Earth's Radiant Energy System Research Articles

Planetary Energy Flow and Entropy Production Rate by Earth from 2002 to 2023.

In this work, satellite data from the Clouds and Earth's Radiant Energy System (CERES) and Moderate Resolution Imaging Spectroradiometer (MODIS) instruments are analyzed to determine how the global absorbed sunlight and global entropy production rates have changed from 2002 to 2023. The data is used to test hypotheses derived from the Maximum Power Principle (MPP) and Maximum Entropy Production Principle (MEP) about the evolution of Earth's surface and atmosphere. The results indicate that both the rate of absorbed sunlight and global entropy production have increased over the last 20 years, which is consistent with the predictions of both hypotheses. Given the acceptance of the MPP or MEP, some peripheral extensions and nuances are discussed.

Thermal infrared observations of a western United States biomass burning aerosol plume

Abstract. Biomass burning smoke particles, due to their submicron particle size in relation to the average thermal infrared (TIR) wavelength, theoretically have negligible signals at the TIR channels. However, nearly instantaneous longwave (LW) signatures of thick smoke plumes can be frequently observed at the TIR channels from remotely sensed data, including at 10.6 µm (IR window), as well as in water-vapor-sensitive wavelengths at 7.3, 6.8, and 6.3 µm (e.g., lower, middle, and upper troposphere). We systematically evaluated multiple hypotheses as to causal factors of these IR signatures of biomass burning smoke using a combination of data from the Aqua MODerate resolution Imaging Spectroradiometer (MODIS), Aqua Cloud and the Earth Radiant Energy System (CERES), Geostationary Operational Environmental Satellite 16/17 (GOES-16/17) Advanced Baseline Imager, and Suomi-NPP Visible Infrared Imaging Radiometer Suite (VIIRS) and Cross-track Infrared Sounder (CrIS). The largely clear transmission of light through wildfire smoke in the near infrared indicates that coarse or giant ash particles are unlikely to be the dominant cause. Rather, clear signals in water vapor and TIR channels suggest that both co-transported water vapor injected to the middle to upper troposphere and surface cooling by the reduction of surface radiation by the plume are more significant, with the surface cooling effect of smoke aloft being the most dominant. Giving consideration of the smoke impacts on TIR and longwave, CERES indicates that large wildfire aerosol plumes are more radiatively neutral. Further, this smoke-induced TIR signal may be used to map very optically thick smoke plumes, where traditional aerosol retrieval methods have difficulties.

On the Consistency of Ice Cloud Optical Models for Spaceborne Remote Sensing Applications and Broadband Radiative Transfer Simulations

AbstractAqua satellite Moderate Resolution Imaging Spectroradiometer (MODIS) 1‐km observations are collocated with Clouds and the Earth's Radiant Energy System (CERES) fields of view taken during July 2008 afternoon satellite passes over the equatorial western Pacific Ocean. Radiation simulations are compared with collocated CERES observations to better understand the sensitivity of computed fluxes to two ice cloud broadband radiation parameterization schemes and inferred ice cloud characteristics. In particular, the radiation computational schemes and ice cloud property retrievals are based on two respective ice particle models, the MODIS Collection 6 (MC6) aggregate model and a more microphysically consistent two‐habit model (THM). The simulation results show that both MC6 and THM overestimate the shortwave (SW) and longwave (LW) cloud radiative effects at the top of the atmosphere, as compared to the CERES observations; the difference between the MC6 and THM‐based ice cloud retrievals is too small to compensate for the differences between the two model‐based radiation schemes. Therefore, the present finding suggests that broadband radiative simulations are more sensitive to the radiation parameterization scheme than to the input cloud properties retrieved using the corresponding ice cloud particle optical property model.

Angular sampling of a monochromatic, wide-field-of-view camera to augment next-generation Earth radiation budget satellite observations

Abstract. Earth radiation budget (ERB) satellite observations require conversion of the measured radiance, which is a remotely sensed quantity, to a derived irradiance, which is the relevant energy balance quantity routinely used in modeling and analysis of the climate system. The state-of-the-art approach for radiance-to-irradiance conversion taken by the Clouds and the Earth's Radiant Energy System (CERES) benefits from the exhaustive sampling of radiance anisotropy by multiple CERES instruments across many years. Unfortunately, the CERES approach is not easily extended to new ERB spectral channels that lack previous sampling, such as the “split-shortwave” planned to be part of the next-generation ERB mission Libera. As an alternative approach, the capability of a monochromatic, wide-field-of-view camera to provide dense angular sampling in a much shorter time frame is assessed. We present a general concept for how this can be achieved and quantify the proficiency of a camera to provide rapid angular distribution model (ADM) generation for the new Libera ultraviolet and visible (VIS) sub-band. A single mid-visible camera wavelength (555 nm) is shown to be ideal for representing the VIS sub-band, requiring only basic scene stratification for 555 nm to VIS conversion. Synthetic camera sampling with realistic operating constraints also demonstrates that the angular radiance field of various scenes can be well populated within a single day of sampling, a notable advance over existing approaches. These results provide a path for generating observationally based VIS ADMs with minimal lag time following Libera's launch. Coupled with efforts to utilize a camera for scene identification, this may also pave the way for future ERB satellite systems to develop stand-alone irradiance products for arbitrary sets of spectral channels, opening up new measurement and science possibilities.

CERESMIP: a climate modeling protocol to investigate recent trends in the Earth's Energy Imbalance

The Clouds and the Earth's Radiant Energy System (CERES) project has now produced over two decades of observed data on the Earth's Energy Imbalance (EEI) and has revealed substantive trends in both the reflected shortwave and outgoing longwave top-of-atmosphere radiation components. Available climate model simulations suggest that these trends are incompatible with purely internal variability, but that the full magnitude and breakdown of the trends are outside of the model ranges. Unfortunately, the Coupled Model Intercomparison Project (Phase 6) (CMIP6) protocol only uses observed forcings to 2014 (and Shared Socioeconomic Pathways (SSP) projections thereafter), and furthermore, many of the ‘observed' drivers have been updated substantially since the CMIP6 inputs were defined. Most notably, the sea surface temperature (SST) estimates have been revised and now show up to 50% greater trends since 1979, particularly in the southern hemisphere. Additionally, estimates of short-lived aerosol and gas-phase emissions have been substantially updated. These revisions will likely have material impacts on the model-simulated EEI. We therefore propose a new, relatively low-cost, model intercomparison, CERESMIP, that would target the CERES period (2000-present), with updated forcings to at least the end of 2021. The focus will be on atmosphere-only simulations, using updated SST, forcings and emissions from 1990 to 2021. The key metrics of interest will be the EEI and atmospheric feedbacks, and so the analysis will benefit from output from satellite cloud observation simulators. The Tier 1 request would consist only of an ensemble of AMIP-style simulations, while the Tier 2 request would encompass uncertainties in the applied forcing, atmospheric composition, single and all-but-one forcing responses. We present some preliminary results and invite participation from a wide group of models.

Comparison between deep learning architectures for the 1 km, 10/15-min estimation of downward shortwave radiation from AHI and ABI

The retrieval of downward shortwave radiation (DSR) with high spatiotemporal resolution and short latency is critical. It is the fundamental driving force of surface energy, carbon, and hydrological circulations, and a key energy source for photovoltaic electricity. However, existing methods face significant challenges owing to cloud heterogeneity and their reliance on other satellite-derived products, which hinder the retrieval of accurate and timely DSR with high spatiotemporal resolution. In addition to the spectral features used in traditional approaches, deep learning (DL) can incorporate the spatial and temporal features of satellite data. This study developed and compared three DL methods, namely the DenseNet, the bidirectional gated recurrent unit without surface albedo as inputs (BiGRUnor), and the convolutional neural network with gated recurrent unit without surface albedo as inputs (CNNGRUnor). These methods were used to estimate DSR at 1 km and 10/15 min resolutions directly from top-of-atmosphere reflectance over the Advanced Himawari Imager (AHI) onboard Himawari-8 and the Advanced Baseline Imager (ABI) onboard GOES-16 coverage, achieving high accuracies. The instantaneous root mean square error (RMSE) and relative RMSE for the three models were 68.4 (16.1%), 69.4 (16.3%), and 67.1 (15.7%) W/m2, respectively, which are lower than the baseline machine learning method, the multilayer perceptron model (MLP), with RMSE at 76.8 W/m2 (18.0%). Hourly accuracies for the three DL methods were 58.6 (14.1%), 57.8 (14.0%), and 57.3 (13.8%) W/m2, which are within the DSR RMSEs that we estimated for existing datasets of the Earth's Radiant Energy System (CERES) (88.8 W/m2, 21.4%) and GeoNEX (77.8 W/m2, 18.8%). The study illustrates that DL models that incorporate temporal information can eliminate the need for surface albedo as an input, which is crucial for timely monitoring and nowcasting of DSR. Incorporating spatial information can enhance retrieval accuracy in overcast conditions, and incorporating infrared bands can further improve the accuracy of DSR estimation.

The seasonal cycle of cloud radiative effects over Congo Basin based on CERES observation and comparison to CMIP6 models

This study investigates the seasonal variability of the cloud radiative effects (CREs) over Congo Basin (CB) using 15-year observations from Clouds and the Earth's Radiant Energy System (CERES) Energy Budget and Filled (EBAF) Ed4.1 level 3b dataset involving CERES and Moderate Resolution Imaging Spectroradiometer (MODIS) instruments on board Terra and Aqua satellites. The relationships between CREs and cloud properties such as total cloud fraction (TCF), cloud top height (CTH), cloud top temperature (CTT) and cloud optical thickness (COT) are checked. An evaluation of Coupled Model Intercomparison Project (CMIP) Phase 6 in capturing the seasonal cycle of CREs as well as the magnitudes of the CREs along the seasonal cycle is also performed. This study shows a net cloud cooling effect of −8.4 W/m2 and − 43.9 W/m2 respectively at the top of the atmosphere (TOA) and at the surface, leading to a net warming effect of 35.67 W/m2 in the atmosphere. This value implies a large energy source over the Central Africa (CA) atmospheric column. The associated relationships between CREs and cloud properties show that the shortwave CRE is more sensitive to TCF and optical thickness whereas its longwave counterparts is more sensitive to CTH, CTT and COT at the TOA and in the atmosphere. All of the four CMIP6 models used in this study can capture the spatial pattern of CREs as well as their seasonal cycle but misrepresent intensity of CREs. Results also show that a better-simulated TCF considerably reduces the intensity of the annual mean underestimation in both longwave and shortwave CRE for some CMIP6 models, but not for models with overestimated shortwave CRE.

Assessment of Top of Atmosphere, Atmospheric and Surface Energy Budgets in CMIP6 Models on Regional Scales

AbstractWe examine top of atmosphere (TOA), atmospheric, and surface energy budget components of 53 CMIP6 models for the period 2000–2009 on regional scales with respect to two reference data sets: the NASA Energy and Water cycle Study (NEWS), from which we adopt the regional decomposition, and the Clouds and the Earth's Radiant Energy System (CERES) Energy Balanced and Filled (EBAF). Focusing on regional scale, CMIP6 models tend to have more energy entering or less energy leaving the climate systems at TOA over the Northern Hemisphere land, Southern Hemisphere ocean and the polar regions compared to CERES EBAF, while the contrary applies in other regions. Atmospheric net shortwave and longwave fluxes both tend to be underestimated in CMIP6 models as compared to EBAF, with substantial regional differences. Regional surface radiative fluxes as reported by NEWS and EBAF can differ substantially. Nevertheless, robust regional biases exist in CMIP6. Surface upward shortwave radiation is overestimated by 43 (81%) models over Eurasia. For almost all surface radiative flux components over the North Atlantic and Indian Ocean, there are at least 21 (40%) models which fall outside the NEWS uncertainty range. Latent heat flux is overestimated over most of the land and ocean regions while firm conclusions for sensible heat flux remain elusive due to discrepancies between different reference data sets. Compared to CMIP5, there is an overall improvement in CMIP6 on regional scale. Still, substantial deficiencies and spreads on regional scale remain, which are potentially translated into an inadequate simulation of atmospheric dynamics or hydrological cycle.

Revisiting the global mean ocean mass budget over 2005–2020

Abstract. We investigate the performances of Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) satellite gravimetry missions in assessing the ocean mass budget at the global scale over 2005–2020. For that purpose, we focus on the last years of the record (2015–2020) when GRACE and GRACE Follow-On faced instrumental problems. We compare the global mean ocean mass estimates from GRACE and GRACE Follow-On to the sum of its contributions from Greenland, Antarctica, land glaciers, terrestrial water storage and atmospheric water content estimated with independent observations. Significant residuals are observed in the global mean ocean mass budget at interannual timescales. Our analyses suggest that the terrestrial water storage variations based on global hydrological models likely contribute in large part to the misclosure of the global mean ocean mass budget at interannual timescales. We also compare the GRACE-based global mean ocean mass with the altimetry-based global mean sea level corrected for the Argo-based thermosteric contribution (an equivalent of global mean ocean mass). After correcting for the wet troposphere drift of the radiometer on board the Jason-3 altimeter satellite, we find that mass budget misclosure is reduced but still significant. However, replacing the Argo-based thermosteric component by the Ocean Reanalysis System 5 (ORAS5) or from the Clouds and the Earth's Radiant Energy System (CERES) top of the atmosphere observations significantly reduces the residuals of the mass budget over the 2015–2020 time span. We conclude that the two most likely sources of error in the global mean ocean mass budget are the thermosteric component based on Argo and the terrestrial water storage contribution based on global hydrological models. The GRACE and GRACE Follow-On data are unlikely to be responsible on their own for the non-closure of the global mean ocean mass budget.

Retrieval of cloud microphysical properties from Himawari-8/AHI infrared channels and its application in surface shortwave downward radiation estimation in the sun glint region

Satellite remote sensing of cloud property retrieval and shortwave downward radiation (SWDR) estimation is essential for global radiation budget and climate change studies. Sun glint areas remain a challenge for the existing cloud and SWDR algorithms based on the visible channel since surface specular reflection has a significant impact on satellite retrieval. In this study, a set of algorithms for cloud detection and cloud microphysical parameter estimation were developed using infrared multichannel data from the new generation geostationary satellite Himawari-8 based on the random forest method. The results indicated that the cloud retrieval algorithm exhibited better performance in the sun glint areas where the official Himawari-8 products (cloud detection and cloud optical thickness) were overestimated. We developed a new SWDR estimation algorithm combining the radiative transfer model and machine learning techniques by considering the cloud properties from the cloud retrieval algorithm. The results indicated that the SWDR and cloud radiative forcing derived by the new algorithm were more consistent with those of the well-known radiation products Cloud and the Earth's Radiant Energy System than those estimated using the official-based cloud product, with decreases in the root mean square error of approximately 22% and 41%, respectively. The new algorithms effectively addressed sun glint contamination by providing more data coverage and exhibiting stable performance on a spatiotemporal scale.

Quantifying Uncertainties in CERES/MODIS Downwelling Radiation Fluxes in the Global Tropical Oceans

The Clouds and the Earth's Radiant Energy System program, which uses the Moderate Resolution Imaging Spectroradiometer (CM), has been updated with the launch of new satellites and the availability of newly upgraded radiation data. The spatial and temporal variability of daily averaged synoptic 1-degree CM version 3 (CMv3) (old) and version 4 (CMv4) (new) downwelling shortwave (Q S ) and longwave radiation (Q L ) data in the global tropical oceans spanning 30°S–30°N from 2000 to 2017 is investigated. Daily in situ data from the Global Tropical Moored Buoy Array were used to validate the CM data from 2000 to 2015. When compared to CMv3, both Q S and Q L in CMv4 show significant improvements in bias, root-mean-square error, and standard deviations. Furthermore, a long-term trend analysis shows that Q S has been increasing by 1 W m −2 per year in the Southern Hemisphere. In contrast, the Northern Hemisphere has a −0.7 W m −2 annual decreasing trend. Q S and Q L exhibit similar spatial trend patterns. However, in the Indian Ocean, Indo-Pacific warm pool region, and Southern Hemisphere, Q L spatial patterns in CMv3 and CMv4 differ with an opposite trend (0.5 W m −2 ). These annual trends in Q S and Q L could cause the sea surface temperature to change by −0.2 to 0.3 °C per year in the tropical oceans. These results stress the importance of accurate radiative flux data, and CMv4 can be an alternative to reanalysis or other model-simulated data.

A Comparison of Top‐Of‐Atmosphere Radiative Fluxes From CERES and ARISE

AbstractUncertainty in Arctic top‐of‐atmosphere (TOA) radiative flux observations stems from the low sun angles and the heterogeneous scenes. Advancing our understanding of the Arctic climate system requires improved TOA radiative fluxes. We compare Cloud and Earth's Radiant Energy System (CERES) TOA radiative fluxes with Arctic Radiation‐IceBridge Sea and Ice Experiment (ARISE) airborne measurements using two approaches: grid box averages and instantaneously matched footprints. Both approaches indicate excellent agreement in the longwave and good agreement in the shortwave (SW), within 2σ uncertainty considering all error sources (CERES and airborne radiometer calibration, inversion, and sampling). While the SW differences are within 2σ uncertainty, both approaches show a ∼−10 W m−2 average CERES‐aircraft flux difference. Investigating the source of this negative difference, we find a substantial sensitivity of the flux differences to the sea ice concentration data set. Switching from imager‐based to passive microwave‐based sea ice data in the CERES inversion process reduces the differences in the grid box average fluxes and in the sea ice partly cloudy scene anisotropy in the matched footprints. In the long‐term, more accurate sea ice concentration data are needed to reduce CERES TOA SW flux uncertainties. Switching from imager to passive microwave sea ice data, in the short‐term, could improve CERES TOA SW fluxes in polar regions, additional testing is required. Our analysis indicates that calibration and sampling uncertainty limit the ability to place strong constraints (<±7%) on CERES TOA fluxes with aircraft measurements.

Southern Ocean Solar Reflection Biases in CMIP6 Models Linked to Cloud Phase and Vertical Structure Representations

AbstractOver the Southern Ocean (SO, 40°S–70°S), climate models have consistently underestimated solar reflection. Here we evaluate the relationship between cloud profiles, cloud phase and radiation over the SO in Coupled Model Intercomparison Project Phase 6 (CMIP6) models against Clouds and the Earth's Radiant Energy System and Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observations. We find that the lack of solar reflection is slightly improved in CMIP6 models compared to CMIP5's, attributable to a better representation of cloud fraction and phase. We show that clouds have a different vertical structure and radiative effect south and north of where the 0°C isotherm meets the surface (∼55°S). Although the models capture the greater vertical extent of clouds south of 55°S, they fail to reproduce the observed increase in solar reflection, which we pinpoint to cloud phase biases. Increasing CMIP6 supercooled liquid cloud opacity should help reduce their persistent shortwave biases.

Top-of-the-atmosphere reflected shortwave radiative fluxes from GOES-R

Abstract. Under the GOES-R activity, new algorithms are being developed at the National Oceanic and Atmospheric Administration (NOAA)/Center for Satellite Applications and Research (STAR) to derive surface and top-of-the-atmosphere (TOA) shortwave (SW) radiative fluxes from the Advanced Baseline Imager (ABI), the primary instrument on GOES-R. This paper describes a support effort in the development and evaluation of the ABI instrument capabilities to derive such fluxes. Specifically, scene-dependent narrow-to-broadband (NTB) transformations are developed to facilitate the use of observations from ABI at the TOA. Simulations of NTB transformations have been performed with MODTRAN 4.3 using an updated selection of atmospheric profiles and implemented with the final ABI specifications. These are combined with angular distribution models (ADMs), which are a synergy of ADMs from the Clouds and the Earth's Radiant Energy System (CERES) and from simulations. Surface conditions at the scale of the ABI products as needed to compute the TOA radiative fluxes come from the International Geosphere–Biosphere Programme (IGBP). Land classifications at 1/6∘ resolution for 18 surface types are converted to the ABI 2 km grid over the contiguous United States (CONUS) and subsequently re-grouped to 12 IGBP types to match the classification of the CERES ADMs. In the simulations, default information on aerosols and clouds is based on that used in MODTRAN. Comparison of derived fluxes at the TOA is made with those from CERES, and the level of agreement for both clear and cloudy conditions is documented. Possible reasons for differences are discussed. The product is archived and can be downloaded from the NOAA Comprehensive Large Array-data Stewardship System (CLASS).

Generating 5 km resolution 1981–2018 daily global land surface longwave radiation products from AVHRR shortwave and longwave observations using densely connected convolutional neural networks

Surface longwave radiation (SLWR) components, including downward longwave radiation (DLR), upward longwave radiation (ULR), and net longwave radiation (NLR), are major contributors to the Earth's surface radiation budget and play important roles in ecological, hydrological, and atmospheric processes. Previous SLWR products have different drawbacks, such as being temporally short (after 2000), spatially coarse (≥ 25 km), and instantaneous values, which hinder their in-depth applications in land surface process modeling and climate trends analysis. Here, we reported the Advanced Very High-Resolution Radiometer (AVHRR)-based Global LAnd Surface Satellites (GLASS-AVHRR) SLWR products over the global land surface at a 5 km spatial resolution and 1 day temporal resolution between 1981 and 2018. These products were generated using multiple densely connected convolutional neural networks (DesCNNs) from the AVHRR top-of-atmosphere (TOA) reflected and emitted observations and European Centre for Medium-Range Weather Forecasts (ECMWF) fifth generation reanalysis (ERA5) near-surface meteorological data. DesCNNs were trained using integrated SLWR samples derived from the Moderate Resolution Imaging Spectroradiometer (MODIS)-based GLASS, Clouds and the Earth's Radiant Energy System Synoptic (CERES-SYN), and ERA5 SLWR products. In situ measurements from 231 globally distributed sites were used to evaluate the GLASS-AVHRR SLWR estimates. The results illustrated the overall high accuracies of GLASS-AVHRR SLWR products with root-mean-square-errors (RMSEs) of 18.66, 14.92, and 16.29 Wm−2, and mean bias errors (MBEs) of −2.69, −3.77, and 0.49 Wm−2 for all-sky DLR, ULR, and NLR, respectively. We found good correlation and consistency between GLASS-AVHRR and both CERES-SYN and ERA5 in terms of spatial patterns, latitudinal gradient, and temporal evolution. Our results revealed the significant contribution of shortwave observations to SLWR estimation owing to the high amounts of clouds over polar regions and water vapor and clouds in tropical areas, which was not previously widely recognized by the remote sensing community. GLASS-AVHRR SLWR products were updated, documented, and made available to the public at www.glass.umd.edu and www.geodata.cn.

Diurnal Variation in Clouds and Radiative Budgets Over the Tibetan Plateau During Summer Using CERES Data

AbstractDiurnal variations in clouds and radiation budgets over four subareas of the Tibetan Plateau (TP) during summer (June–August) are analyzed using the Clouds and the Earth's Radiant Energy System (CERES) synoptic 1° (SYN1deg) data from 2000 to 2020. The results show that the total cloud amount decreases from southeast to northwest and is larger during daytime (71.1%) than nighttime (67.2%) over the entire TP. High‐clouds develop in the afternoon, persist during nighttime, and dissipate after sunrise. Low clouds develop after sunrise and dissipate in the afternoon over the entire TP, but show opposite temporal variation over the Kunlun Mountains. The net radiation budget at the top‐of‐atmosphere reaches its maximum at noon. The surface net radiation budget is positive in the daytime and negative at nighttime. These features are mainly adjusted by the cloud distribution. The diurnal variations in heating rate over the four subareas are similar in the upper atmosphere but different in the lower layer. The low‐atmosphere heating rate shows a maximum value over the center‐south (CS) subarea, while it is lowest over the west (W) subarea. Internal cloud forcing has distinct regional differences over the four subareas: it shows a heating effect in the low atmosphere and a cooling effect in the middle atmosphere over the CS subarea, whereas over the W subarea it shows a radiative cooling effect in the low atmosphere and no significant radiative effect in the middle layer. The findings of this study help toward improving our understanding of the TP's energy cycle.

Evaluation of aerosol optical depths and clear-sky radiative fluxes of the CERES Edition 4.1 SYN1deg data product

Abstract. Aerosol optical depths (AODs) used for the Edition 4.1 Clouds and the Earth's Radiant Energy System (CERES) Synoptic 1∘ (SYN1deg) product are evaluated. AODs are derived from Moderate Resolution Imaging Spectroradiometer (MODIS) observations and assimilated by an aerosol transport model (the Model for Atmospheric Transport and Chemistry – MATCH). As a consequence, clear-sky AODs closely match with those derived from MODIS instruments. AODs under all-sky conditions are larger than AODs under clear-sky conditions, which is supported by ground-based AErosol RObotic NETwork (AERONET) observations. When all-sky MATCH AODs are compared with Modern-Era Retrospective analysis for Research and Applications (Version 2; MERRA-2) AODs, MATCH AODs are generally larger than MERRA-2 AODs, especially over convective regions (e.g., the Amazon, central Africa, and eastern Asia). This variation is largely due to the differing methods of assimilating the MODIS AOD data product and the use of quality flags in our assimilation. Including AODs with larger retrieval uncertainty makes AODs over the convective regions larger. When AODs are used for clear-sky irradiance computations and computed downward shortwave irradiances are compared with ground-based observations, the computed instantaneous irradiances are 1 %–2 % larger than observed irradiances. The comparison of top-of-atmosphere clear-sky irradiances with those derived from CERES observations suggests that AODs used for surface radiation observation sites are 0.01–0.03 larger, which is within the uncertainty of instantaneous MODIS AODs. However, the comparison with AERONET AODs suggests that AODs used for computations over desert sites are 0.08 larger. The cause of positive biases in downward shortwave irradiance and in AOD for the desert sites is possibly due to the dust particle size and distribution, as defined by the MATCH transport model, and the transfer of that information into the radiative transfer model.

Diffuse Radiation Forcing Constraints on Gross Primary Productivity and Global Terrestrial Evapotranspiration

AbstractThe diffuse radiation fertilization effect—the increase in plant productivity in the presence of higher diffuse radiation (K↓,d)—is an important yet understudied aspect of atmosphere‐biosphere interactions and can modify the terrestrial carbon, energy, and water budgets. The K↓,d fertilization effect links the carbon cycle with clouds and aerosols, all of which are large sources of uncertainties for our current understanding of the Earth system and for future climate projections. Here we establish to what extent observational and modeling uncertainty in sunlight's diffuse fraction (kd) affects simulated gross primary productivity (GPP) and terrestrial evapotranspiration (λE). We find only 48 eddy covariance sites with simultaneous sufficient measurements of K↓,d with none in the tropical climate zone, making it difficult to constrain this mechanism globally using observations. Using a land modeling framework based on the latest version of the Community Land Model, we find that global GPP ranges from 114 Pg C year−1 when using kd forcing from the Modern‐Era Retrospective analysis for Research and Applications, version 2 reanalysis to a ∼7% higher value of 122 Pg C year−1 when using the Clouds and the Earth's Radiant Energy System satellite product, with especially strong differences apparent over the tropical region (mean increase ∼9%). The differences in λE, although smaller (−0.4%) due to competing changes in shaded and sunlit leaf transpiration, can be greater than regional impacts of individual forcing agents like aerosols. Our results demonstrate the importance of comprehensively and systematically validating the simulated kd by atmosphere modules as well as the response to differences in kd within land modules across Earth System Models.

Non‐Gaussian Distributions of TOA SW Flux as Observed by MISR and CERES

AbstractThe Top of Atmosphere (TOA) shortwave (SW) flux, converted from Terra Multi‐angle Imaging SpectroRadiometer (MISR) narrow band albedos, is compared with that measured from Clouds and the Earth's Radiant Energy System (CERES). We describe the probability density function (PDF) of the monthly TOA SW flux and how the statistical third moment, skewness, can impact the quantification of the flux. The PDF of the SW flux is not normally distributed but positively skewed. In both sets of observations, the near‐global (80 S–80 N) median value of the SW flux is ∼3 W/m2 less than the mean value, due to the positive skewness of the distribution. The near‐global mean TOA SW flux converted from MISR is about 7 W/m2 (∼7%) less than CERES measured flux during the last two decades. Surprisingly, a hemispheric asymmetry exists with TOA SW observations from the Terra platform. SH reflects 3.92 and 1.15 W/m2 more mean SW flux than NH, from MISR and CERES Single Scanner Footprint products, respectively. We can infer that the offsetting by morning clouds in the SH is greater than the effect of hemispheric imbalance of SW flux caused by different land masses in two hemispheres. While the characteristics of the two SW fluxes broadly agree with each other, differences in the regional PDF from two different SW fluxes are substantial over high cloud regions and high altitude regions. Our analysis shows that some parts of the different skewness from the two measurements may be attributed to the different calibration of the radiance anisotropy over high cloud scenes.

Evaluating Twenty‐Year Trends in Earth's Energy Flows From Observations and Reanalyses

AbstractSatellite, reanalysis, and ocean in situ data are analyzed to evaluate regional, hemispheric and global mean trends in Earth's energy fluxes during the first 20 years of the twenty‐first century. Regional trends in net top‐of‐atmosphere (TOA) radiation from the Clouds and the Earth's Radiant Energy System (CERES), ECMWF Reanalysis 5 (ERA5), and a model similar to ERA5 with prescribed sea surface temperature (SST) and sea ice differ markedly, particularly over the Eastern Pacific Ocean, where CERES observes large positive trends. Hemispheric and global mean net TOA flux trends for the two reanalyses are smaller than CERES, and their climatological means are half those of CERES in the southern hemisphere (SH) and more than nine times larger in the northern hemisphere (NH). The regional trend pattern of the divergence of total atmospheric energy transport (TEDIV) over ocean determined using ERA5 analyzed fields is similar to that inferred from the difference between TOA and surface fluxes from ERA5 short‐term forecasts. There is also agreement in the trend pattern over ocean for surface fluxes inferred as a residual between CERES net TOA flux and ERA5 analysis TEDIV and surface fluxes obtained directly from ERA5 forecasts. Robust trends are observed over the Gulf Stream associated with enhanced surface‐to‐atmosphere transfer of heat. Within the ocean, larger trends in ocean heating rate are found in the NH than the SH after 2005, but the magnitude of the trend varies greatly among datasets.