The Arctic Oscillation and its impact on temperature and precipitation in Northern Eurasia in the 20th Century

Kryzhov, V. N.; Gorelits, O. V.

doi:10.3103/s1068373915110011

Cited by 40 publications

(15 citation statements)

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“…This mode explains about 15.8% of the total variance for 1983–2009 (Figures S17a and S18a). The second EOF mode of observation for the global 2‐m temperature, which explains 13.8% of the variance, mainly shows the Arctic Oscillation (AO)‐related impact over the land in the Northern Hemisphere (Kryzhov and Gorelits, 2015; He et al ., 2017). The correlation coefficient between PC2 and the AO index obtained from CPC (available at http://www.cpc.ncep.noaa.gov/products/precip/CWlink/daily_ao_index/ao_index.html) is 0.52 (Figures S19a and S20a).…”

Section: Discussion and Summarymentioning

confidence: 99%

Assessment of MME methods for seasonal prediction using WMO LC‐LRFMME hindcast dataset

Kim

Ahn

Kryjov

et al. 2020

Intl Journal of Climatology

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Different multi-model ensemble (MME) methods were investigated for their potential to improve the skill of 1-month lead seasonal forecast products, based on six models from Global Producing Centers (GPCs) for long-range forecasts (LRFs) designated by the World Meteorological Organization (WMO). We first compared the hindcast performance of seven MME methods (simple composite method, SCM; simple linear regression, SLR; multiple linear regression, MLR; best selection anomaly, BSA; multilayer perceptron, MLP; radial basis function, RBF; genetic algorithm, GA) for the global 2-m temperature and precipitation for 1983-2009. The reference datasets for 2-m temperature and precipitation are the ERA-Interim from European Centre for Medium-Range Weather Forecasts (ECMWF) and Global Precipitation Climatology Project (GPCP) for hindcast verification. For real-time verification, the data from the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis 1 for 2-m temperature and climate anomaly monitoring system and outgoing longwave radiation precipitation index (CAMS OPI) for precipitation are used. According to our analysis, GA was the most successful MME method in predicting both the global 2-m temperature and precipitation for all four seasons. GA also showed good performance in predicting the 2-m temperature and precipitation over the 13 regional climate outlook forum (RCOF) regions in all four seasons, but the range in performance among the RCOF regions varied significantly. In a realtime forecast period (MAM 2012-DJF 2015/16), GA outperformed in terms of time-averaged anomaly pattern correlation coefficient (ACC) and root-meansquare error (RMSE) of the 2-m temperature, although the forecast skill difference (0.02) between GA and SCM was not statistically significant. For the precipitation, both SCM and GA also reveal better performance than other MME methods. During the very strong El Niño event in 2015, individual models show better performance than other years. Nonetheless, these two MME methods outperform all the individual models.

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Section: Discussion and Summarymentioning

confidence: 99%

Assessment of MME methods for seasonal prediction using WMO LC‐LRFMME hindcast dataset

Kim

Ahn

Kryjov

et al. 2020

Intl Journal of Climatology

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“…Meanwhile, it is believed that variability in the Southern and Northern Annular Modes (SAM and NAM respectively) is correlated with the corresponding Ferrel cell and the poleward branch of the corresponding Hadley cell [67][68][69]. The NAM, also called the Arctic Oscillation (AO), appears to be well correlated to temperature and precipitation trends in Northern Eurasia [67,70,71]. The AO is itself highly correlated to another atmospheric index, the North Atlantic Oscillation (NAO) [71,72].…”

Section: Regional Atmospheric Circulation Patterns and The Relationships Between Themmentioning

confidence: 99%

“…The NAM, also called the Arctic Oscillation (AO), appears to be well correlated to temperature and precipitation trends in Northern Eurasia [67,70,71]. The AO is itself highly correlated to another atmospheric index, the North Atlantic Oscillation (NAO) [71,72]. However, Qian et al (2017) argue that after their proposed splitting of the "polar cell" into two cells (mentioned above), the changes in the strength of these two components of the "polar cell" explain more of the northern hemisphere climate changes than the AO/NAO [55].…”

Section: Regional Atmospheric Circulation Patterns and The Relationships Between Themmentioning

confidence: 99%

Analyzing Atmospheric Circulation Patterns Using Mass Fluxes Calculated from Weather Balloon Measurements: North Atlantic Region as a Case Study

Connolly¹,

Connolly²,

Soon

et al. 2021

Atmosphere

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In recent decades, efforts to investigate atmospheric circulation patterns have predominantly relied on either semi-empirical datasets (i.e., reanalyses) or modeled output (i.e., global climate models, GCMs). While both approaches can provide important insights, there is a need for more empirical data to supplement these approaches. In this paper, we demonstrate how the application of relatively simple calculations to the basic measurements from a standard weather balloon radiosonde can provide a vertical profile of the horizontal atmospheric mass fluxes. These mass fluxes can be resolved into their meridional (north/south) and zonal (east/west) components. This provides a new useful empirical tool for analyzing atmospheric circulations. As a case study, we analyze the results for a selected five stations along a fairly constant meridian in the North Atlantic sector from 2015–2019. For each station, we find the atmospheric mass flux profiles from the lower troposphere to mid-stratosphere are surprisingly coherent, suggesting stronger interconnection between the troposphere and stratosphere than previously thought. Although our five stations span a region nominally covered by the classical polar, Ferrel and Hadley meridional circulation cells, the results are inconsistent with those expected for polar and Ferrel cells and only partially consistent with that of a Hadley cell. However, the region is marked by very strong prevailing westerly (west to east) mass fluxes for most of the atmosphere except for the equatorial surface easterlies (“trade winds”). We suggest that the extension of the techniques of this case study to other stations and time periods could improve our understanding of atmospheric circulation patterns and their time variations.

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“…The NAO is described as the oscillation of atmospheric mass between the Azores Islands and Iceland, which determines the strength of the westerlies and the position of storm tracks (Hurrell, 1995). The AO is associated with changes in the pressure systems between the Arctic and mid-latitudes, known as the polar vortex, where the cold Arctic air is either confined to the Arctic or allowed to move to the equator (Kryzhov & Gorelits, 2015). Lacking any dominant periodicities, both the NAO and the AO fluctuate in magnitude and direction as they oscillate between negative and positive phases at the monthly to the decadal scale.…”

Section: Introductionmentioning

confidence: 99%

Local and regional climate variables driving spring phenology of tortricid pests: a 36 year study

Pak

Biddinger

Bjørnstad

2018

Ecological Entomology

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1. Insect phenology is driven by local climate variables, most notably temperature. Increased warming has been linked to advancements in critical phenophases such as the spring flight of reproductive adults in the mid-Atlantic region of the U.S.A.2. Local climate is governed by the fluctuations of large-scale climate oscillations. In the northern hemisphere, both the North Atlantic Oscillation (NAO) and the Arctic Oscillation (AO) control the local autumn and winter severity. Low NAO and AO indices are associated with colder autumns and winters, which can delay spring phenology.3. In this study, 36 years of data from experimental fruit orchards in Biglerville, Pennsylvania, were used to run partial least-squares regressions in order to determine the climate variables related to the spring phenology of five tortricid pest species.4. The phenology of the tortricid pests did not advance, even though there was evidence of warming at the research site.5. Spring temperatures were found to be the most significant climate variables in determining the timing of the spring flights. However, autumn-winter temperatures were also important.6. For the NAO and the AO, it was found that these oscillations affected the tortricid moths by influencing autumn-winter conditions. The oscillations of the NAO and AO can obscure long-term changes in phenology.7. These findings suggest that the inclusion of large-scale climate oscillations can provide important insights into how climate conditions can influence insect phenology, and presents an opportunity for improving the ability to forecast spring emergence.

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The Arctic Oscillation and its impact on temperature and precipitation in Northern Eurasia in the 20th Century

Cited by 40 publications

References 1 publication

Assessment of MME methods for seasonal prediction using WMO LC‐LRFMME hindcast dataset

Assessment of MME methods for seasonal prediction using WMO LC‐LRFMME hindcast dataset

Analyzing Atmospheric Circulation Patterns Using Mass Fluxes Calculated from Weather Balloon Measurements: North Atlantic Region as a Case Study

Local and regional climate variables driving spring phenology of tortricid pests: a 36 year study

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