Climate change is widely seen as one of the most important threats to humanity. As a result of our emissions of greenhouse gases, global temperatures are rising. However, the implications of our carbon emissions for regional changes in weather are poorly understood.
As discussed in an important paper published in the Proceedings of the National Academy of Sciences, I argued that we need to increase the resolution of our climate models substantially (i.e. reduce the size of the basic model grid boxes) before we can be confident in predicting regional climate change, particularly in relation to extreme events.
Clearly, to increase the resolution of climate models requires enhanced computational capability. However, this alone is not enough: we additionally need to improve substantially the computational efficiency of our climate models.
Improving the computational efficiency of weather and climate models is the primary goal of ITHACA.
Developing high-resolution weather and climate predictions is vital for a number of reasons.
For weather prediction, it enables more reliable forecasts of extreme weather events to be made.
In addition, if society is to be able to adapt to climate change, we need to know whether it is primarily adapting to hotter and drier conditions on the one hand, or wetter and stormier conditions on the other. Knowing this will determine the type of infrastructure investments needed to make society more resilient to climate change. Unfortunately, the IPCC reports show that for most regions of the world, even the sign of annual-mean precipitation change is profoundly uncertain.
Secondly, if we are ever to consider climate geoengineering, e.g. spraying aerosols in the stratosphere to reflect sunlight, we have to be sure we are not making the climate worse in some parts of the world.
Thirdly, the emerging field of “loss and damage” requires some kind of quantitative attribution of specific weather events to human greenhouse gas emissions.
Finally, we need to understand the climate system better. For example, are there significant tipping points in the climate system and are our emissions likely to take us over a tipping point in the coming years?
p
In ITHACA we have studied three ways of improving the computational efficiency of our climate models: a) reducing the numerical precision of the variables in the models from 64 bits – the historical default – to 32, and even 16 bits, b) using quantum computers which could in principle improve computational speed exponentially, c) use AI methods to represent parts of the climate model.
