Abstract

Potential ENSO-related predictability of wintertime daily extreme precipitation and temperature frequencies is investigated. This is done empirically using six decades of daily data at 168 stations distributed over the contiguous United States. ENSO sensitivity in the extreme ranges of intraseasonal precipitation and temperature probability density functions is demonstrated via a compositing technique. Potential predictability of extremes is then investigated with a simple statistical model. Given a perfect forecast of ENSO, the frequency of intraseasonal extremes is specified as the average frequency of occurrence during similar-phased ENSO winters on record. Specification skill is assessed as the cross-validated proportion of local variance explained by this method. The skill depends on varying ENSO sensitivity in different geographic regions and quantile ranges and on consistency or variability from one like-phased ENSO event to another. ENSO sensitivity also varies according to the intensity of the tropical forcing; however, not always in the expected sense. Good predictability is likely for variables and in regions displaying a strong and consistent ENSO signal. This is found in some coherent regions of the United States for various combinations of frequency variable and ENSO phase.

ENSO-based predictability of heavy and extreme precipitation frequency is potentially good along the Gulf Coast, central plains, Southwest, and in the Ohio River valley for El Niño winters and in the Southwest and Florida for La Niña winters. Not all large magnitude signals translate into significant specification skill. Extreme precipitation frequency in the Southwest is a good example of this. Extreme warm temperature frequency (EWF) is potentially predictable in the southern and eastern United States during El Niño winters and in the Midwest during the strongest events. La Niña winters exhibit potentially very good EWF predictability in a large area of the southern United States centered on Texas. Despite showing coherent ENSO patterns, extreme cold temperature frequency (ECF) signals are mostly weak and inconsistent, especially during strong ENSO events. Curiously, specification skill improves in the northern United States, along the West Coast and in the southeast during weaker El Niño winters. An improvement in potential ECF predictability is also observed in the Midwest during weaker La Niña winters.

Abstract

This study examines the health impacts of recent heat waves statewide and for six subregions of California: the north and south coasts, the Central Valley, the Mojave Desert, southern deserts, and northern forests. By using canonical correlation analysis applied to daily maximum temperatures and morbidity data in the form of unscheduled hospitalizations from 1999 to 2009, 19 heat waves spanning 3–15 days in duration that had a significant impact on health were identified. On average, hospital admissions were found to increase by 7% on the peak heat-wave day, with a significant impact seen for several disease categories, including cardiovascular disease, respiratory disease, dehydration, acute renal failure, heat illness, and mental health. Statewide, there were 11 000 excess hospitalizations that were due to extreme heat over the period, yet the majority of impactful events were not accompanied by a heat advisory or warning from the National Weather Service. On a regional basis, the strongest health impacts are seen in the Central Valley and the north and south coasts. The north coast contributes disproportionately to the statewide health impact during heat waves, with a 10.5% increase in daily morbidity at heat-wave peak as compared with 8.1% for the Central Valley and 5.6% for the south coast. The temperature threshold at which an impact is seen varies by subregion and timing within the season. These results suggest that heat-warning criteria should consider local percentile thresholds to account for acclimation to local climatological conditions as well as the seasonal timing of a forecast heat wave.

Seasonal climate anomalies over North America exhibit rather large variability between years characterized by the same ENSO phase. This lack of consistency reduces potential statistically based ENSO-related climate predictability. The authors show that the North Pacific oscillation (NPO) exerts a modulating effect on ENSO teleconnections. Sea level pressure (SLP) data over the North Pacific, North America, and the North Atlantic and daily rainfall records in the contiguous United States are used to demonstrate that typical ENSO signals tend to be stronger and more stable during preferred phases of the NPO. Typical El Niño patterns (e.g., low pressure over the northeastern Pacific, dry northwest, and wet southwest, etc.) are strong and consistent only during the high phase of the NPO, which is associated with an anomalously cold northwestern Pacific. The generally reversed SLP and precipitation patterns during La Niña winters are consistent only during the low NPO phase. Climatic anomalies tend to be weak and spatially incoherent during low NPO–E1 Niño and high NPO–La Niña winters. These results suggest that confidence in ENSO-based long-range climate forecasts for North America should reflect interdecadal climatic anomalies in the North Pacific.

Abstract

The signature of ENSO in the wintertime frequencies of heavy precipitation and temperature extremes is derived from both observations and atmospheric general circulation model output for the contiguous United States. ENSO signals in the frequency of occurrence of heavy rainfall are found in the Southeast, Gulf Coast, central Rockies, and the general area of the Mississippi–Ohio River valleys. Strong, nonlinear signals in extreme warm temperature frequencies are found in the southern and eastern United States. Extreme cold temperature frequencies are found to be less sensitive to ENSO forcing than extreme warm temperature frequencies. Observed ENSO signals in extreme temperature frequencies are not simply manifestations of shifts in mean seasonal temperature. These signals in the wintertime frequency of extreme rainfall and temperature events appear strong enough to be useful in long-range regional statistical prediction.

Comparisons of observational and model results show that the model climate is sensitive to ENSO on continental scales and provide some encouragement to modeling studies of intraseasonal sensitivity to low-frequency climatic forcing. However, large regional disagreements exist in all variables. Continental-scale El Niño signatures in intraseasonal temperature variability are not correctly modeled. Modeled signals in extreme temperature event frequencies are much more directly related to shifts in seasonal mean temperature than they are in nature.

Abstract

Running correlations between pairs of stochastic time series are typically characterized by low-frequency evolution. This simple result of sampling variability holds for climate time series but is not often recognized for being merely noise. As an example, this paper discusses the historical connection between El Niño–Southern Oscillation (ENSO) and average Indian rainfall (AIR). Decades of strong correlation (∼−0.8) alternate with decades of insignificant correlation, and it is shown that this decadal modulation could be due solely to stochastic processes. In fact, the specific relationship between ENSO and AIR is significantly less variable on decadal timescales than should be expected from sampling variability alone.

Abstract

By matching large-scale patterns in climate fields with patterns in observed station precipitation, this work explores seasonal predictability of precipitation in the contiguous United States for all seasons. Although it is shown that total seasonal precipitation and frequencies of less-than-extreme daily precipitation events can be predicted with much higher skill, the focus of this study is on frequencies of daily precipitation above the seasonal 90th percentile (P90), a variable whose skillful prediction is more challenging. Frequency of heavy daily precipitation is shown to respond to ENSO as well as to non-ENSO interannual and interdecadal variability in the North Pacific.

Specification skill achieved by a statistical model based on contemporaneous SST forcing with and without an explicit dynamical atmosphere is compared and contrasted. Statistical models relating the SST forcing patterns directly to observed station precipitation are shown to perform consistently better in all seasons than hybrid (dynamical–statistical) models where the SST forcing is first translated to atmospheric circulation via three separate general circulation models and the dynamically computed circulation anomalies are statistically related to observed precipitation. Skill is summarized for all seasons, but in detail for January–February–March, when it is shown that predictable patterns are spatially robust regardless of the approach used. Predictably, much of the skill is due to ENSO. While the U.S. average skill is modest, regional skill levels can be quite high. It is also found that non-ENSO-related skill is significant, especially for the extreme Southwest and that this is due mostly to non-ENSO interannual and decadal variability in the North Pacific SST forcing.

Although useful specification skill is achieved by both approaches, hybrid predictability is not pursued further in this effort. Rather, prognostic analysis is carried out with the purely statistical approach to analyze P90 predictability based on antecedent SST forcing. Skill at various lead times is investigated and it is shown that significant regional skill can be achieved at lead times of several months even in the absence of strong ENSO forcing.

Abstract

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°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.

Abstract

Climate in the North Pacific and North American sectors has experienced interdecadal shifts during the twentieth century. A network of recently developed tree-ring chronologies for Southern and Baja California extends the instrumental record and reveals decadal-scale variability back to 1661. The Pacific decadal oscillation (PDO) is closely matched by the dominant mode of tree-ring variability that provides a preliminary view of multiannual climate fluctuations spanning the past four centuries. The reconstructed PDO index features a prominent bidecadal oscillation, whose amplitude weakened in the late l700s to mid-1800s. A comparison with proxy records of ENSO suggests that the greatest decadal-scale oscillations in Pacific climate between 1706 and 1977 occurred around 1750, 1905, and 1947.

Abstract

The Dustbowl Era drought in the 1930s was the principal Midwest drought of the twentieth century, occurring primarily in late spring–summer [April–August (AMJJA)] when >70% of annual rainfall normally occurred. Another major Midwest drought occurred in the 1950s but primarily in fall–early winter [September–December (SOND)] when normal rainfall was ∼1/2 as much. Optimized canonical correlation analysis (CCA) is applied to forecast AMJJA and SOND Midwest rainfall variability in cross-validated fashion from antecedent DJF and JJA sea surface temperature (SST) variability in the surrounding oceans. These CCA models simulate (i.e., hindcast, not forecast) the Dustbowl Era drought of the 1930s and four of seven secondary AMJJA droughts (≥3-yr duration) during the twentieth century, and the principal Midwest drought of the 1950s and one of three secondary SOND droughts. Diagnosing the model canonical correlations finds the superposition of tropical Pacific cool phases of the quasi-decadal oscillation (QDO) and interdecadal oscillation (IDO) responsible for secondary droughts in AMJJA when ENSO was weak and finds the eastern equatorial Pacific cool phase of the ENSO responsible for secondary droughts during SOND when ENSO was strong. These explain why secondary droughts in AMJJA occurred more often (nearly every decade) and were of longer duration than secondary droughts in SOND when decadal drought tendencies were usually interrupted by ENSO. These diagnostics also find the AMJJA Dustbowl Era drought in the 1930s and the principal SOND drought in the 1950s driven primarily by different phases (i.e., in quadrature) of the pentadecadal signal in the Pacific decadal oscillation (PDO).

Abstract

Most of the great California–Nevada heat waves can be classified into primarily daytime or nighttime events depending on whether atmospheric conditions are dry or humid. A rash of nighttime-accentuated events in the last decade was punctuated by an unusually intense case in July 2006, which was the largest heat wave on record (1948–2006). Generally, there is a positive trend in heat wave activity over the entire region that is expressed most strongly and clearly in nighttime rather than daytime temperature extremes. This trend in nighttime heat wave activity has intensified markedly since the 1980s and especially since 2000. The two most recent nighttime heat waves were also strongly expressed in extreme daytime temperatures. Circulations associated with great regional heat waves advect hot air into the region. This air can be dry or moist, depending on whether a moisture source is available, causing heat waves to be expressed preferentially during day or night. A remote moisture source centered within a marine region west of Baja California has been increasing in prominence because of gradual sea surface warming and a related increase in atmospheric humidity. Adding to the very strong synoptic dynamics during the 2006 heat wave were a prolonged stream of moisture from this southwestern source and, despite the heightened humidity, an environment in which afternoon convection was suppressed, keeping cloudiness low and daytime temperatures high. The relative contributions of these factors and possible relations to global warming are discussed.