This page picks out a few key publications in the history of the climateprediction.net project:
- Weather@home Experimental Setup, Quarterly Journal of the Royal Meteorological Society, 2015
- Weather@home Results, BAMS, 2013
- Russian Heatwave, Geophysical Research Letters, 2012
- Did climate change increase the risk of Flooding in the UK in 2000?, Nature 2011
- Geoengineering Experiment Results, Nature Geoscience, 2010
- First Results from climateprediction.net, Nature, 2005
- Establishing the Principle of climateprediction.net, Nature, 1999
Weather@home Experimental Setup
“weather@home — development and validation of a very large ensemble modelling system for probabilistic event attribution”
Authors: N. Massey, R. Jones, F. E. L. Otto, T. Aina, S. Wilson, J. M. Murphy, D. Hassell, Y. H. Yamazaki, and M. R. Allen
This key paper sets out the core experimental setup used in all of our weather@home experiments. This is the innovative method of running both a global climate model and a higher resolution regional climate model on a volunteer’s home computer. Such a set-up allows the simulation of weather on a scale that is of most use to studies of the attribution of extreme events.
Quarterly Journal of the Royal Meteorological Society, Volume 141, Issue 690, pages 1528–1545, July 2015 Part A, DOI: 10.1002/qj.2455 [OPEN ACCESS]
Demonstrating the effect that climate change is having on regional weather is a subject which occupies climate scientists, government policy makers and the media. After an extreme weather event occurs, the question is often posed, ‘Was the event caused by anthropogenic climate change?’ Recently, a new branch of climate science (known as attribution) has sought to quantify how much the risk of extreme events occurring has increased or decreased due to climate change. One method of attribution uses very large ensembles of climate models computed via volunteer distributed computing. A recent advancement is the ability to run both a global climate model and a higher resolution regional climate model on a volunteer’s home computer. Such a set-up allows the simulation of weather on a scale that is of most use to studies of the attribution of extreme events. This article introduces a global climate model that has been developed to simulate the climatology of all major land regions with reasonable accuracy. This then provides the boundary conditions to a regional climate model (which uses the same formulation but at higher resolution) to ensure that it can produce realistic climate and weather over any region of choice. The development process is documented and a comparison to previous coupled climate models and atmosphere-only climate models is made. The system (known as weather@home) by which the global model is coupled to a regional climate model and run on volunteers’ home computers is then detailed. Finally, a validation of the whole system is performed, with a particular emphasis on how accurately the distributions of daily mean temperature and daily mean precipitation are modelled in a particular application over Europe.
Climateprediction.net featured twice in the special report of the Bulletin of the American Meteorological Society: Explaining Extreme Events of 2013 from a Climate Perspective, which presented assessments of how climate change may have affected the strength and likelihood of individual extreme events.
In the first paper, looking at the heavy rainfall in the Upper Danube and Elbe Basins in Germany in 2013, the study found no evidence that climate change made this event more likely.
In a second paper, looking at the extreme snowfall in the Spanish Pyrenees in 2013, the study again found that climate change was not the key driver of this extreme weather event.
Chapter 20. The heavy precipitation event of May–June 2013 in the Upper Danube and Elbe Basins
Authors: Nathalie Schaller, Friederike E. L. Otto, Geert Jan van Oldenborgh, Neil R. Massey, Sarah Sparrow, and Myles R. Allen
An observation-based analysis and large simulation ensembles show no evidence that climate change made heavy precipitation in the upper Danube and Elbe basins in May–June, such as observed in 2013, more likely
After an anomalously cold, cloudy, and rainy spring in central Europe, regions in Germany, Switzerland, Austria, and the Czech Republic received large amounts of precipitation between 30 May and 2 June 2013, with some places receiving the usual monthly precipitation amount within one or two days (CIB 2013). As shown in Fig. 20.1a, the maximum precipitation fell in the upper Danube and Elbe catchments, which led to severe flooding along these rivers in the following weeks. Grams et al. (2014) identified that during the four-day event, three consecutive low pressure systems moved from east to west over central Europe, due to a Rossby wave breaking, with the Alps acting as a wall. Thus, the low pressure systems remained stationary—a rare weather situation that occasionally occurs in summer but is extremely unusual in spring. Hydrological processes, in particular the late snow melt and saturated soils in some regions in Germany even before the event caused by the unusual spring weather, played an important role in the ensuing Danube and Elbe floods (BfG-DWD 2013). It has been suggested that Arctic warming has increased the chances of flooding on the Elbe and Danube (Petoukhov et al. 2013). However, Hirabayashi et al. (2013) showed that floods in central Europe should decrease with climate change, even as flooding in other parts of Europe has been attributed to anthropogenic warming (Pall et al. 2011). In this study, we analyze whether and to what extent anthropogenic climate change changed the odds of high precipitation in the upper Elbe and Danube catchments in May–June.
Chapter 21: The extreme snow accumulation in the western Spanish Pyrenees during winter and spring 2013
Authors: Juan A. Añel, Juan Ignacio López-Moreno, Friederike E. L. Otto, Sergio Vicente-Serrano, Nathalie Schaller, Neil Massey, Samuel T. Buisán, and Myles R. Allen
Natural climatic variability was apparently the main driver in the extreme cumulative snowfall that fell in the Pyrenees in 2013
Snow accumulation in the Pyrenees has shown a statistically significant negative trend since 1950 (López-Moreno 2005) in a similar way to other European mountain areas (Marty 2008). In the Pyrenees, the reduction in snow cover has mostly been associated with decreasing winter precipitation, which in turn has been related to a positive trend of the North Atlantic Oscillation index (NAOi, López-Moreno and Vicente-Serrano 2007; López-Moreno et al. 2010). However, this long-term trend is superimposed upon a high interannual variability, which leads to frequent changes between snow-poor and snow-rich years (Buisán et al. 2014). In the last decade the Pyrenees have recorded 5 years that have clearly exceeded the long-term average (above 75th percentile) winter precipitation, leading to deeper than normal snow-cover. This has been associated with a continuing trend of negative NAO conditions (Vicente-Serrano et al. 2011). Thus we investigate whether a different driver, anthropogenic emissions, played a role in changing the frequency of occurrence of deep and extensive snow cover using the example of the wet and snow rich winter and spring 2013. This year, despite having temperatures close to the long-term average for winter and spring, recorded far above normal precipitation from January to June over the Atlantic coast of the Iberian Peninsula and western Pyrenees. This wet anomaly was a consequence of an above average frequency of advections from the north and north-west, leading to the January–June precipitation events exceeding 100–200 year return periods (considering a reference period of only 33 years, 1980–2012, and using a generalized Pareto distribution). In the case of the Pyrenees, the anomaly of recorded precipitation was less extreme in the east than in the west.
“Reconciling two approaches to attribution of the 2010 Russian heat wave”
Authors:F. E. L. Otto, N. Massey, G. J. van Oldenborgh, R. G. Jones, M. R. Allen
Geophysical Research Letters, Volume 39, Issue 4, February 2012; DOI: 10.1029/2011GL050422
This is another key weather@home paper that looked at the effect of climate change on the risk of the severe heatwave that hit Russia in 2010. The conclusion was that the framing of your question is very important – whether you ask about the effect climate change had on the severity of an event, or the risk of that event happening. Climate change can effect either, both or neither of these things and this particular instance, climate modelling experiments concluded that the severity of the heatwave was not influenced by climate change, but the risk of it happening in the first place was.
In the summer 2010 Western Russia was hit by an extraordinary heat wave, with the region experiencing by far the warmest July since records began. Whether and to what extent this event is attributable to anthropogenic climate change is controversial. Dole et al. (2011) report the 2010 Russian heat wave was “mainly natural in origin” whereas Rahmstorf and Coumou (2011) write that with a probability of 80% “the 2010 July heat record would not have occurred” without the large-scale climate warming since 1980, most of which has been attributed to the anthropogenic increase in greenhouse gas concentrations. The latter explicitly state that their results “contradict those of Dole et al. (2011).” Here we use the results from a large ensemble simulation experiment with an atmospheric general circulation model to show that there is no substantive contradiction between these two papers, in that the same event can be both mostly internally-generated in terms of magnitude and mostly externally-driven in terms of occurrence-probability. The difference in conclusion between these two papers illustrates the importance of specifying precisely what question is being asked in addressing the issue of attribution of individual weather events to external drivers of climate.
Did climate change increase the risk of Flooding in the UK in 2000?
“Anthropogenic greenhouse gas contribution to flood risk in England and Wales in Autumn 2000”
Authors: P. Pall, T. Aina, D. Stone, P. Stott, T. Nozawa, A. Hilberts, D. Lohmann & M.R. Allen
Nature, 470, 382–385, 2011. Download pdf (1.4 MB)
One of our first regional extreme weather event attribution studies, this experiment looked at the contribution of climate change to the risk of the severe flooding experienced in the UK in the Autumn of 2000.
As with our later weather@home studies, we compared thousands of models of the climate as it was actually observed with a “world that might have been without climate change”.
Our conclusions were that “the precise magnitude of the anthropogenic contribution remains uncertain, but in nine out of ten cases our model results indicate that twentieth century anthropogenic greenhouse gas emissions increased the risk of floods occurring in England and Wales in autumn 2000 by more than 20%, and in two out of three cases by more than 90%”
Interest in attributing the risk of damaging weather-related events to anthropogenic climate change is increasing. Yet climate models used to study the attribution problem typically do not resolve the weather systems associated with damaging events2 such as the UK floods of October and November 2000. Occurring during the wettest autumn in England and Wales since records began in 1766, these floods damaged nearly 10,000 properties across that region, disrupted services severely, and caused insured losses estimated at £1.3 billion. Although the flooding was deemed a ‘wakeup call’ to the impacts of climate change at the time, such claims are typically supported only by general thermodynamic arguments that suggest increased extreme precipitation under global warming, but fail to account fully for the complex hydrometeorology associated with flooding. Here we present a multi-step, physically based ‘probabilistic event attribution’ framework showing that it is very likely that global anthropogenic greenhouse gas emissions substantially increased the risk of flood occurrence in England and Wales in autumn 2000. Using publicly volunteered distributed computing, we generate several thousand seasonal-forecast-resolution climate model simulations of autumn 2000 weather, both under realistic conditions, and under conditions as they might have been had these greenhouse gas emissions and the resulting large-scale warming never occurred. Results are fed into a precipitation-runoff model that is used to simulate severe daily river runoff events in England and Wales (proxy indicators of flood events). The precise magnitude of the anthropogenic contribution remains uncertain, but in nine out of ten cases our model results indicate that twentieth century anthropogenic greenhouse gas emissions increased the risk of floods occurring in England and Wales in autumn 2000 by more than 20%, and in two out of three cases by more than 90%.
Geoengineering Experiment Results
“Regional climate response to solar-radiation management”
Authors: Katharine L. Ricke, M. Granger Morgan & Myles R. Allen
Nature Geoscience 3, 537 – 541 (2010), doi:10.1038/ngeo915
Working in collaboration with Kate Ricke and Granger Morgan of Carnegie Mellon University in the USA, climateprediction.net developed a geoengineering experiment to investigate the effect of geoengineering the climate by using changes in volcanic aerosol to mimic geoengineering activities.
They concluded that “solar-radiation management would generally lead to less extreme temperature and precipitation anomalies, compared with unmitigated greenhouse gas emissions. However, they also illustrate that it is physically not feasible to stabilize global precipitation and temperature simultaneously as long as atmospheric greenhouse gas concentrations continue to rise”
Concerns about the slow pace of climate mitigation have led to renewed dialogue about solar-radiation management, which could be achieved by adding reflecting aerosols to the stratosphere. Modelling studies suggest that solar-radiation management could produce stabilized global temperatures and reduced global precipitation. Here we present an analysis of regional differences in a climate modified by solar-radiation management, using a large-ensemble modelling experiment that examines the impacts of 54 scenarios for global temperature stabilization. Our results confirm that solar-radiation management would generally lead to less extreme temperature and precipitation anomalies, compared with unmitigated greenhouse gas emissions. However, they also illustrate that it is physically not feasible to stabilize global precipitation and temperature simultaneously as long as atmospheric greenhouse gas concentrations continue to rise. Over time, simulated temperature and precipitation in large regions such as China and India vary significantly with different trajectories for solar-radiation management, and they diverge from historical baselines in different directions. Hence, it may not be possible to stabilize the climate in all regions simultaneously using solar-radiation management. Regional diversity in the response to different levels of solar-radiation management could make consensus about the optimal level of geoengineering difficult, if not impossible, to achieve.
First Results from climateprediction.net
“Uncertainty in predictions of the climate response to rising levels of greenhouse gases”
Authors: D.A. Stainforth, T. Aina, C. Christensen, M. Collins, N. Faull, D.J. Frame, J.A. Kettleborough, S. Knight, A. Martin, J.M. Murphy, C. Piani, D. Sexton, L.A. Smith, R.A. Spicer, A.J. Thorpe & M.R. Allen
Nature, 433, 403–406, January 2005. Download pdf (720 KB)
This paper presented the first results from the climateprediction.net experiment. Following on from Professor Allen’s initial idea to use volunteer computers to run large ensembles of climate models, it took several years to gain the research, set up the team and develop the climate models that would be suitable to run people’s home computers. The project was launched in 2003, and by 2005 we published the first results in the prestigious journal, Nature.
This experiment was the first multi-thousand-member grand ensemble of climate simulations.
The key finding was that some realistic models showed climate sensitivity (the temperature change associated with a doubling of the concentration of carbon dioxide in Earth’s atmosphere) of greater than 11°C and some less than 2°C. As the conclusions of this paper explained, “models with such extreme sensitivities are critical for the study of the full range of possible responses of the climate system to rising greenhouse gas levels, and for assessing the risks associated with specific targets for stabilizing these levels.”
The paper included the following acknowledgement: “We thank all participants in the ‘climateprediction.net’ experiment and the many individuals who have given their time to make the project a reality and a success.”
The range of possibilities for future climate evolution1–3 needs to be taken into account when planning climate change mitigation and adaptation strategies. This requires ensembles of multidecadal simulations to assess both chaotic climate variability and model response uncertainty4–9. Statistical estimates of model response uncertainty, based on observations of recent climate change10–13, admit climate sensitivities—defined as the equilibrium response of global mean temperature to doubling levels of atmospheric carbon dioxide—substantially greater than 5 K. But such strong responses are not used in ranges for future climate change14 because they have not been seen in general circulation models. Here we present results from the ‘climateprediction.net’ experiment, the first multi-thousand-member grand ensemble of simulations using a general circulation model and thereby explicitly resolving regional details15–21. We find model versions as realistic as other state-of-the-art climate models but with climate sensitivities ranging from less than 2 K to more than 11 K. Models with such extreme sensitivities are critical for the study of the full range of possible responses of the climate system to rising greenhouse gas levels, and for assessing the risks associated with specific targets for stabilizing these levels.
Establishing the Principle of climateprediction.net
“Do it yourself climate prediction”
Author: M. Allen
Nature, 401, 642, October 1999. Download pdf (50 KB)
This paper, by climateprediction.net founder and Primary Investigator, Professor Myles Allen, sets out the unique concept behind the project – using volunteers’ home computers to run vast ensembles of climate models – something that had not been considered until this point. As he says in this paper, “the ability to perform million-member ensemble simulations, even at a relatively coarse resolution, would profoundly affect climate modelling”.
The tortuous process of drafting, reviewing and revising the third assessment report of the Intergovernmental Panel on Climate Change (IPCC) is well under way, following the meeting of chapter authors in Arusha, Tanzania, on 1–3 September. Even those of us only peripherally involved have been burning the midnight oil to complete analyses ‘in time for IPCC’, and this is nothing compared with the heroic efforts of the lead authors. Only the most hardened cynic would deny the value of this five-yearly stimulus to the climate research community, and the usefulness of the reports it generates. One of the IPCC’s greatest achievements has been to bring national governments ‘on board’. Nevertheless, any such intergovernmental process sits uneasily in the era of stakeholders and focus groups. Large numbers of scientists gather together periodically and attempt to forge a consensus about the nature and scale of the problem of global warming. This is followed by a gathering of an even larger number of policy-makers, primarily politicians and civil servants, to decide what to do about it. A bemused public must then be persuaded, for its own good, to go along with the solution.