IDEAS home Printed from https://ideas.repec.org/a/spr/climat/v140y2017i3d10.1007_s10584-016-1858-z.html
   My bibliography  Save this article

The effects of time-varying observation errors on semi-empirical sea-level projections

Author

Listed:
  • Kelsey L. Ruckert

    (The Pennsylvania State University
    The Pennsylvania State University)

  • Yawen Guan

    (The Pennsylvania State University)

  • Alexander M. R. Bakker

    (The Pennsylvania State University)

  • Chris E. Forest

    (The Pennsylvania State University
    The Pennsylvania State University
    The Pennsylvania State University)

  • Klaus Keller

    (The Pennsylvania State University
    The Pennsylvania State University
    Carnegie Mellon University)

Abstract

Sea-level rise is a key driver of projected flooding risks. The design of strategies to manage these risks often hinges on projections that inform decision-makers about the surrounding uncertainties. Producing semi-empirical sea-level projections is difficult, for example, due to the complexity of the error structure of the observations, such as time-varying (heteroskedastic) observation errors and autocorrelation of the data-model residuals. This raises the question of how neglecting the error structure impacts hindcasts and projections. Here, we quantify this effect on sea-level projections and parameter distributions by using a simple semi-empirical sea-level model. Specifically, we compare three model-fitting methods: a frequentist bootstrap as well as a Bayesian inversion with and without considering heteroskedastic residuals. All methods produce comparable hindcasts, but the parametric distributions and projections differ considerably based on methodological choices. Our results show that the differences based on the methodological choices are enhanced in the upper tail projections. For example, the Bayesian inversion accounting for heteroskedasticity increases the sea-level anomaly with a 1% probability of being equaled or exceeded in the year 2050 by about 34% and about 40% in the year 2100 compared to a frequentist bootstrap. These results indicate that neglecting known properties of the observation errors and the data-model residuals can lead to low-biased sea-level projections.

Suggested Citation

  • Kelsey L. Ruckert & Yawen Guan & Alexander M. R. Bakker & Chris E. Forest & Klaus Keller, 2017. "The effects of time-varying observation errors on semi-empirical sea-level projections," Climatic Change, Springer, vol. 140(3), pages 349-360, February.
  • Handle: RePEc:spr:climat:v:140:y:2017:i:3:d:10.1007_s10584-016-1858-z
    DOI: 10.1007/s10584-016-1858-z
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10584-016-1858-z
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s10584-016-1858-z?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Gabriele C. Hegerl & Thomas J. Crowley & William T. Hyde & David J. Frame, 2006. "Climate sensitivity constrained by temperature reconstructions over the past seven centuries," Nature, Nature, vol. 440(7087), pages 1029-1032, April.
    2. Michael C. Runge & Julienne C. Stroeve & Andrew P. Barrett & Eve McDonald-Madden, 2016. "Detecting failure of climate predictions," Nature Climate Change, Nature, vol. 6(9), pages 861-864, September.
    3. James Neumann & Daniel Hudgens & John Herter & Jeremy Martinich, 2011. "The economics of adaptation along developed coastlines," Wiley Interdisciplinary Reviews: Climate Change, John Wiley & Sons, vol. 2(1), pages 89-98, January.
    4. Malte Meinshausen & S. Smith & K. Calvin & J. Daniel & M. Kainuma & J-F. Lamarque & K. Matsumoto & S. Montzka & S. Raper & K. Riahi & A. Thomson & G. Velders & D.P. Vuuren, 2011. "The RCP greenhouse gas concentrations and their extensions from 1765 to 2300," Climatic Change, Springer, vol. 109(1), pages 213-241, November.
    5. Donald Resio & Jennifer Irish & Joannes Westerink & Nancy Powell, 2013. "The effect of uncertainty on estimates of hurricane surge hazards," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 66(3), pages 1443-1459, April.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Gupta, Rishabh & Mishra, Ashok, 2019. "Climate change induced impact and uncertainty of rice yield of agro-ecological zones of India," Agricultural Systems, Elsevier, vol. 173(C), pages 1-11.
    2. Melissa Dell & Benjamin F. Jones & Benjamin A. Olken, 2014. "What Do We Learn from the Weather? The New Climate-Economy Literature," Journal of Economic Literature, American Economic Association, vol. 52(3), pages 740-798, September.
    3. Jiří Mikšovský & Rudolf Brázdil & Petr Štĕpánek & Pavel Zahradníček & Petr Pišoft, 2014. "Long-term variability of temperature and precipitation in the Czech Lands: an attribution analysis," Climatic Change, Springer, vol. 125(2), pages 253-264, July.
    4. Tony E. Wong & Alexander M. R. Bakker & Klaus Keller, 2017. "Impacts of Antarctic fast dynamics on sea-level projections and coastal flood defense," Climatic Change, Springer, vol. 144(2), pages 347-364, September.
    5. Gregory Casey & Stephie Fried & Ethan Goode, 2023. "Projecting the Impact of Rising Temperatures: The Role of Macroeconomic Dynamics," IMF Economic Review, Palgrave Macmillan;International Monetary Fund, vol. 71(3), pages 688-718, September.
    6. Eliseev, Alexey V. & Mokhov, Igor I., 2008. "Eventual saturation of the climate–carbon cycle feedback studied with a conceptual model," Ecological Modelling, Elsevier, vol. 213(1), pages 127-132.
    7. Schaeffer, Michiel & Gohar, Laila & Kriegler, Elmar & Lowe, Jason & Riahi, Keywan & van Vuuren, Detlef, 2015. "Mid- and long-term climate projections for fragmented and delayed-action scenarios," Technological Forecasting and Social Change, Elsevier, vol. 90(PA), pages 257-268.
    8. Ramos, Rodrigo Soares & Kumar, Lalit & Shabani, Farzin & Picanço, Marcelo Coutinho, 2019. "Risk of spread of tomato yellow leaf curl virus (TYLCV) in tomato crops under various climate change scenarios," Agricultural Systems, Elsevier, vol. 173(C), pages 524-535.
    9. Rashid, Muhammad Adil & Jabloun, Mohamed & Andersen, Mathias Neumann & Zhang, Xiying & Olesen, Jørgen Eivind, 2019. "Climate change is expected to increase yield and water use efficiency of wheat in the North China Plain," Agricultural Water Management, Elsevier, vol. 222(C), pages 193-203.
    10. Carl-Friedrich Schleussner & Joeri Rogelj & Michiel Schaeffer & Tabea Lissner & Rachel Licker & Erich M. Fischer & Reto Knutti & Anders Levermann & Katja Frieler & William Hare, 2016. "Science and policy characteristics of the Paris Agreement temperature goal," Nature Climate Change, Nature, vol. 6(9), pages 827-835, September.
    11. Qun'ou Jiang & Yuwei Cheng & Qiutong Jin & Xiangzheng Deng & Yuanjing Qi, 2015. "Simulation of Forestland Dynamics in a Typical Deforestation and Afforestation Area under Climate Scenarios," Energies, MDPI, vol. 8(10), pages 1-26, September.
    12. Rungruang Janta & Laksanara Khwanchum & Pakorn Ditthakit & Nadhir Al-Ansari & Nguyen Thi Thuy Linh, 2022. "Water Yield Alteration in Thailand’s Pak Phanang Basin Due to Impacts of Climate and Land-Use Changes," Sustainability, MDPI, vol. 14(15), pages 1-19, July.
    13. J. Annan & J. Hargreaves, 2011. "On the generation and interpretation of probabilistic estimates of climate sensitivity," Climatic Change, Springer, vol. 104(3), pages 423-436, February.
    14. Minh Ha-Duong, 2008. "Hierarchical fusion of expert opinion in the Transferable Belief Model, application on climate sensitivity," Post-Print halshs-00112129, HAL.
    15. Detlef Vuuren & Elke Stehfest & Michel Elzen & Tom Kram & Jasper Vliet & Sebastiaan Deetman & Morna Isaac & Kees Klein Goldewijk & Andries Hof & Angelica Mendoza Beltran & Rineke Oostenrijk & Bas Ruij, 2011. "RCP2.6: exploring the possibility to keep global mean temperature increase below 2°C," Climatic Change, Springer, vol. 109(1), pages 95-116, November.
    16. Gregory Casey & Soheil Shayegh & Juan Moreno-Cruz & Martin Bunzl & Oded Galor & Ken Caldeira, 2019. "The Impact of Climate Change on Fertility," Department of Economics Working Papers 2019-04, Department of Economics, Williams College.
    17. Catherine C. Ivanovich & Tianyi Sun & Doria R. Gordon & Ilissa B. Ocko, 2023. "Future warming from global food consumption," Nature Climate Change, Nature, vol. 13(3), pages 297-302, March.
    18. Donald T. Resio & Taylor G. Asher & Jennifer L. Irish, 2017. "The effects of natural structure on estimated tropical cyclone surge extremes," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 88(3), pages 1609-1637, September.
    19. Erik O. Sterner & Tom Adawi & U. Martin Persson & Ulrika Lundqvist, 2019. "Knowing how and knowing when: unpacking public understanding of atmospheric CO2 accumulation," Climatic Change, Springer, vol. 154(1), pages 49-67, May.
    20. Matthew A. Thomas & Ting Lin, 2018. "A dual model for emulation of thermosteric and dynamic sea-level change," Climatic Change, Springer, vol. 148(1), pages 311-324, May.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:climat:v:140:y:2017:i:3:d:10.1007_s10584-016-1858-z. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.