Improved Historical Temperature and Precipitation Time Series for U.S. Climate Divisions

Vose, Russell S.; Applequist, Scott; Squires, Mike; Durre, Imke; Menne, Matthew J.; Williams, Claude N.; Fenimore, Chris; Gleason, Karin; Arndt, Derek S.

doi:10.1175/jamc-d-13-0248.1

Cited by 372 publications

(287 citation statements)

References 52 publications

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“…Based on GA14, the analyses presented here use averages of the June/July/August (JJA) Palmer Drought Severity Index (PDSI) obtained from (1) NOAA's 1895 to 2015 climate division (instrumental) data for California [Vose et al, 2014] and (2) version 2a of the North American Drought Atlas (NADA), which is a gridded reconstruction of PDSI based on tree ring records that extend back more than 1200 years [Cook et al, 2004[Cook et al, , 2008. The JJA period matches the instrumental data to the long-term tree ring record and the highly persistent nature of PDSI allows for a close representation of several months prior to JJA for a given year [Guttman, 1998;Heim, 2005], including the bulk of each water year's wet season.…”

Section: Methodsmentioning

confidence: 99%

Revisiting the recent California drought as an extreme value

Robeson

2015

Geophysical Research Letters

193

174

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Spatially weighted averages of Palmer Drought Severity Index (PDSI) over central and southern California show that the 1 year 2014 drought was not as severe as previously reported, but it still is the most severe in the 1895–2014 instrumental record. Using the typical adjustment procedure that matches the mean and standard deviation of tree ring PDSI values to those of instrumental data shows over 10 droughts from 800 to 2006 that were more severe than the 1 year 2014 drought, with the 2014 drought having a return period of 140–180 years. Quantile mapping allows for a closer correspondence between instrumental and tree ring PDSI probability distributions and produces return periods of 700–900 years for the 1 year 2014 drought. Associated cumulative 3 and 4 year droughts, however, are estimated to be much more severe. The 2012–2014 drought is nearly a 10,000 year event, while the 2012–2015 drought has an almost incalculable return period and is completely without precedent.

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Section: Methodsmentioning

confidence: 99%

Revisiting the recent California drought as an extreme value

Robeson

2015

Geophysical Research Letters

193

174

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“…NARR soil moisture is generated by the NOAH land surface model, which is equivalent to the land surface model that is used within the WRF simulation. For verification, both the quality controlled NCEP Stage IV radar and gauge data [59] and the Tropical Rainfall Measuring Mission (TRMM) satellite data were used to compare measured rainfall to model rainfall. Geostationary Operational Environmental Satellite (GOES) 10 satellite data is used in comparison against the WRF simulations.…”

Section: Observational Datamentioning

confidence: 99%

“…The 60-year average precipitation of July and August is shown in Figure 12 for the simulation with the native and modified KF CPSs, and a 0.5 • by 0.5 • gridded National Oceanic and Atmospheric Administration (NOAA) long-term U.S.-Mexico precipitation data set product (P-NOAA) [59] over the region of the Southwest U.S. and northwest Mexico. The maximum in precipitation in the P-NOAA data is located just west of the SMO crest, with precipitation amounts during the monsoon on the order of 200-250 mm per month.…”

Section: Changes In Model-simulated Monsoon Precipitation In the 60-ymentioning

confidence: 99%

Improvement in the Modeled Representation of North American Monsoon Precipitation Using a Modified Kain–Fritsch Convective Parameterization Scheme

et al. 2018

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Abstract:A commonly noted problem in the simulation of warm season convection in the North American monsoon region has been the inability of atmospheric models at the meso-β scales (10 s to 100 s of kilometers) to simulate organized convection, principally mesoscale convective systems. With the use of convective parameterization, high precipitation biases in model simulations are typically observed over the peaks of mountain ranges. To address this issue, the Kain-Fritsch (KF) cumulus parameterization scheme has been modified with new diagnostic equations to compute the updraft velocity, the convective available potential energy closure assumption, and the convective trigger function. The scheme has been adapted for use in the Weather Research and Forecasting (WRF). A numerical weather prediction-type simulation is conducted for the North American Monsoon Experiment Intensive Observing Period 2 and a regional climate simulation is performed, by dynamically downscaling. In both of these applications, there are notable improvements in the WRF model-simulated precipitation due to the better representation of organized, propagating convection. The use of the modified KF scheme for atmospheric model simulations may provide a more computationally economical alternative to improve the representation of organized convection, as compared to convective-permitting simulations at the kilometer scale or a super-parameterization approach.

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“…The data set consists of divisional and statewide monthly precipitation X and average temperature anomalies Y (computed with respect to 1901-2000 average) from 1896 to 2014 over the CONUS, obtained from area-weighted averages of grid-point estimates resulting from station data gridded via climatologically aided interpolation (Karl and Koss 1984;Vose et al 2014). The statewide scale provides a synoptic picture useful for management purposes involving statewide authorities, while the divisional scale yields more refined spatially smoothed results that allow the recognition of physically coherent hydroclimatological patterns (e.g., Wolock and McCabe 1999;McCabe and Wolock 2002).…”

Section: Data Setmentioning

confidence: 99%

Can we tell more than we can know? The limits of bivariate drought analyses in the United States

Serinaldi

2015

Stoch Environ Res Risk Assess

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The joint occurrence of extreme hydroclimatic events, such as simultaneous precipitation deficit and high temperature, results in the so-called compound events, and has a serious impact on risk assessment and mitigation strategies. Multivariate frequency analysis (MFA) allows a probabilistic quantitative assessment of this risk under uncertainty. Analyzing precipitation and temperature records in the contiguous United States (CONUS), and focusing on the assessment of the degree of rarity of the 2014 California drought, we highlight some critical aspects of MFA that are often overlooked and should be carefully taken into account for a correct interpretation of the results. In particular, we show that an informative exploratory data analysis (EDA) devised to check the basic hypotheses of MFA, a suitable assessment of the sampling uncertainty, and a better understanding of probabilistic concepts can help to avoid misinterpretation of univariate and multivariate return periods, and incoherent conclusions concerning the risk of compound extreme hydroclimatic events. Empirical results show that the dependence between precipitation deficit and temperature across the CONUS can be positive, negative or not significant and does not exhibit significant changes in the last three decades. Focusing on the 2014 California drought as a compound event and based on the data used, the probability of occurrence strongly depends on the selected variables and how they are combined, and is affected by large uncertainty, thus preventing definite conclusions about the actual degree of rarity of this event.

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Improved Historical Temperature and Precipitation Time Series for U.S. Climate Divisions

Cited by 372 publications

References 52 publications

Revisiting the recent California drought as an extreme value

Revisiting the recent California drought as an extreme value

Improvement in the Modeled Representation of North American Monsoon Precipitation Using a Modified Kain–Fritsch Convective Parameterization Scheme

Can we tell more than we can know? The limits of bivariate drought analyses in the United States

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