IMPROVING CIRRUS ESTIMATES OF RADIATIVE FORCING: BACKSCATTERING FOR MODELS AND OBSERVATIONS (ICE-RF)

One of the big uncertainties in both weather forecasting and prediction of future climate change is cloud. In both cases the models that estimate the future weather and climate must represent cloud in a simplified way. If we can improve the way that clouds are represented in the models, then we can improve the model outputs.
Cirrus are the high, often thin and wispy-looking clouds, consisting entirely of ice particles. Their influence on weather and climate change is very hard to determine because of the different ways they interact with radiation. They reflect solar radiation (a cooling effect) but also absorb and re-radiate longwave radiation from the Earth (resulting in a warming effect). Improving the way that cirrus are represented in models will bring advances in both weather forecasting and climate change prediction. This project aims to both develop and test a new scheme for representing cirrus that will be used in the Met Office UKV forecast model and in the Met Office Earth System Model used for climate change prediction.
One of the problems with understanding cirrus is the difficulty in knowing the microphysical form of the cloud - that is the size, shape and roughness of the ice particles in the cloud - the properties that determine how the cloud interacts with radiation. Research aircraft can fly through the cloud and sample the ice particles, but that is not a practical method for widespread use or routine monitoring. Alternatively the clouds can be systematically interrogated from below by ground-based lidar, or from above by satellite. Both systems rely on radiation back-scattered from the ice particles, using two wavelengths, one strongly and one weakly absorbed by ice particles. However, to make sense of the lidar data we need to know how different habits (sizes and shapes) of ice crystal backscatter radiation at the lidar wavelengths. This can be determined using the Manchester Ice Cloud Chamber (MICC).
Ice clouds of different habits can be made and characterized in the MICC. As the ice particles fall they pass through a scattering chamber. We will shine lasers into the scattering chamber, at paired lidar wavelengths (for ground-based or satellite systems), and measure the exact backscatter from the different ice clouds, as well as polarization and scattering in other directions. The two wavelengths of a pair interact with the particles in different ways, depending on their size, shape and roughness, so we can determine a colour ratio (the ratio of the two backscattered signals) that tells us something about the ice particles in the cloud.
Having developed a catalogue of laboratory ice cloud colour ratios and scattering functions we will model these known conditions using the discrete dipole approximation method to ensure that this method can reproduce the laboratory results, and provide for a parameterisation of colour ratio according to particle size distribution (PSD). Further colour ratios will be modeled and parameterised for ice clouds with PSDs that we have not reproduced in the MICC, but are possible in nature.
Now, if we take the colour ratio measured from real cirrus by satellite lidars we can use our parameterisation of the backscatter signal to obtain the mean mass weighted ice crystal size (Dmmw). The ice microphysics scheme in the UKV model also predicts the Dmmw, thus we can directly validate the UKV microphysics scheme by comparing the predicted and actual Dmmw for cirrus.
The same microphysics scheme is part of the Met Office Earth System Model used for climate change prediction, so its improvement also increases confidence in estimates of future climate change that inform Government policy. The backscatter parameterisations will be made across a range of wavelengths that can be applied to ground-based, current and future satellite missions, to tell us whether particular cirrus are having a net warming or cooling effect on the atmosphere.

The end result of this project (ICE-RF) is to reduce uncertainties in weather forecasting and climate change predictions, through a better understanding and model representation of the backscatter from ice particles and the radiative effects of cirrus. The net effect of cirrus is a large unknown in current climate models, and yet it is key to understanding current climate and future climate response. In the broadest sense, anyone who is or will be affected by climate change and climate change policies will benefit from this work, as will any business that would be positively impacted by improvements to weather forecasting brought about by a better treatment of cirrus.
More specifically, through improved climate models, direct beneficiaries of the work will include the Department of Energy and Climate Change who will benefit from the increased realism associated with the improved parameterisations of cirrus in global and regional projections of climate change. Only when cirrus cloud complexity is understood, and accurately parameterised, can cirrus cloud feedbacks and cirrus cloud indirect effects be adequately assessed in Global Climate Model (GCM) scenarios. Such scenarios are used to inform policy makers of likely economic and societal impacts. More reliable GCM forecasts due to reduced uncertainties allows for more robust planning and mitigation strategies. Such strategies have impacts in many areas, including the energy industries (traditional, renewables and future technologies), construction, town and civic planning, transport, agriculture and tourism. The timescale of these indirect impacts (i.e. via better informed climate change policies) varies with the industry. The effect on the energy industries could be rapid, with either an increase or a relaxation in the immediate need for clean energy supplies. On a longer timescale industries such as agriculture and tourism that are directly impacted by climate change will be better prepared and able to adapt to expected changes, and will have a more reliable timeframe for their adaptation.
Thus, improved climate change forecasts have broad policy implications both nationally and internationally, with current and future impacts on business, the economy and our way of life.
More immediately, the enhancements in UKV will improve weather forecasts, providing widespread benefit, both commercially and at a personal level for the public. Although cirrus are often considered as relatively thin, high cloud, and not rain bearing, they nonetheless influence surface temperatures, and heating in the mid- to upper troposphere. The heating, in turn, will affect the dynamics with all the consequences that can bring, and also influence transport of air between the troposphere and stratosphere. An example beneficiary is the transport industry: local authorities, road and rail transport will be influenced by better surface temperature forecasts, determining whether frozen surfaces are a risk, while the aviation industry will also benefit from better prognostic information about the upper troposphere.
The success of our work can also encourage the development of new instruments to further interrogate cirrus clouds - for example lidars in the higher frequency UV range, where ice becomes absorbing again, or further development of hyperspectral lidars. While this is a specialized industry, the resulting data applications will have further widespread impact.