Arctic warming from a high-latitude effusive volcanic eruption

The effusive Holuhraun eruption in Iceland emitted large quantities of sulfur into the troposphere during the fall and winter of 2014-15. Previous studies have shown that the resulting volcanic aerosols led to reduced insolation, and thus surface cooling, through increased cloud shortwave reflectance, mostly over the North Atlantic and Europe. Less attention has been paid to the Arctic, which at the time of the eruption received limited sunlight. Based on evidence from observations and model simulations, here we argue that increased cloud liquid water path and cloud cover following the 2014-15 Holuhraun eruption led to surface warming in the Arctic through trapping of longwave radiation. Our results show that sulfur emissions from the eruption led to extended lifetime of low and middle level clouds, reducing the longwave radiative cooling of the surface. This is the first time, to our knowledge, that an effusive volcanic eruption is shown to have this effect. Given the high level of volcanic activity in Iceland, these findings demonstrate the need to further investigate the climate impacts of high-latitude effusive volcanic eruptions. Moreover, marine cloud brightening through cloud seeding has been suggested as one way to combat anthropogenic climate change but, as our results suggest, such actions might have counteractive regional consequences.

Moderate climate sensitivity due to opposing mixed-phase cloud feedbacks

Earth's climate sensitivity quantifies the ultimate change in global mean surface air temperature in response to a doubling of atmospheric CO2 concentrations. Recent assessments estimate that Earth's climate sensitivity very likely lies between 2.3 °C and 4.7 °C, with the representation of clouds in climate models accounting for a large portion of its uncertainty. Here, we adjust the climate sensitivity of individual contemporary climate models after using satellite observations to alleviate biases in their representation of mixed-phase clouds. A resulting moderate average climate sensitivity of 3.63 ± 0.98(1σ) °C arises due to opposing responses of clouds. While increasing the proportion of liquid within cold clouds prior to CO2 doubling increases climate sensitivity via transitions from solid to liquid hydrometeors, a strongly opposing increase in reflective cloud cover decreases climate sensitivity. This emphasizes the need to reconsider the role of mixed-phase cloud cover changes in climate sensitivity assessments.

Evaluation of CanESM Cloudiness, Cloud Type and Cloud Radiative Forcing Climatologies Using the CALIPSO-GOCCP and CERES Datasets

In this study, the annual and seasonal climatology of cloud fraction (CF) and cloud type simulated by the Canadian Environmental System Models (CanESMs) version 5 (CanESM5) and version 2 (CanESM2) at their fully coupled and AMIP configurations were validated against the CALIPSO-GOCCP-based CF. The CFs produced using the CALIPSO-COSP simulator based on the CanESMs data at their atmospheric (AMIP) configuration are also evaluated. The simulated shortwave, longwave, and net cloud radiative forcing using the AMIP version of the CanESM5 were also validated against satellite observations based on the recent CERES radiation satellite products. On average, all models have a negative bias in the total CF with global mean biases (MBs) of 2%, 2.4%, 3.9%, 6.4,%, 5.6%, and 7.1% for the coupled-CanESM5, AMIP-CanESM5, COSP-AMIP-CanESM5, coupled-CanESM2, AMIP-CanESM2, and COSP-AMIP-CanESM2, respectively, indicating that the CanESM5 has a smaller MB. There were no significant differences between AMIP and coupled versions of the model, but the COSP-based model-simulated data showed larger biases. Although the models captured well the climatological features of CF, they also exhibited a significant bias in CF reaching up to 40% over some geographical locations. This is particularly prevalent over the low level (LL) marine stratocumulus/cumulus, convectively active tropical latitudes that are normally dominated by high level (HL) clouds and at the polar regions where all models showed negative, positive, and positive bias corresponding to these locations, respectively. The AMIP-CanESM5 model performed reasonably well simulating the global mean cloud radiative forcing (CRF) with slight negative biases in the NetCRF at the TOA and surface that would be expected if the model has a positive bias in CF. This inconsistent result may be attributed to the parameterization of the optical properties in the model. The geographical distributions of the model bias in the NetCRF, however, can be significant reaching up to ±40 Wm−2 depending on the location and atmospheric level. The Pearson correlation showed that there is a strong correlation between the global distribution of model bias in NetCRF and CF and it is significantly influenced by the LL and HL clouds.

Characteristics of High-Latitude Climate and Cloud Simulation in Community Atmospheric Model Version 6 (CAM6)

Many global climate models (GCMs) have difficulty in simulating climate variabilities over high northern latitudes. One of the main reasons is the inability of GCMs to simulate proper cloud fraction and the amount of liquid-containing cloud over the region. This study assessed the impact of cloud simulation in high latitudes by comparing the long-term parallel simulations of Community Atmosphere Model version 6 (CAM6) and CAM5, the previous version. The results show that the CAM6 simulation exhibits a considerable improvement in the Arctic, especially by reducing the cold bias of CAM5 throughout the year. Over the sub-Arctic region, however, CAM6 produces an excessive cold bias in summer and a warm bias in winter compared to the observation, which is closely related to the overestimation of cloud fraction and the amount of cloud liquid. In summer, the overestimation of the cloud in CAM6 tends to alleviate the cold bias compared to CAM5 due to an increase in downward longwave radiation over the high latitudes, while causing the excessive cold bias by blocking downward shortwave radiation over the sub-Arctic land area. In winter, when there is little incidence of shortwave radiation, the overestimation of the cloud in CAM6 increases the downward longwave radiation, which alleviates the cold bias in CAM5 over the Arctic but induces an excessive warm bias over the sub-Arctic land. The excessive cloudiness in CAM6 could weaken the high-latitude internal variability, exacerbating the deteriorating climate variability and long-term trend simulations in the region.

Comparison of the Spatial and Temporal Variability of Cloud Amounts over China Derived from Different Satellite Datasets

Various cloud cover products have been developed over the past few decades, but their uncertainties have not been sufficiently assessed, especially at a regional scale, which is vital for the application of satellite products to climate studies. In this study, we compare the spatial–temporal variability of the cloud amount over China from the 11 datasets provided by the Global Energy and Water Cycle Experiment (GEWEX) cloud assessment project at a horizontal resolution of 1° × 1° from the 1980s to 2000s, using the site data as a reference. The differences among these datasets are quantified in terms of the standard deviations and the correlation coefficients between different datasets. Most of the datasets show a similar spatial distribution of total cloud amounts (TCAs), but their magnitudes differ. The standard deviations of the annual, winter, and summer mean TCA are approximately 9–18% for the regional mean TCAs over the four typical regions of China, including the northwestern region (NW), northeastern region (NE), Tibetan Plateau region (TP), and southern China region (SC), with the largest standard deviations of 13–18% in the TP. By analyzing the factors that influence the satellite inversion data, such as the observation instrument, inversion algorithm, and observation time, we found that the difference caused by the observation instrument or algorithm is greater than the effect of the observation time, and the satellite cloud datasets with better recognition capability for cloud types show lower uncertainties when compared with the station observation. In terms of seasonal cycle, except HIRS and MODIS-ST, most satellite datasets can reproduce the observed seasonal cycle with the largest TCA in summer and the smallest TCA in autumn and winter. For the interannual variation, ISCCP-D1, MODIS-CE, and MODIS-ST are most consistent with the site data for the annual mean TCA, and two of the remaining datasets (PATMOSX and TOVSB) show more consistent temporal variations with the site observation in summer than in winter, especially over NW and NE regions. In general, MODIS-CE shows the best performance in reproducing the spatial pattern and interannual variation of TCA amongst the 11 satellite datasets, and PATMOSX, MODIS-ST, CALIPSO-GOCCP, and CALIPSO-ST also show relatively good performance.

Using Satellite Observations to Evaluate Model Microphysical Representation of Arctic Mixed-Phase Clouds

Mixed-phase clouds play an important role in determining Arctic warming, but are parametrized in models and difficult to constrain with observations. We use two satellite-derived cloud phase metrics to investigate the vertical structure of Arctic clouds in two global climate models that use the Community Atmosphere Model version 6 (CAM6) atmospheric component. We report a model error limiting ice nucleation, produce a set of Arctic-constrained model runs by adjusting model microphysical variables to match the cloud phase metrics, and evaluate cloud feedbacks for all simulations. Models in this small ensemble uniformly overestimate total cloud fraction in the summer, but have variable representation of cloud fraction and phase in the winter and spring. By relating modeled cloud phase metrics and changes in low-level liquid cloud amount under warming to longwave cloud feedback, we show that mixed-phase processes mediate the Arctic climate by modifying how wintertime and springtime clouds respond to warming.

Evaluations of the Climatologies of Three Latest Cloud Satellite Products Based on Passive Sensors (ISCCP-H, Two CERES) against the CALIPSO-GOCCP

In this study, the climatologies of three different satellite cloud products, all based on passive sensors (CERES Edition 4.1 [EBAF4.1 and SYN4.1] and ISCCP–H), were evaluated against the CALIPSO-GOCCP (GOCCP) data, which are based on active sensors and, hence, were treated as the reference. Based on monthly averaged data (ocean + land), the passive sensors underestimated the total cloud cover (TCC) at lower (TCC < 50%), but, overall, they correlated well with the GOCCP data (r = 0.97). Over land, the passive sensors underestimated the TCC, with a mean difference (MD) of −2.6%, followed by the EBAF4.1 and ISCCP-H data with a MD of −2.0%. Over the ocean, the CERES-based products overestimated the TCC, but the SYN4.1 agreed better with the GOCCP data. The ISCCP-H data on average underestimated the TCC both over oceanic and continental regions. The annual mean TCC distribution over the globe revealed that the passive sensors generally underestimated the TCC over continental dry regions in northern Africa and southeastern South America as compared to the GOCCP, particularly over the summer hemisphere. The CERES datasets overestimated the TCC over the Pacific Islands between the Indian and eastern Pacific Oceans, particularly during the winter hemisphere. The ISCCP-H data also underestimated the TCC, particularly over the southern hemisphere near 60° S where the other datasets showed a significantly enhanced TCC. The ISCCP data also showed less TCC when compared against the GOCCP data over the tropical regions, particularly over the southern Pacific and Atlantic Oceans near the equator and also over the polar regions where the satellite retrieval using the passive sensors was generally much more challenging. The calculated global mean root meant square deviation value for the ISCCP-H data was 6%, a factor of 2 higher than the CERES datasets. Based on these results, overall, the EBAF4.1 agreed better with the GOCCP data.

Aerosol-boundary-layer-monsoon interactions amplify semi-direct effect of biomass smoke on low cloud formation in Southeast Asia

Low clouds play a key role in the Earth-atmosphere energy balance and influence agricultural production and solar-power generation. Smoke aloft has been found to enhance marine stratocumulus through aerosol-cloud interactions, but its role in regions with strong human activities and complex monsoon circulation remains unclear. Here we show that biomass burning aerosols aloft strongly increase the low cloud coverage over both land and ocean in subtropical southeastern Asia. The degree of this enhancement and its spatial extent are comparable to that in the Southeast Atlantic, even though the total biomass burning emissions in Southeast Asia are only one-fifth of those in Southern Africa. We find that a synergetic effect of aerosol-cloud-boundary layer interaction with the monsoon is the main reason for the strong semi-direct effect and enhanced low cloud formation in southeastern Asia.

Aerosol and Cloud Detection Using Machine Learning Algorithms and Space-Based Lidar Data

Clouds and aerosols play a significant role in determining the overall atmospheric radiation budget, yet remain a key uncertainty in understanding and predicting the future climate system. In addition to their impact on the Earth’s climate system, aerosols from volcanic eruptions, wildfires, man-made pollution events and dust storms are hazardous to aviation safety and human health. Space-based lidar systems provide critical information about the vertical distributions of clouds and aerosols that greatly improve our understanding of the climate system. However, daytime data from backscatter lidars, such as the Cloud-Aerosol Transport System (CATS) on the International Space Station (ISS), must be averaged during science processing at the expense of spatial resolution to obtain sufficient signal-to-noise ratio (SNR) for accurately detecting atmospheric features. For example, 50% of all atmospheric features reported in daytime operational CATS data products require averaging to 60 km for detection. Furthermore, the single-wavelength nature of the CATS primary operation mode makes accurately typing these features challenging in complex scenes. This paper presents machine learning (ML) techniques that, when applied to CATS data, (1) increased the 1064 nm SNR by 75%, (2) increased the number of layers detected (any resolution) by 30%, and (3) enabled detection of 40% more atmospheric features during daytime operations at a horizontal resolution of 5 km compared to the 60 km horizontal resolution often required for daytime CATS operational data products. A Convolutional Neural Network (CNN) trained using CATS standard data products also demonstrated the potential for improved cloud-aerosol discrimination compared to the operational CATS algorithms for cloud edges and complex near-surface scenes during daytime.

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.

Subtropical Marine Low Stratiform Cloud Deck Spatial Errors in the E3SMv1 Atmosphere Model

Marine low-level clouds continue to be poorly simulated in models despite many studies and field experiments devoted to their improvement. Here we focus on the spatial errors in the cloud decks in the Department of Energy Earth system model (the Energy Exascale Earth System Model [E3SM]) relative to the satellite climatology by calculating centroid distances, area ratios, and overlap ratios. Since model dynamics is better simulated than clouds, these errors are attributed primarily to the model physics. To gain additional insight, we performed a sensitivity run in which model winds were nudged to those of reanalysis. This results in a large change (but not necessarily an improvement) in the simulated cloud decks. These differences between simulations are mainly due to the interactions between model dynamics and physics. These results suggest that both model physics (widely recognized) and its interaction with dynamics (less recognized) are important to model improvement in simulating these low-level clouds.

Strong Dependence of Atmospheric Feedbacks on Mixed-Phase Microphysics and Aerosol-Cloud Interactions in HadGEM3

We analyze the atmospheric processes that explain the large changes in radiative feedbacks between the two latest climate configurations of the Hadley Centre Global Environmental model. We use a large set of atmosphere-only climate change simulations (amip and amip-p4K) to separate the contributions to the differences in feedback parameter from all the atmospheric model developments between the two latest model configurations. We show that the differences are mostly driven by changes in the shortwave cloud radiative feedback in the midlatitudes, mainly over the Southern Ocean. Two new schemes explain most of the differences: the introduction of a new aerosol scheme and the development of a new mixed-phase cloud scheme. Both schemes reduce the strength of the preexisting shortwave negative cloud feedback in the midlatitudes. The new aerosol scheme dampens a strong aerosol-cloud interaction, and it also suppresses a negative clear-sky shortwave feedback. The mixed-phase scheme increases the amount of cloud liquid water path (LWP) in the present day and reduces the increase in LWP with warming. Both changes contribute to reducing the negative radiative feedback of the increase of LWP in the warmer climate. The mixed-phase scheme also enhances a strong, preexisting, positive cloud fraction feedback. We assess the realism of the changes by comparing present-day simulations against observations and discuss avenues that could help constrain the relevant processes.

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.

Inter-Comparison and Evaluation of the Four Longest Satellite-Derived Cloud Climate Data Records: CLARA-A2, ESA Cloud CCI V3, ISCCP-HGM, and PATMOS-x

Results from four global cloud climate data records (ISCCP-HGM, ESA Cloud CCI V3, CLARA-A2 and PATMOS-x) have been inter-compared in global time series plots, in global maps and in zonal region plots covering the period in common, 1984–2009. The investigated cloud parameters were total cloud fraction and cloud top pressure. Averaged seasonal cycles of cloud cover, as observed by the CALIPSO-CALIOP sensor over the 2007–2015 period, were also used as an additional independent and high-quality reference for the study of global cloud cover. All CDRs show good agreement on global cloud amounts (~65%) and also a weak negative trend (0.5–1.9% per decade) over the period of investigation. Deviations between the CDRs are seen especially over the southern mid-latitude region and over the poles. Particularly good results are shown by PATMOS-x and by ESA Cloud CCI V3 when compared to the CALIPSO-CALIOP reference. Results for cloud top pressure show large differences (~60 hPa) between ISCCP-HGM and the other CDRs for the global mean. The two CDR groups show also opposite signs in the trend over the period.

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

Cloud profiling from active lidar and radar in the A-train satellite constellation has significantly advanced our understanding of clouds and their role in the climate system. Nevertheless, the response of clouds to a warming climate remains one of the largest uncertainties in predicting climate change and for the development of adaptions to change. Both observation of long-term changes and observational constraints on the processes responsible for those changes are necessary. We review recent progress in our understanding of the cloud feedback problem. Capabilities and advantages of active sensors for observing clouds are discussed, along with the importance of active sensors for deriving constraints on cloud feedbacks as an essential component of a global climate observing system.

New Insights about Cloud Vertical Structure from CloudSat and CALIPSO observations

Active cloud observations from A-Train's CloudSat and CALIPSO satellites offer new opportunities to examine the vertical structure of hydrometeor layers. We use the 2B-CLDCLASS-LIDAR merged CloudSat-CALIPSO product to examine global aspects of hydrometeor vertical stratification. We group the data into major Cloud Vertical Structure (CVS) classes based on our interpretation of how clouds in three standard atmospheric layers overlap, and provide their global frequency of occurrence. The two most frequent CVS classes are single-layer (per our definition) low and high clouds which represent ~53% of cloudy skies, followed by high clouds overlying low clouds, and vertically extensive clouds that occupy near-contiguously a large portion of the troposphere. The prevalence of these configurations changes seasonally and geographically, between daytime and nighttime, and between continents and oceans. The radiative effects of the CVS classes reveal the major radiative warmers and coolers from the perspective of the planet as a whole, the surface, and the atmosphere. Single-layer low clouds dominate planetary and atmospheric cooling, and thermal infrared surface warming. We also investigate the consistency between passive and active views of clouds by providing the CVS breakdowns of MODIS cloud regimes for spatiotemporally coincident MODIS-Aqua (also on the A-Train) and CloudSat-CALIPSO daytime observations. When the analysis is expanded for a more in-depth look at the most heterogeneous of the MODIS cloud regimes, it ultimately confirms previous interpretations of their makeup that did not have the benefit of collocated active observations.

Clouds at Barbados are representative of clouds across the trade wind regions in observations and climate models

Trade wind regions cover most of the tropical oceans, and the prevailing cloud type is shallow cumulus. These small clouds are parameterized by climate models, and changes in their radiative effects strongly and directly contribute to the spread in estimates of climate sensitivity. This study investigates the structure and variability of these clouds in observations and climate models. The study builds upon recent detailed model evaluations using observations from the island of Barbados. Using a dynamical regimes framework, satellite and reanalysis products are used to compare the Barbados region and the broader tropics. It is shown that clouds in the Barbados region are similar to those across the trade wind regions, implying that observational findings from the Barbados Cloud Observatory are relevant to clouds across the tropics. The same methods are applied to climate models to evaluate the simulated clouds. The models generally capture the cloud radiative effect, but underestimate cloud cover and show an array of cloud vertical structures. Some models show strong biases in the environment of the Barbados region in summer, weakening the connection between the regional biases and those across the tropics. Even bearing that limitation in mind, it is shown that covariations of cloud and environmental properties in the models are inconsistent with observations. The models tend to misrepresent sensitivity to moisture variations and inversion characteristics. These model errors are likely connected to cloud feedback in climate projections, and highlight the importance of the representation of shallow cumulus convection.

Europe's forest management did not mitigate climate warming

Afforestation and forest management are considered to be key instruments in mitigating climate change. Here we show that since 1750, in spite of considerable afforestation, wood extraction has led to Europe's forests accumulating a carbon debt of 3.1 petagrams of carbon. We found that afforestation is responsible for an increase of 0.12 watts per square meter in the radiative imbalance at the top of the atmosphere, whereas an increase of 0.12 kelvin in summertime atmospheric boundary layer temperature was mainly caused by species conversion. Thus, two and a half centuries of forest management in Europe have not cooled the climate. The political imperative to mitigate climate change through afforestation and forest management therefore risks failure, unless it is recognized that not all forestry contributes to climate change mitigation.