On the Association of the Summertime Shortwave Cloud Radiative Effect in Northern Russia with Atmospheric Circulation and Climate over East Asia

2022 ◽  
Author(s):  
Le Liu ◽  
Bingyi Wu ◽  
Shuoyi Ding
Keyword(s):  
East Asia ◽  
Atmospheric Circulation ◽  
Radiative Effect ◽  
Cloud Radiative Effect ◽  
Northern Russia

Quantifying the interplay of clouds and their environment through an energetic lens during EUREC4A

2021 ◽  
Author(s):  
Anna Lea Albright ◽  
Sandrine Bony ◽  
Bjorn Stevens ◽  
Raphaela Vogel
Keyword(s):  
Large Scale ◽  
Hydrological Cycle ◽  
Trade Wind ◽  
Energy Budgets ◽  
Moist Static Energy ◽  
Radiative Effect ◽  
The North ◽  
Cloud Radiative Effect ◽  
Moist Static Energy Budget ◽  
Static Energy

<p>The trades form an important link in the atmospheric energy supply, transporting moisture and momentum to the deep tropics and influencing the global hydrological cycle. Trade-wind cumuli are the most ubiquitous cloud type over tropical oceans, yet models disagree in simulating their response to warming. Our study takes advantage of extensive in-situ soundings performed during the EUREC4A campaign, which took place in the downstream trades of the North Atlantic in winter 2020. We employ 1068 dropsondes made in a ca. 2deg x 2deg area to close the moisture and energy budgets of the subcloud layer and atmospheric column. Our motivation for closing moisture and energy budgets using EUREC4A data is two-fold. First, we try to understand which large-scale environmental factors control variability in subcloud layer moisture and moist static energy, given their influence on setting convective potential. Second, we quantify the interplay between clouds and their environment through an energetic lens. The cloud radiative effect emerges as a residual from the total column moist static energy budget, yielding an energetic estimate of clouds. We quantify how this cloud radiative effect compares with coincident satellite and geometric (i.e. cloud fraction) estimates of cloudiness, varies on different scales, and relates to large-scale environmental conditions.</p>

The Surface Longwave Cloud Radiative Effect from Space Lidar Observations

2021 ◽  
Author(s):  
Assia Arouf
Keyword(s):  
Cloud Cover ◽  
Temporal Variations ◽  
Radiative Effect ◽  
Cloud Properties ◽  
Cloud Radiative Effect ◽  
Long Time ◽  
The One ◽  
Natural Climate ◽  
The Impact ◽  
Lidar Observations

<p>Clouds exert important effects on Earth's surface energy balance through their effects on longwave (LW) and shortwave (SW) radiation. Indeed, clouds radiatively warm the surface in the LW domain by emitting LW radiation back to the ground. The surface LW cloud radiative effect (CRE) quantifies this warming effect. To study the impact of clouds on the interanual natural climate variability, we need to observe them on a long time scale over all kinds of surfaces. The CALIPSO space lidar provides these observations by sampling the atmosphere along its track over all kinds of surfaces for over than 14 years (2006-2020).</p><p>In this work, we propose new estimates of the surface LW CRE from space-based lidar observations only. Indeed, we show from 1D atmospheric column radiative transfer calculations, that surface LW CRE at sea level linearly decreases with the cloud altitude. Thus, these computations allow to establish simple relationships between the surface LW CRE, and five cloud properties observed by the CALIPSO space lidar: the opaque cloud cover and altitude, the thin cloud cover, altitude, and emissivity. Over the 2008–2011, CALIPSO-based retrieval (27.7 W m<sup>-2</sup>) is 1.2 W m<sup>-2</sup> larger than the one derived from combined space radar, lidar, and radiometer observations. Over the 2008–2018 period, the global mean CALIPSO-based retrieval (27.5 W m<sup>-2</sup>) is 0.1 W m<sup>-2</sup> larger than the one derived from CERES space radiometer. Our estimates show that globally, opaque clouds warm the surface by 23.3 W m<sup>-2</sup> and thin clouds contribute only by 4.2 W m<sup>-2</sup>. At high latitudes North and South over oceans, the largest surface LW opaque CRE occurs in fall (40.4 W m<sup>-2</sup>, 31.6 W m<sup>-2</sup>) due to the formation of additional opaque low clouds after sea ice melting over a warmer ocean.</p><p>To quantify the cloud property that drives the temporal variations of the surface LW CRE, the surface LW CRE needs to be related by simple relationships to a finite number of cloud properties such as cloud opacity, cloud altitude and cloud cover. This study allows a decomposition and attribution approach of the surface LW CRE variations and shows that they are driven by the variations occurring in the opaque cloud properties. Moreover, opaque cloud cover drives over than 73% of global surface LW CRE interannual variations.</p>

scholarly journals Toward a Consistent Definition between Satellite and Model Clear-Sky Radiative Fluxes

2020 ◽  
Vol 33 (1) ◽  
pp. 61-75 ◽  
Author(s):  
Norman G. Loeb ◽  
Fred G. Rose ◽  
Seiji Kato ◽  
David A. Rutan ◽  
Wenying Su ◽  
...  
Keyword(s):  
Tropical Pacific ◽  
Radiative Effect ◽  
The West ◽  
Model Calculations ◽  
Radiative Fluxes ◽  
Clear Sky ◽  
Cloud Radiative Effect ◽  
Adjustment Factors ◽  
The Impact ◽  
Global Mean

AbstractA new method of determining clear-sky radiative fluxes from satellite observations for climate model evaluation is presented. The method consists of applying adjustment factors to existing satellite clear-sky broadband radiative fluxes that make the observed and simulated clear-sky flux definitions more consistent. The adjustment factors are determined from the difference between observation-based radiative transfer model calculations of monthly mean clear-sky fluxes obtained by ignoring clouds in the atmospheric column and by weighting hourly mean clear-sky fluxes with imager-based clear-area fractions. The global mean longwave (LW) adjustment factor is −2.2 W m−2 at the top of the atmosphere and 2.7 W m−2 at the surface. The LW adjustment factors are pronounced at high latitudes during winter and in regions with high upper-tropospheric humidity and cirrus cloud cover, such as over the west tropical Pacific, and the South Pacific and intertropical convergence zones. In the shortwave (SW), global mean adjustment is 0.5 W m−2 at TOA and −1.9 W m−2 at the surface. It is most pronounced over sea ice off of Antarctica and over heavy aerosol regions, such as eastern China. However, interannual variations in the regional SW and LW adjustment factors are small compared to those in cloud radiative effect. After applying the LW adjustment factors, differences in zonal mean cloud radiative effect between observations and climate models decrease markedly between 60°S and 60°N and poleward of 65°N. The largest regional improvements occur over the west tropical Pacific and Indian Oceans. In contrast, the impact of the SW adjustment factors is much smaller.

On the Relationships between Subtropical Clouds and Meteorology in Observations and CMIP3 and CMIP5 Models*

2015 ◽  
Vol 28 (8) ◽  
pp. 2945-2967 ◽  
Author(s):  
Timothy A. Myers ◽  
Joel R. Norris
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Cloud Fraction ◽  
Sea Surface ◽  
Radiative Effect ◽  
Low Level ◽  
Temperature Advection ◽  
Cloud Radiative Effect ◽  
High Level ◽  
The Relationship

Abstract Climate models’ simulation of clouds over the eastern subtropical oceans contributes to large uncertainties in projected cloud feedback to global warming. Here, interannual relationships of cloud radiative effect and cloud fraction to meteorological variables are examined in observations and in models participating in phases 3 and 5 of the Coupled Model Intercomparison Project (CMIP3 and CMIP5, respectively). In observations, cooler sea surface temperature, a stronger estimated temperature inversion, and colder horizontal surface temperature advection are each associated with larger low-level cloud fraction and increased reflected shortwave radiation. A moister free troposphere and weaker subsidence are each associated with larger mid- and high-level cloud fraction and offsetting components of shortwave and longwave cloud radiative effect. It is found that a larger percentage of CMIP5 than CMIP3 models simulate the wrong sign or magnitude of the relationship of shortwave cloud radiative effect to sea surface temperature and estimated inversion strength. Furthermore, most models fail to produce the sign of the relationship between shortwave cloud radiative effect and temperature advection. These deficiencies are mostly, but not exclusively, attributable to errors in the relationship between low-level cloud fraction and meteorology. Poor model performance also arises due to errors in the response of mid- and high-level cloud fraction to variations in meteorology. Models exhibiting relationships closest to observations tend to project less solar reflection by clouds in the late twenty-first century and have higher climate sensitivities than poorer-performing models. Nevertheless, the intermodel spread of climate sensitivity is large even among these realistic models.

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