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Register a new userReconstruction of solar irradiance using the Group sunspot number
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We present a reconstruction of total solar irradiance since 1610 to the present based on variations of the surface distribution of the solar magnetic field. The latter is calculated from the historical record of the Group sunspot number using a simple but consistent physical model. Our model successfully reproduces three independent data sets: total solar irradiance measurements available since 1978, total photospheric magnetic flux from 1974 and the open magnetic flux since 1868 (as empirically reconstructed from the geomagnetic aa-index). The model predicts an increase in the total solar irradiance since the Maunder Minimum of about 1.3 rm{Wm$^{-2}$}.
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One of the important open questions in solar irradiance studies is whether long-term variability (i.e. on timescales of years and beyond) can be reconstructed by means of models that describe short-term variability (i.e. days) using solar proxies as inputs. Preminger and Walton (2005, GRL, 32, 14109) showed that the relationship between spectral solar irradiance and proxies of magnetic-flux emergence, such as the daily sunspot area, can be described in the framework of linear system theory by means of the impulse response. We significantly refine that empirical model by removing spurious solar-rotational effects and by including an additional term that captures long-term variations. Our results show that long-term variability cannot be reconstructed from the short-term response of the spectral irradiance, which cautions the extension of solar proxy models to these timescales. In addition, we find that the solar response is nonlinear in such a way that cannot be corrected simply by applying a rescaling to sunspot area.
Reliable historical records of total solar irradiance (TSI) are needed for climate change attribution and research to assess the extent to which long-term variations in the Suns radiant energy incident on the Earth may exacerbate (or mitigate) the more dominant warming in recent centuries due to increasing concentrations of greenhouse gases. We investigate potential impacts of the new Sunspot Index and Long-term Solar Observations (SILSO) sunspot-number time series on model reconstructions of TSI. In contemporary TSI records, variations on time scales longer than about a day are dominated by the opposing effects of sunspot darkening and facular brightening. These two surface magnetic features, retrieved either from direct observations or from solar activity proxies, are combined in TSI models to reproduce the current TSI observational record. Indices that manifest solar-surface magnetic activity, in particular the sunspot-number record, then enable the reconstruction of historical TSI. Revisions to the sunspot-number record therefore affect the magnitude and temporal structure of TSI variability on centennial time scales according to the model reconstruction methodologies. We estimate the effects of the new SILSO record on two widely used TSI reconstructions, namely the NRLTSI2 and the SATIRE models. We find that the SILSO record has little effect on either model after 1885 but leads to greater amplitude solar-cycle fluctuations in TSI reconstructions prior, suggesting many 18th and 19th century cycles could be similar in amplitude to those of the current Modern Maximum. TSI records based on the revised sunspot data do not suggest a significant change in Maunder Minimum TSI values, and comparing that era to the present we find only very small potential differences in estimated solar contributions to climate with this new sunspot record.
We use 5 test data series to quantify putative discontinuities around 1946 in 5 annual-mean sunspot number or group number sequences. The series tested are: the original and n
The Earths primary source of energy is the radiant energy generated by the Sun, which is referred to as solar irradiance, or total solar irradiance (TSI) when all of the radiation is measured. A minor change in the solar irradiance can have a significant impact on the Earths climate and atmosphere. As a result, studying and measuring solar irradiance is crucial in understanding climate changes and solar variability. Several methods have been developed to reconstruct total solar irradiance for long and short periods of time; however, they are physics-based and rely on the availability of data, which does not go beyond 9,000 years. In this paper we propose a new method, called TSInet, to reconstruct total solar irradiance by deep learning for short and long periods of time that span beyond the physical models data availability. On the data that are available, our method agrees well with the state-of-the-art physics-based reconstruction models. To our knowledge, this is the first time that deep learning has been used to reconstruct total solar irradiance for more than 9,000 years.
Ground-based whole sky cameras are extensively used for localized monitoring of clouds nowadays. They capture hemispherical images of the sky at regular intervals using a fisheye lens. In this paper, we propose a framework for estimating solar irradiance from pictures taken by those imagers. Unlike pyranometers, such sky images contain information about cloud coverage and can be used to derive cloud movement. An accurate estimation of solar irradiance using solely those images is thus a first step towards short-term forecasting of solar energy generation based on cloud movement. We derive and validate our model using pyranometers co-located with our whole sky imagers. We achieve a better performance in estimating solar irradiance and in particular its short-term variations as compared to other related methods using ground-based observations.
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