Solar irradiance measurements

Solar Output Variability.- Assessing Solar Variability.- Solar Variability of Possible Relevance for Planetary Climates.- Measurement of Total and Spectral Solar Irradiance.- Solar Irradiance Variability Since 1978.- Solar Variability Over the Past Several Millennia.- Solar and Heliospheric Modulation of Galactic Cosmic Rays.- What Do Cosmogenic Isotopes Tell Us about Past Solar Forcing of Climate?.- Tropospheric Aerosols, Radiation Budget, and Changes.- Observed Long-Term Variations of Solar Irradiance at the Earth's Surface.- Aerosol Effects on Clouds and Climate.- Satellite Observations of Natural and Anthropogenic Aerosol Effects on Clouds and Climate.- Aerosol-Cloud Interactions Control of Earth Radiation and Latent Heat Release Budgets.- Atmospheric Ion-Induced Aerosol Nucleation.- Atmospheric Aerosol and Cloud Condensation Nuclei Formation: A Possible Influence of Cosmic Rays?.- Climate Change: Detection and Attribution.- On the Response of the Climate System to Solar Forcing.- Detection and Attribution of Climate Change, and Understanding Solar Influence on Climate.- The Climate Response to the Astronomical Forcing.- Regional Response of the Climate System to Solar Forcing: The Role of the Ocean.- Middle Atmospheric Response to Solar Variability.- Discussion of the Solar UV/Planetary Wave Mechanism.- Solar Variation and Stratospheric Response.- Signature of the 11-Year Cycle in the Upper Atmosphere.- The Middle Atmospheric Ozone Response to the 11-Year Solar Cycle.- Influence of the Solar Cycle on the General Circulation of the Stratosphere and Upper Troposphere.- Multidecadal Signal of Solar Variability in the Upper Troposphere During The 20th Century.- The Role of Dynamics in Solar Forcing.- Solar Influences on Dynamical Coupling Between the Stratosphere and Troposphere.- The Response of the Middle Atmosphere to Solar Cycle Forcing in the Hamburg Model of the Neutral and Ionized Atmosphere.- A Possible Transfer Mechanism for the 11-Year Solar Cycle to the Lower Stratosphere.- Recent Space Data.- Recent Space Data.- Satellite Measurements of Middle Atmospheric Impacts by Solar Proton Events in Solar Cycle 23.- Impact of Solar Activity on Stratospheric Ozone and No2 Observed by GOMOS/ENVISAT.- The Stratospheric and Mesospheric NOy in the 2002-2004 Polar Winters as Measured by MIPAS/ENVISAT.- Early Data from Aura and Continuity from UARS and Toms.- Planetary Science.- What Do We Know about the Climate of Terrestrial Planets?.- Solar Variability and Climate Impact on Terrestrial Planets.- Climate Variability on Venus and Titan.- The Orbital Forcing of Climate Changes on Mars.

The solar activity has been observed to vary at various time scales, of which the 11-year solar cycle is the most prominent one. Since the Sun provides the main external energy to the Earth, knowledge of solar variability is highly crucial for understanding the influence of the Sun on the Earth's climate system. One of the important measurements related to solar variability is the solar irradiance: the total solar irradiance (TSI) and the spectral solar irradiance (SSI). However, space-based TSI/SSI measurements only cover the last four decades, which is unfortunately not sufficient for studying long-term solar variability and its influence on climate. Therefore, reconstructions of the solar irradiance on longer time scales are required. Reconstructions of the solar irradiance require knowledge of proxies of solar magnetic activity. The only directly-observed solar quantity back to 1610 is the group sunspot number, while one has to rely on indirect proxies going further back in time. The commonly used indirect proxy is the concentration of the cosmogenic isotopes (14C and 10Be) retrieved from natural archives. Cosmogenic isotopes are produced in the upper terrestrial atmosphere by the impinging galactic cosmic rays, whose flux is modulated by the heliospheric magnetic field. The signals in the 14C record are well globally-mixed while in the 10Be records they are highly subjected to the local climate. We constructed the first multi-isotope composite based on one global 14C and six local 10Be records, using a new Bayesian approach (Chap. 3). All six 10Be records were first synchronized with respect to the 14C record using a wiggle-matching method. Next a Monte Carlo simulation was performed to search for that solar modulation potential which best fits all the available isotope data sets at any given time. This composite is considered more robust compared to other composites constructed linearly. Hence, it is further used in this thesis as a proxy of solar magnetic activity on a millennial time scale. In this thesis, we use two SATIRE (Spectral And Total Irradiance REconstruction) versions, SATIRE-T and SATIRE-M, to reconstruct the long-term changes in the solar irradiance. The SATIRE-T model uses the sunspot number to deduce the evolution of the solar surface magnetic components and to reconstruct the TSI/SSI back to the beginning of the 17th century. The SATIRE-M model employs the cosmogenic isotopes as proxies of solar activity to reconstruct the TSI/SSI over the Holocene. Since the SATIRE-M model is based partially on the SATIRE-T model, we first re-visited the SATIRE-T model with various modifications (Chap. 4). With these improvements, the free parameters in the SATIRE-T model are constrained and further employed in the SATIRE-M model. Next, we use the SATIRE-M model and the first multi-isotope composite to reconstruct the solar irradiance over the last 9\,000 years. This is the first SSI reconstruction that not only uses physics-based models to describe all involved non-linear physical processes, but also bases on a multi-isotope composite. The TSI/SSI reconstructions have been recommended for studies of long-term climate changes within the Palaeoclimate Modelling Intercomparison Project-phase 4 (PMIP4). Due to the sampling and quality of the cosmogenic isotope data, the reconstructions with the SATIRE-M model have only a resolution of 10 years, which, unfortunately, might cause biases in the climate models. Therefore, we developed a statistical approach to simulate the quasi 11-year solar cycle from the decadally-averaged sunspot numbers (Chap. 5). This is done by characterising the solar cycle properties and finding the linear relationships between these properties and the decadally-averaged sunspot numbers. This simulated sunspot number series with 11-year solar cycles has annual resolution, and is further employed in the SATIRE model to reconstruct the annual values of TSI/SSI over the Holocene. The TSI/SSI reconstructions with simulated cycles are consistent with the reconstructions based on the directly-observed sunspot numbers. This final solar irradiance reconstruction has been provided as a solar forcing input to climate models. This hopefully will help us to better understand the degree of the solar influence on the Earth's climate on long time scales.

[1] Foukal's [2010] comment (hereinafter referred to as Fcom) on Balmaceda et al. [2009] makes a number of claims and assertions that require a response, since many of them are without a factual basis. For one, the conclusion presented in the abstract is that “There is no basis for the claim [ascribed by the authors to me] that UV irradiance variations have a much smaller influence on climate than total solar irradiance variations.” Over 25% of the main body of the paper is devoted to disproving this claim. I have never made such a claim. Differences in time-variation between total and ultraviolet solar irradiance could help in separating their influence on climate…. Correlation of our time series of UV irradiance with global temperature, T, accounts for only 20% of the global temperature variance during the 20th century. Correlation of our total irradiance time series with T accounts statistically for 80% of the variance in global temperature over that period, although the irradiance variation amplitude is insufficient to influence global warming in present-day climate models. It is the difference in shape between the UV and total irradiance time series, not their amplitude, that interests us as a discriminator between their signatures in the climate record (paragraph 13). Our main finding is that the time-behavior of total and ultraviolet irradiances since 1915 are distinctly different (paragraph 16). The correlation we find between global temperature and FUV over most of the 20th century is relatively low. But it seems sufficient to account for, e.g., the roughly 20% of climate variance seemingly associated with solar variability in the Holocene temperature record (paragraph 17). High correlation between total irradiance variation and T has been noted previously in empirical models. But our study is the first to contrast this with the much lower correlation between T and FUV (paragraph 18). [5] Although F02 [2002, paragraph 20] discusses that the total irradiance amplitude is too low to give a significant contribution to global warming, it is then stated, “Nevertheless, the possibility of significant driving of 20th century climate by total irradiance variation cannot be dismissed [e.g., North and Wu, 2001]…. Interpretation of this high correlation between S and T awaits improved understanding of possible climate sensitivity to relatively small total irradiance variation.” [6] 3. Fcom [2010, paragraph 4] claim that a “critical consideration of the analysis by Fligge and Solanki [1997]” was carried out. However, F02 does not cite that paper nor provide any explanation why he ignored its quite relevant results. [7] 4. Foukal [2010, paragraph 4] states that “They…reported a correction factor between Royal Greenwich Observatory (RGO) and U.S. Air Force (USAF) spot areas of 1.2, thus less than half the size now claimed by Balmaceda et al.” (with “They” he means Fligge and Solanki [1997]). This is not correct. The factor of 1.2 obtained by Fligge and Solanki [1997] was between RGO and Rome and between RGO and Yunnan data (using Rome as an intermediary). Since RGO and USAF (Solar Optical Observing Network (SOON)) data do not overlap, their direct comparison is impossible and has to be done via another data set. If we take Rome as an intermediary between RGO and SOON, then the analysis of Fligge and Solanki [1997] returns a factor between ≈1.1 × 1.15 = 1.27 and ≈1.1 × 1.25 = 1.38, while that of BEA09 gives values between 1.3 and 1.46 (see BEA09, Tables 2 and 3), which is roughly consistent with the results obtained by using the Russian data as intermediary (as preferred by BEA09). Thus, the range of 1.2–1.3 quoted by Fcom for the factor between RGO and SOON (Fcom, paragraph 5) is largely below the lower limit allowed even if using the lower quality Rome data as an intermediary. In any case, by using the result of Fligge and Solanki [1997] to reconstruct total irradiance, we still get a result much closer to BEA09 than to F02, see our point 6 below. [8] 5. Foukal insists on using Rome as an intermediary rather than the Russian data chosen by BEA09. There are good reasons for the choice made by BEA09: the Russian data (1) are more similar to RGO data (with a smaller correction factor and the same minimum spot size), (2) have an order of magnitude fewer gaps (BEA09, Table 1), and (3) do not display the inconstant behavior shown by the Rome data (which was noticed even by Fcom (paragraph 6), see also our point 7 below). Thus, e.g., using Rome data as an intermediary would have led to a very gappy data set between 1976 and 1981 that would have had to be filled by as many data points from Russian data as available from Rome data. Note that the Russian record was not available to Fligge and Solanki [1997]. [9] 6. Foukal [2010, paragraph 4] also claims that “This correction [factor of 1.2] would not have changed the finding that TSI and UV flux variations differ…” We have followed up this unsubstantiated statement and reconstructed the total irradiance, S, according to the method of F02 but now using the AS series from the combination of Greenwich, Rome, and Yunnan data, the two latter multiplied by a factor of 1.2 as proposed by Fligge and Solanki [1997]. The resulting S curve is plotted in Figure 1 (dashed curve). Clearly, it lies considerably closer to FUV (dot-dashed curve) and S from the sunspot areas AS calibrated by BEA09 (solid curve) than to the reconstruction based on noncalibrated AS (dotted curve), which is very similar to the S published by F02. This is supported by the RMS difference between the FUV curve and the three reconstructions of S for the period after 1976: whereas the RMS difference to dashed and solid curves (Fligge and Solanki [1997] calibration and BEA09 calibration) is 0.00354 and 0.00314, respectively, and the difference to the dotted curve is 0.00931, i.e., significantly larger. [10] 7. In paragraph 6, Fcom argues that the errors in the sunspot areas do not follow Gaussian statistics but display a bimodal distribution, in particular the ratio between Rome and RGO. In Figure 2, we plot the histogram of ratios between Rome and RGO, together with the best fit Gaussian. Clearly, the statistics are relatively close to being Gaussian (although there is an extended tail) and are far from bimodal. Note also that the ratio of Russian to RGO data follows Gaussian statistics far more closely than the plotted ratio. The more unusual statistics displayed by Rome than by Russian data when compared with RGO (and also with SOON) was another reason for BEA09 to concentrate on the Russian data. [11] Hence, Fcom's main argument against the validity of our result, which is based on (1) his faulty claim about the statistics not being Gaussian, (2) his insistence on using Rome data in spite of their obviously lower quality, and (3) his use of the incorrect factor of 1.2 between RGO and SOON, is without any basis. [12] 8. In paragraphs 8 and 9 of Fcom, Foukal states that we cannot provide a satisfactory explanation for the difference between the various historic data sets. We repeat that the main aim of BEA09 was to create a consistent sunspot areas record, not to find an explanation why data records differ from each other. For historical data such an undertaking will remain difficult. Note that a far bigger and more fundamental question about the physics of the Sun is raised by the drastic change in the relationship between sunspot area and sunspot number between the time that RGO stopped recording and SOON started if one uses uncorrected sunspot area data (BEA09, Figure 3). Why, after displaying on average the same size over many cycles, do sunspots suddenly display only two thirds that average size once another data set is used? After applying the calibration provided by BEA09 (derived quite independently of sunspot numbers), the RGO and SOON relationships with sunspot number agree exceedingly well with each other. Foukal does not even attempt to provide a physical explanation for this sudden and huge change in average sunspot size. A well-founded physical explanation for such a massive effect would give at least some credence to his claims. [13] 9. Foukal [2010, paragraph 12] also makes the claim that “The comparison with sunspot number shown in Figure 3 of Fligge and Solanki [1997] is plotted for cycles 12–20 for the RGO data and for cycles 22–23 for the SOON data. Given the large cycle-to-cycle dispersion in relation between spot areas and spot numbers, it is difficult to conclude anything from this plot.” We are completely at a loss as to how Foukal reaches this conclusion. The difference in relationship is far bigger than the scatter. Consider the slopes to the sunpot area versus sunspot number regression lines: For RGO we find 18.0 ± 0.2; for SOON after correction by the factor of 1.49 we find 17.7 ± 0.3; but for SOON without correction we find 11.8 ± 0.2. Although the first two agree within ∼1–1.5σ with each other, the last differs by over 30σ. [14] We note that recently Hathaway [2010], using an independent analysis, i.e., employing sunspot numbers instead of a third sunspot areas data set, finds a factor of 1.48 between RGO and SOON data, in close agreement with the BEA09 result of 1.49. This further strengthens the result of BEA09 and casts further doubt on the arguments put forward by Fcom for a factor of 1. [15] 10. Nowhere do BEA09 claim that the reconstruction by F02 does not do a reasonable job prior to 1976, when Greenwich data were used. The statement in paragraph 10 of Fcom is consequently irrelevant. [16] 11. We agree with Fcom [2010, paragraph 11] that “solar radiative driving of climate change is problematical,” as long as we consider the period since around 1970. Regarding earlier times, we believe that a final answer is pending, awaiting improvements in climate models. [17] 12. That the calibrated AS record is the better one to use is also indicated by a comparison with independent reconstructions of total solar irradiance that do not make use of the sunspot area [Steinhilber et al., 2009; Schöll et al., 2007]. The first one is based on the relationship between the open flux and total solar irradiance derived by Fröhlich [2009] and makes use of cosmogenic radionuclides. The 11 year smoothed total irradiance curve [Steinhilber et al., 2009, Figure 1d] shows a slightly higher peak around the 1990s compared to the 1960s (with the difference being <0.1 W/m2). Foukal's S curve (F02, Figure 2a) shows for the same time interval, 1960–1990, an increase of ≈0.015% of SQ, which corresponds to 0.2 W/m2, i.e., over twice the value found by Steinhilber et al. [2009]. Schöll et al. [2007] use the sunspot number for the short-term trend and neutron monitor measurements for the long-term reconstruction of irradiance variations [Schöll et al., 2007, Figure 4]. For cycle 19, it exhibits a bigger amplitude than for cycles 21 and 22, very similar to the dotted curve in Figure 6 of BEA09 (see solid curve in Figure 1), based on calibrated AS, but quite different from the reconstruction of F02, where cycles 21 and 22 are considerably stronger than 19. Note that on shorter time scales, neutron monitor measurements are considerably more reliable than cosmogenic isotopes, so that we trust more the result of Schöll et al. [2007]. [18] In conclusion, the criticisms voiced by Foukal in his comment do not hold up to closer scrutiny, as we have shown above. It is amply clear that the use of calibrated sunspot areas data, as proposed by BEA09 in agreement with Hathaway et al. [2002] and Hathaway [2010], is to be preferred over the use of noncalibrated data, as still supported by Foukal. [19] Philippa Browning thanks the reviewer for his assistance in evaluating this paper.

The Sun is the primary external energy source for all planets in the Solar System. Its radiative flux, known as solar irradiance, plays a crucial role in planetary atmosphere modeling. Accurate spectral solar irradiance (SSI) measurements are essential for understanding the temperature, composition, and dynamics of planetary atmospheres, as well as their climate and weather patterns. Climate models depend on accurate SSI data to simulate solar forcing effects. Measurements of SSI at Earth are available from spacecraft missions, but similar data for other planets are limited. Mars is the only planet with SSI measurements, obtained by the Mars Atmosphere and Volatile EvolutioN spacecraft. The lack of SSI measurements for other planets hinders the study of solar influence on planetary atmospheres and climate. To address this gap, my dissertation proposes to use information about magnetic activity on both the near and far sides of the Sun to estimate solar irradiance variability from any vantage point in the Solar System. Near side measurements are obtained through direct observations, while far side information is derived from helioseismic holography. Far side information can also be obtained by using a surface flux transport model. This comprehensive approach enables sufficiently accurate irradiance variability estimations for planetary studies using the Spectral And Total Irradiance REconstruction (SATIRE) model, which attributes irradiance variations over periods longer than a day to the emergence and evolution of solar surface magnetic features -- i.e., sunspots and faculae. Irradiance is then calculated as the sum of contributions from the quiet Sun and these magnetic features. Others have also addressed this gap by interpolating Earth-based irradiance measurements and scaling them according to the Sun-planet distance in order to estimate solar irradiance variability at other planets. However, this interpolation approach overlooks the inherent solar irradiance variability over various timescales and the evolution of solar surface magnetic fields. The estimation of solar irradiance variability relies on area coverage calculations of magnetic features. In Chapter 2, synthetic full surface magnetograms generated by a Surface Flux Transport Model are converted into sunspots and faculae areas, and the wavelength-integrated spectral irradiance (total solar irradiance, or TSI) and the S-index (a proxy for ultraviolet irradiance variability) are calculated. The results demonstrate that simple phase-shifted Earth-based irradiance measurements are unreliable for other planets due to the dynamic nature of sunspots and faculae. The study finds agreement in S-index variability between the traditional interpolation method and the new approach, since this variability is dominated by faculae, which have longer lifetimes and cover a larger portion of the solar surface. However, significant discrepancies are observed in TSI variability estimates, particularly because sunspots, which are short-lived magnetic features that dominate the TSI variability, are not adequately captured by the interpolation approach. This finding highlights the limitations of an interpolation method that disregards far side solar activity. Chapter 3 builds on the previous chapter by addressing the challenge of estimating solar irradiance variability with limited data from the far side of the Sun. High-resolution magnetograms and continuum intensity images used for Earth-based irradiance reconstruction are unavailable for the far side, and therefore, magnetic field data alone must suffice. Here, I use Far side Active Region Magnetograms (FARM) -- which are derived from far side seismic images -- to estimate the area coverage of sunspots and faculae on the far side of the Sun. The results show that magnetic field data can serve as a reliable proxy for the area coverage of sunspots and faculae, enabling the estimation of solar irradiance variability at different positions in the ecliptic plane. The estimates are not as accurate as those obtained from the SATIRE model, but they provide a more reliable alternative to the interpolation method when near side data are unavailable.

Context. The variability of Solar Spectral Irradiance over the rotational period and its trend over the solar activity cycle are important for understanding the Sun-Earth connection as well as for observational constraints for solar models. Recently the SIM experiment on SORCE has published an unexpected negative correlation with Total Solar Irradiance of the visible spectral range. It is compensated by a strong and positive variability of the near UV range. Aims. We aim to verify whether the anti-correlated SIM/SORCE-trend in the visible can be confirmed by independent observations of the VIRGO experiment on SOHO. The challenge of all space experiments measuring solar irradiance are sensitivity changes of their sensors due to exposure to intense UV radiation, which are difficult to assess in orbit. Methods. We analyze a 10-year time series of VIRGO sun photometer data between 2002 and 2012. The variability of Spectral Solar Irradiance is correlated with the variability of the Total Solar Irradiance, which is taken as a proxy for solar activity. Results. Observational evidence indicates that after six years only one single long-term process governs the degradation of the backup sun photometer in VIRGO which is operated once in a month. This degradation can be well approximated by a linear function over ten years. The analysis of the residuals from the linear trend yield robust positive correlations of spectral irradiance at 862, 500 and 402 nm with total irradiance. In the analysis of annual averages of these data the positive correlations change into weak negative correlations, but of little statistical significance, for the 862 nm and 402 nm data. At 500 nm the annual spectral data are still positively correlated with Total Solar Irradiance. The persisting positive correlation at 500 nm is in contradiction to the SIM/SORCE results.

The solar X‐ray radiation varies more than other wavelengths during flares; thus solar X‐ray irradiance measurements are relied upon for detecting flare events as well as used to study flare parameters. There is new information about the spectral and temporal variations of flares using solar irradiance measurements from NASA's Solar Radiation and Climate Experiment (SORCE) and the Thermosphere, Ionosphere, Mesosphere, Energetics, and Dynamics (TIMED) missions. For one, the improved measurement precision for the total solar irradiance (TSI) measurements by the SORCE Total Irradiance Monitor (TIM) has enabled the first detection of flares in the TSI. These flare observations indicate a total flare energy that is about 105 times more than the X‐ray measurements in the 0.1–0.8 nm range. In addition, solar spectral irradiance instruments aboard TIMED and SORCE have observed hundreds of flare events in the 0.1 nm to 190 nm range. These solar ultraviolet measurements show that the ultraviolet irradiance changes during flares account for 50% or more of the flare variations seen in the TSI, with most of the ultraviolet contribution coming from the ultraviolet shortward of 14 nm. The remaining part of the flare energy is assumed to come from the wavelengths longward of 190 nm, typically only needing to be about 100 ppm increase for the largest flares. Another result is that the flare variations in the TSI have a strong limb darkening effect, whereby the flares near the limb indicate variations in the TSI being almost entirely from the ultraviolet shortward of 14 nm.

For more than a decade, total solar irradiance has been monitored from several satellites, namely the Nimbus-7, Solar Maximum Mission (SMM), the NASA ERBS, NOAA9 and NOAA1O, EURECA, and the Upper Atmospheric Research Satellite (UARS) (e.g. Willson and Hudson, 1991; Hoyt et al., 1992; Mecherikunnel et al., 1988; Romero et al., 1994). These observations have revealed variations in total irradiance ranging from minutes to the 11-year solar cycle (Figure 1, from Frohlich 1994). The very small, rapid irradiance fluctuations are due to solar oscillations (Woodard and Hudson, 1983; Frohlich, 1992). The short-term variations (from days to months) are directly related to the evolution of active regions via the combined effect of dark sunspots and bright faculae (Chapman, 1987). The most important discovery of irradiance observations is the 0.1% peak-to-peak variation in total solar irradiance over the solax cycle (Willson and Hudson, 1991). This solar-cycle-related variation of total irradiance is attributed to the changing emission of bright magnetic elements, including faculae and the magnetic network (Foukal and Lean 1988). This solar cycle variability may also be related to changes in the photospheric temperature; however it is not clear as yet whether this change can be linked to the bright network component (Kuhn et al., 1988).

Abstract. The lack of long and reliable time series of solar spectral irradiance (SSI) measurements makes an accurate quantification of solar contributions to recent climate change difficult. Whereas earlier SSI observations and models provided a qualitatively consistent picture of the SSI variability, recent measurements by the SORCE (SOlar Radiation and Climate Experiment) satellite suggest a significantly stronger variability in the ultraviolet (UV) spectral range and changes in the visible and near-infrared (NIR) bands in anti-phase with the solar cycle. A number of recent chemistry-climate model (CCM) simulations have shown that this might have significant implications on the Earth's atmosphere. Motivated by these results, we summarize here our current knowledge of SSI variability and its impact on Earth's climate. We present a detailed overview of existing SSI measurements and provide thorough comparison of models available to date. SSI changes influence the Earth's atmosphere, both directly, through changes in shortwave (SW) heating and therefore, temperature and ozone distributions in the stratosphere, and indirectly, through dynamical feedbacks. We investigate these direct and indirect effects using several state-of-the art CCM simulations forced with measured and modelled SSI changes. A unique asset of this study is the use of a common comprehensive approach for an issue that is usually addressed separately by different communities. We show that the SORCE measurements are difficult to reconcile with earlier observations and with SSI models. Of the five SSI models discussed here, specifically NRLSSI (Naval Research Laboratory Solar Spectral Irradiance), SATIRE-S (Spectral And Total Irradiance REconstructions for the Satellite era), COSI (COde for Solar Irradiance), SRPM (Solar Radiation Physical Modelling), and OAR (Osservatorio Astronomico di Roma), only one shows a behaviour of the UV and visible irradiance qualitatively resembling that of the recent SORCE measurements. However, the integral of the SSI computed with this model over the entire spectral range does not reproduce the measured cyclical changes of the total solar irradiance, which is an essential requisite for realistic evaluations of solar effects on the Earth's climate in CCMs. We show that within the range provided by the recent SSI observations and semi-empirical models discussed here, the NRLSSI model and SORCE observations represent the lower and upper limits in the magnitude of the SSI solar cycle variation. The results of the CCM simulations, forced with the SSI solar cycle variations estimated from the NRLSSI model and from SORCE measurements, show that the direct solar response in the stratosphere is larger for the SORCE than for the NRLSSI data. Correspondingly, larger UV forcing also leads to a larger surface response. Finally, we discuss the reliability of the available data and we propose additional coordinated work, first to build composite SSI data sets out of scattered observations and to refine current SSI models, and second, to run coordinated CCM experiments.

The NOAA‐9 Solar Backscatter Ultraviolet Model 2 (SBUV/2) instrument is one of a series of instruments providing daily solar spectral irradiance measurements in the middle and near ultraviolet since 1978. The SBUV/2 instruments are primarily designed to measure the stratospheric profile and total column amount of ozone, using the directional albedo as the input to the ozone retrieval algorithm. Almost all optical components are common to both radiance and irradiance measurements, whose ratio forms the directional albedo, so that most response changes cancel out. As a result, the SBUV/2 instrument does not require onboard monitoring of time‐dependent sensitivity changes for production of ozone data. We use vicarious comparisons with coincident Shuttle SBUV (SSBUV) solar irradiance measurements during 1989–1996, combined with observed calibration drift during the solar activity minimum in 1985–1986, to determine the long‐term instrument characterization for NOAA‐9 SBUV/2. This approach allows us to derive more accurate solar spectral irradiances for the period from March 1985 to May 1997, spanning two solar cycle minima with a single instrument. The NOAA‐9 irradiance data show an amplitude of approximately 9.3% at 200–205 nm for solar cycle 22. This is consistent with the result of ΔF200–205 = 8.3% for cycle 21 from Nimbus‐7 SBUV and ΔF200–205 = 10% for cycle 23 from UARS SUSIM. NOAA‐9 data at 245–250 nm show a solar cycle amplitude of ∼5.7%. The observed irradiance change at 200–205 nm between the minima of solar cycles 21 and 22 is not consistent with the UV irradiance change expected from the total solar irradiance trend suggested by Willson and Mordvinov [2003]. NOAA‐9 SBUV/2 data can be combined with data from other instruments to create a 25‐year record of solar UV irradiance.

Measurement of solar spectral irradiance is required in an increasingly wide variety of technical applications, such as atmospheric studies, health, and solar energy, among others. The solar spectral irradiance at ground level has a strong dependence on many atmospheric parameters. In addition, spectroradiometer optics and detectors have high sensitivity. Because of this, it is necessary to compare with a reference instrumentation or light source to verify the quality of measurements. A simple and realistic test for validating solar spectral irradiance measurements is presented in this study. This methodology is applicable for a specific spectral range inside the broadband range from 280 to 4000 nm under cloudless sky conditions. The method compares solar spectral irradiance measurements with both predictions of clear-sky solar spectral irradiance and measurements of broadband instruments such as pyrheliometers. For the spectral estimation, a free atmospheric transmittance simulation code with the air mass calculation as the mean parameter was used. The spectral direct normal irradiance (Gbλ) measurements of two different spectroradiometers were tested at Plataforma Solar de Almería, Spain. The results are presented in this article. Although only Gbλ measurements were considered in this study, the same methodology can be applied to the other solar irradiance components.

Attempt to look into the nature of solar activity and variability have increased importance in recent days because of their terrestrial relationships. In the present work we have attempted to compare the solar activity events during first six years (2008–2013) of the ongoing solar cycle 24 with first six years (1996–2001) of solar cycle 23. To that end, we have considered sunspot numbers, F10.7 cm solar flux, halo CMEs and geomagnetic storms as comparison parameters. Sunspot number during the year 2008–2013 varied from 0 to 96.7 while during the year 1996 to 2001 it was observed from 0.9 to 170.1. Solar radio flux (F10.7 cm index) varied from 65 to 190 during the years 2008–2013 while it was observed from 65 to 283 during the years 1996–2001. 197 cases of halo CMEs (width=360∘) in solar cycle 23 (1996–2001) and 177 cases of halo CMEs (width=360∘) in solar cycle 24 (2008–2013) are investigated. 287 and 104 geomagnetic storm cases (Dst varies between −50 and −350 nT) are analysed during the half period of solar cycle 23 and 24 respectively. Comparative results indicate that solar cycle 23 was more pronounced in comparison of solar cycle 24.

The Solar Radiation and Climate Experiment (SORCE) was a NASA mission that operated from 2003 to 2020 to provide key climate-monitoring measurements of total solar irradiance (TSI) and solar spectral irradiance (SSI). This 17-year mission made TSI and SSI observations during the declining phase of Solar Cycle 23, during all of Solar Cycle 24, and at the very beginning of Solar Cycle 25. The SORCE solar-variability results include comparisons of the solar irradiance observed during Solar Cycles 23 and 24 and the solar-cycle minima levels in 2008 – 2009 and 2019 – 2020. The differences between these two minima are very small and are not significantly above the estimate of instrument stability over the 11-year period. There are differences in the SSI variability for Solar Cycles 23 and 24, notably for wavelengths longer than 250 nm. Consistency comparisons with SORCE variability on solar-rotation timescales and solar-irradiance model predictions suggest that the SORCE Solar Cycle 24 SSI results might be more accurate than the SORCE Solar Cycle 23 results. The SORCE solar-variability results have been useful for many Sun–climate studies and will continue to serve as a reference for comparisons with future missions studying solar variability.

Radiation from the Sun is the dominant source of energy input to the Earth's climate system; even small variations in solar irradiance can produce noticeable climate changes on global and regional scales. Determining how much of the observed global change can be attributed to variations in the Sun's output and how much can be attributed to human or other influences requires an accurate record of solar irradiance. Measurements of solar irradiance made by the Total Irradiance Monitor (TIM) on NASA's Solar Radiation and Climate Experiment (SORCE) satellite give a value of total solar irradiance that is significantly lower than previously accepted values. Kopp and Lean show that this new, lower value is more accurate than measurements made using older instruments. They used laboratory studies and satellite calibrations to diagnose and quantify error sources on TIM and other space‐based solar radiometers and found that earlier radiometers measured higher values of solar irradiance because they included scattered instrument light in their signals, while the different optical design of the TIM radiometer reduces this spurious signal and acquires more accurate measurements. They also show that TIM's high stability gives improved agreement with models estimating solar variability, concluding that this new instrument provides the most accurate value of solar irradiance and helps improve estimates of the Sun's influence on climate. (Geophysical Research Letters, doi:10.1029/2010GL045777, 2011)

UV solar irradiance strongly affects the chemical and physical properties of the Earth’s atmosphere. UV radiation is also a fundamental input for modeling the habitable zones of stars and the atmospheres of their exoplanets. Unfortunately, measurements of solar irradiance are affected by instrumental degradation and are not available before 1978. For other stars, the situation is worsened by interstellar medium absorption. Therefore, estimates of solar and stellar UV radiation and variability often rely on modeling. Recently, Lovric et al. used Solar Radiation and Climate Experiment (SORCE)/Stellar Irradiance Comparison Experiment (SOLSTICE) data to investigate the variability of a color index that is a descriptor of the UV radiation that modulates the photochemistry of planets’ atmospheres. After correcting the SOLSTICE data for residual instrumental effects, the authors found the color index to be strongly correlated with the Mg ii index, a solar activity proxy. In this paper, we employ an irradiance reconstruction to synthetize the UV color and Mg ii index with the purpose of investigating the physical mechanisms that produce the strong correlation between the color index and the solar activity. Our reconstruction, which extends back to 1989, reproduces very well the observations, and shows that the two indices can be described by the same linear relation for almost three cycles, thus ruling out an overcompensation of SORCE/SOLTICE data in the analysis of Lovric et al. We suggest that the strong correlation between the indices results from the UV radiation analyzed originating in the chromosphere, where atmosphere models of quiet and magnetic features present similar temperature and density gradients.

Total solar and UV irradiances have been measured from various space platforms for more than two decades. These measurements established conclusively that solar irradiance changes on a wide range of time scales: from minutes to the 11 years solar cycle. The first results on the spectral distribution of total irradiance variations have been provided by the SunPhotometers on the SOHO/VIRGO experiment at 402, 500, and 862 nm, showing that spectral irradiance at these particular wavelengths changes in a fashion similar to total irradiance with amplitudes being higher at the shorter wavelengths.Although considerable information exist on irradiance variations, their physical origin is not yet fully understood. Current empirical models assume that most of the irradiance variations can be explained by the effect of surface magnetic activity features, and it is assumed that there is a linear relation between solar indices and irradiance changes. In contrast, current results show that both UV and total irradiances were higher at the maximum of solar cycle 23 than magnetic indices, such as the sunspot number and the full disk magnetic field strength. In addition, there is a growing evidence that global effects, like temperature changes, may also contribute to irradiance variations. In this paper we give an overview of the current results on total and spectral irradiance variations, their relation to magnetic activity using measurements from the National Solar Observatory an Kitt Peak and SOHO-MDI. Climate implications of irradiance variations are also discussed.KeywordsSolar CycleSolar IrradianceTotal Solar IrradianceSpectral IrradianceSingular Spectrum AnalysisThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.