Accumulation of greenhouse gases in the atmosphere alters the spectrum of sunlight that reaches the Earth’s surface. Atmospheric parameters forecasted by ﬁve climate models from the Coupled Model Intercomparison Project were input to the radiative transfer model SMARTS to generate spectra over six decades (2018-2082). The potential geographic and temporal variation of photovoltaic and photosynthetic output was forecast for the spectral responses of one-, two- and three-junction PV cells, and three crop plants: corn, wheat, and rice. Introduction As sunlight passes through the Earth’s atmosphere, it’s spectrum modulated by the gases and larger particles it encounters before reaching the surface. As heat trapping from climate change accelerates, actions and interactions across the planet are shifting the atmospheric composition. The role of water vapor (as well as CO 2) to absorb solar radiation was demonstrated by Eunice Foote in 18561. In addition to accumulation of carbon dioxide and increasing atmospheric pressures2, the water content is rising due to enhanced air and water temperatures. The burning of fossil fuels produces both waste gases and particulates (aerosols). The transition towards cleaner fuels leads to a reduction in aerosols, which may be compounded by decreases in wind speeds3. (a) (b) Figure 1. (a) Quantum e Ưiciencies of a single -junction (1J) silicon (Si), tandem (2J) perovskite -silicon (PVSK/Si) and three -junction (3J) III -V multijunction cells4–6. The standard terrestrial solar spectrum (AM1.5G) is shown against a spectrum for which the precipitable water (PWV) has been changed from 1.42 to 0.00 [g/cm2]. (b) Spectra of quantum yield of photosynthesis for maize, wheat, and rice7 shown against the standard terrestrial solar spectrum (AM1.5G) and a spectrum for which the aerosol optical depth (AOD) at 550 nm has been changed from 0.084 to 0.15. EƯorts to both measure and model these trends, and their interactions, span decades8. The Coupled Model Intercomparison Project (CMIP) was established in 1995 to improve model accuracy and provide a knowledge base to inform international action to mitigate climate change damage. The sixth iteration (CMIP69) began in 2013 and comprises 23 models which provide the underlying data for the multi-decade forecasts in reports by the Intergovernmental Panel on Climate Change (IPCC)10. Various future scenarios (Shared Socioeconomic Pathways, SSPs) are evaluated based on the expected level of radiative forcing from additional solar radiation being retained in the atmosphere, land, and water. The scenarios are numbered sequentially by the expected forcing by 2100: scenario 1 assumes aggressive carbon reduction and low forcing of 1.9 W/m2 (SSP119); with continued expansion of fossil-fuel combustion, scenario 5 expects forcing to reach 8.5 W/m2 by 2100 (SSP585). An intermediate condition considered here is scenario 2: 4.5 W/m2 (SSP245). The e Ưects of climate change on photovoltaics due to alterations in temperature, broadband irradiance, and cloud cover, have been studied previously11–29. To isolate the impact of spectrum variation, results here are taken at 25° C and normalized to the broadband irradiances. CMIP6 models produce over a thousand parameters for forecasting changes to the geosphere. The ﬁve models selected produce the atmospheric parameters used by the radiative transfer model SMARTS to generate representative solar spectra at the Earth’s surface. The parameters of interest are the aerosol optical depth at 550 nm (AOD, Figure 3), carbon dioxide, relative humidity, ozone, precipitable water (Figure 2), surface pressure (Figure 4), surface air temperature, and surface maximum temperature. Inputs were drawn from: ESM2-1 (National Centre for Meteorological Research, “CNRM”), UKESM1-0-LL (Met O Ưice Hadley Center, “MOHC”), ES2L ( Japan Agency for Marine-Earth Science and Technology, Centre for Climate System Research / National Institute for Environmental Studies “MIROC”), GISS-E2-1-G (National Aeronautics and Space Administration, “NASA”), and GFDL-ESM4 (National Oceanic and Atmospheric Administration, “NOAA”). Data was sampled once per month. Table 1. Predicted 2020 solar cell short -circuit current relative to that under the standard spectrum (AM1.5G) at 25° C (scenario: SSP585) . Currents are normalized to 1000 W/m2 irradiance. “Summer”/ “winter” correspond to months 6-8/12-2 in the northern hemisphere and 12- 2/6-8 in the southern hemisphere. CNRM MOHC MIROC NASA NOAA Si (1J) winter summer PVSK/Si (2J) winter summer III-V (3J) winter summer For solar cells, the spectra generated by SMARTS combined with the quantum e Ưiciency (Figure 1a) of the solar cell gives the predicted short-circuit current of the cell. The spectral response of each cell was drawn from cells with the current-best laboratory e Ưiciency4–6. An analogous process for crop plants, using the relative spectra of quantum yield of photosynthesis (Figure 1b) provides a prediction of the crop growth rates that might result when nutrient and temperature conditions are su Ưicient to give growth limited by the available sunlight. Geographic variation of photovoltaic output: 2020 Using 2020 as a baseline year, the geographic distribution of photovoltaic output can be compared against the standard reference spectrum (AM1.5G). Results for the three solar cell designs are shown in Table 1. AM1.5G was established in the 1980s in the USA to represent mid-latitude (37°), ﬁxed (non-tracking) installations. It has persisted as the go-to reference standard for laboratory e Ưiciencies, commercial modules, and power plant performance ratings, even as PV has spread across diverse climates and tracking systems have become the backbone of utility- scale PV energy generation. As with temperature and broadband irradiance, evaluation under more than one condition would reduce forecast errors30,31. Approximately 760 GW of PV was operating in 202032, so (assuming 1500 kWh of generation per kW of capacity) a 1% increase/decrease equates to 11 TWh of generation gained/lost, equivalent to the annual electricity consumption of the Democratic Republic of the Congo33. (a) (b) (c) (d) Figure 2. Precipitable water under SSP245 winter (a) & summer (b) and SSP585 winter (c) & summer (d). Winter/summer correspond to months 12-2/6-8 in the northern hemisphere and 6-8/12-2 in the southern hemisphere. For solar cells with more than one junction, series connection of the sub-cells means that the output current is limited by whichever junction is producing the least. The resulting increase in spectrum sensitivity makes the use of a single spectrum less predictive of operating e Ưiciency and insu Ưicient for comparing cells with di Ưerent spectrum sensitivities30,31,34. If a cell with more than one junction is tuned so that its sub-cell currents match under the standard spectrum, a performance peak is obtained: performance will fall o Ư under the range of spectrum conditions found in operation. This is evident for the two-junction (2J) perovskite-silicon and the three-junction (3J) III-V multijunction cells, for which the predicted output is often more than 5% lower in latitudes below +/- 30°. Table 2. Decadal projections of the change in seasonal short-circuit current of silicon (Si) solar cells relative to 2020 values: aerosol optical depth is held constant at 2020 values. Data is normalized by the relative irradiances. Si (1J) Decadal forecasts The ability of solar PV to slow the destabilization of the climate will be altered as those changes take e Ưect. Increases in global surface temperatures also increase evaporation and therefore the precipitable water content in the column of atmosphere above the surface35. The decrease in stability that results from thermal energy accumulating in the atmospheric system is expected to lead to both changes in magnitude and larger ﬂuctuations. Absorption of radiation by water vapor occurs at wavelengths both within and beyond the absorption bands of solar cells, so the proportional impact on their outputs di Ưers from what might be found from analysis of changes in the (broadband) solar irradiance alone. For example, the number of photons in the standard spectrum (precipitable water=1.42 g/cm2) is about 22% lower than that of a spectrum from an atmosphere lacking water vapor (precipitable water=0 g/cm2), Figure 1(a). The decrease within the absorption range of silicon solar cells (300-1200 nm) is 10%. The forecasts for precipitable water (PWV) are given in Figure 2. Precipitable water vapor increases with temperature, so the downward trends in cell current over time (and between summer and winter) are expected with anthropogenic climate change36. (a) (b) (c) (d) Figure 3. Aerosol optical depth at 550 nm (AOD550) under SSP245 winter (a) & summer (b) and SSP585 winter (c) & summer (d). Winter/summer correspond to months 12-2/6-8 in the northern hemisphere and 6-8/12-2 in the southern hemisphere. Table 3. Decadal projections of the change in seasonal short-circuit current of single-junction (1J) silicon solar cells relative to 2020 values. Data is normalized by the relative irradiance. Aerosol levels in the atmosphere (quantiﬁed as aerosol optical depth, AOD) are more di Ưicult to forecast than precipitable water vapor. Sources include fossil fuel combustion, forest ﬁres, windborne desert dust and ocean salt, and volcanism. A patchwork approach to climate mitigation globally will give rise to both increases and decreases in aerosol levels locally and over time. While the transition from coal to natural gas (and zero-carbon sources) reduces the aerosols emitted per unit of energy, this may be more than o Ưset by growth in total energy demand worldwide. As climate zones shift, large-scale burning of biomass may accelerate as permafrost thaws37 and forest areas no longer contain su Ưicient moisture to support them38. Conversely, decreases in aerosols are expected from climate-change-driven reductions in wind speeds3. The variation in model forecasts for aerosol optical depth at 550 nm (Figure 3) reﬂects this uncertainty. The forecast for solar cell short circuit currents with aerosol variation included is shown in Table 3. With aerosol changes included, the downward trends seen in Table 2 are reduced or (for the NASA and NOAA models in fall/winter) reversed. Table 4. Silicon (1J) short-circuit current in 2050, relative to 2020 (SSP585). (The step change seen at high latitudes in winter is due to a decline in the number of values averaged (from three to two), when the sun is below the horizon.) CNRM MOHC MIROC NASA NOAA winter spring summer fall EƯect on photosynthesis For plants, an analogous metric to the quantum e Ưiciency of solar cells is the spectrum of quantum yield of photosynthesis7. Integrating across the “action spectrum” (the range of wavelengths that drive photosynthesis reactions) gives the quantum yield, a measure of the e Ưiciency at which photon energy is absorbed and converted to chemical energy via ﬁxation of carbon. Figure 1(b) shows normalized spectra of quantum yields for three widely distributed commercial crops7. Borrowing the concept of a standard spectrum from PV provides a sense of the global variation in the rate of photosynthesis from these crops. The relative quantum yield of photosynthesis for maize predicted for 2020 is shown in Table 5. The absorption of plant pigments such as chlorophylls and carotenoids cuts oƯ beyond ~700 nm, so is largely una Ưected by variations in atmospheric water vapor. Table 5. Predicted 2020 quantum yield of photosynthesis for maize, relative to that under the standard spectrum (AM1.5G), for scenario SSP585. Data is normalized to 1000 W/m2 irradiance. “Winter”/ “spring” /“summer”/“fall” correspond to months 12-2/3-5/6-8/9-11 in the northern hemisphere and 6-8/9-11/12-2/3-5 in the southern hemisphere. CNRM MOHC MIROC NASA NOAA winter spring summer fall The change in quantum yield of photosynthesis for maize in 2050, relative to 2020 is shown in Table 6. The results for wheat and rice are found in the Supplemental Materials (Table 11 & Table 12). Table 6. Maize quantum yield of photosynthesis in 2050, relative to 2020, scenario SSP585. (The step change seen at high latitudes in winter is due to a reduction in the number of values averaged (from three to two), when the sun falls below the horizon.) CNRM MOHC MIROC NASA NOAA winter spring summer fall Supplemental Materials (a) (b) (c) (d) Figure 4. Surface a tmospheric pres sure under SSP245 winter (a) & summer (b) and SSP585 winter (c) & summer (d). Winter/summer correspond to months 12-2/6-8 in the northern hemisphere and 6-8/12-2 in the southern hemisphere. Table 7. Decadal projections of the change in seasonal short-circuit current of tandem (2J) perovskite-silicon (PVSK/Si) and three-junction (3J) III-V multijunction (III-V) solar cells relative to 2020 values: aerosol optical depth is held constant at 2020 values. Data is normalized by the relative irradiances. PVSK/Si (2J) III-V (3J) Table 8. Decadal projections of the change in seasonal short-circuit current of perovskite/silicon (2J) and III-V (3J) solar cells relative to 2020 values. Data is normalized by the relative irradiance. PVSK/Si (2 J) III-V (3J) Table 9. PVSK/Si (2J) short-circuit current in 2050, relative to 2020 (SSP585). (The step change seen at high latitudes in winter is due to a reduction in the number of values averaged (from three to two), when the sun falls below the horizon.) CNRM MOHC MIROC NASA NOAA winter spring summer fall Table 10. III-V (3J) short-circuit current in 2050, relative to 2020 (SSP585). (The step change seen at high latitudes in winter is due to a reduction in the number of values averaged (from three to two), when the sun falls below the horizon.) CNRM MOHC MIROC NASA NOAA winter spring summer fall Table 11. Wheat quantum yield of photosynthesis in 2050, relative to 2020, scenario SSP585. (The step change seen at high latitudes in winter is due to a reduction in the number of values averaged (from three to two), when the sun falls below the horizon.) CNRM MOHC MIROC NASA NOAA winter spring summer fall Table 12. Rice quantum yield of photosynthesis in 2050, relative to 2020, scenario SSP585. (The step change seen at high latitudes in winter is due to a reduction in the number of values averaged (from three to two), when the sun falls below the horizon.) CNRM MOHC MIROC NASA NOAA winter spring summer fall