Clouds And The Earth's Radiant Energy System Research Articles

Tracing the physical signatures among the calculated global clear-sky spectral shortwave radiative flux distribution

This study utilized the high-spectral resolution radiative transfer model (MODerate resolution atmospheric TRANsmission, MODTRAN6.0.2.5) to compute global clear-sky shortwave (SW) radiative flux and compared it with NASA’s Clouds and the Earth’s Radiant Energy System (CERES) Synoptic Radiative Fluxes and Clouds (SYN1deg) product. The comparison revealed that the global distributions of clear-sky downwelling SW fluxes at the surface from the M6.0 calculations and SYN1 results are similar, with annual means of 246.51 Wm-2 and 242.42 Wm-2, respectively. Analysis further showed that most of the M6.0 calculations are slightly higher from low to mid-latitudes, particularly in the Northern Hemisphere (NH), but lower in higher latitudes compared to SYN1 results. However, these differences mostly fall within the CERES estimated uncertainty (6 Wm-2) of monthly mean clear-sky downwelling SW flux at the surface. The sensitivity of clear-sky SW/μ0 fluxes to changes in Precipitable Water Vapor (PWV), represented by the clear-sky water vapor radiative kernel, is about -0.7 Wm-2/(kgm-2) over oceans for both M6.0 and CERES SYN1 products, except for SYN1 results over the Southern Hemisphere (SH) ocean. Additionally, the zonal means of land coverage and SW/VIS/NIR albedos from M6.0 calculations indicate that VIS albedos are highest in polar regions (>60°), followed by SW and NIR albedos, while NIR albedos become highest from low to mid-latitudes (<60°). Generally, SW/VIS/NIR albedos and their differences increase monotonically with increased land coverage from 60°S to 60°N. The consistent clear-sky water vapor radiative kernels derived from both products exceeded our expectations, suggesting their potential use to trace physical signatures in climate model calculations. It is recommended that these model-derived radiative kernels should be validated by the long-term global and regional surface observations in order to enhance confidence to implement these radiative kernels in climate models.

Roles of Earth’s Albedo Variations and Top-of-the-Atmosphere Energy Imbalance in Recent Warming: New Insights from Satellite and Surface Observations

Past studies have reported a decreasing planetary albedo and an increasing absorption of solar radiation by Earth since the early 1980s, and especially since 2000. This should have contributed to the observed surface warming. However, the magnitude of such solar contribution is presently unknown, and the question of whether or not an enhanced uptake of shortwave energy by the planet represents positive feedback to an initial warming induced by rising greenhouse-gas concentrations has not conclusively been answered. The IPCC 6th Assessment Report also did not properly assess this issue. Here, we quantify the effect of the observed albedo decrease on Earth’s Global Surface Air Temperature (GSAT) since 2000 using measurements by the Clouds and the Earth’s Radiant Energy System (CERES) project and a novel climate-sensitivity model derived from independent NASA planetary data by employing objective rules of calculus. Our analysis revealed that the observed decrease of planetary albedo along with reported variations of the Total Solar Irradiance (TSI) explain 100% of the global warming trend and 83% of the GSAT interannual variability as documented by six satellite- and ground-based monitoring systems over the past 24 years. Changes in Earth’s cloud albedo emerged as the dominant driver of GSAT, while TSI only played a marginal role. The new climate sensitivity model also helped us analyze the physical nature of the Earth’s Energy Imbalance (EEI) calculated as a difference between absorbed shortwave and outgoing longwave radiation at the top of the atmosphere. Observations and model calculations revealed that EEI results from a quasi-adiabatic attenuation of surface energy fluxes traveling through a field of decreasing air pressure with altitude. In other words, the adiabatic dissipation of thermal kinetic energy in ascending air parcels gives rise to an apparent EEI, which does not represent “heat trapping” by increasing atmospheric greenhouse gases as currently assumed. We provide numerical evidence that the observed EEI has been misinterpreted as a source of energy gain by the Earth system on multidecadal time scales.

Intensification mechanisms and moisture dynamics of super cyclonic storm ‘Amphan’ over the Bay of Bengal: Implications for aerosol re-distribution

The present research investigates the dynamics and underlying causes contributing to the exceptional intensity of Super Cyclonic Storm (SuCS) Amphan (16th to 21st May 2020) over the Bay of Bengal (BoB), as well as its impact on aerosol redistribution along the four cities of eastern coast and north-eastern India. Notably, the SuCS was formed during the first phase of the COVID-19 lockdown in India, giving it a unique aspect of study and analysis. Our analysis based on 30 years of climatology data from Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) reanalysis reveals ‘positive’ monthly anomalous winds (0.8 to 1.6 m/s) prevailed over the central BoB for May 2020. The present study further found the evolution of ‘barrier layer thickness’(BLT) leading up to landfall, noting a thickening trend from 8 to 3 days before landfall, contributing to maintaining warmer sea surface temperatures near the coast. Additionally, utilizing European Centre for Medium-Range Weather Forecasts (ECMWF), reanalysis version-5 (ERA-5) data, a mean positive sea surface temperature (SST) anomaly of 0.8 to 1 °C was observed ‘before’ cyclone period (10–15 May 2020) near the cyclogenesis point. A detailed examination of Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) vertical cross-section plots during the cyclone's intensification stage reveals the presence of high-altitude clouds composed primarily of ice crystals. Further, analysis also indicates that the cyclone transported Sea-salt PM2.5 aerosols from the ocean, dispersing them in the landfall region.The aerosol optical Depth (AOD) data obtained from the National Aeronautics and Space Administration's (NASA) ‘Clouds and the Earth's Radiant Energy System (CERES)’ mission and MERRA-2 were also analysed, revealing that the cyclone redistributed aerosols over the Bengal basin region (mainly over ‘Kolkata') and three other nearby cities along the track of the cyclone (i.e., Bhubaneswar (Odisha) Agartala (Tripura) and Shillong (Meghalaya) respectively).

Unfiltering of the EarthCARE Broadband Radiometer (BBR) observations: the BM-RAD product

Abstract. The methodology to determine the unfiltered solar and thermal radiances from the measured EarthCARE Broadband Radiometer (BBR) shortwave (SW) and total-wave (TW) filtered radiances is presented. Within the EarthCARE ground processing, the correction for the effect of the BBR spectral responses, the unfiltering, is performed by the so-called BM-RAD processor which produces the level-2 BM-RAD product. The BM-RAD product refers to unfiltered broadband radiances that are derived from the BBR and the Multi-Spectral Imager (MSI) instruments on board the forthcoming EarthCARE satellite. The method is based on theoretical regressions between filtered and unfiltered radiances, as is done for the Clouds and the Earth's Radiant Energy System (CERES) and the Geostationary Earth Radiation Budget (GERB) instruments. The regressions are derived from a large geophysical database of spectral radiance curves simulated using radiative transfer models. Based on the radiative transfer computations, the unfiltering error, i.e., the error introduced by the small spectral variations of the BBR instrument response, is expected to remain well below 0.5 % in the shortwave (SW) and 0.1 % in the longwave (LW), at 1 standard deviation. These excellent performances are permitted by the very simple optics used in the BBR instrument: a telescope with a single paraboloid mirror. End-to-end verification of the unfiltering algorithm has been performed by running the BM-RAD processor on modelled level-1 BBR radiances obtained for three EarthCARE orbits simulated by an integrated forecasting and data assimilation system. The resulting unfiltered radiances are eventually compared to the solar and thermal radiances derived by radiative transfer simulations over the three EarthCARE orbits. In addition, this end-to-end verification has provided further evidence on the high accuracy of the unfiltered radiance process, with accuracies better than 0.5 % for SW and better than 0.1 % for LW.

Investigation of Dust‐Induced Direct Radiative Forcing Over the Arabian Peninsula Based on High‐Resolution WRF‐Chem Simulations

AbstractThis study investigates the impact of dust on radiation over the Arabian Peninsula (AP) during the reported high, low, and normal dust seasons (March–August) of 2012, 2014, and 2015, respectively. Simulations were performed using the Weather Research and Forecasting model coupled to a Chemistry module (WRF‐Chem). The simulated seasonal horizontal and vertical dust concentrations, and their interannual distinctions, match well with those from two ground‐based AERONET observations, and measurements from MODIS and CALIOP satellites. The maximum dust concentrations over the dust‐source regions in the southern AP reach vertically upto 700 hPa during the high dust season, but only upto 900–950 hPa during the low/normal dust seasons. Stronger incoming low‐level winds along the southern Red Sea and those from Iraq bring in higher‐than‐normal dust during the high dust summers. We conducted a sensitivity experiment by switching‐off the dust module to assess the radiative perturbations due to dust. The results suggest that active dust‐module improved the fidelity of simulated radiation fluxes distributions at the surface and top of the atmosphere vis‐à‐vis Clouds and the Earth's Radiant Energy System (CERES) measurements. Dust results in a 26 Wm−2 short‐wave (SW) radiative forcing in the tropospheric‐column over the AP. The SW radiative forcing increases by another 6–8 Wm−2 during the high dust season due to the increased number of extreme dust days, which also amplifies atmospheric heating. During extreme dust days, the heating rate exhibits a dipolar structure, with cooling over the Iraq region and warming of 40%–60% over the southern‐AP.

Next-generation radiance unfiltering process for the Clouds and the Earth's Radiant Energy System instrument

Abstract. The filtered radiances measured by the Clouds and the Earth's Radiant Energy System (CERES) instruments are converted to shortwave (SW), longwave (LW), and window unfiltered radiances based on regressions developed from theoretical radiative transfer simulations to relate filtered and unfiltered radiances. This paper describes an update to the existing Edition 4 CERES unfiltering algorithm (Loeb et al., 2001), incorporating the most recent developments in radiative transfer modeling, ancillary input datasets, and increased computational and storage capabilities during the past 20 years. Simulations are performed with the updated Moderate Resolution Atmospheric Transmission (MODTRAN) 5.4 version. Over land and snow, the surface bidirectional reflectance distribution function (BRDF) is characterized by a kernel-based representation in the simulations, instead of the Lambertian surface used in the Edition 4 unfiltering process. Radiance unfiltering is explicitly separated into four seasonally dependent land surface groups based on the spectral radiation similarities of different surface types (defined by the International Geosphere-Biosphere Programme); over snow, it is separated into fresh snow, permanent snow, and sea ice. This differs from the Edition 4 unfiltering process where only one set of regressions was used for land and snow, respectively. The instantaneous unfiltering errors are estimated with independent test cases generated from radiative transfer simulations in which the “true” unfiltered radiances from radiative transfer simulations are compared with the unfiltered radiances calculated from the regressions. Overall, the relative errors are mostly within ±0.5 % for SW, within ±0.2 % for daytime LW, and within ±0.1 % for nighttime LW for both CERES Terra Flight Model 1 (FM1) and Aqua FM3 instruments. The unfiltered radiances are converted to fluxes and compared to CERES Edition 4 fluxes. The global mean instantaneous fluxes for Aqua FM3 are reduced by 0.34 to 0.45 W m−2 for SW and increased by 0.25 to 0.46 W m−2 for daytime LW; for Terra FM1, they are reduced by 0.24 to 0.34 W m−2 for SW and increased by 0.08 to 0.28 W m−2 for daytime LW. Nighttime LW flux differences are negligible for both instruments.

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.

Assessing the cloud radiative bias at Macquarie Island in the ACCESS-AM2 model

Abstract. As a long-standing problem in climate models, large positive shortwave radiation biases exist at the surface over the Southern Ocean, impacting the accurate simulation of sea surface temperature, atmospheric circulation, and precipitation. Underestimations of low-level cloud fraction and liquid water content are suggested to predominantly contribute to these radiation biases. Most model evaluations for radiation focus on summer and rely on satellite products, which have their own limitations. In this work, we use surface-based observations at Macquarie Island to provide the first long-term, seasonal evaluation of both downwelling surface shortwave and longwave radiation in the Australian Community Climate and Earth System Simulator Atmosphere-only Model version 2 (ACCESS-AM2) over the Southern Ocean. The capacity of the Clouds and the Earth’s Radiant Energy System (CERES) product to simulate radiation is also investigated. We utilize the novel lidar simulator, the Automatic Lidar and Ceilometer Framework (ALCF), and all-sky cloud camera observations of cloud fraction to investigate how radiation biases are influenced by cloud properties. Overall, we find an overestimation of +9.5±33.5 W m−2 for downwelling surface shortwave radiation fluxes and an underestimation of -2.3±13.5 W m−2 for downwelling surface longwave radiation in ACCESS-AM2 in all-sky conditions, with more pronounced shortwave biases of +25.0±48.0 W m−2 occurring in summer. CERES presents an overestimation of +8.0±18.0 W m−2 for the shortwave and an underestimation of -12.1±12.2 W m−2 for the longwave in all-sky conditions. For the cloud radiative effect (CRE) biases, there is an overestimation of +4.8±28.0 W m−2 in ACCESS-AM2 and an underestimation of -7.9±20.9 W m−2 in CERES. An overestimation of downwelling surface shortwave radiation is associated with an underestimated cloud fraction and low-level cloud occurrence. We suggest that modeled cloud phase is also having an impact on the radiation biases. Our results show that the ACCESS-AM2 model and CERES product require further development to reduce these radiation biases not just in shortwave and in all-sky conditions, but also in longwave and in clear-sky conditions.

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.

Interannual Variation in Top-of-Atmosphere Upward Shortwave Flux over the Arctic Related to Sea Ice, Snow Cover, and Land Cloud Cover in Spring and Summer

Abstract We investigated the interannual variations in the annual mean and seasonal cycle of upward shortwave radiation at the top of the atmosphere (TOA SW↑) over the Arctic using the Clouds and the Earth’s Radiant Energy System (CERES) observation data during 2001–20. The annual mean TOA SW↑ over the Arctic showed a decreasing trend from 2001 to 2012 (−2.5 W m−2 decade−1) and had a large interannual variability after 2012. The standard deviation of detrended TOA SW↑ increased from 0.4 W m−2 in 2001–12 to 1.1 W m−2 in 2012–20. Over land, TOA SW↑ variation was related to snow cover in May; snow cover, cloud fraction, and cloud optical depth (COD) in June; and cloud fraction and COD in July. Over ocean, TOA SW↑ variation in June and July was linked to sea ice cover. TOA SW↑ variation over ocean in June and July after 2012 was highly related to the North Atlantic Oscillation (NAO). This study suggests that changes in the large annual mean TOA SW↑ variability after 2012 are explained by the timing of land snow and sea ice melt in spring and summer and cloud variability over land in summer.

A Model for Estimating the Earth’s Outgoing Radiative Flux from A Moon-Based Radiometer

A Moon-based radiometer can provide continuous measurements for the Earth’s full-disk broadband irradiance, which is useful for studying the Earth’s Radiation Budget (ERB) at the height of the Top of the Atmosphere (TOA). The ERB describes how the Earth obtains solar energy and emits energy to space through the outgoing broadband Short-Wave (SW) and emitted thermal Long-Wave (LW) radiation. In this work, a model for estimating the Earth’s outgoing radiative flux from the measurements of a Moon-based radiometer is established. Using the model, the full-disk LW and SW outgoing radiative flux are gained by converting the unfiltered entrance pupil irradiances (EPIs) with the help of the anisotropic characteristics of the radiances. Based on the radiative transfer equation, the unfiltered EPI time series is used to validate the established model. By comparing the simulations for a Moon-based radiometer with the satellite-based data from the National Institute of Standards and Technology Advanced Radiometer (NISTAR) and the Clouds and the Earth’s Radiant Energy System (CERES) datasets, the simulations show that the daytime SW fluxes from the Moon-based measurements are expected to vary between 194 and 205 Wm−2; these simulations agree well with the CERES data. The simulations are about 5 to 20 Wm−2 smaller than the NISTAR data. For the simulated Moon-based LW fluxes, the range is 251~287 Wm−2. The Moon-based and NISTAR fluxes are consistently 5~15 Wm−2 greater than CERES LW fluxes, and both of them also show larger diurnal variations compared with the CERES fluxes. The correlation coefficients of SW fluxes for Moon-based data and NISTAR data are 0.97, 0.63, and 0.53 for the months of July, August, and September, respectively. Compared with the SW flux, the correlation of LW fluxes is more stable for the same period and the correlation coefficients are 0.87, 0.69, and 0.61 for July to September 2017.

An ENSO Prediction Model Based on Backtracking Multiple Initial Values: Ordinary Differential Equations–Memory Kernel Function

This article presents a new prediction model, the ordinary differential equations–memory kernel function (ODE–MKF), constructed from multiple backtracking initial values (MBIV). The model is similar to a simplified numerical model after spatial dimension reduction and has both nonlinear characteristics and the low-cost advantage of a time series model. The ODE–MKF focuses on utilizing more temporal information and includes machine learning to solve complex mathematical inverse problems to establish a predictive model. This study first validates the feasibility of the ODE–MKF via experiments using the Lorenz system. The results demonstrate that the ODE–MKF prediction model could describe the nonlinear characteristics of complex systems and exhibited ideal predictive robustness. The prediction of the El Niño-Southern Oscillation (ENSO) index further demonstrates its effectiveness, as it achieved 24-month lead predictions and effectively improved nonlinear problems. Furthermore, the reliability of the model was also tested, and approximately 18 months of prediction were achieved, which was verified with the Clouds and the Earth’s Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) radiation fluxes. The short-term memory index Southern Oscillation (SO) was further used to examine the applicability of ODE–MKF. A six-month lead prediction of the SO trend was achieved, indicating that the predictability of complex systems is related to their inherent memory scales.

Evaluation of Five Global Top-of-Atmosphere Outgoing Longwave Radiation Products

Five global monthly top-of-atmosphere (TOA) outgoing longwave radiation (OLR) products are evaluated in this study, including the products derived from the High-Resolution Infrared Radiation Sounder (HIRS), Clouds and the Earth’s Radiant Energy System (CERES), Advanced Very High Resolution Radiometer (AVHRR), the CM SAF cLoud, Albedo and surface RAdiation dataset from AVHRR data (CLARA), and the Global Energy and Water Cycle EXchanges (GEWEX) project. Results show that overall there is good consistency among these five products. Larger differences are found between GEWEX and CERES (HIRS) after (before) 2000 (RMSE ~ 5 W/m2), particularly in the tropical regions. In terms of global mean values, GEWEX shows large differences with the other products from the year 1992 to 2002, and CLARA shows large differences from the year 1979 to 1981, which are more obvious in the global ocean values. Large discrepancies among these products exist at low latitudinal bands, particularly before the year 2000. Australia and Asia (mid–low latitude part) are two typical regions in which larger differences are found.

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.

Long-term trends in albedo as seen from a lunar observatory

The Earth’s albedo is the fraction of short-wave solar radiation that is reflected back to space, and is key to understand the observed trends in climate change. Although there have been many observational approaches to estimate the Earth’s albedo, different methods have their own drawbacks, for example, artificial satellites have a finite life time, and are operating in a hard environment that could quickly degrade the detectors. The Moon, the only natural satellite of Earth, offers an additional platform for monitoring the Earth’s albedo. In this paper, we calculate the global TOA (top-of-atmosphere) albedo that would be observed by a theoretical observatory on the Moon, and compare it with long-term trends derived from CERES (Clouds and the Earth’s Radiant Energy System, 2000–2020) and earthshine data (1999–2017). We find that the global hourly mean albedo observed in the direction of the Moon is more variable because of the orbital movement of the Moon. However, the regional and long-term global mean albedo anomalies that would be observed by CERES and has a good general agreement by a lunar observer over 20 years the data spans. Similarly, earthshine data show a steady decline during two decades of about 0.7 W/m2 which is in line with the decline of 0.5 W/m2 that would be observed from the Moon. Besides, to compare the regional changes of albedo with CERES, the spatially-resolved decadal anomalies in the albedo are also calculated for analysis. We find that a lunar-based observatory would be capable of detecting similar changes seen from CERES. Thus, it is a a practical option to monitor the long-term trends in albedo from the point of view of capturing the variability in the TOA fluxes of the Earth’s atmosphere.

Generation of global 1-km daily top-of-atmosphere outgoing longwave radiation product from 2000 to 2021 using machine learning

ABSTRACT Top-of-atmosphere (TOA) outgoing longwave radiation (OLR), a key component of the Earth’s energy budget, serves as a diagnostic of the Earth’s climate system response to incoming solar radiation. However, existing products are typically estimated using broadband sensors with coarse spatial resolutions. This paper presents a machine learning method to estimate TOA OLR by directly linking Moderate Resolution Imaging Spectroradiometer (MODIS) TOA radiances with TOA OLR determined by Clouds and the Earth’s Radiant Energy System (CERES) and other information, such as the viewing geometry, land surface temperature and cloud top temperature determined by Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2). Models are built separately under clear- and cloudy-sky conditions using a gradient boosting regression tree. Independent test results show that the root mean square errors (RMSEs) of the clear-sky and cloudy-sky models for estimating instantaneous values are 4.1 and 7.8 W/m2, respectively. Real-time conversion ratios derived from CERES daily and hourly OLR data are used to convert the instantaneous MODIS OLR to daily results. Inter-comparisons of the daily results show that the RMSE of the estimated MODIS OLR is 8.9 W/m2 in East Asia. The developed high resolution dataset will be beneficial in analyzing the regional energy budget.

Assessing the Performance of the South American Land Data Assimilation System Version 2 (SALDAS-2) Energy Balance across Diverse Biomes

Understanding the exchange of energy between the surface and the atmosphere is important in view of the climate scenario. However, it becomes a challenging task due to a sparse network of observations. This study aims to improve the energy balance estimates for the Amazon, Cerrado, and Pampa biomes located in South America using the radiation and precipitation forcing obtained from the Clouds and the Earth’s Radiant Energy System (CERES) and the precipitation CPTEC/MERGE datasets. We employed three surface models—Noah-MP, Community Land Model (CLSM), and Integrated Biosphere Simulator (IBIS)—and conducted modeling experiments, termed South America Land Data Assimilation System (SALDAS-2). The results showed that SALDAS-2 radiation estimates had the smallest errors. Moreover, SALDAS-2 precipitation estimates were better than the Global Land Data Assimilation System (GLDAS) in the Cerrado (MBE = −0.16) and Pampa (MBE = −0.19). Noah-MP presented improvements compared with CLSM and IBIS in 100% of towers located in the Amazon. CLSM tends to overestimate the latent heat flux and underestimate the sensible heat flux in the Amazon. Noah-MP and Ensemble outperformed GLDAS in terms latent and sensible heat fluxes. The potential of SALDAS-2 should be emphasized to provide more accurate estimates of surface energy balance.

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.

An observation-based method to assess tropical stratocumulus and shallow cumulus clouds and feedbacks in CMIP6 and CMIP5 models

In the Earth system models (ESMs) participating in the Coupled Models Intercomparison Project phase 6 (CMIP6), the tropical low-cloud feedback is 50% more positive than its predecessors (CMIP5) and continues to dominate the spread in simulated climate sensitivity. In the context of recent studies reporting larger feedbacks for stratocumulus (Sc) than shallow cumulus (Cu) clouds, it appears crucial to faithfully represent the geographical extent of each cloud type to simulate realistic low-cloud feedbacks. Here we use a novel observation-based method to distinguish Sc and Cu clouds together with satellite data from Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) and Clouds and the Earth’s Radiant Energy System (CERES) to evaluate Sc and Cu cloud fractions, cloud radiative effects and cloud feedbacks in the two latest generations of CMIP ESMs. Overall, the CMIP6 models perform better than the CMIP5 models in most aspects considered here, indicating progress. Yet the ensemble mean continues to underestimate the marine tropical low-cloud fraction, mostly attributable to Sc. Decomposition of the bias reveals that the Sc-regime cloud fraction is better represented in CMIP6, although Sc regimes occur too infrequently—even less frequently than in CMIP5. Building on our Sc and Cu discrimination method, we demonstrate that CMIP6 models also simulate more realistic low-cloud feedbacks than CMIP5 models, especially the Sc component. Finally, our results suggest that part of the CMIP6 low-cloud feedback increase can be traced back to greater cloud fraction in Sc-dominated regions.