Observational Constraints on Cloud Feedbacks: The Role of Active Satellite Sensors

Clouds reduce downwelling longwave radiation over land in a warming climate

Clouds greatly influence the Earth's energy balance1,2. Observationally constraining cloud radiative feedback, a notably uncertain climate feedback mechanism3-5, is crucial for improving predictions of climate change5-7 but, so far, remains an elusive objective, and the feedback may be different over the ocean versus over land8-10. Here we show a local negative surface longwave cloud feedback over land at the southern Great Plains site, constrained by direct long-term observation of spectrally resolved downwelling longwave radiance11. This negative cloud feedback at the southern Great Plains site causes a -1.77 ± 1.15 W m-2 per decade change in downwelling longwave radiation and suggests that cloud changes may partially modulate the warming effect of increased greenhouse gas concentrations and atmospheric temperatures over land. Specifically, our results are derived from an optimal spectral fingerprinting method12-15 designed to separate surface longwave cloud feedback from other surface forcings and feedbacks, by making use of their unique spectral signatures13-18 in the long-term record of spectrally resolved radiances. Furthermore, we show that the results are not site specific: negative surface longwave cloud feedbacks, primarily induced by decreasing low cloud cover in warming climates, are commonly observed over land in reanalysis and satellite datasets. Our findings establish a pivotal observational benchmark of radiative forcing and feedback needed for validating climate model performance over land.

Assessment of Nighttime Cloud Cover Products from MODIS and Himawari-8 Data with Ground-Based Camera Observations

Comparing cloud cover (CC) products from different satellites with the same ground-based CC dataset provides information on the similarities or differences of values among satellite products. For this reason, 42-month CC products from Moderate Resolution Imaging Spectrometer’s (MODIS) Collection 6.1 daily cloud cover products (MOD06_L2, MYD06_L2, MOD08_D3, and MYD08_D3) and Himawari-8 are compared with the ground-based camera datasets. The comparison shows that CC from MODIS differs from ground measurement CC by as much as 57% over Chiba, Japan, when low CC is observed by the camera. This indicates MODIS’s ability to capture high-level clouds that are not effectively seen from the ground. When the camera detects high CC, an indication of the presence of low-level clouds, CC from MODIS is relatively higher than the CC from the camera. In the case of Himawari-8 data, when the camera observes low CC, this difference is around 0.7%. This result indicates that high-level clouds are not effectively observed, but the Himawari-8 data correlates well with camera observations. When the camera observes high CC, Himawari-8-derived CC is lower by around 10% than CC from the camera. These results show the potential of continuous observations of nighttime clouds using the camera to provide a dataset that can be used for intercomparison among nighttime satellite CC products.

Uncertainty Assessment of the Vertically-Resolved Cloud Amount for Joint CloudSat–CALIPSO Radar–Lidar Observations

The joint CloudSat–Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) climatology remains the only dataset that provides a global, vertically-resolved cloud amount statistic. However, data are affected by uncertainty that is the result of a combination of infrequent sampling, and a very narrow, pencil-like swath. This study provides the first global assessment of these uncertainties, which are quantified using bootstrapped confidence intervals. Rather than focusing on a purely theoretical discussion, we investigate empirical data that span a five-year period between 2006 and 2011. We examine the 2B-Geometric Profiling (GEOPROF)-LIDAR cloud product, at typical spatial resolutions found in global grids (1.0°, 2.5°, 5.0°, and 10.0°), four confidence levels (0.85, 0.90, 0.95, and 0.99), and three time scales (annual, seasonal, and monthly). Our results demonstrate that it is impossible to estimate, for every location, a five-year mean cloud amount based on CloudSat–CALIPSO data, assuming an accuracy of 1% or 5%, a high confidence level (>0.95), and a fine spatial resolution (1°–2.5°). In fact, the 1% requirement was only met by ~6.5% of atmospheric volumes at 1° and 2.5°, while the more tolerant criterion (5%) was met by 22.5% volumes at 1°, or 48.9% at 2.5° resolution. In order for at least 99% of volumes to meet an accuracy criterion, the criterion itself would have to be lowered to ~20% for 1° data, or to ~8% for 2.5° data. Our study also showed that the average confidence interval: decreased four times when the spatial resolution increased from 1° to 10°; doubled when the confidence level increased from 0.85 to 0.99; and tripled when the number of data-months increased from one (monthly mean) to twelve (annual mean). The cloud regime arguably had the most impact on the width of the confidence interval (mean cloud amount and its standard deviation). Our findings suggest that existing uncertainties in the CloudSat–CALIPSO five-year climatology are primarily the result of climate-specific factors, rather than the sampling scheme. Results that are presented in the form of statistics or maps, as in this study, can help the scientific community to improve accuracy assessments (which are frequently omitted), when analyzing existing and future CloudSat–CALIPSO cloud climatologies.

Space lidar observations constrain longwave cloud feedback

Some of the most challenging questions in atmospheric science relate to how clouds will respond as the climate warms. On centennial scales, the response of clouds could either weaken or enhance the warming due to greenhouse gas emissions. Here we use space lidar observations to quantify changes in cloud altitude, cover, and opacity over the oceans between 2008 and 2014, together with a climate model with a lidar simulator to also simulate these changes in the present-day climate and in a future, warmer climate. We find that the longwave cloud altitude feedback, found to be robustly positive in simulations since the early climate models and backed up by physical explanations, is not the dominant longwave feedback term in the observations, although it is in the model we have used. These results suggest that the enhanced longwave warming due to clouds might be overestimated in climate models. These results highlight the importance of developing a long-term active sensor satellite record to reduce uncertainties in cloud feedbacks and prediction of future climate.