The 1980 Summer Heat Wave and Drought in Historical Perspective

Karl, Thomas R.; Quayle, Robert G.

doi:10.1175/1520-0493(1981)109<2055:tshwad>2.0.co;2

Cited by 82 publications

(45 citation statements)

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“…Midwest events Heat waves affecting the Midwest have anticyclonic flow at mid-levels, either as a closed anticyclone or as a strong ridge (Klein 1952;Karl and Quayle 1981;Namias 1982;Chang and Wallace 1987;Lyon and Dole 1995;Kunkel et al 1996;Livezey and Tinker 1996;Palecki et al 2001;Meehl and Tebaldi 2004;Lau and Nath 2012;Loikith and Broccoli 2012;Teng et al 2013), with associated clear skies allowing maximum solar heating of the surface as well as adiabatic warming from subsidence.…”

Section: Summer Heat Wave Eventsmentioning

confidence: 99%

“…The regional-scale flow also interacts with local mechanisms, particularly the Urban Heat Island (UHI) effect (Kunkel et al 1996;Palecki et al 2001). Another common element identified in several studies is the presence of drought (Chang and Wallace 1987;Karl and Quayle 1981;Namias 1982;Lyon and Dole 1995) as well as simultaneous precipitation deficits (Lau and Nath 2012).…”

Section: Summer Heat Wave Eventsmentioning

confidence: 99%

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North American extreme temperature events and related large scale meteorological patterns: a review of statistical methods, dynamics, modeling, and trends

et al. 2015

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of: the physics of LSMP life cycles, comprehensive model assessment of LSMP-extreme temperature event linkages, and LSMP properties are needed. Generally, climate models capture observed properties of heat waves and cold air outbreaks with some fidelity. However they overestimate warm wave frequency and underestimate cold air outbreak frequency, and underestimate the collective influence of low-frequency modes on temperature extremes. Modeling studies have identified the impact of large-scale circulation anomalies and land-atmosphere interactions on changes in extreme temperatures. However, few studies have examined changes in LSMPs to more specifically understand the role of LSMPs on past and future extreme temperature changes. Even though LSMPs are resolvable by global and regional climate models, they are not necessarily well simulated. The paper concludes with unresolved issues and research questions.Abstract The objective of this paper is to review statistical methods, dynamics, modeling efforts, and trends related to temperature extremes, with a focus upon extreme events of short duration that affect parts of North America. These events are associated with large scale meteorological patterns (LSMPs). The statistics, dynamics, and modeling sections of this paper are written to be autonomous and so can be read separately. Methods to define extreme events statistics and to identify and connect LSMPs to extreme temperature events are presented. Recent advances in statistical techniques connect LSMPs to extreme temperatures through appropriately defined covariates that supplement more straightforward analyses. Various LSMPs, ranging from synoptic to planetary scale structures, are associated with extreme temperature events. Current knowledge about the synoptics and the dynamical mechanisms leading to the associated LSMPs is incomplete. Systematic studies

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Section: Summer Heat Wave Eventsmentioning

confidence: 99%

Section: Summer Heat Wave Eventsmentioning

confidence: 99%

North American extreme temperature events and related large scale meteorological patterns: a review of statistical methods, dynamics, modeling, and trends

et al. 2015

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“…At their most severe, extreme climate events, such as the mid-American heat waves of 1980 and 2011 and the 2003 European heat wave, involve months of hot, dry weather (3,4), increasing mortality in humans and wildlife (5,6) while reducing agricultural and natural-systems productivity (7)(8)(9)(10)). An increase in climate extremes would have unambiguously negative effects on ecosystems.…”

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confidence: 99%

Timing of climate variability and grassland productivity

Craine

Nippert

Elmore

et al. 2012

Proc. Natl. Acad. Sci. U.S.A.

278

185

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Future climates are forecast to include greater precipitation variability and more frequent heat waves, but the degree to which the timing of climate variability impacts ecosystems is uncertain. In a temperate, humid grassland, we examined the seasonal impacts of climate variability on 27 y of grass productivity. Drought and highintensity precipitation reduced grass productivity only during a 110-d period, whereas high temperatures reduced productivity only during 25 d in July. The effects of drought and heat waves declined over the season and had no detectable impact on grass productivity in August. If these patterns are general across ecosystems, predictions of ecosystem response to climate change will have to account not only for the magnitude of climate variability but also for its timing.Konza | net primary production | streamflow | critical climate periods F uture climates are likely to include more frequent droughts, high-intensity precipitation patterns, and heat waves, (i.e., periods of elevated air temperatures) (1, 2). At their most severe, extreme climate events, such as the mid-American heat waves of 1980 and 2011 and the 2003 European heat wave, involve months of hot, dry weather (3, 4), increasing mortality in humans and wildlife (5, 6) while reducing agricultural and natural-systems productivity (7-10). An increase in climate extremes would have unambiguously negative effects on ecosystems. However, most climate variability would not be considered extreme and occurs on much shorter time scales throughout the growing season with temperature and precipitation frequently disassociated. The response of ecosystems to short-term climate variability at different times of year is thought to vary (11-16), but we know little about how the timing of short-duration climate variability impacts key ecosystem dynamics such as plant productivity.To understand better how the timing of climate variability affects grassland productivity, we applied the critical climate period approach (17, 18) to long-term measurements of grass productivity in a humid, temperate grassland. Aboveground net primary productivity of grass (ANPP G ) was measured at the time of peak standing biomass from 1984-2010 in both shallow-soil upland and deep-soil lowland topographic positions in an annually burned, ungrazed watershed that is dominated by grasses with the C 4 photosynthetic pathway. In attempting to understand how the timing of climate variability affects grass productivity, we analyzed long-term records of precipitation, stream discharge, and air temperature to examine how variation in drought, precipitation intensity, and heat waves affect grass productivity at different times of the growing season. Results and DiscussionAcross 27 y, drought reduced grass productivity over a wide range of dates but had declining effects as the season progressed. ANPP G decreased with decreasing precipitation summed from April 15 to August 2 [day of year (DOY) 105-214] (Fig. 1). ANPP G declined 0.60 ± 0.12 g·m −2 for each millimeter decline in p...

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“…The first comprehensive effort to evaluate the drought severity is the Palmer drought severity index (PDSI; Palmer 1965), based on a soil water balance equation. Utilizing precipitation and temperature records to calculate moisture supply and demand, PDSI and its ensuing variants have been widely used for monitoring (e.g., Karl and Quayle 1981;Jones et al 1996) and forecasting (e.g., Kim and Valdés 2003;Özger et al 2009) regional droughts, and assessing the global warming effect on the areal extent and severity of global droughts (Dai 2011). The primary limitation of PDSI is its inherent time scale making it more suitable for monitoring agricultural droughts rather than hydrological droughts (Guttman 1998).…”

Section: Introductionmentioning

confidence: 99%

Constructing a 1-km Gridded Multi-Scalar Drought Index Dataset (1960 - 2012) in Taiwan Based on the Standardized Precipitation Evapotranspiration Index-SPEI

Weng¹

2016

Terr. Atmos. Ocean. Sci.

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An index sensitive to global warming, the standardized precipitation evapotranspiration index (SPEI), is employed in this study to construct a 1-km gridded multi-scalar drought index data bank in Taiwan. A site-and scale-dependent posterior fitness assessment procedure regarding determination of the most appropriate statistical distribution is used to standardize the station's water deficit/surplus series, thereby SPEI at various time scales. Model uncertainty at different scales is evaluated and the results show that the uncertainty is higher for the shorter 10-day scale. Contrasts in the climatic means between SPEI and popular standardized precipitation index (SPI) are compared. The climatic pre-summer monsoon heating and midsummer drought phenomena are better captured by the SPEI. The established data bank, one of the data integration tasks in the Taiwan Climate Change Projection and Information Platform (TCCIP) project, is helpful in historical drought diagnostics. It also serves as the ground truth to correct model biases while using the regional model to project hydro-climate change and impact assessment, and historical baseline in the statistical downscaling while developing the statistical relationship between the general circulation model outputs and fine-scale observations. As an example of applications, the gridded SPEI at 3-month time scale is used to study the interannual variability in springtime drought. The results show that Taiwan's southwestern plains region is most vulnerable to the risk of droughts. Composite analysis reveals that possible causes of island-wide drought link to the turnabout of cold El Niño-Southern Oscillation (ENSO) phase but also to the interannual variability in Pacific Decadal Oscillation when the analysis is initiated from the regional perspective.

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The 1980 Summer Heat Wave and Drought in Historical Perspective

Cited by 82 publications

References 0 publications

North American extreme temperature events and related large scale meteorological patterns: a review of statistical methods, dynamics, modeling, and trends

North American extreme temperature events and related large scale meteorological patterns: a review of statistical methods, dynamics, modeling, and trends

Timing of climate variability and grassland productivity

Constructing a 1-km Gridded Multi-Scalar Drought Index Dataset (1960 - 2012) in Taiwan Based on the Standardized Precipitation Evapotranspiration Index-SPEI

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