scholarly journals Temporal and Spatial Characteristics of Short-Term Cloud Feedback on Global and Local Interannual Climate Fluctuations from A-Train Observations

2019 ◽  
Vol 32 (6) ◽  
pp. 1875-1893 ◽  
Author(s):  
Qing Yue ◽  
Brian H. Kahn ◽  
Eric J. Fetzer ◽  
Sun Wong ◽  
Xianglei Huang ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Temperature Change ◽  
Temporal Variability ◽  
Short Term ◽  
Surface Temperature Change ◽  
Feedback Parameter ◽  
Cloud Feedbacks ◽  
Local Feedback ◽  
Temporal And Spatial ◽  
Global Mean

AbstractObservations from multiple sensors on the NASA Aqua satellite are used to estimate the temporal and spatial variability of short-term cloud responses (CR) and cloud feedbacks λ for different cloud types, with respect to the interannual variability within the A-Train era (July 2002–June 2017). Short-term cloud feedbacks by cloud type are investigated both globally and locally by three different definitions in the literature: 1) the global-mean cloud feedback parameter λGG from regressing the global-mean cloud-induced TOA radiation anomaly ΔRG with the global-mean surface temperature change ΔTGS; 2) the local feedback parameter λLL from regressing the local ΔR with the local surface temperature change ΔTS; and 3) the local feedback parameter λGL from regressing global ΔRG with local ΔTS. Observations show significant temporal variability in the magnitudes and spatial patterns in λGG and λGL, whereas λLL remains essentially time invariant for different cloud types. The global-mean net λGG exhibits a gradual transition from negative to positive in the A-Train era due to a less negative λGG from low clouds and an increased positive λGG from high clouds over the warm pool region associated with the 2015/16 strong El Niño event. Strong temporal variability in λGL is intrinsically linked to its dependence on global ΔRG, and the scaling of λGL with surface temperature change patterns to obtain global feedback λGG does not hold. Despite the shortness of the A-Train record, statistically robust signals can be obtained for different cloud types and regions of interest.

scholarly journals The Dependence of Radiative Forcing and Feedback on Evolving Patterns of Surface Temperature Change in Climate Models

2015 ◽  
Vol 28 (4) ◽  
pp. 1630-1648 ◽  
Author(s):  
Timothy Andrews ◽  
Jonathan M. Gregory ◽  
Mark J. Webb
Keyword(s):  
Surface Temperature ◽  
Temperature Change ◽  
Radiative Forcing ◽  
General Circulation ◽  
Climate Feedback ◽  
Climate Feedbacks ◽  
Surface Temperature Change ◽  
Feedback Parameter ◽  
Surface Warming ◽  
Global Mean

Abstract Experiments with CO2 instantaneously quadrupled and then held constant are used to show that the relationship between the global-mean net heat input to the climate system and the global-mean surface air temperature change is nonlinear in phase 5 of the Coupled Model Intercomparison Project (CMIP5) atmosphere–ocean general circulation models (AOGCMs). The nonlinearity is shown to arise from a change in strength of climate feedbacks driven by an evolving pattern of surface warming. In 23 out of the 27 AOGCMs examined, the climate feedback parameter becomes significantly (95% confidence) less negative (i.e., the effective climate sensitivity increases) as time passes. Cloud feedback parameters show the largest changes. In the AOGCM mean, approximately 60% of the change in feedback parameter comes from the tropics (30°N–30°S). An important region involved is the tropical Pacific, where the surface warming intensifies in the east after a few decades. The dependence of climate feedbacks on an evolving pattern of surface warming is confirmed using the HadGEM2 and HadCM3 atmosphere GCMs (AGCMs). With monthly evolving sea surface temperatures and sea ice prescribed from its AOGCM counterpart, each AGCM reproduces the time-varying feedbacks, but when a fixed pattern of warming is prescribed the radiative response is linear with global temperature change or nearly so. It is also demonstrated that the regression and fixed-SST methods for evaluating effective radiative forcing are in principle different, because rapid SST adjustment when CO2 is changed can produce a pattern of surface temperature change with zero global mean but nonzero change in net radiation at the top of the atmosphere (~−0.5 W m−2 in HadCM3).

scholarly journals An Analysis of the Short-Term Cloud Feedback Using MODIS Data

2013 ◽  
Vol 26 (13) ◽  
pp. 4803-4815 ◽  
Author(s):  
Chen Zhou ◽  
Mark D. Zelinka ◽  
Andrew E. Dessler ◽  
Ping Yang
Keyword(s):  
Surface Temperature ◽  
Negative Feedback ◽  
Cloud Feedback ◽  
Short Term ◽  
Cloud Feedbacks ◽  
Global Mean Surface Temperature ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Terra Satellite ◽  
Global Mean

Abstract The cloud feedback in response to short-term climate variations is estimated from cloud measurements combined with offline radiative transfer calculations. The cloud measurements are made by the Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA's Terra satellite and cover the period 2000–10. Low clouds provide a strong negative cloud feedback, mainly because of their impact in the shortwave (SW) portion of the spectrum. Midlevel clouds provide a positive net cloud feedback that is a combination of a positive SW feedback partially canceled by a negative feedback in the longwave (LW). High clouds have only a small impact on the net cloud feedback because of a close cancellation between large LW and SW cloud feedbacks. Segregating the clouds by optical depth, it is found that the net cloud feedback is set by a positive cloud feedback due to reductions in the thickest clouds (mainly in the SW) and a cancelling negative feedback from increases in clouds with moderate optical depths (also mainly in the SW). The global average SW, LW, and net cloud feedbacks are +0.30 ±1.10, −0.46 ±0.74, and −0.16 ±0.83 W m−2 K−1, respectively. The SW feedback is consistent with previous work; the MODIS LW feedback is lower than previous calculations and there are reasons to suspect it may be biased low. Finally, it is shown that the apparently small control that global mean surface temperature exerts on clouds, which leads to the large uncertainty in the short-term cloud feedback, arises from statistically significant but offsetting relationships between individual cloud types and global mean surface temperature.

Correction to “GISS analysis of surface temperature change” by J. Hansen et al.

2000 ◽  
Vol 105 (D10) ◽  
pp. 12517-12517 ◽  
Author(s):  
J. Hansen ◽  
R. Ruedy ◽  
J. Glascoe ◽  
M. Sato
Keyword(s):  
Surface Temperature ◽  
Temperature Change ◽  
Surface Temperature Change

GISS analysis of surface temperature change

1999 ◽  
Vol 104 (D24) ◽  
pp. 30997-31022 ◽  
Author(s):  
J. Hansen ◽  
R. Ruedy ◽  
J. Glascoe ◽  
M. Sato
Keyword(s):  
Surface Temperature ◽  
Temperature Change ◽  
Surface Temperature Change
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