Surface Incident Shortwave Radiation Research Articles

A review and comparison of surface incident shortwave radiation from multiple data sources: satellite retrievals, reanalysis data and GCM simulations

ABSTRACT Surface incident shortwave radiation (Rs ) can promote the circulation of substance and energy, and the accuracy of its estimation is of great significance for climate studies. The Rs can be acquired from satellite retrievals, reanalysis predictions and general circulation model (GCM) simulations. Although Rs estimates have been evaluated and compared in previous studies, most of them focus on evaluating the Rs estimates over specific regions using ground measurements from limited stations. Therefore, it is essential to comprehensively validate Rs estimates from multiple data sources. In this study, ground measurements of 690 stations from BSRN, GEBA, CMA, GC-NET and buoys were employed to validate the Rs estimates from seven representative products (GLASS, GEWEX-SRB, CERES-EBAF, ERA5, MERRA2, CFSR and CMIP6). The validation results indicated that the selected products overestimated Rs globally, with biases ranged from 0.48 to 21.27 W/m2. The satellite retrievals showed relatively better accuracy among seven datasets compared to ground measurements at the selected stations. Moreover, the selected seven products were all in poor accuracy at high-latitude regions with RMSEs greater than 50 W/m2. The long-term variation trends were also analyzed in this study.

Estimation of Land Surface Incident Shortwave Radiation From Geostationary Advanced Himawari Imager and Advanced Baseline Imager Observations Using an Optimization Method

Surface incident shortwave radiation (ISR) is an important component of the surface radiation budget. We refined the optimization method developed for polar-orbiting satellite data [1] and applied it to estimate ISR from the new generation geostationary Advanced Himawari Imager (AHI) onboard the Himawari-8/9 satellite and Advanced Baseline Imager (ABI) onboard the Geostationary Operational Environmental Satellite-R Series. Validation of the AHI ISR estimation at 2-km resolution showed an R² of 0.93, bias of 0.52 W/m², and RMSE of 106.52 W/m² for instantaneous estimates; an R² of 0.95, bias of -0.12 W/m², and RMSE of 22.49 W/m² for daily mean ISR; and a bias of -0.18 W/m² and RMSE of 7.72 W/m² for monthly mean ISR. Validation of the ABI ISR at 2-km spatial resolution showed an R² value of 0.93, bias of 8.71 W/m², and RMSE of 102.30 W/m² for instantaneous estimates; an R² of 0.95, bias of -2.38 W/m², and RMSE of 27.17 W/m² for daily mean ISR; and a bias of 1.40 W/m² and RMSE of 14.75 W/m² for monthly mean ISR. Our study also demonstrated that AHI and ABI observations have realized much better estimations for hourly and diurnal ISR than previous polar-orbiting satellite data because of their higher frequency of sampling on the atmospheric conditions.

Evaluation of Bayesian Multimodel Estimation in Surface Incident Shortwave Radiation Simulation over High Latitude Areas

Surface incident shortwave radiation (SSR) is crucial for understanding the Earth’s climate change issues. Simulations from general circulation models (GCMs) are one of the most practical ways to produce long-term global SSR products. Although previous studies have comprehensively assessed the performance of the GCMs in simulating SSR globally or regionally, studies assessing the performance of these models over high-latitude areas are sparse. This study evaluated and intercompared the SSR simulations of 48 GCMs participating in the fifth phase of the Coupled Model Intercomparison Project (CMIP5) using quality-controlled SSR surface measurements at 44 radiation sites from three observation networks (GC-NET, BSRN, and GEBA) and the SSR retrievals from the Clouds and the Earth’s Radiant Energy System, Energy Balanced and Filled (CERES EBAF) data set over high-latitude areas from 2000 to 2005. Furthermore, this study evaluated the performance of the SSR estimations of two multimodel ensemble methods, i.e., the simple model averaging (SMA) and the Bayesian model averaging (BMA) methods. The seasonal performance of the SSR estimations of individual GCMs, the SMA method, and the BMA method were also intercompared. The evaluation results indicated that there were large deficiencies in the performance of the individual GCMs in simulating SSR, and these GCM SSR simulations did not show a tendency to overestimate the SSR over high-latitude areas. Moreover, the ensemble SSR estimations generated by the SMA and BMA methods were superior to all individual GCM SSR simulations over high-latitude areas, and the estimations of the BMA method were the best compared to individual GCM simulations and the SMA method-based estimations. Compared to the CERES EBAF SSR retrievals, the uncertainties of the SSR estimations of the GCMs, the SMA method, and the BMA method are relatively large during summer.

Estimation of all-sky instantaneous surface incident shortwave radiation from Moderate Resolution Imaging Spectroradiometer data using optimization method

Surface incident shortwave radiation (ISR) is a crucial parameter in the land surface radiation budget. Many reanalysis, observation-based, and satellite-derived global radiation products have been developed but often have insufficient accuracy and spatial resolution for many applications. In this paper, we propose a method based on a radiative transfer model for estimating surface ISR from Moderate Resolution Imaging Spectroradiometer (MODIS) Top of Atmosphere (TOA) observations by optimizing the surface and atmospheric variables with a cost function. This approach consisted of two steps: retrieving surface bidirectional reflectance distribution function parameters, aerosol optical depth (AOD), and cloud optical depth (COD); and subsequently calculating surface ISR. Validation against measurements at seven Surface Radiation Budget Network (SURFRAD) sites resulted in an R2 of 0.91, a bias of −6.47 W/m2, and a root mean square error (RMSE) of 84.17 W/m2 (15.12%) for the instantaneous results. Validation at eight high-latitude snow-covered Greenland Climate Network (GC-Net) sites resulted in an R2 of 0.86, a bias of −21.40 W/m2, and an RMSE of 84.77 W/m2 (20.96%). These validation results show that the proposed method is much more accurate than the previous studies (usually with RMSEs of 80-150 W/m2). We further investigated whether incorporating additional satellite products, such as the MODIS surface broadband albedo (MCD43), aerosol (MOD/MYD04), and cloud products (MOD/MYD06), as constraints in the cost function would improve the accuracy. When the AOD and COD estimates were constrained, RMSEs were reduced to 62.19 W/m2 (12.12%) and 71.70 W/m2 (17.74%) at the SURFRAD and GC-Net sites, respectively. This algorithm could estimate surface ISR with MODIS TOA observations over both snow-free and seasonal/permanent snow-covered surfaces. The algorithm performed well at high-latitude sites, which is very useful for radiation budget research in the polar regions.

Evaluation of the Reanalysis Surface Incident Shortwave Radiation Products from NCEP, ECMWF, GSFC, and JMA Using Satellite and Surface Observations

Solar radiation incident at the Earth’s surface (Rs) is an essential component of the total energy exchange between the atmosphere and the surface. Reanalysis data have been widely used, but a comprehensive validation using surface measurements is still highly needed. In this study, we evaluated the Rs estimates from six current representative global reanalyses (NCEP–NCAR, NCEP-DOE; CFSR; ERA-Interim; MERRA; and JRA-55) using surface measurements from different observation networks [GEBA; BSRN; GC-NET; Buoy; and CMA] (674 sites in total) and the Earth’s Radiant Energy System (CERES) EBAF product from 2001 to 2009. The global mean biases between the reanalysis Rs and surface measurements at all sites ranged from 11.25 W/m2 to 49.80 W/m2. Comparing with the CERES-EBAF Rs product, all the reanalyses overestimate Rs, except for ERA-Interim, with the biases ranging from −2.98 W/m2 to 21.97 W/m2 over the globe. It was also found that the biases of cloud fraction (CF) in the reanalyses caused the overestimation of Rs. After removing the averaged bias of CERES-EBAF, weighted by the area of the latitudinal band, a global annual mean Rs values of 184.6 W/m2, 180.0 W/m2, and 182.9 W/m2 were obtained over land, ocean, and the globe, respectively.

Local Adaptive Calibration of the Satellite-Derived Surface Incident Shortwave Radiation Product Using Smoothing Spline

Incident solar radiation $(R_s)$ over the Earth's surface plays an important role in determining the Earth's climate and environment. Generally, $R_s$ can be obtained from direct measurements, remotely sensed data, or reanalysis and general circulation model (GCM) data. Each type of product has advantages and limitations: the surface direct measurements provide accurate but sparse spatial coverage, whereas other global products may have large uncertainties. Ground measurements have been normally used for validation and occasionally calibration, but transforming their “true values” spatially to improve the satellite products is still a new and challenging topic. In this paper, an improved thin-plate smoothing spline approach is presented to locally “calibrate” the Global LAnd Surface Satellite (GLASS) $R_s$ product using the reconstructed $R_s$ data from surface meteorological measurements. The influence of surface elevation on $R_s$ estimation was also considered in the proposed method. The point-based surface reconstructed $R_s$ was used as the response variable, and the GLASS $R_s$ product and the surface elevation data at the corresponding locations as explanatory variables to train the thin-plate spline model. We evaluated the performance of the approach using the cross-validation method at both daily and monthly time scales over China. We also validated the estimated $R_s$ based on the thin-plate spline method using independent ground measurements and independent satellite estimates of $R_s$ . These validation results indicated that the thin-plate smoothing spline method can be effectively used for calibrating satellite-derived $R_s$ products using ground measurements to achieve better accuracy.

Impact of geolocations of validation data on the evaluation of surface incident shortwave radiation from Earth System Models

AbstractGround‐based observations of surface incident solar radiation (Rs) have been used to evaluate simulations of global climate models. Existing studies have shown that biases in simulated clouds have a significant spatial pattern, which may be transferred to the simulated Rs. Therefore, the evaluation results of Rs simulations may depend on the locations of the ground‐based observations. In this study, Rs simulations of 48 models participating in the fifth phase of the Coupled Model Intercomparison Project (CMIP5) were first evaluated with ground‐based observations from different networks (446 stations in total) from 2000 to 2005. The global mean biases of the CMIP5 Rs simulations were found to vary from 4.8 to 11.9 W m−2 when Rs observations from different networks were used as reference data. To reduce the location impact on the evaluation results, CMIP5 simulated Rs was then evaluated with the latest satellite Rs retrieval at 1° × 1° spatial resolution by the Clouds and the Earth's Radiant Energy System, Energy Balanced and Filled (CERES EBAF). It was found that the CMIP5 simulated multimodel mean Rs has a small bias of 2.6 W m−2 compared with CERES EBAF over the globe, 4.7 W m−2 and 1.7 W m−2 over land and oceans, respectively. CERES EBAF Rs was found to have a positive bias of 1.3 W m−2 compared with ground‐based observations. After removing this bias of CERES EBAF Rs, global mean Rs was estimated to be 185 W m−2.

Analysis of surface incident shortwave radiation from four satellite products

Incident solar radiation (Rs) over the Earth's surface is important for studying our climate and environment. Global observation networks have been established, but many land surfaces are under-represented. Satellite remote sensing is the only way to estimate Rs at both global and regional scales. Many efforts have been made to evaluate the accuracy of current Rs products generated from satellite observations, but only a limited amount of ground measurements was generally used and the individual satellite products were used for analyzing Rs variability. In this study, four satellite estimates of Rs, including the Global Energy and Water Cycle Experiment — Surface Radiation Budget (GEWEX-SRB V3.0), the International Satellite Cloud Climatology Project — Flux Data (ISCCP-FD), the University of Maryland (UMD)/Shortwave Radiation Budget (SRB) (UMD-SRB V3.3.3) product, and the Earth's Radiant Energy System (CERES) EBAF, were evaluated using comprehensive ground measurements at 1151 sites around the world from the Global Energy Balance Archive (GEBA) and the China Meteorological Administration (CMA). It was found that these satellite estimates of Rs agree better with surface measurements at monthly than at daily time scale and can capture the seasonal variation of Rs very well, but these satellite products overestimated Rs by approximately 10wm−2. The mean bias and the root mean square error (RMSE) of the monthly mean estimates from these four data sets were 10.2wm−2 and 24.8wm−2 respectively. The global annual mean values of Rs were 186.7wm−2, 185.4wm−2, and 188.6wm−2 for CERES-EBAF, ISCCP-FD, and GEWEX-SRB V3.0 respectively. The averaged global annual mean Rs value from ground-measured-calibrated three satellite derived Rs products was 180.6wm−2, which is smaller than that estimated from individual satellite-derived products. The CERES-EBAF product shows the best accuracy among these four data sets, which indicates that including more accurate cloud information from active instruments can improve the accuracy of Rs. These satellite products show different temporal trends. Both GEWEX-SRB V3.0 and ISCCP-FD showed similar trends at the global scale but with different magnitudes. A significant dimming was found between 1984 and 1991, followed by brightening from 1992 to 2000, and then by a significant dimming over 2001–2007. The CERES-EBAF product showed a brightening trend, but not significantly since 2000. The variability from satellite estimates at pixel level was also analyzed. The results are comparable with previous studies based on observed Rs at the surface for specific regions, although some inconsistencies still exist and the magnitudes of the variations should be further quantified. We also found that clouds contribute more to the long-term variations of Rs derived from satellite observations than aerosols.

Comprehensive evaluation of polar weather research and forecasting model performance in the Antarctic

Recent versions of the Polar Weather Research and Forecasting model are evaluated over the Antarctic to assess the impact of model improvements, resolution, large‐scale circulation variability, and uncertainty in initial and lateral boundary conditions. The model skill differs more between forecasts using different sources of lateral boundary data than between forecasts from different model versions or simulated years. Using the ERA‐Interim reanalysis for initial and lateral boundary conditions produces the best skill. The forecasts have a cold summer and a warm winter bias in 2 m air temperatures, with similar but smaller bias in dew point temperatures. Upper air temperature biases are small and remain less than 1 °C except at the tropopause in summer. Geopotential height biases increase with height in both seasons. Deficient downward longwave radiation in all seasons and an under representation of clouds enhance radiative loss, leading to the cold summer bias. Excess summer surface incident shortwave radiation plays a secondary role, because 80% of it is reflected, leading to greater skill for clear compared with cloudy skies. The positive wind speed bias produces a warm surface bias in winter resulting from anomalously large downward flux of sensible heat toward the surface. Low temperatures on the continent limit sublimation and hence the precipitable water amounts over the ice sheet. ERA‐Interim experiments with higher precipitable water showed reduced biases in downwelling shortwave and longwave radiation. Increasing horizontal resolution from 60 to 15 km improves the skill of surface wind forecasts.

Statistical Analysis of Forecasting Models across the North Slope of Alaska during the Mixed-Phase Arctic Clouds Experiment

Abstract The National Centers for Environmental Prediction’s (NCEP) Eta Model, the models of the European Centre for Medium-Range Weather Forecasts (ECMWF) and the National Aeronautics and Space Administration’s (NASA) Global Modeling and Assimilation Office (GMAO) models, and the Regional Atmospheric Modeling System (RAMS) model are all examined during the Mixed-Phase Arctic Clouds Experiment (MPACE) that took place from 27 September through 22 October 2004. During two intensive observation periods, soundings were launched every 6 h from four sites across the North Slope of Alaska (NSA): Barrow, Atqasuk, Oliktok Point, and Toolik Lake. Measurements of temperature, moisture, and winds, along with surface measurements of radiation and cloud cover, were compared to model outputs from the Eta, ECMWF, GMAO, and RAMS models using the bootstrap statistical technique to ascertain if differences in model performance were statistically significant. Ultimately, three synoptic regimes controlled NSA weather during the MPACE period for varying amounts of time. Each posed a unique challenge to the forecasting models during the study period. Temperature forecasts for all models were good at the MPACE sites with mean bias errors generally under 2 K, and the models had the fewest significant errors predicting temperature. Forecasting moisture and wind proved to be more difficult for the models, especially aloft in the 500–300-hPa layer. The largest errors occurred in the GMAO model, with significant moist biases of 40% and wind errors of 10 m s−1 or more. The RAMS, Eta, and ECMWF models had smaller moist biases in this layer. Both the Eta and RAMS models overestimated the surface incident shortwave radiation, underestimated longwave radiation, and underestimated cloud cover fraction. Overall, the bootstrapping results coincided with findings from conventional statistical comparisons as model outputs with the largest errors were most likely to be captured and declared statistically significant in the bootstrapping process. The significant model errors during MPACE were predominantly traced to the inability of the models to simulate disturbances in synoptic regime I, warm or cold biases over higher inland terrain, a warm bias along the NSA coastal waters in the Beaufort Sea, and difficulty in forecasting the intensity of the explosive cyclone in synoptic regime III.

Modeling orbital forcing of lake level change: Lake Gosiute (Eocene), North America

The sedimentary record of Lake Gosiute, a lake that existed in southwestern Wyoming during the Eocene, contains evidence for lake level fluctuations thought to be caused by the earth's precession cycle. However, it is not clear how the effects of these orbital variations were transferred through the climate system and into the sedimentary record. We carry out a series of experiments using a general circulation model (GCM), a lake energy balance model and a lake water balance model to better understand the processes by which these orbital variations could have altered lake evaporation, on-lake precipitation and runoff from the lake's catchment. GCM simulations indicate significant differences in surface incident shortwave radiation between the two end-members of the precession cycle. These differences cause lake evaporation to be ∼25% higher when perihelion occurs at the summer solstice. GCM simulations also indicate significant seasonal changes in the amount of precipitation between the two end-members, but no change in the annual mean precipitation. Preliminary experiments with a lake water balance model show that local effects such as changes in vegetation, in snowmelt runoff, or in the area of mudflats surrounding the lake could have a large impact on lake level. However, more data need to be collected to determine the importance of these effects. Our results challenge previous interpretations of paleoclimate that were based on geologic data and simple assumptions regarding the effects of orbital variations on the water balance of the lake. In particular, we find that (1) changes in shortwave radiation may have been more important than changes in temperature or moisture in causing lake level fluctuations and (2) changes in catchment and lake characteristics should be further examined. In order to make an accurate reconstruction of past climatic change from a lake level record, climate system processes and local non-climatic variables must be considered explicitly.