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  • 1
    Publication Date: 2019-05-23
    Description: 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 (1850–2014), 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 Maunder-minimum-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 one (NRLTSI2–NRLSSI2) and a semi-empirical one (SATIRE). 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 time-slice 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 with a well-resolved shortwave radiation scheme are encouraged to prescribe SSI changes and include solar-induced stratospheric ozone variations, in order to better represent solar climate variability compared to models that only prescribe TSI and/or exclude the solar-ozone response. We show that monthly-mean solar-induced ozone variations are implicitly included in the SPARC/CCMI CMIP6 Ozone Database for historical simulations, which is derived from transient chemistry–climate model simulations and has been developed for climate models that do not calculate ozone interactively. CMIP6 models without chemistry that perform a preindustrial control simulation with time-varying solar forcing will need to use a modified version of the SPARC/CCMI Ozone Database that includes solar variability. CMIP6 models with interactive chemistry are also encouraged to use the particle forcing datasets, which will allow the potential long-term effects of particles to be addressed for the first time. The consideration of particle forcing has been shown to significantly improve the representation of reactive nitrogen and ozone variability in the polar middle atmosphere, eventually resulting in further improvements in the representation of solar climate variability in global models.
    Type: Article , PeerReviewed , info:eu-repo/semantics/article
    Format: text
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  • 2
    Publication Date: 2017-06-22
    Description: 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 (1850–2014), 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 Maunder-minimum-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 one (NRLTSI2–NRLSSI2) and a semi-empirical one (SATIRE). A new and lower TSI value is recommended: the contemporary solar-cycle average is now 1361.0Wm−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 −0.04Wm−2. In the 200–400nm 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 time-slice 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 −0.35Kday−1 at the stratopause), cooler stratospheric temperatures (−1.5K 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 (+0.2Kday−1 at the stratopause), temperatures ( ∼ 1K 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 with a well-resolved shortwave radiation scheme are encouraged to prescribe SSI changes and include solar-induced stratospheric ozone variations, in order to better represent solar climate variability compared to models that only prescribe TSI and/or exclude the solar-ozone response. We show that monthly-mean solar-induced ozone variations are implicitly included in the SPARC/CCMI CMIP6 Ozone Database for historical simulations, which is derived from transient chemistry–climate model simulations and has been developed for climate models that do not calculate ozone interactively. CMIP6 models without chemistry that perform a preindustrial control simulation with time-varying solar forcing will need to use a modified version of the SPARC/CCMI Ozone Database that includes solar variability. CMIP6 models with interactive chemistry are also encouraged to use the particle forcing datasets, which will allow the potential long-term effects of particles to be addressed for the first time. The consideration of particle forcing has been shown to significantly improve the representation of reactive nitrogen and ozone variability in the polar middle atmosphere, eventually resulting in further improvements in the representation of solar climate variability in global models.
    Print ISSN: 1991-959X
    Electronic ISSN: 1991-9603
    Topics: Geosciences
    Published by Copernicus on behalf of European Geosciences Union (EGU).
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  • 3
    Publication Date: 2019-03-28
    Description: Measurements of thermospheric composition via ground-based instrumentation are challenging to make and so details about this important region of the upper atmosphere are currently sparse. We present a technique that deduces quantitative estimates of thermospheric composition from ionospheric data, for which there is a global network of stations. The visibility of the F1 peak in ionospheric soundings from ground-based instrumentation is a sensitive function of thermospheric composition. The ionospheric profile in the transition region between F1 and F2 peaks can be expressed by the G factor, a function of ion production rate and loss rates via ion-atom interchange reactions and dissociative recombination of molecular ions. This in turn can be expressed as the square of the ratio of ions lost via these processes. We compare estimates of the G factor obtained from ionograms recorded at Kwajalein (9° N, 167.2° E) for 25 times during which the TIMED spacecraft recorded approximately co-located measurements of the neutral thermosphere. We find a linear relationship between √G and the molecular: atomic composition ratio, with a gradient of 2.23 ± 0.17 and an offset of 1.66 ± 0.19. This relationship reveals the potential for using ground-based ionospheric measurements to infer quantitative variations in the composition of the neutral thermosphere. Such information can be used to investigate spatial and temporal variations in thermospheric composition which in turn has applications such as understanding the response of thermospheric composition to climate change and the efficacy of the upper atmosphere on satellite drag.
    Electronic ISSN: 2568-6402
    Topics: Geosciences , Physics
    Published by Copernicus on behalf of European Geosciences Union (EGU).
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  • 4
    Publication Date: 2018-01-01
    Electronic ISSN: 2115-7251
    Topics: Geosciences , Physics
    Published by EDP Sciences
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  • 5
    Publication Date: 2018-01-01
    Electronic ISSN: 2115-7251
    Topics: Geosciences , Physics
    Published by EDP Sciences
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  • 6
    Publication Date: 2017-06-01
    Print ISSN: 1542-7390
    Electronic ISSN: 1542-7390
    Topics: Geosciences , Physics
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  • 7
  • 8
    Publication Date: 2018-01-01
    Electronic ISSN: 2115-7251
    Topics: Geosciences , Physics
    Published by EDP Sciences
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  • 9
    Publication Date: 2019-01-01
    Description: Aims: To elucidate differences between commonly-used mid-latitude geomagnetic indices and study quantitatively the differences in their responses to solar forcing as a function of Universal Time (UT), time-of-year (F), and solar-terrestrial activity level. To identify the strengths, weaknesses and applicability of each index and investigate ways to correct for any weaknesses without damaging their strengths. Methods: We model how the location of a geomagnetic observatory influences its sensitivity to solar forcing. This modelling for a single station can then be applied to indices that employ analytic algorithms to combine data from different stations and thereby we derive the patterns of response of the indices as a function of UT, F and activity level. The model allows for effects of solar zenith angle on ionospheric conductivity and of the station’s proximity to the midnight-sector auroral oval: it employs coefficients that are derived iteratively by comparing data from the current aa index stations (Hartland and Canberra) to simultaneous values of the am index, constructed from chains of stations in both hemispheres. This is done separately for eight overlapping bands of activity level, as quantified by the am index. Initial estimates were obtained by assuming the am response is independent of both F and UT and the coefficients so derived were then used to compute a corrected F-UT response pattern for am. This cycle was repeated until it resulted in changes in predicted values that were below the adopted uncertainty level (0.001%). The ideal response pattern of an index would be uniform and linear (i.e., independent of both UT and F and the same at all activity levels). We quantify the response uniformity using the percentage variation at any activity level, V = 100 (σS/〈S〉), where S is the index’s sensitivity at that activity level and σS is the standard deviation of S: both S and σS were computed using the eight UT ranges of the 3-hourly indices and 20 equal-width ranges of F. As an overall metric of index performance, we take an occurrence-weighted mean of V, Vav, over the eight activity-level bins. This metric would ideally be zero and a large value shows that the index compilation is introducing large spurious UT and/or F variations into the data. We also study index performance by comparisons with the SME and SML indices, compiled from a very large number of stations, and with an optimum solar wind “coupling function”, derived from simultaneous interplanetary observations. Results: It is shown that a station’s response patterns depend strongly on the level of geomagnetic activity because at low activity levels the effect of solar zenith angle on ionospheric conductivity dominates over the effect of station proximity to the midnight-sector auroral oval, whereas the converse applies at high activity levels. The metric Vav for the two-station aa index is modelled to be 8.95%, whereas for the multi-station am index it is 0.65%. The ap (and hence Kp) index cannot be analyzed directly this way because its construction employs tabular conversions, but the very low Vav for am allows us to use 〈ap〉/〈am〉 to evaluate the UT-F response patterns for ap. This yields Vav = 11.20% for ap. The same empirical test applied to the classical aa index and the new “homogenous” aa index, aaH (derived from aa using the station sensitivity model), yields Vav of, respectively, 10.62% (i.e., slightly higher than the modelled value) and 5.54%. The ap index value of Vav is shown to be high because it exaggerates the average semi-annual variation and has an annual variation giving a lower average response in northern hemisphere winter. It also contains a strong artefact UT variation. We derive an algorithm for correcting for this uneven response which gives a corrected ap value, apC, for which Vav is reduced to 1.78%. The unevenness of the ap response arises from the dominance of European stations in the network used and the fact that all data are referred to a European station (Niemegk). However, in other contexts, this is a strength of ap, because averaging similar data gives increased sensitivity and more accurate values on annual timescales, for which the UT-F response pattern is averaged out.
    Electronic ISSN: 2115-7251
    Topics: Geosciences , Physics
    Published by EDP Sciences
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  • 10
    Publication Date: 2017-09-01
    Print ISSN: 1542-7390
    Electronic ISSN: 1542-7390
    Topics: Geosciences , Physics
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