Climate Change Opinion

Investing in Science to Improve Climate Risk Management

Integrating Earth science research and observations into adaptation planning helps identify effective strategies to manage climate risks.

By , Richard Alley, and Klaus Keller

Climate change caused by past and ongoing emissions from fossil fuel burning poses sizable risks for current and future generations through its impacts on multiple interacting sectors, including, for example, food and water supplies and public health [O’Neill et al., 2017].

The extent of these risks is subject to deep uncertainties and tipping points, suggesting the need for flexible approaches to climate adaptation. One example of a deep uncertainty in our understanding of climate is the degree to which local and regional storm surge intensities are modulated by a warming climate [Lee et al., 2017; Wong et al., 2018].

In climate risk management, these uncertainties often affect estimates of potentially damaging impacts, thus amplifying the importance of the uncertainties [Wong et al., 2017a].

Even with strong mitigation of anthropogenic climate forcing, communities will still need to adapt to impending changes resulting from historical greenhouse gas emissions. Successful strategies require the right information, such as observations of relevant environmental indicators, or signposts, to trigger needed changes in the approach, and updated risk assessments that account for new information.

Fundamental Earth science research provides a foundation for supplying this information. Integrating research about regional- and global-scale Earth system processes into adaptation planning can help identify strategies to manage climate risks in the face of the uncertainties to ensure sustainable and resilient communities. We illustrate this point with an example of decision-making to adapt to sea level rise in Norfolk, Va.

Protecting Norfolk: A Case Study

Consider the decision of how high to build a levee to help protect Norfolk, which sits along the Elizabeth River at the south end of the Chesapeake Bay. Suppose that decision-makers seek flood defense systems to limit the chance that by the year 2070, floodwaters will overtop the levee to 1% in a given year, corresponding to an event with a 100-year return period. Planning to meet this target requires projections of sea level rise and storm surge. These projections hinge critically on assumptions about climate policies and the strength of physical feedback mechanisms governing, for example, ice sheet and storm surge dynamics [Wong et al., 2017a].

Diagram showing water height anomalies plotted against return periods for four sets of model assumptions, and the maximum water height of a flood event with 100-year return period for each scenario.
Fig. 1. Effects of deep uncertainties (about storm surge dependence on global mean temperatures) and positive feedbacks (triggering of rapid West Antarctic melting dynamics) on projected coastal flooding by 2070 modeled for Norfolk, Va. (a) Water height anomalies in 2070 (relative to 2015) plotted against return periods for four different sets of model assumptions. (b) Maximum water height of a flood event with a 100-year return period for each modeled scenario. Changing storm surge distributions are based on changes in global mean temperature. Mean sea level and global mean temperature projections were obtained using the Building blocks for Relevant Ice and Climate Knowledge (BRICK) sea level rise emulator from Wong et al. [2017b]. Storm surge models were calibrated using data from Sewells Point in Norfolk. Land subsidence estimates are from Kopp et al. [2014].
According to our modeling, making seemingly reasonable assumptions about future flooding on the basis of recent historical tide gauge records alone could lead planners to suggest a levee height of roughly 2.5 meters (Figure 1, green curve). However, this choice of levee height could result in drastically higher (and arguably unacceptable) flooding risks if some of these assumptions fail. For example, if Earth’s ice sheets respond rapidly and nonlinearly to increased climate forcing (as they have likely done in the past [Wong et al., 2017a] and may be doing now [Joughin et al., 2014]) and if storm surge frequency and/or intensity increase with a warming climate (which may be consistent with existing evidence [Lee et al., 2017]), then the projected probability of floodwaters overtopping a 2.5-meter-tall levee rises to just over 5% per year (Figure 1, pink curve) in this example analysis. In other words, what might have been considered a once-in-a-century flood event would occur approximately every 19 years on average.

An alternative might be to build a levee high enough to defend against the perceived worst-case scenario. Using an example worst-case assumption established by the National Oceanic and Atmospheric Administration in the case of Norfolk might lead to construction of a levee roughly 4 meters tall [Sweet et al., 2017]. This strategy might be logistically infeasible, however, and would likely require very large investments that some might feel could be better spent elsewhere.

How can we manage trade-offs between competing concerns given the deep uncertainties in our knowledge of climate? One approach is to hedge in the short term against immediate and foreseeable risks, then adapt as new information becomes available. By analogy, if a doctor tells you that you have an increased risk of heart disease, it might be prudent to adapt your behavior initially by moderately modifying your diet and exercise habits while leaving open the option to use more intensive approaches, such as prescription medication, if your perceived risk does not decrease sufficiently in the future.

Health risks can be better managed with sustained observations (checkups, blood tests, etc.) and analyses. Similarly, climate risks can be better managed with sustained Earth observation systems and research, so that science can inform decisions. What does this mean for designing, implementing, and resourcing mission-oriented basic science?

For coastal flood risk management, several unknowns can be addressed by analyzing basic Earth science questions: (1) What are the impacts of possible future greenhouse gas emissions trajectories, including on relatively low probability but high-risk events? (2) Will the West Antarctic Ice Sheet (WAIS) collapse, and if so, on what timescale [Joughin et al., 2014]? (3) What would be the resulting contribution of WAIS collapse to local sea level changes? (4) Are there detectable changes in the frequency and severity of storm tides? (5) Are storm tracks changing as a result of climate change? (6) What regions and metropolitan areas are more likely to be affected?

Designing adaptive strategies that can react to new information from Earth science observations can drastically improve future outcomes in the event of potentially damaging flood events [U.S. Army Corps of Engineers, 2014]. Current mitigation measures can be planned with an eye toward flexibility and expandability so that the full suite of appropriate options is available in the future. As an example, while new levees are being built or existing levees heightened, it might also be prudent to build them so they could be widened and further heightened in the future. In the Norfolk example, consider again a levee designed to defend against floods with return periods shorter than 100 years as of 2070 when the levee height—2.5 meters—is based only on linear extrapolation from the historical record (Figure 1, green curve). By comparison, even under relatively conservative model assumptions, considering a consistent storm surge pattern out to 2070 but ignoring the potential for accelerated West Antarctic melting (Figure 1, orange curve), that 2.5-meter levee would protect only against floods with a 46-year average return period, less than half of the target standard.

Integrating disciplines such as Earth science, statistics, and decision analysis into adaptation planning can help identify signposts that can be used to design monitoring systems and trigger potentially needed changes in strategy [Haasnoot et al., 2013; Weatherhead et al., 2018]. A simplified and potentially effective adaptive approach at Sewells Point, near Naval Station Norfolk, might involve the following steps:

First, heighten the levee in stages over the next few decades to maintain a 100-year protection standard against well-characterized risks—say, the nonaccelerated sea level rise scenario in Figure 1 (orange curve)—with enough width built in to increase the height in the future if necessary. Second, plan for transitions between scenarios (e.g., from orange to blue to pink in Figure 1) to avoid being locked into a single approach. And if the projected levee height needed to meet the target protection standard in certain scenarios is more than what might be tolerated on the basis of cost or aesthetic objections, the possibility of using other resiliency measures—elevating houses, property buyouts, or land use changes, for example—should be left open. Third, monitor signposts, such as changes in ice sheet dynamics and trends in regional tide gauge records, for indications that the local flood risk could change. Finally, update risk assessments and mitigation strategies with this new information.

Effective Adaptation Requires Investment

Such adaptive strategies can drastically reduce risks and/or costs [Haasnoot et al., 2013; Yohe, 1991], but they do require sustained investment to realize these benefits. The example outlined above requires investments in sustained Earth observations and analysis aimed at understanding and early detection of climate change impacts. Remote sensing observation platforms provide broad-based benefits [Weatherhead et al., 2018], increasing the economic value of the gathered information. The benefits of such information are regional in the case of storm surge trends and global in relation to ice sheet observations.

Finally, various adaptation strategies can be analyzed and compared to shed light on the trade-offs associated with choosing among the strategies (trade-offs related to costs, externalities like property values, and flood hazards, among others). Ultimately, the selection of a strategy requires balancing and compromising among diverse stakeholder perspectives and objectives. Multiobjective decision analysis can help identify strategies that best navigate the often hard trade-offs that arise [Kasprzyk et al., 2013], and careful articulation of objectives and trade-offs helps to improve the transparency of the decision-making process.

This opinion focuses on the issues of sea level rise and flooding affecting one example site. Similar plans would be required to address a wide range of other issues posed by the changing climate, which are likely to become more challenging if mitigation measures are not implemented.

Efforts to defend against climate-related risks benefit from sustained commitments to getting the right science, getting the science right, and getting the science to the decision-makers. Investments in basic Earth science observations and research enhance our ability to identify meaningful signposts for adaptation, to understand the risks associated with tipping points, and to realize the benefits of sound risk management strategies.

Acknowledgments

We thank Tony Wong, Robert Nicholas, Kelsey Ruckert, Nancy Tuana, and Robert Lempert for their contributions. This work was supported by the Penn State Center for Climate Risk Management. Any opinions, findings, conclusions, and recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the funding entity. Any errors and opinions are those of the authors. All model codes, analysis codes, data, and model output used for analysis are freely available at github.com/vsrikrish/SPSLAM and are distributed under the GNU General Public License. The data sets, software tools, and other resources on this website are provided as is, without warranty of any kind, express or implied. In no event shall the authors or copyright holders be liable for any claim, damages, or other liability in connection with the use of these resources. V.S., R.A., and K.K. conceptualized the commentary and wrote the paper; V.S. wrote the analysis codes. The authors are not aware of any competing interests.

References

Haasnoot, M., et al. (2013), Dynamic adaptive policy pathways: A method for crafting robust decisions for a deeply uncertain world, Global Environ. Change, 23(2), 485–498, https://doi.org/10.1016/j.gloenvcha.2012.12.006.

Joughin, I., B. E. Smith, and B. Medley (2014), Marine ice sheet collapse potentially under way for the Thwaites Glacier Basin, West Antarctica, Science, 344(6185), 735–738, https://doi.org/10.1126/science.1249055.

Kasprzyk, J. R., et al. (2013), Many objective robust decision making for complex environmental systems undergoing change, Environ. Modell. Software, 42, 55–71, https://doi.org/10.1016/j.envsoft.2012.12.007.

Kopp, R. E., et al. (2014), Probabilistic 21st and 22nd century sea-level projections at a global network of tide-gauge sites, Earth’s Future, 2(8), 383–406, https://doi.org/10.1002/2014EF000239.

Lee, B. S., M. Haran, and K. Keller (2017), Multidecadal scale detection time for potentially increasing Atlantic storm surges in a warming climate, Geophys. Res. Lett., 44(20), 10,617–10,623, https://doi.org/10.1002/2017GL074606.

O’Neill, B. C., et al. (2017), IPCC reasons for concern regarding climate change risks, Nat. Clim. Change, 7, 28–37, https://doi.org/10.1038/nclimate3179.

Sweet, W. V., et al. (2017), Global and regional sea level rise scenarios for the United States, NOAA Tech. Rep. NOS CO-OPS 083, 65 + vii pp., Natl. Oceanic and Atmos. Admin., Silver Spring., Md., tidesandcurrents.noaa.gov/publications/techrpt83_Global_and_Regional_SLR_Scenarios_for_the_US_final.pdf.

U.S. Army Corps of Engineers (2014), Procedures to evaluate sea level change: Impacts, responses, and adaptation, Tech. Lett. 1100-2-1, 254 pp., Washington, D.C., www.publications.usace.army.mil/Portals/76/Publications/EngineerTechnicalLetters/ETL_1100-2-1.pdf.

Weatherhead, E. C., et al. (2018), Designing the climate observing system of the future, Earth’s Future, 6(1), 80–102, https://doi.org/10.1002/2017EF000627.

Wong, T. E., A. M. R. Bakker, and K. Keller (2017a), Impacts of Antarctic fast dynamics on sea-level projections and coastal flood defense, Clim. Change, 144(2), 347–364, https://doi.org/10.1007/s10584-017-2039-4.

Wong, T. E., et al. (2017b), BRICK v0.2, a simple, accessible, and transparent model framework for climate and regional sea-level projections, Geosci. Model Dev., 10(7), 2,741–2,760, https://doi.org/10.5194/gmd-10-2741-2017.

Wong, T. E., et al. (2018), Neglecting model structural uncertainty underestimates upper tails of flood risk, Environ. Res. Lett., 13(7), 074019, https://doi.org/10.1088/1748-9326/aacb3d.

Yohe, G. W. (1991), Uncertainty, climate change and the economic value of information: An economic methodology for evaluating the timing and relative efficacy of alternative response to climate change with application to protecting developed property from greenhouse induced sea level rise, Policy Sci., 24(3), 245–269, https://doi.org/10.1007/BF00186329.

Author Information

Vivek Srikrishnan ([email protected]), Earth and Environmental Systems Institute, Pennsylvania State University, University Park; and Richard Alley and Klaus Keller, Earth and Environmental Systems Institute and Department of Geosciences, Pennsylvania State University, University Park

Citation: Srikrishnan, V., R. Alley, and K. Keller (2019), Investing in science to improve climate risk management, Eos, 100, https://doi.org/10.1029/2019EO131077. Published on 16 August 2019.
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