Flash Drought in CMIP5 Models

David Hoffmann aMonash University, Melbourne, Victoria, Australia
bAustralian Research Council Centre of Excellence for Climate Extremes, Melbourne, Victoria, Australia

Search for other papers by David Hoffmann in
Current site
Google Scholar
PubMed
Close
https://orcid.org/0000-0001-5368-1223
,
Ailie J. E. Gallant aMonash University, Melbourne, Victoria, Australia
bAustralian Research Council Centre of Excellence for Climate Extremes, Melbourne, Victoria, Australia

Search for other papers by Ailie J. E. Gallant in
Current site
Google Scholar
PubMed
Close
, and
Mike Hobbins cCooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, Colorado
dNational Oceanic and Atmospheric Administration/Physical Sciences Laboratory, Boulder, Colorado

Search for other papers by Mike Hobbins in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

“Flash drought” (FD) describes the rapid onset of drought on subseasonal times scales. It is of particular interest for agriculture because it can deplete soil moisture for crop growth in just a few weeks. To better understand the processes causing FD, we evaluate the importance of evaporative demand and precipitation by comparing three different drought indices that estimate this hazard using meteorological and hydrological parameters from the CMIP5 suite of models. We apply the standardized precipitation index (SPI); the evaporative demand drought index (EDDI), derived from evaporative demand E0; and the evaporative stress index (ESI), which connects atmospheric and soil moisture conditions by measuring the ratio of actual and potential evaporation. The results show moderate-to-strong relationships (r2 > 0.5) between drought indices and upper-level soil moisture on daily time scales, especially in drought-prone regions. We find that all indices are able to identify FD in the top 10-cm layer of soil moisture in a similar proportion to that in the models’ climatologies. However, there is significant intermodel spread in the characteristics of the FDs identified. This spread is mainly caused by an overestimation of E0, indicating stark differences in the land surface models and coupling in individual CMIP5 models. Of all indices, the SPI provides the highest skill in predicting FD prior to or at the time of onset in soil moisture, while both EDDI and ESI show significantly lower skill. The results highlight that the lack of precipitation is the main contributor to FDs in climate models, with E0 playing a secondary role.

Significance Statement

This study is the first to assess the representation of rapidly developing drought, commonly referred to as flash drought, in global coupled climate models. This study elucidates how these models simulate flash drought and how they represent flash drought processes to allow for assessment in a changing climate. The work is also the first to compare the skill of drought indices based on precipitation and evaporative demand E0 for flash drought early detection on a global scale. We show that precipitation deficits are the main contributor to flash drought in climate models, with E0 playing a secondary role. However, an overestimation of E0 in some models causes significant intermodel disagreement, reflecting differences in the representation of land–atmosphere interactions.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/JHM-D-20-0262.s1.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Publisher’s Note: This article was revised on 25 May 2021 to correct a mistake in the affiliations of the first two authors.

Corresponding author: David Hoffmann, david.hoffmann@monash.edu

Abstract

“Flash drought” (FD) describes the rapid onset of drought on subseasonal times scales. It is of particular interest for agriculture because it can deplete soil moisture for crop growth in just a few weeks. To better understand the processes causing FD, we evaluate the importance of evaporative demand and precipitation by comparing three different drought indices that estimate this hazard using meteorological and hydrological parameters from the CMIP5 suite of models. We apply the standardized precipitation index (SPI); the evaporative demand drought index (EDDI), derived from evaporative demand E0; and the evaporative stress index (ESI), which connects atmospheric and soil moisture conditions by measuring the ratio of actual and potential evaporation. The results show moderate-to-strong relationships (r2 > 0.5) between drought indices and upper-level soil moisture on daily time scales, especially in drought-prone regions. We find that all indices are able to identify FD in the top 10-cm layer of soil moisture in a similar proportion to that in the models’ climatologies. However, there is significant intermodel spread in the characteristics of the FDs identified. This spread is mainly caused by an overestimation of E0, indicating stark differences in the land surface models and coupling in individual CMIP5 models. Of all indices, the SPI provides the highest skill in predicting FD prior to or at the time of onset in soil moisture, while both EDDI and ESI show significantly lower skill. The results highlight that the lack of precipitation is the main contributor to FDs in climate models, with E0 playing a secondary role.

Significance Statement

This study is the first to assess the representation of rapidly developing drought, commonly referred to as flash drought, in global coupled climate models. This study elucidates how these models simulate flash drought and how they represent flash drought processes to allow for assessment in a changing climate. The work is also the first to compare the skill of drought indices based on precipitation and evaporative demand E0 for flash drought early detection on a global scale. We show that precipitation deficits are the main contributor to flash drought in climate models, with E0 playing a secondary role. However, an overestimation of E0 in some models causes significant intermodel disagreement, reflecting differences in the representation of land–atmosphere interactions.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/JHM-D-20-0262.s1.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Publisher’s Note: This article was revised on 25 May 2021 to correct a mistake in the affiliations of the first two authors.

Corresponding author: David Hoffmann, david.hoffmann@monash.edu

Supplementary Materials

    • Supplemental Materials (PDF 686.80 KB)
Save
  • Allen, R. G., L. S. Pereira, D. Raes, and M. Smith, 1998: Crop evapotranspiration: Guidelines for computing crop water requirements. FAO Irrigation and Drainage Paper 56, 300 pp., http://www.fao.org/3/X0490E/X0490E00.htm.

  • Anderson, M. C., J. M. Norman, J. R. Mecikalski, J. A. Otkin, and W. P. Kustas, 2007: A climatological study of evapotranspiration and moisture stress across the continental United States based on thermal remote sensing: 1. Model formulation. J. Geophys. Res., 112, D10117, https://doi.org/10.1029/2006JD007506.

    • Search Google Scholar
    • Export Citation
  • Arora, V. K., and H. D. Matthews, 2009: Characterizing uncertainty in modeling primary terrestrial ecosystem processes. Global Biogeochem. Cycles, 23, GB2016, https://doi.org/10.1029/2008GB003398.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Arora, V. K., and G. J. Boer, 2010: Uncertainties in the 20th century carbon budget associated with land use change. Global Change Biol., 16, 33273348, https://doi.org/10.1111/j.1365-2486.2010.02202.x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bouchet, R. J., 1963: Évapotranspiration réelle et potentielle, signification climatique. IAHS Publ., 62, 134–142.

  • Entekhabi, D., I. Rodriguez-Iturbe, and F. Castelli, 1996: Mutual interaction of soil moisture state and atmospheric processes. J. Hydrol., 184, 317, https://doi.org/10.1016/0022-1694(95)02965-6.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Farahmand, A., and A. AghaKouchak, 2015: A generalized framework for deriving nonparametric standardized drought indicators. Adv. Water Resour., 76, 140145, https://doi.org/10.1016/j.advwatres.2014.11.012.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ford, T. W., and S. M. Quiring, 2014: Comparison and application of multiple methods for temporal interpolation of daily soil moisture. Int. J. Climatol., 34, 26042621, https://doi.org/10.1002/joc.3862.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ford, T. W., and C. F. Labosier, 2017: Meteorological conditions associated with the onset of flash drought in the eastern United States. Agric. For. Meteor., 247, 414423, https://doi.org/10.1016/j.agrformet.2017.08.031.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ford, T. W., D. B. McRoberts, S. M. Quiring, and R. E. Hall, 2015: On the utility of in situ soil moisture observations for flash drought early warning in Oklahoma, USA. Geophys. Res. Lett., 42, 97909798, https://doi.org/10.1002/2015GL066600.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hobbins, M. T., A. Wood, D. J. McEvoy, J. L. Huntington, C. Morton, M. Anderson, and C. Hain, 2016: The evaporative demand drought index. Part I: Linking drought evolution to variations in evaporative demand. J. Hydrometeor., 17, 17451761, https://doi.org/10.1175/JHM-D-15-0121.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hobbins, M. T., D. McEvoy, and C. Hain, 2017: Evapotranspiration, evaporative demand, and drought. Drought and Water Crises, D. Wilhite and R. Pulwarty, Eds., CRC Press, 259–288.

    • Crossref
    • Export Citation
  • Hunt, E. D., M. Svoboda, B. Wardlow, K. Hubbard, M. Hayes, and T. Arkebauer, 2014: Monitoring the effects of rapid onset of drought on non-irrigated maize with agronomic data and climate-based drought indices. Agric. For. Meteor., 191, 111, https://doi.org/10.1016/j.agrformet.2014.02.001.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jeffrey, S. J., L. D. Rotstayn, M. A. Collier, S. M. Dravitzki, C. Hamalainen, C. Moeseneder, K. K. Wong, and J. I. Syktus, 2013: Australia’s CMIP5 submission using the CSIRO Mk3.6 model. Aust. Meteor. Oceanogr. J., 63, 113, https://doi.org/10.22499/2.6301.001.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Koster, R. D., and M. J. Suarez, 2001: Soil moisture memory in climate models. J. Hydrometeor., 2, 558570, https://doi.org/10.1175/1525-7541(2001)002<0558:SMMICM>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Koster, R. D., and Coauthors, 2004: Regions of strong coupling between soil moisture and precipitation. Science, 305, 11381140, https://doi.org/10.1126/science.1100217.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Koster, R. D., Z. Guo, R. Yang, P. A. Dirmeyer, K. Mitchell, and M. J. Puma, 2009a: On the nature of soil moisture in land surface models. J. Climate, 22, 43224335, https://doi.org/10.1175/2009JCLI2832.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Koster, R. D., S. D. Schubert, and M. J. Suarez, 2009b: Analyzing the concurrence of meteorological droughts and warm periods, with implications for the determination of evaporative regime. J. Climate, 22, 33313341, https://doi.org/10.1175/2008JCLI2718.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Koster, R. D., S. D. Schubert, H. Wang, S. P. Mahanama, and A. M. DeAngelis, 2019: Flash drought as captured by reanalysis data: Disentangling the contributions of precipitation deficit and excess evapotranspiration. J. Hydrometeor., 20, 12411258, https://doi.org/10.1175/JHM-D-18-0242.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lisonbee, J., M. Woloszyn, and M. Skumanich, 2021: Making sense of flash drought: Definitions, indicators, and where we go from here. J. Appl. Serv. Climatol., 2021, 119, https://doi.org/10.46275/JOASC.2021.02.001.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, Y., and Coauthors, 2020a: Flash droughts characterization over China: From a perspective of the rapid intensification rate. Sci. Total Environ., 704, 135373, https://doi.org/10.1016/j.scitotenv.2019.135373.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, Y., and Coauthors, 2020b: Two different methods for flash drought identification: Comparison of their strengths and limitations. J. Hydrometeor., 21, 691704, https://doi.org/10.1175/JHM-D-19-0088.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lorenz, R., and Coauthors, 2016: Influence of land-atmosphere feedbacks on temperature and precipitation extremes in the GLACE-CMIP5 ensemble. J. Geophys. Res. Atmos., 121, 607623, https://doi.org/10.1002/2015JD024053.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lukas, J., M. Hobbins, and I. Rangwala, 2017: The EDDI user guide. NOAA, 35 pp., https://psl.noaa.gov/eddi/pdf/EDDI_UserGuide_v1.0.pdf.

  • McEvoy, D. J., J. L. Huntington, M. T. Hobbins, A. Wood, C. Morton, M. Anderson, and C. Hain, 2016: The Evaporative Demand Drought Index. Part II: CONUS-wide assessment against common drought indicators. J. Hydrometeor., 17, 17631779, https://doi.org/10.1175/JHM-D-15-0122.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McKee, T. B., N. J. Doesken, and J. Kleist, 1993: The relationship of drought frequency and duration to time scales. Preprints, Eighth Conf. on Applied Climatology, Anaheim, CA, Amer. Meteor. Soc., 179184.

  • Milly, P. C., and Coauthors, 2014: An enhanced model of land water and energy for global hydrologic and Earth-system studies. J. Hydrometeor., 15, 17391761, https://doi.org/10.1175/JHM-D-13-0162.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mo, K. C., and D. P. Lettenmaier, 2015: Heat wave flash droughts in decline. Geophys. Res. Lett., 42, 28232829, https://doi.org/10.1002/2015GL064018.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mo, K. C., and D. P. Lettenmaier, 2016: Precipitation deficit flash droughts over the United States. J. Hydrometeor., 17, 11691184, https://doi.org/10.1175/JHM-D-15-0158.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nguyen, H., M. C. Wheeler, J. A. Otkin, T. Cowan, A. Frost, and R. Stone, 2019: Using the evaporative stress index to monitor flash drought in Australia. Environ. Res. Lett., 14, 064016, https://doi.org/10.1088/1748-9326/ab2103.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Noguera, I., F. Dominguez-Castro, and S. M. Vicente-Serrano, 2020: Characteristics and trends of flash droughts in Spain, 1961-2018. Ann. N. Y. Acad. Sci., 1472, 155172, https://doi.org/10.1111/nyas.14365.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Orlowsky, B., and S. I. Seneviratne, 2013: Elusive drought: Uncertainty in observed trends and short- and long-term CMIP5 projections. Hydrol. Earth Syst. Sci., 17, 17651781, https://doi.org/10.5194/hess-17-1765-2013.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Otkin, J. A., M. C. Anderson, C. Hain, I. E. Mladenova, J. B. Basara, and M. Svoboda, 2013: Examining rapid onset drought development using the thermal infrared–based evaporative stress index. J. Hydrometeor., 14, 10571074, https://doi.org/10.1175/JHM-D-12-0144.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Otkin, J. A., M. C. Anderson, C. Hain, and M. Svoboda, 2015: Using temporal changes in drought indices to generate probabilistic drought intensification forecasts. J. Hydrometeor., 16, 88105, https://doi.org/10.1175/JHM-D-14-0064.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Otkin, J. A., and Coauthors, 2016: Assessing the evolution of soil moisture and vegetation conditions during the 2012 United States flash drought. Agric. For. Meteor., 218–219, 230242, https://doi.org/10.1016/j.agrformet.2015.12.065.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Otkin, J. A., M. Svoboda, E. D. Hunt, T. W. Ford, M. C. Anderson, C. Hain, and J. B. Basara, 2018: Flash droughts: A review and assessment of the challenges imposed by rapid-onset droughts in the United States. Bull. Amer. Meteor. Soc., 99, 911919, https://doi.org/10.1175/BAMS-D-17-0149.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pendergrass, A. G., and Coauthors, 2020: Flash droughts present a new challenge for subseasonal-to-seasonal prediction. Nat. Climate Change, 10, 191199, https://doi.org/10.1038/s41558-020-0709-0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pitman, A. J., 2003: The evolution of, and revolution in, land surface schemes designed for climate models. Int. J. Climatol., 23, 479510, https://doi.org/10.1002/joc.893.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rippey, B. R., 2015: The U.S. drought of 2012. Wea. Climate Extreme, 10, 5764, https://doi.org/10.1016/j.wace.2015.10.004.

  • Schubert, S. D., H. Wang, R. Koster, M. Suarez, and P. Groisman, 2014: Northern Eurasian heat waves and droughts. J. Climate, 27, 31693207, https://doi.org/10.1175/JCLI-D-13-00360.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Seneviratne, S. I., T. Corti, E. L. Davin, M. Hirschi, E. B. Jaeger, I. Lehner, B. Orlowsky, and A. J. Teuling, 2010: Investigating soil moisture–climate interactions in a changing climate: A review. Earth-Sci. Rev., 99, 125161, https://doi.org/10.1016/j.earscirev.2010.02.004.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Seneviratne, S. I., and Coauthors, 2013: Impact of soil moisture-climate feedbacks on CMIP5 projections: First results from the GLACE-CMIP5 experiment. Geophys. Res. Lett., 40, 52125217, https://doi.org/10.1002/grl.50956.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Svoboda, M., and Coauthors, 2002: The Drought Monitor. Bull. Amer. Meteor. Soc., 83, 11811190, https://doi.org/10.1175/1520-0477-83.8.1181.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Takata, K., S. Emori, and T. Watanabe, 2003: Development of the minimal advanced treatments of surface interaction and run-off. Global Planet. Change, 38, 209222, https://doi.org/10.1016/S0921-8181(03)00030-4.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Taylor, K. E., R. J. Stouffer, and G. A. Meehl, 2012: An overview of CMIP5 and the experiment design. Amer. Meteor. Soc., 93, 485498, https://doi.org/10.1175/BAMS-D-11-00094.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ukkola, A. M., M. G. De Kauwe, A. J. Pitman, M. J. Best, G. Abramowitz, V. Haverd, M. Decker, and N. Haughton, 2016: Land surface models systematically overestimate the intensity, duration and magnitude of seasonal-scale evaporative droughts. Environ. Res. Lett., 11, 104012, https://doi.org/10.1088/1748-9326/11/10/104012.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ukkola, A. M., A. J. Pitman, M. G. De Kauwe, G. Abramowitz, N. Herger, J. P. Evans, and M. Decker, 2018a: Evaluating CMIP5 model agreement for multiple drought metrics. J. Hydrometeor., 19, 969988, https://doi.org/10.1175/JHM-D-17-0099.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ukkola, A. M., A. J. Pitman, M. G. Donat, M. G. De Kauwe, and O. Angélil, 2018b: Evaluating the contribution of land-atmosphere coupling to heat extremes in CMIP5 models. Geophys. Res. Lett., 45, 90039012, https://doi.org/10.1029/2018GL079102.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Van Looy, K., and Coauthors, 2017: Pedotransfer functions in earth system science: Challenges and perspectives. Rev. Geophys., 55, 11991256, https://doi.org/10.1002/2017RG000581.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vereecken, H., and Coauthors, 2016: Modeling soil processes: Review, key challenges, and new perspectives. Vadose Zone J., 15, 157, https://doi.org/10.2136/vzj2015.09.0131.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vicente-Serrano, S. M., S. Beguería, and J. I. López-Moreno, 2010: A multiscalar drought index sensitive to global warming: The standardized precipitation evapotranspiration index. J. Climate, 23, 16961718, https://doi.org/10.1175/2009JCLI2909.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, L., X. Yuan, Z. Xie, P. Wu, and Y. Li, 2016: Increasing flash droughts over China during the recent global warming hiatus. Sci. Rep., 6, 30571, https://doi.org/10.1038/srep30571.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yuan, S., and S. M. Quiring, 2017: Evaluation of soil moisture in CMIP5 simulations over the contiguous United States using in situ and satellite observations. Hydrol. Earth Syst. Sci., 21, 22032218, https://doi.org/10.5194/hess-21-2203-2017.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yuan, X., and Coauthors, 2019: Anthropogenic shift towards higher risk of flash drought over China. Nat. Commun., 10, 4661, https://doi.org/10.1038/s41467-019-12692-7.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, Y., Q. You, C. Chen, and X. Li, 2017: Flash droughts in a typical humid and subtropical basin: A case study in the Gan River Basin, China. J. Hydrol., 551, 162176, https://doi.org/10.1016/j.jhydrol.2017.05.044.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, Y., Q. You, C. Chen, J. Ge, and M. Adnan, 2018: Evaluation of downscaled CMIP5 coupled with VIC model for flash drought simulation in a humid subtropical basin, China. J. Climate, 31, 10751090, https://doi.org/10.1175/JCLI-D-17-0378.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 780 0 0
Full Text Views 880 453 41
PDF Downloads 893 403 19