Do modelled or satellite-based estimates of surface solar irradiance accurately describe its temporal variability?

[1] Observational evidence indicates significant regional trends in solar radiation at the surface in both all-sky and cloud-free conditions. Negative trends in the downwelling solar surface irradiance (SSI) have become known as ‘dimming’ while positive trends have become known as ‘brightening’. We use the Met Office Hadley Centre HadGEM2 climate model to model trends in cloud-free and total SSI from the pre-industrial to the present-day and compare these against observations. Simulations driven by CMIP5 emissions are used to model the future trends in dimming/brightening up to the year 2100. The modeled trends are reasonably consistent with observed regional trends in dimming and brightening which are due to changes in concentrations in anthropogenic aerosols and, potentially, changes in cloud cover owing to the aerosol indirect effects and/or cloud feedback mechanisms. The future dimming/brightening in cloud-free SSI is not only caused by changes in anthropogenic aerosols: aerosol impacts are overwhelmed by a large dimming caused by increases in water vapor. There is little trend in the total SSI as cloud cover decreases in the climate model used here, and compensates the effect of the change in water vapor. In terms of the surface energy balance, these trends in SSI are obviously more than compensated by the increase in the downwelling terrestrial irradiance from increased water vapor concentrations. However, the study shows that while water vapor is widely appreciated as a greenhouse gas, water vapor impacts on the atmospheric transmission of solar radiation and the future of global dimming/brightening should not be overlooked.

Solar surface Radiation and Temperature Trends:Between 2001 and 2020, we explored the hypothesis that the measured increase in solar surface radiation might explain the observed temperature rise at 2 meters above ground in De Bilt. To investigate this, we combined solar surface radiation and temperature data from the Royal Netherlands Meteorological Institute (KNMI) with satellite measurements of radiation and cloud cover at the top of the atmosphere from the CERES satellite. By analyzing the energy and radiation balance of the atmosphere and considering partial balances at both the surface and the top of the atmosphere, we established connections between the components. The study solved for surface temperature, solar radiation reflection fraction, and solar radiation absorption fraction using balance equations. Energy balance calibration involved: Adjusting the temperature difference between the surface and the 2-meter height temperature to match observed temperature differences. Tuning the balance-calculated ratio of total forcing differences to temperature differences (ΔΣFi/ΔT) per season to align with the derivative of the Stefan-Boltzmann radiation law (R^2: 0.9992). Interestingly, we found that the atmosphere’s near surface emissivity could be well approximated by a power function of season dependent cloud cover. Additionally, the reflection and absorption fractions of solar radiation strongly correlated with cloud cover (R^2 > 0.90). Key findings and best estimates related to solar surface radiation and its impact on temperature trends in De Bilt, Netherlands, over the period from 2001 to 2020 at a 95% confidence interval:Surface Warming Contributions:Approximately 74% [43-100] of the surface warming is attributed to an increase in solar surface radiation. The remaining 26% [0-56] results from an increase in downward thermal radiation from the atmosphere near the surface. No contribution from downward thermal radiation cannot be ruled out. Atmospheric Warming at 2 Meters Height:The warming of the atmosphere at 2 meters height is influenced by several factors: Reduced reflection at the top of the atmosphere, contribution 39% [-2-80]. Increased upward thermal radiation, contribution 29% [6-52]. Enhanced latent heat and convection, contribution 28% [-6-63]. Notably, the strong correlation between reflection fraction and cloud cover suggests that the surface-level atmospheric warming is also influenced by decreasing cloud cover. Discussion about the Energy and Radiation Balance Analysis:Unmeasured variables can be inferred from balance equations. Observed warming can be explained by changes in energy and radiation components. Due to the high variability of the yearly data, the propagation of errors leads to high confidence intervals indicating that the true contribution could plausibly range anywhere from small negative to nearly the complete warming considered. In summary, Energy and Radiation Balance Analysis indicates that the solar surface radiation significantly impacts (> 43%) recent warming trends in De Bilt.

A high resolution satellite view of surface solar radiation over the climatically sensitive region of Eastern Mediterranean

The long-term trends of total surface solar radiation (SSR), surface diffuse radiation, and surface air temperature were analyzed in this study based on updated 48-yr data from 55 observational stations in China, and then the correlation between SSR and the diurnal temperature range (DTR) was studied. The effect of total solar radiation on surface air temperature in China was investigated on the basis of the above analyses. A strong correlation between SSR and DTR was found for the period 1961–2008 in China. The highest correlation and steepest regression line slope occurred in winter, indicating that the solar radiation effect on DTR was the largest in this season. Clouds and water vapor have strong influences on both SSR and DTR, and hence on their relationship. The largest correlations between SSR and DTR occurred in wintertime in northern China, regardless of all-day (including clear days and cloudy days) or clear-day cases. Our results also showed that radiation arriving at the surface in China decreased significantly during 1961–1989 (dimming period), but began to increase during 1990–2008 (brightening period), in agreement with previous global studies. The reduction of total SSR offset partially the greenhouse warming during 1961–1989. However, with the increase of SSR after 1990, this offsetting effect vanished; on the contrary, it even made a contribution to the accelerated warming. Nonetheless, the greenhouse warming still played a controlling role because of the increasing of minimum and mean surface temperatures in the whole study period of 1961–2008. We estimated that the greenhouse gases alone may have caused surface temperatures to rise by 0.31–0.46°C (10 yr)−1 during 1961–2008, which is higher than previously estimated. Analysis of the corresponding changes in total solar radiation, diffuse radiation, and total cloud cover indicated that the dimming and brightening phenomena in China were likely attributable to increases in absorptive and scattering aerosols in the atmosphere, respectively.

Short-term forecasts of solar radiation are gaining importance for power grid operators, providing estimates of surface solar irradiance (SSI) and anticipated photovoltaic (PV) power generation for minutes to hours ahead. Geostationary satellites such as the Meteosat Second Generation equipped with the Spinning Enhanced Visible and Infrared Imager (SEVIRI) provide valuable spectral imagery for deriving SSI across wide geographical areas. While these spectral measurements are foundational for many SSI estimation and forecasting methods, current approaches still use Level-2 products of SSI, which leads to significant delays in the forecasting process. We demonstrate that a convolutional residual network can accurately emulate SSI produced by the SARAH-3 algorithm.A generalized deep-learning model is trained to estimate the instantaneous SARAH-3 SSI from all channels of the SEVIRI imager over a large region in Europe. The SSI emulator shows a similar bias and root mean square error (RMSE) for a large validation pyranometer set across Europe and Northern Africa. The emulator directly infers SSI from the Level-1.5 spectral imagery of SEVIRI within just 15 seconds, strongly reducing the 10+ minutes runtime of non-machine-learning radiation retrievals such as SARAH-3. We present a characterisation of the SSI emulator's performance depending on location and season and quantify the channel importance for different regions and surface albedos. We also present the SSI emulator's performance over snowy surfaces where retrieval algorithms such as SARAH-3 have been struggling to distinguish between snow and clouds. To further improve the emulator, advanced fine-tuning methods are applied based on ground observations without degradation in regional bias. The fine-tuning improves the RMSE on average from 80 to 65 W/m^2 and outperforms the SARAH-3 algorithm. 

<p>Previous studies have documented that the surface solar radiation (SSR) over most regions of China has shifted from the ‘global dimming’ since the 1950s to the ‘global brightening’ after 2005. In this paper, the potential factors that affect the annual trends of SSR over East China from 2005 to 2018 based on different satellite-derived products are analyzed. Then, due to the lack of long-term various aerosol species from observation data, the focus of this study is to calculate the contributions from direct effects of different types of cloud fraction on SSR relative to the effects of total cloud fraction over East China during the same period using a BCC_RAD radiative transfer model. The results show that clouds and aerosols are the primary factors that affect the SSR over East China from 2005 to 2018, followed by water vapor and ozone.</p><p>The annual mean all-sky SSR from 2005 to 2018 is significantly increased over the North China Plain, Northeast China, Yunnan, and Eastern Sichuan provinces, with the increases up to 0.6 W m<sup>-2</sup> yr<sup>-1</sup>. This is probably due to the combined reductions of aerosols and clouds during this period, but clouds even play a more important role over Shanxi and northern Shaanxi. Changes in aerosols dominate the increase of SSR over Hunan, Jiangxi, and Fujian provinces, whereas clouds contribute more to the decreases of SSR over Guangdong, Guangxi, Guizhou, and Zhejiang provinces. Meanwhile, the simulations indicate that the marked annual mean decreases in high cloud fraction, especially for low cloud fraction, are the main causes of simulated increases in SSR due to total cloud fraction over most regions of East China, while the increases in high, medium-high, especially for medium-low cloud fraction, play more important roles in reductions of SSR over southern China. Moreover, the direct effects of various types of cloud fraction on changes in SSR for each season are also examined. It seems that the direct effects of low cloud fraction on SSR are likely the strongest among all kinds of clouds. Take southern China as an example, the direct effects of medium-low and low cloud fraction are stronger for spring and autumn, while contributions from low cloud fraction are largest in winter. However, the combined increases in high, medium-high, medium-low cloud fraction exceed decreases in low cloud fraction, thus causing the reduction in SSR in summer. This study highlights that different types of clouds may have different impacts on SSR not only on the annual mean scale but also on seasonal scales.</p><p>Keywords: surface solar radiation, aerosols, different types of cloud fraction</p>

<p>The quantification of Earth’s solar radiation budget and its temporal changes is essential for the understanding of the genesis and evolution of climate on our planet. While the solar radiative fluxes in and out of the climate system can be accurately tracked and quantified from space by satellite programs such as CERES or SORCE, the disposition of solar energy within in the climate system is afflicted with larger uncertainties. A better quantification of the solar radiative fluxes not only under cloudy, but also under cloud-free conditions can help to reduce these uncertainties and is essential for example for the determination of cloud radiative effects or for the understanding of  temporal changes in the solar radiative components of the climate system.</p> <p>We combined satellite observations of Top of Atmosphere fluxes with the information contained in surface flux observations and climate models to infer the absorption of solar radiation in the atmosphere, which we estimated at 73 Wm<sup>-2</sup> globally under cloud-free conditions (Wild et al. 2019 Clim Dyn). The latest generation of climate models participating in CMIP6 is now able to reproduce this magnitude surprisingly well, whereas in previous climate model  generations the cloud-free atmosphere was typically too transparent for solar radiation, which stated a long-standing modelling issue (Wild 2020 Clim Dyn, Wild et al. 1995 JClim).</p> <p>With respect to changes in solar fluxes, there is increasing evidence that the substantial long-term decadal variations in surface solar radiation known as dimming and brightening occur not only under all-sky, but similarly also under clear-sky conditions (Manara et al. 2016 ACP, Yang et al. 2019 JClim; Wild et al. 2021 GRL). This points to aerosol radiative effects as major factor for the explanation of this phenomenon.</p>

<strong class="journal-contentHeaderColor">Abstract.</strong> Accurate solar surface irradiance (SSI) data are a prerequisite for efficient planning and operation of solar energy systems. Respective data are also essential for climate monitoring and analysis. Satellite-based SSI has grown in importance over the last few decades. However, a retrieval method is needed to relate the measured radiances at the satellite to the solar surface irradiance. In a widespread classical approach, these radiances are used directly to derive the effective cloud albedo (CAL) as basis for the estimation of the solar surface irradiance. This approach was already introduced and discussed in the early 1980s. Various approaches are briefly discussed and analysed, including an overview of open questions and opportunities for improvement. Special emphasis is placed on the reflection of fundamental physical laws and atmospheric measurement techniques. In addition, atmospheric input data and key applications are briefly discussed. It is concluded that the well-established observation-based CAL approach is still an excellent choice for the retrieval of the cloud transmission. The coupling with lookup-table-based clear-sky models enables the estimation of solar surface irradiance with high accuracy and homogeneity. This could explain why, despite its age, the direct CAL approach is still used by key players in energy meteorology and the climate community. For the clear-sky input data, it is recommended to use ECMWF forecast and reanalysis data.

<strong class="journal-contentHeaderColor">Abstract.</strong> Accurate solar surface irradiance (SSI) data are a prerequisite for efficient planning and operation of solar energy systems. Respective data are also essential for climate monitoring and analysis. Satellite-based SSI has grown in importance over the last few decades. However, a retrieval method is needed to relate the measured radiances at the satellite to the solar surface irradiance. In a widespread classical approach, these radiances are used directly to derive the effective cloud albedo (CAL) as basis for the estimation of the solar surface irradiance. This approach was already introduced and discussed in the early 1980s. Various approaches are briefly discussed and analysed, including an overview of open questions and opportunities for improvement. Special emphasis is placed on the reflection of fundamental physical laws and atmospheric measurement techniques. In addition, atmospheric input data and key applications are briefly discussed. It is concluded that the well-established observation-based CAL approach is still an excellent choice for the retrieval of the cloud transmission. The coupling with lookup-table-based clear-sky models enables the estimation of solar surface irradiance with high accuracy and homogeneity. This could explain why, despite its age, the direct CAL approach is still used by key players in energy meteorology and the climate community. For the clear-sky input data, it is recommended to use ECMWF forecast and reanalysis data.

New Directions: A facelift for the picture of the global energy balance

The global target&amp;#160;of net-zero emissions and carbon neutrality by the mid-21st&amp;#160;century is accelerating the transition to clean energy. Accurately assessing solar energy potential requires high-quality hourly surface solar radiation (SSR)&amp;#160;and direct radiation (Rd)&amp;#160;datasets.&amp;#160;This study&amp;#160;evaluates&amp;#160;hourly&amp;#160;SSR and Rd&amp;#160;data from two&amp;#160;reanalysis products&amp;#160;(ERA5&amp;#160;and MERRA-2) and three satellite-derived products (CERES, SARAH-E,&amp;#160;and Solcast) against 22&amp;#160;years&amp;#160;of homogeneous&amp;#160;surface observations in&amp;#160;China. This validation utilizes data from 96 stations for SSR and 17 stations for Rd,&amp;#160;and includes both accuracy and stability tests:&amp;#160;According to the accuracy test, SSR and Rdare often overestimated, with lower accuracy observed during&amp;#160;sunrise and sunset.&amp;#160;SSR exhibits larger seasonal variations in accuracy than Rd, with accuracy declining in&amp;#160;the cold season.&amp;#160;SARAH-E and ERA5 demonstrate the&amp;#160;least&amp;#160;overestimation&amp;#160;of&amp;#160;the diurnal cycle of SSR, indicating the&amp;#160;highest accuracy. CERES and SARAH-E demonstrate the highest accuracy&amp;#160;for Rd, with CERES underestimating&amp;#160;and SARAH-E overestimating&amp;#160;throughout the day. &amp;#160; Decadal trends of SSR and Rdare also overestimated by most products. SSR stability is&amp;#160;lower in the cold season compared to the warm season.&amp;#160;Rd&amp;#160;stability&amp;#160;decreases&amp;#160;notably in cloudy and polluted MERRA-2 and CERES exhibit the highest stability for&amp;#160;SSR, while ERA5 demonstrates the highest stability for&amp;#160;Rd. In summary, as highlighted by the bold lines in Figure 1, ERA5 excels in capturing the diurnal cycle of SSR, and&amp;#160;CERES demonstrates superior performance for Rd&amp;#160;across China.&amp;#160;&amp;#160;&amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; &amp;#160; Figure 1. Overall comparison&amp;#160;of products over China for&amp;#160;hourly surface solar radiation&amp;#160;(a) and direct radiation (b),&amp;#160;regarding accuracy index (nMABD, the normalized mean absolute bias deviation, %) and stability index (absolute decadal trend bias, % decadal&amp;#8211;1). The&amp;#160;performance of each product correlates positively with the size of its hexagon.

Surface solar irradiance (SSI) is a crucial component in climatological and agricultural applications. Because the use of renewable energy is crucial, the importance of SSI has increased. In situ measurements are often used to investigate SSI; however, their availability is limited in spatial coverage. To precisely estimate the distribution of SSI with fine spatiotemporal resolutions, we used the GEOstationary Korea Multi-Purpose SATellite 2A (GEO-KOMPSAT 2A, GK2A) equipped with the Advanced Meteorological Imager (AMI). To obtain an optimal model for estimating hourly SSI around Korea using GK2A/AMI, the convolutional neural network (CNN) model as a machine learning (ML) technique was applied. Through statistical verification, CNN showed a high accuracy, with a root mean square error (RMSE) of 0.180 MJ m−2, a bias of −0.007 MJ m−2, and a Pearson’s R of 0.982. The SSI obtained through a ML approach showed an accuracy higher than the GK2A/AMI operational SSI product. The CNN SSI was evaluated by comparing it with the in situ SSI from the Ieodo Ocean Research Station and from flux towers over land; these in situ SSI values were not used for training the model. We investigated the error characteristics of the CNN SSI regarding environmental conditions including local time, solar zenith angle, in situ visibility, and in situ cloud amount. Furthermore, monthly and annual mean daily SSI were calculated for the period from 1 January 2020 to 31 January 2022, and regional characteristics of SSI around Korea were analyzed. This study addressed the availability of satellite-derived SSI to resolve the limitations of in situ measurements. This could play a principal role in climatological and renewable energy applications.

Surface solar irradiance varies on scales down to seconds or meters due to clouds. This highly variable nature of irradiance is not resolved by atmospheric models, yet heterogeneity in surface irradiance impacts the overlying cloud field. The inability to resolve irradiance variability, aside from insufficient model resolution, is explained by our limited understanding of cloud-driven solar irradiance variability at short spatiotemporal scales and the lack of high resolution spatial observational data. Cloud resolving models utilizing ray tracing techniques are a useful research tool, but ultimately require validation against observations.In 2021, we gathered new observational data with a network of radiometers, specifically designed to gather data on cloud-driven surface patterns of irradiance. I will present results on various kinds of surface patterns in relation to cloud type and atmospheric conditions, based on these observations. Our radiometers sample surface solar irradiance at 10 Hz for 18 wavelengths, which we deployed in different setups in the FESSTVaL (Germany) and LIAISE (Spain) field campaigns. Our results highlight the complexity and wide range of regimes in spatiotemporal irradiance variability, but also provide insights into its driving mechanisms. These insights help guide the development of improved radiative transfer calculations, in order to move towards models that can accurately resolve irradiance variability in an operational setting.

Abstract. The recent release of the International Satellite Cloud Climatology Project (ISCCP) HXG cloud products and new ERA5 reanalysis data enabled us to produce a global surface solar radiation (SSR) dataset: a 16-year (2000–2015) high-resolution (3 h, 10 km) global SSR dataset using an improved physical parameterization scheme. The main inputs were cloud optical depth from ISCCP-HXG cloud products; the water vapor, surface pressure and ozone from ERA5 reanalysis data; and albedo and aerosol from Moderate Resolution Imaging Spectroradiometer (MODIS) products. The estimated SSR data were evaluated against surface observations measured at 42 stations of the Baseline Surface Radiation Network (BSRN) and 90 radiation stations of the China Meteorological Administration (CMA). Validation against the BSRN data indicated that the mean bias error (MBE), root mean square error (RMSE) and correlation coefficient (R) for the instantaneous SSR estimates at 10 km scale were −11.5 W m−2, 113.5 W m−2 and 0.92, respectively. When the estimated instantaneous SSR data were upscaled to 90 km, its error was clearly reduced, with RMSE decreasing to 93.4 W m−2 and R increasing to 0.95. For daily SSR estimates at 90 km scale, the MBE, RMSE and R at the BSRN were −5.8 W m−2, 33.1 W m−2 and 0.95, respectively. These error metrics at the CMA radiation stations were 2.1 W m−2, 26.9 W m−2 and 0.95, respectively. Comparisons with other global satellite radiation products indicated that our SSR estimates were generally better than those of the ISCCP flux dataset (ISCCP-FD), the global energy and water cycle experiment surface radiation budget (GEWEX-SRB), and the Earth's Radiant Energy System (CERES). Our SSR dataset will contribute to the land-surface process simulations and the photovoltaic applications in the future. The dataset is available at https://doi.org/10.11888/Meteoro.tpdc.270112 (Tang, 2019).

This paper discusses the differences, due to different measuring mechanisms, of satellite-derived and ground measured surface solar irradiance (SSI) direct and diffuse components, and the impacts of the differences when validating satellite-derived data using ground measurements, using SSI products derived from the Advanced Himawari Imager. The issue of satellite-ground mismatch, though previously recognized, has not been given sufficient attention in the relevant scientific literature due to: (i) satellite SSI have mostly focused on relatively longer-term data, such as hourly, daily and longer; and/or (ii) most studies assessed only global (or total) irradiance. Also, studies addressing this satellite-ground mismatch issue often require a dense ground network to characterize the spatial representativeness of ground measurements, which likely limited performing such studies. With shorter-term satellite SSI data, such as 10-min or less, and the direct and diffuse components becoming increasingly available, further investigation of this satellite-ground mismatch issue is warranted. Our analysis, using direct and diffuse component SSI observations from satellite and sparse ground sites showed that a large part of apparent validation errors may have been due to the satellite-ground mismatch, often exceeding 50% in the case of 10-min direct normal irradiance. Greater appreciation and understanding of this issue should lead to more objective assessment of the real quality of satellite SSI products. Our study also showed that several factors, such as spatial averaging, temporal averaging, solar zenith angle and view zenith angle, all have often substantial impacts on the validation outcomes, over 100 W/m2 in some cases, highlighting the necessity for the ground- and satellite-based solar radiation communities to co-develop protocols for satellite SSI data validation.