Clouds And 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.

VIIRS Edition 1 Cloud Properties for CERES, Part 2: Evaluation with CALIPSO

The decades-long Clouds and Earth’s Radiant Energy System (CERES) Project includes both cloud and radiation measurements from instruments on the Aqua, Terra, and Suomi National Polar-orbiting Partnership (SNPP) satellites. To build a reliable long-term climate data record, it is important to determine the accuracies of the parameters retrieved from the sensors on each satellite. Cloud amount, phase, and top height derived from radiances taken by the Visible Infrared Imaging Radiometer Suite (VIIRS) on the SNPP are evaluated relative to the same quantities determined from measurements by the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) on the Cloud Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) spacecraft. The accuracies of the VIIRS cloud fractions are found to be as good as or better than those for the CERES amounts determined from Aqua MODerate-resolution Imaging Spectroradiometer (MODIS) data and for cloud fractions estimated by two other operational algorithms. Sensitivities of cloud fraction bias to CALIOP resolution, matching time window, and viewing zenith angle are examined. VIIRS cloud phase biases are slightly greater than their CERES MODIS counterparts. A majority of cloud phase errors are due to multilayer clouds during the daytime and supercooled liquid water clouds at night. CERES VIIRS cloud-top height biases are similar to those from CERES MODIS, except for ice clouds, which are smaller than those from CERES MODIS. CERES VIIRS cloud phase and top height uncertainties overall are very similar to or better than several operational algorithms, but fail to match the accuracies of experimental machine learning techniques. The greatest errors occur for multilayered clouds and clouds with phase misclassification. Cloud top heights can be improved by relaxing tropopause constraints, improving lapse-rate to model temperature profiles, and accounting for multilayer clouds. Other suggestions for improving the retrievals are also discussed.

High-resolution outgoing long wave radiation data (2014–2020) of INSAT-3D Imager and its comparison with Clouds and Earth’s Radiant Energy System (CERES) data

As a proxy of convection INSAT-3D satellite-derived product Outgoing Long Wave Radiation (OLR) data is available in both high temporal and spatial ranges over 40°N–40°S & 35°E–135°E. Daily gridded data set of 7 years of data (2014–2020) has been generated at 10 km × 10 km resolution and the same is compared with Clouds and Earth’s Radiant Energy System (CERES) instrument data taken from CERES as reference. Almost all the INSAT-3D data set generated is Global Space-based Inter-Calibration System (GSICS) corrected. The spatiotemporal consistency of the data set was statistically analyzed and found to be reasonably good agreement having a bias of ∼±5–6 W/m2 over above said domain.This inter-comparison is essential to get confidence in the data sets and release it further in the public domain for any scientific study. Again, this data set will be very useful in diagnosing the variations of convection at different scales (daily, weekly, monthly, annual, seasonal, intra-seasonal, etc.) & an important repository of Daily Climate Data Records (DCDR) for future studies. The specified domain of the present study is affected throughout the year with variable (weak, moderate, intense, or severe) spatiotemporal Inter-Tropical Convergence Zone (ITCZ) convection streams due to different types of weather activities (winter, pre-monsoon, monsoon, and post-monsoon) throughout the year. To visualize the importance of this high-resolution OLR data set a case study of Cyclone Amphan and Vayu is presented. The extremely severe intense convection (OLR departure −112 W/m2 with INSAT-3D new data set whereas −104 W/m2 in CERES data) was observed in both the data sets on 18th May-2020 at 13.7–16°N & 86.2–86.8°E during the super cyclonic stage of Amphan. A similar type of variation in the OLR has been noticed for Vayu Cyclone (OLR departure −94 W/m2 with INSAT-3D new data set whereas −86 W/m2 in CERES data). This information is very useful in impact-based forecasting and further future actions for disaster managers/decision-makers. The localized convective features during cyclone activity over the Indian Ocean region both in the Arabian Sea and the Bay of Bengal are well captured with this new data set and the difference in OLR of INSAT -3D and CERES -9 W/m2 and -12 W/m2 respectively.

Observations of aerosol optical properties during tropical forest fires in Indonesia

This study aims to determine the direct effect of aerosol on one element of the radiation energy balance that includes short-wavelength (SW). The AOD data from the Moderate Resolution Imaging Spectroradiometer, MODIS level 3 instrument, Terra satellite, and the Clouds and Earth's Radiant Energy System (CERES) radiation component instrument were obtained to observe the spatial and temporal variations in the aerosol optical depth (AOD) at 550 nm and their relationship to the radiation balance element. These data were analyzed from January 2000 to April 2019 in Indonesia. The results of the analysis show that the average daily temporal fluctuation of AOD showed a respectively high value, namely 1.03 on 23 October 2015 and 0.68 on 9 October 2006. This very high AOD value was closely related to forest fires in Sumatra and Kalimantan. The immediate reaction in Kalimantan was when there was an increase in the accumulation of aerosol particles in the atmosphere in urban areas in this region, namely Palangka Raya, Pontianak and Banjarmasin. From the results of processing using the scatter plot statistical method, the relationship between AOD and SW obtained a negative correlation value (r) which was quite high for Pontianak with r = 0.62.

The Effect of Spatial Resolution and Temporal Sampling Schemes on the Measurement Error for a Moon-Based Earth Radiation Observatory

The Moon-Based Earth Radiation Observatory (MERO) is a new platform, which is expected to advance current Earth radiation budget (ERB) research with better observations. For the instrument design of a MERO system, ascertaining the spatial resolution and sampling scheme is important. However, current knowledge about this is still limited. Here we proposed a simulation method for the MERO-measured Earth top of atmosphere (TOA) outgoing shortwave radiation (OSR) and outgoing longwave radiation (OLR) fluxes and constructed the “true” Earth TOA OSR and OLR fluxes based on the Clouds and Earth’s Radiant Energy System (CERES) data. Then we used them to reveal the effects of spatial resolution and temporal scheme (sampling interval and the temporal sampling sequence) on the measurement error of a MERO. Our results indicate that the spatial sampling error in the unit of percentage reduces linearly as the spatial resolution varies from 1000 km to 100 km; the rate is 2.5%/100 km for the Earth TOA OSR flux, which is higher than that (1%/100 km) of the TOA OLR flux. Besides, this rate becomes larger when the spatial resolution is finer than 40 km. It is also demonstrated that a sampling temporal sequence of starting time of 64 min with a sampling interval of 90 min is the optimal sampling scheme that results in the least temporal sampling error for the MERO system with a 40 km spatial resolution, note that this conclusion depends on the temporal resolution and quality of the data used to construct the “true” Earth TOA OSR and OLR fluxes. The proposed method and derived results in this study could facilitate the ascertainment of the optimal spatial resolution and sampling scheme of a MERO system under certain manufacturing budget and measurement error limit.

Validation of Near-Real-Time NOAA-20 CrIS Outgoing Longwave Radiation with Multi-Satellite Datasets on Broad Timescales

The Outgoing Longwave Radiation (OLR) package was first developed as a stand-alone application, and then integrated into the National Oceanic and Atmospheric Administration (NOAA) Unique Combined Atmospheric Processing System (NUCAPS) hyperspectral sounding retrieval system. An objective of this package is to provide near-real-time OLR products derived from the Cross Track Infrared Sounder (CrIS) onboard the Joint Polar Satellite System (JPSS) satellites. It was initially developed and validated with CrIS onboard the Suomi National Polar-orbiting Partnership (SNPP) satellite, and has been expanded to JPSS-1 (renamed NOAA-20 after launch) datasets that are currently available to the public. In this paper, we provide the results of detailed validation tests with NOAA-20 CrIS for large and wide representative conditions at a global scale. In our validation tests, the observations from Clouds and Earth’s Radiant Energy System (CERES) on Aqua were treated as the absolute reference or “truth”, and those from SNPP CrIS OLR were used as the transfer standard. The tests were performed on a 1°×1° global spatial grid over daily, monthly, and yearly timescales. We find that the CrIS OLR products from NOAA-20 agree exceptionally well with those from Aqua CERES and SNPP CrIS OLR products in all conditions: the daily bias is within ±0.6 Wm−2, and the standard deviation (STD) ranges from 4.88 to 9.1 Wm−2. The bias and the STD of OLR monthly mean are better, within 0.3 and 2.0 Wm−2, respectively. These findings demonstrate the consistency between NOAA-20 and SNPP CrIS OLR up to annual scales, and the robustness of NUCAPS CrIS OLR products.

Reduction of surface radiative forcing observed from remote sensing data during global COVID-19 lockdown

The calamity of the COVID-19 pandemic during the early half of 2020 not only caused a huge physical and economic loss but altered the social behavior of the whole world. The social and economic stagnation imposed in many countries and served as a major cause of perturbation in atmospheric composition. This paper utilized the relation between atmospheric composition and surface radiation and analyzed the impact of global COVID-19 lockdown on land surface solar and thermal radiation. Top of atmosphere (TOA) and surface radiation are obtained from the Clouds and Earth's Radiant Energy System (CERES) and European Reanalysis product (ERA5) reanalysis product. Aerosol Optical Depth (AOD) is obtained from Moderate Resolution Imaging Spectroradiometer (MODIS) while Nitrogen dioxide (NO2), and sulfur dioxide (SO2) are obtained from Ozone Monitoring Instrument (OMI). Observations of all mentioned parameters are studied for the global lockdown period of 2020 (from January to July) and compared with the corresponding months of the previous four years (2016–19) observations. Regarding surface radiation, April 2020 is the most affected month during the pandemic in which 0.2% increased net solar radiation (NSR), while 3.45% and 4.8% decreased net thermal radiation (NTR) and net radiation (NR) respectively was observed. Average radiative forcing during March–May 2020 was observed as 1.09 Wm−2, −2.19 Wm−2 and −1.09 Wm−2 for NSR, NTR and NR, respectively. AOD was reduced by 0.2% in May 2020 while NO2 and SO2 were reduced by 5.4% and 8.8%, respectively, in April 2020. It was observed that NO2 kept on reducing since January 2020 while SO2 kept on reducing since February 2020 which were the pre-lockdown months. These results suggest that a more sophisticated analysis is needed to explain the atmosphere-radiation relation.

Subdiurnal to Interannual Frequency Analysis of Observed and Modeled Reflected Shortwave Radiation From Earth

AbstractEstimates of global top‐of‐atmosphere radiation on monthly, seasonal, annual, and longer time‐scales require estimates of the diurnal variability in both insolation and surface and atmospheric reflection. We compare Earth Polychromatic Imaging Camera (EPIC) and National Institute of Standards and Technology Advanced Radiometer (NISTAR) observations from the Deep Space Climate Observatory (DSCOVR) satellite with Clouds and Earth’s Radiant Energy System (CERES) hourly synoptic fluxes, which are diurnally filled through geostationary observations, and find that their power spectral density functions substantially agree, showing strong relative power at subdiurnal, diurnal, seasonal, and annual time‐scales, and power growing from diurnal to seasonal time‐scales. Frequency analysis of fluxes from several coupled model intercomparison project 5 model (CMIP5) and CMIP6 models shows that they distribute too much power over periods greater than 1 day but less than one year, indicating that a closer look is needed into how models achieve longer‐term stability in reflected shortwave radiation. Model developers can consider using these datasets for time‐varying energetic constraints, since tuning parameter choices will impact modeled planetary shortwave radiation across timescales ranging from subdiurnal to decadal.

Does ERA5 outperform satellite products in estimating atmospheric downward longwave radiation at the surface?

Atmospheric downward longwave radiation (DLR) is a key component of the surface energy budget in the Earth system. Satellite retrievals and atmospheric reanalysis estimates are the two typical approaches to obtaining a spatio-temporally continuous DLR product. In this study, we evaluated the DLR product from the latest ERA5 atmospheric reanalysis and the well-known Clouds and Earth's Radiant Energy System (CERES) satellite retrievals, against high-quality observations collected at 46 Baseline Surface Radiation Network (BSRN) stations over land surfaces and at 9 Global Tropical Moored Buoy Array (GTMBA) buoy stations. The accuracy of the ERA5 DLR product over land was found to be higher on average than that of CERES at hourly to monthly time scales. Conversely, ERA5 performed slightly worse than CERES-SYN when estimating DLR over the ocean surface. This is the first time that atmospheric reanalysis has performed better than satellite retrievals in estimating DLR over the land surface, demonstrating the potentially extensive application prospects for ERA5 as well as setting new challenges for quantitative remote sensing research.

Assessment of regional aerosol radiative effects under the SWAAMI campaign – Part 2: Clear-sky direct shortwave radiative forcing using multi-year assimilated data over the Indian subcontinent

Abstract. Clear-sky, direct shortwave aerosol radiative forcing (ARF) has been estimated over the Indian region, for the first time employing multi-year (2009–2013) gridded, assimilated aerosol products, as an important part of the South West Asian Aerosol Monsoon Interactions (SWAAMI) which is a joint Indo-UK research field campaign focused at understanding the variabilities in atmospheric aerosols and their interactions with the Indian summer monsoon. The aerosol datasets have been constructed following statistical assimilation of concurrent data from a dense network of ground-based observatories and multi-satellite products, as described in Part 1 of this two-part paper. The ARF, thus estimated, is assessed for its superiority or otherwise over other ARF estimates based on satellite-retrieved aerosol products, over the Indian region, by comparing the radiative fluxes (upward) at the top of the atmosphere (TOA) estimated using assimilated and satellite products with spatiotemporally matched radiative flux values provided by CERES (Clouds and Earth's Radiant Energy System) single-scan footprint (SSF) product. This clearly demonstrated improved accuracy of the forcing estimates using the assimilated vis-à-vis satellite-based aerosol datasets at regional, subregional and seasonal scales. The regional distribution of diurnally averaged ARF estimates has revealed (a) significant differences from similar estimates made using currently available satellite data, not only in terms of magnitude but also the sign of TOA forcing; (b) the largest magnitudes of surface cooling and atmospheric warming over the Indo-Gangetic Plain (IGP) and arid regions from north-western India; and (c) negative TOA forcing over most parts of the Indian region, except for three subregions – the IGP, north-western India and eastern parts of peninsular India where the TOA forcing changes to positive during pre-monsoon season. Aerosol-induced atmospheric warming rates, estimated using the assimilated data, demonstrate substantial spatial heterogeneities (∼0.2 to 2.0 K d−1) over the study domain with the IGP demonstrating relatively stronger atmospheric heating rates (∼0.6 to 2.0 K d−1). There exists a strong seasonality as well, with atmospheric warming being highest during pre-monsoon and lowest during winter seasons. It is to be noted that the present ARF estimates demonstrate substantially smaller uncertainties than their satellite counterparts, which is a natural consequence of reduced uncertainties in assimilated vis-à-vis satellite aerosol properties. The results demonstrate the potential application of the assimilated datasets and ARF estimates for improving accuracies of climate impact assessments at regional and subregional scales.

Invariability of Arctic Top‐of‐Atmosphere Radiative Response to Surface Temperature Changes

AbstractRecent studies have used satellite data to estimate the response of top‐of‐atmosphere (TOA) radiative fluxes to surface temperature changes in the Arctic. The satellite‐observed radiative response is indicative of Arctic climate sensitivity that determines future Arctic warming. However, it remains ambiguous whether the satellite‐observed radiative response is invariable because the time period covered by satellite data reflects a rapidly changing transient Arctic climate state with considerable sea ice loss. Using NASA's Clouds and Earth's Radiant Energy System (CERES) observations from 2000 to 2018, this study evaluates the invariability of the radiative response by comparing the radiative response of the high sea ice concentration (SIC) period to that of the low SIC period. The results show that the net radiative response remains approximately unchanged regardless of the SIC (−0.19 ± 0.44 and 0.15 ± 0.16 W m−2 K−1 for high and low SIC periods, respectively). In addition, seven of the 11 models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) demonstrated that the modeled radiative responses are stable. The ERA‐interim reanalysis estimates show that regionally confined changes in individual radiative feedbacks such as albedo, lapse rate, water vapor, and clouds do not vary considerably. Consequently, we infer that the radiative response in the Arctic may remain stable even under rapid Arctic climate change. Hence, the Arctic climate sensitivity can be quantified with present satellite observations.

Changes in cloud amount over the Tibetan Plateau and impacts of large-scale circulation

Using the Clouds and Earth's Radiant Energy System (CERES) Edition 4 dataset, characteristics and variations of cloud amounts over the Tibetan Plateau (Tibet) during 2001–2019 was analyzed. Our results reveal that the mid–high cloud cover (MHCC) constitutes the major proportion and shows similar seasonal variations and annual cycle to the total cloud cover (TCC). The high cloud cover (HCC) has the greatest seasonal variation, whereas the mid–low cloud cover (MLCC) has the least variation. TCC, MHCC, and HCC exhibit the largest values in summer. The summer TCC, MHCC and MLCC exhibited decreasing trends and MHCC is more significant. The summer HCC shows increasing trend. Clouds at different heights show different correlations with skin temperature, and decreased TCC likely influences recent warming over the Tibet. The increased skin temperature is mainly adjusted by the decreased cloud amount especially MHCC. Cloud amounts are highly responsible for the precipitation, and the summer precipitation over the Tibet is mainly influenced by HCC, followed by MHCC. The decreasing TCC is related to two Rossby wave trains over Eurasia, corresponding to the Eurasian teleconnection pattern and Silk Road pattern. They induce an anomalous anti-cyclone in north Tibet and restrain ascending motions. Meanwhile, the South Asia High weakens and further enhances the sinking movements.

Trutinor: A Conceptual Study for a Next-Generation Earth Radiant Energy Instrument

Uninterrupted and overlapping satellite instrument measurements of Earth’s radiation budget from space are required to sufficiently monitor the planet’s changing climate, detect trends in key climate variables, constrain climate models, and quantify climate feedbacks. The Clouds and Earth’s Radiant Energy System (CERES) instruments are currently making these vital measurements for the scientific community and society, but with modern technologies, there are more efficient and cost-effective alternatives to the CERES implementation. We present a compact radiometer concept, Trutinor (meaning “balance” in Latin), with two broadband channels, shortwave (0.2–3 μm) and longwave (5–50 μm), capable of continuing the CERES record by flying in formation with an existing imager on another satellite platform. The instrument uses a three-mirror off-axis anastigmat telescope as the front optics to image these broadband radiances onto a microbolometer array coated with gold black, providing the required performance across the full spectral range. Each pixel of the sensor has a field of view of 0.6°, which was chosen so the shortwave band can be efficiently calibrated using the Moon as an on-orbit light source with the same angular extent, thereby reducing mass and improving measurement accuracy, towards the goal of a gap-tolerant observing system. The longwave band will utilize compact blackbodies with phase-change cells for an absolute calibration reference, establishing a clear path for SI-traceability. Trutinor’s instrument breadboard has been designed and is currently being built and tested.

Aerosols enhance cloud lifetime and brightness along the stratus-to-cumulus transition

Anthropogenic aerosols are hypothesized to enhance planetary albedo and offset some of the warming due to the buildup of greenhouse gases in Earth's atmosphere. Aerosols can enhance the coverage, reflectance, and lifetime of warm low-level clouds. However, the relationship between cloud lifetime and aerosol concentration has been challenging to measure from polar orbiting satellites. We estimate two timescales relating to the formation and persistence of low-level clouds over [Formula: see text] spatial domains using multiple years of geostationary satellite observations provided by the Clouds and Earth's Radiant Energy System (CERES) Synoptic (SYN) product. Lagrangian trajectories spanning several days along the classic stratus-to-cumulus transition zone are stratified by aerosol optical depth and meteorology. Clouds forming in relatively polluted trajectories tend to have lighter precipitation rates, longer average lifetime, and higher cloud albedo and cloud fraction compared with unpolluted trajectories. While liquid water path differences are found to be negligible, we find direct evidence of increased planetary albedo primarily through increased drop concentration ([Formula: see text]) and cloud fraction, with the caveat that the aerosol influence on cloud fraction is positive only for stable atmospheric conditions. While the increase in cloud fraction can be large typically in the beginning of trajectories, the Twomey effect accounts for the bulk (roughly 3/4) of the total aerosol indirect radiative forcing estimate.

The Climate Monitoring SAF Outgoing Longwave Radiation from AVHRR

Data from the Advanced Very High Resolution Radiometer (AVHRR) have been used to create several long-duration data records of geophysical variables describing the atmosphere and land and water surfaces. In the Climate Monitoring Satellite Application Facility (CM SAF) project, AVHRR data are used to derive the Cloud, Albedo, and Radiation (CLARA) climate data records of radiation components (i.a., surface albedo) and cloud properties (i.a., cloud cover). This work describes the methodology implemented for the additional estimation of the Outgoing Longwave Radiation (OLR), an important Earth radiation budget component, that is consistent with the other CLARA variables. A first step is the estimation of the instantaneous OLR from the AVHRR observations. This is done by regressions on a large database of collocated observations between AVHRR Channel 4 (10.8 µm) and 5 (12 µm) and the OLR from the Clouds and Earth’s Radiant Energy System (CERES) instruments. We investigate the applicability of this method to the first generation of AVHRR instrument (AVHRR/1) for which no Channel 5 observation is available. A second step concerns the estimation of daily and monthly OLR from the instantaneous AVHRR overpasses. This step is especially important given the changes in the local time of the observations due to the orbital drift of the NOAA satellites. We investigate the use of OLR in the ERA5 reanalysis to estimate the diurnal variation. The developed approach proves to be valuable to model the diurnal change in OLR due to day/night time warming/cooling over clear land. Finally, the resulting monthly mean AVHRR OLR product is intercompared with the CERES monthly mean product. For a typical configuration with one morning and one afternoon AVHRR observation, the Root Mean Square (RMS) difference with CERES monthly mean OLR is about 2 Wm−2 at 1° × 1° resolution. We quantify the degradation of the OLR product when only one AVHRR instrument is available (as is the case for some periods in the 1980s) and also the improvement when more instruments are available (e.g., using METOP-A, NOAA-15, NOAA-18, and NOAA-19 in 2012). The degradation of the OLR product from AVHRR/1 instruments is also quantified, which is done by “masking” the Channel 5 observations.

Estimation of Surface Downward Shortwave Radiation over China from Himawari-8 AHI Data Based on Random Forest

Downward shortwave radiation (RS) drives many processes related to atmosphere–surface interactions and has great influence on the earth’s climate system. However, ground-measured RS is still insufficient to represent the land surface, so it is still critical to generate high accuracy and spatially continuous RS data. This study tries to apply the random forest (RF) method to estimate the RS from the Himawari-8 Advanced Himawari Imager (AHI) data from February to May 2016 with a two-km spatial resolution and a one-day temporal resolution. The ground-measured RS at 86 stations of the Climate Data Center of the Chinese Meteorological Administration (CDC/CMA) are collected to evaluate the estimated RS data from the RF method. The evaluation results indicate that the RF method is capable of estimating the RS well at both the daily and monthly time scales. For the daily time scale, the evaluation results based on validation data show an overall R value of 0.92, a root mean square error (RMSE) value of 35.38 (18.40%) Wm−2, and a mean bias error (MBE) value of 0.01 (0.01%) Wm−2. For the estimated monthly RS, the overall R was 0.99, the RMSE was 7.74 (4.09%) Wm−2, and the MBE was 0.03 (0.02%) Wm−2 at the selected stations. The comparison between the estimated RS data over China and the Clouds and Earth’s Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) RS dataset was also conducted in this study. The comparison results indicate that the RS estimates from the RF method have comparable accuracy with the CERES-EBAF RS data over China but provide higher spatial and temporal resolution.

Global Cloud Detection for CERES Edition 4 Using Terra and Aqua MODIS Data

The Clouds and Earth’s Radiant Energy System (CERES) has been monitoring clouds and radiation since 2000 using algorithms developed before 2002 for CERES Edition 2 (Ed2) products. To improve cloud amount accuracy, CERES Edition 4 (Ed4) applies revised algorithms and input data to Terra and Aqua MODerate-resolution Imaging Spectroradiometer (MODIS) radiances. The Ed4 cloud mask uses 5–7 additional channels, new models for clear-sky ocean and snow/ice-surface radiances, and revised Terra MODIS calibrations. Mean Ed4 daytime and nighttime cloud amounts exceed their Ed2 counterparts by 0.035 and 0.068. Excellent consistency between average Aqua and Terra cloud fraction is found over nonpolar regions. Differences over polar regions are likely due to unresolved calibration discrepancies. Relative to Ed2, Ed4 cloud amounts agree better with those from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). CALIPSO comparisons indicate that Ed4 cloud amounts are more than or as accurate as other available cloud mask systems. The Ed4 mask correctly identifies cloudy or clear areas 90%–96% of the time during daytime over nonpolar areas depending on the CALIPSO–MODIS averaging criteria. At night, the range is 88%–95%. Accuracy decreases over land. The polar day and night accuracy ranges are 90%–91% and 80%–81%, respectively. The mean Ed4 cloud fractions slightly exceed the average for seven other imager cloud masks. Remaining biases and uncertainties are mainly attributed to errors in Ed4 predicted clear-sky radiances. The resulting cloud fractions should help CERES produce a more accurate radiation budget and serve as part of a cloud property climate data record.

Aerosol and cloud radiative forcing over various hot spot regions in India

Twelve years of NASA CERES (Clouds and Earth’s Radiant Energy System) data have been used to examine the spatio-temporal variability of aerosol– and cloud– induced shortwave radiative forcing over selected hot spot regions in India. Four regions (northern semiarid – R1; monsoon trough – R2; densely populated urban – R3; and southern peninsula – R4) are selected with different surface characteristics and notable difference in meteorological and geographical features. The analysis shows that three out of the four regions (viz. R1, R2, and R3) experience high aerosol loading and forcing in the monsoon season followed by moderate forcing in pre-monsoon season. While all the seasons except the post-monsoon period show a positive linear relation between cloud optical depth and aerosol optical depth for all the regions, the post-monsoon season shows a negative relation. However, the relation between aerosol forcing and cloud forcing shows adequate non-linearity owing to the numerous factors that control cloud radiative effect. The estimated aerosol induced heating rate shows exponential decrease with height, but with high variability during each season. Irrespective of any region, the maximum heating rate is observed in the pre-monsoon season (2.86 ± 0.78, 2.49 ± 0.78, 1.89 ± 0.57, and 0.88 ± 0.28 K/day for R1, R2, R3, and R4, respectively). Plausible reasons for the variation in the above parameters are discussed. The results suggest that increased anthropogenic activities affect the thermodynamics and hence the dynamics through retention and exchange of heat, and it could affect the precipitation pattern adversely.

A statistical and process-oriented evaluation of cloud radiative effects in high-resolution global models

Abstract. This study evaluates the impact of atmospheric horizontal resolution on the representation of cloud radiative effects (CREs) in an ensemble of global climate model simulations following the protocols of the High Resolution Model Intercomparison Project (HighResMIP). We compare results from four European modelling centres, each of which provides data from “standard”- and “high”-resolution model configurations. Simulated radiative fluxes are compared with observation-based estimates derived from the Clouds and Earth's Radiant Energy System (CERES) dataset. Model CRE biases are evaluated using both conventional statistics (e.g. time and spatial averages) and after conditioning on the phase of two modes of internal climate variability, namely the El Niño–Southern Oscillation (ENSO) and the North Atlantic Oscillation (NAO). Simulated top-of-atmosphere (TOA) and surface CREs show large biases over the polar regions, particularly over regions where seasonal sea-ice variability is strongest. Increasing atmospheric resolution does not significantly improve these biases. The spatial structure of the cloud radiative response to ENSO and NAO variability is simulated reasonably well by all model configurations considered in this study. However, it is difficult to identify a systematic impact of atmospheric resolution on the associated CRE errors. Mean absolute CRE errors conditioned on the ENSO phase are relatively large (5–10 W m−2) and show differences between models. We suggest this is a consequence of differences in the parameterization of SW radiative transfer and the treatment of cloud optical properties rather than a result of differences in resolution. In contrast, mean absolute CRE errors conditioned on the NAO phase are generally smaller (0–2 W m−2) and more similar across models. Although the regional details of CRE biases show some sensitivity to atmospheric resolution within a particular model, it is difficult to identify patterns that hold across all models. This apparent insensitivity to increased atmospheric horizontal resolution indicates that physical parameterizations play a dominant role in determining the behaviour of cloud–radiation feedbacks. However, we note that these results are obtained from atmosphere-only simulations and the impact of changes in atmospheric resolution may be different in the presence of coupled climate feedbacks.

Exploring the Potential of Satellite Solar-Induced Fluorescence to Constrain Global Transpiration Estimates

The opening and closing of plant stomata regulates the global water, carbon and energy cycles. Biophysical feedbacks on climate are highly dependent on transpiration, which is mediated by vegetation phenology and plant responses to stress conditions. Here, we explore the potential of satellite observations of solar-induced chlorophyll fluorescence (SIF)—normalized by photosynthetically-active radiation (PAR)—to diagnose the ratio of transpiration to potential evaporation (‘transpiration efficiency’, τ). This potential is validated at 25 eddy-covariance sites from seven biomes worldwide. The skill of the state-of-the-art land surface models (LSMs) from the eartH2Observe project to estimate τ is also contrasted against eddy-covariance data. Despite its relatively coarse (0.5°) resolution, SIF/PAR estimates, based on data from the Global Ozone Monitoring Experiment 2 (GOME-2) and the Clouds and Earth’s Radiant Energy System (CERES), correlate to the in situ τ significantly (average inter-site correlation of 0.59), with higher correlations during growing seasons (0.64) compared to decaying periods (0.53). In addition, the skill to diagnose the variability of in situ τ demonstrated by all LSMs is on average lower, indicating the potential of SIF data to constrain the formulations of transpiration in global models via, e.g., data assimilation. Overall, SIF/PAR estimates successfully capture the effect of phenological changes and environmental stress on natural ecosystem transpiration, adequately reflecting the timing of this variability without complex parameterizations.