Precipitation from mixed-phase clouds at high-latitudes is difficult to represent correctly in numerical weather prediction models. Paired water vapour and precipitation isotope measurements provide a constraint on the integrated effect of evaporation and condensation processes, but have rarely been collected in a way that allows to use these for model validation and improvement. Here we present a collection of spatially distributed measurements of water isotopes in the different phases at high time resolution during the ISLAS2021 field campaign over the period 15 to 30 March 2021. The main observational site of this campaign was Andenes, Norway (69.3144 degrees N, 16.1194 degrees E). Isotopic measurements were conducted simultaneously at sea level and a mountain observatory, as well as additional coastal sites at distances of 120 km (Troms & oslash;, Norway) and 1100 km (Bergen, Norway), enabling the assessment of spatial representativeness of vapour isotope measurements. Precipitation samples for water isotope analysis were collected on site at sub-event time resolution, and along a transect across the Vester & aring;len archipelago. These measurements were complemented by a suite of aerosol measurements, including ice-nucleating particles, and additional in situ and remote sensing observations of meteorological variables. During the two weeks of the ISLAS2021 field campaign, frequent alternations between mid-latitude and arctic weather systems were encountered, providing a range of different cases for more detailed process studies. The dataset is available at 10.1594/PANGAEA.984616 , and can serve as a test bed for assessing the spatial representativeness and sampling strategies for water isotope measurements on meteorological time scales. Furthermore, we anticipate our data to be useful in various aspects related to cloud microphysics, for example the quantification of riming processes in convective clouds, the role of ice nucleating particles in marine cold-air outbreaks, and on the condensation efficiency of mid-latitude storms.

Supercooled liquid-containing clouds (sLCCs) play a significant role in Earth's radiative budget and the hydrological cycle, especially through surface snowfall production. Evaluating state-of-the-art climate models with respect to their ability to simulate the frequency of occurrence of sLCCs and the frequency with which they produce snow is, therefore, critically important. Here, we compare these quantities as derived from satellite observations, reanalysis datasets, and Earth system models from Phase 6 of the Coupled Model Intercomparison Project (CMIP6) and find significant discrepancies between the datasets for mid- and high latitudes in both hemispheres. Specifically, we find that the ERA5 reanalysis and 10 CMIP6 models consistently overestimate the frequency of sLCCs and snowfall frequencies from sLCCs compared to CloudSat–CALIPSO satellite observations. The biases are very similar for ERA5 and the CMIP6 models, which indicates that the discrepancies in cloud phase and snowfall stem from differences in the representation of cloud microphysics rather than the representation of meteorological conditions. This, in turn, highlights the need for refinements in the models’ parameterizations of cloud microphysics in order for them to represent cloud phase and snowfall accurately. The thermodynamic phase of clouds and precipitation has a strong influence on simulated climate feedbacks and, thus, projections of future climate. Understanding the origin(s) of the biases identified here is, therefore, crucial for improving the overall reliability of climate models.

Marine cold-air outbreaks (mCAOs) are a characteristic type of high-impact weather in the European Arctic and are characterized by an intense water cycle where polar cloud processes play an important role. Model simulations and weather forecasts of mCAO events are challenging and associated with poor predictability. One reason is that processes related to the water cycle interact with one another on a wide range of scales. In regional models, some of these processes are resolved and others are fully or partly parameterised. To test and improve numerical weather prediction models, additional observations and novel types of measurements of water vapour are highly demanded. Stable water isotopes are an increasingly available measurement, allowing to trace sub-grid scale processes, and providing the potential to constrain the mass budget of the atmospheric water cycle during mCAO events. During the ISLAS2022 field experiment (21 March to 10 April 2022), the stable isotope composition of water vapour and liquid samples, cloud structures, and other meteorological parameters were collected between Svalbard and Northern Scandinavia on various measurement platforms. Airborne survey flights to Svalbard provided the ocean evaporation signature and subsequent processing of water vapour during mCAO conditions. During a number of flights, mCAO airmasses were repeatedly sampled over a course of hours to days, allowing to characterize their thermodynamic evolution as clouds were first forming, then glaciating and precipitating. In addition, vapour isotope and sea water isotope measurements were taken continuously onboard R/V Helmer Hanssen between Tromsø and the Greenland west coast. Finally, coordinated land-based measurement activity over Northern Norway and Sweden allowed collection of precipitation samples, thus closing the mass budget of the mCAO events. Furthermore, using buoyancy-controlled meteorological balloons launched from Ny Ålesund, we additionally obtained continuous in-situ measurements of the boundary-layer evolution during the mCAO. We provide an overview over the airborne and ground-based measurement activities during the campaign and provide several examples to highlight the potential of the stable water isotope measurements to constrain the water budget of mCAOs in conjunction with traditional meteorological observations.

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 (CRE). 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, which was implemented into the Norwegian Earth System Model (NorESM). 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 CRE of 2 W m−2. This large surface flux increase brings the simulation into better agreement with Arctic ground-based measurements. Despite the fact that the model cannot respond fully to the INP parameterization change due to fixed sea surface temperatures, Arctic surface air temperature increases by 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.

Abstract. Supercooled Liquid-Containing Clouds (sLCCs) play a significant role in Earth's radiative budget and the hydrological cycle, especially through surface snowfall production. Evaluating state-of-the-art climate models with respect to their ability to simulate the frequency of occurrence of sLCCs and the frequency with which they produce snow is, therefore, critically important. Here, we compare these quantities as derived from satellite observations, reanalysis datasets, and Earth System Models from Phase 6 of the Coupled Model Intercomparison Project (CMIP6) and find significant discrepancies between the data sets for mid and high latitudes in both hemispheres. Specifically, we find that the ERA5 reanalysis and ten CMIP6 models consistently overestimate the frequency of sLCCs and snowfall frequencies from sLCCs compared to CloudSat-CALIPSO satellite observations, especially over open ocean regions. The biases are very similar for ERA5 and the CMIP6 models, which indicates that the discrepancies in cloud phase and snowfall stem from differences in the representation of cloud microphysics rather than the representation of meteorological conditions. This, in turn, highlights the need for refinements in the models’ parameterizations of cloud microphysics in order for them to represent cloud phase and snowfall accurately. The thermodynamic phase of clouds and precipitation has a strong influence on simulated climate feedbacks and, thus, projections of future climate. Understanding the origin(s) of the biases identified here is, therefore, crucial for improving the overall reliability of climate models.

Abstract Clouds are the main source of uncertainties when projecting climate change. Mixed-phase clouds that contain ice and supercooled-liquid particles are especially hard to constrain, and climate models neither agree on their phase nor their spatial extent. This is problematic, as models that underestimate contemporary supercooled-liquid in mixed-phase clouds will underestimate future warming. Furthermore, it has recently been shown that supercooled-liquid water in mixed-phase clouds is not homogeneously-mixed, neither vertically nor horizontally. However, while there have been attempts at observationally constraining mixed-phase clouds to constrain uncertainties in future warming, all studies only use the phase of the interior of mixed-phase clouds. Here we show, using novel satellite observations that distinguish between cloud-top and interior phase in mixed-phase clouds, that mixed-phase clouds are more liquid at the cloud top globally. We use these observations to constrain the cloud top phase in addition to the interior in a global climate model, leading to +1 °C more 21st century warming in NorESM2 SSP5-8.5 climate projections. We anticipate that the difference between cloud top and interior phase in mixed-phase clouds is an important new target metric for future climate model development, because similar mixed-phase clouds related biases in future warming are likely present in many climate models.

Cloud feedbacks are a major contributor to the spread of climate sensitivity in global climate models (GCMs) [1]. Among the most poorly understood cloud feedbacks is the one associated with the cloud phase, which is expected to be modified with climate change [2]. Cloud phase bias, in addition, has significant implications for the simulation of radiative properties and glacier and ice sheet mass balances in climate models. In this context, this work aims to expand our knowledge on how the representation of the cloud phase affects snow formation in GCMs. Better understanding this aspect is necessary to develop climate models further and improve future climate predictions. This study aims to improve the understanding of the link between the representation of cloud phase and surface snowfall in historical simulations, comparing them to a combination of satellite remote sensing and reanalysis data. We use the cloud and snowfall products from CloudSat satellite and the European Centre for Medium-Range Weather Forecast Re-Analysis 5 (ERA5), producing a global surface snowfall rate climatology for each dataset. We compare the outputs from the Coupled Model Intercomparison Project Phase 6 (CMIP6) climate models to the snowfall rate climatology produced by CloudSat and ERA5. Comparing historical simulations from climate models with CloudSat will relate the already identified cloud phase biases with snowfall biases in specific regions for the past decade. Statistical analysis is carried out to determine cloud phase and precipitation (liquid and solid) biases and their potential connection to each other in the climate models. The results show that, globally, CMIP6 models can reproduce some of the characteristics of liquid water content and snowfall, as seen in ERA5. In addition, a comparison between ERA5 and the CMIP6 models shows that CMIP6 models underestimate the ice water path. Then, we look at the regional differences of ice water path, liquid water path, and surface snowfall to better understand how the individual CMIP6 models perform in different regions—showing an overestimation of ice water path in the southern and northern hemisphere extratropics. At the same time, surface snowfall is within the ERA5 standard deviation. When comparing the ERA5 ice water path to surface snowfall, we identify a positive relationship between the two, independent of the latitudes. On the other hand, the CMIP6 ensemble mean or CMIP6 models individually do not indicate a positive relationship. The next step of this ongoing research is to investigate the relationship between cloud phase (ice, liquid water path) and surface snowfall rate based on CloudSat retrievals. [1] Zelinka, M. D., Myers, T. A., McCoy, D. T., Po-Chedley, S., Caldwell, P. M., Ceppi, P., et al. (2020). Causes of higher climate sensitivity in CMIP6 models. Geophysical Research Letters, 47, e2019GL085782. https://doi-org.ezproxy.uio.no/10.1029/2019GL085782 [2] Bjordal, J., Storelvmo, T., Alterskjær, K. et al. Equilibrium climate sensitivity above 5 °C plausible due to state-dependent cloud feedback. Nat. Geosci. 13, 718–721 (2020). https://doi-org.ezproxy.uio.no/10.1038/s41561-020-00649-1

The High- Latitude Measurement of Snowfall (HiLaMS) campaign explored variability in snowfall properties and processes at meteorologically distinct field sites located in Haukeliseter, Norway, and Kiruna, Sweden, during the winters of 2016/17 and 2017/18, respectively. Campaign activities were founded upon the sensitivities of a low-cost, core instrumentation suite consisting of Micro Rain Radar, Precipitation Imaging Package, and Multi-Angle Snow Camera. These instruments are highly portable to remote field sites and, considered together, provide a unique and complementary set of snowfall observations including snowflake habit, particle size distributions, fall speeds, surface snowfall accumulations, and vertical profiles of radar moments and snow water content. These snow-specific parameters, used in combination with existing observations from the field sites such as snow gauge accumulations and ambient weather conditions, allow for advanced studies of snowfall processes. HiLaMS observations were used to 1) successfully develop a combined radar and in situ microphysical property retrieval scheme to estimate both surface snowfall accumulation and the vertical profile of snow water content, 2) identify the predominant snowfall regimes at Haukeliseter and Kiruna and characterize associated macrophysical and microphysical properties, snowfall production, and meteorological conditions, and 3) identify biases in the HARMONIE-AROME numerical weather prediction model for forecasts of snowfall accumulations and vertical profiles of snow water content for the distinct snowfall regimes observed at the mountainous Haukeliseter site. HiLaMS activities and results suggest value in the deployment of this enhanced snow observing instrumentation suite to new and diverse high-latitude locations that may be underrepresented in climate and weather process studies.

Cloud feedbacks are a major contributor to the spread of climate sensitivity in global climate models (GCMs) [1]. Among the most poorly understood cloud feedbacks is the one associated with the cloud phase, which is expected to be modified with climate change [2]. Cloud phase bias, in addition, has significant implications for the simulation of radiative properties and glacier and ice sheet mass balances in climate models. In this context, this work aims to expand our knowledge on how the representation of the cloud phase affects snow formation in GCMs. Better understanding this aspect is necessary to develop climate models further and improve future climate predictions. This study will compare surface snowfall, ice, and liquid water content from the Coupled Model Intercomparison Project Phase 6 (CMIP 6) climate models (accessed through Pangeo) to the European Centre for Medium-Range Weather Forecast Re-Analysis 5 (ERA5) data from 1985 to 2014. We conduct statistical analysis at the annual and seasonal timescales to determine the biases in cloud phase and precipitation (liquid and solid) in the CMIP6 models and their potential connection between them. For the analysis, we use the Jupyter notebook on the CMIP6 analysis (https://github.com/franzihe/eosc-nordic-climate-demonstrator/blob/master/work/), which guides the user step by step. The use of the Pangeo.io intake package makes it possible to browse the CMIP6 online catalog for the required variables, models, and experiments and stores it in xarray dask datasets. Vertical variables in sigma pressure levels had to be interpolated to standard pressure levels as provided in ERA5. We also interpolated the horizontal and vertical variables to the exact horizontal grid resolution before calculating the climatology. A global comparison between the reanalysis (ERA5) and the CMIP6 models shows that models tend to underestimate the ice water path compared to the reanalysis even if most of them can reproduce some of the characteristics of liquid water content and snowfall. To better understand the link between biases in cloud phase and surface snowfall rate, we try to find a relationship between ice water path and surface snowfall in GCMs. Linear regressions within extratropical areas show a positive relationship between ice water content and surface snowfall in the reanalysis data, while CMIP6 models do not have these characteristics. [1] Zelinka, M. D., Myers, T. A., McCoy, D. T., Po-Chedley, S., Caldwell, P. M., Ceppi, P., et al. (2020). Causes of higher climate sensitivity in CMIP6 models. Geophysical Research Letters, 47, e2019GL085782. https://doi-org.ezproxy.uio.no/10.1029/2019GL085782 [2] Bjordal, J., Storelvmo, T., Alterskjær, K. et al. Equilibrium climate sensitivity above 5 °C plausible due to state-dependent cloud feedback. Nat. Geosci. 13, 718–721 (2020). https://doi-org.ezproxy.uio.no/10.1038/s41561-020-00649-1 Github: https://github.com/franzihe

In the winter, orographic precipitation falls as snow in the mid to high latitudes where it causes avalanches, affects local infrastructure, or leads to flooding during the spring thaw. We present a technique to validate operational numerical weather prediction model simulations in complex terrain. The presented verification technique uses a combined retrieval approach to obtain surface snowfall accumulation and vertical profiles of snow water at the Haukeliseter test site, Norway. Both surface observations and vertical profiles of snow are used to validate model simulations from the Norwegian Meteorological Institute’s operational forecast system and two simulations with adjusted cloud microphysics.Retrieved surface snowfall is validated against measurements conducted with a double-fence automated reference gauge (DFAR). In comparison, the optimal estimation snowfall retrieval produces + 10.9% more surface snowfall than the DFAR. The predicted surface snowfall from the operational forecast model and two additional simulations with microphysical adjustments (CTRL and ICE-T) are overestimated at the surface with +41.0 %, +43.8 %, and +59.2 %, respectively. Simultaneously, the CTRL and ICE-T simulations underestimate the mean snow water path by -1071.4% and -523.7 %, respectively.The study shows that we would reach false conclusions only using surface accumulation or vertical snow water content profiles. These results highlight the need to combine ground-based in-situ and vertically-profiling remote sensing instruments to identify biases in numerical weather prediction.