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decadal climate simulations using the original radiation parameterizations (the control simulation) and their NN emulations (the NN run). In the study, we apply the NN approach to approximating both the longwave radiation (LWR) and shortwave radiation (SWR) parameterizations in the NCAR CAM [e.g., see the special issue of the Journal of Climate (1998, Vol. 11, No. 6)]. Calculation of the LWR and SWR or the full/total model radiation is the most time-consuming part of the atmospheric physics
decadal climate simulations using the original radiation parameterizations (the control simulation) and their NN emulations (the NN run). In the study, we apply the NN approach to approximating both the longwave radiation (LWR) and shortwave radiation (SWR) parameterizations in the NCAR CAM [e.g., see the special issue of the Journal of Climate (1998, Vol. 11, No. 6)]. Calculation of the LWR and SWR or the full/total model radiation is the most time-consuming part of the atmospheric physics
1. Introduction Surface net radiation R n is the sum of incident downward and upward shortwave and longwave radiation: where S ↓ and S ↑ are the surface downward and upward shortwave radiation, L ↓ and L ↑ are the surface downward and upward longwave radiation, α is surface albedo, and S n is surface net shortwave radiation. The downward components of R n are controlled by solar zenith angle (i.e., time of day, season, and latitude), cloud amount, atmospheric water vapor amount
1. Introduction Surface net radiation R n is the sum of incident downward and upward shortwave and longwave radiation: where S ↓ and S ↑ are the surface downward and upward shortwave radiation, L ↓ and L ↑ are the surface downward and upward longwave radiation, α is surface albedo, and S n is surface net shortwave radiation. The downward components of R n are controlled by solar zenith angle (i.e., time of day, season, and latitude), cloud amount, atmospheric water vapor amount
also have a positive bias in sea surface temperature ( Hyder et al. 2018 ), and a poleward shift of the Southern Hemisphere jet ( Ceppi et al. 2014 ). Importantly, a lack of knowledge about how clouds might respond to a changing climate stems from a poor understanding of clouds in extratropical regions such as the Southern Ocean ( Zelinka et al. 2020 ; Tan et al. 2016 ). A key reason for the bias in simulated shortwave radiation over the Southern Ocean is a relative lack of long-term surface
also have a positive bias in sea surface temperature ( Hyder et al. 2018 ), and a poleward shift of the Southern Hemisphere jet ( Ceppi et al. 2014 ). Importantly, a lack of knowledge about how clouds might respond to a changing climate stems from a poor understanding of clouds in extratropical regions such as the Southern Ocean ( Zelinka et al. 2020 ; Tan et al. 2016 ). A key reason for the bias in simulated shortwave radiation over the Southern Ocean is a relative lack of long-term surface
surface shortwave radiation through increasing frequency of overcast skies and modified cloud properties ( Dai et al. 1999 ; Liepert 2002 ; Stone and Weaver 2003 ). Modeling studies suggest that such trends will continue in this region under various climate change scenarios ( Walsh et al. 2014 ). In spite of this, expected energy output at proposed PV plant sites is estimated from observed shortwave radiation data that usually span 10 yr or less. Such data may not adequately capture long-term trends
surface shortwave radiation through increasing frequency of overcast skies and modified cloud properties ( Dai et al. 1999 ; Liepert 2002 ; Stone and Weaver 2003 ). Modeling studies suggest that such trends will continue in this region under various climate change scenarios ( Walsh et al. 2014 ). In spite of this, expected energy output at proposed PV plant sites is estimated from observed shortwave radiation data that usually span 10 yr or less. Such data may not adequately capture long-term trends
-specific numerical weather prediction (NWP) capabilities able to support the energy sector. We assessed local solar (shortwave) radiation forecasts in South Africa, provided by the SAWS high-resolution NWP models, the SA models at 4- and 1.5-km spatial resolutions. Our primary aim was to investigate the skill of high-resolution SA models, to help inform future core model developments. By understanding the SA model’s performance in forecasting short-term (up to 1 week ahead) location-based solar radiation, we
-specific numerical weather prediction (NWP) capabilities able to support the energy sector. We assessed local solar (shortwave) radiation forecasts in South Africa, provided by the SAWS high-resolution NWP models, the SA models at 4- and 1.5-km spatial resolutions. Our primary aim was to investigate the skill of high-resolution SA models, to help inform future core model developments. By understanding the SA model’s performance in forecasting short-term (up to 1 week ahead) location-based solar radiation, we
cover is trending toward a larger fraction of darker (less reflective) and relatively warmer open ocean surfaces more susceptible to absorbing shortwave radiation ( Kashiwase et al. 2017 ). The abundance of multiyear sea ice is being replaced by thinner first year ice more prone to earlier melt at the onset of the sea ice melt season (e.g., Serreze and Stroeve 2015 ; Mortin et al. 2016 ). Anomalies in atmospheric circulation are impacting the heat and moisture content that is transported poleward
cover is trending toward a larger fraction of darker (less reflective) and relatively warmer open ocean surfaces more susceptible to absorbing shortwave radiation ( Kashiwase et al. 2017 ). The abundance of multiyear sea ice is being replaced by thinner first year ice more prone to earlier melt at the onset of the sea ice melt season (e.g., Serreze and Stroeve 2015 ; Mortin et al. 2016 ). Anomalies in atmospheric circulation are impacting the heat and moisture content that is transported poleward
shortwave radiation (SWR; Pinker et al. 2009 ; Kumar et al. 2012 ). Nevertheless, the meteorological sensors on the moorings are exposed to elements such as sea spray, natural and anthropogenic aerosols, and severe weather during each year-long deployment. The sensors therefore occasionally develop time-dependent drifts or biases. In most cases, systematic errors are identified from the near-real-time data streams or from the internally stored data after a mooring is recovered ( Freitag et al. 1994
shortwave radiation (SWR; Pinker et al. 2009 ; Kumar et al. 2012 ). Nevertheless, the meteorological sensors on the moorings are exposed to elements such as sea spray, natural and anthropogenic aerosols, and severe weather during each year-long deployment. The sensors therefore occasionally develop time-dependent drifts or biases. In most cases, systematic errors are identified from the near-real-time data streams or from the internally stored data after a mooring is recovered ( Freitag et al. 1994
spectral albedo of snow. I: Pure snow . J. Atmos. Sci. , 37 , 2712 – 2733 , doi: 10.1175/1520-0469(1980)037<2712:AMFTSA>2.0.CO;2 . 10.1175/1520-0469(1980)037<2712:AMFTSA>2.0.CO;2 Zender , C. , and Coauthors , 1997 : Atmospheric absorption during the Atmospheric Radiation Measurement (ARM) Enhanced Shortwave Experiment (ARESE) . J. Geophys. Res. , 102 , 29 901 – 29 915 , doi: 10.1029/97JD01781 . 10.1029/97JD01781 1 Despite the large intraseasonal variability in incident SW and ozone column
spectral albedo of snow. I: Pure snow . J. Atmos. Sci. , 37 , 2712 – 2733 , doi: 10.1175/1520-0469(1980)037<2712:AMFTSA>2.0.CO;2 . 10.1175/1520-0469(1980)037<2712:AMFTSA>2.0.CO;2 Zender , C. , and Coauthors , 1997 : Atmospheric absorption during the Atmospheric Radiation Measurement (ARM) Enhanced Shortwave Experiment (ARESE) . J. Geophys. Res. , 102 , 29 901 – 29 915 , doi: 10.1029/97JD01781 . 10.1029/97JD01781 1 Despite the large intraseasonal variability in incident SW and ozone column
, the SW surface cloud forcing in this model is stronger during the summer months than in GISS-ER. This may be caused by stronger absorption or reflection within the HadCM3 clouds due to different cloud droplet size parameterization. b. Sea ice, surface albedo, and clear-sky surface shortwave radiation The presence of highly reflective ice plays a dominant role in defining the Arctic Ocean surface albedo. Both the sea ice concentrations and the ice properties controlling the albedo of the sea ice
, the SW surface cloud forcing in this model is stronger during the summer months than in GISS-ER. This may be caused by stronger absorption or reflection within the HadCM3 clouds due to different cloud droplet size parameterization. b. Sea ice, surface albedo, and clear-sky surface shortwave radiation The presence of highly reflective ice plays a dominant role in defining the Arctic Ocean surface albedo. Both the sea ice concentrations and the ice properties controlling the albedo of the sea ice
1. Introduction It has been documented that in recent decades the Arctic has warmed and that northern Alaska is one of the most significantly impacted regions from global warming. The evidence of environmental changes in the Arctic regions with a focus on Alaska has been summarized in Hinzman et al. (2005) . Studies related to surface shortwave radiation (SWR) in the Alaska region can be traced back to 1880s ( Ray 1885 ) and the first comprehensive measurement of SWR started in the early 1960s
1. Introduction It has been documented that in recent decades the Arctic has warmed and that northern Alaska is one of the most significantly impacted regions from global warming. The evidence of environmental changes in the Arctic regions with a focus on Alaska has been summarized in Hinzman et al. (2005) . Studies related to surface shortwave radiation (SWR) in the Alaska region can be traced back to 1880s ( Ray 1885 ) and the first comprehensive measurement of SWR started in the early 1960s
