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Near-forward-scattered radiation coming from the vicinity of the sun’s direction impacts the interpretation of measurements of direct solar radiation by pyrheliometers and sun photometers, and it is also relevant for concentrating solar technology applications. Here, a Monte Carlo radiative transfer model is employed to study the apparent direct solar transmittance t(α), that is, the transmittance measured by an instrument that receives the radiation within a half-field-of-view (half-FOV) angle α from the center of the solar disk, for various ice cloud, water cloud, and aerosol cases. The contribution of scattered radiation to t(α) increases with increasing particle size, and it also depends strongly on ice crystal morphology. The Monte Carlo calculations are compared with a simple approach, in which t(α) is estimated through Beer’s law, using a scaled optical depth that excludes the part of the phase function corresponding to scattering angles smaller than α. Overall, this optical depth scaling approach works very well, although with some degradation of the performance for ice clouds for very small half-FOV angles (α < 0.5°–1°), and in optically thick cases. The errors can be reduced by fine-tuning the optical depth scaling factors based on the Monte Carlo results. Parameterizations are provided for computing the optical depth scaling factors for water clouds, ice clouds, aerosols, and for completeness, Rayleigh scattering to allow for a simple calculation of t(α). It is also shown that the optical depth scaling used in delta-two-stream approximations is inappropriate for simulating the direct solar radiation received by pyrheliometers.

To support the planning of future solar energy production in India, forthcoming changes in incoming surface solar radiation and the main physical factors contributing to the change were inferred from simulations performed with 27 global CMIP5 climate models. According to the multimodel-mean response, radiation diminishes by 0.5%–4% by the period 2030–59 (relative to 1971–2000), in tandem with strengthening aerosol and water vapor dimming. The largest reduction is anticipated for northern India. The evolution of incident radiation in the mid- and late twenty-first century depends substantially on the emission scenario. According to the representative concentration pathways RCP2.6 and RCP4.5, solar radiation would gradually recover close to the level that prevailed in the late twentieth century. This results from the peaking of aerosol loading before midcentury while the water vapor content continuously increases somewhat. Conversely, under RCP8.5, incident radiation would still decline, although more slowly than during the early century. This coincides with a substantial increase in atmospheric water vapor content and a modest decrease in aerosol forcing. In cloud forcing, multimodel-mean changes are minor, but divergence among the model simulations is substantial. Moreover, cloud forcing proved to be the factor that correlates most strongly with intermodel differences in the solar radiation response. Multimodel-mean changes in solar radiation are small and would not crucially affect the conditions of solar energy production. Nevertheless, some individual models simulate far more substantial reductions of up to ~10%.

Future seasonal changes in surface incident solar radiation and relative humidity (RH) over Europe and adjacent ocean areas were assessed based on phase 3 of the Coupled Model Intercomparison Project (CMIP3) model ensemble. Under the A1B scenario, by 2070–99, summertime solar radiation is projected to increase by 5%–10% in central and southern Europe. In winter, radiation decreases in most of northern and eastern Europe by 5%–15%. RH drops in summer in the southern European inland by 8%–12%, whereas in winter a small increase of 2%–3% is projected for northeastern Europe. In spring, the change is an intermediate between those in the extreme seasons, while in autumn the patterns resemble summer. Over the northern Atlantic Ocean, RH increases in all seasons by 1%–2%. The intermodel agreement on the sign of all these shifts is good, and the patterns recur in the responses to the A2 and B1 scenarios. Substantial changes are already simulated to occur before the midcentury, for example, in summer RH decreases by more than 5% in the inner Balkan Peninsula. Projected changes in these two variables agree well and are also mainly consistent with precipitation responses both in the multimodel mean and in individual models. According to all indicators, southern European summers become more arid, while winters, in the north particularly, become moister and darker. The increasing radiation and declining RH exacerbate summertime drought in southern Europe, whereas excessive humidity in the north may, for example, inflict moisture damages in constructions.

The Monte Carlo Independent Column Approximation (McICA) method for computing domain-average radiative fluxes allows a flexible treatment of unresolved cloud structure, and it is unbiased with respect to the full ICA, but its flux estimates contain conditional random noise. Here, tests of McICA in the ECHAM5 atmospheric GCM are reported. ECHAM5 provides an interesting test bed for McICA because it carries prognostic variables for the subgrid-scale probability distribution of total water content, which allows us to determine subgrid-scale cloud variability directly from the resolved-scale model variables. Three experiments with differing levels of radiative noise, each consisting of ten 6-yr runs, are performed to estimate the impact of McICA noise on simulated climate. In an experiment that attempted to deliberately maximize McICA noise, a systematic reduction in low cloud fraction occurred. For a more reasonable implementation of McICA, the impact of noise is very small, although statistically discernible. In terms of the impacts of noise, McICA appears to be a viable approach for use in ECHAM5. However, to improve the simulation of cloud radiative effects, realistic representation of both unresolved and resolved cloud structures is needed, which remains a challenging problem. Comparison of ECHAM5 data with a global cloud system–resolving model dataset and with International Satellite Cloud Climatology Project data suggested two problems related to unresolved cloud structures. First, ECHAM5 appears to underestimate subgrid-scale cloud variability. This problem seems partly related to the use of the beta distribution scheme for total water content in ECHAM5: in its current form, the scheme is unable to generate highly inhomogeneous clouds (relative standard deviation of condensate amount >1). Second, it appears that in ECHAM5, overcast cloud layers occur too frequently and partially cloudy layers too rarely. This problem is not unique to the beta distribution scheme; in fact, it is more pronounced when using an alternative, relative humidity–based cloud fraction scheme.

The Monte Carlo Independent Column Approximation (McICA) method for computing domain-average radiative fluxes is unbiased with respect to the full ICA, but its flux estimates contain conditional random noise. Results for five experiments are used to assess the impact of McICA-related noise on simulations of global climate made by the NCAR Community Atmosphere Model (CAM). The experiment with the least noise (an order of magnitude below that of basic McICA) is taken as the reference. Two additional experiments help demonstrate how the impact of noise depends on the time interval between calls to the radiation code. Each experiment is an ensemble of seven 15-month simulations. Experiments with very high noise levels feature significant reductions to cloudiness in the lowermost model layer over tropical oceans as well as changes in highly related quantities. This bias appears immediately, stabilizes after a couple of model days, and appears to stem from nonlinear interactions between clouds and radiative heating. Outside the Tropics, insignificant differences prevail. When McICA sampling is confined to cloudy subcolumns and when, on average, 50% more samples, relative to basic McICA, are drawn for selected spectral intervals, McICA noise is much reduced and the results of the simulation are almost statistically indistinguishable from the reference. This is true both for mean fields and for the nature of fluctuations on scales ranging from 1 day to at least 30 days. While calling the radiation code once every 3 h instead of every hour allows the CAM additional time to incorporate McICA-related noise, the impact of noise is enhanced only slightly. In contrast, changing the radiative time step by itself produces effects that generally exceed the impact of McICA’s noise.

The primary purpose of this study is to assess the performance of 1D solar radiative transfer codes that are used currently both for research and in weather and climate models. Emphasis is on interpretation and handling of unresolved clouds. Answers are sought to the following questions: (i) How well do 1D solar codes interpret and handle columns of information pertaining to partly cloudy atmospheres? (ii) Regardless of the adequacy of their assumptions about unresolved clouds, do 1D solar codes perform as intended? One clear-sky and two plane-parallel, homogeneous (PPH) overcast cloud cases serve to elucidate 1D model differences due to varying treatments of gaseous transmittances, cloud optical properties, and basic radiative transfer. The remaining four cases involve 3D distributions of cloud water and water vapor as simulated by cloud-resolving models. Results for 25 1D codes, which included two line-by-line (LBL) models (clear and overcast only) and four 3D Monte Carlo (MC) photon transport ...

The parameterization of cloud shortwave absorption poses a difficult problem in broadband radiation schemes that treat the near-IR region as a single interval. This problem arises because the spectral variation of the single-scattering co-albedo 1 − ω of cloud droplets and ice crystals is enormous in the near-IR region, and because the cloud particle absorption is overlapped by sharply varying water vapor absorption. In this paper, several parameterization methods of cloud near-IR (0.68–4.00 μm) 1 − ω are intercompared using a large set of atmospheric columns generated by a GCM. The methods include 1) linear averaging of 1 − ω, weighting with the TOA solar flux; 2) “thick averaging” by Edwards and Slingo; 3) Fouquart’s formula, which presents water cloud near-IR 1 − ω as a function of optical thickness; and 4) the “correlated ω” technique by Espinoza and Harshvardhan. An extension of the correlated ω technique to ice clouds is suggested. In addition, a new “adaptive ω” broadband parameterization ...

In emissivity-type (i.e., nonscattering) longwave radiation schemes, the cloud fraction N and emissivity e are often merged into an effective cloud fraction, Ne = Ne. It is shown that if this approximation is combined with the maximum–random cloud overlap assumption (or with pure maximum overlap), the results can be strongly sensitive to vertical resolution. This problem occurs at least in the European Centre for Medium-Range Weather Forecasts longwave scheme. It is further shown that the maximum–random overlap assumption can be implemented so that this problem is avoided, provided that the effects of N and e are considered separately. The proposed method is applicable to emissivity-type longwave schemes in general.