scholarly journals Microphysical, macrophysical and radiative signatures of volcanic aerosols in trade wind cumulus observed by the A-Train

2011 ◽  
Vol 11 (14) ◽  
pp. 7119-7132 ◽  
Author(s):  
T. Yuan ◽  
L. A. Remer ◽  
H. Yu
Keyword(s):  
Large Scale ◽  
Climate Models ◽  
Cloud Amount ◽  
Trade Wind ◽  
Climate Mitigation ◽  
Cloud Albedo ◽  
Order Of Magnitude ◽  
Volcanic Aerosols ◽  
Planetary Albedo ◽  
Aerosol Cloud Interactions

Abstract. Increased aerosol concentrations can raise planetary albedo not only by reflecting sunlight and increasing cloud albedo, but also by changing cloud amount. However, detecting aerosol effect on cloud amount has been elusive to both observations and modeling due to potential buffering mechanisms and convolution of meteorology. Here through a natural experiment provided by long-term degassing of a low-lying volcano and use of A-Train satellite observations, we show modifications of trade cumulus cloud fields including decreased droplet size, decreased precipitation efficiency and increased cloud amount are associated with volcanic aerosols. In addition we find significantly higher cloud tops for polluted clouds. We demonstrate that the observed microphysical and macrophysical changes cannot be explained by synoptic meteorology or the orographic effect of the Hawaiian Islands. The "total shortwave aerosol forcin", resulting from direct and indirect forcings including both cloud albedo and cloud amount, is almost an order of magnitude higher than aerosol direct forcing alone. Furthermore, the precipitation reduction associated with enhanced aerosol leads to large changes in the energetics of air-sea exchange and trade wind boundary layer. Our results represent the first observational evidence of large-scale increase of cloud amount due to aerosols in a trade cumulus regime, which can be used to constrain the representation of aerosol-cloud interactions in climate models. The findings also have implications for volcano-climate interactions and climate mitigation research.

scholarly journals Microphysical, macrophysical and radiative signatures of volcanic aerosols in trade wind cumulus observed by the A-Train

2011 ◽  
Vol 11 (2) ◽  
pp. 6415-6455 ◽  
Author(s):  
T. Yuan ◽  
L. A. Remer ◽  
H. Yu
Keyword(s):  
Large Scale ◽  
Climate Models ◽  
Cloud Amount ◽  
Trade Wind ◽  
Climate Mitigation ◽  
Cloud Albedo ◽  
Aerosol Forcing ◽  
Order Of Magnitude ◽  
Planetary Albedo ◽  
Aerosol Cloud Interactions

Abstract. Increased aerosol concentrations can raise planetary albedo not only by reflecting sunlight and increasing cloud albedo, but also by changing cloud amount. However, detecting aerosol effect on cloud amount has been elusive to both observations and modeling due to potential buffering mechanisms and convolution of meteorology. Here through a natural experiment provided by long-term degassing of a low-lying volcano and use of A-Train satellite observations, we show aerosols are associated strongly with modification of trade cumulus cloud fields including decreased droplet size, decreased precipitation efficiency and increased cloud amount. We also demonstrate that the observed microphysical and macrophysical changes cannot be explained by synoptic meteorology or the orographic effect of the Hawaiian Islands. The "total shortwave aerosol forcing", resulting from direct and indirect forcings including both cloud albedo and cloud amount, is almost an order of magnitude higher than aerosol direct forcing alone. The precipitation reduction associated with enhanced aerosol leads to large changes in the energetics of air-sea exchange. Our results represent the first observational evidence of large-scale increase of cloud amount due to aerosols in a trade cumulus regime, which can be used to constrain the representation of aerosol-cloud interactions in climate models. The findings also have implications for volcano-climate interactions and climate mitigation research.

scholarly journals Cities Exacerbate Climate Warming

Urban Science ◽  
2021 ◽  
Vol 5 (1) ◽  
pp. 27
Author(s):  
Lahouari Bounoua ◽  
Kurtis Thome ◽  
Joseph Nigro
Keyword(s):  
Surface Temperature ◽  
Land Surface ◽  
Large Scale ◽  
Climate Models ◽  
Warming Trend ◽  
Climate Mitigation ◽  
Temperature Changes ◽  
Surface Warming ◽  
The Us

Urbanization is a complex land transformation not explicitly resolved within large-scale climate models. Long-term timeseries of high-resolution satellite data are essential to characterize urbanization within land surface models and to assess its contribution to surface temperature changes. The potential for additional surface warming from urbanization-induced land use change is investigated and decoupled from that due to change in climate over the continental US using a decadal timescale. We show that, aggregated over the US, the summer mean urban-induced surface temperature increased by 0.15 °C, with a warming of 0.24 °C in cities built in vegetated areas and a cooling of 0.25 °C in cities built in non-vegetated arid areas. This temperature change is comparable in magnitude to the 0.13 °C/decade global warming trend observed over the last 50 years caused by increased CO2. We also show that the effect of urban-induced change on surface temperature is felt above and beyond that of the CO2 effect. Our results suggest that climate mitigation policies must consider urbanization feedback to put a limit on the worldwide mean temperature increase.

Seasonal modulation of trapped gravity waves and their imprints on trade wind clouds

2020 ◽  
Author(s):  
Claudia Stephan
Keyword(s):  
Gravity Waves ◽  
Large Scale ◽  
Climate Models ◽  
Global Climate ◽  
Wave Theory ◽  
Internal Gravity Waves ◽  
Global Climate Models ◽  
Trade Wind ◽  
Trapped Waves ◽  
Convective Boundary

<p>Idealized simulations have shown decades ago that shallow clouds generate internal gravity waves, which under certain atmospheric background conditions become trapped inside the troposphere and influence the development of clouds. These feedbacks, which occur at horizontal scales of up to several tens of km are neither resolved, nor parameterized in traditional global climate models (GCMs), while the newest generation of GCMs is starting to resolve them. The interactions between the convective boundary layer and trapped waves have almost exclusively been studied in highly idealized frameworks and it remains unclear to what degree this coupling affects the organization of clouds and convection in the real atmosphere. Here, the coupling between clouds and trapped waves is examined in storm-resolving simulations that span the entirety of the tropical Atlantic and are initialized and forced by meteorological analyses. The coupling between clouds and trapped waves is sufficiently strong to be detected in these simulations of full complexity.  Stronger upper-tropospheric westerly winds are associated with a stronger cloud-wave coupling. In the simulations this results in a highly-organized scattered cloud field with cloud spacings of about 19 km, matching the dominant trapped wavelength. Based on the large-scale atmospheric state wave theory can reliably predict the regions and times where cloud-wave feedbacks become relevant to convective organization. Theory, the simulations and satellite imagery imply a seasonal cycle in the trapping of gravity waves. </p>

scholarly journals A global satellite view of aerosol cloud interactions

2004 ◽  
Vol 4 (5) ◽  
pp. 6823-6836 ◽  
Author(s):  
C. Luo
Keyword(s):  
North Africa ◽  
North Atlantic ◽  
Radiative Forcing ◽  
General Circulation ◽  
Large Scale ◽  
Regional Scale ◽  
Cloud Amount ◽  
General Circulation Models ◽  
Aerosol Cloud ◽  
Aerosol Cloud Interactions

Abstract. Long-term and large-scale correlations between Advanced Very High-Resolution Radiometer (AVHRR) aerosol optical depth and International Satellite Cloud Climatology Project (ISCCP) monthly cloud amount data show significant regional scale relationships between cloud amount and aerosols, consistent with aerosol-cloud interactions. Positive correlations between aerosols and cloud amount are associated with North American and Asian aerosols in the North Atlantic and Pacific storm tracks, and mineral aerosols in the tropical North Atlantic. Negative correlations are seen near biomass burning regions of North Africa and Indonesia, as well as south of the main mineral aerosol source of North Africa. These results suggest that there are relationships between aerosols and clouds in the observations that can be used by general circulation models to verify the correct forcing mechanisms for both direct and indirect radiative forcing by clouds.

scholarly journals Constraints on aerosol processes in climate models from vertically-resolved aircraft observations of black carbon

2013 ◽  
Vol 13 (1) ◽  
pp. 437-473 ◽  
Author(s):  
Z. Kipling ◽  
P. Stier ◽  
J. P. Schwarz ◽  
A. E. Perring ◽  
J. R. Spackman ◽  
...  
Keyword(s):  
Vertical Distribution ◽  
Black Carbon ◽  
Biomass Burning ◽  
Large Scale ◽  
Climate Models ◽  
Convective Transport ◽  
Convective Precipitation ◽  
Scale Model ◽  
Modest Improvement ◽  
Order Of Magnitude

Abstract. Evaluation of the aerosol schemes in current climate models is dependent upon the available observational data. In-situ observations from flight campaigns can provide valuable data about the vertical distribution of aerosol that is difficult to obtain from satellite or ground-based platforms, although they are localised in space and time. Using single-particle soot-photometer (SP2) measurements from the HIAPER Pole-to-Pole Observations (HIPPO) campaign, which consists of many vertical profiles over a large region of the Pacific, we evaluate the meridional and vertical distribution of black carbon (BC) aerosol simulated by the HadGEM3-UKCA and ECHAM5-HAM2 models. Both models show a similar pattern of overestimating the BC column burden compared to that derived from the observations, in many areas by an order of magnitude. However, by sampling the simulated BC mass mixing ratio along the flight track and comparing to the observations, we show that this discrepancy has a rather different vertical structure in the two models. Using this methodology, we conduct sensitivity tests on two specific elements of the models: biomass-burning emissions and scavenging by convective precipitation. We show that, by coupling the convective scavenging more tightly with convective transport, both the column burden and vertical distribution of BC in HadGEM3–UKCA are significantly improved with respect to the observations, demonstrating the importance of a realistic representation of this process. In contrast, updating from GFED2 to GFED3.1 biomass-burning emissions makes a more modest improvement in both models, which is not statistically significant. We also demonstrate the important role that nudged simulations (where the large-scale model dynamics are continuously relaxed towards a reanalysis) can play in this type of evaluation, allowing statistically significant differences between configurations of the aerosol scheme to be seen where the differences between the corresponding free-running simulations would not be significant.

Large Differences in Diffuse Solar Radiation Among Current-Generation Reanalysis and Satellite-Derived Products

2021 ◽  
pp. 1-52
Author(s):  
T. Chakraborty ◽  
X. Lee
Keyword(s):  
Solar Radiation ◽  
Cloud Cover ◽  
Large Scale ◽  
Climate Models ◽  
Shortwave Radiation ◽  
Current Generation ◽  
Diffuse Fraction ◽  
Diffuse Solar Radiation ◽  
Order Of Magnitude ◽  
Model Variability

AbstractThough the partitioning of shortwave radiation (K↓) at the surface into its diffuse (K↓,d) and direct beam (K↓,b) components is relevant for, among other things, the terrestrial energy and carbon budgets, there is a dearth of large-scale comparisons of this partitioning across reanalysis and satellite-derived products. Here we evaluate K↓, K↓,d, and K↓,b, as well as the diffuse fraction (kd) of solar radiation in four current-generation reanalysis (NOAA-CIRES-DOE, NCEP/NCAR, MERRA-2, ERA5) datasets and one satellite-derived product (CERES) using ≈1400 site years of observations. Although the systematic positive biases in K↓ is consistent with previous studies, the biases in gridded K↓,d and K↓,b vary in direction and magnitude, both annually and across seasons. The inter-model variability in cloud cover strongly explains the biases in both K↓,d and K↓,b. Over Europe and China, the long-term (10-year plus) trends in K↓,d in the gridded products are noticeably differ from corresponding observations and the grid-averaged 35-year trends show an order of magnitude variability. In the MERRA-2 reanalysis, which includes both clouds and assimilated aerosols, the reduction in both clouds and aerosols reinforce each other to establish brightening trends over Europe, while the effect of increasing aerosols overwhelm the effect of decreasing cloud cover over China. The inter-model variability in kd seen here (0.27 to 0.50 from CERES to MERRA-2) suggests substantial differences in shortwave parameterization schemes and their inputs in climate models and can contribute to inter-model variability in coupled simulations. Based on these results, we call for systematic evaluations of K↓,d and K↓,b in CMIP6 models.

scholarly journals On the Seasonal and Synoptic Time-Scale Variability of the North Atlantic Trade Wind Region and Its Low-Level Clouds

2015 ◽  
Vol 72 (4) ◽  
pp. 1428-1446 ◽  
Author(s):  
Matthias Brueck ◽  
Louise Nuijens ◽  
Bjorn Stevens
Keyword(s):  
Wind Speed ◽  
Time Scales ◽  
North Atlantic ◽  
Large Scale ◽  
Sensible Heat Flux ◽  
Cloud Amount ◽  
Trade Wind ◽  
Low Level ◽  
The North ◽  
The North Atlantic

Abstract The seasonality in large-scale meteorology and low-level cloud amount (CClow) is explored for a 5° × 5° area in the North Atlantic trades, using 12 years of ERA-Interim and MODIS data, supported by 2 years of Barbados Cloud Observatory (BCO) measurements. From boreal winter to summer, large-scale subsiding motion changes to rising motion, along with an increase in sea surface temperature, a clockwise turning and weakening of low-level winds, and reduced cold-air advection, lower-tropospheric stability (LTS), and surface fluxes. However, CClow is relatively invariant around 30%, except for a minimum of 20% in fall. This minimum is only pronounced when MODIS scenes with large high-level cloud amount are excluded, and a winter maximum in CClow is more pronounced at the BCO. On monthly time scales, wind speed has the best correlation with CClow. Existing large-eddy simulations suggest that the wind speed–CClow correlation may be explained by a direct deepening response of the trade wind layer to stronger winds. Large correlations of wind direction and advection with CClow also suggest that large-scale flow patterns matter. Smaller correlations with CClow are observed for LTS and surface evaporation, as well as negligible correlations for relative humidity (RH) and vertical velocity. However, these correlations considerably increase when only summer is considered. On synoptic time scales, all correlations drop substantially, whereby wind speed, RH, and surface sensible heat flux remain the leading parameters. The lack of a single strong predictor emphasizes that the combined effect of parameters is necessary to explain variations in CClow in the trades.

scholarly journals Polar Cooling Effect Due to Increase of Phytoplankton and Dimethyl-Sulfide Emission

Atmosphere ◽  
2018 ◽  
Vol 9 (10) ◽  
pp. 384 ◽  
Author(s):  
Ah-Hyun Kim ◽  
Seong Yum ◽  
Hannah Lee ◽  
Dong Chang ◽  
Sungbo Shim
Keyword(s):  
Sea Ice ◽  
Large Scale ◽  
Climate Models ◽  
Dimethyl Sulfide ◽  
Climate Model ◽  
Cloud Condensation Nuclei ◽  
Marine Phytoplankton ◽  
The Arctic ◽  
Polar Regions ◽  
Cloud Albedo

The effects of increased dimethyl-sulfide (DMS) emissions due to increased marine phytoplankton activity are examined using an atmosphere-ocean coupled climate model. As the DMS emission flux from the ocean increases globally, large-scale cooling occurs due to the DMS-cloud condensation nuclei (CCN)-cloud albedo interactions. This cooling increases as DMS emissions are further increased, with the most pronounced effect occurring over the Arctic, which is likely associated with a change in sea-ice fraction as sea ice mediates the air-sea exchange of the radiation, moisture and heat flux. These results differ from recent studies that only considered the bio-physical feedback that led to amplified Arctic warming under greenhouse warming conditions. Therefore, climate negative feedback from DMS-CCN-cloud albedo interactions that involve marine phytoplankton and its impact on polar climate should be properly reflected in future climate models to better estimate climate change, especially over the polar regions.

scholarly journals Technical Note: On the use of nudging for aerosol-climate model intercomparison studies

2014 ◽  
Vol 14 (7) ◽  
pp. 10311-10343 ◽  
Author(s):  
K. Zhang ◽  
H. Wan ◽  
X. Liu ◽  
S. J. Ghan ◽  
G. J. Kooperman ◽  
...  
Keyword(s):  
Large Scale ◽  
Climate Models ◽  
Climate Model ◽  
Cloud Amount ◽  
Temperature Fields ◽  
Specific Humidity ◽  
Radiation Budget ◽  
Anthropogenic Aerosols ◽  
Anthropogenic Aerosol ◽  
The Impact

Abstract. Nudging is an assimilation technique widely used in the development and evaluation of climate models. Constraining the simulated wind and temperature fields using global weather reanalysis facilitates more straightforward comparison between simulation and observation, and reduces uncertainties associated with natural variabilities of the large-scale circulation. On the other hand, the forcing introduced by nudging can be strong enough to change the basic characteristics of the model climate. In the paper we show that for the Community Atmosphere Model version 5, due to the systematic temperature bias in the standard model and the sensitivity of simulated ice formation to anthropogenic aerosol concentration, nudging towards reanalysis results in substantial reductions in the ice cloud amount and the impact of anthropogenic aerosols on longwave cloud forcing. In order to reduce discrepancies between the nudged and unconstrained simulations and meanwhile take the advantages of nudging, two alternative experimentation methods are evaluated. The first one constrains only the horizontal winds. The second method nudges both winds and temperature, but replaces the long-term climatology of the reanalysis by that of the model. Results show that both methods lead to substantially improved agreement with the free-running model in terms of the top-of-atmosphere radiation budget and cloud ice amount. The wind-only nudging is more convenient to apply, and provides higher correlations of the wind fields, geopotential height and specific humidity between simulation and reanalysis. This suggests nudging the horizontal winds but not temperature is a good strategy for the investigation of aerosol indirect effects through ice clouds, since it provides well-constrained meteorology without strongly perturbing the model's mean climate.

scholarly journals Combining Crowdsourcing and Deep Learning to Explore the Mesoscale Organization of Shallow Convection

2020 ◽  
Vol 101 (11) ◽  
pp. E1980-E1995 ◽  
Author(s):  
Stephan Rasp ◽  
Hauke Schulz ◽  
Sandrine Bony ◽  
Bjorn Stevens
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Climate Models ◽  
Training Dataset ◽  
Trade Wind ◽  
Radiation Balance ◽  
Shallow Convection ◽  
Wide Range ◽  
Shallow Clouds ◽  
New Phenomena

AbstractHumans excel at detecting interesting patterns in images, for example, those taken from satellites. This kind of anecdotal evidence can lead to the discovery of new phenomena. However, it is often difficult to gather enough data of subjective features for significant analysis. This paper presents an example of how two tools that have recently become accessible to a wide range of researchers, crowdsourcing and deep learning, can be combined to explore satellite imagery at scale. In particular, the focus is on the organization of shallow cumulus convection in the trade wind regions. Shallow clouds play a large role in the Earth’s radiation balance yet are poorly represented in climate models. For this project four subjective patterns of organization were defined: Sugar, Flower, Fish, and Gravel. On cloud-labeling days at two institutes, 67 scientists screened 10,000 satellite images on a crowdsourcing platform and classified almost 50,000 mesoscale cloud clusters. This dataset is then used as a training dataset for deep learning algorithms that make it possible to automate the pattern detection and create global climatologies of the four patterns. Analysis of the geographical distribution and large-scale environmental conditions indicates that the four patterns have some overlap with established modes of organization, such as open and closed cellular convection, but also differ in important ways. The results and dataset from this project suggest promising research questions. Further, this study illustrates that crowdsourcing and deep learning complement each other well for the exploration of image datasets.

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