A Climatology of Clouds in Marine Cold Air Outbreaks in Both Hemispheres

2016 ◽  
Vol 29 (18) ◽  
pp. 6677-6692 ◽  
Author(s):  
Jennifer K. Fletcher ◽  
Shannon Mason ◽  
Christian Jakob

Abstract A climatology of clouds within marine cold air outbreaks, primarily using long-term satellite observations, is presented. Cloud properties between cold air outbreaks in different regions in both hemispheres are compared. In all regions marine cold air outbreak clouds tend to be low level with high cloud fraction and low-to-moderate optical thickness. Stronger cold air outbreaks have clouds that are optically thicker, but not geometrically thicker, than those in weaker cold air outbreaks. There is some evidence that clouds deepen and break up over the course of a cold air outbreak event. The top-of-the-atmosphere longwave cloud radiative effect in cold air outbreaks is small because the clouds have low tops. However, their surface longwave cloud radiative effect is considerably larger. The rarity of cold air outbreaks in summer limits their shortwave cloud radiative effect. They do not contribute substantially to global shortwave cloud radiative effect and are, therefore, unlikely to be a major source of shortwave cloud radiative effect errors in climate models.

2021 ◽  
Author(s):  
Miguel Perpina ◽  
Vincent Noel ◽  
Helene Chepfer ◽  
Rodrigo Guzman ◽  
Artem Feofilov

<p><span>Climate models predict a weakening of the tropical atmospheric circulation, more specifically a slowdown of Hadley and Walker circulations. Many climate models predict that global warming will have a major impact on cloud properties, including their geographic and vertical distribution. Climate feedbacks from clouds, which amplify warming when positive, are today the main source of uncertainty in climate forecasts. Tropical clouds play a key role in the redistribution of solar energy and their evolution will likely affect climate. Therefore, it is crucial to better understand how tropical clouds will evolve in a changing climate. Among cloud properties, the vertical distribution is sensitive to climate change. Active sensors integrated into satellites, such as CALIOP (Cloud-Aerosol LIdar with Orthogonal Polarization), make it possible to obtain a detailed vertical distribution of clouds. CALIOP measurements and calibration are more stable over time and more precise than passive remote sensing satellite detectors. CALIOP observations can be simulated in the atmospheric conditions predicted by climate models using lidar simulators such as COSP (</span><span>CFMIP Observation Simulator Package). Moreover, </span><span>cloud properties directly drive the Cloud Radiative Effect (CRE). Understanding how models predict cloud vertical distribution will evolve in the future has implications for how models predict the Cloud Radiative Effect (CRE) at the Top of the Atmosphere (TOA) will evolve in the future. </span></p><p><span>The purpose of our study is to compare, firstly, based on satellite observations (GOCCP) and reanalyzes (ERA5), we will establish the relationship between atmospheric dynamic circulation, opaque cloud properties and TOA CRE. Then, we will compare this observed relationship with the one found in climate model simulations of current climate conditions (CESM1 and IPSL-CM6). Finally, we will identify how model biases in present climate conditions influence the cloud feedback spread between models in a warmer climate.</span></p>


2021 ◽  
pp. 1-64
Author(s):  
Man Yue ◽  
Minghuai Wang ◽  
Jianping Guo ◽  
Haipeng Zhang ◽  
Xinyi Dong ◽  
...  

AbstractThe planetary boundary layer (PBL) plays an essential role in climate and air quality simulations. Nevertheless, large uncertainties remain in understanding the drivers for long-term trend of PBL height (PBLH) and its simulation. Here we combinate the radiosonde data and reanalysis datasets to analyze PBLH long-term trends over China, and to further explore the performance of CMIP6 climate models in simulating these trends. Results show that the observed long-term “positive to negative” trend shift of PBLH is related to the variation in the surface upward sensible heat flux (SHFLX), and the SHFLX is further controlled by the synergistic effect of low cloud cover (LCC) and soil moisture (SM) changes. Variabilities in LCC and SM directly influence the energy balance via surface net downward shortwave flux (SWF) and the latent heat flux (LHFLX), respectively. The CMIP6 climate models, however, cannot reproduce the observed PBLH long-term trend shift over China. The CMIP6 results illustrate an overwhelming continuous downward PBLH trend during the 1979–2014 period, which is largely caused by the poor capability in simulating long-term variations of cloud radiative effect. Our results reveal that the long-term cloud radiative effect simulation is critical for CMIP6 models in reproducing the long-term trend of PBLH. This study highlights the importance of processes associated with LCC and SM in modulating PBLH long-term variations and calls attentions to improve these processes in climate models in order to improve the PBLH long-term trend simulations.


2018 ◽  
Vol 31 (22) ◽  
pp. 9293-9312 ◽  
Author(s):  
A. Lacour ◽  
H. Chepfer ◽  
N. B. Miller ◽  
M. D. Shupe ◽  
V. Noel ◽  
...  

Using lidar and radiative flux observations from space and ground, and a lidar simulator, we evaluate clouds simulated by climate models over the Greenland ice sheet, including predicted cloud cover, cloud fraction profile, cloud opacity, and surface cloud radiative effects. The representation of clouds over Greenland is a central concern for the models because clouds impact ice sheet surface melt. We find that over Greenland, most of the models have insufficient cloud cover during summer. In addition, all models create too few nonopaque, liquid-containing clouds optically thin enough to let direct solar radiation reach the surface (−1% to −3.5% at the ground level). Some models create too few opaque clouds. In most climate models, the cloud properties biases identified over all Greenland also apply at Summit, Greenland, proving the value of the ground observatory in model evaluation. At Summit, climate models underestimate cloud radiative effect (CRE) at the surface, especially in summer. The primary driver of the summer CRE biases compared to observations is the underestimation of the cloud cover in summer (−46% to −21%), which leads to an underestimated longwave radiative warming effect (CRELW = −35.7 to −13.6 W m−2 compared to the ground observations) and an underestimated shortwave cooling effect (CRESW = +1.5 to +10.5 W m−2 compared to the ground observations). Overall, the simulated clouds do not radiatively warm the surface as much as observed.


2016 ◽  
Author(s):  
Elisa T. Sena ◽  
Allison McComiskey ◽  
Graham Feingold

Abstract. Empirical estimates of the microphysical response of cloud droplet size distribution to aerosol perturbations are commonly used to constrain aerosol–cloud interactions in climate models. Instead of empirical microphysical estimates, here macroscopic variables are analyzed to address the influences of aerosol particles and meteorological descriptors on instantaneous cloud albedo and radiative effect of shallow liquid water clouds. Long-term ground-based measurements from the Atmospheric Radiation Measurement (ARM) Program over the Southern Great Plains are used. A broad statistical analysis was performed on 14-years of coincident measurements of low clouds, aerosol and meteorological properties. Two cases representing conflicting results regarding the relationship between the aerosol and the cloud radiative effect were selected and studied in greater detail. Microphysical estimates are shown to be very uncertain and to depend strongly on the methodology, retrieval technique, and averaging scale. For this continental site, the results indicate that the influence of aerosol on shallow cloud radiative effect and albedo is weak and that macroscopic cloud properties and dynamics play a much larger role in determining the instantaneous cloud radiative effect compared to microphysical effects.


2007 ◽  
Vol 7 (6) ◽  
pp. 17117-17146
Author(s):  
T. Wagner ◽  
S. Beirle ◽  
T. Deutschmann ◽  
M. Grzegorski ◽  
U. Platt

Abstract. Cloud climate feedback constitutes the most important uncertainty in climate modelling, and currently even its sign is still unknown. In the recently published report of the intergovernmental panel on climate change (IPCC), from 20 climate models 6 showed a positive and 14 a negative cloud radiative feedback in a doubled CO2 scenario. The radiative budget of clouds has also been investigated by experimental methods, especially by studying the relation of satellite observed broad band shortwave and longwave radiation to sea surface temperature. Here we present a new method for the investigation of the dependence of cloud properties on temperature changes, derived from spectrally resolved UV/vis satellite observations. Our study differs from previous investigations in three important ways: first, we directly extract cloud properties (amount and altitude) and relate them to surface temperature. Second, we retrieve the cloud altitude from the atmospheric O2 absorption instead from thermal IR radiation. Third, our correlation analysis is performed using 7.5 years of global monthly anomalies (with respect to the average of the same month for all years). For most parts of the globe (except the tropics) we find a negative correlation of cloud fraction versus surface-near temperature. In contrast, for the cloud top height a positive correlation is found for almost the whole globe. Both findings might serve as an indicator for an overall positive cloud climate feedback. Another peculiarity of our study is that the cloud-temperature relationships are determined for fixed locations (instead to spatial variations over selected areas) and are based on the "natural" variability over several years (instead the anomaly for a strong El-Nino event). Thus our results might be especially representative for the extrapolation to long term climate changes. Climate models should aim to reproduce our findings: if substantial differences are found, this might indicate that important details are not yet well captured by these models. If good agreement is found, from the models reliable information on the magnitude and the detail mechanisms of cloud climate feedback could be gained.


2017 ◽  
Author(s):  
Pamela Trisolino ◽  
Alcide di Sarra ◽  
Fabrizio Anello ◽  
Carlo Bommarito ◽  
Tatiana Di Iorio ◽  
...  

Abstract. Measurements of global and diffuse photosynthetically active radiation (PAR) have been carried out on the island of Lampedusa, in the central Mediterranean Sea, since 2002. PAR is derived from observations made with multi filter rotating shadowband radiometers (MFRSRs) by comparison with a freshly calibrated PAR sensor and by relying on the on-site Langley plots. In this way, a long-term calibrated record covering the period 2002–2016 is obtained and is presented in this work. The monthly mean global PAR peaks in June, with about 160 W m−2, while the diffuse PAR reaches 60 W m−2 in some cases in spring or summer. The global PAR displays a clear annual cycle with a semi amplitude of about 52 W m−2. The diffuse PAR annual cycle has a semi amplitude of about 12 W m−2 (about 23 % of the annual mean value). The diffuse PAR is about 39 % of the global, with a marked seasonal variation, between about 25–30 % in winter and about 50 % in summer. A simple method to retrieve the cloud-free PAR global and diffuse irradiances in days characterized by partly cloudy conditions has been implemented and applied to the dataset. This method allows to retrieve the cloud-free evolution of PAR, and to calculate the cloud radiative effect. CRE, for downwelling PAR. The cloud-free monthly mean global PAR reaches 175 W m−2 in summer, and the diffuse PAR about 40 W m−2. The annual semi amplitudes are similar for all-sky and cloud-free data. The diffuse PAR for the cloud-free cases is about 24 % of the global. The cloud radiative effect, CRE, on global and diffuse PAR is calculated as the difference between all-sky and cloud-free measurements. The average CRE is about −14.7 W m−2 for the global, and +8.1 W m−2 for the diffuse PAR. The smallest CRE is observed in July, due to the high cloud-free conditions frequency. Maxima (negative for the global, and positive for the diffuse component) occur in March–April and in October, due to the combination of elevated PAR irradiances and high occurrence of cloudy conditions. Largest monthly mean values of CRE are at about −31 W m−2 for the global (April 2007), and +18 W m−2 for the diffuse component (April 2010). Summer clouds appear to be characterized by a low frequency of occurrence, low altitude, and low optical thickness, possibly linked to the peculiar marine boundary layer structure. These properties also contribute to produce small radiative effects on PAR in summer. The cloud radiative effect has been de-seasonalized to remove the influence of annual irradiance variations. The monthly mean normalized CRE for global PAR shows a statistically significant high correlation with monthly cloud fraction, cloud top pressure, and cloud optical thickness, as determined from satellite MODIS observations. The normalized CRE for diffuse PAR show lower correlations, although still statistically significant, with cloud fraction and cloud top pressure, while displays a limited correlation with cloud optical thickness. Cloud fraction seems to be the most relevant parameter driving the cloud radiative effects. Normalized CRE data have been de-seasonalized and related with variations of the de-seasonalized PAR. A statistically significant correlation is found between the de-seasonalized PAR and the de-seasonalized normalized CRE. This correlation is seasonally dependent, and suggests that about 77 % of the global PAR interannual variability may be ascribed to clouds variability in winter.


2008 ◽  
Vol 8 (9) ◽  
pp. 2299-2312 ◽  
Author(s):  
T. Wagner ◽  
S. Beirle ◽  
T. Deutschmann ◽  
M. Grzegorski ◽  
U. Platt

Abstract. Cloud climate feedback constitutes the most important uncertainty in climate modelling, and currently even its sign is still unknown. In the recently published report of the intergovernmental panel on climate change (IPCC), 6 out of 20 climate models showed a positive and 14 a negative cloud radiative feedback in a doubled CO2 scenario. The radiative budget of clouds has also been investigated by experimental methods, especially by studying the relation of satellite observed broad band shortwave and longwave radiation to sea surface temperature. Here we present a new method for the investigation of the dependence of cloud properties on temperature changes, derived from spectrally resolved satellite observations in the visible spectral range. Our study differs from previous investigations in three important ways: first, we directly extract cloud properties (effective cloud fraction and effective cloud top height) and relate them to surface temperature. Second, we retrieve the cloud altitude from the atmospheric O2 absorption instead from thermal IR radiation. Third, our correlation analysis is performed using 7.5 years of global monthly anomalies (with respect to the average of the same month for all years). For most parts of the globe (except the tropics) we find a negative correlation of effective cloud fraction versus surface-near temperature. In contrast, for the effective cloud top height a positive correlation is found for almost the whole globe. Both findings might serve as an indicator for an overall positive cloud radiative feedback. Another peculiarity of our study is that the cloud-temperature relationships are determined for fixed locations (instead to spatial variations over selected areas) and are based on the "natural" variability over several years (instead the anomaly for a strong El-Nino event). From a detailed comparison to cloud properties from the International Satellite Cloud Climatology Project (ISCCP), in general good agreement is found. However, also systematic differences occurred indicating that our results provide independent and complementary information on cloud properties. Climate models should thus aim to reproduce our findings. Recommendations for the development of a "processor" to convert model results into the cloud sensitive quantities observed by the satellite are given.


2007 ◽  
Vol 7 (4) ◽  
pp. 11797-11837 ◽  
Author(s):  
E. I. Kassianov ◽  
L. K. Berg ◽  
C. Flynn ◽  
S. McFarlane

Abstract. The objective of this study is to investigate, by observational means, the magnitude and sign of the actively discussed relationship between cloud fraction N and aerosol optical depth τa. Collocated and coincident ground-based measurements and Terra/Aqua satellite observations at the Atmospheric Radiation Measurement (ARM) Climate Research Facility (ACRF) Southern Great Plains (SGP) site form the basis of this study. The N–τa relationship occurred in a specific 5-year dataset of fair-weather cumulus (FWC) clouds and mostly non-absorbing aerosols. To reduce possible contamination of the aerosols on the cloud properties estimation (and vice versa), we use independent datasets of τa and N obtained from the Multi-filter Rotating Shadowband Radiometer (MFRSR) measurements and from the ARM Active Remotely Sensed Clouds Locations (ARSCL) value-added product, respectively. Optical depth of the FWC clouds τcld and effective radius of cloud droplets re are obtained from the MODerate resolution Imaging Spectroradiometer (MODIS) data. We found that relationships between cloud properties (N,τcld, re) and aerosol optical depth are time-dependent (morning versus afternoon). Observed time-dependent changes of cloud properties, associated with aerosol loading, control the variability of surface radiative fluxes. In comparison with pristine clouds, the polluted clouds are more transparent in the afternoon due to smaller cloud fraction, smaller optical depth and larger droplets. As a result, the corresponding correlation between the surface radiative flux and τa is positive (warming effect of aerosol). Also we found that relationship between cloud fraction and aerosol optical depth is cloud size dependent. The cloud fraction of large clouds (larger than 1 km) is relatively insensitive to the aerosol amount. In contrast, cloud fraction of small clouds (smaller than 1 km) is strongly positively correlated with τa. This suggests that an ensemble of polluted clouds tends to be composed of smaller clouds than a similar one in a pristine environment. One should be aware of these time- and size-dependent features when qualitatively comparing N–τa relationships obtained from the satellite observations, surface measurements, and model simulations.


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