NAMAs, NAPAs, LAPAs, NAPs, INDCs, NAPCCs… The alphabet soup of climate action is enoug...
NAMAs, NAPAs, LAPAs, NAPs, INDCs, NAPCCs… The alphabet soup of climate action is enoug...
An advisory released this August by the US National Weather Service warned this yea...
Skateboarding and climate change resilience; what on earth can these two activities have in c...
The Black Death struck Europe in 1347, killing 30-50% of the European population in six viole...
Over the past decade, the media, climate crusaders and skeptics alike have echoed the same mantra “no single extreme event can be attributed to human induced climate change”. Whether this statement was used from the vantage point of climate deniers or doubters, to negate the contribution of man-made activities to climate change, or by climate activists to highlight the inherent uncertainties that exist in climate science but the impetus to act on established scientific knowledge and precaution, it has been used widely across the spectrum. However, in recent years considerable advances have been made in the science of risk attribution that have allowed scientists to quantify the role of man-made versus natural drivers in reference to specific weather and climate events (Stott, et al., 2011). It’s time to rephrase the attribution tag line to reflect the advances in science.
When I asked Dr. Thomas Peterson, President of the World Meteorological Organization’s Commission for Climatology, how he would rephrase the catchy mantra he said, “no single event can be entirely attributed to man-made climate change however man-made actions are changing the magnitude and frequency of some events”. Then he added, “the science has moved on in probabilistic terms” (personal communication). There were important nuances embedded within this refined statement. First of all, magnitude and frequency were mentioned and distinguished as separate impacts. Secondly, which events was Dr. Peterson referring to? And most fundamentally, how do scientists arrive at a probabilistic conclusion of whether a given event is man-made or naturally caused?
Scientists derive a probabilistic conclusion by examining the fraction of applied risk of a particular extreme event by modeling natural drivers in the climate (for example, changes in solar output and sulfate particulate release due to volcanic eruptions) and man-made drivers (emitting greenhouse gases, including CO2, into the atmosphere). The probability of a particular extreme event occurring is represented in a series of model simulations; for example one representing current climate conditions at the time of the extreme event (including man-made and natural drivers) compared with a parallel scenario where the man-made drivers are absent. By comparing various models, scientists can determine the fraction of the event that was attributed to human influence (Stott, et al., 2011).
To put this into context, when investigating the European summer heat wave of 2003, Stott et al. (2004) examined the long term trends in mean European summer (June-August) temperatures and determined the fraction of the heat wave attributable to man-made changes to the climate system, versus the fraction attributable to natural internal variability. They then estimated how the risk of an average June-August temperature exceeding the extreme threshold had changed as a result of man-made activities with the climate system. Their analysis showed that it was over 90 percent likely that human influence at least doubled the risk of a heat wave of this magnitude.
A useful analogy to understand the effects of climate changes on extreme weather goes as follows. In an average season a baseball player hits a home run 30% of the time. The following season the player’s ability to hit a home run increases considerably, by 20%, and now the player is hitting a home run 50% of the time. That season the team starts to undergo routine drug testing and it is discovered that the player has been using steroids (Meehl 2012). So did the player hit the home run that won the final game as a result of taking steroids?
The answer to this question, like a climate attribution study, can only be accurately answered with a probability estimate. For any particular home run over the course of the season, one would not know whether it was the result of steroids (20%) or natural athletic ability (30%). What is known is that the player’s ability to hit a home run has increased by 20% versus prior seasons. Therefore, one could make the attribution statement that, all other things being equal, steroids increased the probability of a home-run occurrence by 20% (Meehl, 2012). Climate attribution assessments distinguish the effects of man-made climate change or a different external factor (steroids in this analogy) from natural variability (the players natural athletic ability) (Peterson, et al., 2012).
Indeed as natural variability plays a fundamental role, not all attribution assessments show that man-made actions were the main driver of an extreme event –it depends on the event.
For example, while the US drought of 2012 was exacerbated by record high temperatures, scientists found that the majority of the event was attributed to natural variability and lacked a clear detection to human influence (Rupp, et al., 2013). The 2014 Bulletin of American Meteorological Society (BAMS) Report, which assessed extreme events of 2013 from a climate perspective, indicated that human caused climate change greatly increased the risk of extreme heat waves that were reviewed in the report. The ways in which man-made drivers affected other types of events including storms, droughts and heavy downpours was less clear indicating that natural variability played an important role in such extremes (Herring et al., 2014).
It is important to nuance between attribution findings that show increased frequency versus increased magnitude of extreme events. Some events are occurring on a more frequent time scale, for example events that once occurred once in 285 years may now be occurring once in 70 years, while other events may be occurring on a similar time scale however are increasing in magnitude (personal communication). For example, the 2011 BAMs study found that, the probability of a Hurricane Sandy-level flooding event in New York City has doubled since 1950 (frequency) (Sweet, et at., 2013). These findings have important implications for adaptation decisions as decision-makers need to know which types of extremes they are planning for and whether they should anticipated increased frequency, magnitude or some combination of both.
Assessments of extreme event attribution are important for society as they could lead to improved adaptation decisions. If a particular event is slated to increase in magnitude and/or frequency for a particular region, decision makers could re-allocate resources towards insulating communities from the consequences of such events through adapting building codes to future climate conditions and extreme weather events, developing permeable roads to absorb excess water, creating forested spaces in urban areas that provide shade and cooling, building flood defenses with seawalls and storm surge barriers, and allocating money aside for emergency measures (Stott et al., 2011).
The science has indeed come a long way since the original mantra caught wind over a decade ago. While we cannot say in definitive terms that an event was undoubtedly caused by man-made actions, or natural variation for that matter, we can provide probability estimates which reflect the extent to which each driver was involved. As Dr. Peterson suggested, the new mantra could broadly be “no single event can be entirely attributed to man-made climate change however man-made actions are changing the magnitude and frequency of some events” (personal communication). However, more specifically it involves waiting for an individual peer reviewed study that assesses the fraction of risk attributed to each individual extreme event. It’s not as catchy and it doesn’t roll of the tongue for a media sound bite however, bottom line, it keeps up with the science.
Herring, S.C., et al. (2014). Explaining Extreme Events of 2013 from a Climate Perspective. Bulletin of the American Meteorological Society. 95 (9), S1-S96.
Meehl, G.A. (2012). As animated in steroids, baseball and climate change: What do home runs and weather extremes have in common? UCAR video. [Available online at http://www2.ucar.edu/atmosnews/attribution/steroids-baseball-climate-change/].
Peterson, T.C., et al. (2012). Explaining Extreme Events of 2011 from a Climate Perspective. Bulletin of the American Meteorological Society. 93, 1041-1067.
Peterson, et al. (2013). Explaining Extreme Events of 2012 from a Climate Perspective. Bulletin of the American Meteorological Society. 94 (9), S1-S74.
Peterson, T.C., Personal Communication, Nov. 4, 2014.
Rupp, D, E., et al (2013). “Human influence of the probability of low precipitation in the central united states in 2012 in Peterson, T.C., et al. (2013). Explaining Extreme Events of 2012 from a Climate Perspective. Bulletin of the American Meteorological Society. 94 (9), 2-6.
Stott, P. A., et al. (2004). Human contribution to the European heatwave of 2003. Nature, 432, 610–614.
Stott, P.A., et al. (2011). Attribution of Weather and Climate Related Events. Climate Science for Serving Society: Research Modeling and Prediction Priorities. J. W. Hurrell and G. Asrar, Eds., Springer, 2013. 307-337.
Sweet, W., et al (2013) “Hurricane Sandy Inundation Problems Today and Tomorrow,” in Peterson, T.C., et al. (2013). Explaining Extreme Events of 2012 from a Climate Perspective. Bulletin of the American Meteorological Society. 94 (9), 17-20
Amanda L. Rycerz, is a Research Officer at HabitatSeven; an interactive design studio that works with governments, academia, non-profits and forward-thinking corporations.