IDEAS home Printed from https://ideas.repec.org/p/osf/eartha/bmskp.html

Observation-based Simulations of Humidity and Temperature Using Quantile Regression

Author

Listed:
  • Poppick, Andrew
  • McKinnon, Karen A.

Abstract

The human impacts of changes in heat events depend on changes in the joint behavior of temperature and humidity. Little is currently known about these complex joint changes, either in observations or projections from general circulation models (GCMs). Further, GCMs do not fully reproduce the observed joint distribution, implying a need for simulation methods that combine information from GCMs with observations for use in impact studies. We present an observation-based, conditional quantile mapping approach for the simulation of future temperature and humidity. A temperature simulation is first produced by transforming historical temperature observations to include projected changes in the mean and temporal covariance structure from a GCM. Next, a humidity simulation is produced by transforming humidity observations to account for projected changes in the conditional humidity distribution given temperature, using a quantile regression model. We use the Community Earth System Model Large Ensemble (CESM1-LE) to estimate future changes in summertime (June - August) temperature and humidity over the Continental United States (CONUS), and then use the proposed method to create future simulations of temperature and humidity at stations in the Global Summary of the Day dataset. We find that CESM1-LE projects decreases in summertime humidity across CONUS for a given deviation in temperature from the forced trend, but increases in the risk of high dew point on historically hot days. In comparison with CESM1-LE, our observation-based simulation largely projects smaller changes in the future risk of either high or low humidity on days with historically warm temperatures.

Suggested Citation

  • Poppick, Andrew & McKinnon, Karen A., 2020. "Observation-based Simulations of Humidity and Temperature Using Quantile Regression," Earth Arxiv bmskp, Center for Open Science.
    • Handle: RePEc:osf:eartha:bmskp
    DOI: 10.31219/osf.io/bmskp
    as

    Download full text from publisher

    File URL: https://osf.io/download/5ecfe4e917ac9e02e66216d4/
    Download Restriction: no
    File URL: https://libkey.io/10.31219/osf.io/bmskp?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
    ---><---

    References listed on IDEAS

    as
    1. Barreca, Alan I., 2012. "Climate change, humidity, and mortality in the United States," Journal of Environmental Economics and Management, Elsevier, vol. 63(1), pages 19-34.
    2. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    3. Aiguo Dai & John C. Fyfe & Shang-Ping Xie & Xingang Dai, 2015. "Decadal modulation of global surface temperature by internal climate variability," Nature Climate Change, Nature, vol. 5(6), pages 555-559, June.
    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. repec:spo:wpmain:info:hdl:2441/5rkqqmvrn4tl22s9mc4b6ga2g is not listed on IDEAS
    6. Brian J. Reich, 2012. "Spatiotemporal quantile regression for detecting distributional changes in environmental processes," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 61(4), pages 535-553, August.
    7. Jeremy S. Pal & Elfatih A. B. Eltahir, 2016. "Future temperature in southwest Asia projected to exceed a threshold for human adaptability," Nature Climate Change, Nature, vol. 6(2), pages 197-200, February.
    8. Keith W. Dixon & John R. Lanzante & Mary Jo Nath & Katharine Hayhoe & Anne Stoner & Aparna Radhakrishnan & V. Balaji & Carlos F. Gaitán, 2016. "Evaluating the stationarity assumption in statistically downscaled climate projections: is past performance an indicator of future results?," Climatic Change, Springer, vol. 135(3), pages 395-408, April.
    9. Victor Chernozhukov & Iv·n Fern·ndez-Val & Alfred Galichon, 2010. "Quantile and Probability Curves Without Crossing," Econometrica, Econometric Society, vol. 78(3), pages 1093-1125, May.
    10. E. M. Fischer & R. Knutti, 2013. "Robust projections of combined humidity and temperature extremes," Nature Climate Change, Nature, vol. 3(2), pages 126-130, February.
    11. Machado, Jose A.F. & Silva, J. M. C. Santos, 2005. "Quantiles for Counts," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 1226-1237, December.
    12. Karen A. McKinnon & Andrew Poppick, 2020. "Estimating Changes in the Observed Relationship Between Humidity and Temperature Using Noncrossing Quantile Smoothing Splines," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 25(3), pages 292-314, September.
    13. repec:spo:wpecon:info:hdl:2441/5rkqqmvrn4tl22s9mc4b6ga2g is not listed on IDEAS
    14. Thomas R. Knutson & Jeffrey J. Ploshay, 2016. "Detection of anthropogenic influence on a summertime heat stress index," Climatic Change, Springer, vol. 138(1), pages 25-39, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Dina A. Awad & Hazem A. Masoud & Ahmed Hamad, 2024. "Climate changes and food-borne pathogens: the impact on human health and mitigation strategy," Climatic Change, Springer, vol. 177(6), pages 1-25, June.

    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. Karen A. McKinnon & Andrew Poppick, 2020. "Estimating Changes in the Observed Relationship Between Humidity and Temperature Using Noncrossing Quantile Smoothing Splines," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 25(3), pages 292-314, September.
    2. Paolo Frumento & Nicola Salvati, 2021. "Parametric modeling of quantile regression coefficient functions with count data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(4), pages 1237-1258, October.
    3. repec:hum:wpaper:sfb649dp2016-057 is not listed on IDEAS
    4. Viviana Carcaiso & Leonardo Grilli, 2023. "Quantile regression for count data: jittering versus regression coefficients modelling in the analysis of credits earned by university students after remote teaching," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(4), pages 1061-1082, October.
    5. Chawarat Rotejanaprasert & Andrew B. Lawson, 2018. "A Bayesian Quantile Modeling for Spatiotemporal Relative Risk: An Application to Adverse Risk Detection of Respiratory Diseases in South Carolina, USA," IJERPH, MDPI, vol. 15(9), pages 1-15, September.
    6. Paul Hewson & Keming Yu, 2008. "Quantile regression for binary performance indicators," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 24(5), pages 401-418, September.
    7. Charlier, Isabelle & Paindaveine, Davy & Saracco, Jérôme, 2015. "Conditional quantile estimation based on optimal quantization: From theory to practice," Computational Statistics & Data Analysis, Elsevier, vol. 91(C), pages 20-39.
    8. Fan, Yanqin & Liu, Ruixuan, 2016. "A direct approach to inference in nonparametric and semiparametric quantile models," Journal of Econometrics, Elsevier, vol. 191(1), pages 196-216.
    9. Pedro H. C. Sant'Anna & Xiaojun Song & Qi Xu, 2022. "Covariate distribution balance via propensity scores," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(6), pages 1093-1120, September.
    10. Cariou, Pierre & Wolff, Francois-Charles, 2015. "Identifying substandard vessels through Port State Control inspections: A new methodology for Concentrated Inspection Campaigns," Marine Policy, Elsevier, vol. 60(C), pages 27-39.
    11. Filip Žikeš & Jozef Baruník, 2016. "Semi-parametric Conditional Quantile Models for Financial Returns and Realized Volatility," Journal of Financial Econometrics, Oxford University Press, vol. 14(1), pages 185-226.
    12. Paul Contoyannis & Jinhu Li, 2017. "The dynamics of adolescent depression: an instrumental variable quantile regression with fixed effects approach," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(3), pages 907-922, June.
    13. Victor Chernozhukov & Iván Fernández-Val & Blaise Melly & Kaspar Wüthrich, 2020. "Generic Inference on Quantile and Quantile Effect Functions for Discrete Outcomes," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 115(529), pages 123-137, January.
    14. Michael L. Polemis & Mike G. Tsionas, 2023. "The environmental consequences of blockchain technology: A Bayesian quantile cointegration analysis for Bitcoin," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(2), pages 1602-1621, April.
    15. A Ford Ramsey, 2020. "Probability Distributions of Crop Yields: A Bayesian Spatial Quantile Regression Approach," American Journal of Agricultural Economics, John Wiley & Sons, vol. 102(1), pages 220-239, January.
    16. Pereda-Fernández, Santiago, 2023. "Identification and estimation of triangular models with a binary treatment," Journal of Econometrics, Elsevier, vol. 234(2), pages 585-623.
    17. Henry R. Scharf & Xinyi Lu & Perry J. Williams & Mevin B. Hooten, 2022. "Constructing Flexible, Identifiable and Interpretable Statistical Models for Binary Data," International Statistical Review, International Statistical Institute, vol. 90(2), pages 328-345, August.
    18. Tengyuan Liang, 2022. "Universal prediction band via semi‐definite programming," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(4), pages 1558-1580, September.
    19. Guodong Li & Yang Li & Chih-Ling Tsai, 2015. "Quantile Correlations and Quantile Autoregressive Modeling," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(509), pages 246-261, March.
    20. Wu, Qi & Yan, Xing, 2019. "Capturing deep tail risk via sequential learning of quantile dynamics," Journal of Economic Dynamics and Control, Elsevier, vol. 109(C).
    21. Manzan, Sebastiano & Zerom, Dawit, 2013. "Are macroeconomic variables useful for forecasting the distribution of U.S. inflation?," International Journal of Forecasting, Elsevier, vol. 29(3), pages 469-478.

    More about this item

    Statistics

    Access and download statistics

    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:osf:eartha:bmskp. 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: OSF (email available below). General contact details of provider: https://eartharxiv.org .

    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.