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Article

Projection of the UV Radiation for Vitamin D Production Changes Between 2015–2024 and 2090–2099 Periods

1
O3Lab, Saint Petersburg University, St. Petersburg 199034, Russia
2
Voeikov Main Geophysical Observatory, St. Petersburg 194021, Russia
3
Physical-Meteorological Observatory Davos/World Radiation Centre, 7260 Davos, Switzerland
*
Author to whom correspondence should be addressed.
Atmosphere 2025, 16(6), 686; https://doi.org/10.3390/atmos16060686
Submission received: 22 April 2025 / Revised: 23 May 2025 / Accepted: 28 May 2025 / Published: 6 June 2025
(This article belongs to the Special Issue Ozone Evolution in the Past and Future (2nd Edition))

Abstract

:
We evaluate changes in the daily doses of surface ultraviolet radiation (UV) necessary for vitamin D production (UVpD) during the 21st century caused by the evolution of the Earth’s climate and the atmospheric ozone layer. Experiments with the Earth system model SOCOLv4 (version 4 of the Solar-Climate Ozone Links Chemistry-Climate Model) and an atmospheric radiative transfer model indicated a significant (20–80%) decrease in UVpD doses at the Earth’s surface between 2015–2024 and 2090–2099 in middle latitudes in both hemispheres and an increase of 30–40% in some areas of lower latitudes. These changes are driven by strong greenhouse gas growth and ozone-depleting substance reductions. The experiments also provided estimates of the relative contributions of the total ozone column (TOC), cloud parameters, and surface albedo changes to the corresponding variations in UVpD daily doses. Outside the tropics, the primary factor contributing to the decrease in UVpD doses (50% to 80%) is the increase in TOC. Changes in cloud parameters account for 20% to 30% of the decrease, while the decline in surface albedo contributes less than 20%. However, in the polar regions of the Northern Hemisphere, this contribution can reach up to 50%. In the lower latitudes, diminishing TOC and liquid water column of cloud (LWCC) provide the main contributions to the increase in UVpD doses.

1. Introduction

Changes in climate system parameters, the ozone layer, and ultraviolet radiation (UV) fluxes are linked via numerous feedback sources, including dynamic, radiation, and photochemical processes. Therefore, to project the evolution of UV surface radiation, it is necessary to exploit chemistry–climate models (CCM), which consider the main interactions between the above-mentioned processes [1]. At the same time, the calculation of surface UV radiation fluxes is currently carried out using radiation transfer models, which consider all the main factors affecting UV radiation fluxes, including the effects of multiple scattering and reflection of light within the Earth’s atmosphere and on its surface [2]. These models have been used in recent decades to evaluate the changes in the surface UV irradiance during the 21st century (see, e.g., [3], Section 9 and the references therein). In particular, many model projections have been performed to estimate the century-long evolution of surface UV radiation causing negative effects on human health and the biosphere: redness of the human skin (erythema), suppression of plant growth, and so on [4,5,6,7,8,9]. These studies helped to elucidate, along with astronomical (nearly periodic) factors (changes in the Earth–Sun position, the solar zenith angle, the tilt of the Earth’s rotation axis, and solar activity), the main atmospheric (non-periodic) factors that crucially control the surface UV fluxes: (1) the total ozone column (TOC); (2) the cloud parameters liquid cloud water column (LWCC) and total cloud cover); (3) the reflective properties of the Earth’s surface; (4) atmospheric aerosol properties (aerosol optical depth (AOD) and other optical parameters); and (5) concentrations of some minor gases in the atmosphere [3].
The century-long evolution of the surface erythemal UV irradiance at local noon was evaluated in [4,5] based on CCM projections but without considering variations in cloud parameters, aerosol content, and surface albedo. The calculations show a significant decrease in noon erythemal UV fluxes during the 21st century in middle and high latitudes in both hemispheres, especially in polar regions, as a response to the total ozone recovery. The results of [5] were reevaluated by [6], considering the influence of the cloud parameter variations. They found that the erythemal surface irradiance will decrease until 2100 at the middle and high latitudes of the Northern Hemisphere and even fall below its 1980 level. At low latitudes, surface erythemal radiation is projected to increase slightly in some regions (~2%) by the end of the 21st century.
The changes in the surface erythemal UV radiation using the 21st-century projections were evaluated by [7] utilizing the outputs from models participating in the 5th Phase of the Model Intercomparison Project (CMIP-5) driven by the socioeconomic scenario RCP 4–5. They considered changes in all main atmospheric factors (1)–(4), controlling the UV fluxes at the surface. The results of the calculations were found to be similar to those from [6] for low and middle latitudes in both hemispheres, despite the additional consideration of the surface albedo and aerosol load changes. However, in the Arctic, the variation in surface reflectivity emerged as the dominant factor affecting changes in erythemal radiation in the 21st century. The impact of the aerosol changes on the surface erythema doses calculated in [7] was considered to be rather uncertain.
More recent evaluations of the century-long changes in the UV index (erythemal daily maximum dose rate in mW/m2 divided by 25 mW/m2) and the plant growth-weighted irradiance were discussed in [9]. The authors considered the evolution of factors (1)–(4) according to three Shared Socioeconomic Pathway (SSP) scenarios—SSP1-2.6, SSP3-7.0, and SSP5-8.5—in the model projections from CMIP6 modeling outputs for the 21st century. The most significant negative changes in the UV index occurred at middle and high latitudes in both hemispheres under the SSP5-8.5 scenario, primarily due to the super-recovery of the ozone layer. However, in the tropics, TOC decreases due to changes in dynamics, which leads to a small increase in the UV index (~2%) over some regions. At high and partly middle latitudes of both hemispheres, changes in cloudiness parameters and surface albedo lead to a significant decrease in surface erythemal radiation for all seasons. However, July is an exception because the attenuation of cloudiness significantly increases the UV index over Europe, Asia, and North America, despite an increase in total ozone content over these areas.
Along with this, the variations of surface UV radiation, which can have beneficial effects (vitamin D production in the human body skin (UVpD)), may be of significant interest [10,11,12,13,14,15]. According to modern research, human beings require significantly higher doses of vitamin D for optimal functioning than was previously believed [16]. These adequate levels of vitamin D cannot be solely obtained from food and dietary supplements, and exposure to sufficient UVpD is also required. Consequently, a decrease in surface UV radiation during the 21st century could result in a considerable increase in the number of people worldwide who are deficient in vitamin D.
However, only a limited number of studies have been devoted to the possible changes in UVpD during the 21st century. In [14], the influence of TOC and AOD variations on UVpD irradiance was investigated over Europe for two RCP scenarios in the 21st century (RCP-2.6 and RCP-8.5). It was found that UVpD doses are very sensitive to TOC variability for RCP-8.5. The decrease in UVpD can reach up to 20% for all seasons and latitudes from 2006 to 2100 as a result of ozone layer recovery according to the Montreal Protocol implementation [17]. The exception is the summertime period when decreasing UVpD is minimal due to the decline in aerosol loading, which compensates for the negative effect of the TOC recovery on the production of vitamin D.
Fountoulakis and Bais [15] evaluated UVpD changes at high latitudes in the Northern Hemisphere between 2010–2020 and 2085–2095 according to RCP-4.5 and RCP-8.6 scenarios. Their analysis included variations of the total ozone column, surface reflectivity, and aerosol optical depth based on data from the corresponding CMIP5 model runs. They found a substantial decrease in UVpD fluxes (up to 50%) for clear-sky conditions (only the effect of the TOC variations was considered) compared to cases when the changes in all atmospheric factors mentioned above were considered.
In all published papers, the uncertainties and differences between models were estimated as rather high; therefore, more models should be applied to reach a better understanding of the future UVpD changes and responsible processes. To achieve this goal, a century-long (2000–2099) numerical experiment was performed using the Earth’s system model SOCOLv4 [18], forced by the SSP5-8.5 scenario for the evolution of atmospheric greenhouse gases and ozone-depleting substances in the 21st century [19]. This model helps to assess global changes in daily doses of surface UV radiation, which is important for the production of vitamin D, through to the end of the 21st century as a result of the corresponding variations of TOC, cloud parameters, and surface reflectivity. In this paper, we limited our consideration to the influence of the main radiation factors on changes in UV radiation: (1) variations in TOC, (2) changes in cloud parameters (cloud liquid water column and total cloud cover), and (3) surface albedo. The corresponding changes in the aerosol content and NOx (nitrogen oxides) concentration in the atmosphere of urbanized territories may also significantly impact the variations in UVpD during the 21st century. However, there are currently no reliable data on the likely changes in the atmospheric concentration mentioned above. For example, powerful explosive volcanic eruptions can substantially disturb the atmospheric aerosol layer during the 21st century [2]. Also, UVpD changes have a spectral response mainly in the UV-B (290–315 nm wavelengths) range of the solar spectrum and consequently depend on the first line of the TOC, surface reflectivity, and cloudiness variation [3,15]. Therefore, in the model experiment performed, the concentrations of nitrogen oxides and atmospheric aerosol were set by their values at the beginning of the 21st century [15]. The values of factors (1)–(3) obtained from SOCOLv4 in the model experiment were used as input information in the FASTRT (Fast simulations of downward UV doses, indices, and irradiances at the Earth’s surface) model for calculating radiation fluxes on the surface [10] to estimate doses of UVpD radiation over the two time intervals (2015–2024 and 2090–2099) for each model grid cell.
Further, we evaluated the contributions of each factor from (1)–(3) to the changes in UVpD doses from the period of 2015–2024 to the period of 2090–2099. All cells of the SOCOLv4 surface model grid have been grouped into geographical regions. In each region, all factors affecting UVpD doses changed in the same direction by the end of the 21st century. This approach enabled us to identify the areas of the Earth’s surface most sensitive to global warming regarding UVpD irradiance. Additionally, it allowed us to accurately estimate the UVpD response to each factor in these regions.

2. Description of Models and Numerical Experiments

2.1. Earth System Model SOCOLv4 and Experimental Design

SOCOLv4 is based on the Max–Planck model (MPI-ESM1.2) and was extended to interactively include the atmospheric chemical module MEZON and the sulfate aerosol microphysics module AER [18]. The model operates with a spectral resolution T63 (~1.9° × 1.9°) horizontally and features 47 vertical layers that extend from the surface to 0.01 hPa (approximately 80 km). The dynamical and physical fields of the model are recalculated every 15 min, while the basic radiation and photochemical fields are updated every 2 h. SOCOLv4 calculates the concentrations of approximately 100 atmospheric gases that are involved in 216 gas-phase reactions, 72 photolysis reactions, and 16 heterogeneous reactions on the surface or within sulfate aerosol particles and polar stratospheric clouds. SOCOLv4 was described in detail in [18].
The SOCOLv4 model participated in several international Chemistry Climate Model validation projects, focusing on evaluating the models and assessing future ozone layer variability. The comparison of the SOCOLv4 results with some other models, e.g., [20], as well as with ground-based and satellite observations [21], generally indicates its high quality and applicability for climate and ozone layer projections.
With SOCOLv4, we performed simulations from 2000 to 2099 driven by evolving boundary conditions prescribed by the CMIP6 SSP5-8.5 scenario [19]. SSP5-8.5 is the most pessimistic scenario, which assumes continued growth in anthropogenic emissions without significant measures to reduce them. ODSs were determined with the scenario from the Ozone Depletion assessment [22]. The spatial distributions of the ocean surface temperature and sea ice area are calculated interactively.
The results from this model simulation were analyzed by comparing two 10-year-long intervals (2015–2024 and 2090–2099). For the specified periods, the FASTRT (UVSPEC) program was employed to compute monthly mean daily surface UV doses, which are essential for vitamin D production. These UVpD daily dose calculations were based on the monthly mean values of the above-mentioned parameters (TOC, cloud liquid water column, cloud cover, and surface albedo taken from the SOCOLv4 results) under illumination conditions corresponding to the 15th day of January, April, July, and October. Thus, for each of the 4 months mentioned above and for each surface cell of the model grid, we generated two ensembles of monthly mean UVpD daily doses: the first for the period from 2015 to 2024 and the second for the period from 2090 to 2099. Both of the ensembles consisted of 10 members. After that, we calculated ensemble means and estimated the interannual variability for both ensembles. The ensemble approach allows us to calculate the statistical significance of the ensemble mean UVpD changes between 2015–2024 and 2090–2099, resulting from changes in boundary conditions (ODSs, GHGs) following WMO and SSP-8.5 scenarios. The level of significance was evaluated by Student’s t-test [23].
The radiation scheme of the SOCOLv4 model does not allow for direct determination of the surface albedo in the UV part of the spectrum. Therefore, to estimate this value, we apply a simplified scheme, which accounts for the strong dependence of the albedo in the UV radiation range on the presence of ice, snow cover, and sea ice in each cell of the model grid. Therefore, if any of these elements were detected in a model grid cell, the albedo was fixed at 0.8; otherwise, it was set at 0.03. The ice, snow cover, and sea ice in each model grid cell were accumulated during the numerical experiment with the SOCOLv4, along with the TOC, cloud liquid water column, and cloud cover fields. They were subsequently used to calculate the albedo in the UV spectral range.

2.2. FASTRT (UVSPEC)

To calculate the surface fluxes of UV radiation, we used the UVSPEC module of the numerical model of radiative transfer of radiation in a plane-parallel atmosphere with a reflective surface, libRadtran developed at the Institute of Atmospheric Physics in Oberpfaffenhofen (Germany, website: http://www.libradtran.org). This model uses modern algorithms to calculate radiation transfer in a wide spectrum range, accounting for multiple scattering.
To minimize the computational costs associated with calculating UV radiation fluxes, UV irradiance is calculated using linear interpolation using the multidimensional FASTRT look-up table, based on the UVSPEC module [10]. The main input parameters for calculating UV radiation fluxes include TOC, day of the year, surface albedo, cloud cover, cloud liquid water column, and geographic coordinates (latitude and longitude). The full list of the input parameters of FASTRT can be found at https://fastrt.nilu.no/README.html, accessed on 30 May 2025). Along with UV spectral fluxes on the surface, the daily doses of ultraviolet radiation that are effective in producing vitamin D can be calculated using this method.
The influence of cloudiness on the surface characteristics of UV radiation is parameterized in the FASTRT (UVSPEC) model using the cloud liquid water column and the total cloud cover fraction according to [10,24]. The LWCC determines the cloud’s optical thickness for UV atmospheric transmission. The total cloud cover fraction is important for computing surface irradiance. The surface fluxes (E) calculated by FASTRT are a linear combination of the irradiances under a homogeneous cloud cover (Ecloud) and cloudless conditions (Enocloud):
E = CF × Ecloud + (1CF) × Enocloud
where CF is the total cloud cover fraction. Extensive testing of this model under various atmospheric conditions was conducted by analyzing the output data from the European ultraviolet radiation monitoring network. Also, the comparison of the FASTRT results with the LibRadtran results (considered a benchmark) has shown that the average errors in calculating UV radiation fluxes in the atmosphere and at the surface using the FASTRT module are about 1% for a cloudless atmosphere, with a standard deviation of 1%. For cloudy conditions, the average error rises to about 8%, with a standard deviation of 9% https://fastrt.nilu.no/README.html [10].

3. Results and Discussion

Model calculations conducted with the SOCOLv4 for the SSP5-8.5 scenario indicate that the 21st century will witness substantial changes in total ozone content, cloud parameters, and surface albedo in the UV radiation spectral range.
Figure 1 illustrates the geographical distribution of the monthly and ensemble-averaged TOC evolution between 2015–2024 and 2090–2099 for January (top-left), April (top-right), July (bottom-left), and October (bottom-right). In both hemispheres outside the tropics, TOC is projected to increase in the middle latitudes by 5–20% in January, April, and July and 10–20% in October. Notably, during the 2090–2099 period, the ozone “hole” over Antarctica in October is anticipated to be filled with excess, and the TOC changes will be up to 80% over the South Pole region. This super-recovery is significantly influenced by the Montreal Protocol’s restrictions on ozone-depleting substances [17], alongside changes in middle atmosphere temperature and circulation.
Conversely, in tropical latitudes, there is a 2–3% decrease in TOC between 2015–2024 and 2090–2099, attributed to strengthened meridional circulation leading to increased ascending motions in the upper troposphere and lower stratosphere, causing a decline in ozone column content in the tropics [25]. This can be explained by the fact that, if the strengthening of the meridional circulation contributes to the ozone layer recovery in the middle and high latitudes, along with a decrease in atmospheric concentrations of halogen-containing ODSs and a decrease in temperature, then this strengthening in the tropics leads to an increase in ascending atmospheric currents in the upper troposphere and lower stratosphere, and thus, a predominant decrease in ozone concentration in the 21st century [25].
Figure 2 illustrates the latitude–longitude distribution of the monthly and ensemble mean changes in LWCC between 2015–2024 and 2090–2099 for January (top-left), April (top-right), July (bottom-left), and October (bottom-right). It shows that, except in the low and partly middle latitudes of both hemispheres and for all seasons, the liquid water column of clouds increased significantly (by 50% on average and up to 100% for individual regions) during the 21st century. The exception is the middle latitude over land in July (North America, Europe, and Russia in NH and Southern America in SH), where the projected cloud liquid water column decreases significantly by 2090–2099. These LWCC behaviors are consistent with the climate change influence on cloudiness [26] and are in good qualitative agreement with the corresponding results from projection outputs of CMIP6 models for the SSP5-8.5 scenario [27]. The corresponding changes in the total cloud cover between 2015–2024 and 2090–2099 for January (top-left), April (top-right), July (bottom-left), and October (bottom-right) are shown in Figure A1 (Appendix A). The geographical distributions of these changes are close to the LWCC changes (Figure 2), but the magnitudes of the total cloud cover variations are significantly smaller in size than the LWCC changes. Also, the FASTRT UVpD doses depend linearly on the total cloud cover (see Equation (1)), but their dependence on the LWCC values is exponential. Thus, UVpD sensitivity is substantially higher than LWCC changes in comparison with the corresponding sensitivity to the total cloud cover.
Figure 3 depicts the changes in the surface reflectivity for UV radiation during the 21st century for January (top-left), April (top-right), July (bottom-left), and October (bottom-right). The figure indicates a notable decline in the monthly and ensemble mean albedo values across this period. This decrease is primarily driven by the poleward movement of the snow cover boundary, which is linked to rising surface air temperatures and the overall warming attributed to the greenhouse effect (North America, Europe, and Asia in January and April). Furthermore, a substantial factor contributing to this reduction in albedo is the diminishing of the ice-covered area observed in both hemispheres. This trend is especially pronounced in the Northern Hemisphere in July and October, as shown in the bottom panels of Figure 3. By the end of the 21st century in autumn, the Arctic Ocean is projected to be largely ice-free, with glaciation becoming seasonal [28]. This shift causes a decline in the UV radiation levels at the surface, as lower albedo leads to increased absorption of short-wavelength solar irradiance and diminishes their multiscattering in the surface layer.
Figure 4 for January (top-left) and April (bottom-left) and Figure 5 for July (top-left) and October (bottom-left), respectively, depict the latitude–longitude distributions of changes in the daily dose of UVpD radiation between 2015–2024 and 2090–2099, considering the corresponding changes in all radiation factors, which are considered. At the same time, here and further, only statistically significant changes in doses of UVpD during the 21st century are shown with a significance of more than 90%. It can be seen from the figures that the daily dose of UVpD reaches its maximum decrease at the middle and high latitudes of both hemispheres. The evolution of radiation factors that weaken the level of UV radiation at the surface (increasing in TOC and liquid water column of clouds, decreasing in albedo) contributes to the reduction in the daily UV radiation dose by 20–50% in the middle latitudes of the Northern Hemisphere and by 10–30% in the Southern Hemisphere.
To evaluate the individual contribution of TOC, LWCC/total cloud cover, and surface albedo, three additional simulations with FASTRT (UVSPEC) were performed. In addition to calculating the impact of all factors for the 2090–2099 time period (referred to as ALL), which is described in Section 2.2, we also calculated the daily dose when only one of these factors applied to the 2090–2099 range, while the other two factors were maintained at their levels of the 2015–2025 time period. The results of these runs are named TOC (only TOC presents the 2090–2099 conditions), H2O (only the total cloud cover and LWCC present the 2090–2099 conditions), and ALB (only the surface albedo presents the 2090–2099 conditions).
Further, to correctly assess the contribution of various factors to the change in UVpD, for each month, we divided all cells of the model grid into two main groups: a group in which all the factors act toward reducing the daily dose of UVpD (named S U M N ); and a group of cells in which all the factors increase the daily dose (named S U M P ). It should be noted that the ALB effect associated with the melting of sea ice and snow cover is completely negative for changes in UVpD. Therefore, all ALB cells fall into group (1). Thus, the following groups of model cells are calculated for each month:
S U M N   g r o u p   c o n s i t s   o f   t h e   c e l l s   i n   e a c h   o f   w h i c h T O C   e f f e c t   o n   U V p D < 0             a n d   H 2 O   e f f e c t   o n   U V p D < 0     a n d   A L B   e f f e c t   o n   U V p D < 0    
S U M P   g r o u p   c o n s i t s   o f   t h e   c e l l s   i n   e a c h   o f   w h i c h T O C   e f f e c t   o n   U V p D > 0                                         a n d   H 2 O   e f f e c t   o n   U V p D > 0        
S U M   g r o u p   c o s i s t s   o f                                                                                   S U M N   g r o u p   o f   c e l l s   a n d   S U M P   g r o u p   o f   c e l l s
Figure 4 shows the changes in daily doses of UVpD for runs of ALL (top-left for January, bottom-left for April) and S U M (bottom-left for January, bottom-right for April). In just the same order, the UVpD dose variations for ALL and S U M   runs are presented in Figure 5, but for July and October instead of January and April, respectively. By comparing the ALL and SUM distributions of the UVpD values in Figure 4 and Figure 5, we can identify geographical areas where all factors influence UVpD changes in either a negative (ALB impact is always negative) or a positive direction. This is true for geographical regions where ALL and SUM distributions are closely aligned. From the figures, we can see that the ALL and S U M approaches yield fairly similar results in most cells of the horizontal model grid.
However, there are some areas where UVpD dose changes in both the ALL and S U M show significant differences. The model grid cells in these regions do not fall into either of groups (2) or (3). In these areas, the TOC/ALB and H2O factors impact UVpD dose variations in opposite ways, compensating for each other’s effects. Specifically, the influence of H2O is positive, which overlaps with the negative impact of TOC/surface albedo. These regions are notably found in Southern Africa and South America in January, and Southern America in April (Figure 4, top and bottom, respectively). However, this situation is particularly evident in July over land in the mid-latitudes of North America, Europe, and Russia. The total ozone column increases between 2015–2024 and 2090–2099 (see Figure 1, bottom-left), and the cloud parameters (LWCC and total cloud cover) decrease, which means the clouds become thinner and their fractions are reduced (see Figure 2, bottom-left, and Figure A1, bottom-left). Thus, the TOC changes over the 21st century led to a diminution of UVpD doses, but the H2O changes led to an increase in them. The TOC and H2O effects compensate for each other, but the positive changes in UVpD are clearly visible for the ALL group (Figure 5, top-left), especially for Europe and North America. It means the changes in cloud parameters have a much greater impact on UVpD than TOC, resulting in a substantial increase (0–30%) in daily doses of UVpD by the end of the 21st century (Figure 5, top-left).
By dividing all model grid cells at the surface into groups (2) and (3), we identify the geographical areas where all factors have the same sign of their impact on the UVpD variations (positive or negative). In these regions, the surface UVpD doses are particularly sensitive to climate change, as the factors do not compensate for each other but instead amplify each other’s effects. Additionally, this grouping allows for more accurate attribution of the influence of each factor on the long-term changes in the surface UVpD irradiance.
Figure 6 and Figure 7 illustrate the relative contribution of variations in TOC, cloud parameters, and surface albedo to the changes in the daily dose of UVpD between the periods 2015–2024 and 2090–2099. This is shown as a percentage for July (on the left side) and October (on the right side). By “relative contribution”, we refer to the ratio of daily dose changes from one factor to the absolute values of the daily dose changes from all factors. For the S U M N group, these are TOC/abs( S U M N ), H2O/abs( S U M N ), and ALB/abs( S U M N ), while for the S U M P group, these are TOC/abs( S U M P ) and H2O/abs( S U M P ).
The percentage contributions of the factors from the S U M N group are shown in Figure 6 for TOC (top-left), H2O (center-left), and ALB (bottom-left) in July and for TOC (top-right), H2O (center-right), and ALB (bottom-right) in October. An analysis of the data shows that significant reductions in daily UV doses have occurred in July and October during the 21st century. These reductions are observed across all factors and are particularly evident in the middle and high latitudes of both hemispheres.
The primary factor affecting changes in the daily dose of ultraviolet radiation (UVpD) is the recovery of the ozone layer, which accounts for 50 to 80% of these changes. In contrast, changes in cloud cover contribute 20 to 30%, while alterations in surface reflectivity contribute no more than 20%. However, an exception exists in the polar regions of the Northern Hemisphere, where changes in albedo can reach 50%. This is linked to the diminishing reflective properties of the surface due to the loss of Arctic Ocean ice cover during the warmer half of the year by the end of the 21st century. Similar findings apply to the attribution of UVpD changes observed in January and April (see Appendix A, Figure A2). It is also important to consider the seasonal shift between the Northern and Southern Hemispheres, which switch from July to January and from April to October. During polar night conditions, low UVpD values and minor changes in the UVpD dose result in significant uncertainty in the attribution results. The percentage contributions of the factors from the S U M P group are presented in Figure 7 for TOC (top-left) and H2O (bottom-left) in July and for TOC (top-right) and H2O (bottom-right) in October. By the end of the 21st century, there is an increase in daily UVpD doses, primarily in the low latitudes of both hemispheres, including Southeast Asia, the Hawaii region, and Southern America. The main factor contributing to the variations of the daily UVpD doses is the LWCC decrease, which accounts for the 60–80% increase in UVpD doses. The corresponding contribution of the TOC changes in the UVpD dose increase does not exceed 20–40%. The exclusions are part of the Hawaii region and the Central Pacific Ocean, where the TOC changes are the primary drivers of the UVpD variation during the 21st century.
In January and April, the total increases in UVpD doses from all factors are negligible from the years 2015–2024 to 2090–2099 (see Appendix A, Figure A3).

4. Summary and Conclusions

The SOCOLv4 model of the Earth’s climate system and the FASTRT (UVSPEC) code for calculating radiation fluxes in the atmosphere were used to estimate changes in daily doses of surface UV radiation necessary for vitamin D production, considering the Earth’s climate and atmospheric ozone layer changes in the 21st century. From the model experiments driven by the SSP5-8.5 scenario of greenhouse gas concentrations and the WMO scenario of reducing the atmospheric content of ozone-depleting substances, significant changes in radiation factors affecting the surface daily doses of UVpD (TOC, cloud parameters, and surface albedo) have been obtained.
As a result of the Montreal Protocol implementation [17], atmospheric ozone levels are expected to increase by 5–15% in the middle latitudes and up to 80% in the polar regions from the period 2015–2024 to the period 2090–2099. However, in lower latitudes, a corresponding decline in total ozone column values of 2–3% is anticipated, primarily due to climate warming and the intensification of meridional atmospheric circulation.
The application of the SSP5-8.5 scenario leads to an increase in the liquid water column and total cloud cover by the end of the 21st century, particularly in middle and high latitudes. However, a decrease in surface albedo is expected due to the reduced land snow cover and loss of sea ice. The decline in albedo will be especially noticeable in the high latitudes of the Northern Hemisphere, mainly as a result of melting sea ice in the Arctic Ocean during the summer and autumn seasons. Additionally, significant reductions in cloud liquid water column are anticipated in certain areas of lower latitudes by the years 2090–2099.
The output from the model runs provided evaluations based on the relative contributions of variations in total ozone content, cloud parameters, and surface albedo to the corresponding changes in ultraviolet radiation (UVpD) during the 21st century. The comparison of the obtained results with the evaluations of the UVpD changes at the end of the 21st century over Europe [14] and over the high and middle latitudes of the NH [15] demonstrates a strong qualitative agreement. Consequently, we can draw the following conclusions regarding the projected changes in UVpD at the surface:
  • In the middle and high latitudes of both hemispheres across all seasons, changes in the above-mentioned radiation factors are expected to lead to a decrease in UVpD daily doses by 20–80% from the years 2015–2024 to 2090–2099. The primary contributor to the reduction in UV doses is the variation in total ozone content (TOC), accounting for 50–80% of the decrease. This effect is further supplemented by the influence of cloud parameters, which contribute an additional 20–30%, while variations in surface albedo contribute less than 20%. However, in the polar regions of the Northern Hemisphere, this contribution can reach up to 50%.
  • The most pronounced exception to changes in UVpD behavior occurs during the summer months. In July, across the mid-latitudes of North America, Europe, and Russia, the effects of changes in cloud parameters and total ozone column (TOC) tend to compensate for each other. However, the diminishing impact of LWCC/total cloud cover significantly outweighs the recovery effect of the TOC. As a result, this leads to a substantial (up to 30%) increase in daily UVpD doses by the end of the 21st century.
  • In the tropics of both hemispheres, during July and October, the combined effects of the TOC and LWCC/total cloud cover changes are expected to increase UVpD daily doses by 30–40% from the years 2015–2024 to 2090–2099 in certain areas of the lower latitudes (South-East Asia, the Hawaii region, and Southern America, Figure 7).
  • These findings highlight the significant public health concerns associated with decreased UV exposure, particularly regarding vitamin D synthesis and overall health. As we navigate a world where outdoor activities may be limited due to lifestyle changes or environmental factors, it is imperative to recognize and address the ramifications of decreased UV exposure on public health, ensuring that communities are informed and equipped to maintain optimal health conditions. Therefore, understanding and addressing the implications of decreased UV exposure is vital for promoting long-term well-being in the population. The impact of decreasing UV levels on human health is not covered in this discussion, as it requires insights from experts in various fields, including biology, medicine, and nutrition. It is important to clarify that we do not make predictions. Instead, we concentrate solely on the most severe scenario outlined by the Intergovernmental Panel on Climate Change (IPCC) to inform society about the potential negative consequences of future climate conditions. The likelihood of this scenario is uncertain; therefore, its accuracy cannot be assessed.

Author Contributions

Conceptualization, V.Z., E.R. and T.E.; methodology, V.Z.; software, V.Z.; validation, E.R., T.E. and V.Z.; investigation, T.E. and V.Z.; resources, E.R.; data curation, T.E.; writing—original draft preparation, V.Z.; writing—review and editing, T.E., E.R. and V.Z.; visualization, V.Z.; supervision, E.R.; project administration, E.R.; funding acquisition, E.R. All authors have read and agreed to the published version of the manuscript.

Funding

The work of E.R. and V.Z. on the model experiments, analysis of the results, and writing of the manuscript was supported by St. Petersburg State University under research grant 124032000025-1. The work of T.E. was supported by the Swiss National Science Foundation project 200021L-22814 10001350 (STOA).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data on SOCOLv4 are available from the Zenodo general-purpose open repository at https://doi.org/10.5281/zenodo.7234665. Data supporting the presented results can be found by writing to the author’s e-mail: v_zubov@rambler.ru.

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A

Figure A1. Latitude–longitude distribution of changes in total cloud cover (%) in January (top-left), April (top-right), July (bottom-left), and October (bottom-right), between 2015–2024 and 2090–2099 according to the SSP5-8.5 scenario.
Figure A1. Latitude–longitude distribution of changes in total cloud cover (%) in January (top-left), April (top-right), July (bottom-left), and October (bottom-right), between 2015–2024 and 2090–2099 according to the SSP5-8.5 scenario.
Atmosphere 16 00686 g0a1
Figure A2. Latitude–longitude cross-section of the relative contribution of TOC, H2O, and ALB to negative changes in the daily dose of UVpD (%), between 2015–2024 and 2090–2099 according to the SSP5-8.5 scenario: for JANUARY, the ratios of TOC/abs S U M N (top-left), H2O/abs S U M N (center-left), and ALB/abs S U M N (bottom-left), and for APRIL, the ratios of TOC/abs S U M N (top-right), H2O/abs S U M N (center-right), and ALB/abs S U M N (bottom-right) are shown. The areas of UV radiation changes with statistical significance at the level of 90% and higher are marked in color.
Figure A2. Latitude–longitude cross-section of the relative contribution of TOC, H2O, and ALB to negative changes in the daily dose of UVpD (%), between 2015–2024 and 2090–2099 according to the SSP5-8.5 scenario: for JANUARY, the ratios of TOC/abs S U M N (top-left), H2O/abs S U M N (center-left), and ALB/abs S U M N (bottom-left), and for APRIL, the ratios of TOC/abs S U M N (top-right), H2O/abs S U M N (center-right), and ALB/abs S U M N (bottom-right) are shown. The areas of UV radiation changes with statistical significance at the level of 90% and higher are marked in color.
Atmosphere 16 00686 g0a2aAtmosphere 16 00686 g0a2b
Figure A3. Latitude–longitude cross-section of the relative contribution of TOC and H2O to positive changes in the daily dose of UVpD (%), between 2015–2024 and 2090–2099 according to the SSP5-8.5 scenario: for JANUARY, the ratios of TOC/abs S U M P (top-left) and H2O/abs S U M P (top-right), and for APRIL, the ratios of TOC/abs S U M P (bottom-left) and H2O/abs S U M P (bottom-right) are shown. The areas of UV radiation changes with statistical significance at the level of 90% and higher are marked in color.
Figure A3. Latitude–longitude cross-section of the relative contribution of TOC and H2O to positive changes in the daily dose of UVpD (%), between 2015–2024 and 2090–2099 according to the SSP5-8.5 scenario: for JANUARY, the ratios of TOC/abs S U M P (top-left) and H2O/abs S U M P (top-right), and for APRIL, the ratios of TOC/abs S U M P (bottom-left) and H2O/abs S U M P (bottom-right) are shown. The areas of UV radiation changes with statistical significance at the level of 90% and higher are marked in color.
Atmosphere 16 00686 g0a3

References

  1. SPARC. Report on the Evaluation of Chemistry-Climate Models; SPARC Report No. 5, WCRP-132, WMO/TD-NO. 1526; Eyring, V., Shepherd, T.G., Waugh, D.W., Eds.; SPARC: Zurich, Switzerland, 2010. [Google Scholar]
  2. WMO. Scientific Assessment of Ozone Depletion: 2006; Global Ozone Research and Monitoring Project—Report No. 50; World Meteorological Organization: Geneva, Switzerland, 2007; 572p. [Google Scholar]
  3. Zerefos, C.; Fountoulakis, I.; Eleftheratos, K.; Kazantzidis, A. Long-term variability of human health related solar ultraviolet-B radiation doses from the 1980s to the end of the 21st century. Physiol. Rev. 2023, 103, 1789–1826. [Google Scholar] [CrossRef] [PubMed]
  4. Hegglin, M.; Shepherd, T. Large climate-induced changes in ultraviolet index and stratosphere to troposphere ozone flux. Nat. Geosci. 2009, 2, 687–691. [Google Scholar] [CrossRef]
  5. Tourpali, K.; Bais, A.F.; Kazantzidis, A.; Zerefos, C.S.; Akiyoshi, H.; Austin, J.; Brühl, C.; Butchart, N.; Chipperfield, M.P.; Dameris, M.; et al. Clear sky UV simulations for the 21st century based on ozone and temperature projections from Chemistry-Climate Models. Atmos. Chem. Phys. 2009, 9, 1165–1172. [Google Scholar] [CrossRef]
  6. Bais, A.F.; Tourpali, K.; Kazantzidis, A.; Akiyoshi, H.; Bekki, S.; Braesicke, P.; Chipperfield, M.P.; Dameris, M.; Eyring, V.; Garny, H.; et al. Projections of UV radiation changes in the 21st century: Impact of ozone recovery and cloud effects. Atmos. Chem. Phys. 2011, 11, 7533–7545. [Google Scholar] [CrossRef]
  7. Bais, A.; McKenzie, R.; Bernhard, G.; Aucamp, P.; Ilyas, M.; Madronich, S.; Tourpali, K. Ozone depletion and climate change: Impacts on UV radiation. Photochem. Photobiol. Sci. 2015, 14, 19–52. [Google Scholar] [CrossRef] [PubMed]
  8. Pastukhova, A.; Chubarova, N.E.; Zhdanova, Y.Y.; Galin, V.Y.; Smyshlyaev, S.P. Numerical Simulation of Variations in Ozone Content, Erythemal Ultraviolet Radiation, and Ultraviolet Resources over Northern Eurasia in the 21st Century. Izv. Atmos. Ocean. Phys. 2019, 55, 242–250. [Google Scholar] [CrossRef]
  9. Chatzopoulou, A.; Tourpali, K.; Bais, A.F.; Braesicke, P. Twenty-first century surface UV radiation changes deduced from CMIP6 models. Part II: Effects on UV index and plant growth weighted irradiance. Photochem. Photobiol. Sci. 2024, 24, 89–109. [Google Scholar] [CrossRef] [PubMed]
  10. Engelsen, O.; Kylling, A. Fast simulation tool for ultraviolet radiation at the Earth’s surface. Opt. Eng. 2005, 44, 041012. [Google Scholar] [CrossRef]
  11. MacLaughlin, J.A.; Anderson, R.R.; Holick, M.F. Spectral character of sunlight modulates photosynthesis of pre-vitamin D3 and its photoisomers in human skin. Science 1982, 216, 1001–1003. [Google Scholar] [CrossRef] [PubMed]
  12. Holick, M.; Bouillon, R.; Eisman, J.; Garabedian, M.; Kleinschmidt, J.; Suda, T.; Terenetskaya, I.; Webb, A. Action Spectrum for the Production of Pre-Vitamin D3 in Human Skin; CIE Technical Report TC 6-54; CIE: Vienna, Austria, 2006. [Google Scholar]
  13. Zubov, V.A.; Rozanov, E.V.; Karol, I.L.; Egorova, T.A.; Kiselev, A.; Ozolin, Y.E. Modelling variability of the vitamin D UV-radiation for 21st century. Proc. Voeikov Main Geophys. Obs. 2013, 568, 118–136. [Google Scholar]
  14. Corrêa, M.D.; Godin-Beekmann, S.; Haeffelin, M.; Bekki, S.; Saiag, P.; Badosa, J.; Jégou, F.; Pazmiño, A.; Mahé, E. Projected changes in clear sky erythemal and vitamin D effective UV doses for Europe over the period 2006 to 2100. Photochem. Photobiol. Sci. 2013, 12, 1053–1064. [Google Scholar] [CrossRef] [PubMed]
  15. Fountoulakis, I.; Bais, A.F. Projected changes in erythemal and vitamin D effective irradiance over northern-hemisphere high latitudes. Photochem. Photobiol. Sci. 2015, 14, 1251–1264. [Google Scholar] [CrossRef] [PubMed]
  16. ScienceDaily. University of California—Riverside: More Than Half the World’s Population Gets Insufficient Vitamin D, Says Biochemist. ScienceDaily, 19 July 2010. [Google Scholar]
  17. Godin-Beekmann, S.; Newman, P.A.; Petropavlovskikh, I. 30th anniversary of the Montreal Protocol: From the safeguard of the ozone layer to the protection of the Earth’s climate. Comptes Rendus Géosci. 2018, 350, 331–333. [Google Scholar] [CrossRef]
  18. Sukhodolov, T.; Egorova, T.; Stenke, A.; Ball, W.T.; Brodowsky, C.; Chiodo, G.; Feinberg, A.; Friedel, M.; Karagodin-Doyennel, A.; Peter, T.; et al. Atmosphere–ocean–aerosol–chemistry–climate model SOCOLv4.0: Description and evaluation. Geosci. Model Dev. 2021, 14, 5525–5560. [Google Scholar] [CrossRef]
  19. Riahi, K.; van Vuuren, D.P.; Kriegler, E.; Edmonds, J.; O’Neill, B.C.; Fujimori, S.; Bauer, N.; Calvin, K.; Dellink, R.; Fricko, O.; et al. The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview. Glob. Environ. Change 2017, 42, 153–168. [Google Scholar] [CrossRef]
  20. Dhomse, S.S.; Kinnison, D.; Chipperfield, M.P.; Salawitch, R.J.; Cionni, I.; Hegglin, M.I.; Abraham, N.L.; Akiyoshi, H.; Archibald, A.T.; Bednarz, E.M.; et al. Estimates of ozone return dates from Chemistry-Climate Model Initiative simulations. Atmos. Chem. Phys. 2018, 18, 8409–8438. [Google Scholar] [CrossRef]
  21. Nerobelov, G.; Timofeyev, Y.; Polyakov, A.; Virolainen, Y.; Rozanov, E.; Zubov, V. An Investigation of the SOCOLv4 Model’s Suitability for Predicting the Future Evolution of the Total Column Ozone. Atmosphere 2024, 15, 1491. [Google Scholar] [CrossRef]
  22. World Meteorological Organization (WMO). Scientific Assessment of Ozone Depletion: 2022; GAW Report No. 278; WMO: Geneva, Switzerland, 2022; 509p. [Google Scholar]
  23. von Storch, H.; Zwiers, F.W. Statistical Analysis in Climate Research; Cambridge University Press: Cambridge, UK, 1999. [Google Scholar]
  24. Shettle, E.P. Models of Aerosol, Clouds and Precipitation for Atmospheric Propagation Studies. In Atmospheric Propagation in the UV, Visible, IR and mm-Region and Related System Aspects; AGARD Conference Proceedings No. 454; AGARD: Neuilly-sur-Seine, France, 1989; p. 15. [Google Scholar]
  25. Zubov, V.; Rozanov, E.; Egorova, T.; Karol, I.; Schmutz, W. Role of external factors in the evolution of the ozone layer and stratospheric circulation in 21st century. Atmos. Chem. Phys. 2013, 13, 4697–4706. [Google Scholar] [CrossRef]
  26. Mendoza, V.; Pazos, M.; Garduno, R.; Mendoza, B. Thermodynamics of climate change between cloud cover, atmospheric temperature and humidity. Sci. Rep. 2021, 11, 21244. [Google Scholar] [CrossRef] [PubMed]
  27. Chatzopoulou, A.; Tourpali, K.; Bais, A.F.; Braesicke, P. 21st century surface UV radiation changes deduced from CMIP6 models: Part I—Evolution of major influencing factors. Photochem. Photobiol. Sci. 2025, 24, 89–109. [Google Scholar] [CrossRef] [PubMed]
  28. IPCC. Climate Change 2007: The Scientific Basis—Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change; Houghton, J.T., Ding, Y., Griggs, D.J., Noguer, M., van der Linden, P.J., Dai, X., Maskell, K., Johnson, C.A., Eds.; Cambridge University Press: Cambridge, UK, 2007. [Google Scholar]
Figure 1. Latitude–longitude distribution of TOC change (%) in January (top-left), April (top-right), July (bottom-left), and October (bottom-right), between 2015–2024 and 2090–2099 according to the SSP5-8.5 scenario.
Figure 1. Latitude–longitude distribution of TOC change (%) in January (top-left), April (top-right), July (bottom-left), and October (bottom-right), between 2015–2024 and 2090–2099 according to the SSP5-8.5 scenario.
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Figure 2. Latitude–longitude distribution of changes in cloud liquid water column (%) in January (top-left), April (top-right), July (bottom-left), and October (bottom-right), between 2015–2024 and 2090–2099 according to the SSP5-8.5 scenario.
Figure 2. Latitude–longitude distribution of changes in cloud liquid water column (%) in January (top-left), April (top-right), July (bottom-left), and October (bottom-right), between 2015–2024 and 2090–2099 according to the SSP5-8.5 scenario.
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Figure 3. Latitude–longitude distribution of changes in the surface albedo for UV radiation in January (top-left), April (top-right), July (bottom-left), and October (bottom-right), between 2015–2024 and 2090–2099 according to the SSP5-8.5 scenario.
Figure 3. Latitude–longitude distribution of changes in the surface albedo for UV radiation in January (top-left), April (top-right), July (bottom-left), and October (bottom-right), between 2015–2024 and 2090–2099 according to the SSP5-8.5 scenario.
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Figure 4. Latitude–longitude distribution of changes in the daily dose of UV radiation for the production of vitamin D (%), between 2015–2024 and 2090–2099 according to the SSP5-8.5 scenario: for January, the experiments ALL (top-left panel) and SUM (top-right panel), for April the experiments ALL (bottom-left panel) and SUM (bottom-right panel). The color-coded values of UV radiation changes with statistical significance at the level of 90% or higher are marked.
Figure 4. Latitude–longitude distribution of changes in the daily dose of UV radiation for the production of vitamin D (%), between 2015–2024 and 2090–2099 according to the SSP5-8.5 scenario: for January, the experiments ALL (top-left panel) and SUM (top-right panel), for April the experiments ALL (bottom-left panel) and SUM (bottom-right panel). The color-coded values of UV radiation changes with statistical significance at the level of 90% or higher are marked.
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Figure 5. Latitude–longitude distribution of changes in the daily dose of UV radiation to produce vitamin D (%), between 2015–2024 and 2090–2099 according to the SSP5-8.5 scenario: for July, experiments ALL (top-left panel) and SUM (top-right panel), for October, the experiments ALL (bottom-left panel) and SUM (bottom-right panel). The color-coded values of UV radiation changes with statistical significance at the level of 90% or higher are marked.
Figure 5. Latitude–longitude distribution of changes in the daily dose of UV radiation to produce vitamin D (%), between 2015–2024 and 2090–2099 according to the SSP5-8.5 scenario: for July, experiments ALL (top-left panel) and SUM (top-right panel), for October, the experiments ALL (bottom-left panel) and SUM (bottom-right panel). The color-coded values of UV radiation changes with statistical significance at the level of 90% or higher are marked.
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Figure 6. Latitude–longitude cross-section of the relative contribution of TOC, H2O, and ALB to negative changes in the daily dose of UVpD (%), between 2015–2024 and 2090–2099 according to the SSP5-8.5 scenario: for July, the ratios of TOC/abs( S U M N ) (top-left), H2O/abs( S U M N ) (center-left), and ALB/abs( S U M N ) (bottom-left), and for October, the ratios of TOC/abs( S U M N ) (top-right), H2O/abs( S U M N ) (center-right), and ALB/abs( S U M N ) (bottom-right) are shown. The areas of UV radiation changes with statistical significance at the level of 90% or higher are marked in color.
Figure 6. Latitude–longitude cross-section of the relative contribution of TOC, H2O, and ALB to negative changes in the daily dose of UVpD (%), between 2015–2024 and 2090–2099 according to the SSP5-8.5 scenario: for July, the ratios of TOC/abs( S U M N ) (top-left), H2O/abs( S U M N ) (center-left), and ALB/abs( S U M N ) (bottom-left), and for October, the ratios of TOC/abs( S U M N ) (top-right), H2O/abs( S U M N ) (center-right), and ALB/abs( S U M N ) (bottom-right) are shown. The areas of UV radiation changes with statistical significance at the level of 90% or higher are marked in color.
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Figure 7. Latitude–longitude cross-section of the relative contribution of TOC and H2O to positive changes in the daily dose of UVpD (%), between 2015–2024 and 2090–2099 according to the SSP5-8.5 scenario: for July, the ratio of TOC/abs( S U M P ) (top-left) and H2O/abs( S U M P ) (bottom-left); for October, the ratio of TOC/abs( S U M P ) (top-right) and H2O/abs( S U M P ) (bottom-right). The areas of UV radiation changes with statistical significance at the level of 90% or higher are marked in color.
Figure 7. Latitude–longitude cross-section of the relative contribution of TOC and H2O to positive changes in the daily dose of UVpD (%), between 2015–2024 and 2090–2099 according to the SSP5-8.5 scenario: for July, the ratio of TOC/abs( S U M P ) (top-left) and H2O/abs( S U M P ) (bottom-left); for October, the ratio of TOC/abs( S U M P ) (top-right) and H2O/abs( S U M P ) (bottom-right). The areas of UV radiation changes with statistical significance at the level of 90% or higher are marked in color.
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Zubov, V.; Rozanov, E.; Egorova, T. Projection of the UV Radiation for Vitamin D Production Changes Between 2015–2024 and 2090–2099 Periods. Atmosphere 2025, 16, 686. https://doi.org/10.3390/atmos16060686

AMA Style

Zubov V, Rozanov E, Egorova T. Projection of the UV Radiation for Vitamin D Production Changes Between 2015–2024 and 2090–2099 Periods. Atmosphere. 2025; 16(6):686. https://doi.org/10.3390/atmos16060686

Chicago/Turabian Style

Zubov, Vladimir, Eugene Rozanov, and Tatiana Egorova. 2025. "Projection of the UV Radiation for Vitamin D Production Changes Between 2015–2024 and 2090–2099 Periods" Atmosphere 16, no. 6: 686. https://doi.org/10.3390/atmos16060686

APA Style

Zubov, V., Rozanov, E., & Egorova, T. (2025). Projection of the UV Radiation for Vitamin D Production Changes Between 2015–2024 and 2090–2099 Periods. Atmosphere, 16(6), 686. https://doi.org/10.3390/atmos16060686

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