Complexity in estimating past and future extreme short-duration rainfall

Journal name:
Nature Geoscience
Volume:
10,
Pages:
255–259
Year published:
DOI:
doi:10.1038/ngeo2911
Received
Accepted
Published online

Abstract

Warming of the climate is now unequivocal. The water holding capacity of the atmosphere increases by about 7% per °C of warming, which in turn raises the expectation of more intense extreme rainfall events. Meeting the demand for robust projections for extreme short-duration rainfall is challenging, however, because of our poor understanding of its past and future behaviour. The characterization of past changes is severely limited by the availability of observational data. Climate models, including typical regional climate models, do not directly simulate all extreme rainfall producing processes, such as convection. Recently developed convection-permitting models better simulate extreme precipitation, but simulations are not yet widely available due to their computational cost, and they have their own uncertainties. Attention has thus been focused on precipitation–temperature relationships in the hope of obtaining more robust extreme precipitation projections that exploit higher confidence temperature projections. However, the observed precipitation–temperature scaling relationships have been established almost exclusively by linking precipitation extremes with day-to-day temperature variations. These scaling relationships do not appear to provide a reliable basis for projecting future precipitation extremes. Until better methods are available, the relationship of the atmosphere's water holding capacity with temperature provides better guidance for planners in the mid-latitudes, albeit with large uncertainties.

At a glance

Figures

  1. Long-term trends in, and relationship between, extreme precipitation and dew-point temperatures.
    Figure 1: Long-term trends in, and relationship between, extreme precipitation and dew-point temperatures.

    a, Time series of wet-day dew-point temperature anomalies. The black and red lines show the linear trend (least-squares fit) in regional average dew-point temperature and its 90% confidence interval. b, Time series of normalized extreme hourly precipitation. The black dashed line shows the trend (which is not statistically significant) in the median value obtained by fitting a generalized extreme value (GEV) distribution with year as a covariate in the location parameter. Red dashed lines show the 90% confidence interval for the trend. c, Scatter plots of summer mean wet-day dew-point temperature anomalies and normalized (Supplementary Information) summer maximum 1-hour precipitation at the same stations over 1957–2015. A significant relationship between extreme precipitation and dew-point temperature can be identified — at 6.8% intensification in the median value per 1 °C dew-point temperature change — by fitting a GEV distribution with dew-point temperature as a covariate in the location parameter. The black and red lines show the estimated median values of precipitation and their 90% confidence intervals conditional on dew-point temperature anomalies. Different stations are marked by different colours.

  2. Relationship between extreme 1-hour precipitation and the daily dew-point temperatures during wet days in summer.
    Figure 2: Relationship between extreme 1-hour precipitation and the daily dew-point temperatures during wet days in summer.

    Binning method estimates of 99.9th and the 99th conditional percentiles of hourly precipitation (conditional on daily dew-point temperature) based on data from five observing stations in the Netherlands. Super CC scaling (around 14% per °C) between precipitation and dew-point temperature is clearly seen. The dotted lines represent the 7% per °C (black) and the 14% per °C (red) rates. JJA, June–July–August.

  3. Schematic representation of possible shifts of binning curves in the warmer world assuming no circulation change.
    Figure 3: Schematic representation of possible shifts of binning curves in the warmer world assuming no circulation change.

    The binning curve in the warmer climate (red) shifts along the current climate (blue) curve if the binning rate is the same as the trend rate (left); it shifts right-downwards if the binning rate is larger than the trend rate (middle), and right-upwards if the binning rate is smaller than the trend rate (right). The black lines represent changes in the heaviest precipitation from the current to the future climates. The slopes of the binning curves and the black lines represent the binning scaling rates and trend scaling rates, respectively.

  4. The binning curves of hourly precipitation shift right-downwards in simulations of the future warmer climate with conventional RCMs.
    Figure 4: The binning curves of hourly precipitation shift right-downwards in simulations of the future warmer climate with conventional RCMs.

    The 99th and 95th conditional percentiles are computed based on daily maximum hourly precipitation simulated by the Rossby Centre regional climate model when driven by a Bergen climate model climate change simulation over the region spanned by the five observing stations in the Netherlands. Thick solid and dashed lines represent binning curves in the historical and future climates respectively. The thin solid lines show predictions based on the CC relation that are obtained by shifting the current climate binning curve by the projected future warming and the multiplying by 1.07. The dotted lines represent the 7% per °C (black) and the 14% per °C (red) rates.

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Author information

Affiliations

  1. Climate Research Division, Environment and Climate Change Canada, Toronto, Ontario M3H 5T4, Canada

    • Xuebin Zhang,
    • Guilong Li &
    • Hui Wan
  2. Pacific Climate Impacts Consortium, University of Victoria, Victoria, British Columbia V8W 2Y2, Canada

    • Francis W. Zwiers
  3. Climate Research Division, Environment and Climate Change Canada, Victoria, British Columbia V8W 2Y2, Canada

    • Alex J. Cannon

Contributions

X.Z. conceived of and designed the study. G.L., H.W. and A.J.C. undertook the analyses and produced the figures. X.Z. and F.W.Z. wrote the paper. All co-authors helped edit the paper.

Competing financial interests

The authors declare no competing financial interests.

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