Using reports of forest losses caused directly by large scale windstorms (or primary damage, PD) from the European forest institute database (comprising 276 PD reports from 1951–2010), total growing stock (TGS) statistics of European forests and the daily North Atlantic Oscillation (NAO) index, we identify a statistically significant change in storm intensity in Western, Central and Northern Europe (17 countries). Using the validated set of storms, we found that the year 1990 represents a change-point at which the average intensity of the most destructive storms indicated by PD/TGS > 0.08% increased by more than a factor of three. A likelihood ratio test provides strong evidence that the change-point represents a real shift in the statistical behaviour of the time series. All but one of the seven catastrophic storms (PD/TGS > 0.2%) occurred since 1990. Additionally, we detected a related decrease in September–November PD/TGS and an increase in December–February PD/TGS. Our analyses point to the possibility that the impact of climate change on the North Atlantic storms hitting Europe has started during the last two and half decades.

Abstract. Significant discrepancies remain in estimates of climate impacts of anthropogenic aerosols between different general circulation models (GCMs). Here, we demonstrate that eliminating differences in model aerosol or radiative forcing fields results in close agreement in simulated globally averaged temperature and precipitation responses in the studied GCMs. However, it does not erase the differences in regional responses. We carry out experiments of equilibrium climate response to modern-day anthropogenic aerosols using an identical representation of anthropogenic aerosol optical properties and the first indirect effect of aerosols, MACv2-SP (a simple plume implementation of the second version of the Max Planck Institute Aerosol CLimatology), in two independent climate models (NorESM, Norwegian Earth System Model, and ECHAM6). We find consistent global average temperature responses of −0.48 (±0.02) and −0.50 (±0.03) K and precipitation responses of −1.69 (±0.04) % and −1.79 (±0.05) % in NorESM1 and ECHAM6, respectively, compared to modern-day equilibrium climate without anthropogenic aerosols. However, significant differences remain between the two GCMs' regional temperature responses around the Arctic circle and the Equator and precipitation responses in the tropics. The scatter in the simulated globally averaged responses is small in magnitude when compared against literature data from modern GCMs using model intrinsic aerosols but same aerosol emissions −(0.5–1.1) K and −(1.5–3.1) % for temperature and precipitation, respectively). The Pearson correlation of regional temperature (precipitation) response in these literature model experiments with intrinsic aerosols is 0.79 (0.34). The corresponding correlation coefficient for NorESM1 and ECHAM6 runs with identical aerosols is 0.78 (0.41). The lack of improvement in correlation coefficients between models with identical aerosols and models with intrinsic aerosols implies that the spatial distribution of regional climate responses is not improved via homogenizing the aerosol descriptions in the models. Rather, differences in the atmospheric dynamic and snow/sea ice cover responses dominate the differences in regional climate responses. Hence, even if we would have perfect aerosol descriptions inside the global climate models, uncertainty arising from the differences in circulation responses between the models would likely still result in a significant uncertainty in regional climate responses.

Abstract. The number of cloud droplets formed at the cloud base depends on both the properties of aerosol particles and the updraft velocity of an air parcel at the cloud base. As the spatial scale of updrafts is too small to be resolved in global atmospheric models, the updraft velocity is commonly parameterised based on the available turbulent kinetic energy. Here we present alternative methods through parameterising updraft velocity based on high-resolution large-eddy simulation (LES) runs in the case of marine stratocumulus clouds. First we use our simulations to assess the accuracy of a simple linear parameterisation where the updraft velocity depends only on cloud top radiative cooling. In addition, we present two different machine learning methods (Gaussian process emulation and random forest) that account for different boundary layer conditions and cloud properties. We conclude that both machine learning parameterisations reproduce the LES-based updraft velocities at about the same accuracy, while the simple approach employing radiative cooling only produces on average lower coefficient of determination and higher root mean square error values. Finally, we apply these machine learning methods to find the key parameters affecting cloud base updraft velocities.

<p><strong>Abstract.</strong> Significant discrepancies remain in estimates of climate impacts of anthropogenic aerosols between different general circulation models (GCMs). Here, we demonstrate that eliminating differences in model aerosol or radiative forcing fields results in close agreement in simulated globally averaged temperature and precipitation responses in the studied GCMs. However, it does not erase the differences in regional responses. We carry out experiments of equilibrium climate response to modern day anthropogenic aerosols using an identical representation of anthropogenic aerosol optical properties and aerosol-cloud interactions, MACv2-SP, in two independent climate models (NorESM and ECHAM6). We find consistent global average temperature responses of −0.48 K and −0.50 K and precipitation responses of −1.69 % and −1.79 % in NorESM1 and ECHAM6, respectively, compared to modern-day equilibrium climate without anthropogenic aerosols. However, significant differences remain between the two GCMs regional temperature responses around the Arctic circle and the equator and precipitation responses in the tropics. The scatter in the simulated globally averaged responses is small in magnitude when compared against literature data from modern GCMs using model intrinsic aerosols but same aerosol emissions (−(0.5–1.1) K and −(1.5–3.1) % for temperature and precipitation, respectively). The Pearson correlation of regional temperature (precipitation) response in these literature model experiments with intrinsic aerosols is 0.79 (0.34). The corresponding correlation coefficients for NorESM1 and ECHAM6 runs with identical aerosols are 0.78 (0.41). The lack of improvement in correlation coefficients between models with identical aerosols and models with intrinsic aerosols implies that the spatial distribution of regional climate responses is not improved via homogenizing the aerosol descriptions in the models. Rather, differences in the atmospheric dynamic and high latitude cloud and snow/sea ice cover responses dominate the differences in regional climate responses. Hence, further improvements in the model aerosol descriptions can be expected to have a limited value in improving our understanding of regional aerosol climate impacts, unless the dynamical cores of the climate models are improved as well.</p>

Abstract. The number of cloud droplets formed at the cloud base depends both on the properties of aerosol particles and the updraft velocity of an air parcel at the cloud base. As the spatial scale of updrafts is too small to be resolved in global atmospheric models, the updraft velocity is commonly parameterised based on the available turbulent kinetic energy. Here we present alternative methods through parameterising updraft velocity based on high-resolution large eddy simulation (LES) runs in the case of marine stratocumulus clouds. First we use our simulations to assess the accuracy of a simple linear parametrisation where the updraft velocity depends only on cloud top radiative cooling. In addition, we present two different machine learning methods (Gaussian process emulation and random forest) that account for different boundary layer conditions and cloud properties. We conclude that both machine learning parameterisations reproduce the LES-based updraft velocities at about the same accuracy, while the simple approach employing radiative cooling only produce on average lower coefficient of determination and higher root mean square error values. Finally, we apply these machine learning methods to find the key parameters affecting cloud base updraft velocities.