Solar energy resource availability under extreme and historical wildfire smoke conditions

Domain: earth_science
Published: 2025-01-02
Editorial outcome: minor_revision

Abstract
By 2050, the U.S. plans to increase solar energy from 3% to 45% of the nation’s electricity generation. Quantifying wildfire smoke’s impact on solar photovoltaic (PV) generation is essential to meet this goal, especially given previous studies documenting sizable PV output losses due to smoke. We quantify smoke-driven changes in baseline solar resource availability [i.e., amount of direct normal (DNI) and global horizontal (GHI) irradiance] at different spatial and temporal scales using radiative transfer model output and satellite-based smoke, aerosol, and cloud observations. We show that irradiance decreases as smoke frequency increases at the state, regional, and national scale. DNI is more sensitive to smoke with sizable losses persisting downwind of fires. Large reductions in GHI – the main PV resource – are possible close to fires, but mean GHI declines minimally (<5%) due to transported smoke. PV resources remain relatively stable across most of CONUS even in extreme fire seasons.

Introduction
Addressing climate change necessitates a rapid transition to renewable energy; one path to a sustainable future lies in solar energy production. Since 2010, advances in solar cell efficiency have decreased the cost of utility-scale photovoltaics (PV) by 82%1, increasing the feasibility of large-scale solar power. As of 2020, solar power constituted only 3% (80 gigawatts) of U.S. energy production, but the U.S. aims to receive 45% of its energy from solar power by 20502. Meeting this target requires rapid widespread solar energy development, which means identifying locations with suitable solar resources given current and future climate, population, land use, and technological constraints.

Solar resources are assessed by the amount of shortwave direct normal (DNI) and global horizontal (GHI) irradiance a location receives. Concentrating solar-thermal power (CSP) relies on DNI while PV relies primarily on GHI3,4. However, combined, GHI and DNI indicate the amount of diffuse radiation, which affects PV panel technologies differently5. The most prominent commercially-employed panels are constructed from monocrystalline silicon (c-Si; ~95% of installations) and polycrystalline cadmium telluride (CdTe; most of the remaining 5% of installations)6,7. CdTe and c-Si are direct and indirect bandgap materials, respectively, meaning that CdTe better absorbs solar radiation. About 1 μm of thin film CdTe absorbs nearly 99% of incident light8 by continuing to function under diffuse radiation. c-Si requires a thick wafer of over 100 μm and performance is reduced under diffuse radiation9. The tradeoff between direct and diffuse conditions – resulting from clouds and aerosols – carries important implications for solar energy production.

Anthropogenic pollution10,11 and dust12 reduce PV generation by decreasing solar radiation via atmospheric attenuation (absorption/scattering) and deposition on panels (soiling). In northern and eastern China, air pollution decreases annual average (peak) irradiance at PV arrays by 20–25% (34%)10. Air pollution in India reduced GHI by 29% from 2001 to 2018, decreasing PV output by 12–41% depending on the panel architecture13. Globally, particulate matter (PM) reduces PV output by >50% in polluted and desert regions, and soiling accounts for over two-thirds of energy lost11. In some regions, PM’s impact on PV generation can rival that of clouds11. PM includes a wide range of aerosol types, including pollution, dust, and smoke, which interact with solar radiation differently based on their optical properties. As such, general examinations of PM’s impact on solar energy may not fully capture the specific effects of increasingly abundant wildfire smoke.

Since the mid-1980s, higher temperatures, earlier snowmelt, and increased fuel aridity have led to extended wildfire seasons, longer-burning fires, and larger burn areas14–16, trends projected to continue as the climate changes. Most of this increase in wildfire activity has occurred in the Pacific Northwest and Southwest15, the latter having the highest U.S. solar resource potential. More active wildfire seasons result in more wildfire smoke emissions17,18 that travel far from fires19 to impact air quality across the contiguous U.S. (CONUS). Historical20,21 and projected18,22 air quality trends show that wildfire smoke emissions offset declining anthropogenic PM emissions. Smoke’s increasing impact on air quality is detectable over the Pacific Northwest20,21 and will grow more significant in the future23.

Recent case studies document sizable smoke-driven reductions in PV output during severe local wildfires over short time intervals and at specific locations and aerosol optical depths (AOD). Average PV output declined 34% over two days at a PV plant in Spain due to smoke24, 7% at a solar lab in Australia during a controlled burn when AOD reached 0.25 (27% at peak)25, 8.3% across 53 utility-scale PV plants in the western U.S. during the 2018 wildfire season26, and ~10–50% at southern California solar plants during the 2020 wildfire season when AOD ranged from 0.5 to 4.527. Other models of the California 2020 wildfire season support a 7.7% decline in PV generation and project an additional 0.4% reduction under further enhanced smoke conditions28. Examinations of solar resource and forecast models demonstrate the need to include aerosol parameters that account for smoke. Solar forecasts improved nearly 50% when using aerosol inputs from a 3-hr reanalysis product rather than a monthly aerosol climatology29. Similarly, using a satellite-derived AOD product that better detects smoke’s presence and spatial variability improved a solar resource model’s accuracy compared to ground-based observations30.

In this paper, we study wildfire smoke’s impact on baseline (i.e., average) solar resource availability across multiple temporal and spatial resolutions. We leverage daily case studies, monthly aggregations, and multi-year analyses to quantify smoke’s impact on DNI and GHI at state, regional, and national levels. At the finest scale, we examine daily localized smoke impacts in California during the 2020 wildfire season under different aerosol and cloud conditions to demonstrate the range of smoke’s influence on irradiance. We ask how smoke plume optical thickness affects the daily severity of smoke-driven irradiance changes and how cloud-driven irradiance changes compare. At larger scales, we compare regional and national solar resources under high (2020) and low (2019) smoke conditions to bookend potential future and historical smoke impacts, respectively. We ask how much daily smoke-driven irradiance changes affect monthly mean DNI and GHI, how these impacts vary for local and transported smoke, and how these impacts relate to AOD. We use the resulting mean irradiances to model smoke’s impact on PV output at 13 locations across CONUS for c-Si and CdTe panels. Finally, we expand our analysis to a 16-year period (2006–2021) to characterize longer-term, regional smoke-irradiance interactions even as impacts may increase into the future. While we examine both DNI and GHI, our discussion focuses on GHI because PV panels are the main source of U.S. solar energy. Importantly, our study reaches beyond quantifying the impact of local smoke on areas close to active fires to examine the role of both local and transported smoke in determining average solar resource availability at the regional and national level. We find considerable losses in model-based daily mean DNI (32–42%) and GHI (11–17%) in California due to local smoke. Local smoke can also greatly reduce monthly mean DNI (max: 61%) and GHI (max: 25%) with impacts on DNI persisting downwind of fires. However, the impact of transported smoke on GHI is relatively minimal (<5%) on average over CONUS, even during an extreme wildfire season. The scale of the GHI reductions implies that the average impact of smoke plumes on PV resources is relatively minimal across the U.S., which is encouraging as grids incorporate more utility-scale battery storage capacity to provide greater stability when local smoke causes potentially large irradiance shifts at finer time scales.

Results
For this study, we rely on modeled clear-sky and all-sky DNI and GHI from the National Renewable Energy Laboratory’s National Solar Radiation Database (NSRDB) at 4-km spatial and 30-min temporal resolution across CONUS. All-sky values include cloud and aerosol impacts; clear-sky values include aerosols but exclude clouds. We compute (1) daily means for each variable using times when the solar zenith angle (SZA) is <75°, (2) cloud-driven changes by subtracting the clear-sky (i.e., cloud-free) and all-sky values, and (3) smoke-driven changes by subtracting clear-sky values on smoke-impacted and reference smoke-free days. We pair daily irradiances with smoke-impacted and smoke-free flags derived from the National Oceanic and Atmospheric Administration’s Hazard Mapping System (HMS) Smoke Product. Although HMS does not identify the source of smoke (e.g., wildfire, prescribed burn, agricultural), our analysis focuses on months when western U.S. and Canadian wildfire activity is greatest with most smoke traced to wildfires. We integrate satellite-derived aerosol and cloud parameters from the Multi-Angle Implementation of Atmospheric Correction (MAIAC) product and the Clouds and the Earth’s Radiant Energy System (CERES). Finally, we design a model to assess PV performance given smoke impacts.

Case study of localized smoke-irradiance interactions
Driven by increased aridity31, the 2020 wildfire season set records for acres burned in California32, and previous studies and news articles documented large reductions in PV output27. Smoke’s impact on solar resources depends on its optical properties, which vary by fuel type and load, combustion style, age, composition, and density19,33,34. Similarly, clouds introduce variability into solar energy forecasts based on differences in cloud coverage, type, shape, optical depth, and altitude35–40. Typically, clouds scatter light more efficiently than smoke and reach far greater optical depths. In California, monthly averaged cloud fractions are typically <50% in August and reach a minimum of 30–45% in September as estimated from 25 years of CERES data. Given that smoke is becoming commonplace and localized plumes are often optically thick and absorbing, we explore whether smoke-driven changes in DNI (ΔDNI) and GHI (ΔGHI) may rival those from clouds.

We introduce a case study of four California days to demonstrate how smoke’s impact on clear-sky DNI and GHI compares to the magnitude of cloud-driven changes. We calculated smoke-driven ΔDNI and ΔGHI across California by comparing two smoke-impacted days in 2020 against analogous smoke-free conditions on the same days in 2019 (Fig. 1). August 20 and 30 were selected because both featured expansive smoke plumes in 2020, different mean AODs at 550nm (AOD550), no smoke in 2019, and minimal cloud cover in 2019 and 2020. The AOD in smoke-impacted locations averaged 0.56 on August 20 (median: 0.40) and 0.43 on August 30 (median: 0.32) according to MAIAC observations. The NSRDB assumed a higher average AOD (August 20: 0.99; August 30: 0.6), and the lower resolution distributed the highest AODs near fires over larger areas. As such, the NSRDB may compute larger reductions in irradiance further from a fire than finer resolution models. We calculated cloud-driven ΔDNI and ΔGHI as the difference between modeled all-sky and clear-sky irradiances on two cloudy days in 2019 that were relatively smoke-free with different cloud optical depths (COD). The average COD observed (all-sky modeled) by CERES (NSRDB) was 2.93 (2.14) on September 7 and 10.76 (19.26) on September 18.

Optical depth modulates the impact of both smoke and clouds on DNI and GHI, but clouds can drive substantially larger declines in irradiance. Mean DNI declined 42% (32%) under the optically thicker (thinner) smoke (Fig. 1). Decreases in GHI were smaller under both optically thicker (-17%) and thinner (-11%) smoke conditions. The influence of optical depth was more pronounced for DNI than GHI as DNI decreased 10% compared to 6% for GHI with the increase in AOD. Overall, ΔDNI consistently exceeded ΔGHI due to the heightened sensitivity of DNI to cloud droplets and aerosol particles. Losses were greater for optically thicker smoke and clouds than optically thinner smoke and clouds, respectively. Daily ΔDNI and ΔGHI values were more variable for clouds than smoke, and the maximum cloud-driven reduction far exceeded the maximum smoke-driven reduction. ΔDNI reached −910 Wm−2 for thicker clouds and −797 Wm−2 for thinner clouds compared to −756 Wm−2 for thicker smoke. ΔGHI reached −547 Wm−2 for thicker clouds and −421 Wm−2 for thinner clouds compared to −350 Wm−2 for thicker smoke. For context, PV panel efficiency is tested using a standard irradiance value of 1000 Wm−2. Thinner smoke and clouds resulted in similar irradiance reductions, particularly for GHI, while thicker smoke typically drove equal or greater losses than thinner clouds. Occasionally, thicker smoke achieved similar reductions to thicker clouds. Since localized wildfire smoke can persist longer than clouds – which develop, move, and dissipate throughout the day, limiting their impact – adding more smoke to a relatively cloud-free region functionally increased the number of cloudy (i.e., high optical depth) days in California in 2020.

2020 monthly regional and national smoke impacts as a future analogue
Exceptional fire years like 2020 will become more frequent as the west continues to warm41 and relatively low fire years like 2019 will stand as reminders of past conditions. We contrast 2020 and 2019 as an analogue for future smoke impacts on solar resources relative to more traditional smoke conditions. Since smoke impacts the U.S. every year from wildfires, prescribed burns, and agricultural fires, we focus on low and high smoke conditions rather than smoke-impacted and smoke-free conditions. Smoke-impacted days increased in 2020 across 72% of CONUS in August (Fig. 2a) and 88% in September (Fig. 2b). In 2020, the percentage of CONUS experiencing smoke overhead for at least 15 days was 22% in August (2019: 1%) and 40% in September (2019: 4%). The percentage experiencing smoke for at least eight days was 38% in August (2019: 17%) and 47% in September (2019: 15%). As wildfires grew across California, Oregon, Washington, and Colorado, smoke was transported north and east leading to a widespread increase in smoke-impacted days.

AOD provides context for interpreting surface-level irradiance changes since extinction efficiency varies with smoke particle density, plume vertical extent, and composition. Smoke-driven changes in AOD550 observed MAIAC monthly mean AOD550 across CONUS were minor in August (Fig. 2c), increasing 23% (2019: 0.13; 2020: 0.16), and more substantial in September (Fig. 2d), increasing 127% (2019: 0.11; 2020: 0.25). Few areas surpassed an AOD550 of 0.5 in August 2019 (<0.01%), September 2019 (<0.01%), or August 2020 (0.08%). However, in September 2020, AOD550 exceeded 0.5 across 3% of CONUS, in areas close to active fires. The more substantial increase in smoke frequency in September was associated with a more than doubling of the mean CONUS AOD550. Observed monthly mean AODs align well with those underlying the NSRDB model. A comparison of the two products is available in Supplementary Figs. 1 and 2. Overall, AOD patterns and changes are consistent, but the NSRDB is biased low at high AODs and distributes high AODs over larger distances. Thus, projected changes in irradiance may be lower directly above active fires but extend further than implied by AOD observations.

As smoke frequency and AOD increased, modeled clear-sky DNI (Fig. 2e, f) and GHI (Fig. 2g, h) typically decreased, particularly in areas close to large wildfires in California and Colorado in August and California, Colorado, Oregon, and Washington in September. For clear-sky DNI, monthly means decreased across 63% and 96% of CONUS in August (Fig. 2e) and September, respectively (Fig. 2f). In August, mean clear-sky DNI decreased over 20% in areas of California (max: 30%; 255 Wm−2) and Colorado (max: 24%; 203 Wm−2) near active wildfires. In September, average losses exceeded 20% across much of California (max: 61%; 483 Wm−2) and in parts of Oregon (max: 29%; 238 Wm−2), Northern Colorado (max: 24%; 225 Wm−2), and Southern Wyoming (max: 25%; 229 Wm−2). Clear-sky DNI losses reached 40–60% in some areas of California’s Central Valley and Northern Coastal Ranges. DNI reductions persisted downwind to affect areas far from emission sources but within the transport pathway for western wildfire smoke. In September, clear-sky DNI decreased 5–10% as far east as Maine and 10–15% in Nebraska and parts of South Dakota, Minnesota, Iowa, Wisconsin, Kansas, and Texas.

Mean clear-sky GHI (Fig. 2g, h) decreased less than DNI, but spatial trends in GHI losses largely mirrored those for DNI. In 2020, clear-sky GHI decreased across 66% and 95% of CONUS in August and September, respectively. On average, reductions were <5% in nearly all areas (≥95%) that experienced GHI losses in August and September. The largest reductions occurred in California. By percent, the greatest decrease was 12% in August and 25% in September, and by value, the greatest decrease was 83 Wm−2 in August and 156 Wm−2 in September. While substantial reductions in clear-sky GHI occurred across California, Oregon, and Colorado, severe decreases were restricted to areas immediately adjacent to large wildfires (e.g., California Central Valley and Coastal Ranges; Oregon Willamette Valley and Coast Range). Outside these areas, the reduction in monthly mean clear-sky GHI was minimal (<5%), which indicates that monthly mean clear-sky GHI changes little with increased smoke in areas primarily affected by transported plumes.

Figure 3 summarizes how monthly mean irradiance changed alongside smoke frequency at the national and regional scale for the entire wildfire season (April–October). Nationally, clear-sky DNI varied with changes in smoke frequency throughout the fire season, while clear-sky GHI remained relatively stable (Fig. 3a). Early in the season (April–July), smoke frequency across CONUS was the same or slightly lower in 2020 and irradiance was slightly higher (maximum increase of 2.5% for clear-sky DNI and 0.8% for clear-sky GHI). Later in the season (August–October), 2020’s smoke-impacted days far exceeded 2019’s with smoke frequency more than doubling in August (110%) and more than tripling in September (219%). Corresponding clear-sky DNI decreased by 3% and 8% while clear-sky GHI decreased by 1% and 2%, respectively. Decreases in CONUS-averaged clear-sky irradiance were relatively small compared to the large increases in smoke frequency, especially when considering GHI.

Regionally (Fig. 3b), smoke frequency increased and clear-sky irradiance decreased in the late summer and early fall, particularly in the Southwest (SW), Pacific Northwest (PNW), and North Central (NC) regions. (Fig. 3c, d, f). The SW recorded the largest rise in smoke-impacted days of any region, increasing by 16.6 days in August (+882%) and 19.2 days in September (+963%). The largest decrease in clear-sky irradiance during these months also occurred in the SW with DNI losses of 10% in August and 12% in September. GHI losses only reached 3%. In September, smoke-impacted days increased substantially in the PNW (+544%), NC (+180%), and Northeast (NE; +1169%) regions; clear-sky DNI decreased 11%, 8%, and 5%, respectively; and clear-sky GHI decreased 3%, 1%, and 1%, respectively. Weaker trends in smoke frequency and irradiance characterized the South Central (SC) and Southeast (SE) regions (Fig. 3e, g). Trends in the SW, PNW, and NC regions largely drove the variation in CONUS averages (Fig. 3a). Additionally, the limited fluctuation of GHI over a wide range of smoke frequencies indicates the relative stability of the baseline PV resource.

We focus on smoke-driven irradiance shifts, but the main societal concern is how much these changes, particularly in GHI, affect PV power output. We modeled average daily output at an idealized 1 MW fixed-tilt PV installation for September 2019 and 2020 at 13 locations across CONUS using the monthly mean clear-sky DNI and GHI computed in this study (Supplementary Table 1). While proportional changes in output are similar across fixed-tilt and single-axis tracking panels, the absolute change will be greater for tracking panels, which have higher baseline production. Below we present results assuming c-Si panels (Fig. 4), and results for CdTe panels are provided in Supplementary Fig. 3.

Under standard test conditions (STC; 25°C, air mass 1.5), power loss is greatest in areas near active wildfires with optically dense, persistent smoke plumes (Fig. 4). For example, given an 11% or 65 Wm−2 reduction in mean September GHI in Modesto, CA (2019 baseline: 620 Wm−2), the model projects an 11% (-6.6 megawatt hours (MWh)) reduction in average daily power output. PV output reductions are substantially lower (~1 MWh) in areas with optically thinner, transported smoke plumes (e.g., NC, SC, and NE regions). However, using the STC ignores how site location, wind conditions, and smoke-driven reductions in surface radiation influence panel temperature, which greatly affects PV performance as panels operate more efficiently at lower temperatures42,43. PV output is lower under ambient air temperatures, which are hotter than STC in September, but proportional reductions remain the same. Cooling effects from wind and smoke-driven GHI reductions increase PV output relative to that calculated solely using air temperature.

Smoke-irradiance interactions across CONUS from 2006–2021
Although 2020 smoke conditions exemplify future conditions, U.S. wildfire activity has been increasing since the mid-1980s14–16. As such, we examined regional daily fractional smoke spatial coverage and clear-sky irradiance trends from 2006 to 2021. Mean irradiance refers to the daily average clear-sky DNI and GHI for each region. Long-term analyses better constrain smoke’s impact on baseline resources given spatio-temporal variations in smoke extent and optical properties. At a high level, regional smoke coverage has increased in recent years (Fig. 5), and smoke-irradiance interactions exhibit a clear seasonality (Fig. 6a, b). Overall, correlations were significant (p<0.05) for 86% of DNI (Fig. 6a) and 60% of GHI analyses (Fig. 6b). Correlations and regression equations are provided in Supplementary Table 2.

Mean clear-sky DNI was more sensitive to plume size (i.e., regional coverage) in areas where plumes are denser, indicating nearby fires and fresh smoke. During years with above average regional smoke coverage (Fig. 5), DNI reductions were largest in the SW (August: -7%; September: -7%) and PNW (August: -11%; September: -6%) where plumes from local wildfires are fresh and optically thick19,44. Average smoke coverage was greatest in the PNW (August: 51%; September: 27%) and NC regions (August: 47%; September: 27%), with the latter influenced primarily by transported smoke. Despite similar smoke coverage, the decline in clear-sky DNI was smaller in the NC region (August: -5%; September: -4%) due to differences in smoke density and optical properties.

Correlations (Fig. 6a) and regression slopes (Fig. 6c, d) indicate that the connection between smoke coverage and DNI is strongest in the SW and PNW, particularly during the local fire season. August (r = -0.81; r = -0.68) and September (r = -0.68; r = -0.65) correlations are high, and the regression analysis (Fig. 6c) projects a 17% (156 Wm−2) decline in August clear-sky DNI in the PNW and a 16% (144 Wm−2) decline in the SW as smoke coverage increases from 0% to 100%. Similar decreases of 16% (147 Wm−2) and 14% (125 Wm−2) are projected for September (Fig. 6d) in the PNW and SW, respectively. In August, smoke coverage explains 66% of the variance in clear-sky DNI in the SW, but the greatest rate of change in DNI is in the PNW.

Clear-sky DNI was less sensitive to regional smoke coverage in areas with optically thin, laterally extensive, dilute, aged plumes overhead, such as the NC, SC, NE, and SE regions. The strongest correlation in the NC, SC, and NE regions occurred in September (r = -0.46; r = -0.31; r = -0.3), and the strongest in the SE occurred in July and August (r = -0.32). Smoke coverage explained less than 10% of the variance in clear-sky DNI for these regions and months, except in the NC region during July (17%), August (16%), and September (21%).

Regional clear-sky GHI remained remarkably stable across this 16-year timeframe regardless of smoke coverage (Fig. 5). During high smoke years, each region’s mean clear-sky GHI decreased by ≤3% compared to low smoke years. GHI declines peaked at 3% in August (PNW) and 2% in September (PNW and SW). Regions mainly affected by transported smoke experienced decreases in clear-sky GHI of ≤1%.

Correlations between clear-sky GHI and smoke coverage were generally weaker with few significant values (Fig. 6b). The strongest correlations occurred in the SW and PNW regions in July (r = -0.33, p<0.001; r = -0.51, p<0.001) and August (r = -0.56, p<0.001; r = -0.44, p<0.001). These moderate correlations were associated with decreases in clear-sky GHI of 3% and 5%, respectively (July: -22 to -23 Wm−2; August: -30 to -35 Wm−2), as regional smoke coverage increased from 0% to 100% (Fig. 6c, d). In the SW, correlations were weaker for September (r = -0.21, p<0.001) with smoke coverage explaining <5% of the variance in clear-sky GHI. The regression analysis projects a 3% (-16 Wm−2) decline in September’s clear-sky GHI in the SW when smoke increases from 0% to 100% coverage. In other regions, correlations were weaker with mixed significance. Ultimately, across these 16 years, smoke failed to drive major shifts in regional mean clear-sky GHI.

While we discuss how increasing smoke coverage from 0% to 100% impacts irradiance, regions rarely experienced 100% smoke coverage (Supplementary Fig. 4). At most, 5% of August days in the PNW and NE regions were entirely smoke-impacted, and fewer days reached 100% smoke coverage in September (2% for the NE, NC, SW, and PNW regions). The PNW and NC regions reached ≥50% smoke coverage on nearly half of August days and a quarter of September days. In the SW, 1% (16%) of August and 2% (11%) of September days reached 100% (50%) smoke coverage.

Methods
Below we describe the datasets and variables used in this study as well as the data processing and analysis, which were performed using Python version 3.10.

Irradiance data
We assessed solar resources using modeled clear-sky and all-sky DNI and GHI from the National Renewable Energy Laboratory’s National Solar Radiation Database (NSRDB)47 Physical Solar Model (PSM) version 3.2.2. The NSRDB combines atmospheric and land surface properties from satellite and reanalysis data in a two-step physical model. Cloud properties are derived from Geostationary Operational Environmental Satellite (GOES) observations using the Advanced Very High Resolution Radiometer (AVHRR) Pathfinder Atmospheres-Extended (PATMOS-x) algorithms. AODs are determined using a combination of data from the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) reanalysis product and monthly values from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument aboard the Terra and Aqua satellites. When MODIS monthly data are missing, only the MERRA-2 data are used, as is standard for the eastern and northern U.S. Otherwise, an optimal linear combination of the two datasets is computed. Surface albedo inputs are sourced from MODIS and the Interactive Multisensor Snow and Ice Mapping System. MERRA-2 is used for all other inputs. The NSRDB regrids these data to a 4×4-km spatial resolution and interpolates to a 30-min temporal resolution before being input into the Fast All-sky Radiation Model for Solar Applications (FARMS)48. GHI is directly computed using FARMS, as is DNI when the sky is cloud-free. To compute DNI when clouds are present, the Direct Insolation Simulation Code is used to decompose the DNI component of GHI. REST2 is used for clear-sky irradiance computations49. Comparisons to ground observations of radiation indicate a 5% bias for GHI and 10% bias for DNI with the NSRDB47.

Smoke plume locations
We use the HMS to distinguish between smoke-free and smoke-impacted days at each NSRDB grid location across CONUS. The HMS was developed by the National Oceanic and Atmospheric Administration’s (NOAA) National Environmental Satellite, Data, and Information Service (NESDIS) and provides daily U.S. smoke plume locations50. Analysts use visual-band imagery mainly from the National Aeronautics and Space Administration (NASA) GOES to identify smoke from wild, agricultural, and prescribed fires. No attempt is made to determine the smoke’s source. Analysts manually outline plume extents and qualitatively estimate plume concentration. Occasionally, analysts use polar orbiting satellite imagery to identify smoke or infrared imagery to distinguish smoke from clouds51.

The HMS is considered a conservative estimate of smoke plume number and extent19. While error and confidence metrics are not currently available for the HMS smoke product, we anticipate minimal sensitivity to the known limitations. Edge detection issues are the most prominent limitation relevant to this study. The HMS can struggle to distinguish plume edges when (1) the smoke is diffuse and mixed with anthropogenic haze or (2) the plume edge is obscured by clouds. The former issue mainly affects observations over the southeastern U.S., but the fire season in the southeast generally occurs much earlier in the year and is characterized by smaller agricultural fires. By focusing on the western U.S. wildfire season, we work with plumes that the HMS is best suited to characterize. For local smoke, the HMS performs well in detecting the sharp edges of fresh, dense, western plumes. The smoke transported far downwind from these fires mainly affects areas over the Northern Great Plains and Midwest where anthropogenic haze mixing is less common. We anticipate high accuracy for the HMS detections in areas where smoke’s impact on irradiance is greatest (i.e., optically dense plumes) with edge detection issues isolated to locations where plumes are most diffuse and light extinction is lowest.

Cloud and aerosol observations
We use the cloud mask and 550 nm AOD retrieval from the MAIAC daily atmosphere product (MCD19A2). MAIAC processes MODIS observations from both Terra and Aqua overpasses, which capture morning (~10:30 AM LT) and afternoon (~1:30 PM LT) conditions, respectively. MAIAC uses the contrasting spatial and temporal variability of land surfaces and aerosols to improve cloud masking and retrieve 1-km resolution AOD52,53. Unlike other algorithms, MAIAC specifically tests for the presence of smoke, and when detected, retrieves AOD for both clear and possibly cloudy skies. This results in more AOD retrievals during smoke-impacted conditions.

We combine cloud observations from MAIAC and CERES. We use the cloud mask from MAIAC to determine cloud locations on a fine spatial scale (1-km) and the CERES Synoptic 1-degree hourly (SYN1deg-1Hour) Edition 4.1 product to obtain the cloud visual optical depth. The SYN1deg cloud properties are derived from visible and infrared MODIS, Visible Infrared Imaging Radiometer Suite (VIIRS), and geostationary (GEO) satellite imagery54–56. Values are spatially averaged and temporally interpolated to the SYN1deg-1Hour resolution.

Data processing
We downloaded the 30-min, 4-km, yearly netCDF files for the NSRDB PSM version 3.2.2 for all years from 2006 to 2021 from the Registry of Open Data on Amazon Web Services (AWS), which is part of the Department of Energy’s Open Energy Data Initiative. We extracted the clear-sky DNI, clear-sky GHI, all-sky DNI, all-sky GHI, and solar zenith angle (SZA) data sets for April–October of each year and for grid locations within the 48 states comprising the CONUS. The SZA values were used to filter their radiance data to exclude times when the SZA is greater than 75°. This removes times near sunrise and sunset when solar energy generation is low. Using an SZA filter provides consistency in analysis across time zones.

The four data products used in this analysis have different spatial and temporal resolutions. Spatially, we converted the HMS, MAIAC, and CERES data to the NSRDB 4-km grid using two spatial gridding processes. First, the HMS smoke polygons were gridded by determining if the center point (latitude, longitude) of an NSRDB grid cell was located beneath a smoke-plume polygon. If a polygon overlapped an NSRDB point, the location was considered smoke-impacted, and if not, the location was considered smoke-free. Second, we mapped the MAIAC and CERES grids to the NSRDB using a nearest-neighbor approach in which the haversine distance between each grid point is calculated and the minimum distance determined. Each NSRDB location was assigned a MAIAC AOD, MAIAC cloud mask, and CERES COD based on the closest location match. A few NSRDB grid points (0.01%) had more than one matching MAIAC neighbor, and as such, an average AOD was calculated for these pixels. Interpolating the CERES COD in this manner provides a rough estimate of the observed cloud variability on each day but does not explicitly resolve the cloud shape within each grid box. We include the NSRDB COD in Fig. 1 to illustrate the finer variability resolved by the model.

Temporally, we averaged the MAIAC, CERES, and NSRDB data to the daily level of the HMS smoke product. AOD values were averaged across the different Terra and Aqua retrievals in MAIAC to determine a daily mean AOD at 550 nm. The MAIAC cloud mask classifies each pixel as cloudy, possibly cloudy, or clear. If a location is identified as cloudy or possibly cloudy in any of the Terra or Aqua retrievals for the day, we consider the location to be cloudy. The daily COD was calculated by averaging the hourly CERES COD values for times when the SZA was less than 75°. Similarly, the NSRDB 30-min irradiance values were averaged for each day to obtain daily clear-sky and all-sky DNI and GHI. Once converted to the same spatial and temporal resolution, we merged all four data products for analysis.

Analysis of California case study dates
Four dates were selected for this case study to (1) highlight the role that AOD plays in determining smoke’s impact on irradiance and (2) compare the impact of smoke and clouds on solar resources. We focused on August and September when selecting dates for this case study because major fire activity and smoke production in California is generally greatest during these months and was so in 2020. We identified days based on visual examination of the NASA Worldview true-color imagery of California for all August and September days in 2019 and 2020. We selected August 20 and August 30 to illustrate the influence of smoke optical properties, via AOD, on smoke-driven changes in solar resources. We restricted our analysis to locations that were smoke-impacted in 2020 and smoke-free in 2019 based on the HMS polygons. In 2020, the observed (MAIAC) average AOD550 was 0.56 on August 20 and 0.43 on August 30, indicating optically thicker and thinner smoke plumes, respectively. We calculated the smoke-driven change in DNI (ΔDNI) and GHI (ΔGHI) as the difference between clear-sky values in 2020 (smoke-impacted) and 2019 (smoke-free). Comparing the same date across both years better controls for variation in solar position and climate. We then compared these smoke-driven changes in irradiance to those from clouds of different optical depths. We analyzed locations across California that were categorized as cloudy or possibly cloudy by MAIAC on September 7, 2019 (CODmean = 3.26) and September 18, 2019 (CODmean = 10.75). We calculated the difference between the all-sky and clear-sky values on each day to determine the ΔDNI and ΔGHI attributable to clouds. A cloud filter was not applied when assessing smoke-driven changes because MAIAC does not retrieve an AOD when a pixel is categorized as cloudy and only retrieves an AOD under possibly cloudy conditions when smoke is detected. Boxplots were used to present the distribution of either the clear-sky DNI and GHI values or the ΔDNI and ΔGHI on each of the case study days. Negative values indicate the reduction of irradiance.

Analysis of monthly CONUS trends in 2019 and 2020
We computed monthly smoke frequency by summing the number of HMS-indicated smoke-impacted days at each NSRDB grid point for each month. We then calculated the difference between the number of smoke-impacted days in 2019 and 2020. Similarly, we computed the mean monthly AOD550 for all CONUS pixels by averaging the daily MAIAC AOD550 and calculated the difference between the 2019 and 2020 monthly values. For irradiance, we averaged daily clear-sky DNI and GHI to obtain the mean monthly clear-sky DNI and GHI. We then calculated the average smoke-driven changes in DNI (ΔDNI) and GHI (ΔGHI) by subtracting the monthly clear-sky value in 2019 from that in 2020 for each irradiance measure. National and regional monthly averages were then calculated for smoke frequency and irradiance from the monthly means at each grid location to summarize trends at different spatial scales.

Modeling photovoltaic array performance
We designed a model of a 1 MW solar PV installation to assess the change in PV output under average low smoke (2019) and high smoke (2020) conditions for September (i.e., when smoke was most abundant in 2020). To provide a comprehensive and realistically relevant analysis of PV performance under non-standard (i.e., real world) conditions, we used monthly mean irradiance values computed in this study for low and high smoke years as input. Based on the computed clear-sky DNI and GHI values, we derived the solar cell temperature since different solar panel technologies exhibit different temperature dependencies, and thus, different temperature coefficients for each PV technology. Below we describe in detail the model design and relevant computations for evaluating the performance of the PV modules under different conditions.

Traditionally, all PV modules are tested, and their performance is rated under STC57. The STC for terrestrial solar panels use an irradiance level of 1000 Wm–2, cell temperature of 25°C (77°F), and air mass of 1.5. A solar radiation intensity of 1000 Wm–2 is approximately the average solar radiation level on a clear day at noon. The cell temperature refers to the temperature of the solar cells themselves, not the ambient temperature. Although the STC rating is based on this standardized cell temperature, solar panels often operate at higher temperatures under real-world conditions. The air mass represents the spectral distribution of sunlight and is a measure of the path length through the Earth’s atmosphere. An air mass of 1.5 approximates the sun’s angle in the sky at mid-latitudes on an averaged day.

The temperature coefficient of solar panels is a measure of how much a solar panel’s performance is affected by changes in temperature. Specifically, the measure quantifies the change in electrical output (usually power or voltage) for each degree Celsius (°C) rise in temperature above a standard reference point, typically 25°C (STC) and is expressed as a percentage change per °C. For example, a temperature coefficient of –0.3% per °C means that for each °C increase in temperature above 25°C, the solar panel’s output power decreases by 0.3%. The temperature coefficient (Tcoefficient) is specified via Eq. (1).

Tcoefficient = (OutputatToperating - OutputatTSTC) / ((Toperating - TSTC) * OutputatTSTC) (1)

Often, the operating temperatures (Toperating) for PV panels significantly exceeds the STC temperature (TSTC) with actual temperatures of over 65°C in hot desert climates and even reaching 35–45°C in cold or arctic climates. Therefore, we account for the effect of operating temperatures on performance when evaluating the effect of smoke on the performance of PV panels using two methods. First, we calculated operating temperature using a simplified approach that relies solely on ambient air temperature to estimate the module operating temperature58,59. Operating temperature (°C) of the PV cell or module (Tcell) is calculated using the relationship in Eq. (2).

Tcell = Tair + (NOCT - 20) / 800 * S (2)

where Tair is the ambient air temperature (°C), NOCT is Nominal Operating Cell Temperature (°C), and S is the solar insolation in W cm–2. NOCT is defined as the temperature reached by open-circuited cells in a PV module under incident irradiance of 800 Wm–2, air temperature of 20°C, wind velocity of 1 ms–1, and the assumption that the panel mount exposes the back surface of the panel to air (e.g., as opposed to residential mountings where the roof covers the back surface). In the context of PV panels, S refers to the amount of solar radiation energy received on a given surface area in a given amount of time. The fraction (NOCT−20)/800 represents the rate at which the cell temperature increases per unit increase in solar irradiance above a baseline of 20°C at an irradiance of 800 Wm–2. When using Eq. (2), the actual module temperature will be lower than calculated when wind velocity is high and higher under still conditions58,60.

Second, a more accurate estimate for operating temperature of PV panels in the field can be calculated using the relationship in Eq. (3)61.

Tcell = Tair + (NOCT - 20) / 800 * GHI * WindFactor (3)

where GHI is the Global Horizontal Irradiance, and the Wind Factor is a term that accounts for the cooling effect of the wind. We approximated the Wind Factor via Eq. (4).

WindFactor = WindCoefficient * (WindSpeed - 1) (4)

where the wind coefficient parameter depends on the installation specifics and panel type.

For our simulations, we evaluated panel performance at 13 locations across CONUS (Fig. 4) for the month of September in 2019 and 2020, when smoke impacts were greatest. We assumed fixed tilt panels oriented at the angle of each location’s latitude, consistent with industry standards. As inputs, we used the monthly mean clear-sky GHI calculated in the current study; normalized NOCT values of 46°C for c-Si PV and 45°C for CdTe PV; and temperature coefficients of –0.45% for c-Si and –0.21% for CdTe62. We assume a 1 MW solar installation of each solar PV (c-Si and CdTe) system modeled. The highest reported record efficiencies for each panel technology were used (i.e., 24.5% and 20.2% for c-Si and CdTe, respectively). Under STC, the solar panel efficiency equates to the percent of incident irradiance that would be efficiently converted to useful electrical power63. In other words, at 25°C and 1000 Wm–2 incident solar irradiance, c-Si and CdTe PV modules were assumed to produce 245 W pm–2 and 202 W pm–2 of the PV module, respectively. Hence, the total area required for 1 MW installation for c-Si and CdTe PV was estimated to be 4081.63 m2 and 4950.5 m2, respectively. For a better perspective, this is approximately the volume of solar PV installation that can power houses for about 130 American families.

The ambient temperatures and wind speeds were drawn from the NSRDB. Considering only daytime hours (i.e., when enough solar irradiance was available to produce measurable PV power output), we calculated the average ambient temperature and wind speed for September across both years. We averaged across 2019 and 2020 to ensure that changes in operating temperature resulted from smoke-driven GHI adjustments rather than independent meteorological conditions in a given year. We then calculated the average PV panel operating temperatures. Ultimately, output was modeled at each location in 2019 and 2020 for c-Si and CdTe solar panels using four model input configurations: two at the STC temperature and two at location-specific temperatures. First, we estimated power output at 25°C using only clear-sky DNI, which excludes the impact of clouds, operating temperatures, and diffuse radiation but includes the impact of smoke. Second, we estimated power output at 25°C under more realistic irradiance conditions that used GHI as the input. Third, we estimated power output using GHI as the irradiance input and a location-based cell temperature calculated via Eq. (2), which accounts for ambient air temperature but excludes the effects of wind and GHI on temperature. Fourth, we again estimated power output using GHI but used location-specific cell temperatures calculated via Eq. (3), which includes temperature changes driven by wind speed and actual clear-sky GHI. This fourth configuration is considered the most realistic evaluation of smoke-driven changes in PV performance that we present.

Analysis of longitudinal data from 2006-2021
Using the daily mean all-sky and clear-sky DNI and GHI, we calculated the mean DNI and GHI values for all days from 2006 to 2021 for six regions: the Southwest, Pacific Northwest, North Central, South Central, Southeast, and Northeast (Figs. 5–6). We computed percent smoke coverage for each region on these days by determining the percentage of pixels flagged as smoke-impacted relative to the number of pixels in the region. We performed a linear regression analysis for each region and month combination for both clear-sky DNI and GHI using the scipy.stats.linregress function in Python. We performed 84 linear regressions and selected an alpha of 0.05 for significance.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Review of “Solar energy resource availability under extreme and historical wildfire smoke conditions” by Corwin et al.\nThis paper combines several observational datasets to develop a novel synergistic approach to examine the impact of fire smoke plumes on quantities relevant to solar energy, namely global horizontal irradiance (GHI) and direct normal irradiance (DNI). This work is clearly motivated by not only the increase in deployment, and planned future deployments, of solar resources but also recent studies showing a notable decrease in solar energy resulting from wildfire smoke plumes.\nThe authors first focus on the very active 2020 wildfire season over California, which has received much attention in the literature recently. Similar to these previous studies, they find that DNI, and to a lesser extent GHI, is reduced in accordance with smoke “thickness”, represented by the aerosol optical depth. The relatively minimal impact of smoke on GHI is important since most of the deployed solar resources (photovoltaic) are more sensitive to it rather than to DNI. The authors then take a broader approach to understand how far-reaching the impacts of smoke can be on DNI and GHI during the 2019 and 2020 seasons. They conclude that while these quantities can be drastically reduced nearby a plume, the downstream impacts very quickly diminish, such that the fingerprint from fires across the western U.S. is nearly unidentifiable across the eastern U.S. (in terms of irradiance). This is especially true for GHI, where reductions are confined to 100s or even 10s of km from an active burning region. A longer period from 2006-2021 is also used to clarify regional trends. Finally, a model of photovoltaic output is used to clarify actual performance impacts due to the smoke. Accounting for the location-based temperature rather than a standard temperature is found to be important.\nOverall, I think this is a nice and comprehensive study. I find the manuscript to be well written with explanations that are clear. The logic was mostly easy to follow, and I appreciate the authors provide some discussion of the limitations of their study. I think this will nicely complement recent literature on the subject and hopefully motivate additional studies. I have one major comment and some minor comments that should be addressed before the manuscript can be acceptable for publication.\nMajor comment:\n1. One important piece missing from the Discussion and/or Methods section is the impact of the approach for the HMS product. In the Methods section, you talk about the HMS producing a “conservative estimate of plume number and extent”. Do you have any feel for how conservative this may be? And are there any error/confidence metric available with this product? I wonder how often smoke plumes, especially the ones transported further distances (i.e., more diffuse smoke and generally higher in the atmosphere, so probably harder to detect with confidence manually), would be misclassified. If it's a conservative estimate, then more actual smoke cells would be classified as non-smoke cells, thus making the two data sets more alike (i.e., showing smaller differences between smoky and non-smoky periods). I think the authors need to comment on this and provide additional discussions somewhere in their manuscript.\nSpecific comments:\n1. L31-59: Please consider citing a recent study by Ghosh et al. (2022), which examined the impact of aerosols from air pollution on India’s solar production.\nReference:\nGhosh, S., Dey, S., Ganguly, D., Roy, S. B., & Bali, K. (2022). Cleaner air would enhance India’s annual solar energy production by 6–28 TWh. Environmental Research Letters, 17(5), 054007.\n2. L72-82: Please consider citing a recent study by Gilletly et al. (2023), which quantified smoke impacts on solar resources over the western U.S.\nReference:\nGilletly, S. D., Jackson, N. D., & Staid, A. (2023). Evaluating the impact of wildfire smoke on solar photovoltaic production. Applied Energy, 348, 121303.\n3. L142: Are clouds truly “rare” over this region in August and September? Some supporting studies would be good to reference here.\n4. L161-163: Maybe not to the same extent, but isn’t the same true for smoke due to plume height, smoke age/composition, etc?\n5. L164-166: I have not heard this before; can you cite a paper, news article, etc. where this type of question is being raised?\n6. L275-277 and Figure 5: Can you comment on the lack of sensitivity resulting from the two methods shown in the right panel?\n7. L287-288: I don’t understand this statement – so you’re saying that even though the southern sites have warmer baseline temps, the smoke reduces temp more there than at the northern sites such that the smoke-influenced temp is lower at the southern sites? Maybe I am missing something.\n8. L316: I don’t see the Corwin et al. (2022) citation in the reference list.\n9. L318: How often do these regions see 100% smoke coverage? Similar question for L392.\n10. L368-370: You specifically call out California, which has historically had the largest wildfires, but really isn’t this a potential risk for other regions considering we are seeing an increase in burning frequency/intensity in regions that have not traditionally experienced wildfire? I think the recent fires in Texas are a good example of this, and Texas is also responsible for a large fraction of the U.S. solar production.\n11. L370-371: What is the cost difference between solar PV and natural gas?\n12. L427-429: Does this largely depend on where we put our solar resources? I think your statement could be framed differently; that is, results stemming from your study can help inform where deployment of future solar farms would be minimally impacted by smoke. Perhaps combined with knowledge of cloud impacts on DNI/GHI, and modeling of expected future fire/clouds conditions, this could highlight ideal locations to deploy solar.\n13. L446-447: Is this a limitation that would affect your results considering the rather coarse resolution of MERRA-2 (which I believe it is 0.625 deg x 0.5 deg)? Please comment.\n14. L502: So CERES is interpolated from 1 deg to 4 km? Do you expect these COD values to be reliable considering this huge spatial difference?\n15. L516-517: I am not familiar with this product, but why assume all “possibly cloudy” pixels are cloudy? Are results insensitive if you assume equal chance cloudy or clear for example?\n16. L529-530: Over which region are these mean values computed? Same question for L534-535.\n17. L539-540: Does this miss the potential impact of pyrocumulus clouds which can be important for radiative transfer?\n18. L598: Is there a more official citation for this website?\n19. L601 and L616: Is it necessary to use different variable names for what seem like the same parameter?\n20. L623: I presume these are the 12 locations shown in Fig. 5? Would be good to reference it if so.\n21. L660: Similar point here, would be good to reference the six regions or just list them here.\n22. L896, Fig. 2: I don’t understand how delta GHI could be positive in some locations for the smoke day? Is this due to some physical phenomenon (e.g., multiple scattering), error/bias in the NSRDB product, or something else? Please explain.\n23. L896, Fig. 2: The optically thick (thin) cloud days have smaller (larger) COD values. Isn’t this counterintuitive? Also, the NASA Worldview snapshots don’t really sync up to the COD plots to my eyes. Is this just a timing issue?\n24. L911, Fig. 3: Would it be better to use a normalized delta AOD metric? For instance, if the background AOD is very small, then even a small increase due to smoke may be important.\n25. L922, Fig. 5: You talk about consistent decreases in GHI magnitude across the different sites. However, I think it would be good to visualize this using percent difference or some normalized metric. For instance, in the right panel, a decrease in 7 MWh at Dallas is more impactful than a decrease in 8 MWh at Portland due to their different baseline values. I suggest adding additional subplots here or make a new plot and add it to the Supplemental.", "reviewer_2": "Review of the study entitled “Solar energy resource availability under extreme and historical wildfire smoke conditions” by Corwin et al.\nThis a very nice and well-structured study analyzing the impact of smoke from wildfire to irradiance across the continental U.S. and subsequent threat on solar photovoltaic (PV) energy generation. Results rely on modeled clear-sky and all-sky direct normal irradiance (DNI) and global horizontal irradiance (GHI) from a high spatial and temporal resolution database. I find the results reliable and recommend publication after minor revisions.\nMinor comments\n1. Line 121: Losses in model-based daily mean GHI (11–17 %) in California associated with localized smoke are consistent with results from ground-based measurements of solar irradiance in Athens, Greece by Masoom et al. (2023) (https://doi.org/10.5194/acp-23-8487-2023), who measured losses in GHI due to smoke from extreme wildfires up to 17 %. California and Greece have the same climate (Mediterranean climate). Of course, the area of Greece is much smaller than California, and the fires in California are larger, but personally I think that finding consistent results in different parts of the world with the same climate shows that results reliable.\n2. Line 153: 10-point and 6-point. Point is vague, do you mean 10 % and 6 %?\n3. Line 170: “smoke-impacted and smoke-free days from the previous section (Aug 20, Aug 30)” is confusing. If you refer to Aug 30, 2020, then it is smoke-thin not smoke-free day.\n4. Line 188: Put cloudy in “”.\n5. Figure 2: the two plots showing the COD should switch position, i.e. move plot for COD=10.76 on top and plot for COD=3.26 to bottom. Fig. 2 caption: Here is how understand the Differences (Δ), please clarify:\nΔ smoke-thin (Aug 30, 2020) – smoke-free (Aug 30, 2019)\nΔ smoke-thick (Aug 20, 2020) – smoke-free (Aug 20, 2019)\nΔ cloud-thin (Sep 07, 2019) – cloud-free (Aug 30, 2019)\nΔ cloud-thick (Sep 18, 2019) – cloud-free (Aug 20, 2019)\n6. Figure 3 and lines 216-241: percentages in lines do not match the ΔIrradiance scale in Figure 3. I assume that the scale must be multiplied by 100 to be correct, but the authors should check.\n7. Lines 246, 258", "reviewer_266": "['Figure 4.a)', ['Figure 4.b-d)', ['Figure 4.a). The indices a', 'b-d are not given in the plots.\\n8. Line 298: I understand that you apply correlation analysis per month. But I cannot understand how you get n=42', 'since from 2006 to 2021 it’s 16 years. Please clarify.\\n9. Lunes 543-544: “Cloud-driven changes were calculated by subtracting the clear-sky values from the all-sky values on the same day”. I think that you subtract the all-sky values from the clear-sky values', 'but cannot understand the “on the same day”.\\n10. Equation 2 and line 608: denominator 80 should be 800.', {'reviewer_3': 'This manuscript seeks to understand the impact of increasing wildfires on solar generation.\\nThere is a comprehensive publication on the same topic which uses the same solar radiation data (https://www.nrel.gov/docs/fy23osti/86640.pdf). The authors are requested to provide detailed rationale for why this manuscript should be published given that this previous publication has already dealt with this issue. In this reviewer’s opinion this previously publication has better treatment of –\\n(1) Consistency of solar radiation and clouds and aerosols as they come from the same source unlike this manuscript.\\n(2) The AOD is validated against ground observations.\\n(3) The PV generation is computed using hourly data using more detailed technical inputs(single-axis tracking etc.) unlike this manuscript which has simplistic treatment (horizontal and fixed and monthly instead of hourly).\\nAdditional comments –\\n(1) Line 98-100: Authors claim that there is more fixed tilt than PV without any evidence. Actually, the US has more utility-scale (tracking) deployments than roof-top fixed. Please see the first figure in https://www.seia.org/solar-industry-research-data.\\n(2) Line 113-115 : How does the AOD from MAIAC compare with ground observations and the NSRDB AOD. This is important as solar radiation comes from the NSRDB and the AOD is NSRDB might be different from MAIAC.\\n(3) Line 121: Please provide references to the “prior studies”.\\n(4) Line 125-126: This statement assumes that panels have fixed tilt. Again, please check figure 1 in https://www.seia.org/solar-industry-research-data which shows utility scale capacity to be much larger than rooftop. Most utility scale deployments are tracking.\\n(5) Line 205-208: Please compare with this report. (https://www.nrel.gov/docs/fy23osti/86640.pdf) The AOD used in this study is not the same AOD used in the NSRDB. Has the AOD from NSRDB been compared with the AOD from this study to demonstrate that they are similar. How about AERONET comparisons?\\n(6) Line 271-272: Why is generation computed monthly instead of hourly. This treatment is too simplistic.\\n(7) Line 483-489: Why are all these different datasets being used. Solar radiation from NSRDB, AOD and cloud mask from MAIAC, cloud optical depth from CERES. The NSRDB already has all this information. So two questions –\\n(a) How does all this interpolated data compare with the NSRDB AOD, COD and cloud mask.\\n(b) How do you account for differences in cloud mask from MAIAC and NSRDB. Pixels might be cloudy in MAIAC and clear in NSRDB and vice versa.\\n(8) Line 559: Again, this whole exercise is too simplistic. Why is this computation done on monthly averages. PVlib provides all the PV modeling tools to compute generation for hourly data. Also, single-axis tracking should be considered as that is what drives utility scale generation. Finally, rooftop still have a particular orientation so horizontal is not a good treatment.'}, {'author_response': 'INTRODUCTORY RESPONSE:\\nWe thank the reviewers for their comments. We considered each critique and recommendation and have made several updates to the original manuscript. We begin this document with a detailed discussion of our contributions to the literature and relative to the NREL Technical Report entitled “Final Technical Report: Impact of Wildfires on Solar Generation, Reserves, and Energy Prices” (Cai et al., 2023), as requested. We then respond to Reviewer #3’s additional comments as some are partially addressed in our primary response. Afterward, we respond to Reviewer #1 and Reviewer #2’s comments. Figure numbers in the response document refer to the original figure numbers for consistency. Following our primary response, responses to individual comments are included in blue text. We thank you for your time and consideration of our manuscript.\\n1\\nREVIEWER #3 OVERALL COMMENT:\\nThis manuscript seeks to understand the impact of increasing wildfires on solar generation.\\nThere is a comprehensive publication on the same topic which uses the same solar radiation data\\n(https://www.nrel.gov/docs/fy23osti/86640.pdf). The authors are requested to provide detailed\\nrationale for why this manuscript should be published given that this previous publication has\\nalready dealt with this issue. In this reviewer’s opinion this previously publication has better\\ntreatment of –\\n(1) Consistency of solar radiation and clouds and aerosols as they come from the same source\\nunlike this manuscript.\\n(2) The AOD is validated against ground observations.\\n(3) The PV generation is computed using hourly data using more detailed technical inputs\\n(single-axis tracking etc.) unlike this manuscript which has simplistic treatment (horizontal and\\nfixed and monthly instead of hourly).\\nPrimary Response:\\nWe thank Reviewer 3 for drawing our attention to the recent NREL Technical Report entitled\\n“Final Technical Report: Impact of Wildfires on Solar Generation, Reserves, and Energy Prices”\\n(Cai et al., 2023). However, we strongly disagree with Reviewer 3’s assertion that Cai et al.\\ncomprehensively addressed the issue of smoke impacts on solar energy as well as the implication\\nthat our work only replicates their contributions. Cai et al. provide a valuable assessment of\\nwildfire smoke’s impact on average monthly DNI and GHI across California, but their study is\\nlimited in time and space. In fact, Cai et al. highlight the limitations of their scope within the\\nreport and express a need for future work that robustly examines smoke-irradiance interactions at\\nlarger scales with a specific focus on transported smoke:\\n“[Our] wildfire scenarios were focused on fires in California alone, although\\nwildfire smoke effects on AOD can occur at large downwind distances…Moreover,\\nalthough the western United States is where most fires occur, the atmospheric\\ntransport of smoke can also affect other parts of the country…Future analysis can\\nbe more comprehensive by including all of North America to understand the\\nimpacts in a scenario with simultaneous fires in several regions. This analysis\\n2\\nfocused on only California, but the fire impact on solar generation and the\\nsubsequent impact is likely to be observed in other regions of the United States as\\nwell as outside the United States…where large fires recurrence has been becoming\\nmore frequent” (pg. 50).\\nOur novel contributions outlined below directly address Cai et al.’s call for a more\\ncomprehensive investigation of smoke’s impact on solar resources beyond California. We also\\naddress the needs highlighted in the U.S. Department of Energy’s 2021 Solar Futures Study\\n(Ardani et al., 2021) for assessments that move “beyond traditional forecasting requirements to\\nadd considerations for evaluating localized or regional impacts on PV generation such\\nas…wildfires, or smoke (Cole, Greer, et al. 2020; Gómez -Amo et al. 2019).” We examine the\\nentire contiguous U.S. in our analysis (Results sections 2 and 3), which includes regions with\\ndistinctly different smoke characteristics (e.g., Bian et al., 2020). We assess impacts over\\nvariable time scales from daily case studies (Results section 1) to a 16-year longitudinal analysis\\n(Results section 3) and capture the variability caused by different fire start locations and smoke\\ntransport paths from one wildfire season to the next. We also perform analysis that position our\\nresults relative to cloud-driven changes to PV resources. Our approach ensures that we\\ncharacterize regional differences and the impact of smoke along a continuum from local to\\ntransported smoke. Overall, our work targets and provides answers to significant gaps in the\\ncurrent literature on smoke and PV production, which is often restricted to individual wildfire\\nevents or seasons and geographically limited to near-source areas.\\nContribution 1 – Comprehensive quantification of wildfire smoke’s impact on solar\\nresources across the contiguous U.S.: The properties of wildfire smoke vary substantially based\\non combustion conditions, fuel type, dilution, and atmospheric aging. Consequently, the impact\\nof smoke on PV production is highly dependent on the proximity of solar panels to fire locations,\\nunderscoring the importance of a national-level analysis. Our article presents the national\\nperspective on smoke-irradiance interactions needed to explore the continuum from local to\\ndistantly transported smoke to fully understand smoke’s potential impact on PV production.\\nCai et al. attempt to differentiate between local (source) and transported (downwind) smoke by\\ncomparing smoke-driven changes in clearsky GHI from September 10-14 in the Greater Bay\\nArea and Los Angeles metropolitan area, respectively. The authors find minimal difference\\nbetween local and transported smoke impacts, with the former producing clear-sky GHI\\nreductions of 10-23% and the latter 12-19% (Figure 18 in Cai et al.). Such results support large\\nsolar resource reductions for both types of smoke. Ultimately, Cai et al.’s assumption that the\\nsmoke in the Los Angeles area represents downwind or transported smoke is inaccurate. While\\ntransported smoke from Northern California wildfires was present over the Los Angeles area in\\nmid-September, two large local wildfires, in addition to multiple smaller fires, produced\\nsubstantial smoke emissions that directly affected air quality in the Los Angeles area. The El\\nDorado and Bobcat fires ignited on September 5 and 6, respectively, and burned through\\n3\\nNovember. Both are visible along with the associated large smoke plumes generated in the\\nNASA Worldview images below (Response Figure 1). Given this, smoke in the Los Angeles\\narea was of mixed source and age with a large component representing fresh, dense smoke from\\nlocal sources. Such smoke would have distinctly different optical properties from the aged,\\ndilute, transported plumes characteristic of areas further north and east (e.g., Bian et al. 2020).\\nResponse Figure 1: NASA Worldview images (Terra/Aqua true color with MODIS/VIIRS\\nhotspots) of the Los Angeles area showing a mixture of transported and local smoke impacting\\nthe area during the timeframe represented by Cai et al. and a small inset in the middle panel\\nshowing the LA subregion from Cai et al.’s map.\\nBecause we worked at a national scale, our methodology comprehensively characterizes the\\ncontinuum of smoke impacts in the U.S., which vary substantially across different regions and\\nwith distance from fires. For example, we examine trends in the North Central (northern Great\\nPlains and Midwest) and Northeast regions that are primarily affected by transported smoke from\\nthe Southwest, Pacific Northwest, Rocky Mountains, and Canada. In these downwind regions,\\nsmoke conditions differ substantially from those in California in terms of smoke hours, source,\\nand optical properties (e.g., Brey et al., 2018; Bian et al., 2020). In contrast to Cai et al., we find\\n4\\nthat transported or downwind smoke results in much lower reductions in clear-sky GHI. Our\\nfindings support the stability of solar energy resources in many regions of the U.S. Thus, the\\nfindings of Cai et al. cannot be generalized. With this broader perspective, we demonstrate that\\nlocal and transported smoke have substantially different impacts on solar resources with\\nimportant implications for solar development across different regions.\\nContribution 2 – Variability of smoke-driven changes in irradiance across temporal scales:\\nWe quantify smoke-driven changes in clear-sky DNI and GHI across timescales ranging from\\ndaily to multi-year analyses. Our daily assessment of California resources reveals the substantial\\nshort-term impacts of smoke on solar resources. We then assess changes in monthly solar\\nresources between a single low smoke year and an extreme smoke year to characterize how the\\nimpact of future smoke conditions may differ from historical conditions. Finally, we investigate\\nchanges over a 16-year period with variable smoke production and impacts across U.S. regions.\\nBy grounding our daily analysis in historical empirical observations (NOAA HMS) of smoke, we\\nensure that our findings are grounded in accurate geographically resolved representations of the\\ndistribution of smoke.\\nIn contrast to studies solely focused on monthly averages (i.e., baseline or background\\nresources), we conduct daily case studies that allow us to demonstrate the broad range of\\nsmoke’s impacts on short-term solar resources. Our case studies specifically target days with\\ndifferent smoke optical conditions to highlight the importance of AOD in determining smoke’s\\nimpact on solar resources.With this analysis, we juxtapose the magnitude of short-term\\nfluctuations against the impacts on long-term baseline resources. The former is useful for\\nplanning day-to-day production and the latter for determining placement and architecture for\\nfuture developments. For example, simply stating that PV resources declined by 20% at a\\nmonthly level fails to acknowledge that daily losses of 50% occurred. Both metrics provide\\ninvaluable information for preparing adequately for future smoke conditions.\\nWe also perform the longest longitudinal study of smoke and solar resource impacts based on\\ndirect observations of historical smoke conditions. The multi-year correlation and regression\\nanalyses in our final results section present these results. However, to better emphasize this\\nmajor contribution to the field, we have added the figure below (Response Figure 2) to our third\\nresults section on trends from 2006-2021. This figure highlights the regional differences in\\nsmoke-irradiance trends resulting from shifts in fire locations and meteorological conditions\\nfrom one fire season to the next. Such variability is ignored in studies that focus exclusively on\\n2020 or assume 2020 fire locations in future climate models.\\n5\\nResponse Figure 2: Time series (upper six panels) of the August (solid) and September (dashed)\\ndaily mean smoke coverage (teal line), clear-sky DNI (purple line), and clear-sky GHI (orange\\nline) for each CONUS region from 2006-2021. Line plot (bottom panel) indicating the percent\\nchange in irradiance during years with high (i.e., above average) smoke coverage compared to\\nyears with low smoke coverage based on the regional average smoke coverage for each month.\\nSmoke coverage refers to the fraction of each region that was smoke-impacted on a given day.\\n6\\nContribution 3 – Comparative impact of smoke on PV resources relative to clouds\\nClouds are one of the major atmospheric phenomena that impacts solar production and\\nsignificant attention has been paid to cloud effects in the literature (e.g., citations). Since smoke\\nand cloud optical properties differ substantially, it is essential to compare the effects of clouds\\nand smoke on solar resources. We show that optically dense clouds can result in far larger losses\\nto short-term solar resources, but smoke-driven reductions can reach similar magnitudes to those\\ncaused by optically thinner clouds. Anecdotally, a comparison to the cloud effect was the main\\nquestion and request we received when presenting earlier versions of this work. Ultimately, the\\nassessment of daily level changes in solar irradiance due to smoke and clouds is necessary for\\nframing our subsequent findings on monthly and multiyear variability in solar resources.\\nContribution 4 – Novel application of premier smoke observational dataset: Importantly, we\\nground our analysis in the novel use of a well-understood observational and operational smoke-\\nspecific data product. We chose to determine smoke-impacted locations based on the NOAA\\nHMS, which is based on direct observations of smoke by trained analysts examining\\ngeostationary satellite images throughout the day. Often studies, including Cai et al., rely on an\\nincrease in AOD to indicate when smoke is present and assess smoke-driven changes in\\nirradiance. However, AOD is a measure of all aerosols present in the columns from a range of\\nsources including wildfire smoke, dust, sea salt spray, and anthropogenic pollution. So, while the\\nprimary aerosol change in California from 2019 to 2020 can be attributed to smoke, other\\nvariations in aerosols due to meteorological conditions and anthropogenic emissions are possible.\\nAdditionally, AOD measurements are sensitive to the retrieval algorithm used and with some\\nalgorithms are better able to characterize smoke than others. We use AOD to provide additional\\ncontext but do not rely on AOD as a smoke indicator or proxy. AOD provides essential\\ninformation for understanding smoke’s impact on solar energy, but by using smoke observations\\ncoupled with AOD, we ensure accurate classification of smoke-free and smoke-impacted\\nconditions.\\nConcluding remarks: As we have demonstrated, our work represents a novel advancement of\\nour knowledge of how local and transported smoke impact solar resources across different U.S.\\nregions and nationally. In contrast to Cai et al. we rely on a well-understood observationally\\ngrounded smoke mapping product to isolate smoke-impacted locations rather than AOD, which\\ncan include multiple aerosol sources. We also add a new figure that demonstrates the stability of\\nclear-sky GHI over time regardless of variations in the intensity of individual wildfire seasons or\\nthe location of burning, which importantly contrasts with Cai et al.’s results that only consider\\nirradiance changes given emissions from 2020 wildfire locations. Finally, Reviewer 3 notes the\\nfollowing as benefits of Cai et al.’s study relative to ours, and we address each below.\\n7\\n(1) Consistent source of solar radiation, cloud, and aerosol data.\\nOur analysis focuses on two data sources: the NSRDB for solar radiation and the NOAA HMS\\nfor smoke location. We chose to include observed aerosol and cloud data to provide additional\\ncontext around the real-world smoke and atmospheric conditions experienced. We elected not to\\nuse the NSRDB AOD for this because of its reliance on reanalysis data at a moderate resolution.\\nOur goal was to tie observed conditions to modeled expectations, but we did not mean to imply\\nthat the MAIAC AOD or CERES COD drove the NSRDB irradiance calculations. To provide\\ngreater clarity, we have added the NSRDB AOD and COD values to maps in a new figure that\\ncombines Figures 1 and 2 (below). We have adjusted our captions and now note in our results\\nsection how the NSRDB compares to the satellite observations. We have also added two figures\\nto the supplementary information that compare the monthly average AODs from MAIAC and\\nNSRDB and reference these figures in our discussion of changes to monthly irradiance.\\nResponse Figure 3: Combined version of Figures 1 and 2 showing the change in clear-sky DNI\\nand GHI due to smoke and clouds given different optical conditions. We have incorporated the\\nAOD and COD maps into this single figure and added a column with the AOD and COD values\\nused by NSRDB to model irradiance on these days.\\n8\\n(2) AOD validation against ground observations.\\nValidating the AOD retrieved by MAIAC and used by the NSRDB is well outside the scope of\\nthis study. Such validations have already been performed for MAIAC (e.g., Jethva et al. 2019)\\nand NSRDB (e.g., Lin et al. 2023). As we only use the MAIAC AOD to provide additional\\ncontext to our results (i.e., not to model irradiance or identify smoke position), we did not\\ninclude independent product validations in our study. However, we have compared the mean\\nmonthly AOD for August and September of 2019 and 2020 as calculated using MAIAC (Figure\\n3.c & 3.d) and NSRDB. Below are maps (Response Figure 4) of the change in AOD from 2019\\nto 2020 for both months from each source. Both products show the largest increase in AOD\\noccurs near large wildfires, particularly along the West Coast. Increases in AOD extend across\\nCONUS but are much smaller, and the increase in AOD is greater and more extensive in\\nSeptember than August. The main difference between the two products is the level of precision\\nfor MAIAC. MAIAC has a much finer spatial resolution (1-km) than MERRA-2 (0.625 x 0.5\\ndeg), which is used to inform the NSRDB AOD. As such, the NSRDB distributes the increased\\nAOD near fire sources over a larger area than MAIAC. Given this, we have added the sentence\\nbelow regarding the impact such a discrepancy in spatial resolution would have on our results.\\nPrimarily, this likely indicates that the NSRDB may compute larger reductions in irradiance\\nfurther from fire than would be modeled given an AOD with a finer resolution:\\n● “The AOD in smoke-impacted locations averaged 0.56 on August 20 (median: 0.40)\\n550\\nand 0.43 on August 30 (median: 0.32) according to MAIAC observations. The NSRDB\\nassumed a higher average AOD (August 20: 0.99; August 30: 0.6), and the lower\\nresolution distributed the highest AODs near fire sources over larger areas. Thus, the\\nNSRDB may compute larger reductions in irradiance further from a fire than models at a\\nfiner resolution.”\\nAdditionally, we include a linear regression comparison of the AODs from MAIAC and the\\nNSRDB (Response Figure 5) that mirrors Cai et al.’s comparison of the NSRDB and AERONET\\n(Response Figure 6). We find nearly identical results to Cai et al.’s comparison. However, we\\nnote that the NSRDB performance declines at higher AODs characteristic of optically dense\\nsmoke plumes. By underestimating the AOD, NSRDB would likely underestimate smoke-driven\\nchanges in irradiance directly beneath fresh, dense plumes. As such, using the NSRDB to\\ncalculate changes in DNI and GHI would likely result in an underestimate of the reduction\\ndirectly beneath fresh, dense plumes and a slight overestimate of the reduction in the immediate\\nvicinity of such a plume as the increased AOD is averaged across the MERRA-2 resolution.\\n9\\nResponse Figure 4: Comparison of the change in AOD from 2019 to 2020 for August and\\nSeptember given two sources for the AOD values (MAIAC vs. NSRDB)\\nResponse Figure 5: Comparison of the NSRDB and MAIAC AOD for August and September\\n2019 and 2020 with a linear regression fit.\\n10\\nResponse Figure 6: Cai et al. comparison of the NSRDB and AERONET AOD.\\n(3) Computation of PV generation using hourly data and more detailed technical inputs (single-\\naxis tracking etc.).\\nThe main focus of our paper is on quantifying smoke’s impact on resource availability (i.e., DNI\\nand GHI) at a variety of spatial and temporal scales. We include a brief estimation of the\\nsubsequent impact on PV performance given standardized installations at 13 locations across the\\nU.S. (Figure 5, Supplementary Figure 1). The goal of this analysis is to contextualize our\\ncalculated changes in baseline average solar resources during a particularly extreme smoke\\nseason and given regional differences in background irradiance and temperature. We do not\\nimply a robust calculation of PV generation, but rather a first approximation of smoke-driven\\nchanges. Exhaustively modeling changes in PV generation was outside the scope of our study.\\nUse of daily average irradiance for assessing PV performance: Panel performance\\nassessments are performed at a variety of temporal scales for different reasons. Some are\\nperformed at the 5-minute level to assist in load balancing while others are at hourly levels to\\nassist in planning for reserve capacity and nodal prices (Cai et al.). Still other tools, like NREL’s\\nPVWatts, focus on assessing longer time scales to assist solar installers and homeowners in\\nplanning future developments. Our focus is on long-term performance. As such, we use the daily\\naverage smoke-free and smoke-impacted irradiance for September–the month with the greatest\\n11\\nsmoke impact in 2020–to estimate the change in panel performance given reductions in direct\\nand increases in diffused light. Our approach is similar to benchmark PV performance modeling–\\nwhich routinely models output under a set of standard test conditions that assume a uniform\\nirradiance value of 1000 W/m2–and PVWatts-style models that allow users to adjust irradiance\\nby applying a uniform percent reduction monthly irradiance. The benefit of our approach is that\\nwe introduce regionally realistic irradiances, temperatures, and wind inputs in a manner that\\nallows us to isolate changes in PV performance based on smoke-driven changes in irradiance,\\nnot temperature or meteorology.\\nAdditionally, if our goal had been to characterize short-term PV generation, the HMS smoke\\nproduct presents a practical limitation to such an analysis. The HMS allows us to determine\\nwhether a location experienced smoke on a given day but not the exact timing of the smoke\\nplume. So, while the NSRDB is available at the daily level, the status of modeled irradiance\\nvalues as smoke-free or smoke-impacted cannot be determined reliably at the hourly level. An\\nhourly analysis requires precise resolution of a smoke plume’s location, particularly for fresh,\\ndense, local plumes, which can be narrowly constrained and shift rapidly throughout the day.\\nTaking these factors into account, a larger time scale provides a broader picture of the impact of\\natmospheric smoke on long-term PV performance.\\nFixed panel architecture: Solar panels perform optimally under direct irradiation from the light\\nsource when the radiation is perpendicular to the surface of the panels. The primary impact of\\nsmoke is to change the irradiation condition from direct to diffused light. Under such a condition,\\nthe impact on power generation of fixed axis and single axis tracking installations would be\\nreasonably similar proportionally. For example, the NREL PVWatts application can be used to\\nquickly estimate how panel architecture influences smoke’s impact on PV performance. The\\ntable below presents the PV generation modeled by PVWatts given fixed and single-axis\\ntracking panel configurations with different tilts. While we include additional parameters in our\\nmodel, the PVWatts results demonstrate that the relative reduction in PV generation largely\\nmirrors the change in irradiance. Panel architecture changes the absolute generation but has little\\neffect on the proportional change in generation between smoke-free and smoke-impacted\\nconditions. We have updated Figure 5 per the request of Reviewer #1 to represent the change in\\nPV output proportionally to account for these and regional differences. We also added a sentence\\nto the discussion of Figure 5 describing the potential impact of different panel architectures:\\n“While proportional changes in output are similar across fixed-tilt and single-axis tracking\\npanels, the absolute change will be greater for tracking panels, which have higher baseline\\nproduction.” Ultimately, quantitatively evaluating the PV output for fixed axis installations\\nallowed the manuscript to maintain its focus on the primary topic of resource availability while\\nusing PV performance as supporting evidence to frame the overarching implications of our\\nfindings.\\n12\\nSeptember PV Generation (kWh/m2/day)\\nin Los Angeles (34.13, -118.42)\\n9.3% decrease in Percent\\nTilt Configuration Baseline Difference\\nmonthly irradiance Reduction\\nFixed (open rack) 6.11 5.54 -0.57 -9.3%\\n0° Fixed (roof mount) 6.11 5.54 -0.57 -9.3%\\nTracking 1-axis 8.04 7.29 -0.75 -9.3%\\nFixed (open rack) 7.01 6.36 -0.65 -9.3%\\n34° Fixed (roof mount) 7.11 6.45 -0.66 -9.3%\\nTracking 1-axis 8.81 8 -0.81 -9.2%\\nAnalysis applications: Practitioners can use our estimates of resource availability to model the\\npotential impact of smoke on solar productivity in different regions. We consider these to be\\npotential impacts because the future distribution of smoke is not known. Our PV simulations\\nprovide an example of how practitioners can use our DNI and GHI results to plan for future solar\\ninstallations. The PV simulations are not intended to represent real historical losses in PV\\ngeneration as a consequence of smoke. Analyses of short-term impacts of smoke on PV\\ngeneration have been explored in other studies that we discuss (e.g., Donaldson et al., 2021;\\nPerry and Troccoli, 2015; Gómez-Amo et al. 2019; Juliano et al., 2022) and to which Cai et al.\\ncan be added. The Cai et al. simulation is geared toward energy system balancing and planning\\nand our simulation is geared towards assessing solar potential across the U.S. for future PV\\ninstallation planning among other potential uses.\\n13\\nREVIEWER #3 ADDITIONAL COMMENTS:\\n(1) Line 98-100: Authors claim that there is more fixed tilt than PV without any evidence.\\nActually, the US has more utility-scale (tracking) deployments than roof-top fixed. Please see the\\nfirst figure in https://www.seia.org/solar-industry-research-data.\\nLines 98-100 were meant to refer to the abundance of PV production in the U.S. relative to\\nConcentrating Solar Power (CSP) and should not have included the word “stationary.” We have\\nrewritten this sentence as follows: “Overall, while we examine both DNI and GHI, our\\ndiscussion focuses on GHI because most solar energy in the U.S. comes from PV panels rather\\nthan CSP.”\\nRegarding our focus on fixed panels for Figure 5, as discussed in the primary response, the focus\\nof this work was on evaluating the resource availability rather than PV output. However, we\\nagree with the reviewer that most utility-scale PV deployments employ a single-axis tracking\\nsystem. Still, fixed tilt panels represent a substantial share of the nation’s PV installations.\\nUtility-scale installations are not exclusively composed of tracking panels. As per the EIA’s\\nAnnual Electric Generators Report (https://www.eia.gov/todayinenergy/detail.php?id=30912),\\nthere were approximately equal amounts of fixed tilt and single-axis tracking installations in the\\nU.S. as of 2015. The distribution of fixed and tracking installations are not evenly distributed\\nnationally (see maps below). Tracking panels are far more abundant in the West where 56% of\\ninstallations used single-axis tracking systems. In the East, 80% of panels were fixed tilt. Utility\\nsolar has increased substantially since 2015, but 28% of utility-scale net capacity (MW) came\\nfrom fixed tilt panels in 2022 (EIA Form 860 data on solar energy;\\nhttps://www.eia.gov/electricity/data/eia860/). Finally, in addition to showing that most U.S. solar\\nenergy is produced at utility-scale installations, the SEIA report shows that residential\\ninstallations (usually fixed) produce a large share of U.S. solar energy (19%). The SEIA also\\nprojects that 15% of U.S. households will have a PV system installed by 2030\\n(https://www.seia.org/sites/default/files/2024-06/SolarCheatSheetQ2_2024.pdf). As such,\\nunderstanding the impact on fixed tilt deployments has important implications for homeowners\\nand the utility grids. Fixed axis systems remain a significantly large proportion of U.S.\\nrenewable energy production to warrant consideration.\\nAdditional details regarding our focus on fixed tilt panels are included in our primary response.\\n14\\n(2) Line 113-115 : How does the AOD from MAIAC compare with ground observations and the\\nNSRDB AOD. This is important as solar radiation comes from the NSRDB and the AOD is\\nNSRDB might be different from MAIAC.\\nThis comment was addressed in our primary response above. We have added the NSRDB AODs\\nto our revised Figure 1 (combination of original Figures 1 and 2). We’ve also added a\\ncomparison of the MAIAC and NSRDB monthly mean AOD for August and September of 2019\\nand 2020 to the Supplementary Information. These results demonstrate that MAIAC and\\nNSRDB AODs are largely similar. At a daily level, the NSRDB shows the same spatial patterns\\nfor the smoke but estimates a higher average AOD. The coarser resolution of the inputs for the\\nNSRDB AOD also leads to higher AODs being distributed across larger areas. At the monthly\\nlevel, the NSRDB is biased low at higher AODs and, again, distributes higher AODs across\\nlarger areas. We have added the following details to the monthly results section:\\n● “Observed monthly mean AODs align well with those underlying the NSRDB model\\noutput. A comparison of the two products is available in Supplementary Figures 1 and 2.\\nOverall, AOD patterns and changes are consistent across both products, but the NSRDB\\nis biased low at high AODs and distributes high AODs over larger distances. As such,\\nprojected changes in irradiance may be lower directly above active fires, but extend\\nfurther from fires than what observed AODs suggest.”\\n(3) Line 121: Please provide references to the “prior studies”.\\nWe have added the following text to highlight three examples of these prior studies: “We find\\nconsiderable losses in model-based daily mean DNI (32-44%) and GHI (11-17%) in California\\ndue to local smoke. Such reductions are consistent with observations from Athens, Greece during\\nthe 2021 wildfire season where GHI declined 10-20% due to smoke (Masoom et al., 2023);\\nCanberra, Australia where DNI and GHI reductions reached 26% and 32%, respectively,\\napproximately 1 km from a controlled burn (Perry and Troccoli, 2015); and in Golden, Colorado\\n15\\nwhere smoke from the 1988 Yellowstone National Park fires decreased daily DNI and GHI by\\n37% and 9%, respectively (Hulstrom & Stoffel, 1990).”\\n(4) Line 125-126: This statement assumes that panels have fixed tilt. Again, please check figure\\n1 in https://www.seia.org/solar-industry-research-data which shows utility scale capacity to be\\nmuch larger than rooftop. Most utility scale deployments are tracking.\\nPlease see our primary response above and our response to Additional Comment #1. Our text in\\nthe cited lines (125-126) refers to the changes in clear-sky GHI resources and potential\\nimplications. These lines do not present or discuss results from PV performance modeling. We\\nhave updated the sentence to clarify by replacing “This result implies” with “The scale of the\\nGHI reductions implies.”\\n(5) Line 205-208: Please compare with this report.\\n(https://www.nrel.gov/docs/fy23osti/86640.pdf) The AOD used in this study is not the same\\nAOD used in the NSRDB. Has the AOD from NSRDB been compared with the AOD from this\\nstudy to demonstrate that they are similar. How about AERONET comparisons?\\nPlease see our primary response above and our response to Additional Comment #2.\\n(6) Line 271-272: Why is generation computed monthly instead of hourly. This treatment is too\\nsimplistic.\\nPlease see our primary response above in the section “Use of daily average irradiance for\\nassessing PV performance” under point 3.\\n(7) Line 483-489: Why are all these different datasets being used. Solar radiation from NSRDB,\\nAOD and cloud mask from MAIAC, cloud optical depth from CERES. The NSRDB already has\\nall this information. So two questions –\\n(a) How does all this interpolated data compare with the NSRDB AOD, COD and cloud mask.\\n(b) How do you account for differences in cloud mask from MAIAC and NSRDB. Pixels might\\nbe cloudy in MAIAC and clear in NSRDB and vice versa.\\nPlease see our primary response above as well as our response to Additional Comment #2. We\\nhave addressed the comparison between the NSRDB and MAIAC AODs as well as NSRDB and\\nCERES CODs in these responses. The satellite products provide context as to the observed\\nconditions relative to the modeled changes in irradiance. We added comparison maps to the new\\ncase study figure (Response Figure 3) to highlight the differences between the products. We also\\nadded Supplementary Figures 1 and 2 to directly compare monthly AODs from the two products.\\n16\\nWe used the MAIAC cloud mask for two reasons. First, our goal was to remain observationally\\ngrounded in our analysis. Using a satellite-based cloud mask allows us to describe the observed\\nconditions and connect them to the NSRDB’s projected change in irradiance. Second, the\\nNSRDB literature indicates that its cloud mask is derived from the AVHRR Pathfinder\\nAtmospheres-Extended (PATMOS-x) algorithms, but a designated cloud mask variable is not\\nclearly included in the dataset. The NSRDB provides a cloud type variable that includes\\ncategories for clear, possibly clear, fog, water, super-cooled water, mixed, opaque ice, cirrus,\\noverlapping, overshooting, dust, smoke, and unknown scenes. However, this cloud type variable\\ncombines both cloud (e.g., cirrus) and aerosol (dust/smoke) designations. Due to complexities\\nand ambiguities in these categories, we decided against constructing a cloud mask based on the\\ncloud type variable. In contrast, MAIAC provides a well-documented and high-resolution cloud\\nmask that indicates cloudy, possibly cloudy, and clear scenes separate from aerosol designations.\\nAdditionally, using MODIS-based observations for characterizing cloud conditions is\\ncommonplace with the understanding that MODIS provides a snapshot rather than continuous\\ncloud observations.\\n(8) Line 559: Again, this whole exercise is too simplistic. Why is this computation done on\\nmonthly averages. PVlib provides all the PV modeling tools to compute generation for hourly\\ndata. Also, single-axis tracking should be considered as that is what drives utility scale\\ngeneration. Finally, rooftop still have a particular orientation so horizontal is not a good\\ntreatment.\\nPlease see point 3 of our primary response above as well as our response to Additional Comment\\n#1. In our modeling, we assume the fixed tilt panels are oriented at the same angle as the\\nlocation’s latitude. We have added adjusted Lines 623-624 to clarify the panel tilt angle as\\nfollows: “For our simulations, we evaluated panel performance at 13 locations for the month of\\nSeptember in 2019 and 2020, when smoke impacts were greatest. We assumed fixed tilt panels\\noriented at the angle of each location’s latitude, consistent with industry standards.”\\n17\\nREVIEWER #1 COMMENTS:\\nReview of “Solar energy resource availability under extreme and historical wildfire smoke\\nconditions” by Corwin et al.\\nThis paper combines several observational datasets to develop a novel synergistic approach to\\nexamine the impact of fire smoke plumes on quantities relevant to solar energy, namely global\\nhorizontal irradiance (GHI) and direct normal irradiance (DNI). This work is clearly motivated\\nby not only the increase in deployment, and planned future deployments, of solar resources but\\nalso recent studies showing a notable decrease in solar energy resulting from wildfire smoke\\nplumes. The authors first focus on the very active 2020 wildfire season over California, which\\nhas received much attention in the literature recently. Similar to these previous studies, they find\\nthat DNI, and to a lesser extent GHI, is reduced in accordance with smoke “thickness”,\\nrepresented by the aerosol optical depth. The relatively minimal impact of smoke on GHI is\\nimportant since most of the deployed solar resources (photovoltaic) are more sensitive to it rather\\nthan to DNI. The authors then take a broader approach to understand how far-reaching the\\nimpacts of smoke can be on DNI and GHI during the 2019 and 2020 seasons. They conclude that\\nwhile these quantities can be drastically reduced nearby a plume, the downstream impacts very\\nquickly diminish, such that the fingerprint from fires across the western U.S. is nearly\\nunidentifiable across the eastern U.S. (in terms of irradiance). This is especially true for GHI,\\nwhere reductions are confined to 100s or even 10s of km from an active burning region. A longer\\nperiod from 2006-2021 is also used to clarify regional trends. Finally, a model of photovoltaic\\noutput is used to clarify actual performance impacts due to the smoke. Accounting for the\\nlocation-based temperature rather than a standard temperature is found to be important.\\nOverall, I think this is a nice and comprehensive study. I find the manuscript to be well written\\nwith explanations that are clear. The logic was mostly easy to follow, and I appreciate the\\nauthors provide some discussion of the limitations of their study. I think this will nicely\\ncomplement recent literature on the subject and hopefully motivate additional studies. I have one\\nmajor comment and some minor comments that should be addressed before the manuscript can\\nbe acceptable for publication.\\nMajor comment:\\n1. One important piece missing from the Discussion and/or Methods section is the impact of the\\napproach for the HMS product. In the Methods section, you talk about the HMS producing a\\n“conservative estimate of plume number and extent”. Do you have any feel for how conservative\\nthis may be? And are there any error/confidence metrics available with this product? I wonder\\nhow often smoke plumes, especially the ones transported further distances (i.e., more diffuse\\nsmoke and generally higher in the atmosphere, so probably harder to detect with confidence\\nmanually), would be misclassified. If it\\'s a conservative estimate, then more actual smoke cells\\n18\\nwould be classified as non-smoke cells, thus making the two data sets more alike (i.e., showing\\nsmaller differences between smoky and non-smoky periods). I think the authors need to\\ncomment on this and provide additional discussions somewhere in their manuscript.\\nThank you for this comment. The HMS smoke product is often presented as a conservative\\nestimate of smoke locations (e.g., Brey et al., 2018) for three main reasons. First, the HMS is\\nonly available during the day as overnight observations are not possible given the current\\nmethodology. As we are interested in smoke’s impact on solar energy, this limitation does not\\napply to the current study. Second, analysts can struggle to accurately detect smoke at the edge\\nof diffuse plumes in areas with high levels of anthropogenic haze, but this is primarily an issue\\nover the southeastern U.S. Since we are focused on smoke from large western U.S. wildfires, this\\nlimitation is expected to have little impact on our results, particularly given that the fire season in\\nthe Southeast generally occurs much earlier in the year and is characterized by smaller\\nagricultural fires. Third, clouds can pose a challenge if the edge of a plume is obscured by the\\ncloud.\\nWhile noting these limitations is necessary when using the HMS and generally included in most\\nHMS-related articles, these limitations are not expected to result in meaningful changes given\\nour focus on large western wildfires, which produce the optimal conditions for plume detection\\nin the HMS. For local smoke, the HMS performs well in detecting the sharp edges of fresh,\\ndense plumes in the western U.S. The smoke transported far downwind from these fires mainly\\naffects areas over the Northern Great Plains and Midwest where anthropogenic haze mixing is\\nless prominent. Finally, we focus on core daylight hours (solar zenith angle < 75 deg), which is\\nwhen the HMS performs best for smoke identification. Given this, we anticipated high degrees of\\naccuracy for the HMS detections in areas where smoke’s impact on irradiance is greatest (i.e.,\\noptically dense plumes). The main locations where HMS edge detection limitations could cause\\nerrors for us are locations where the plumes are most diffuse. As such, any impact on surface\\nirradiance would be at its smallest in these locations.\\nUnfortunately, while these are known limitations, no error or confidence metrics are available for\\nthe HMS and computing such metrics is beyond the scope of this study.\\nWe have added relevant details from this response to the Methods section:\\n● “The HMS is considered a conservative estimate of smoke plume number and extent\\n[18]. While error and confidence metrics are not currently available for the HMS smoke\\nproduct, we anticipate minimal sensitivity to the known limitations. Edge detection issues\\nare the most prominent limitation relevant to this study. The HMS can struggle to\\ndistinguish plume edges when 1) the smoke is diffuse and mixed with anthropogenic haze\\nor 2) the plume edge is obscured by clouds. The former issue mainly affects observations\\nover the southeastern U.S., but the fire season in the southeast generally occurs much\\n19\\nearlier in the year and is characterized by smaller agricultural fires. By focusing on the\\nwestern U.S. wildfire season, we work with plumes that the HMS is best suited to\\ncharacterize. For local smoke, the HMS performs well in detecting the sharp edges of\\nfresh, dense, western plumes. The smoke transported far downwind from these fires\\nmainly affects areas over the Northern Great Plains and Midwest where anthropogenic\\nhaze mixing is less common. We anticipate high accuracy for the HMS detections in\\nareas where smoke’s impact on irradiance is greatest (i.e., optically dense plumes) with\\nedge detection issues isolated to locations where plumes are most diffuse and light\\nextinction is lowest.”\\nSpecific comments:\\n1. L31-59: Please consider citing a recent study by Ghosh et al. (2022), which examined the\\nimpact of aerosols from air pollution on India’s solar production.\\nReference:\\nGhosh, S., Dey, S., Ganguly, D., Roy, S. B., & Bali, K. (2022). Cleaner air would\\nenhance India’s annual solar energy production by 6–28 TWh. Environmental Research\\nLetters, 17(5), 054007.\\nThank you for bringing this study to our attention. We have added the following sentence to this\\nparagraph: “Air pollution in India reduced GHI by 29% from 2001-2018, decreasing PV output\\nby 12-41% depending on panel architecture [13].”\\n2. L72-82: Please consider citing a recent study by Gilletly et al. (2023), which quantified smoke\\nimpacts on solar resources over the western U.S.\\nReference:\\nGilletly, S. D., Jackson, N. D., & Staid, A. (2023). Evaluating the impact of wildfire\\nsmoke on solar photovoltaic production. Applied Energy, 348, 121303.\\nTo incorporate Gilletly et al. (2023), we added the following highlighted text to the sentence\\nbeginning at line 74: “Average PV output declined 34% over two days at a PV plant in Spain due\\nto smoke [24], 7% at a solar lab in Australia during a controlled burn when AOD reached 0.25 (-\\n27% at peak) [25], 8.3% across 53 utility-scale PV plants in the western U.S. during the 2018\\nwildfire season [26], and ~10-50% at southern California solar plants during the 2020 wildfire\\nseason when AOD ranged from 0.5 - 4.5 [27].”\\n3. L142: Are clouds truly “rare” over this region in August and September? Some supporting\\nstudies would be good to reference here.\\nWe agree that the term “rare” is subjective and imprecise. We have modified this sentence to\\nbetter characterize the typical cloud conditions. We examined 25 years of CERES data that\\n20\\nestimates cloud fractions over the California area at the monthly level (below). The yearly\\nminimum cloud fraction occurs in September for 16 out of 25 years. The next most common\\nmonth with the minimum cloud faction is October. Overall, California’s minimum cloud\\nfractions vary from 30 to 45%. August cloud fractions are typically less than 50%. We have\\nrevised this sentence as follows: “In California, monthly averaged cloud fractions typically fall\\nbelow 50% in August and usually reach a minimum range of 30-45% in September as estimated\\nfrom 25 years of CERES data.”\\nTime series of monthly averaged cloud fraction from CERES observations between 2001 and\\n2024. Red circles indicated the month of September.\\n4. L161-163: Maybe not to the same extent, but isn’t the same true for smoke due to plume\\nheight, smoke age/composition, etc?\\nCorrect, the impact of smoke on irradiance varies substantially based on multiple factors. We\\nmentioned this briefly in L149/150 (\"The impact of wildfire smoke plumes on AOD varies\\nsubstantially, resulting in large differences in how plumes affect DNI and GHI”), but did not\\nelaborate in as much detail as we did for clouds. We have added the following sentence:\\n“Smoke’s impact on solar resources depends on its optical properties, which vary by fuel type\\nand load, combustion style, age, composition, and density [e.g., 19, 34, 35].” Additionally, we\\nhave chosen to reorganize and streamline the daily case study results into a single section to\\nbetter highlight the key takeaways more concisely.\\n21\\n5. L164-166: I have not heard this before; can you cite a paper, news article, etc. where this type\\nof question is being raised?\\nWe intended this statement to introduce a question/hypothesis that we were posing in this\\nanalysis, rather than a comment on previous articles. We recognize that this was unclear and\\nhave updated these two sentences as follows: “In California, summertime clouds are infrequent,\\nbut smoke impacts are becoming commonplace. Given that localized plumes are often quite\\noptically thick and absorbing, we explore whether the magnitude of smoke impacts may rival\\nthose of clouds and effectively increase the number of “cloudy” days. Our adjustments to address\\nthis comment (#5) and the previous (#4) create a more streamlined presentation of these results.\\n6. L275-277 and Figure 5: Can you comment on the lack of sensitivity resulting from the two\\nmethods shown in the right panel?\\nIn addressing this comment and comment #25, we found a copy-paste error that affected the\\nmagnitude of the change in PV output for the right panel. We have resolved the issue and\\nupdated the plot (see comment #25). The corrected figure shows greater sensitivity to changes in\\nGHI across locations, but similar proportional changes across GHI-based methods within a\\nlocation. The left panel in the original Figure 5 showed the impact of smoke on panels given\\nchanges in DNI (i.e., assuming ideal conditions where all incident irradiation is direct) compared\\nto GHI (direct + diffuse). The difference between these two methods is significant as smoke\\nmore strongly affects DNI. The panel on the right compared two approaches for handling\\ntemperature. Both methods rely on GHI as the irradiance input, meaning there is no difference in\\nthe incident irradiation. The difference in performance stems from changes in the panel operating\\ntemperature as a result of wind (increase = cooling) and irradiance (increase = warming). The\\nconsistency within a location across the GHI-based methods indicates that GHI is the dominant\\nforce in changing the PV output while temperature shifts the baseline output higher or lower. We\\nhave adjusted the Figure 5 caption to clarify the model inputs.\\n7. L287-288: I don’t understand this statement – so you’re saying that even though the southern\\nsites have warmer baseline temps, the smoke reduces temp more there than at the northern sites\\nsuch that the smoke-influenced temp is lower at the southern sites? Maybe I am missing\\nsomething.\\nAfter redesigning Figure 5 in response to Reviewer #1 Comment #25 (i.e., including percent\\nchange), we have decided to remove this sentence. The normalized change in power output does\\nnot indicate this pattern across the northern and southern sites. Additionally, we determined that\\nthe magnitude of the change does not vary meaningfully across the sites along a north-south\\ntemperature gradient.\\n22\\n8. L316: I don’t see the Corwin et al. (2022) citation in the reference list.\\nWe added the following to the reference list and updated the citation numbering:\\n● [45] Corwin, K. A., Corr, C. A., Burkhardt, J., & Fischer, E. V. (2022). Smoke‐driven\\nchanges in photosynthetically active radiation during the U.S. agricultural growing\\nseason. Journal of Geophysical Research, 127(23).\\n9. L318: How often do these regions see 100% smoke coverage? Similar question for L392.\\nTo provide greater context around the smoke coverage variable, we have added the following\\nparagraph to this section: “While we discuss how increasing smoke coverage from 0% to 100%\\nimpacts irradiance, regions rarely experienced 100% smoke coverage (Supplementary Figure 4).\\nAt most, 5% of August days in the PNW and NE regions were entirely smoke-impacted, and\\nfewer days reached 100% smoke coverage in September (2% for the NE, NC, SW, and PNW\\nregions). The PNW and NC regions reached ≥ 50% smoke coverage on nearly half of August\\ndays and a quarter of September days. In the SW, 1% (16%) of August and 2% (11%) of\\nSeptember days reached 100% (50%) smoke coverage.” We also added the following\\nhighlighted text to L392: “Average clear-sky DNI can decrease 14-17% in the SW and PNW in\\nAugust and September if wildfire smoke fully covers these regions, although such conditions are\\ninfrequent.” Finally, we have added the following figure to the Supplementary Information to\\nshow the proportion of days with smoke coverage below a given fraction.\\nSupplementary Figure 4: Empirical cumulative distribution function (ECDF) showing the\\ndistribution of the daily fraction of each region that was smoke-impacted in August (purple) and\\nSeptember (orange) from 2006-2021.\\n23\\n10. L368-370: You specifically call out California, which has historically had the largest\\nwildfires, but really isn’t this a potential risk for other regions considering we are seeing an\\nincrease in burning frequency/intensity in regions that have not traditionally experienced\\nwildfire? I think the recent fires in Texas are a good example of this, and Texas is also\\nresponsible for a large fraction of the U.S. solar production.\\nSince our case studies focused on California, we limited our discussion to California. However,\\nwe agree with the reviewer that many other regions are experiencing increased wildfire activity,\\nincluding those with high solar potential. We have added the following sentence to this\\nparagraph to connect these results to this broader context and impact: \"Additionally, localized\\nshort-term impacts are not isolated to California. Increased wildfire activity across the western\\nU.S. is exposing other areas rich in solar resources to local, dense smoke plumes (e.g., 2022 Calf\\nCanyon/Hermits Peak fire in New Mexico, 2024 Smokehouse Creek fire in Texas).”\\n11. L370-371: What is the cost difference between solar PV and natural gas?\\nIn the paper, we are referring to electricity generation costs per MWh. In a nutshell, solar\\ngeneration costs per MWh, often referred to as variable or marginal costs, are nearly $0/MWh\\nbecause the input, sunlight, is free. Natural gas has a positive variable cost because the input,\\nnatural gas, is not free. Therefore, natural gas generation is more expensive than solar generation\\nin $/MWh produced. We have updated the paragraph so that this context is clearer:\\n● “Since the current grid lacks large-scale electricity storage capacity, short-term declines\\nin solar generation must be offset by alternative generation sources, typically natural gas,\\nwith nonzero marginal costs. As such, more wildfire smoke could increase short-term use\\nof dirtier and more expensive—in $/MWh—natural gas generation or battery storage.”\\nMore broadly, if we want to compare two generation technologies such as natural gas and\\nsolar, there are several notions of cost we may want to consider. First, power plants have fixed\\ncosts including construction costs and daily operating and maintenance costs. Second, power\\nplants have variable (marginal) costs, or the costs associated with producing an additional MWh\\nof electricity. The variable cost of natural gas power plants is primarily made up of input costs,\\nthat is, the cost of natural gas in $/MMBtu. The amount of electricity a natural gas power plant\\nproduces is proportional to the amount of natural gas used in generation. Other costs such as\\npersonnel and maintenance are typically considered fixed costs. Solar PV plants have $0\\nmarginal costs because the input to generation is free sunlight. Once the solar panel is installed, it\\nproduces an additional MWh for free. There are maintenance costs associated with solar\\ngeneration, but again, they are typically considered fixed costs because a marginal MWh\\nproduced by a solar panel does not depend on maintenance.\\nWhy might natural gas replace solar power? In the present context, we are interested in\\nwhich power plants would replace solar PV generation when smoke causes a short-term decline\\nin generation. The answer to this depends on the aggregate electricity supply curve, sometimes\\nreferred to as the merit order, marginal cost curve, or dispatch curve. In competitive wholesale\\nelectricity markets, the dispatch curve is determined by variable costs and not fixed costs.\\nTherefore, variable costs determine which generation type (e.g., hydro, coal, natural gas) will\\nreplace declines in solar generation. There are numerous examples of dispatch curves available\\nand we paste an example from the US Energy Information Administration (EIA) below.\\nSource: https://www.eia.gov/todayinenergy/detail.php?id=7590\\nIn the Figure, each dot represents a different electricity generator. The y-axis represents\\neach generator’s variable cost. The generators are sorted from lowest to highest variable cost.\\nThe x-axis represents generation capacity with the distance between proximal dots representing\\neach individual generator’s capacity. The grid’s aggregate generation capacity is represented by\\nthe sum of the capacities along the x-axis.\\nRenewable generators, such as solar and wind, are the green dots and the furthest to the\\nleft on the supply curve with $0/MWh variable costs. Renewable generators are always\\ndispatched when available because they are always furthest to the left on the supply curve.\\nNuclear, hydro, and coal are also almost always dispatched, referred to as base load, because\\nthey have relatively low variable costs, and it is difficult for these generating units to quickly\\nramp up and down to offset short and medium-term losses in renewable generation capacity.\\nMoreover, California does not have coal generation so the base load in California, the left most\\nportion of the supply curve, consists of renewables, hydro, nuclear, and natural gas.\\nFinally, when solar generation declines, the entire supply curve shifts inwards (imagine\\nremoving the green dots and shifting the remaining dots to the left). The aggregate load, or\\ndemand, is represented by the vertical lines on the graph. The load can vary by the minute. The\\ntwo lines on this figure are hypothetical loads at different times of the day. The important thing\\nto consider is when the renewables go offline (e.g., when smoke blocks much of the sunlight),\\n25\\nthe supply curve shifts inwards, and a marginal producer comes online to meet the demand. The\\nmarginal producers are represented by the dots near the vertical lines, the hypothetical demands.\\nIn most electricity markets around the US, the marginal producers are natural gas generating\\nunits. In summary, when solar generation declines, the loss in generation is typically offset by\\nadditional natural gas generators coming online, and those natural gas generators have higher\\n(nonzero) variable costs than solar generators. This increases the wholesale electricity price,\\nwhich is what we meant when stating that declines in solar generation are met by more expensive\\nnatural gas generation. Additional battery or other storage could provide an alternative to natural\\ngas generation, but grid-level storage is not currently economically feasible.\\n12. L427-429: Does this largely depend on where we put our solar resources? I think your\\nstatement could be framed differently; that is, results stemming from your study can help inform\\nwhere deployment of future solar farms would be minimally impacted by smoke. Perhaps\\ncombined with knowledge of cloud impacts on DNI/GHI, and modeling of expected future\\nfire/clouds conditions, this could highlight ideal locations to deploy solar.\\nWe appreciate the reviewer’s rewording here to better highlight our major contribution. We have\\nupdated the closing statement of our article as follows: “These findings inform where long-term\\nsolar resources are most stable given climate-induced increases in wildfire smoke that drive\\nhigher AODs. Combining assessments of long-term smoke-irradiance interactions with projected\\nfire vulnerability and cloud conditions could highlight ideal locations for solar deployment.”\\n13. L446-447: Is this a limitation that would affect your results considering the rather coarse\\nresolution of MERRA-2 (which I believe it is 0.625 deg x 0.5 deg)? Please comment.\\nThe coarser resolution of MERRA-2 impacts the distance over which changes in irradiance are\\nexperienced due to smoke-driven increases in AOD. In Response Figure 3 (combination of\\noriginal Figures 1 and 2), we have added panels showing the AOD used by the NSRDB to drive\\nirradiance calculations. We have also added two figures to the Supplementary Information (SI\\nFigures 1 and 2) comparing the monthly average AOD calculated from MAIAC and that from\\nthe NSRDB. We describe both of these additions in our response to Reviewer #1. These figures\\ndemonstrate that the NSRDB AOD distributes the higher AOD observed near active fires with\\ndense plumes across a larger area. The high AOD from the smoke substantially increases the\\naverage AOD for the entire pixel. This may result in an overestimate of the distance over which\\nthe largest reductions in irradiance are felt. We have added text discussing the difference\\nbetween the MAIAC and NSRDB AODs throughout and the potential impacts on our results.\\n26\\n14. L502: So CERES is interpolated from 1 deg to 4 km? Do you expect these COD values to be\\nreliable considering this huge spatial difference?\\nThe CERES COD is determined from each footprint in a CERES overpass. Each CERES pixel is\\n20 km at nadir. The cloud fraction and CODs in each footprint are determined from convolved\\nMODIS pixels within the footprint. However, to grid the data multiple CERES pixels are\\naveraged together in classes and the cloudy pixels are counted over the entire grid box in each\\nclass. CERES CODs are averaged logarithmically in each class to preserve reflected energy.\\nThese properties are then temporally interpolated to daily and monthly. For this study, we used a\\nnearest neighbor approach to assign each NSRDB pixel the COD from the nearest or overlapping\\nCERES pixel. This approach roughly represents cloud variability but does not explicitly resolve\\nthe cloud shapes within each grid box. Since we were interested in using CERES to provide\\ncontext for the observed conditions on the case study days, we determined that the CERES COD\\nwas sufficient for our purposes. In addressing other comments, we have updated Figures 1 and 2\\ninto Response Figure 3, which includes both the CERES and NSRDB COD fields. This allows\\nthe reader to assess the variability across the observed and modeled conditions. The figure\\ndemonstrates that the CERES and NSRDB CODs show similar patterns but highlights the\\nsmoothing effects of the CERES algorithm and resolution. We have added the following\\nsentence to the methods section: “Interpolating the CERES COD in this manner provides a rough\\nestimate of the observed cloud variability on each day but does not explicitly resolve the cloud\\nshape within each grid box. We include the NSRDB COD in Figure 1 to illustrate the finer\\nvariability resolved by the model.”\\n15. L516-517: I am not familiar with this product, but why assume all “possibly cloudy” pixels\\nare cloudy? Are results insensitive if you assume equal chance cloudy or clear for example?\\nTreating “possibly cloudy” pixels as “cloudy” in binary cloud masks is a common practice in\\nsatellite imagery classification. For example, the MODIS cloud fraction product uses a similar\\napproach. The MODIS cloud mask categorizes 1-km pixels as confirmed cloudy, possibly\\ncloudy, possibly clear, and confirmed clear. To determine the cloud fraction at a coarser 5-km\\nresolution, the confirmed and possibly cloudy pixels are reclassified as cloudy and the algorithm\\ncalculates the fraction of 1-km pixels within the 5-km cell that are cloudy. For the AOD\\ncomparison in the California case study, we selected August 20 and 30 because both days were\\nalmost entirely cloud-free, particularly in the areas near and downwind of the plumes. As such,\\nthe classification of possibly cloudy pixels as cloudy or clear would have minimal impacts on\\nour results. For our analysis of cloud-driven changes in DNI and GHI, we examine two relatively\\nsmoke-free days to reduce any confusion between smoke and clouds that might increase\\n“possibly cloudy” classifications. If we exclude “possibly cloudy” pixels, we reduce the number\\nof locations considered in this analysis by 196 (-2.5%) on September 7 and 228 (-1.8%) on\\nSeptember 18. Excluding “possibly cloudy” pixels does not result in meaningful changes to our\\n27\\nresults as shown in the figure below. The COD increases by < 0.29 in all cases and boxplot\\npatterns remain the same.\\nCloudy includes “possibly cloudy” Cloudy excludes “possibly cloudy”\\nComparison of our California daily case study results given two approaches for working with the\\nMAIAC cloud mask.\\n16. L529-530: Over which region are these mean values computed? Same question for L534-\\n535.\\nThe mean values for AOD and COD were computed at the locations with data in the original\\nFigures 1 and 2. This was unclear and we have reorganized this paragraph to introduce our\\nlocation restrictions prior to stating the AOD or COD\\nUpdated AOD introduction: “We selected August 20 and August 30 to illustrate the influence of\\nsmoke optical properties, via AOD, on smoke-driven changes in solar resources. We restricted\\nour analysis to locations that were smoke-impacted in 2020 and smoke-free in 2019 based on the\\nHMS polygons. In 2020, the observed (MAIAC) average AOD was 0.56 on August 20 and\\n550\\n0.43 on August 30, indicating optically thicker and thinner smoke plumes, respectively.”\\nUpdated COD introduction: “We then compared these smoke-driven changes in irradiance to\\nthose from clouds of different optical depths. We analyzed locations across California that were\\ncategorized as “cloudy” or “possibly cloudy” by MAIAC on September 7, 2019 (COD =3.26) \\nmean\\nand September 18, 2019 (COD =10.75). We calculated the difference between the all-sky and\\nmean\\nclear-sky values on each day to determine the ΔDNI and ΔGHI attributable to clouds.”\\n28\\n17. L539-540: Does this miss the potential impact of pyrocumulus clouds which can be\\nimportant for radiative transfer?\\nWhile we do not apply a cloud filter when assessing smoke-driven changes, the MAIAC\\nalgorithm filters out cloudy locations by default. MAIAC cannot retrieve an AOD when the\\nground is obscured by clouds and only performs a retrieval on “possibly cloudy” pixels when\\nsmoke is detected. We have updated the text to provide these details: “A cloud filter was not\\napplied when assessing smoke-driven changes because MAIAC does not retrieve an AOD under\\n“cloudy” conditions and only retrieves an AOD under “possibly cloudy” conditions when smoke\\nis detected.”\\nRegarding pyrocumulus clouds, this approach can miss some of their impact but not entirely.\\nAerosol laden clouds are much more highly absorbing. Most likely the thickest\\npyrocumulonimbus with water and/or ice in the tops would be identified as “clouds” in the\\ncurrent algorithms due to the colder tops, but lower thicker aerosol laden clouds would be\\nidentified as “smoke” even if there is water/ice near the top. So, there would be some\\ndifferentiation but it would be imperfect, meaning at least part of the radiative impact of\\npyrocumulus would be captured in this approach. Further analysis would be required to more\\nfinely separate between the cloud types and identify smoke-cloud mixtures but would be outside\\nthe scope of the current analysis.\\n18. L598: Is there a more official citation for this website?\\nWe have updated the citation for Equation 2 to the following sources:\\n● Ross, R. G. Flat-plate photovoltaic array design optimization. 14th IEEE Photovoltaic\\nSpecialists Conference, San Diego, CA 1126-1132 (1980).\\n● Masters, G. M. Renewable and Efficient Electric Power Systems: Masters/Electric Power\\nSystems. (John Wiley & Sons, Nashville, TN, 2004).\\n19. L601 and L616: Is it necessary to use different variable names for what seem like the same\\nparameter?\\nThese variables refer to the same parameters. For consistency, we have updated the variable\\nnames in Equations 2 and 3 to use 𝑇 and 𝑇 for the respective terms.\\n!\"## $%&\\n29\\n20. L623: I presume these are the 12 locations shown in Fig. 5? Would be good to reference it if\\nso.\\nWe have added the reference to Figure 5 at Line 623 to improve clarity as these are the 13\\nlocations referenced. The sentence now reads as follows: “For our simulations, we evaluate panel\\nperformance at 13 locations across CONUS (Figure 5) for the month of September in 2019 and\\n2020.”\\n21. L660: Similar point here, would be good to reference the six regions or just list them here.\\nAs above, we have added the reference to Figure 6 and included the region names in the sentence\\nindicated: “Using the daily mean all-sky and clear-sky DNI and GHI, we calculated the mean\\nDNI and GHI values for all days from 2006-2021 for six regions: the Southwest, Pacific\\nNorthwest, North Central, South Central, Southeast, and Northeast (Figure 6).”\\n22. L896, Fig. 2: I don’t understand how delta GHI could be positive in some locations for the\\nsmoke day? Is this due to some physical phenomenon (e.g., multiple scattering), error/bias in the\\nNSRDB product, or something else? Please explain.\\nThe boxplot whisker for the thin smoke case study (August 30) very slightly exceeds zero in a\\nhandful of locations. Below is a plot of the AOD in 2019 and 2020 (scaled to the 2019 AOD\\nrange) as well as the locations (blue) of areas where irradiance increased (+ΔI).\\nSince we are interested in ground-level irradiance, we anticipate that multiple scattering would\\nnot cause these positive values. Most of these areas are in northern CA where AOD was\\nconsistently low across both years. The median AOD was 0.039 in 2019 and 0.032 in 2020\\nNSRDB\\nin areas where the NSRDB projected an increase in irradiance. Given the extremely low AODs\\nand their similar magnitude, these positive ΔIs are associated with uncertainty in generally clean\\nareas. We did not add this to the manuscript given length limitations and other comments that\\nrequired text additions.\\n30\\n23. L896, Fig. 2: The optically thick (thin) cloud days have smaller (larger) COD values. Isn’t\\nthis counterintuitive? Also, the NASA Worldview snapshots don’t really sync up to the COD\\nplots to my eyes. Is this just a timing issue?\\nThank you for bringing this error to our attention. We accidentally swapped the cloud optical\\ndepths maps, placing the optically thick panel under the optically thin heading and vice versa.\\nWe have corrected the issue in the updated version of this plot included in our earlier responses\\n(Response Figure 3).\\n24. L911, Fig. 3: Would it be better to use a normalized delta AOD metric? For instance, if the\\nbackground AOD is very small, then even a small increase due to smoke may be important.\\nThank you for this suggestion. We have adjusted Figure 3 to show the change in AOD in terms\\nof percent change, as we used to show the change in irradiance. We agree that showing percent\\nchange better demonstrates the correlation between the relative changes in AOD due to smoke\\nand changes in irradiance. Using a normalized metric also better accounts for the fact that\\nbackground AOD can vary substantially across the U.S. and small magnitude changes may be\\nsignificant in areas with low background AODs (and vice versa).\\n31\\n25. L922, Fig. 5: You talk about consistent decreases in GHI magnitude across the different sites.\\nHowever, I think it would be good to visualize this using percent difference or some normalized\\nmetric. For instance, in the right panel, a decrease in 7 MWh at Dallas is more impactful than a\\ndecrease in 8 MWh at Portland due to their different baseline values. I suggest adding additional\\nsubplots here or make a new plot and add it to the Supplemental.\\nWe have updated Figure 5 to include both the change in power output in MWh and as a percent\\ndifference. We agree that including a normalized metric better demonstrates the relative changes\\nin potential panel performance across areas with different baseline irradiance values. We also\\nupdated the corresponding CdTe version of this plot in the Supplementary Information. As noted\\nin comment #6, while addressing this comment, we discovered an error that resulted in the plot\\ndisplaying the difference between the two location-based methods rather than the difference\\nbetween 2019 and 2020. We have resolved this error and corrected the plot.\\n32\\nREVIEWER #2 COMMENTS:\\nReview of the study entitled “Solar energy resource availability under extreme and historical\\nwildfire smoke conditions” by Corwin et al.\\nThis a very nice and well-structured study analyzing the impact of smoke from wildfire to\\nirradiance across the continental U.S. and subsequent threat on solar photovoltaic (PV) energy\\ngeneration. Results rely on modeled clear-sky and all-sky direct normal irradiance (DNI) and\\nglobal horizontal irradiance (GHI) from a high spatial and temporal resolution database. I find\\nthe results reliable and recommend publication after minor revisions.\\nMinor comments\\n1. Line 121: Losses in model-based daily mean GHI (11–17 %) in California associated with\\nlocalized smoke are consistent with results from ground-based measurements of solar irradiance\\nin Athens, Greece by Masoom et al. (2023) (https://doi.org/10.5194/acp-23-8487-2023), who\\nmeasured losses in GHI due to smoke from extreme wildfires up to 17 %. California and Greece\\nhave the same climate (Mediterranean climate). Of course, the area of Greece is much smaller\\nthan California, and the fires in California are larger, but personally I think that finding\\nconsistent results in different parts of the world with the same climate shows that results are\\nreliable.\\nThank you for bringing this study to our attention. We have included a comparison to Masoom et\\nal.’s findings as suggested. We combined this with details from two additional studies to also\\naddress Reviewer #3’s request to describe the “prior studies” that we mentioned. This section\\nreads as follows:\\n● We find considerable losses in model-based daily mean DNI (32-44%) and GHI (11-\\n17%) in California due to local smoke. Such reductions are consistent with observations\\nfrom Athens, Greece during the 2021 wildfire season where GHI declined 10-20% due to\\nsmoke (Masoom et al., 2023); Canberra, Australia where DNI and GHI reductions\\nreached 26% and 32%, respectively, approximately 1 km from a controlled burn (Perry\\nand Troccoli, 2015); and in Golden, Colorado where smoke from the 1988 Yellowstone\\nNational Park fires decreased daily DNI and GHI by 37% and 9%, respectively\\n(Hulstrom & Stoffel, 1990).\\n2. Line 153: 10-point and 6-point. Point is vague, do you mean 10 % and 6 %?\\nWe agree that this phrasing is unclear and have updated this sentence as follows: On average, the\\n0.13 increase in AOD resulted in a 10% decrease in DNI compared to a 6% decrease in GHI.\\n33\\n3. Line 170: “smoke-impacted and smoke-free days from the previous section (Aug 20, Aug 30)”\\nis confusing. If you refer to Aug 30, 2020, then it is smoke-thin not smoke-free day.\\nThis phrasing was meant to indicate that smoke-driven ΔDNI and ΔGHI were computed using\\nthe dates (i.e., August 20 & 30) from the previous results section on “Variation with aerosol\\noptical depth” (Line 145). We calculated the difference between the smoke-free (2019) and\\nsmoke-impacted (2020) conditions for both days. We agree that this meaning was unclear, and\\nupon further review, we determined that dividing the California case study results into two\\nsections/figures leads to confusing and unnecessary repetition. Ultimately, the change in DNI\\nand GHI due to smoke is shown in both Figures 1 and 2. As such, we have combined these\\nfigures (Response Figure 3) and streamlined the text. This provides greater clarity around each\\nvariable and makes a single concise section that better highlights the overall contribution\\nregarding smoke and cloud optical properties. Additionally, while combining the two figures,\\nwe determined that the smoke filter applied to the cloud analyses (September 7 and 18) was\\nunnecessary since aerosols are included in both the all-sky and clear-sky model results. We have\\nremoved this filter to incorporate more cloudy regions on these days.\\n4. Line 188: Put cloudy in “”.\\nThank you for catching this. We’ve added the quotation marks as requested.\\n5. Figure 2: the two plots showing the COD should switch position, i.e. move plot for\\nCOD=10.76 on top and plot for COD=3.26 to bottom. Fig. 2 caption: Here is how understand the\\nDifferences (Δ), please clarify:\\nΔ smoke-thin (Aug 30, 2020) – smoke-free (Aug 30, 2019)\\nΔ smoke-thick (Aug 20, 2020) – smoke-free (Aug 20, 2019)\\nΔ cloud-thin (Sep 07, 2019) – cloud-free (Aug 30, 2019)\\nΔ cloud-thick (Sep 18, 2019) – cloud-free (Aug 20, 2019)\\nWe have swapped the COD maps to correspond to the correct days and revised this section to\\ncombine Figures 1 and 2 (Response Figure 3). We also streamlined our presentation of the\\nresults. The reviewer’s understanding of how Δsmoke-thin and Δsmoke-thick are calculated is\\ncorrect. Δcloud-thin represents the difference between all-sky and clear-sky on Sep 07, 2019 and\\nΔcloud-thick is the difference between all-sky and clear-sky on Sep 18, 2019. We have updated\\nthe first three sentences of the caption to clarify how the changes are calculated: “Comparison of\\nsmoke, clouds, and irradiance conditions across California on four case study days. Smoke-\\ndriven changes in irradiance under optically thinner (light purple; 8/30/2020) and thicker (brown;\\n8/20/2020) wildfire smoke in 2020 are calculated relative to smoke-free conditions on 8/30/2019\\nand 8/20/2019, respectively. Cloud-driven changes in irradiance under smoke-free conditions\\n34\\nwith optically thin (light gray; 9/7/2019) and thick (dark gray; 9/18/2019) clouds in 2019 are\\ncalculated as the difference on each day between modeled all-sky and clear-sky values.”\\n6. Figure 3 and lines 216-241: percentages in lines do not match the ΔIrradiance scale in Figure\\n3. I assume that the scale must be multiplied by 100 to be correct, but the authors should check.\\nThe reviewer is correct that the ΔIrradiance scale in Figure 3 was showing the change as a\\nfraction rather than percentage. We have addressed the issue and remade the figure with the\\ncorrect scale in percentage.\\n7. Lines 246, 258', '266': ['Figure 4.a)', ['Figure 4.b-d)', ['Figure 4.a). The indices a', 'b-d are not given\\nin the plots.\\nWe have updated Figure 4 to include the panel labels (a-g).\\n8. Line 298: I understand that you apply correlation analysis per month. But I cannot understand\\nhow you get n=42', 'since from 2006 to 2021 it’s 16 years. Please clarify.\\nSince we calculated the correlation coefficient for each month in each region', 'this \"n=42\" was\\nmeant to refer to 7 𝑚𝑜𝑛𝑡ℎ𝑠 ×6 𝑟𝑒𝑔𝑖𝑜𝑛𝑠 = 42 𝑐𝑎𝑙𝑐𝑢𝑙𝑎𝑡𝑖𝑜𝑛𝑠. The reviewer is correct that the n-\\nvalue for each individual correlation coefficient is 16', 'referring to the 16 years included in our\\nstudy. We have replaced the “n=42” text with references to the respective figure panels:\\n● “Correlation coefficients were significant (p < 0.05) for 86% of clear-sky DNI analyses\\n(Figure 6.a) and 60% of clear-sky GHI analyses (Figure 6.b).”\\n9. Lines 543-544: “Cloud-driven changes were calculated by subtracting the clear-sky values\\nfrom the all-sky values on the same day”. I think that you subtract the all-sky values from the\\nclear-sky values', 'but cannot understand the “on the same day”.\\nTo calculate the impact of aerosols on irradiance with the NSRDB', 'we had to identify smoke-\\nfree days against which to compare smoke-impacted days. In contrast', 'we are able to use the all-\\nsky and clear-sky irradiances from the NSRDB on a single day (e.g.', 'Sept 7', 2019], 'to calculate\\nthe impact of clouds on irradiance for that day. As such', 'Δcloud is calculated as follows:\\n○ 𝛥 𝑖𝑟𝑟𝑎𝑑𝑖𝑎𝑛𝑐𝑒 = 𝑎𝑙𝑙𝑠𝑘𝑦 −𝑐𝑙𝑒𝑎𝑟𝑠𝑘𝑦\\n𝑡ℎ𝑖𝑛 𝑐𝑙𝑜𝑢𝑑 𝑠𝑒𝑝𝑡 7 2019 𝑠𝑒𝑝𝑡 7 2019\\n○ 𝛥 𝑖𝑟𝑟𝑎𝑑𝑖𝑎𝑛𝑐𝑒 = 𝑎𝑙𝑙𝑠𝑘𝑦 −𝑐𝑙𝑒𝑎𝑟𝑠𝑘𝑦\\n𝑡ℎ𝑖𝑐𝑘 𝑐𝑙𝑜𝑢𝑑 𝑠𝑒𝑝𝑡 18 2019 𝑠𝑒𝑝𝑡 18 2019\\nWe have updated this paragraph to provide greater clarity around the calculation of both smoke-\\ndriven and cloud-driven irradiance changes. In particular', 'we aimed to clarify the handling of the\\ncloud case studies (highlighted below):\\n35\\n● Four dates were selected for this case study to 1) highlight the role that AOD plays in\\ndetermining smoke’s impact on irradiance and 2) compare the impact of smoke and\\nclouds on solar resources. We focused on August and September when selecting dates for\\nthis case study because major fire activity and smoke production in California is\\ngenerally greatest during these months and was so in 2020. We identified days based on\\nvisual examination of the NASA Worldview true-color imagery of California for all\\nAugust and September days in 2019 and 2020. We selected August 20 and August 30 to\\nillustrate the influence of smoke optical properties', 'via AOD', 'on smoke-driven changes in\\nsolar resources. In 2020', 'the observed (MAIAC) average AOD was 0.56 on August 20\\n550\\nand 0.43 on August 30', 'indicating optically thicker and thinner smoke plumes', 'nrespectively. These dates were relatively smoke- and cloud-free in 2019. As such', 'we\\ncalculated the smoke-driven change in DNI (ΔDNI) and GHI (ΔGHI) as the difference\\nbetween clear-sky values in 2020 (smoke-impacted) and 2019 (smoke-free). Comparing\\nthe same date across both years better controls for variation in solar position and climate.\\nWe compared these smoke-driven changes in irradiance to those from clouds of different\\noptical depths. For this comparison', 'we used September 7', 2019, ['COD =3.26) and\\nmean\\nSeptember 18', 2019, ['COD =10.75)', 'which were relatively smoke-free cloudy days.\\nmean\\nWe calculated the difference between the all-sky and clear-sky values on each day to\\ndetermine the ΔDNI and ΔGHI attributable to clouds.\\n10. Equation 2 and line 608: denominator 80 should be 800.\\nThe denominator in Equation 2 was originally correct as 80 given that we defined S as having\\nunits of mW/cm2 in the text. However', 'this is confusing given that we used W/m2 in Equation 3.\\nFor consistency', \"we have corrected the denominator to 800 and redefined S as having units of\\nW/m2.\\n36\\nREVIEWERS' COMMENTS\\nReviewer #1 (Remarks to the Author):\\nThe authors have responded very well to my comments; I appreciate their detailed responses. Well\\ndone! My only remaining comment is to double check the figure numbering in the main manuscript.\\nOnce that is resolved\", 'I believe the manuscript will be worthy of publication.\\nReviewer #3 (Remarks to the Author):\\nThe manuscript has been sufficiently revised and I believe that the authors have taken care of all\\nthe issues raised in the review. It is now ready for publication.\\nREVIEWER #1:\\nMy only remaining comment is to double check the figure numbering in the main manuscript.\\nRESPONSE:\\nThank you for catching this error. While adding the new figure and combining two of the\\noriginal figures', 'we accidentally labeled the last two figures as Fig. 6 and Fig. 7 when they\\nshould have been labeled Fig. 5 and Fig. 6', 'respectively. We have fixed this throughout the text\\nin the final revisions.'], 'decision_letter\": \"']]]}]]]"}, "author_response": ""}]

Decision letter