Detection and attribution of aerosol–cloud interactions in large-domain large-eddy simulations with the ICOsahedral Non-hydrostatic model

Abstract

Abstract. Clouds and aerosols contribute the largest uncertainty to current estimates and interpretations of the Earth’s changing energy budget. Here we use a new-generation large-domain large-eddy model, ICON-LEM (ICOsahedral Non-hydrostatic Large Eddy Model), to simulate the response of clouds to realistic anthropogenic perturbations in aerosols serving as cloud condensation nuclei (CCN). The novelty compared to previous studies is that (i) the LEM is run in weather prediction mode and with fully interactive land surface over a large domain and (ii) a large range of data from various sources are used for the detection and attribution. The aerosol perturbation was chosen as peak-aerosol conditions over Europe in 1985, with more than fivefold more sulfate than in 2013. Observational data from various satellite and ground-based remote sensing instruments are used, aiming at the detection and attribution of this response. The simulation was run for a selected day (2 May 2013) in which a large variety of cloud regimes was present over the selected domain of central Europe. It is first demonstrated that the aerosol fields used in the model are consistent with corresponding satellite aerosol optical depth retrievals for both 1985 (perturbed) and 2013 (reference) conditions. In comparison to retrievals from ground-based lidar for 2013, CCN profiles for the reference conditions were consistent with the observations, while the ones for the 1985 conditions were not. Similarly, the detection and attribution process was successful for droplet number concentrations: the ones simulated for the 2013 conditions were consistent with satellite as well as new ground-based lidar retrievals, while the ones for the 1985 conditions were outside the observational range. For other cloud quantities, including cloud fraction, liquid water path, cloud base altitude and cloud lifetime, the aerosol response was small compared to their natural variability. Also, large uncertainties in satellite and ground-based observations make the detection and attribution difficult for these quantities. An exception to this is the fact that at a large liquid water path value (LWP > 200 g m−2), the control simulation matches the observations, while the perturbed one shows an LWP which is too large. The model simulations allowed for quantifying the radiative forcing due to aerosol–cloud interactions, as well as the adjustments to this forcing. The latter were small compared to the variability and showed overall a small positive radiative effect. The overall effective radiative forcing (ERF) due to aerosol–cloud interactions (ERFaci) in the simulation was dominated thus by the Twomey effect and yielded for this day, region and aerosol perturbation −2.6 W m−2. Using general circulation models to scale this to a global-mean present-day vs. pre-industrial ERFaci yields a global ERFaci of −0.8 W m−2.

Estimation of cloud condensation nuclei number concentrations and comparison to in situ and lidar observations during the HOPE experiments

“…The main objective is to improve our understanding of clouds and precipitation using a model for very high resolution simulations. In the ICON-LEM (ICOsahedral Non-hydrostatic Large Eddy Model; Zängl et al, 2015;Dipankar et al, 2015;Heinze et al, 2017), which is the model used in HD(CP) 2 , there is no online aerosol transport scheme, which indicates the need for prescribing the aerosol and CCN concentrations in order to be considered for aerosol-cloud interaction.…”

Tropospheric and stratospheric wildfire smoke profiling with lidar: Mass, surface area, CCN and INP retrieval

“…The POLIPHON method is well validated in a variety of field activities (Mamali al., 2018;Düsing et al, 2018;Marinou et al, 2019;Haarig et al, 2019;Genz et al, 2011) and applied in numerous studies (Cordoba-Jabonero et al, 2018;Georgoulias et al, 2020;Marinou et al, 2019;Ansmann et al, 2019b;Baars et al, 2019;Costa-Surós et al, 2020;Hofer et al, 2020). In the following, we briefly summarize the different parts of the data analysis with special focus on wildfire smoke.…”

The behavior of high-CAPE (convective available potential energy) summer convection in large-domain large-eddy simulations with ICON

“…The Synergistic Passive Atmospheric Retrieval Experiment-ICE (SPARE-ICE) features a pair of artificial neural networks that use infrared and microwave radiances as input to detect ice clouds and retrieve their IWP (Holl et al, 2014). The networks were trained by collocating AVHRR channel 3B, 4, and 5 (3.7, 10.8, 12 µm) and MHS channel 3, 4, and 5 (183 ± 1, 183 ± 3, 190 GHz) radiances with IWP retrievals from the CloudSat/CALIPSO radar-lidar synergy product 2C-ICE (Deng et al, 2010). The exclusion of solar reflectances from SPARE-ICE allows retrievals both day and night; however, the reliance on microwave measurements results in fairly large footprints varying from 16 km in diameter at nadir to 52 × 27 km 2 in areas at the edge of the scan.…”