Anomalies in the US precipitation extremes and their association with different modes of climate variability

Armal, Saman; Khanbilvardi, R.

doi:10.1080/02626667.2019.1662026

Cited by 5 publications

(3 citation statements)

References 69 publications

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“…Studies confirm the potential of applying NAO to predict eastern United States' climate and its association with the 1960s drought and the following wet period (Bradbury, Dingman, & Keim, 2002; Coleman & Budikova, 2013; Seager et al, 2012). Our recent studies also demonstrated that variation in NAO partly explains the frequency of precipitation extremes in the Northeast (Armal et al, 2018; Armal & Khanbilvardi, 2019).…”

Section: Methodsmentioning

confidence: 65%

“…We use two climate variables in the model: the annual average of 500‐hPa geopotential height anomaly (GPH) and the North Atlantic Oscillation (NAO) index. Among the general climate indices, it is known that for the Northeast, NAO is one of the dominant modes (Armal et al, 2018; Armal & Khanbilvardi, 2019), which impacts the regional pressure anomalies (Seager, Pederson, Kushnir, Nakamura, & Jurburg, 2012). In our model, NAO index captures the large‐scale effect of climate, while GPH represents the local or regional climate effect.…”

Section: Methodsmentioning

confidence: 99%

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Simulating precipitation in the Northeast United States using a climate‐informed K‐nearest neighbour algorithm

Armal

Devineni

Krakauer

et al. 2020

Hydrological Processes

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Decadal prediction using climate models faces long‐standing challenges. While global climate models may reproduce long‐term shifts in climate due to external forcing, in the near term, they often fail to accurately simulate interannual climate variability, as well as seasonal variability, wet and dry spells, and persistence, which are essential for water resources management. We developed a new climate‐informed K‐nearest neighbour (K‐NN)‐based stochastic modelling approach to capture the long‐term trend and variability while replicating intra‐annual statistics. The climate‐informed K‐NN stochastic model utilizes historical data along with climate state information to provide improved simulations of weather for near‐term regional projections. Daily precipitation and temperature simulations are based on analogue weather days that belong to years similar to the current year's climate state. The climate‐informed K‐NN stochastic model is tested using 53 weather stations in the Northeast United States with an evident monotonic trend in annual precipitation. The model is also compared to the original K‐NN weather generator and ISIMIP‐2b GFDL general circulation model bias‐corrected output in a cross‐validation mode. Results indicate that the climate‐informed K‐NN model provides improved simulations for dry and wet regimes, and better uncertainty bounds for annual average precipitation. The model also replicates the within‐year rainfall statistics. For the 1961–1970 dry regime, the model captures annual average precipitation and the intra‐annual coefficient of variation. For the 2005–2014 wet regime, the model replicates the monotonic trend and daily persistence in precipitation. These improved modelled precipitation time series can be used for accurately simulating near‐term streamflow, which in turn can be used for short‐term water resources planning and management.

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Section: Methodsmentioning

confidence: 65%

Section: Methodsmentioning

confidence: 99%

Simulating precipitation in the Northeast United States using a climate‐informed K‐nearest neighbour algorithm

Armal

Devineni

Krakauer

et al. 2020

Hydrological Processes

Self Cite

View full text Add to dashboard Cite

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“…В настоящее время мы наблюдаем на планете стремительное изменение климата [1,2,3]. Локальные его изменения в средней полосе, умеренной по погодно-климатическим условиям, привели к сдвигу границ растениеводства севернее предшествующих долгие годы границ [4,5].…”

Section: Introductionunclassified

The Results of the Mung Bean’s Introduction (Vigna Radiata (L.) Wilczek) in the Middle Volga Region for 2013-2020

Kuryanovich¹

2022

Izvestiya of SSC RAS. Agricultural Sciences

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The work’s results on introducing a new (for the region) agricultural cropper – mung bean, Vignaradiata (L.) Wilczek are presented in the article. This is an ancient cropper of southern origin, which has a set of valuable economical and biological properties and qualities. The spread of this cropper was limited recently by its requirements for climatic conditions. However, global and local climate changes on the planet contribute to the advancement of crop production’s boundaries to the north. In particular, research shows that the Samara region turned out to be currently the farthest northern geographical point in the world where the mung bean can grow. The mung bean study has been carried out since 2013 in order to determine the possibility of breeding’s work beginning leading to the creation of varieties with the purpose to the environmental conditions of the Middle Volga. The starting breeding material was developed. The “Saltan” variety was created on the base of single-seed descent method, which exceeded the seed parent form in terms of productivity and the bush height. This variety was transferred to the Russian Federation’s State Expert Committee on Examining and Protection of successful breeds in 2020. On the basis of the previous studies, it can be concluded that the mung bean plants in the Middle Volga region do not have distortion in the cycle’s developmental scion. The stage processes are stable and adapted to the local climatic conditions; the productivity and size of these plants correspond to the required crop’s indicators; the life form bushing-is saved; self-seeding is not formed. The plants successfully breed by artificial means; the vegetative way of breeding was not revealed. It is necessary to continue studying the influence of biotic and abiotic environmental factors on the production process in specific weather and climatic conditions of the area alongside the selection process.

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Interannual variabilities of the summer and winter extreme daily precipitation in the Southeastern United States

Wang¹,

Asefa²,

Erkyihun³

2021

Journal of Hydrology

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Anomalies in the US precipitation extremes and their association with different modes of climate variability

Cited by 5 publications

References 69 publications

Simulating precipitation in the Northeast United States using a climate‐informed K‐nearest neighbour algorithm

Simulating precipitation in the Northeast United States using a climate‐informed K‐nearest neighbour algorithm

The Results of the Mung Bean’s Introduction (Vigna Radiata (L.) Wilczek) in the Middle Volga Region for 2013-2020

Interannual variabilities of the summer and winter extreme daily precipitation in the Southeastern United States

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