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Ice formation in clouds and the implications for climate

This SENSE project combines weather-scale and climate modelling with satellite data to understand ice formation in clouds and the effect on future climate.


Climate models are very poor at representing the mixture of ice and supercooled water drops in clouds. Models therefore diverge dramatically in the amounts of ice and water that they simulate and the agreement with satellite observations is extremely poor for many models. The formation of ice crystals in a cloud reduces cloud reflectivity and can even result in the complete removal of cloud water through strong precipitation, leading to substantial reductions in cloud coverage. The huge range of model predictions is partly due to inadequate (and sometimes non-phy

sical) representations of ice-related processes in models, with most models even neglecting any representation of the particles that trigger ice formation – ice-nucleating particles (INPs).

Ice formation is a critical process in determining the magnitude of cloud feedbacks (how much a change in cloud properties in a future climate could alter the climate). It therefore represents a major uncertainty in climate projections. Two key factors determining the strength of the feedback for mixed-phase clouds are the amount of ice in present-day clouds and the response of this ice content to increases in temperature or other alterations in the meteorological conditions. This SENSE project will use satellite observations to understand and then constrain these factors in the UK Earth System Model (UKESM).


  1. Use satellite observations of mixed-phase clouds to understand the environmental factors that control the fraction of supercooled water and ice
  2. Combine satellite observations and in situ measurements from previous campaigns to link ice formation to ice-nucleating particle (INP) concentrations in the air
  3. Quantify the response of cloud ice content to changes in meteorological conditions
  4. Evaluate the cloud processes in UKESM and regional high-resolutions versions of the model in order to improve the estimates of cloud feedback.


The project will use a combination of MODIS and CALIPSO satellite observations of the cloud properties (including mixed-phase fraction) together with satellite radar observations of precipitation. These will be combined with in situ measurements from several past aircraft and ship campaigns over the Southern Ocean and at high northern latitudes. The project will focus on these high-latitude clouds which previous studies have shown have the greatest potential to alter cloud feedbacks. The project also offers the opportunity to use machine learning to disentangle the complex factors that control the relationships between ice, supercooled water, precipitation and environmental conditions.

Model simulations showing the effect of ice-nucleating particles on cloud reflection of solar radiation