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Michael F. Wehner

Abstract

Twenty-year return values of annual and seasonal maxima of daily precipitation are calculated from a set of transiently forced coupled general circulation model simulations. The magnitude and pattern of return values are found to be highly dependent on the seasonal cycle. A similar dependence is found for projected future changes in return values.

The correlation between the spatial pattern of return value changes and mean precipitation changes is found to be low. Hence, the changes in mean precipitation do not provide significant information about changes in precipitation extreme values.

Full access
Yujing Jiang
,
Daniel Cooley
, and
Michael F. Wehner

Abstract

We propose a method for analyzing extremal behavior through the lens of a most efficient basis of vectors. The method is analogous to principal component analysis, but is based on methods from extreme value analysis. Specifically, rather than decomposing a covariance or correlation matrix, we obtain our basis vectors by performing an eigendecomposition of a matrix that describes pairwise extremal dependence. We apply the method to precipitation observations over the contiguous United States. We find that the time series of large coefficients associated with the leading eigenvector shows very strong evidence of a positive trend, and there is evidence that large coefficients of other eigenvectors have relationships with El Niño–Southern Oscillation.

Free access
Kevin Reed
,
Michael F. Wehner
,
Alyssa M. Stansfield
, and
Colin M. Zarzycki
Open access
Dáithí A. Stone
,
Kamoru A. Lawal
,
Chris Lennard
,
Mark Tadross
,
Piotr Wolski
, and
Michael F. Wehner
Open access
Hamed Ashouri
,
Soroosh Sorooshian
,
Kuo-Lin Hsu
,
Michael G. Bosilovich
,
Jaechoul Lee
,
Michael F. Wehner
, and
Allison Collow

Abstract

This study evaluates the performance of NASA’s Modern-Era Retrospective Analysis for Research and Applications (MERRA) precipitation product in reproducing the trend and distribution of extreme precipitation events. Utilizing the extreme value theory, time-invariant and time-variant extreme value distributions are developed to model the trends and changes in the patterns of extreme precipitation events over the contiguous United States during 1979–2010. The Climate Prediction Center (CPC) U.S. Unified gridded observation data are used as the observational dataset. The CPC analysis shows that the eastern and western parts of the United States are experiencing positive and negative trends in annual maxima, respectively. The continental-scale patterns of change found in MERRA seem to reasonably mirror the observed patterns of change found in CPC. This is not previously expected, given the difficulty in constraining precipitation in reanalysis products. MERRA tends to overestimate the frequency at which the 99th percentile of precipitation is exceeded because this threshold tends to be lower in MERRA, making it easier to be exceeded. This feature is dominant during the summer months. MERRA tends to reproduce spatial patterns of the scale and location parameters of the generalized extreme value and generalized Pareto distributions. However, MERRA underestimates these parameters, particularly over the Gulf Coast states, leading to lower magnitudes in extreme precipitation events. Two issues in MERRA are identified: 1) MERRA shows a spurious negative trend in Nebraska and Kansas, which is most likely related to the changes in the satellite observing system over time that has apparently affected the water cycle in the central United States, and 2) the patterns of positive trend over the Gulf Coast states and along the East Coast seem to be correlated with the tropical cyclones in these regions. The analysis of the trends in the seasonal precipitation extremes indicates that the hurricane and winter seasons are contributing the most to these trend patterns in the southeastern United States. In addition, the increasing annual trend simulated by MERRA in the Gulf Coast region is due to an incorrect trend in winter precipitation extremes.

Full access
Mark D. Risser
,
Christopher J. Paciorek
,
Travis A. O’Brien
,
Michael F. Wehner
, and
William D. Collins

Abstract

The gridding of daily accumulated precipitation—especially extremes—from ground-based station observations is problematic due to the fractal nature of precipitation, and therefore estimates of long period return values and their changes based on such gridded daily datasets are generally underestimated. In this paper, we characterize high-resolution changes in observed extreme precipitation from 1950 to 2017 for the contiguous United States (CONUS) based on in situ measurements only. Our analysis utilizes spatial statistical methods that allow us to derive gridded estimates that do not smooth extreme daily measurements and are consistent with statistics from the original station data while increasing the resulting signal-to-noise ratio. Furthermore, we use a robust statistical technique to identify significant pointwise changes in the climatology of extreme precipitation while carefully controlling the rate of false positives. We present and discuss seasonal changes in the statistics of extreme precipitation: the largest and most spatially coherent pointwise changes are in fall (SON), with approximately 33% of CONUS exhibiting significant changes (in an absolute sense). Other seasons display very few meaningful pointwise changes (in either a relative or absolute sense), illustrating the difficulty in detecting pointwise changes in extreme precipitation based on in situ measurements. While our main result involves seasonal changes, we also present and discuss annual changes in the statistics of extreme precipitation. In this paper we only seek to detect changes over time and leave attribution of the underlying causes of these changes for future work.

Open access
Gabriele Villarini
,
David A. Lavers
,
Enrico Scoccimarro
,
Ming Zhao
,
Michael F. Wehner
,
Gabriel A. Vecchi
,
Thomas R. Knutson
, and
Kevin A. Reed

Abstract

Heavy rainfall and flooding associated with tropical cyclones (TCs) are responsible for a large number of fatalities and economic damage worldwide. Despite their large socioeconomic impacts, research into heavy rainfall and flooding associated with TCs has received limited attention to date and still represents a major challenge. The capability to adapt to future changes in heavy rainfall and flooding associated with TCs is inextricably linked to and informed by understanding of the sensitivity of TC rainfall to likely future forcing mechanisms. Here a set of idealized high-resolution atmospheric model experiments produced as part of the U.S. Climate Variability and Predictability (CLIVAR) Hurricane Working Group activity is used to examine TC response to idealized global-scale perturbations: the doubling of CO2, uniform 2-K increases in global sea surface temperature (SST), and their combined impact. As a preliminary but key step, daily rainfall patterns of composite TCs within climate model outputs are first compared and contrasted to the observational records. To assess similarities and differences across different regions in response to the warming scenarios, analyses are performed at the global and hemispheric scales and in six global TC ocean basins. The results indicate a reduction in TC daily precipitation rates in the doubling CO2 scenario (on the order of 5% globally) and an increase in TC rainfall rates associated with a uniform increase of 2 K in SST (both alone and in combination with CO2 doubling; on the order of 10%–20% globally).

Full access
Julio T. Bacmeister
,
Michael F. Wehner
,
Richard B. Neale
,
Andrew Gettelman
,
Cecile Hannay
,
Peter H. Lauritzen
,
Julie M. Caron
, and
John E. Truesdale

Abstract

Extended, high-resolution (0.23° latitude × 0.31° longitude) simulations with Community Atmosphere Model versions 4 and 5 (CAM4 and CAM5) are examined and compared with results from climate simulations conducted at a more typical resolution of 0.9° latitude × 1.25° longitude. Overall, the simulated climate of the high-resolution experiments is not dramatically better than that of their low-resolution counterparts. Improvements appear primarily where topographic effects may be playing a role, including a substantially improved summertime Indian monsoon simulation in CAM4 at high resolution. Significant sensitivity to resolution is found in simulated precipitation over the southeast United States during winter. Some aspects of the simulated seasonal mean precipitation deteriorate notably at high resolution. Prominent among these is an exacerbated Pacific “double ITCZ” bias in both models. Nevertheless, while large-scale seasonal means are not dramatically better at high resolution, realistic tropical cyclone (TC) distributions are obtained. Some skill in reproducing interannual variability in TC statistics also appears.

Full access
Christine A. Shields
,
Jonathan J. Rutz
,
L. Ruby Leung
,
F. Martin Ralph
,
Michael Wehner
,
Travis O’Brien
, and
Roger Pierce
Full access
Reinhard Schiemann
,
Marie-Estelle Demory
,
Len C. Shaffrey
,
Jane Strachan
,
Pier Luigi Vidale
,
Matthew S. Mizielinski
,
Malcolm J. Roberts
,
Mio Matsueda
,
Michael F. Wehner
, and
Thomas Jung
Full access