CL2.1

What is the Earth’s incoming energy, that is, the value of the total solar irradiance (TSI) powering the entire Earth’s climate system on an absolute scale? How accurate are the instruments providing these data? How stable are they on climate-relevant timescales? How well can the 43-year spaceborne TSI measurement record from over a dozen instruments be put into a single time-series composite for the climate- and solar-research communities? How can that composite be extrapolated to historical times using other solar-activity proxies via reconstructions? How do these historical-reconstruction models differ from each other, and how well do they agree with the current measurements? And what is the future of those measurements?

How to cite: Kopp, G.: The TSI Instruments – What’s Old, What’s New, and What’s Next, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10660, https://doi.org/10.5194/egusphere-egu22-10660, 2022.

The Joint Total Solar Irradiance Monitor (JTSIM) onboard the Fengyun-3E spacecraft has been launched successfully the 4^th of July 2021. It aims at measuring the Total Solar Irradiance (TSI) in orbit. The instruments on the Fengyun-3E/JTSIM include the Digital Absolute Radiometer (DARA) from the Physikalisch Meteorologisches Observatorium, Davos and World Radiation Center (PMOD/WRC) and the Solar Irradiance Absolute Radiometer (SIAR) from the Changchun Institute of Optics, Fine Mechanics and Physics Chinese Academy of Sciences (CIOMP/CAS). The JTSIM experiment will use the two different types of TSI radiometers to track the stability of TSI measurements, and to better understand instrumental degradation in space. We will present results from this new experiment at first light. We will compare the measurements from DARA and SIAR over the first few months and relate them to other active missions (SOHO/VIRGO/PMO6v, SORCE/TSIS). 

How to cite: Montillet, J.-P., Finsterle, W., Haberreiter, M., Schmutz, W., Pfiffner, D., Koller, S., Gyo, M., Fang, W., Ye, X., Yang, D., and Wu, D.:  Total Solar Irradiance monitored by  DARA/JTSIM : first light observations, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-616, https://doi.org/10.5194/egusphere-egu22-616, 2022.

The Climate Monitoring SAF (CM SAF) of EUMETSAT is finalizing the third version of the CLoud Albedo and RAdiation based on AVHRR climate data record (CLARA-A3). This new version will cover the 1979-2020 time period and will provide the Top-of-Atmosphere (TOA) radiative fluxes as new products.  

Although the Clouds and the Earth’s Radiant Energy System (CERES) products are acknowledged to be the golden standard w.r.t. TOA radiative flux data records, two limitations can be identified: (1) the products are relatively recent, e.g. starting in year 2000 for the EBAF product, and (2) the products have a relatively coarse spatial resolution of 1°x1° (lat-lon equal angle grid). The products developed within CM SAF aim to bridge these gaps, respectively by (1) a prolongation back in time to the late 1970s and (2) by increasing the spatial resolution to 0.25°x0.25°. A third advantage of the new CDRs lies in their synergy and compatibility with the other CDRs from the CM SAF CLARA product family (cloud mask and other cloud parameters, surface radiation, surface albedo, etc.) sharing common algorithms and processing chains.

The CLARA-A3 data record has been completed but not yet released, and hence we can present a validation of daily and monthly Reflected Solar Flux (RSF) and Outgoing Longwave Radiation (OLR). CLARA-A3 performance is assessed in terms of bias, regional uncertainty (spatial RMSE), and stability. This is done primarily with the relatively recent CERES and GERB broadband-based reference products, and additionally also with long-term data records such as from HIRS, ERA-5, ISCCP, and ESA-CCI to assess the stability throughout the entire data record. Overall, the performance is within the expected target requirements. Regional uncertainty is however related to the number and of observations per day and their local time, which are both variable throughout the AVHRR and MetOp constellation history.

How to cite: Clerbaux, N. and Akkermans, T.: Evaluation of the new TOA radiation fields in the CM SAF CLARA-A3 Climate Data Record., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9786, https://doi.org/10.5194/egusphere-egu22-9786, 2022.

With a few exceptions, spaceborne measurements of Earth’s top-of-atmosphere (TOA) outgoing reflected shortwave and emitted longwave radiation have been made over broad spectral bands covering the entirety of the solar spectral region, terrestrial infrared spectral region or the combination of both. Evidence suggests that separating the solar band into just two sub-bands, roughly equal in incoming solar irradiance levels but coincidently, where the atmosphere is nearly transparent to solar radiation in the visible (λ<700nm) and partially absorbing in the near-infrared sub-band (λ >700nm) primarily due to water vapor and clouds, provides great insight into the deposition of radiative energy in the atmosphere. Moreover, the two sub-bands also demarcate reflectance differences at the ground from different surface types such as vegetation, desert, ice and snow. Therefore, TOA reflected shortwave radiation in the two sub-bands are differently affected by changes in surface and atmospheric properties and support the characterization of processes relevant for shortwave absorption by the climate system, climate feedbacks, and Earth’s albedo variability with added insight into hemispheric albedo symmetry given the hemispheric differences in ocean, continent and cloud distributions. A new NASA Earth Radiation Budget mission, Libera, will directly measure the two sub-bands. We use UKESM1 simulations, Fu-Liou RTM calculations, SCIAMACHY reflectance and CLARREO OSSE output as proxies for Libera’s future data record to demonstrate applications of the shortwave sub-band knowledge in climate science.

How to cite: Hakuba, M., Pilewskie, P., Stephens, G., and Science Team, T. L.: Libera's Split-shortwave Measurements and Their Application in Climate Research, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13306, https://doi.org/10.5194/egusphere-egu22-13306, 2022.

The imagery from geostationary meteorological satellites (GEO) is broadly used to produce various datasets describing the Earth's surface and atmosphere, or else energy fluxes. Some GEOs are not on a strictly geostationary orbit, with an inclination relatively to the equatorial plane that can reach several degrees. A striking example is Meteosat-8: due to operation constraints, its orbit is currently oscillating on a daily basis between latitudes circa 7° N and 7° S. A consequence is that the actual viewing geometry differs from the simple calculation based on the stationary nominal subsatellite position. In the case of Meteosat-8, the viewing zenith angles can differ up to ±7°.

We study how these differences of viewing angles can influence top-of-atmosphere (TOA) reflectance of Earth scenes. To achieve this, we simulate both clear-sky (i.e. cloudless) and overcast TOA reflectances corresponding to nominal and non-stationary viewing geometries of Meteosat-8 for the 0.6 µm channel during the period 2017-2018. Simulations are performed using the 1-D radiative transfer model DISORT within the libRadtran software package. They consider notably the anisotropy of surface reflectance which is modeled by the RossTick-LiSparse model of bidirectional reflectance distribution function and associated parameters of the product MCD43C1v6 derived from the imagery of the Moderate Resolution Imaging Spectroradiometer (MODIS). Overcast TOA reflectances are modeled with a plane-parallel thick liquid cloud.

In this presentation, we will describe how errors on TOA reflectances due to erroneous viewing geometry are distributed on the Meteosat-8 field of view and for different solar geometries. Both for clear-sky and overcast conditions, typical absolute errors range between 0.01 and 0.05, but can reach much higher values for specific geometries, notably close to the forward scattering direction.

How to cite: Tournadre, B., Chen, X., Gschwind, B., and Blanc, P.: Geostationary or not: can we consider Meteosat-8 viewing geometries as stationary?, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8860, https://doi.org/10.5194/egusphere-egu22-8860, 2022.

The Deep Space Climate Observatory (DSCOVR) was launched in February 2015 to a Sun-Earth Lagrange-1 (L1) orbit, approximately 1.5 million kilometers from Earth towards the Sun.  It observes the full, sunlit disk of Earth from a unique vantage point with the two instruments: the Earth Polychromatic Imaging Camera (EPIC) and the NIST (National Institute of Standards and Technology) Advanced Radiometer (NISTAR).  The Earth-observing geometry of the EPIC instrument is characterized by a phase angle between 4° and 12°.  After March 2020 the range of phase angles for DSCOVR EPIC and NISTAR has been substantially decreased towards backscattering reaching 1.95 degrees in December 2020.  This provides a unique opportunity to study correlation between Earth reflectance and phase angle.  The dependence of reflection on scattering angle (180^o – phase angle) is shown separately for ocean and land areas, for cloudy and clear pixels, while cloudy pixels are also separated to liquid and ice clouds.  A strong increase of reflectance towards back-scattering direction observed for all wavelengths.  The spectral signature of the dependence indicates the strongest increase at near IR (780 nm) where contribution from vegetation dominates.  Surface Bidirectional Reflectance Factor (BRF) acquired by EPIC and Terra MISR sensors over the Amazon basin is used to demonstrate the bi-directional effects of solar zenith and scattering angles on variation of reflected radiation from rainforest.  NISTAR observations also demonstrate an increase with scattering angle for all bands but the strongest one is for B-band radiance (0.2–4 μm).

How to cite: Marshak, A., Delgado Bonal, A., and Knyazikhin, Y.: Effect of phase angle on estimation of Earth reflectance, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4908, https://doi.org/10.5194/egusphere-egu22-4908, 2022.

The Earth’s Energy Imbalance (EEI) represents the balance of heat fluxes between
the Earth and outer space in response to radiative forcings and associated climate feedbacks,
and as such is a key metric to understand and define global climate change. Recent
publications have shown that the EEI has doubled in the last two decades, which would have
major impacts on the different components of the Earth’s system. However, these results also
show inconsistencies in the quantification of this increase depending on the observing system
considered. In this study, we investigate two independent ways to estimate EEI from ocean
observations and from energy budget at the top of the atmosphere inferred from satellite. We
show that these two observing systems lead to consistent estimates of EEI variability and
amplitude over the period 2005-2019. Global Ocean Heat Content (GOHC) is derived from a
suite of ocean in situ temperature products, and is also compared to satellite estimate and to
ocean reanalysis estimate. We provide recommendations on how to achieve a consistent and
optimized observation-based comparison between estimates for the EEI budget constraint
approach from independent global climate observing system components and at different
time-scale ranging from interannual to decadal.

How to cite: Minière, A., von Schuckmann, K., Monier, M., Le Traon, P.-Y., and Sallée, J.-B.: Observation-based reconciliation of the Earth's Energy Imbalance budget constraint, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13305, https://doi.org/10.5194/egusphere-egu22-13305, 2022.

The highly successful Budyko-Sellers energy balance models are based on the classical continuum mechanics heat equation in two spatial dimensions. When extended to the third dimension using the correct conductive-radiative surface boundary conditions, the surface temperature anomalies obey the (nonclassical) Half-order energy balance equation (HEBE, with exponent h = ½) implying heat is stored in the subsurface with long memory.  In comparison, the classical EBE has h=1 and short (exponential) system memory.  Using short and long wave data from reanalyses, we discuss the empirical foundations of the FEBE.

Empirically, we find that both internal variability and the forced response to external variability are compatible with h ≈ 0.4.  Although already close to the HEBE and classical continuum mechanics, we argue that an even more realistic “effective media” macroweather model is a generalization: the fractional heat equation (FHE) for long-time (e.g. monthly scale anomalies).  This model retains standard diffusive and advective heat transport but generalize the (temporal) storage term.  A consequence of the FHE is that the surface temperature obeys the Fractional EBE (FEBE).  We show how the resulting FEBE can be been used for monthly and seasonal forecasts as well as for multidecadal climate projections. 

How to cite: Lovejoy, S.: Conductive-radiative boundary conditions and the fractional energy balance equation: predictions and projections, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8853, https://doi.org/10.5194/egusphere-egu22-8853, 2022.

Using the 20-year-old climate model, HadCM3, we show that it is possible to calibrate the model to multiple observations by algorithmically changing parameters in the atmospheric model.  Fourteen atmospheric parameters were modified using a state-of-the-art derivative free optimization algorithm (DFOLS). The calibration reduces model-observational difference against hemispheric scale averages of multiple observations for the 2001-2005 period and used about 90 evaluations of the atmospheric model. The observations considered were outgoing longwave and shortwave radiation, land temperature and precipitation, sea level pressure, mid-tropospheric temperature and humidity, and global-average net flux into the Earth system. A 5-member ensemble was generated by starting the calibration from different initial parameter sets.

The calibrated model simulated large scale observations better than almost all CMIP5 and CMIP6 ensemble.  Spatial patterns of variables from the calibrated ensemble except for outgoing SW, land precipitation and mid-tropospheric humidity are as well simulated as in the CMIP6 ensemble. For these variables, spatial patterns are as well simulated as the CMIP5 ensemble.   In the calibrated ensemble, uncertainty in effective climate sensitivity (ECS; relative error of 10%) and the transient climate response (TCR; relative error of 5%) is small. This is the case for the response at doubling and quadrupling of CO2 concentrations. Uncertainties in regional climate change are also small.

A linear analysis which combines observational uncertainty with the Jacobian of observational sensitivity with respect to parameter change gives a parameter covariance matrix. This is in turn can be combined with the Jacobian of climate response with respect to parameter to give a linear estimate of uncertainty in climate response. The linear uncertainty is similar to the ensemble uncertainty. By increasing individual observational uncertainty in the  linear analysis, it is possible to see which observations are providing the constraints in  transient climate response at 4 times CO2. This analysis finds that almost all the constraint comes from land precipitation, outgoing SW radiation and the net flux, suggesting these are key observations to constrain climate model behaviour.

How to cite: Tett, S., Gregory, J., Freychet, N., Cartis, C., Mineter, M., and Roberts, L.: Calibrating Climate Models – what observations matter?, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7895, https://doi.org/10.5194/egusphere-egu22-7895, 2022.

Despite differences in land distribution, and aerosol amount, the Northern and Southern hemispheres have been found to reflect almost exactly the same amount of incoming shortwave radiation. This indicates that clouds compensate for the asymmetry in clear-sky reflection, and make the system maintain an inter-hemispheric albedo symmetry. 

In the mean state, retrievals from satellite-borne CERES measurements suggest that mid-latitude clouds are both in amount and reflectivity contributing to this compensation, together with sub-tropical cloud amount, that is also greater in the Northern hemisphere. Composites of instances with high asymmetry in either direction indicate that the variability in albedo symmetry is driven by variation in tropical and subtropical cloudiness, with patterns in agreement with non-neutral phases of ENSO. 

CMIP6 models are found to typically overestimate the variability in inter-hemispheric asymmetry, and underestimate the degree of symmetry, compared to observations. The bias in models is largely driven by biases in mid-latitude reflected shortwave radiation. Mid-latitude clouds are also found to play a significant role in model albedo symmetry response to strong forcing: models with large loss of mid- and high-latitude clouds in the Southern hemisphere restore the initial asymmetry, due to relative Northern hemisphere darkening, produced in the models in response to abrupt 4xCO2 forcing and subsequent warming.

Here we discuss albedo distribution and variability in satellite-derived products, and across model generations, pointing at inter-hemispheric symmetry as a useful model diagnostic, and as indicator of cloud feedback mechanisms.

How to cite: Bender, F.: Cloud regulation of inter-hemispheric albedo symmetry, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3190, https://doi.org/10.5194/egusphere-egu22-3190, 2022.

Recent work has shown that the hemispheric asymmetry in cloud albedo is maximized over extratropical oceans: the Southern Ocean exhibits greater climatological cloud albedo than its northern counterpart. We investigate the dynamical causes of such asymmetry by evaluating how albedo responds to a series of cloud controlling factors, namely: sea surface temperature (SST), pressure velocity at 500mb (ω₅₀₀), Estimated Inversion Strength (EIS), Marine Cold Air Outbreak (MCAO) index, SST-T_2m (ΔT_sfc), and surface wind (V_sfc). A cloud albedo parameterization applied to MODIS optical thickness and fractional cloud cover is used in conjunction with ERA-Interim reanalysis products over oceanic points in the 50°–65° bands and for a 15-year period. Cloud properties are bin-averaged according to the range of variability of each predictor, using a 1-day timescale. We find that although ω₅₀₀ strongly controls cloud albedo, it cannot explain the observed hemispheric asymmetry. Instead, we find that surface wind most skillfully explains the hemispheric albedo difference, due to the much greater winds in the Southern Ocean. We further show that V_sfc is not only a predictor of cloud albedo but it also controls physical processes in the boundary layer such that stronger winds ultimately lead to thicker and more horizontally extended cloud decks. The interhemispheric albedo asymmetry is significantly reduced in winter, responding to a strengthening of winds in the North Atlantic and Pacific Oceans during this season. Our findings have significant implications regarding GCM cloud biases over the Southern Ocean for the current climate, as well as for cloud feedback in a warming planet.

How to cite: Blanco, J., Caballero, R., Bony, S., Datseris, G., Stevens, B., Kaspi, Y., and Hadas, O.: Cloud albedo's hemispheric asymmetry: why is the Southern Ocean cloudier?, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7962, https://doi.org/10.5194/egusphere-egu22-7962, 2022.

It is now well known that CMIP6 models have a higher average and range of climate sensitivities than previous generations of models, which has been shown to be in part caused by weaker negative short wave radiative cloud feedbacks in response to greenhouse gas (GHG) forcing. These weaker negative cloud feedbacks are understood to be caused by a number of factors, including warm rain precipitation bias and Southern Ocean (SO) deep convection slowdown.

We find that coupled models in strong forcing (abrupt quadrupling of CO₂) experiments with greater reductions in Southern Hemisphere (SH) extratropical and SO cloud cover and thus albedo also exhibit greater polar amplification in the SH, namely: increased poleward heat transport, greater surface warming at high latitudes, and a decrease in Antarctic surface albedo. Precipitation increases in the Antarctic polar region with warming, but not evenly; liquid-phase precipitation increases in the Antarctic sea ice zone while ice-phase precipitation increases on the continent. These responses occur roughly three decades after the onset of forcing, and continued surface warming in models with greater SH extratropical cloud loss beyond this point occurs mainly in the SH extratropics, especially at high latitudes, rather than globally.

Here, we aim to explore the connection between Antarctic warming and cloudiness in the SH extratropics and SO. Detailing the process of Antarctic warming in these models can help to explain some of the intermodel spread in Antarctic polar responses to GHG forcing, as well as to further constrain predictions of future climate changes in response to anthropogenic forcing, as these models also include some of the highest climate sensitivities of the CMIP6 ensemble.

How to cite: Jönsson, A. and Bender, F.: Southern Ocean cloud reductions in CMIP6 forcing experiments as a contributor to intermodel spread in Antarctic warming, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5374, https://doi.org/10.5194/egusphere-egu22-5374, 2022.

Successive IPCC reports have assessed the level of human-induced warming above preindustrial, but much less emphasis has been placed on quantifying the rate of anthropogenic warming, despite the rate of warming being a key variable for ambitious policymaking. The decadal global temperature anomaly trend can be considered a combination of the forced responses from the full range of radiatively-active pollutants, plus the additional trend introduced by natural variability over the previous decade.

The global temperature anomaly trend likely increased in the 2010s, following a temporary pause through the 2000s. Estimates of the globally averaged radiative forcing (RF) timeseries, which are used to attribute the anthropogenic contribution to this recent behaviour, suggest a 50% increase in the anthropogenic RF trend, which largely arises from aerosol RF trend changes since 2000. When these RF timeseries are used to complete a global temperature anomaly attribution (following the technique outlined in the IPCC’s Special Report on the Global Warming of 1.5°C), they suggest that the attributed anthropogenic warming rate has increased by between 50 and 100% since 2000, pushing the estimated rate of net anthropogenic warming up to around 0.3°C/decade since 2010.

We study the global observational evidence supporting the aerosol trends presented in these RF datasets, and thus aim to determine the likely anthropogenic contribution to the perceived warming acceleration behaviour since 2000. We argue that while observations do support the claim that RF trends are partly responsible for the warming trend (and importantly do support the best-estimate RF trend estimates in this ensemble), observational evidence is circumstantial, with a counterhypothesis that aerosol RFs make only a small contribution to the warming trend since 2000 consistently failing to be disproven across the full ensemble of RFs.

This occurs because observed trends in radiative fluxes and global temperatures are significantly influenced by internal variability, principally ENSO and PDO, precluding a clearer assessment of the externally forced behaviour over the short global observational records we have. In light of this uncertainty, considerable caution is required in predictions or policy judgments that depend on the precise current anthropogenic warming trend, such as the time remaining to, or the outstanding carbon budget consistent with, a warming of 1.5°C, since these may be influenced considerably by recent changes in aerosol forcing behaviour.

How to cite: Jenkins, S., Gettelman, A., Stier, P., Grainger, D., and Allen, M.: The aerosol contribution to the rate of anthropogenic warming since 2000, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1554, https://doi.org/10.5194/egusphere-egu22-1554, 2022.

Over the last decade, the total global anthropogenic emissions of aerosol precursors have declined according to the most recent Community Emissions Data System (CEDS) emission inventory. The CEDS emission inventory used in CMIP6 (CEDSv17) has recently been updated and extended from 2014 until 2019 (version v_2021_02_05, CEDSv21). The role of the updated emissions and the trend beyond 2014 on the modeled atmospheric composition and radiative forcing (RF) using an atmospheric chemistry transport model (OsloCTM3), radiative transfer and kernel calculations will be presented. The results post 2014 are also compared to results with SSP2-4.5 scenario emissions as input. In addition, we present consistent modeling results for 2020, with CEDSv21 emissions for 2019 combined with the 2020 CovidMIP-emission perturbation, as aerosol precursor emissions declined further due to containment policies to combat the COVID-19 pandemic.

For sulphate, the radiative forcing in 2014 relative to 2010 is stronger positive (+0.03 W m^-2) using CEDSv21 compared to a neglectable RF using CEDSv17. In 2017 the RF using the SSP scenario and the updated CEDS are equal (+0.07 W m^-2) as the SO₂ emission reduction in China was included at the starting point of the scenarios (year 2015), but not in the historical emissions (CEDSv17) ending in 2014. Including the effect of COVID-19, the sulphate RF in 2020 was +0.11 W m^-2 with 2010 as baseline, with the strongest positive forcing in Eastern China followed by the eastern part of the US. No regions show a negative sulphate RF in 2020 with respect to 2010.

For the total aerosol-radiation RF (including Black Carbon, primary organic aerosol, SOA, nitrate, and biomass burning aerosols) the RF was +0.05 W m^-2 in 2019 relative to 2010 based on OsloCTM3 simulations and the most recent CEDS emission inventory. Extending the results to 2020 using estimates for COVID-19 emissions, the forcing is further strengthened to +0.07 W m^-2.

How to cite: Skeie, R. B., Myhre, G., and Lund, M. T.: Changes in aerosol atmospheric composition and radiative forcing in OsloCTM3 over the past two decades – the effect of the updated CEDS emission inventory , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6320, https://doi.org/10.5194/egusphere-egu22-6320, 2022.

Despite the implication of aerosols for radiative forcing, there are di erences in aerosol estimates from both, observations and models. This study quanti es di erences between current estimates of aerosol optical depth (AOD) to address two questions: (1) How well do we know the large-scale spatio-temporal pattern of present-day AOD across state-of-the-art data? (2) Has the representation of AOD improved across phases of aerosol-climate model intercomparison projects? To answer these questions, we analyze spatio-temporal patterns of the present-day monthly mean AOD from 94 global datasets. The data is taken from eight satellite retrievals, four aerosol-climate model intercomparison projects, two global reanalyses, one operational ensemble product, one climatology and one merged satellite product covering periods between 1998 and 2019. The evaluation includes new satellite data from SLSTR and aerosol-climate models of CMIP6 and AeroCom-III. The comprehensive data assessment allows us to evaluate the performance of individual products and models concerning di erent spatial and temporal aspects. Our assessment is based on metrics for a detailed investigation with respect to di erent spatio-temporal characteristics of AOD.

Our results highlight spatio-temporal di erences in AOD across datasets, were the performance of individual data sets varies with respect to the di erent spatio-temporal metrics assessed. Global mean AOD of individual satellites ranges between -11% to +17% around a satellite mean of 0.14. The ensemble means from the aerosol-climate model intercomparison projects fall within the satellite range, but individual models can di er considerably. Reanalyses and climalologies are typically closer to the satellite mean than aerosol-climate models. No systematic improvement from earlier to later phases of CMIP and AeroCom is found, although some regional biases have been reduced. Compared to the satellite and reanalysis data, all aerosol-climate ensemble means tend to overestimate AOD along extra-tropical storm tracks and underestimate AOD in regions of high aerosol load in South America, South Africa, India, and Southeast Asia. The identi ed di erences may be used to guide further e orts to improve satellite retrievals and model simulations for aerosols. In addition, the uncertainty in observed AOD implies that a model evaluation based on a single satellite product might draw biased conclusions. This underlines the need for continued e orts to improve both model and satellite estimates of AOD to facilitate a better understanding of aerosol e ects in the Earth system. At the same time, our analysis suggests that an assimilation of multiple satellite products for AOD would be bene cial to account for observational uncertainty.

Reference: Vogel, A., Alessa, G., Scheele, R., Weber, L., Dubovik, O., North, P., Fiedler, S. (2022). Uncertainty in aerosol optical depth from modern aerosol-climate models, reanalyses, and satellite products. Journal of Geophysical Research: Atmospheres, 127, e2021JD035483. https://doi.org/10.1029/2021JD035483

How to cite: Vogel, A., Alessa, G., Scheele, R., Weber, L., Dubovik, O., North, P., and Fiedler, S.: Assessment of present-day aerosol optical depth from modern aerosol-climate models, reanalyses, and satellite products, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6578, https://doi.org/10.5194/egusphere-egu22-6578, 2022.

In this communication, we have found that the broadband longwave radiative effect of the cloud-aerosol transition zone conditions is of the order of 8.0 ±3.7 W m⁻² by combining satellite measurements with radiative transfer modeling. It is often required to differentiate clouds and aerosols from each other in atmospheric studies, but the decision on where the boundaries of the clouds should be put is a point of debate. As a result, what detected as cloud by one method/instrument may be labeled differently by another. This is because 1) clouds and aerosols often co-exist and interact with each other, and 2) change in the state of sky from cloudy to cloudless (or vice versa) comprises an additional condition called “transition zone” (or “twilight zone”) at which the characteristics of the particle suspension lay between those corresponding to pure clouds and atmospheric aerosols [Koren et al. (2007) GRL, 34(8): L08805. 10.1029/2007GL029253]. Nevertheless, a vast area that potentially may represent the transition zone is usually neglected in the observations or assumed as an area that contains either aerosols or optically thin clouds. In this communication, we provide quantitative information about the broadband longwave radiative effects of the transition zone conditions at the top of the atmosphere based on the radiative observations made by the CERES and MODIS instruments onboard Aqua spacecraft and radiative transfer simulations. Specifically, we used the MODIS measurements to look for CERES footprints with homogeneous transition zone and clear-sky conditions over the Southeast Atlantic Ocean for August 2010. Then, CERES observations under homogeneous transition and clear-sky conditions were compared with the corresponding clear-sky radiances, which were simulated using the SBDART radiative transfer model, fed with ERA5 reanalysis atmospheric profiles. For the studied period and domain, transition zone broadband longwave radiative effect was on average equal to 8.0 ±3.7 W m⁻² (heating effect; median: 5.4 W m⁻²), although cases with radiative effects as large as 50 W m⁻² were observed. Furthermore, low-level transition zone conditions defined as those with suspension top height below 2 km (determined based on the difference between the layer top and surface temperature) on average produced a radiative effect of about 4.6 W m⁻². The lowest layers (temperature difference less than 4 K) produced on average a radiative effect of 0.8 W m⁻².

How to cite: Jahani, B., Andersen, H., Calbó, J., González, J.-A., and Cermak, J.: How Significant are the Longwave Radiative Effects of the Cloud-Aerosol Transition Zone?, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2298, https://doi.org/10.5194/egusphere-egu22-2298, 2022.

The climate sensitivity is the central metric for evaluating the amplitude of climate change. It is inversionally proportional to the climate feedback parameter which can be estimated with observations of surface temperature (T), radiative forcing (F) and Earth energy imbalance (EEI). Since EEI is proportional to the time derivative of the ocean heat content, EEI can be derived from in situ temperature/salinity measurements or, equivalently, from the thermosteric component of sea level rise. Here we use a regression method applied to T, F and in situ temperature as well as thermosteric sea level to estimate the climate feedback parameter over the 20^th century. Several recent climate studies have shown that the feedback parameter changes with time, because of the spatial pattern of warming. We evaluate the time variations of the climate feedback parameter over 1900- 2020 by applying the regression method to different periods within the 20^th century. For the first time, we confirm with observations that the climate feedback parameter does change with time, and responds to external forcings such as major volcanic eruptions, as well as to climate internal variability. We also demonstrate that we need a consistent and reliable observing systems across time to derive a credible climate feedback parameter time series.

How to cite: Chenal, J., Meyssignac, B., and Guillaume-Castel, R.: Observational study of time-varying climate feedback parameter, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3359, https://doi.org/10.5194/egusphere-egu22-3359, 2022.

How to cite: Guillaume-Castel, R., Meyssignac, B., Roca, R., and Chenal, J.: Dynamics of the global energy budget with a time dependent climate feedback parameter, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7956, https://doi.org/10.5194/egusphere-egu22-7956, 2022.

Climate sensitivity – the response of the Earth’s surface temperature to radiative forcing – and climate feedbacks are important and widely used metrics to gauge global climate change. In recent years it has become clear that climate sensitivity and feedback change over time in numerical climate model experiments but the reasons for this change are not yet well understood. We investigate the abrupt4xCO2 experiment as simulated by multiple members of the Coupled Model Intercomparison Project (CMIP) phases 5 and 6 and apply a radiative kernel method to decompose climate feedback into contributions from physical processes. We extract two groups of models, one with small (G1) and one with large (G2) global mean lapse-rate feedback change over time. It is found that the model groups differ with respect to warming and feedback patterns and that the Arctic stands out as the region with the biggest between-group differences. We retrace these Arctic changes to the different evolution of Arctic sea ice in both model groups. A further finding is that G1 members exhibit much more warming over the simulation period than G2s members. This appears to result from a more positive early cloud feedback in G1 than in G2. Further investigation is needed to uncover possible cause-effect relationships between Arctic changes and global feedbacks.

How to cite: Eiselt, K.-U. and Graversen, R.: On differences in climate feedback evolution in abrupt4xCO2 climate model experiments, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5457, https://doi.org/10.5194/egusphere-egu22-5457, 2022.

For the 1960-2000 period, the latest generation of climate models (CMIP6) shows less global mean surface temperature change relative to pre-industrial than that seen in observations. In contrast, the previous generation of models (CMIP5) warmed in line with observations over this period. It has been hypothesised that this suppressed late-20th Century warming seen in CMIP6 is caused by a stronger aerosol effective radiative forcing (ERF) than in CMIP5. We investigate the role that historical ERF plays in historical global mean warming. 

To diagnose the historical ERF we determine the climate feedback parameter from regression of top-of-atmosphere energy imbalance against temperature in abrupt-4xCO₂ runs and use the diagnosed climate feedback values in the historical simulations from the same models. We evaluate the historical ERF in 35 CMIP6 and 27 CMIP5 models. We show that this method to estimate ERF is a fairly good approximation to more accurate estimates using atmosphere-only integrations from the Radiative Forcing Model Intercomparison Project (RFMIP). The agreement with RFMIP is best in those models with little or no time dependence (curvature) in their climate feedback parameter. However, the historical ERF estimate can be improved by considering the non-linearity in climate feedbacks. To do this we repeat the process using a three time-constant regression model, showing that this method gives results that are much closer to RFMIP in those models that perform poorly with the one-parameter model.

Under both the one- and three-parameter methods, we show that total historical ERF is lower in CMIP6 than in CMIP5 for 1960-2000. This lower forcing at first appears to explain the differences in warming between the CMIP6 and CMIP5 ensembles. To dive deeper into the contribution to historical forcing we also estimate ERF contributions from greenhouse gases, other anthropogenic forcers (including aerosols), and natural forcing in the subset of CMIP6 and CMIP5 models that performed experiments from the Detection and Attribution Model Intercomparison Project (DAMIP). The causes are a stronger negative aerosol ERF and weaker positive greenhouse gas ERF in CMIP6 compared to CMIP5. However, these forcing differences are amplified by differences in climate sensitivity between the CMIP5 and CMIP6 ensemble, which leads to both a stronger aerosol cooling over 1960-1990 and a stronger greenhouse gas induced warming from 1990 in CMIP6.

How to cite: Smith, C., Forster, P., and Fredriksen, H.-B.: Radiative forcing and climate feedbacks explain the cause of the suppressed late 20th century warming in CMIP6 models, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11277, https://doi.org/10.5194/egusphere-egu22-11277, 2022.

Changes in atmospheric composition, such as increasing greenhouse gases, cause an initial radiative imbalance to the climate system, quantified as the instantaneous radiative forcing. This fundamental metric has not been directly observed globally and previous estimates have come from models. In part, this is because current space-based instruments cannot distinguish the instantaneous radiative forcing from the climate’s radiative response. We apply radiative kernels to satellite observations to disentangle these components and find all-sky instantaneous radiative forcing has increased 0.53 ± 0.11 W/m² from 2003 to 2018, accounting for positive trends in the total planetary radiative imbalance. This increase has been due to a combination of rising concentrations of well-mixed greenhouse gases and recent reductions in aerosol emissions. These results highlight distinct fingerprints of anthropogenic activity in Earth’s changing energy budget, which we find observations can detect within 4 years.

How to cite: Soden, B., Kramer, R., and He, H.: Monitoring Global Radiative Forcing from Space, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-682, https://doi.org/10.5194/egusphere-egu22-682, 2022.

The incident solar radiation on the Earth’s surface (SSR) varies on a decadal scale and this phenomenon is called Global Dimming & Brightening (GDB). GDB is known to be caused by anthropogenic and natural climate agents, with clouds and aerosols being the most significant.

This study examines the GDB using Modern-Era Retrospective Analysis for Research and Applications v.2 (MERRA-2) reanalysis data, that is originally provided on a 0.5°×0.625° horizontal grid resolution, for the 41-year period 01/1980 – 12/2020. The mean monthly SSR fluxes and their deseasonalized anomalies are computed and validated against ground truth measurements from two major reference station networks, namely the Global Energy Balance Archive (GEBA), and the Baseline Surface Radiation Network (BSRN). The changes of SSR anomalies (ΔSSR or GDB) are calculated on global (land & ocean), hemispherical and regional scales, over the entire period and for sub-periods too. In each case, it has been examined whether the sign of MERRA-2 GDB (dimming or brightening) agree or disagree with the corresponding GDB sign of stations lying within the MERRA pixel.

Using SSR deseasonalized anomalies, the computed ΔSSR for the 41-year period 1/1980-12/2019 for the Globe is equal to -6.307±0.193 W/m² (on an annual basis), -5.716±0.281 W/m² for the Northern Hemisphere and -6.161±0.379 W/m² for the Southern Hemisphere, indicating an overall dimming, which has counteracted the anthropogenic greenhouse warming. Stronger dimming is found over oceans, equal to -7.805±0.244 W/m², against a weaker dimming over land, equal to -2.582±0.249 W/m², pointing to a less transparent atmosphere over the oceans than over land. A brightening is found over Europe and E. Asia, opposite to a dimming over India . The agreement between the estimated GDB from MERRA-2 and GEBA/BSRN stations ranges from 50% to 77%, either for the entire study period as well or the examined sub-periods (1980-1985,1986-2000,2001-2010,2011-2020), revealing a reasonable agreement adding confidence about the conclusions drawn from this MERRA-2 based analysis.

How to cite: Stamatis, M., Hatzianastassiou, N., Korras Carraca, M. B., Matsoukas, C., Wild, M., and Vardavas, I.: Detailed analysis of the Global Dimming & Brightening from 1980 to 2020 based on MERRA-2 reanalysis data, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-586, https://doi.org/10.5194/egusphere-egu22-586, 2022.

How to cite: Yuan, M., Leirvik, T., and Wild, M.: Global Trends in Downward Surface Solar Radiation from Spatial Interpolated Ground Observations during 1961-2019, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4120, https://doi.org/10.5194/egusphere-egu22-4120, 2022.

For the explanation of the observed decadal variations in surface solar radiation (known as dimming and brightening) the relative importance of clouds and the cloud-free atmosphere (particularly aerosols) is currently disputed. Here we investigate this issue using daily data from the prominent long-term observational radiation record at Potsdam, Germany, over the 71-years period 1947-2017. We identify cloud-free days based on synop cloud observations as well as on days with maximum atmospheric transmission. Irrespective of the cloud-screening method, strong dimming and brightening tendencies in the atmospheric transmission are evident not only under all-sky, but also of similar magnitude under clear-sky conditions, causing multidecadal variations in surface solar radiation on the order of 10 Wm^-2. This points to the cloud-free atmosphere as a main responsible for dimming and brightening in Central Europe and suggests that these variations are anthropogenically forced rather than of natural origin, with aerosol pollutants as likely major drivers.

This study has been published in Geophysical Research Letters (Wild et al. 2021)

Reference:

Wild, M., Wacker, S., Yang, S., and Sanchez-Lorenzo, A. (2021). Evidence for clear-sky dimming and brightening in central Europe. Geophysical Research Letters, 48, e2020GL092216. https://doi. org/10.1029/2020GL092216

How to cite: Wild, M., Wacker, S., Yang, S., and Sanchez-Lorenzo, A.: Evidence for clear-sky dimming and brightening in the long-term Potsdam radiation record, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1194, https://doi.org/10.5194/egusphere-egu22-1194, 2022.

Decadal trends in Surface solar radiation (SSR) have gained attention in the last few decades after several studies identified the non stable behaviour of such measurements. Also referred to as Global Dimming and Global Brightening, these decadal trends are known to be spatially heterogeneous, meaning that different regions might experience different trends, associated with different causes. While measurements have allowed the identification of trends around the world, the understanding of the causes of those trends is limited to a few regional studies mostly focusing on developed countries. The lack of data in developing countries leads to an underrepresentation of those regions in what regards to global dimming and brightening research. In this work, we use around 39 years  (1967-2005) of daily SSR measurements of two stations in Zimbabwe, and apply a new method for clear-sky derivation, using satellite cloud fraction to identify optimal daily transmittance thresholds for clear-sky identification. The all-sky and clear-sky time series of SSR are then compared to cloud fraction and water vapor data from ERA5 reanalysis and to aerosol emissions from the EDGAR database. The SSR time series show a persistent dimming of similar magnitude both in all-sky and clear-sky. The cloud fraction does not show any significant trends, reinforcing the hypothesis that the dimming was caused by cloud-free radiative processes in the atmosphere. The water vapour time series also does not show any significant trend which could justify the negative trends in SSR. However, the monthly interannual variability show that the dimming is stronger between July and September, months with higher emission of biomass burning aerosols in that region. This might also indicate an anthropogenic related cause of the dimming observed in southern Africa. This study intends to contribute to the understanding of the global dimming and brightening phenomena in southern Africa, but also to highlight the importance of studies focusing in underrepresented regions of the world.

How to cite: Ferreira Correa, L., Wild, M., Folini, D., and Chtirkova, B.: Evidence for biomass burning-forced dimming in southern Africa, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2888, https://doi.org/10.5194/egusphere-egu22-2888, 2022.

Internal variability, a natural source of uncertainty in climate projections, is important when one wants to distinguish between the forced signal and the random noise in the climate system. The downwelling surface solar radiation, a key climate variable, has been shown to exhibit unforced trends (i.e. trends exclusively due to internal variability) even on decadal timescales. These long-term unforced trends interfere with the forced signal and contribute to the decadal variations of SSR, known as global dimming and brightening. A common technique in observation analysis, which serves to reduce the contribution of internal variability and therefore give a better estimate of the forced signal, is the use of composite time series of multiple locations (averaging in space). We use annual mean data of 49 models from the pre-industrial control experiment of the Coupled Model Intercomparison Project – Phase 6 (CMIP6) to give a quantitative estimate of how much the system noise is reduced upon spatial averaging. We find that globally the standard deviation σ (which is proportional to the magnitudes of random trends) is reduced almost linearly with the horizontal grid increment Δx in the range 2 – 15 degrees. On coarser resolutions, deviations from a linear fit are observed, possibly because the patterns of ocean oscillations are not concentrated in compact forms in space. Comparing the rate of reduction of the noise with grid resolution (dσ/dΔx), we find that the noise in all-sky SSR is averaged out ~10 times faster (with increasing Δx) than the noise in clear-sky SSR. Numerical values estimated from the CMIP6 multi-model median and uncertainties estimated from the inter-model spread are dσ/dΔx = -0.11 ± 0.03 Wm^-2/deg for all-sky SSR and dσ/dΔx = -0.01 ± 0.004 Wm^-2/deg for clear-sky SSR. The all-sky SSR global mean σ for a 0.5 deg grid is 4.79Wm^-2, while for clear-sky it is 0.66 Wm^-2. Furthermore, dσ/dΔx is strongly dependent on the geographical location, being more than twice as large in China, compared to Europe for both all-sky and clear-sky SSR.

How to cite: Chtirkova, B., Folini, D., Ferreira Correa, L., and Wild, M.: Spatial scales of internal variability of annual mean all-sky and clear-sky surface solar radiation: quantitative estimates using CMIP6, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5751, https://doi.org/10.5194/egusphere-egu22-5751, 2022.

The NASA/GEWEX Surface Radiation Budget (SRB) project produces 3-hrly shortwave and longwave surface and top of atmosphere radiative fluxes for the 1983-near present time period. The new Release 4 Integrated Product (IP) uses the newly recalibrated and processed ISCCP HXS product as its primary input for cloud and radiance data, replacing ISCCP DX with a ninefold increase in pixel count (10 km instead of 30 km).  This first version retains a 1°x1° resolution enabling intercomparison against previous versions and other data sets such as CERES, but spans 34 years from July 1983 through June 2017 and was announced by Kummerow et al. 2019 (GEWEX News). This new IP product also uses an atmospheric temperature and moisture dataset known as nnHIRS and other parameters such as near surface and skin temperatures from SeaFlux and LandFlux data sets.  In addition to the input data improvements, several important algorithm improvements have been made since Release 3. These include recalculated SW atmospheric transmissivities and reflectivities, updated ocean and snow/ice albedos, and variable total solar irradiance consistent with SORCE measurements. The LW code has been updated to improve the optical property treatment for clouds and aerosols are included in this version.  Radiative treatment of ice clouds is also improved in the LW. The variable aerosol optical properties for the SW and LW are specified using a detailed aerosol history from the Max Planck Institute Aerosol Climatology (MAC).

Here we present an assessment of the LW radiative fluxes and the uncertainty of those fluxes relative to the various inputs to surface SW/LW flux measurements from BSRN and PMEL buoys measurements.  We review the validation of the SW and LW fluxes and then in terms of time series and then assess the products in terms of their long-term variability of the surface SW and LW net fluxes compared to multiple other data products including atmospheric reanalysis products.  The comparisons of radiative estimates to observations are performed at various temporal scales and aimed at investigation of agreement at longer time averages but accessing potential change in diurnal magnitude and daily variability.  Utilizing this uncertainty information, to access long-term variability of surface radiation components at selected region and global scales, considering satellite sampling/calibration “artifacts” as necessary.  At the longer time scales, the net SW and net LW the TOA and surface have implications toward closure of the energy budgets at the surface, we assess these compared to other studies on energy budget closure for the same selected global and regional scales.

How to cite: Stackhouse, P., Cox, S., Mikovitz, J. C., and Zhang, T.: Assessing Uncertainties and Variability 34 Years of Surface Radiative Fluxes and Radiative Closure Using GEWEX SRB Release4-IP, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13489, https://doi.org/10.5194/egusphere-egu22-13489, 2022.

Ground-based measurements of downwelling surface solar irradiance (DSSI) play an important role in many topics, such as the design and operation of solar energy systems, the study of the Earth radiation budget, and the validation of gridded DSSI products such as those derived from satellite imagery and reanalyses. For our purpose of validating satellite-based estimates of DSSI over China, we use ground-based hourly measurements at 99 stations operated by China Meteorological Administration (CMA). The measurements might not be perfect, including outliers, biases, sudden shifts or slow instrumental drifts that an extended quality check (QC) can detect. Physical threshold methods like the one proposed by Long and Dutton (2002) are frequently used to detect some of the physically impossible data records on an hourly or sub-hourly basis. However, in our case, we observed many inconsistent measurements that can pass such threshold-based QC. This is especially because diffuse and/or direct components of DSSI are not measured for most CMA stations and only global irradiance-based check for DSSI can be realized.

To detect drifts or jumps that might last for weeks or months in the time series of measured DSSI, we carried out the QC by comparing the clearness indices (i.e., the ratio between DSSI and horizontal irradiance at the top of atmosphere) from measurements with those from the ERA-5 reanalysis, in a similar approach to Urraca et al. (2017). Consistency of ERA-5's clearness indices was checked at first by comparison with high quality measurements from Baseline Surface Radiation Network. The same quality check was then applied to the hourly datasets at CMA stations. Over the years 2017-2018, our QC method led us to keep 52 stations fully and 19 stations partly.

Reference:

Long C.N. and Dutton E.G. BSRN Global Network recommended QC tests, V2.0, BSRN Technical Report, pp 3, 2002.

Urraca R., Gracia-Amillo A., Huld T., et al. Quality control of global solar radiation data with satellite-based products, Solar Energy 158, pp 49-62, 2017. https://doi.org/10.1016/j.solener.2017.09.032.

How to cite: Chen, X., Tournadre, B., Saint-Drenan, Y.-M., Gschwind, B., Qin, K., and Blanc, P.: Quality check of ground-based hourly measurements of downwelling surface solar irradiance in China, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8847, https://doi.org/10.5194/egusphere-egu22-8847, 2022.

The angular distribution of solar downwelling at Earth surface is of interest namely for emerging applications such as in building design and management (energy efficiency, visual and thermal comfort).

The operational service of CAMS Radiation, named McClear, is a webservice providing the three solar downwelling irradiance components global, diffuse, direct normal on the ground under clear sky conditions from CAMS data with good accuracy ((Lefèvre et al., 2013), (Gschwind et al., 2019)). In preparation to the extension of McClear capabilities, we consider the CIE modelling approach (Darula and Kittler, 2002) as a natural option because it is widely used in the literature. The CIE modelling approach consists in the product of two functions: (1) the scattering function that relates the relative radiance of a sky feature to its angular distance from the sun and (2) the gradation function that explains the variation of the radiance with the angular distance of a sky element to the zenith angle. The basic assumption behind this approach is that the distribution of the radiation in the sky vault can be decomposed in these two functions. As a preliminary to the extension of McClear service, we would like to test the validity of this assumption. For this purpose, we have selected the Perez model (Perez et al., 1993, 1990) that is based on the CIE approach and show good poerformances (Alshaibani et al., 2020; Darula and Kittler, 2002).

We compared the downwelling angular solar radiance at the surface of the Earth calculated by the Perez model and by the Radiative Transfer Model libRadtran a software package used for radiative transfer calculations such as the distribution of the spherical radiance and irradiance (Mayer and Kylling, 2005) also used as basis of McClear irradiance model. In this comparison, we consider several meteorological situations with different atmospheric composition in ozone, water vapor, aerosols loads, etc., but also with different Sun-Earth geometry and different bidirectional reflectance distribution function (BRDF) of the ground. This investigation was beneficial to evaluate the domain of validity of the CIE approach assumption. We described the methodology, discuss the results in view of the targeted application. and we provide some idea for an alternative to the CIE approach to extend McClear in a radiance model.

How to cite: Bham, S., Saint-Drenan, Y.-M., Gschwind, B., and Blanc, P.: Assessment of angular distribution model of radiance: a comparison of the CIE model output with radiation transfer calculations., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12382, https://doi.org/10.5194/egusphere-egu22-12382, 2022.