–2018 disaggregated in seven sectors. The regional inventories integrated are CAMS-REG-GHG 5.1 (Europe), DACCIWA 2.0 (Africa), GEAA-AEI 3.0 (Argentina), INEMA 1.0 (Chile), REAS 3.2.1 (East, Southeast, and South Asia), and VULCAN 3.0 (USA). EDGAR 6.0, CAMS-GLOB-SHIP 3.1 and CAMS-GLOB-TEMPO 3.1 are used for gap-filling. CoCO2-MOSAIC 1.0 can be recommended as a global baseline emission inventory for 2015 which is regionally accepted as a reference, and as such we use the mosaic to inter-compare the most widely used global emission inventories: CAMS-GLOB-ANT 5.3, EDGAR 6.0, ODIAC v2020b, and CEDS v2020_04_24. CoCO2-MOSAIC 1.0 has the highest CO₂ff (36.7 Gt) and CO₂bf (5.9 Gt) emissions globally, particularly in the USA and Africa. Regional emissions generally have a higher seasonality representing better the local monthly profiles and are generally distributed over a higher number of pixels, due to the more detailed information available. All super-emitting pixels from regional inventories contain a power station (CoCO2 database), whereas several super-emitters from global inventories are likely incorrectly geolocated, which is likely because regional inventories provide large energy emitters as point sources including regional information on power plant locations. CoCO2-MOSAIC 1.0 is freely available at zenodo (https://doi.org/10.5281/zenodo.7092358; Urraca et al., 2023) and at the JRC Data Catalogue (https://data.jrc.ec.europa.eu/dataset/6c8f9148-ce09-4dca-a4d5-422fb3682389, last access: 15 May 2023; Urraca Valle et al., 2023).