Composite analysis with Monte Carlo methods: an example with cosmic rays and clouds

Laken, Benjamin A.; Čalogović, Jaša

doi:10.1051/swsc/2013051

Cited by 56 publications

(48 citation statements)

References 53 publications

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“…In a data sequence that exhibits a low-amplitude response to a specific trigger event, the response may be obscured by sources of random variability. The AF technique aims to reveal the response to a specific trigger event by averaging the responses to many occurrences of the trigger event, such that over many events random variability will be suppressed and the response will emerge (Laken andČalogović, 2013). Barnard et al (2011) used this technique with the Steinhilber et al (2012) record to estimate the possible future evolution given the expected decline from the grand solar maximum that persisted through the late 20th century.…”

Section: Analogue Forecastmentioning

confidence: 99%

Solar forcing for CMIP6 (v3.2)

et al. 2017

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Abstract. This paper describes the recommended solar forcing dataset for CMIP6 and highlights changes with respect to CMIP5. The solar forcing is provided for radiative properties, namely total solar irradiance (TSI), solar spectral irradiance (SSI), and the F10.7 index as well as particle forcing, including geomagnetic indices Ap and Kp, and ionization rates to account for effects of solar protons, electrons, and galactic cosmic rays. This is the first time that a recommendation for solar-driven particle forcing has been provided for a CMIP exercise. The solar forcing datasets are provided at daily and monthly resolution separately for the CMIP6 preindustrial control, historical (1850CMIP6 preindustrial control, historical ( -2014, and future (2015-2300) simulations. For the preindustrial control simulation, both constant and time-varying solar forcing components are provided, with the latter including variability on 11-year and shorter timescales but no long-term changes. For the future, we provide a realistic scenario of what solar behavior could be, as well as an additional extreme Maunderminimum-like sensitivity scenario. This paper describes the forcing datasets and also provides detailed recommendations as to their implementation in current climate models.For the historical simulations, the TSI and SSI time series are defined as the average of two solar irradiance models that are adapted to CMIP6 needs: an empirical onePublished by Copernicus Publications on behalf of the European Geosciences Union. A new and lower TSI value is recommended: the contemporary solar-cycle average is now 1361.0 W m −2 . The slight negative trend in TSI over the three most recent solar cycles in the CMIP6 dataset leads to only a small global radiative forcing of −0.04 W m −2 . In the 200-400 nm wavelength range, which is important for ozone photochemistry, the CMIP6 solar forcing dataset shows a larger solar-cycle variability contribution to TSI than in CMIP5 (50 % compared to 35 %).We compare the climatic effects of the CMIP6 solar forcing dataset to its CMIP5 predecessor by using timeslice experiments of two chemistry-climate models and a reference radiative transfer model. The differences in the long-term mean SSI in the CMIP6 dataset, compared to CMIP5, impact on climatological stratospheric conditions (lower shortwave heating rates of −0.35 K day −1 at the stratopause), cooler stratospheric temperatures (−1.5 K in the upper stratosphere), lower ozone abundances in the lower stratosphere (−3 %), and higher ozone abundances (+1.5 % in the upper stratosphere and lower mesosphere). Between the maximum and minimum phases of the 11-year solar cycle, there is an increase in shortwave heating rates (+0.2 K day −1 at the stratopause), temperatures (∼ 1 K at the stratopause), and ozone (+2.5 % in the upper stratosphere) in the tropical upper stratosphere using the CMIP6 forcing dataset. This solar-cycle response is slightly larger, but not statistically significantly different from that for the CMIP5 forcing dataset.CMIP6 models wi...

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Section: Analogue Forecastmentioning

confidence: 99%

Solar forcing for CMIP6 (v3.2)

et al. 2017

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“…Laken et al 2012). Laken et al (2013) took a critical look at the consistency of the mathematical methods used for signal detection when looking at the GCRcloud links. The issues in detecting any GCR influence on cloud and meteorological parameters were also discussed by Laken et al (2011Laken et al ( , 2012).…”

Section: Cosmic Raysmentioning

confidence: 99%

What is the solar influence on climate? Overview of activities during CAWSES-II

Seppälä

Matthes

Randall

et al. 2014

Prog. in Earth and Planet. Sci.

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This paper presents an overview of the main advances in the Key Questions identified by the Task Group 'What is the Solar Influence on Climate' by the SCOSTEP CAWSES-II science program. We go through different aspects of solar forcing from solar irradiance, including total solar irradiance (TSI) and solar spectral irradiance (SSI), to energetic particle forcing, including energetic particle precipitation (EPP) and cosmic rays (CR). Besides discussing the main advances in the timeframe 2009 to 2013, we also illustrate the proposed mechanism for climate variability for the different solar variability sources listed above. The key questions are as follows: What is the importance of spectral variations to solar influences on climate? What is the effect of energetic particle forcing on the whole atmosphere and what are the implications for climate? How well do models reproduce and predict solar irradiance and energetic particle influences on the atmosphere and climate?

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“…In reality the only requirement is that the MC-samples span an identical time-period as the original samples (5 consecutive months). For more details on the MC method applied to solar-terrestrial studies, including information related to determining optimal size of the simulations, see Laken & Č alogović (2013a).…”

Section: Monte Carlo Simulations Of the Null Hypothesismentioning

confidence: 99%

Can Open Science save us from a solar-driven monsoon?

Laken

2016

J. Space Weather Space Clim.

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Numerous studies have been published claiming strong solar influences on the Earth's weather and climate, many of which include documented errors and false-positives, yet are still frequently used to substantiate arguments of global warming denial. Recently, Badruddin & Aslam (2015) reported a highly significant relationship between the Indian monsoon and the cosmic ray flux. They found strong and opposing linear trends in the cosmic ray flux during composites of the strongest and weakest monsoons since 1964, and concluded that this relationship is causal. They further speculated that it could apply across the entire tropical and sub-tropical belt and be of global importance. However, examining the original data reveals the cause of this false-positive: an assumption that the data's underlying distribution was Gaussian. Instead, due to the manner in which the composite samples were constructed, the correlations were biased towards high values. Incorrect or problematic statistical analyses such as this are typical in the field of solar-terrestrial studies, and consequently false-positives are frequently published. However, the widespread adoption of Open Science approaches, placing an emphasis on reproducible open-source analyses as demonstrated in this work, could remedy the situation.

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Composite analysis with Monte Carlo methods: an example with cosmic rays and clouds

Cited by 56 publications

References 53 publications

Solar forcing for CMIP6 (v3.2)

Solar forcing for CMIP6 (v3.2)

What is the solar influence on climate? Overview of activities during CAWSES-II

Can Open Science save us from a solar-driven monsoon?

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