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Comparing the output of general circulation models to observations is essential for assessing and improving the quality of models. While numerical weather prediction models are routinely assessed against a large array of observations, comparing climate models and observations usually requires long time series to build robust statistics. Here, we show that by nudging the large-scale atmospheric circulation in coupled climate models, model output can be compared to local observations for individual days. We illustrate this for three climate models during a period in April 2020 when a warm air intrusion reached the MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate) expedition in the central Arctic. Radiosondes, cloud remote sensing and surface flux observations from the MOSAiC expedition serve as reference observations. The climate models AWI-CM1/ECHAM and AWI-CM3/IFS miss the diurnal cycle of surface temperature in spring, likely because both models assume the snowpack on ice to have a uniform temperature. CAM6, a model that uses three layers to represent snow temperature, represents the diurnal cycle more realistically. During a cold and dry period with pervasive thin mixed-phase clouds, AWI-CM1/ECHAM only produces partial cloud cover and overestimates downwelling shortwave radiation at the surface. AWI-CM3/IFS produces a closed cloud cover but misses cloud liquid water. Our results show that nudging the large-scale circulation to the observed state allows a meaningful comparison of climate model output even to short-term observational campaigns. We suggest that nudging can simplify and accelerate the pathway from observations to climate model improvements and substantially extends the range of observations suitable for model evaluation.

We developed a new version of the Alfred Wegener Institute Climate Model (AWI-CM3), which has higher skills in representing the observed climatology and better computational efficiency than its predecessors. Its ocean component FESOM2 (Finite-volumE Sea ice–Ocean Model) has the multi-resolution functionality typical of unstructured-mesh models while still featuring a scalability and efficiency similar to regular-grid models. The atmospheric component OpenIFS (CY43R3) enables the use of the latest developments in the numerical-weather-prediction community in climate sciences. In this paper we describe the coupling of the model components and evaluate the model performance on a variable-resolution (25–125 km) ocean mesh and a 61 km atmosphere grid, which serves as a reference and starting point for other ongoing research activities with AWI-CM3. This includes the exploration of high and variable resolution and the development of a full Earth system model as well as the creation of a new sea ice prediction system. At this early development stage and with the given coarse to medium resolutions, the model already features above-CMIP6-average skills (where CMIP6 denotes Coupled Model Intercomparison Project phase 6) in representing the climatology and competitive model throughput. Finally we identify remaining biases and suggest further improvements to be made to the model.

European CH4 and N2O emissions are estimated for 2006 and 2007 using four inverse modelling systems, based on different global and regional Eulerian and Lagrangian transport models. This ensemble approach is designed to provide more realistic estimates of the overall uncertainties in the derived emissions, which is particularly important for verifying bottom-up emission inventories. We use continuous observations from 10 European stations (including 5 tall towers) for CH4 and 9 continuous stations for N2O, complemented by additional European and global discrete air sampling sites. The available observations mainly constrain CH4 and N2O emissions from north-western and eastern Europe. The inversions are strongly driven by the observations and the derived total emissions of larger countries show little dependence on the emission inventories used a priori. Three inverse models yield 26–56% higher total CH4 emissions from north-western and eastern Europe compared to bottom-up emissions reported to the UNFCCC, while one model is close to the UNFCCC values. In contrast, the inverse modelling estimates of European N2O emissions are in general close to the UNFCCC values, with the overall range from all models being much smaller than the UNFCCC uncertainty range for most countries. Our analysis suggests that the reported uncertainties for CH4 emissions might be underestimated, while those for N2O emissions are likely overestimated.