This dataset containts the data for the article Using a region-specific ice-nucleating particle parameterization improves the representation of Arctic clouds in a global climate model (https://doi.org/10.5194/acp-25-1617-2025), published in Atmospheric Chemistry and Physics (ACP). It consists of ice-nucleating particle (INP) measurements collected as part of the MC2/ISLAS campaign in Andenes, Norway (69° 18’ N, 16° 07’ E) in 2021. Additionally, it consists of simultaneous aerosol measurements, back trajectories for the INP measurement times, and model data from the Norwegian Earth System model (Seland et al., 2020) where INP concentrations where constrained in the Arctic using these INP measurements. 

Abstract:

Projections of global climate change and Arctic amplification are sensitive to the representation of low-level cloud phase in climate models. Ice-nucleating particles (INPs) are necessary for primary cloud ice formation at temperatures above approximately -38 °C, and thus significantly affect cloud phase and cloud radiative effect. Due to their complex and insufficiently understood variability, INPs constitute an important modelling challenge, especially in remote regions with few observations, such as the Arctic. In this study, INP observations were carried out at Andenes, Norway in March 2021. These observations were used as a basis for an Arctic-specific and purely temperature-dependent INP parameterization, and implemented into the Norwegian Earth System Model. This implementation results in an annual average increase in cloud liquid water path (CLWP) of 70 % for the Arctic, and improves the representation of cloud phase compared to satellite observations. The change in CLWP in boreal autumn and winter is found to likely be the dominant contributor to the annual average increase in net surface cloud radiative effect of 2 W m^-2. This large surface flux increase brings the simulation into better agreement with Arctic ground-based measurements. Despite that the model cannot respond fully to the INP parameterization change due to fixed sea surface temperatures, Arctic surface air temperature increases with 0.7 °C in boreal autumn. These findings indicate that INPs could have a significant impact on Arctic climate, and that a region-specific INP parameterization can be a useful tool to improve cloud representation in the Arctic region.

The dataset contains three subsets:

i)  aerosol_data: Observations of Ice-Nucleating Particles (INPs) in Andenes, Norway, March 2021, as well as simultaneous aerosol measurements

ii) INP_trajectories: Back trajectories at the time of INP measurements

iii) model_data: Simulations with NorESM2 using the Andenes 2021 INP observations to constrain INPs in the Arctic

Additionally, scripts for visualizing the data and for reproducing the NorESM2 model setup can be found in the folder scripts. The data folders and scripts folder should be in the same repository when running the scripts.

Some of the scripts use other openly available datasets. The availability of these are listed below. All the specific datasets can also be provided to the user upon request. 

The CALIOP L2 data used to derive SLF metrics (used in Fig07.py) and the CERES EBAF data (used in Fig12.py) can be downloaded freely at https://search.earthdata.nasa.gov/. The derived SLF metrics can also be found at Bruno (2022), and are also described in Hofer et al. (2024) and Shaw et al. (2022). The CALIPSO-GOCCP data product (used in Fig08.py) can be downloaded from https://climserv.ipsl.polytechnique.fr/cfmip-obs/Calipso_goccp.html. The surface radiation flux (used in Fig13.py) can be downloaded freely at https://www.pangaea.de/. The ERA5 data used to produce the back trajectories can be found at https://doi.org/10.24381/cds.bd0915c6. The colormap from Crameri et al. (2020) was used when preparing the figures. 

References:

• Bruno, O. (2022). Distributions of supercooled liquid fraction from CALIOP V4 [Data set]. Zenodo. https://doi.org/10.5281/zenodo.8289058
• Crameri, F., Shephard, G.E. & Heron, P.J. The misuse of colour in science communication. Nat Commun 11, 5444 (2020). https://doi.org/10.1038/s41467-020-19160-7
• Hofer, S., Hahn, L.C., Shaw, J.K. et al. Realistic representation of mixed-phase clouds increases projected climate warming. Commun Earth Environ 5, 390 (2024). https://doi.org/10.1038/s43247-024-01524-2
• Seland, Ø., Bentsen, M., Olivié, D., Toniazzo, T., Gjermundsen, A., Graff, L. S., Debernard, J. B., Gupta, A. K., He, Y.-C., Kirkevåg, A., Schwinger, J., Tjiputra, J., Aas, K. S., Bethke, I., Fan, Y., Griesfeller, J., Grini, A., Guo, C., Ilicak, M., Karset, I. H. H., Landgren, O., Liakka, J., Moseid, K. O., Nummelin, A., Spensberger, C., Tang, H., Zhang, Z., Heinze, C., Iversen, T., and Schulz, M.: Overview of the Norwegian Earth System Model (NorESM2) and key climate response of CMIP6 DECK, historical, and scenario simulations, Geoscientific Model Development, 13, 6165–6200, https://doi.org/10.5194/gmd-13-6165-2020, 2020.
• Shaw, J., McGraw, Z., Bruno, O., Storelvmo, T., & Hofer, S. (2022). Using satellite observations to evaluate model microphysical representation of Arctic mixed-phase clouds. Geophysical Research Letters, 49, e2021GL096191. https://doi.org/10.1029/2021GL096191