Plain Language Summary
The Sun’s irradiance spectrum is used in many applications, such as constraining the solar forcing in climate models and converting measured satellite radiance to reflectance. A growing body of literature has provided evidence that the currently available solar reference spectra differ by more than their reported uncertainties. Such differences lead to biased results when different reference spectra are adopted in the aforementioned applications. This motivates our work to provide a new high-resolution solar reference spectrum at higher accuracy than any previously reported. Our ability to produce such a dataset is due to the state-of-the-art measurements of the Sun’s irradiance spectrum made since March 2018 by the next-generation Spectral Irradiance Monitor (SIM) instrument on the Total and Spectral Solar Irradiance Sensor-1 (TSIS-1) satellite mission and the Compact SIM (CSIM) technology demonstration mission. The TSIS-1 SIM and CSIM have order-of-magnitude reduction in uncertainty relative to predecessor instruments primarily because of a first-of-its-kind spectral radiometric calibration facility capable of characterizing the instruments to higher fidelity. We develop this new, high-resolution, solar irradiance reference spectrum by adjusting high spectral resolution solar line data to the irradiance scale of the more accurate, but lower spectral resolution, TSIS-1 SIM and CSIM observations.
1 Introduction
Reference solar irradiance spectra have broad utility in atmospheric science and climate applications. For example, the solar spectral irradiance (SSI) is used to convert measured satellite radiance to reflectance (e.g., Wielicki et al., 2013) and as the upper boundary condition in radiative transfer models used, for example, in remote sensing algorithms and renewable energy research (e.g., Berk et al., 2014; Apell & McNeill, 2019). Some instruments use solar absorption lines for wavelength calibration (e.g., Kang et al., 2020). Some instruments also use the Sun for radiometric stability monitoring, which requires a baseline solar spectrum to quantify instrumental changes against (e.g., Pan & Flynn, 2015). Instruments that monitor radiometric calibration stability relative to the moon (e.g., Werdell et al., 2019) indirectly rely on a solar reference spectrum to convert lunar radiance to reflectance using, for example, the RObotic Lunar Observatory (ROLO) model (Kieffer & Stone, 2005). Solar reference spectra also constrain solar irradiance variability models (e.g., Coddington et al. 2016), which climate models use to specify solar forcing of climate change (e.g., Kunze et al., 2020).
Various solar reference spectra exist for these applications. Some are from direct solar irradiance observations from one or more satellite instruments. These have relatively high reported accuracy and relatively low (0.1 nm or poorer) spectral resolution compared to ground-based observations and are typically specific to certain solar activity levels (e.g., Thuillier et al., 2004). Other solar reference spectra are constructed by normalizing high spectral resolution solar lines to a higher accuracy, lower resolution, spectrum (e.g., Dobber et al., 2008). Still others are created by concatenating independent datasets from different spectral regions (e.g., Gueymard, 2003). Disagreements have been identified between the available solar reference spectra and independent measurements that exceed quoted accuracies particularly at near-infrared wavelengths where 8% differences have been reported (e.g., Elsey et al., 2017).
Since March 2018, NASA’s Total and Spectral Solar Irradiance Sensor-1 (TSIS-1) Spectral Irradiance Monitor (SIM) hosted on the International Space Station (ISS) has observed SSI with lower radiometric uncertainty (< 0.3%) over the majority of the spectrum than that attained by previous instruments (Richard et al., 2020). Since 2019, independent SSI observations have also been made by the CubeSat Compact SIM (CSIM) instrument (Richard et al., 2019; Tomlin et al., 2020). CSIM observations span 200 nm to 2800 nm, thereby extending further into the infrared than the TSIS-1 SIM that spans 200 nm to 2400 nm. A mutual validation of the TSIS-1 SIM and CSIM irradiance scales was demonstrated by < 1% disagreement in concurrent observations (Stephens et al., 2020). In this work, we produce a new reference spectrum, theTSIS-1 Hybrid Solar Reference Spectrum (TSIS-1 HSRS ), by adjusting high spectral resolution solar line data to the SI-traceable irradiance scale of the TSIS-1 SIM and CSIM instruments. Such an approach is necessary because the technology does not exist to measure the Sun’s spectrum over a broad spectral range from a single instrument with both high accuracy and high (0.01 nm or finer) spectral resolution.
Figure 1 shows spectral differences of three solar irradiance reference spectra to TSIS-1 SIM of order 10% in portions of the spectrum and that cannot be explained by differences in solar activity. While ultraviolet solar cycle variability reaches 10% at 200 nm, it drops to 5% by 210 nm and reduces even further to ~1% by 300 nm with the exception of the Mg II line near 280 nm. Visible and near-infrared solar cycle variability is on the order of 0.1% or less (Ermolli et al., 2013). The ATLAS-3 spectrum (Thuillier et al., 2004), perhaps the most widely-used solar reference in Earth science applications, is a composite of observations from November, 1994 by five different instruments including the SOLar SPECtrometer (SOLSPEC). Additionally, high resolution modeled solar absorption features from Kurucz (1995) were inserted into the lower resolution observations from the visible through the near-infrared. Reported ATLAS-3 uncertainties are 2-3%. Another solar reference spectrum is the Laboratory for Atmospheric and Space Physics (LASP) Whole Heliospheric Interval (WHI) (Woods et al., 2009). The LASP WHI is a composite of observations from April, 2008 with the majority of the spectrum measured by the SOLSTICE and SIM instruments on the SORCE satellite. Observations from SORCE SIM, the predecessor to the TSIS-1 SIM, were adjusted by up to +8% for wavelengths above 1350 nm to agree with the ATLAS-3 spectrum in a recalibration that has been discussed with reference to a systematic bias (Harder et al., 2010). Therefore, the LASP WHI and ATLAS-3 reference spectra are not independent above 1350 nm. Reported LASP WHI uncertainties are 1-3% for wavelengths above 300 nm. The SOLAR-ISS version 2 reference spectrum (Meftah et al., 2020) is from a newer version of the SOLSPEC instrument (Thuillier et al., 2009) integrated on the ISS from 2008 to 2017. The SOLAR-ISS reference irradiance baseline spectrum is from April, 2008 for wavelengths spanning 165-656 nm and an average over a six year period at wavelengths above 656 nm. Revised engineering corrections, improved calibrations, and advanced thermal and degradation corrections are reported as the reason for the changes in baseline between the earlier ATLAS-3 composite spectrum and the newer SOLAR-ISS spectrum (Bolsée et al., 2017). However, particularly in the near-infrared, a thorough understanding of the offset remains under study (Thuillier et al., 2013). Similar to ATLAS-3, higher spectral resolution lines have been incorporated into SOLAR-ISS. The mean reported SOLAR-ISS uncertainty from 165 to 3000 nm is 1.26%, with uncertainties as low as 0.4-0.6% between 800 to 1700 nm and reaching, or exceeding, 2% below 400 nm and above 2200 nm. Hilbig et al. (2018) further summarize these and other solar reference spectra.
The methodology to develop the HSRS is described in Section 2 and the datasets are described in Section 3. In Section 4, we present results of our uncertainty assessment and comparison to independent datasets. Concluding statements follow in Section 5.
2 Methodology
We develop the HSRS using a modified version of the spectral ratio method. In this method, a wavelength-dependent scaling factor adjusts high spectral resolution datasets (\(\mathbf{\beta}\)) to match a lower resolution but higher accuracy spectrum (\(\mathbf{\alpha}\)). The scaling factor, Q , is the ratio of the \(\mathbf{\alpha}\) and\(\mathbf{\beta}\) datasets after first convolving both to the same spectral resolution and interpolating to a common sampling grid. The\(\mathbf{\alpha}\) and \(\mathbf{\beta}\) datasets are described in Section 3.
Typically, Q is derived after a single-step convolution (Eq. 1) of the \(\mathbf{\beta}\) dataset with the instrument line shape of the\(\mathbf{\alpha}\) dataset (\(\mathbf{\text{ILS}}_{\mathbf{\alpha}}\)) that degrades the resolution of the \(\mathbf{\beta}\) dataset (\(\mathbf{\beta}^{\mathbf{*}}\)) to match that of the\(\mathbf{\alpha}\) dataset (e.g., Kang et al., 2017; Dobber et al., 2008). Instead, we derive Q from a two-step convolution: the first step is as described by Eq. 1 and the second step degrades both\(\mathbf{\beta}^{\mathbf{*}}\) and \(\mathbf{\alpha}\) datasets to a common spectral resolution (denoted \(\mathbf{\beta}^{\mathbf{**}}\) and\(\mathbf{\alpha}^{\mathbf{**}}\), respectively) that is coarser than that of the original \(\mathbf{\alpha}\) dataset. We accomplish this with a Gaussian filter (\(\mathbb{N}\)) of specified standard deviation (\(\mathbf{\sigma}\)) (Eq. 2). The two-step convolution reduces the impacts of any uncertainty in \(\mathbf{\text{ILS}}_{\mathbf{\alpha}}\)on Q (Eq. 3), where the subscript \(\mathbf{\ddagger}\) denotes an interpolation of the \(\mathbf{\beta}^{\mathbf{**}}\) dataset to the\(\mathbf{\alpha}^{\mathbf{**}}\) sampling grid. Finally, the adjusted\(\mathbf{\beta}\) dataset (denoted by \(\mathbf{\Upsilon}\)) is computed from the product of the native \(\mathbf{\beta}\) dataset andQ , where the subscript † denotes an interpolation of Q to the native \(\mathbf{\beta}\) sampling grid (Eq. 4).