Publications

Export 4 results:
Sort by: Author [ Title  (Asc)] Type Year
A B C D E F G H I J K L M N O [P] Q R S T U V W X Y Z   [Show ALL]
P
Polade, SD, Gershunov A, Cayan DR, Dettinger MD, Pierce DW.  2017.  Precipitation in a warming world: Assessing projected hydro-climate changes in California and other Mediterranean climate regions. Scientific Reports. 7   10.1038/s41598-017-11285-y   AbstractWebsite

In most Mediterranean climate (MedClim) regions around the world, global climate models (GCMs) consistently project drier futures. In California, however, projections of changes in annual precipitation are inconsistent. Analysis of daily precipitation in 30 GCMs reveals patterns in projected hydrometeorology over each of the five MedClm regions globally and helps disentangle their causes. MedClim regions, except California, are expected to dry via decreased frequency of winter precipitation. Frequencies of extreme precipitation, however, are projected to increase over the two MedClim regions of the Northern Hemisphere where projected warming is strongest. The increase in heavy and extreme precipitation is particularly robust over California, where it is only partially offset by projected decreases in low-medium intensity precipitation. Over the Mediterranean Basin, however, losses from decreasing frequency of low-medium-intensity precipitation are projected to dominate gains from intensifying projected extreme precipitation. MedClim regions are projected to become more sub-tropical, i.e. made dryer via pole-ward expanding subtropical subsidence. California's more nuanced hydrological future reflects a precarious balance between the expanding subtropical high from the south and the south-eastward extending Aleutian low from the north-west. These dynamical mechanisms and thermodynamic moistening of the warming atmosphere result in increased horizontal water vapor transport, bolstering extreme precipitation events.

Gershunov, A, Barnett TP, Cayan DR, Tubbs T, Goddard L.  2000.  Predicting and downscaling ENSO impacts on intraseasonal precipitation statistics in California: The 1997/98 event. Journal of Hydrometeorology. 1:201-210.   10.1175/1525-7541(2000)001<0201:padeio>2.0.co;2   AbstractWebsite

Three long-range forecasting methods have been evaluated for prediction and downscaling of seasonal and intraseasonal precipitation statistics in California. Full-statistical, hybrid-dynamical-statistical and full-dynamical approaches have been used to forecast Fl Nino-Southern Oscillation (ENSO)-related total precipitation, daily precipitation frequency, and average intensity anomalies during the January-March season. For El Nino winters, the hybrid approach emerges as the best performer, while La Nina forecasting skill is poor. The full-statistical forecasting method features reasonable forecasting skill for both La Nina and El Nino winters. The performance of the full-dynamical approach could not be evaluated as rigorously as that of the other two forecasting schemes. Although the full-dynamical forecasting approach is expected to outperform simpler forecasting schemes in the long run, evidence is presented to conclude that, at present, the full-dynamical forecasting approach is the least viable of the three, at least in California. The authors suggest that operational forecasting of any intraseasonal temperature, precipitation, or streamflow statistic derivable from the available-records is possible now for ENSO-extreme years.

Alfaro, EJ, Gershunov A, Cayan D.  2006.  Prediction of summer maximum and minimum temperature over the central and western United States: The roles of soil moisture and sea surface temperature. Journal of Climate. 19:1407-1421.   10.1175/jcli3665.1   AbstractWebsite

A statistical model based on canonical correlation analysis (CCA) was used to explore climatic associations and predictability of June-August (JJA) maximum and minimum surface air temperatures (Tmax and Tmin) as well as the frequency of Tmax daily extremes (Tmax90) in the central and western United States (west of 90 degrees W). Explanatory variables are monthly and seasonal Pacific Ocean SST (PSST) and the Climate Division Palmer Drought Severity Index (PDSI) during 1950-2001. Although there is a positive correlation between Tmax and Tmin, the two variables exhibit somewhat different patterns and dynamics. Both exhibit their lowest levels of variability in summer, but that of Tmax is greater than Tmin. The predictability of Tmax is mainly associated with local effects related to previous soil moisture conditions at short range (one month to one season), with PSST providing a secondary influence. Predictability of Tmin is more strongly influenced by large-scale (PSST) patterns, with PDSI acting as a short-range predictive influence. For both predictand variables (Tmax and Tmin), the PDSI influence falls off markedly at time leads beyond a few months, but a PSST influence remains for at least two seasons. The maximum predictive skill for JJA Tmin, Tmax, and Tmax90 is from May PSST and PDSI. Importantly. skills evaluated for various seasons and time leads undergo a seasonal cycle that has maximum levels in summer. At the seasonal time frame, summer Tmax prediction skills are greatest in the Midwest, northern and central California, Arizona, and Utah. Similar results were found for Tmax90. In contrast, Tmin skill is spread over most of the western region, except for clusters of low skill in the northern Midwest and southern Montana, Idaho, and northern Arizona.

Cavanaugh, NR, Gershunov A, Panorska AK, Kozubowski TJ.  2015.  The probability distribution of intense daily precipitation. Geophysical Research Letters. 42:1560-1567.   10.1002/2015gl063238   AbstractWebsite

The probability tail structure of over 22,000 weather stations globally is examined in order to identify the physically and mathematically consistent distribution type for modeling the probability of intense daily precipitation and extremes. Results indicate that when aggregating data annually, most locations are to be considered heavy tailed with statistical significance. When aggregating data by season, it becomes evident that the thickness of the probability tail is related to the variability in precipitation causing events and thus that the fundamental cause of precipitation volatility is weather diversity. These results have both theoretical and practical implications for the modeling of high-frequency climate variability worldwide.