The role of the Sun in atmosphere–ocean coupling

Roy, Indrani

doi:10.1002/joc.3713

Cited by 40 publications

(65 citation statements)

References 102 publications

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“…van Loon et al, 2004; as well as Chiodo et al, 2012, andMatthes et al, 2013 Frame and Gray, 2010;Misios and Schmidt, 2013;Roy, 2014;Mitchell et al, 2015b;Hood et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, the MLR approach relies on the individual forcings being independent of one another and combining linearly, which might not be the 5 case in a highly non-linear system like the atmosphere. The choice of analysis method and its details has been found to play a substantial role for the derived solar-induced surface responses in the studies of Roy and Haigh (2010;Roy (2014). Therefore, in contrast to the majority of past solar cycle studies, we analyse the simulated atmospheric response to the 11-year solar cycle in UM-UKCA using both techniques and compare them.…”

Section: Introductionmentioning

confidence: 99%

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Simulating the atmospheric response to the 11-year solar cycle forcing with the UM-UKCA model: the role of detection method and natural variability

Bednarz¹,

Maycock²,

Telford³

et al. 2018

Preprint

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The 11-year solar cycle forcing is recognised as a potentially important atmospheric forcing; however, there remain uncertainties in characterising the effects of the solar variability on the atmosphere from observations and models. Here we 15 present the first detailed assessment of the atmospheric response to the 11-year solar cycle in the UM-UKCA chemistryclimate model using an ensemble of integrations over the recent past. Comparison of the model simulations is made with observations and reanalysis. Importantly, in contrast to the majority of previous studies of the solar cycle impacts, we pay particular attention to the role of detection method by comparing the results diagnosed using both a composite and a multiple linear regression method. We show that stratospheric solar responses diagnosed using both techniques largely agree with 20 each other within the associated uncertainties; however, the results show that apparently different signals can be identified by the methods in the troposphere and in the tropical lower stratosphere. Lastly, we focus on the role of internal atmospheric variability on the detection of the 11-year solar responses by comparing the results diagnosed from individual model ensemble members (as opposed to those diagnosed from the full ensemble). We show overall agreement between the ensemble members in the tropical and mid-latitude mid-stratosphere-to-lower-mesosphere, but larger apparent differences at 25 NH high latitudes during the dynamically active season. Our results highlight the need for long data sets for confident detection of solar cycle impacts in the atmosphere, as well as for more research on possible interdependence of the solar cycle forcing with other atmospheric forcings and processes (e.g. QBO, ENSO… etc.).Atmos. Chem. Phys. Discuss., https://doi

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“…van Loon et al, 2004; as well as Chiodo et al, 2012, andMatthes et al, 2013 Frame and Gray, 2010;Misios and Schmidt, 2013;Roy, 2014;Mitchell et al, 2015b;Hood et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Simulating the atmospheric response to the 11-year solar cycle forcing with the UM-UKCA model: the role of detection method and natural variability

Bednarz¹,

Maycock²,

Telford³

et al. 2018

Preprint

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“…Following this methodology, it is possible to minimise noise being interpreted as a signal. The method is discussed in details by Haigh [2010, 2011], Roy and Collins [2015] and Roy [ , 2014 and previously used by many other studies (Gray et al, 2010 among others The CET data is the longest record of station data and described in various publications [Manley et al (1974), Parker et al (1992), Parker and Horton (2005)]. It is available since 1659, and can be downloaded from http://www.metoffice.gov.uk /hadobs/hadcet/data/ download.html.…”

Section: Methodology and Datamentioning

confidence: 99%

“…Regarding energy output, only a 0.1 % change between maximum to minimum of the solar 11-year cycle [Lean and Rind, 2001], which is too negligible to influence climate. However, studies identified significant regional impacts which are felt seasonally (Gray et al 2010;Roy and Haigh, 2010;van Loon and Meehl, 2007) and also depend on overall period chosen (Roy and Haigh, 2012;Roy, 2014). In understanding climate variability and in interpreting signals of climate change it is important to ascertain the actual role of natural factors so that any human influence may be more accurately identified.…”

Section: Introductionmentioning

confidence: 99%

“…Using the same dataset, over a similar time Roy and Haigh [2010] could not detect negative solar signal around tropical Pacific using Regression Technique. Haam and Tung [2012], Haigh [2010, 2012], Roy [ , 2014 addressed those contradictory findings in details and reconciled some of the contradictions and discussed few mechanisms. Discussions still suggest that the solar-ENSO behaviour is a major area of dispute in climate science.…”

Section: Introductionmentioning

confidence: 99%

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The Role of Natural Factors on Major Climate Variability in Northern Winter

Roy¹

2017

Preprint

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Abstract. The role of natural factors mainly solar eleven-year cycle variability, and volcanic eruptions on two major modes of climate variability the North Atlantic Oscillation (NAO) and El Niño Southern Oscillation (ENSO) are studied for around last 150 years period. The NAO is the primary factor to regulate Central England Temperature (CET) during winter throughout the period, though NAO is impacted differently by other factors in various time periods. Solar variability indicates a strong positive influence on NAO during 1978-1997, though suggests opposite in earlier period. Solar NAO lag relationship is also shown sensitive to the chosen times of reference and thus points towards the previously proposed mechanism/ relationship related to the sun and NAO. The ENSO is influenced strongly by solar variability and volcanic eruptions in certain periods. This study observes a strong negative association between the sun and ENSO before the 1950s, which is even opposite during the second half of 20th century. The period 1978-1997, when two strong eruptions coincided with active years of strong solar cycles, the ENSO, and volcano suggested a stronger association, and we discussed the important role played by ENSO. That period showed warming in central tropical Pacific while cooling in the North Atlantic with reference to the later period (1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017) and also from chosen earlier period. Here we show that the mean atmospheric state is important for understanding the connection between solar variability, the NAO and ENSO and associated mechanism. It presents a critical analysis to improve knowledge about major modes of variability and their role in climate. We also discussed the importance of detecting the robust signal of natural variability, mainly the sun.

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Solar influences on spatial patterns of Eurasian winter temperature and atmospheric general circulation anomalies

Chen

Xiao

et al. 2015

JGR Atmospheres

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Solar influences on spatial patterns of Eurasian winter climate and possible mechanisms are investigated based on a multiple linear regression method and multisource observational and reanalysis data. Robust and significant solar signals are detected in Eurasian surface air temperature (SAT), and strong solar activity evidently warms most area of the continent. The spatial pattern of sea level pressure (SLP) responses to solar activity is similar but not identical to that of the North Atlantic Oscillation (NAO). Compared to the NAO, geographic distribution of solar‐induced SLP anomalies shifts eastward, with significantly enhanced influences over northern Eurasia. Relatively weaker solar signals were also found in mid‐to‐upper troposphere. The spatial pattern of 500 hPa geopotential anomalies resembles a negative Scandinavia teleconnection pattern, and the 200 hPa subtropical jet is weakened, while zonal wind at high latitudes is enhanced due to strong solar activity. The anomalous zonal circulations can be attributed to the “top‐down” mechanism. During high solar activity winters, an enhanced stratospheric zonal wind anomaly propagates downward, causing zonal wind anomalies in the troposphere. However, the “bottom‐up” mechanisms may provide more reasonable explanations of the distinct solar influences on Eurasian climate. Solar‐induced strong warm advection in lower atmosphere tends to increase SAT but decrease SLP, resulting in enhanced solar influences over northern Eurasia. Meanwhile, change in the land‐ocean thermal contrast (LOTC) could also amplify the circulation anomaly. Inhomogeneous surface heating caused by anomalous solar activity modifies LOTC, which probably enhances the solar‐induced circulation patterns. Such a positive feedback may potentially strengthen the solar influences.

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The role of the Sun in atmosphere–ocean coupling

Cited by 40 publications

References 102 publications

Simulating the atmospheric response to the 11-year solar cycle forcing with the UM-UKCA model: the role of detection method and natural variability

Simulating the atmospheric response to the 11-year solar cycle forcing with the UM-UKCA model: the role of detection method and natural variability

The Role of Natural Factors on Major Climate Variability in Northern Winter

Solar influences on spatial patterns of Eurasian winter temperature and atmospheric general circulation anomalies

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