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A warming-induced reduction in snow fraction amplifies rainfall extremes

Nature volume 619, pages 305–310 (2023)Cite this article

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Abstract

The intensity of extreme precipitation events is projected to increase in a warmer climate1,2,3,4,5, posing a great challenge to water sustainability in natural and built environments. Of particular importance are rainfall (liquid precipitation) extremes owing to their instantaneous triggering of runoff and association with floods6, landslides7,8,9 and soil erosion10,11. However, so far, the body of literature on intensification of precipitation extremes has not examined the extremes of precipitation phase separately, namely liquid versus solid precipitation. Here we show that the increase in rainfall extremes in high-elevation regions of the Northern Hemisphere is amplified, averaging 15 per cent per degree Celsius of warming—double the rate expected from increases in atmospheric water vapour. We utilize both a climate reanalysis dataset and future model projections to show that the amplified increase is due to a warming-induced shift from snow to rain. Furthermore, we demonstrate that intermodel uncertainty in projections of rainfall extremes can be appreciably explained by changes in snow–rain partitioning (coefficient of determination 0.47). Our findings pinpoint high-altitude regions as ‘hotspots’ that are vulnerable to future risk of extreme-rainfall-related hazards, thereby requiring robust climate adaptation plans to alleviate potential risk. Moreover, our results offer a pathway towards reducing model uncertainty in projections of rainfall extremes.

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Fig. 1: Elevation-dependent amplification of rainfall extremes.
Fig. 2: Sensitivity to global warming levels.
Fig. 3: Reduction in snow fraction amplifies rainfall extremes.
Fig. 4: Projected risk of rainfall extremes.

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Data availability

CMIP6 data of the eight models used in this study are available from the Program for Climate Model Diagnosis and Intercomparison (PCMDI) at https://esgf-node.llnl.gov/projects/cmip6/. ERA5 hourly land data are available from the Copernicus Climate Change Service (C3S) Climate Date Store at https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land?tab=overview. Rand’s Global Elevation dataset is available from the Research Data Archive (RDA) at the National Center for Atmospheric Research (NCAR) at https://rda.ucar.edu/datasets/ds750.1/.

Code availability

The code and supporting data used in this analysis are available at https://doi.org/10.5281/zenodo.7740037 with GitHub access through https://doi.org/10.5281/zenodo.7796633.

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Acknowledgements

This research was supported by Office of Science, Office of Biological and Environmental Research of the US Department of Energy under contract no. DE-AC02-05CH11231 for the CASCADE Scientific Focus (funded by the Regional and Global Model Analysis Program area within the Earth and Environmental Systems Modeling Program) and the iNAIADS Early Career Research Project (funded by the Environmental Systems Science programme).

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Authors and Affiliations

  1. Earth and Environmental Sciences Area, Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Mohammed Ombadi, Mark D. Risser, Alan M. Rhoades & Charuleka Varadharajan

  2. Department of Climate and Space Sciences and Engineering, University of Michigan, Ann Arbor, MI, USA

    Mohammed Ombadi

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Contributions

M.O. devised the idea, designed the study, processed the data, performed the analysis, prepared figures and tables, and drafted the paper. M.D.R. performed the extreme-value analysis and contributed to the review and editing of the paper. A.R. contributed to the review and editing of the paper and provided subject-specific expertise in mountain hydroclimatology. C.V. contributed to the review and editing of the paper. Both M.R. and C.V. acquired funding for this research work.

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Correspondence to Mohammed Ombadi.

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Extended data figures and tables

Extended Data Fig. 1 Elevation-dependent amplification of rainfall extremes.

All panels in the figure show percentage change in rainfall extremes, normalized by degrees of warming and expressed as a function of elevation over the spatial domain (20° N–90° N) land area with masking of hyper-arid regions (Methods). a, ERA5 12-hours annual maximum series (AMS) of rainfall. b, CMIP6 models (colored dashed and dash-dotted lines) and their multi-model mean (solid black line) for 12-hours AMS of rainfall. c,d,e, Percentage change in rainfall extremes at the native spatial resolution of the models (as opposed to resampled data). Note that the model BCC-CSM2-MR is not included because of missing elevation files in the CMIP6 repository. In all panels, regression is based on all grid cells within the spatial domain (not shown in the figure). For panels a and b, black markers and error bars indicate the mean percentage change at different elevation categories and its 90% confidence interval for ERA5 and CMIP6 multi-model mean. The shaded area surrounding regression lines in panels a and b represents the 95% confidence interval of regression estimates. Note that vertical axes in panels have different range of values.

Extended Data Fig. 2 Changes in snow fraction and timing of precipitation extremes.

a, Snow fraction in annual maximum series (AMS) of daily precipitation for all grid cells in the spatial domain of (20° N–90° N) within ERA5 and CMIP6 models. Dark and light green boxplots correspond to the reference period (1950–1979) and the future period (2071–2100), respectively. The two models of AWI-CM-1-1-MR and MPI-ESM1-2-HR are highlighted with an asterisk due to their peculiar patterns of lacking amplification in rainfall extremes at high elevations. The boxes show the interquartile range (IQR; 75th percentile − 25th percentile) while the whiskers show the extent of the distribution, excluding outliers defined as values greater than (75th percentile + 1.5*IQR) or lower than (25th percentile − 1.5*IQR). b–i, Histograms summarize the shift in timing of precipitation extremes across the eight CMIP6 models used in the present study. Each histogram shows the shift in the modal month of precipitation extremes in the future period (2071–2100) compared to the baseline period (1950–1979) across grid cells within the spatial domain of (20° N–90° N). Red vertical lines represent the 80th percentile of timing shift in the modal month.

Extended Data Fig. 3 Reduction in snow fraction amplifies rainfall extremes.

a—c, Scatter plots for the relationship between change in snow fraction (horizontal axis) and percentage change in rainfall extremes (vertical axis) for CMIP6 model AWI-CM-1-1-MR, and rainfall duration of 3, 12- and 24-hours, respectively. Red markers represent individual grid cells whereas black line and shaded area represent least-squares linear regression fit and its 95% confidence interval, respectively. d—f, Same as a-c but for BCC-CSM2-MR. g—i, Same as a-c but for CMCC-CM2-SR5. j—l, Same as a-c but for EC-Earth3. In all panels, the changes in snow fraction and rainfall extremes are computed for the period (2071–2100) relative to the baseline period (1950–1979) for grid cells within the spatial domain of (20° N–90° N) land area with masking of hyper-arid regions (Methods).

Extended Data Fig. 4 Reduction in snow fraction amplifies rainfall extremes.

a—c, Scatter plots for the relationship between change in snow fraction (horizontal axis) and percentage change in rainfall extremes (vertical axis) for CMIP6 model MRI-AGCM-3-2-H, and rainfall duration of 3-, 12- and 24-hours, respectively. Red markers represent individual grid cells whereas black line and shaded area represent least-squares linear regression fit and its 95% confidence interval, respectively. d—f, Same as a-c but for TaiESM1. g—i, Same as a- c but for MPI-ESM1-2-HR. j, Same as a but for GFDL-ESM4. Note that 3- and 12-hours results for the model GFDL-ESM4 are not shown because only daily temporal resolution was available for this model. In all panels, the changes in snow fraction and rainfall extremes are computed for the period (2071–2100) relative to the baseline period (1950–1979) for grid cells within the spatial domain of (20° N–90° N) land area with masking of hyper-arid regions (Methods).

Extended Data Fig. 5 Projected risk of rainfall extremes.

a,b,c, A heatmap of Log10 (Risk Ratio) estimated from a multi-model mean of CMIP6 projections for the period (2071–2100) compared to baseline period (1950–1979). Estimates are averaged for grid cells within each elevation category (e.g., 0–500 m, 500–1,000 m ... 5,500–6,000 m) for rainfall duration of 3-, 12- and 24-hours, respectively. The vertical axis in a,b,c corresponds to T-year return values for T = 2, 5, 10, 20 years. d,e, Same as a,b,c but for the actual values of Risk Ratio (RR) for 3- and 12-hours rainfall, respectively. f, The ratio of risk ratios RR/RRCC−only quantifies the multiplicative change in risk estimated from the multi-model mean projections compared to the risk expected from Clausius-Clapeyron scaling. Results in panel f are shown for 24 hours rainfall.

Extended Data Fig. 6 Mountainous and hyper-arid regions.

The spatial extent of the six mountain ranges used to investigate changes in Risk Ratio (RR) is shown in blue color. The six mountain ranges are: North American Pacific (Pacific), Rockies, Appalachian, Kjølen, Alps and Asian mountain ranges. The Asian ranges consist of the Himalayas, Tian Shan and Hindu Kush mountains. Regions in black shading are hyper-arid regions excluded from analysis (see Methods for details). Continental Map with boundaries of countries is obtained from Esri ArcGIS World Countries (Generalized) shapefiles, whereas the spatial extent of mountainous regions is obtained from the World Land-Based Polygon Features61.

Extended Data Fig. 7 Elevation-dependent amplification of rainfall extremes in observations.

a, Stations from the Global Historical Climatology Network daily (GHCNd) data set used in analysis 1 are shown in blue markers. Total number of stations (n = 13,194) which have measurements of both precipitation and snowfall. b, Stations from GHCNd used in analysis 2 are shown in blue markers. Total number of stations (n = 20,349) which have measurements of both precipitation and daily mean temperature. c, The percentage change in daily rainfall extremes as a function of elevation for the period (1990–2019) compared to that of (1950–1979) for GHCNd stations in analysis 1. d, Same as c but for GHCNd stations in analysis 2. For panels c and d, regression is based on all stations within each analysis (not shown in the figure), and the shaded area surrounding regression line represents the 95% confidence interval. Additionally, black markers and error bars indicate the mean percentage change at different elevation categories and its 90% confidence interval, respectively. Maps in panels a,b were generated using Cartopy47.

Extended Data Fig. 8 Evaluation of ERA5 against ground observations.

a, Histogram in gray color summarizes the relative error in the estimates of ERA5 daily rainfall extremes relative to observations in GHCNd stations of analysis 1 for the baseline period (1950–1979). Cumulative distribution function (cdf) is also shown in blue color. c, Same as a but for the recent past period (1990–2019). b, cdf of average relative error for analysis 1 stations categorized into distinct classes of mean Winter temperature in units of °C for the baseline period (1950–2019). d, Same as b but for the recent past period (1990–2019). e—h, Same as panels a-d but for analysis 2. For panels a,c,e,g, the red arrow and text show the percentage of stations that have relative error values in the range of −20% to 30%. i,j, Scatter plots for the relationship between the relative error of the reference period (horizontal axis) and recent past period (vertical axis) for GHCNd stations in analysis 1 and analysis 2, respectively. For panels i,j, the dotted lines indicate the range of (−10% to +10%) with the percentage of stations falling within the range shown in black text. Additionally, the scatter plots are visualized as density plots to clearly indicate the density of points.

Extended Data Fig. 9 Elevation-dependent amplification of rainfall extremes.

a—c, Percentage change in rainfall extremes, normalized by degrees of warming, and expressed as a function of elevation over the spatial domain (20° N–90° N) land area with masking of hyper-arid regions (Methods) for rainfall duration of 3-, 12- and 24-hours, respectively. Colored dashed and dash-dotted lines and solid black line show least squares linear regression fit for eight CMIP6 models and their multi-model mean, respectively. Black markers and error bars show the mean percentage change at distinct elevation categories and its 90% confidence interval. d—f, Percentage change in rainfall extremes as a function of elevation for ERA5 data set with black markers and error bars showing mean percentage change at different elevation categories and its 90% confidence interval. g—i, Same as d-f but using median instead of mean. For panels d-f, black line shows the least-squares linear regression fit for all grid cells (not shown in the figure). For all panels, shading indicates the 95% confidence interval of regression fit.

Extended Data Fig. 10 Robustness of results to selection of reference period.

a, Results of a statistical simulation study with 10,000 Monte Carlo replicates drawn from two generalized extreme value (GEV) distributions for reference and future period. The horizontal axis shows the true percent change, whereas the vertical axis shows the percent change estimated from simulations. The markers and vertical bars show the mean change and its 90% confidence interval across the Monte Carlo replicates. Markers correspond to different values of shape parameter typical to those estimated from ERA5 data (e.g., 1st quartile, median). b, The table shows the values of location parameter for both baseline and future GEV and the corresponding percent change for the different simulations in this study.

Extended Data Table 1 List of the eight CMIP6 models used in this study with their name, institution and spatial resolution
Full size table
Extended Data Table 2 Warming levels, averaged over global land and oceans, as estimated by ERA5 for the recent past (1990–2019) and CMIP6 models for the future period (2071–2100); both relative to the baseline period (1950–1979)
Full size table
Extended Data Table 3 Data sets used for assessing the sensitivity of results to warming levels of 1.5, 2, 3 and 4 K. A total of 43 data sets are used, spanning 7 CMIP6 models and 4 scenarios (ssp126, ssp245, ssp370 and ssp585). Each data set consists of a consecutive 30 years period for which mean global temperature is increased by increments of 1.5, 2, 3 and 4 degrees relative to the baseline period (1950–1979)
Full size table

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Ombadi, M., Risser, M.D., Rhoades, A.M. et al. A warming-induced reduction in snow fraction amplifies rainfall extremes. Nature 619, 305–310 (2023). https://doi.org/10.1038/s41586-023-06092-7

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