The Frequency of Extreme Rain Events in Satellite Rain-Rate Estimates and an Atmospheric General Circulation Model
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The frequency distributions of surface rain rate are evaluated in the Tropical Rainfall Measuring Mission (TRMM) and Special Sensor Microwave/Imager (SSM/I) satellite observations and the NOAA/GFDL global atmosphere model version 2 (AM2). Instantaneous satellite rain-rate observations averaged over the 2.5° latitude × 2° longitude model grid are shown to be representative of the half-hour rain rate from single time steps simulated by the model. Rain-rate events exceeding 10 mm h−1 are observed by satellites in most regions, with 1 mm h−1 events occurring more than two orders of magnitude more frequently than 10 mm h−1 events. A model simulation using the relaxed Arakawa–Schubert (RAS) formulation of cumulus convection exhibits a strong bias toward many more light rain events compared to the observations and far too few heavy rain events. A simulation using an alternative convection scheme, which includes an explicit representation of mesoscale circulations and an alternative formulation of the closure, exhibits, among other differences, an order of magnitude more tropical rain events above the 5 mm h−1 rate compared to the RAS simulation. This simulation demonstrates that global atmospheric models can be made to produce heavy rain events, in some cases even exceeding the observed frequency of such events. Additional simulations reveal that the frequency distribution of the surface rain rate in the GCM is shaped by a variety of components within the convection parameterization, including the closure, convective triggers, the spectrum of convective and mesoscale clouds, and other parameters whose physical basis is currently only understood to a limited extent. Furthermore, these components interact nonlinearly such that the sensitivity of the rain-rate distribution to the formulation of one component may depend on the formulation of the others. Two simulations using different convection parameterizations are performed using perturbed sea surface temperatures as a surrogate for greenhouse gas–forced climate warming. Changes in the frequency of rain events greater than 2 mm h−1 associated with changing the convection scheme in the model are greater than the changes in the frequency of heavy rain events associated with a 2-K warming using either model. Thus, uncertainty persists with respect to simulating intensity distributions for precipitation and projecting their future changes. Improving the representation of the frequency distribution of rain rates will rely on refinements in the formulation of cumulus closure and the other components of convection schemes, and greater certainty in predictions of future changes in both total rainfall and in rain-rate distributions will require additional refinements in those parameterizations that determine the cloud and water vapor feedbacks.
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Important limitations in the ability of current models to simulate precipitation extremes have been recognized and are related in part to the use of parameterized convection.
“Important limitations in the ability of current models to simulate precipitation extremes have also been recognized and are related in part to the use of parameterized convection [38, 43, 58, 89, 93].”
From Precipitation Extremes Under Climate Change (2015) · cites this paper as [93]
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A Dynamically Computed Convective Time Scale for the Kain–Fritsch Convective Parameterization Scheme
Bullock, O. R., Jr., Alapaty, K., Herwehe, J. A., & Kain, J. S. (2015) · Monthly Weather Review · doi:10.1175/mwr-d-14-00251.1 · Cited by 37 · see citing works
A quantitative assessment of precipitation associated with the ITCZ in the CMIP5 GCM simulations
Stanfield, R. E., Jiang, J. H., Dong, X., Xi, B., Su, H., Donner, L. J., Rotstayn, L. D., Wu, T., Cole, J. N. S., & Shindo, E. (2015) · Climate Dynamics · doi:10.1007/s00382-015-2937-y · Cited by 37 · see citing works
Self-citationEmpirical values and assumptions in the convection schemes of numerical models
Pradas, A. V., & Tapiador, F. J. (2022) · Geoscientific model development · doi:10.5194/gmd-15-3447-2022 · Cited by 40 · see citing works
Deep-learning post-processing of short-term station precipitation based on NWP forecasts
Liu, Q., Lou, X., Yan, Z., Qi, Y., Jin, Y., Yu, S., Yang, X., Zhao, D., & Xia, J. (2023) · Atmospheric Research · doi:10.1016/j.atmosres.2023.107032 · Cited by 36 · see citing works
Toward Narrowing Uncertainty in Future Projections of Local Extreme Precipitation
Marra, F., Armon, M., Adam, O., Zoccatelli, D., Gazal, O. “. N., Garfinkel, C. I., Rostkier‐Edelstein, D., Dayan, U., Enzel, Y., & Morin, E. (2021) · Geophysical Research Letters · doi:10.1029/2020gl091823 · Cited by 35 · see citing works
Ricciardelli, E., Paola, F. D., Gentile, S., Cersosimo, A., Cimini, D., Gallucci, D., Geraldi, E., Larosa, S., Nilo, S. T., Ripepi, E., et al. (2018) · Remote Sensing · doi:10.3390/rs10101549 · Cited by 34 · see citing works
Analysis of Catania Flash Flood Case Study by Using Combined Microwave and Infrared Technique
Paola, F. D., Ricciardelli, E., Cimini, D., Romano, F., Viggiano, M., & Cuomo, V. (2014) · Journal of Hydrometeorology · doi:10.1175/jhm-d-13-092.1 · Cited by 33 · see citing works
Qiao, F., & Liang, X. (2016) · Climate Dynamics · doi:10.1007/s00382-016-3338-6 · Cited by 32 · see citing works
DeAngelis, A. M., Broccoli, A. J., & Decker, S. G. (2012) · Journal of Climate · doi:10.1175/jcli-d-12-00374.1 · Cited by 31 · see citing works
The Impact of Air‐Sea Interactions on the Representation of Tropical Precipitation Extremes
Hirons, L. C., Klingaman, N. P., & Woolnough, S. J. (2018) · Journal of Advances in Modeling Earth Systems · doi:10.1002/2017ms001252 · Cited by 31 · see citing works
Li, F., Collins, W. D., Wehner, M. F., Williamson, D. L., & Olson, J. G. (2011) · Tellus A Dynamic Meteorology and Oceanography · doi:10.1111/j.1600-0870.2011.00543.x · Cited by 31 · see citing works
Norris, J., Hall, A. D., Neelin, J. D., Thackeray, C. W., & Chen, D. (2021) · Journal of Climate · doi:10.1175/jcli-d-20-0182.1 · Cited by 28 · see citing works
Zhou, Y., Lau, K. ‐., Reale, O., & Rosenberg, R. (2010) · Geophysical Research Letters · doi:10.1029/2009gl041494 · Cited by 28 · see citing works
Wei, J., Dirmeyer, P. A., & Guo, Z. (2010) · Journal of Climate · doi:10.1175/2010jcli3178.1 · Cited by 28 · see citing works
Sensitivity of polar ozone to sea surface temperatures and halogen amounts
Austin, J., & Wilson, R. J. (2010) · Journal of Geophysical Research Atmospheres · doi:10.1029/2009jd013292 · Cited by 28 · see citing works
CMIP5 vs. CORDEX over the Indian region: how much do we benefit from dynamical downscaling?
Mishra, S. K., Sahany, S., & Salunke, P. (2017) · Theoretical and Applied Climatology · doi:10.1007/s00704-017-2237-z · Cited by 27 · see citing works
Johnson, N. C., Krishnamurthy, L., Wittenberg, A. T., Xiang, B., Vecchi, G. A., Kapnick, S., & Pascale, S. (2020) · Journal of Climate · doi:10.1175/jcli-d-19-0417.1 · Cited by 27 · see citing works
Maritime Continent seasonal climate biases in AMIP experiments of the CMIP5 multimodel ensemble
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Transport of radon‐222 and methyl iodide by deep convection in the GFDL Global Atmospheric Model AM2
Donner, L. J., Horowitz, L. W., Fiore, A. M., Seman, C. J., Blake, D. R., & Blake, N. J. (2007) · Journal of Geophysical Research Atmospheres · doi:10.1029/2006jd007548 · Cited by 22 · see citing works
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A Structured Graph Neural Network for Improving the Numerical Weather Prediction of Rainfall
Peng, X., Li, Q., Chen, L., Ning, X., Chu, H., & Liu, J. (2023) · Journal of Geophysical Research Atmospheres · doi:10.1029/2023jd039011 · Cited by 20 · see citing works
Williams, I. N. (2018) · Journal of Geophysical Research Atmospheres · doi:10.1029/2018jd029326 · Cited by 20 · see citing works
Simulating Canadian Arctic Climate at Convection-Permitting Resolution
Diro, G. T., & Sushama, L. (2019) · Atmosphere · doi:10.3390/atmos10080430 · Cited by 20 · see citing works
Sun, C., & Liang, X. (2020) · Climate Dynamics · doi:10.1007/s00382-020-05328-w · Cited by 21 · see citing works
Li, Y., Wu, J., Luo, J., & Yang, Y. (2021) · Journal of Climate · doi:10.1175/jcli-d-21-0378.1 · Cited by 19 · see citing works
- Authors
- Wilcox, E. M., & Donner, L. J.
- Affiliations
- Princeton University (US)
- NOAA Geophysical Fluid Dynamics Laboratory (US)
- Venue
- Journal of Climate
- Publisher
- American Meteorological Society
- Published
- 2007-01-01
- Volume
- 20
- Issue
- 1
- Pages
- 53-69
- ISSN
- 0894-8755 (ISSN-L)
- 1520-0442
- OpenAlex
- W2096115864
- Cited by
- 174
- References
- 32
- Citations by year
- 2026: 6 · 2025: 9 · 2024: 6 · 2023: 20 · 2022: 11 · 2021: 11 · 2020: 8 · 2019: 5 · 2018: 11 · 2017: 10 · 2016: 5 · 2015: 10 · 2014: 15 · 2013: 9 · 2012: 11
- FWCI
- 4.00
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of Climate (publishedVersion)
- Funders
- University Corporation for Atmospheric Research
- National Oceanic and Atmospheric Administration
- Marshall Space Flight Center
- Goddard Space Flight Center
- Topics
- Physical Sciences › Earth and Planetary Sciences › Atmospheric Science › Meteorological Phenomena and Simulations
- Physical Sciences › Environmental Science › Global and Planetary Change › Climate variability and models
- Physical Sciences › Environmental Science › Global and Planetary Change › Atmospheric aerosols and clouds
- Keywords
- heavy rainfall, convection parameterization, mesoscale circulation, TRMM, SSM/I, global warming, greenhouse gas forcing, cloud feedback
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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