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Climate Change

Can’t we all just get along! Bridging the gap between climate scientists and decision-makers to help prevent precipitation-related catastrophes

Article: Coughlan de Perez, Erin; Monasso, Fleur; van Aalst, Maarten, Suare, Pablo. (2014) Science to prevent disasters. Nature Geoscience Commentary. Vol. 7, 78-79. doi:10.1038/ngeo2081

Background Information

 There is currently a huge disconnect between climate scientists and governmental organizations. To reword this sentence in a more pressing situation: there is currently a major disconnect between natural disaster experts and the decision-makers in charge of how to prepare for natural disasters. Frustratingly, many catastrophes result from predictable weather events in which no warnings were in place. Clearly, climate researchers need to develop a better relationship and open communication with policy-makers to help place early-warning systems and be more prepared for devastating weather events.

 

The Red Cross and Red Crescent Climate Centre is currently aiming to work with scientists to help with this problem. This intersection between research and risk management may seem obvious, but science and policy-makers speak different languages. A lot of climate research deals with seasonal averages, which is an inaccurate picture of the weather people face day-to-day. These averages often lead to the policy-makers ignoring climate trends and research (Fig. 1).

 

In order to bridge this science-policy gap, some climate scientists have decided to focus on threshold events, or those extreme weather related events that don’t happen very often, but are completely devastating when they do. A recent example of an extreme weather event is the Colorado flooding that occurred this past September. A better understanding of these threshold weather events can help decision-makers implement drainage channels in at-risk regions or help develop proper evacuation routes.

 

An example of a successful threshold event warning system is our current understanding of tropical cyclone frequency and intensity. Although still devastating, the recent tropical typhoon Haiyan in the Philippines could have been even more catastrophic without this type of early warning research.

 

What needs to be done? 

Firstly, we need to understand the variability in the model-predictions of these threshold events up to the year 2100. The year 2100 is currently predicted to be when atmospheric carbon dioxide levels reach their maximum. Unfortunately, many of current climate research is based on the period of weather events from 1980-2000, which is now outdated by almost 15 years. Implementing a policy from collected data before the turn of the century is 1) very limited, and 2) cannot accurately take into account climate change.

 

Additionally, model projections have high uncertainties, so it could be difficult to tell what is an outlier (a mistake in a weather prediction) and what is a threshold event. So, climate researchers need to expand their data to more recent and past years in order to reduce the signal to noise ratio in their models. (Noisiness is this uncertainty). This data expansion would not only increase the accuracy of their models, but it would help differentiate between outliers (statistically bad data points) and what actually could be a threshold event.

 

Climate research often focuses on extreme rainfall amounts. However, current climate trends have found that reduced rainfall amounts are just as important to predict and understand. The expansion of regional drought, especially in Western Africa, has led to major crop failures in recent years, leading to massive food storages. Thus, models and research cannot only focus of high rain events.

 

Perhaps the most important aspect for a governmental decision-maker is for climate models to have a better understanding of uncertainty. The use of a “best-guess” scenario often leads to policy-makers ignoring climate research altogether. A, seemingly easy, way to fix this problem is to compare climate models directly to real (at-the moment) data. The more real-time data used in extreme weather event simulations, the better the model predictions.

 

The authors’ of this commentary have discovered that even providing confidence levels of anticipated extreme events can be more helpful to decision-makers. Rather than a saying “there might be a major drought,” using a more refined model to say “we are 70% sure there will be a major drought” can make all the difference in getting a disaster plan into action.

 

Where to go from here 

Climate researchers are constantly working on improving their models and making better predictions for extreme weather events. So, how can scientists and policy-makers learn to connect?

 

Coughlan de Perez et al. (2014) conclude that research literature needs to be available in a more reader-friendly language; but for that to happen, decision-makers also need to provide more incentives for academia to be directly involved in policy-making. For the scientists, the authors’ propose that valuable climate research should include a short outline aimed towards policy-makers, in a more general and readable language. Additionally, they suggest that the peer-review process (the in-depth critique that happens before a scientific paper is accepted for publication) include a non-academic reviewer. This would allow for decision-makers to more clearly understand current climate research.

 

Overall, the scientific community is trending towards not only improving their predictions of catastrophic weather events, but also towards working with policy-makers so that society can reap all the benefits of our current knowledge.

 

The average daily rainfall for Kenya (2003-2013) with three specific disasters. The red line represents the average rainfall amount for that period. To the left is a histogram representation of the scatter plot. Understanding the data points well above the average is more helpful to decision-makers than the average rainfall in order to prevent major disasters.

The average daily rainfall for Kenya (2003-2013) with three specific disasters. The red line represents the average rainfall amount for that period. To the left is a histogram representation of the scatter plot. Understanding the data points well above the average is more helpful to decision-makers than the average rainfall in order to prevent major disasters.

 

 

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