GeoLog

climate model

How to forecast the future with climate models

How to forecast the future with climate models

Our climate is constantly changing, and with the help of simulation modelling, scientists are working hard to better understand just how these conditions will change and how it will affect society. Science journalist Conor Paul Purcell has worked on Earth System Models during his time as a PhD student and postdoctoral researcher; today he explains how scientists use these models as tools to forecast the future of our climate.

While we can’t predict everything about our future, climate scientists have a good understanding of how our environment will look and feel like in the coming years. Researchers and climate specialists predict that temperatures will increase dramatically in the 21st century, ranging between 1.5°C and 4°C above pre-industrial levels, depending on your location and the amount of carbon dioxide pumped into the atmosphere in the near future. Forecasts of future drought and flood risk, at both regional and global bases, are also provided by climate experts.

Understanding how such features of Earth’s changing climate may manifest, and ultimately impact on our society, takes considerable international collaboration – a collaboration which is largely based around the results of climate modelling. That’s because climate predictions for the future are made using sophisticated computer models, which are built around mathematical descriptions of the physical and biological processes that govern our planet.

These models have become so complex in recent years that they are now referred to as Earth System Models (ESMs). Using ESMs, climate modellers can create simulations of the planet at different times in the future and the past. ESMs are in fact the only tools we have for simulating the global future in this sense. For instance, if we want to know how our climate may look like one hundred years from now, how ocean acidification levels may change and how this might impact ocean life, or how plants will respond to increasing levels of atmospheric carbon dioxide, ESMs are the only tool available.

The models are built in components, each representing a separate part of the Earth system: the atmosphere, the ocean, the land surface and its vegetation, and the ice-sheets and sea-ice. These are constructed by coding each component with the mathematics that describes the environmental processes at work.

Climate models are systems of differential equations based on the basic laws of physics, fluid motion, chemistry, and biology. Pictured here is a schematic of a global atmospheric model. (Credit: NOAA, via Wikimedia Commons)

For example, the winds in the atmosphere are described by the mathematics of fluid motion. Model developers translate these mathematical equations into code that computers can understand, like giving them a set of instructions to follow. Supercomputers can then interpret the code to simulate how winds, for example, are expected to develop at each global location through time. The results are usually plotted on world maps.

As scientists have learned more about our Earth’s systems over time, the complexity of these individual models has been ramped up dramatically. For example, the land surface and vegetation model components become more sophisticated as plant biologists understand more and more about how plants transfer water and carbon between the land and atmosphere.

And it’s not just one giant solo project either: there are tens of ESMs and hundreds of subcomponent models developed and used at research centres around the globe. Collaboration between these facilities is a necessary part of progress, and information is shared at international conferences ever year, like the American Geophysical Union’s Fall Meeting in the United States and the European Geosciences Union’s General Assembly in Europe.

This means that developments are always been made towards increasing the realism of ESMs. On the horizon such developments will include increasing the resolution of the global models for improving accuracy at regional locations, and also incorporating the results from the latest research in atmospherics, oceanography and ice sheet dynamics. One example is research into plants, specifically how they interact with carbon dioxide and water in the atmosphere. Further understanding of this biological process is expected to increase the realism of models over the coming years and decades. In general, improvements to the accuracy of model simulations can help to help society in the future. For example, models will be able to help predict how climate change may impact, say, water scarcity in South Africa, wildfire risk in the western United States, or crop yields in Asia. Indeed, the ESMs of the future should boast incredibly accurate simulations and prediction capabilities unheard of today.

By Conor Purcell, a Science & Nature Writer with a PhD in Earth Science

Conor Purcell is a science journalist with a PhD in Earth Science. He is also founding-editor of www.wideorbits.com and is on twitter @ConorPPurcell and some of his other articles at cppurcell.tumblr.com.

Imaggeo on Mondays: Monitoring the melt

Automatic weather stations (AWS) play a prominent role in making meteorological measurements in remote areas. These measurements can feed into climate models; providing better projections for rainfall, temperature and more. This peculiarly perched piece of equipment is just such a weather station:

“Alpine Meteorology” by James Thornton, distributed by the EGU under a Creative Commons licence. Wondering where it is? Right here: 46.213546 N, 7.092183 W, sitting in the heart of the Vallon de Nant Natural Reserve, Switzerland.

“Alpine Meteorology” by James Thornton, distributed by the EGU under a Creative Commons licence. Wondering where it is? Right here: 46.213546 N, 7.092183 W, sitting in the heart of the Vallon de Nant Natural Reserve, Switzerland.

Out in the Swiss Alps, this AWS is making measurements of temperature, precipitation, wind speed, relative humidity and solar radiation every 15 minutes. The photographer, James Thornton, says “the data provided by a network of these stations measuring over a period of several years can help us understand how the measured variables change over space and time”. Indeed, the more fine-scale measurements there are; the better meteorologists can make their models – for both short- and long-term changes.

The aim of this station – and the network of others like it in the Swiss Alps – is to obtain a better understanding of how the water cycle is changing in alpine environments. The data from these stations has been incorporated into a complex hydrological model known as WaSiM-ETH and applied to a number of high alpine river catchments to determine the temporal and spatial variation in snowmelt and runoff from glacier-covered regions.

Satellite image of the Swiss Alps, taken in September 2002. The northern peaks are snow covered above 2000 metres, but there is little or no snow on the peaks further south. (Credit: NASA)

Satellite image of the Swiss Alps, taken in September 2002. The northern peaks are snow covered above 2000 metres, but there is little or no snow on the peaks further south. (Credit: NASA)

Since the 1940s, the Alpine basin has had no change land use and relatively constant land cover, making it ideal for investigating how climate variability has influenced hydrological processes in the last century. Daniela Balin and colleagues have found that dominant control on the region’s hydrology is the way in which snow is stored over the course of a year. When more snowpack is preserved into the early summer, there is a greater chance of high river flow. This is because high precipitation at this time of year, together with late snowmelt, combines to produce a large amount of runoff. The effects of this are likely to be amplified in future as convective rainfall events increase. These results mean the Swiss Alps are likely to be sensitive to future climate change, particularly as snowmelt (and therefore temperature), has the biggest influence on the region’s hydrology.

This same model has been used to assess how interaction between glaciers and snow cover affect alpine river basins. Comparing the influence of snowmelt, glacier runoff and other factors that influence hydrology, Mark Verbunt and his team found that snow storage and melting processes had a stronger control on runoff in catchments that had a lot of glacier cover. If the glacier was snow-free, there would be more glacial melt than in the areas of the glacier that were snow-covered. This is because snow covered areas are more reflective (they have a higher albedo). The colour of a glacier is highly variable and because ice is darker than bright white snow it absorbs more solar radiation and melts more readily.

For more on how AWS are being used to monitor atmospheric processes, take a look at the Dust in the Desert series by James King, who is using AWS to collect dust emission data from the African continent.

References:

Balin, D. et al. Temperature-driven ‘meteorological memory’ and hydrological response of an Alpine river basin. Conference abstract (EGU2013-11700)

Verbunt, M. et al. The hydrological role of snow and glaciers in alpine river basins and their distributed modelling. Journal of Hydrology 282, 36–55, 2003

Imaggeo is the EGU’s online open access geosciences image repository. All geoscientists (and others) can submit their images to this repository and since it is open access, these photos can be used by scientists for their presentations or publications as well as by the press and public for educational purposes and otherwise. If you submit your images to Imaggeo, you retain full rights of use, since they are licensed and distributed by the EGU under a Creative Commons licence.