In photovoltaic power ratings, a single solar spectrum, AM1.5, is the de facto standard for record laboratory efficiencies, commercial module specifications, and performance ratios of solar power plants. More detailed energy analysis that accounts for local spectral irradiance, along with temperature and broadband irradianc e, reduces forecast errors to expand the grid utility of solar energy. Here, ground -level measurements of spectral irradiance collected worldwide have been pooled to provide a sampling of geographic, seasonal, and diurnal variation. Applied to nine solar c ell types, the resulting divergence in solar cell efficiencies illustrates that a single spectrum is insufficient for comparisons of cells with different spectral responses. Cells with two or more junctions tend to have efficiencies below that under the st andard spectrum. Silicon exhibits the least spectral sensitivity: relative weekly site variation ranges from 1% in Lima, Peru to 14% in Edmonton, Canada, with a mean of 4%. Introduction To anyone who has ever watched a sunset or gazed upon a deep blue sky, the idea of variation in the spectrum of sunlight is hardly surprising. Yet consideration of spectrum variation has often play ed a minor role in both solar photovoltaic development and operations. When comparing solar photovoltaic (PV) efficiencies, asses sing output of solar power plants, and evaluating warranty claims, the power rating is generally used, for which a single standard spectrum (AM1.5, Appendix A) is the reference. Reliance on the single standard spectrum has its advantages, including providi ng continuity over the years and consistency across academia and industry, but neglect of spectrum variation adds to uncertainty in predictions of solar energy generation1–10. For variable sources such as solar (and wind) to displace conventional sources and become the backbone of future energy supply, electrical grids must be buffered against impacts from their limited availability and potential underperformance11. Reduction in forecast errors12 complements other tools , such as battery storage , which brings increasing marginal cost s as variable sources become a larger fraction of the supply13. Three factors determine the efficiency of a solar photovoltaic cell: temperature, irradiance , and the spectrum of irradiance (the spectral irradiance )14. Historically, application of the first two factors has been distinct from that of the third. Temperature and (broadband ) irradiance are routinely measured at solar installations and applied to forecas ts of the grid availability of solar energy . Measurements of the third factor , spectral irradiance , are comparatively sparse , conducted sporadically and at limited locations worldwide . The power output of solar modules is rated using the single spectrum and residential, commercial, and utility -scale PV systems are designed accordingly. For example, t o determine if a solar power plant is performing to its contractual obligations , a key metric is the “performance ratio”15. This is calcula ted as the energy generated in a given time period divided by the broadband irradiance and the efficiency under the AM1.5 spectrum : variation in the spectrum is neglected. The AM1.5 reference spectrum was standardized in 198216 as an analog to the single spectrum used in space (AM0). Given the variability in terrestrial atmospheric conditions, concern s with over -reliance on it followed soon after3,9,17,18. Yet the information gap between spectral irradiance and temperature & broadband irradiance called out specifically in [3] has persisted. Growth of the solar industry has led to temperature and broadband irradiance variation requirements being codified into internat ional power ratings19, data sheets20, and warranty terms21. In application s where use of the power rating (rather than more detailed energy analys es) 2 predominates, the impact of spectral irradiance variation has often been treated as a secondary concern or neglected22. The current international standard for solar module power rating defines four temperatures and four broadband irradiances, but only one spectrum23. The persistence of th e gap has been due partly to the perceived expense in obtaining spectral irradiance measurements , as well as a legacy assumption that the impact on silicon, solar energy’s workhorse material, is negligible. Increasing deployments of silicon PV lead to relatively small factors growin g in absolute terms, so it may be worth revisit ing these assumptions. (a) (b) (c) Figure 1. Spectral irradiance measured at the sites in Table 1: (a) global horizontal irradiance (GHI) (b) global tilt irradiance (GT I) and global one -axis irradiance (G1I); (c) global normal irradiance (GNI) and direct normal irradiance (DNI). Each line is a weekly sum of spectral irradiance sampled at 10 -minute intervals (except for Chajnantor, CHL: 60 -minute intervals). The spectrum for global horizontal irradiance (GHI), measured with senso rs pointed upward at the dome of the sky is, not surprisingly, the “bluest”. As the degree of solar tracking increases, the sensors begin to follow the sun to the horizons; the irradiance passes through a larger air mass and shifts away from “blue” to the “red” and infrared; visible wavelengths (400 -700 nm) are diminished. The spectra are normalized at either 880 nm or 1050 nm; AM1.5 values are substituted outside the measurement ranges. Standard spectra (AM024, AM1.5G, AM1.5D25) are shown for reference . The solar market approaches €200 billion annually26 and impending rapid climate change make s every kWh of renewable energy more precious. Variation of mere t enths of a percent already imply billions of euros gained or lost over the decades of operation - before any cost of carbon is included27. For a single 100-MW PV plant that displaces fossil -fuel generation at 1500 kWh/kW , any 2% variation in generation is equivalent to about €150,000 in annual revenue and ~1,500 tons of avoided CO 2, or nine one-way flights between New York and Paris per year28. As of 202 1, the world has installed PV capacity equivalent to eight thousand such plants29. Public and p rivate sectors are pivot ing to decarbonization of the global economy, so maximizing the extent to which solar energy support s the future grid depends on minimizing uncertaint y in the availability of solar assets. If solar is to provide 22% of the world’s electricity by 205030, 2% of the implied 9,000 TWh /yr would exceed the present annual generation of Poland31. (a) (b) (c) Figure 2. Solar cell quantum efficiencies digitized at 5 -nm intervals from the Solar Cell Efficiency Tables: (a) silicon (Si), cadmium telluride (CdTe), and copper indium gallium selenide (CIGS); (b) perovskite (PVSK), perovskite -CIGS tandem (PVSK -CIGS), perovskit e-silicon tandem (PVSK -Si), and a three -junction III -V; (c) four -, and six -junction III -Vs. Meanwhile, s uccess ive incre ments in the efficiency of silicon PV over the last decade have depleted the reserve of remaining options . As recently as 201 732, more than half of worldwide PV production was still a traditional aluminum/back -surface -field (Al -BSF) design on multicrystalline wafers and the industry could look forward to continuing efficiency increases from the transition to PERC designs ( ~9% relative) on monocrystalline silicon 3 (~5% relative ). For fu rther improvements, t he industry must now turn to smaller increments from more capital - intensive options such as tunnel -oxide contacts on n -type wafers ( ~3% relative) or heterojunction cells ( ~4% relative). With the efficiency of silicon beginning to plateau, t he international PV industry roadmap now anticipates relying on tandem solar cells for some future growth as early as 202333. The extent to which tandem designs c ould deliver not just hi gher rated power, but also lower cost of energy in operat ion, depends in part on their sensitivity to spectrum variation. As designs using novel materials have proliferated34, evidence for a need to incorporate spectr um variation4–6,10,35 –66 in the power ratings has grown harder to ignore . The work presented here dovetails with a larger effort within the PV sector to expand beyond instantaneous power ratings (kW) to more fully consider energy generation over time (kWh)67. In 2018, the internati onal standard for power rating was complemented with energy rating s for six climatic zones68,69 that give irradiance values in twenty - eight wavelength bands (Appendix B). However, given the limited measured spectral irradiance data available, previous studies on its impact have had to rely on relatively small data sets, or synthetic spectra5,28,48,70. Measured spectral irradiance A coalition of solar r esearchers ha s pooled their data from sites in Africa, Asia, Australia, Europe , and the America s that span a range of latitudes, elevations, atmospheric conditions, and orientations (Figure 1). Spectral irradiance is typically measured by spectroradiometers, instruments which employ optical diffraction to measure irradiance across a series of narrow wavelength ranges . A given d iffract ive optic and its detector can sample only a limited wavelength range, so spectroradiometers for PV applications often measure total wavelength ranges of around 300-1100 nm or 900-1700 nm71 (Table 1). A pair of spectroradiometers spanning 300-1700 nm measures about 97% of the power in the extraterrestrial (AM0) spectrum24. An alt ernative is to use a “solar spectral irradiance meter ” that takes measure d irradiance at a relatively small number of specific wavelengths as inputs to a model to reconstruct the full spectrum72,73. This is the instrument used for locations in Table 1 where the wavelength range is described as 280 -4000 n m. Figure 3. Efficiencies under measured spectral irradiance, compared against the values under Standard Test Conditions (symbols with black border). Symbols are arranged in order of increasing sun tracking: GHI, GTI, G1I, GNI, DNI. Lateral positions are adjusted to improve visibility. Each data point is a weekly sum from spectral irradiance sampled at 10 -minute intervals (except for Chajnantor , CHL: 60 -minute intervals). Best Research -Cell Efficiency chart courtesy of NREL. In parallel with direct measurement of ground -level spectral irradiance is the formulation of synthetic spectra. Synthetic spectra are formed by applying physical models using inputs of atmospheric p roperties that, in turn, are derived from radiometry over select wavelength ranges (Appendix G)8,74 –78. Limited ground -level measuremen t of the atmospheric parameters is augmented with satellite telemetry to produce synthetic spectra over extended geographic areas 77,78. While synthetic spectra have demonstrated the ability to predict 4 solar energy generation under a narrow set of field conditions79–81, ground -level measurements such as those provided here are needed to fine -tune the models and verify that the synthetic s pectra remain accurate across the broad er range of atmospheric conditions under which solar energy is now generated worldwide . Synthetic spectra have previously been applied to analysis of the nine cell types in [28]. A pattern of offsets between results from synthetic77,78 and measured spectral irradiance is in Appendix G. Spectr al irradiance sensors are mounted in orientations with varying degrees of solar tracking (Figure 1). Measurement of spectral irradiance has applications beyond solar PV, including monitoring of pollution82 and the radiative forcing arising from water vapor and other greenhouse gases83,84. As such, the most common orientation is horizontal, with the sensor exposed to the “global” spectrum arriving from the full hemisphere of the sky (global horizontal irradiance, “GHI”) . For solar energy applications, preferred orientations are those that match the “plane of array” in which solar modules are mounted. The most common m ounting orientation for PV modules is either a fixed tilt (global tilted irradiance, or “GTI”) or one -axis tracking (global one -axis irradiance, “G1I”). Two-axis tracking keeps the surface perpendicular (normal) to the sun’s rays and global normal irradiance (“GNI”) is obtained . Finally, to capture irradiance coming only directly from the vicinity of the solar disc, two -axis tracking is combined with collimators that exclude scattered sunlight for measurement of direct normal irradiance ( “DNI”). Figure 4. Relative efficiency as a function of cell type and number of junctions: silicon (Si), cadmium telluride (CdTe), CIGS, perovskite (PVSK), perovskite -CIGS tandem (PVSK -CIGS), perovskite -silicon tandem (PVSK -Si), and thr ee-, four, and six -junction III -V multijunctions. For each cell type, data is grouped by orientation: from left to right, GHI, GTI, G1I, GNI, DNI. Lines indica te the relative efficiency of each type under the three standard solar spectra. AM0 is the standa rd spectrum outside Earth’s atmosphere24. Each data point is a weekly sum from spectral irradiance sampled at 10 -minute intervals (except for Chajnantor, CHL: 60 -minute intervals). Analysis The influence of spectral variation on n ine solar cell types is evaluated using their current -best characteristics (Appendix E), as given in the Solar Cell Efficiency Tables85 and NREL’s Best Research -Cell Efficiency Chart34. Four single -junction cells (silicon86, cadmium telluride87, CIGS85, and perovskite88) are considered , along with five two - terminal multijunctions (perovskite -CIGS89, perovskite -silicon89, and three -, four - and six - junction III -Vs85,87,90. Multijunctions convert solar energy using two or more semiconductor junctions. Stacking the junctions increases the cell voltage while splitting the conversion of sunlight into current from each junction. The two - terminal configuration implies a series connection, so the overall device current is limited by whichever junction is producing the least current. As a result, t wo-terminal multijunctions have more sensitiv ity to spectrum variation. In a solar module sunlight must pass through solar glass and encapsulants before reaching the solar cells, but the high transmission through these materials91 makes the bare -cell spectral responses of Figure 2 useful proxies for comm ercial PV modules. 5 Efficiencies from the measured spectra are shown in Figure 3, against a backdrop of the confirmed values under AM1.5G (AM1.5D, for III-V multijunctions). The corresponding r elative efficienc ies are shown in Figure 4. Cells with a single junction (Si, CdTe, CIGS, and PVSK) demonstrate efficiencies c lustered about their efficiency under the AM1.5 standard spectrum. As the number of junctions increases, the degree of spectrum sensitivity increases , and the relative efficiencies in Figure 4 drop. F or cells with two or more junctions, the performance under AM1.5, rather than being a n average value (as with the single -junction cells) , tends to be more of an upper bound of the performance seen under measured spectra. Energy generation will therefore be lower than for a single -junction cell with the same power rating. Series -connected tandems and multijunctions are tuned (by varying the t hickness and/or composition of the individual junctions) to optimize current matching of each junction under a given target spectrum. Cells engineered to perform best under AM1.5 , therefore, may have been de-tuned from optimum performance in operating cond itions . Rating solar efficiency under a single spectrum is a bit like designing a vehicle solely for highway (not city) driving92: it is unlikely to lead to all-around high er performance. Re -designing solar cells for the range of spectral conditions in o peration93,94 will require different thicknesses and (where possible) material compositions. The practice of tuning for operation under AM1.5, alone, may therefore have an adverse effect on the development of these serially -connected tandems and multijun ctions. As shown in Appendix L, Lima and Singapore are coastal sites with particularly high (and stable) levels of humidity where the efficiencies of the more spectr um-sensitive designs converge on that of silicon. Consistent with the existing power ratings that contain several temperature and broadband irradiance s19, power rating under more than one spectrum would enable interpolation and extrapolation to other atmospheric conditions , providing some of the benefits of the more complex energy analyses . Figure 4 suggests that AM0, the standard sp ectrum in space applications24, is one example that could be re -purposed to bracket much of the terrestrial spectrum variation28. Fortunately, characterization under two spectra does not require two measurements: if the spectral response of a cell is known, efficiency under a single condition can be translated to that under a second condition using a calculation known as “spectral mismatch correction95. Figure 5. Relative efficiency vs. location and orientation for cells with one or two junctions. Data is sorted by increasing median efficiency of silicon. Location names are approximate. Each data point is a weekly sum from spectral irradiance sampled at 10 -minute intervals (except for Chajnantor: 60 -minute intervals). Cells with a wider spectral response (Figure 2) are more tolerant to the increased spectral variation that ari ses from sun tracking. In Appendix H, increasing the sun tracking is seen to benefit single -junction cells with a wider spectral response (silicon, CIGS) , but is a detriment to cells with a narrower spectral response ( cadmium 6 telluride , perovskite s), confirming predictions from synthetic spectra28,48,49. For cells with more than one junction, the correlation is more complex , as losses due to current mismatch between the junctions tr ade against the wide r spectral response of the overall stack. The fuel transportation costs for solar energy are tough to beat. Inherent v ariability in the supply , however, brings challeng es; concerns about its availability limit the grid penetration of so lar PV96. Of prime importance to solar array owners, project developer s, power plant managers, and grid operator s is the real -time variation for their specif ic location and mounting orientation. Site-specific relative efficiency values are given in Figure 5, ranked in order of increasing median efficiency for silicon (absolute efficiencies , Appendix F). The performance of CIGS , with a similar spectral response, tracks the increase with silicon ; all other efficiencies diverge . The AM1.5 spectrum was conceived for use with silicon cells in North America, so it is a tribute to its accuracy that most sites in North American exhibit the least offset from the standard. As might be expected, sites at highe r latitudes (with the largest excursions in sun angle and air mass ) see the largest variation, but there are notable exceptions (Appendix I). Similar variation is also seen in Hangzhou (30° latitude) ; this is likely driven by l ight scattering effects of aerosols , as it is a city of over ten million inhabitants that currently ranks in the lower tier for air quality in China97. Spectral variation from urban a erosols compound s attenuation of the broadband irradiance and therefore shifts the calculation of where best to site PV generation to minimize transmission losses98. Both l ong-term oscillations in local aerosol levels (global dimming & brightening99) and accelerating climate change diminish the predictive power of forecasts that depend on archived spectral irradiance values . Figure 6. Sample seasonal and diurnal efficiency variations for cells with one or two junctions. Seasonal changes are delineated using one month of data for March, June, September, and December. Where available, data from additional years is semi -transparent in the background. Each data point is a sum of data at a 10 -minute interval for a month . Results for al l locations are shown in Appendix K. (Refer to Figure 5 for a legend.) Sample s easonal and d iurnal variation s are shown in Figure 6. Figures for the remaining sites , and for annual variation , are included in Appendix K. For power plant and grid operator s, unanticipated fluctuations in generation , be it losses or gains, can be costly. To meet contractual obligations and grid demand , forecasts for the month, day, and hour ahead are often stipulated ; gaps between the expected and actual output result in power surpluses or deficits that can disrupt grid stability . Operat ors of solar p ower plants therefore employ meteorological (“met”) stations to monitor temperature, wind, and plane -of-array (broadband) irradiance. In larger plants, there are often multiple broadband irradiance instrument s100. In the absence of spectral irradiance monito ring, however, the variation of Figure 6 is necessarily conf ounded with other uncertainties : module and inverter degradation, soiling, cable corrosion, etc.101 Unexplained losses of a few percent can trigger costly contract disputes or warranty claims. Conversely, un planned surpluses lead to transmission line congestion and curtail ment of generation , due to cabling and power electronics that are undersized, or grid operator s unprepared to receive the surge of excess power . Implementing site monitoring procedures for spectral 7 irradiance in line with those established for temperature and broadband irradiance will curb these losses and allow for larger allocations of solar energy assets to displace conventional sources . So long as the cost of electricity storage exceeds that of renewable generation102 and efforts to “electrify everything”103 to blunt impending climate disruptions gain traction , solar power will be increasingly most valuable at the very times104 it is least available . An (extreme ) example is the winter storm that hit the Texas, USA grid in February 2021. Temperatures below freezing caused loss of generation from (insufficiently weatherized) sources that depend on moving parts and the flow of liquids : natural gas, coal, nuclear, and wind. Winter is not usually considered solar energy’s best season, but a s other generators dropped offline and electricity prices spiked by factors of more than five105, solar generation was the only source to deliver increased output106. It can be hard to predict exactly when solar energy will be most valuable , so efforts to both quantify and minimize daily and seasonal spectral variation losses are needed to maximize the projected availability of solar energy in grid operations. As solar assets come to take up larger portions of grid capacity , generation during the “shoulder” periods seen in seasonal and diurnal output (Appendix K & L) will increase in value. Silicon produces the most stable output under seasonal spectrum variation: its weekly site-level variation ranges from 1% to 14%, with a me an of 4% (Table 1 & Appendix J). Though this is the least of the cells evaluated, the me an remains equivalent to more than 10° C of temperature variation , or five years of module degradation107. Where AM1.5 substitut ions were made due to limited measurement range (Appendix D), these are underestimates. Conclusion The variation in measured spectral irradiance data reveals patterns in the impact on solar photovoltaic performance worldwide. The magnitude of the cell efficiency variation indicates that the convention of rating PV power under a single spectrum is insufficient for comparison of different cell types. Spectrum sensitivity increase s with the number of junctions and efficiency correspondingly declines. For tandems and multijunctions, efficiency under the standard spectrum is closer to a maximum than a representative value: the practice of designing for AM1.5 may be reducing the poten tial for energy generation in operation. Having two spectra in the power rating (AM1.5 and AM0, for example) would serve to bracket the operating performance range, better inform solar cell development, and accelerate deployment. Silicon is less sensitive than other candidate materials, but the declining options for further improvements in its efficiency, combined with the sheer scale of future PV deployments, enhance the value of spectrum variation in both relative and absolute terms. Solar energy can prov ide a larger percentage of grid electricity if f orecast errors are reduced via expanded monitoring, in line with that of temperature and broadband irradiance. 8 Table 1. Relative efficiency variation using weekly sums of spectral irradiance at the sensor locations (source shown in brackets) . The orientations are global horizontal irradiance (GHI), global tilted irradiance (GTI), global, 1 -axis tracking irradiance (G1I), global normal irradiance (GNI), and direct normal irradiance (DNI). Spectral irradiance is sampled at 10 -minute intervals (except at Chajnantor: 60 - minute) and summed over each week for each data point. Location names are approximate. The Berlin site has sensors tilted at local latitude (35°) and vertically (90°). Numbers in brackets indicate the contributing authors; the source for superscript numbers is given in the