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.

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 Does Antarctic glaciation cool the world?

2013 ◽  
Vol 9 (1) ◽  
pp. 173-189 ◽  
Author(s):  
A. Goldner ◽  
M. Huber ◽  
R. Caballero
Keyword(s):  
Surface Temperature ◽  
Climate Sensitivity ◽  
Radiative Forcing ◽  
Climate Models ◽  
Ice Sheet ◽  
Cloud Feedback ◽  
Modern World ◽  
Global Mean Surface Temperature ◽  
Effective Radiative Forcing ◽  
Global Mean

Abstract. In this study, we compare the simulated climatic impact of adding an Antarctic ice sheet (AIS) to the "greenhouse world" of the Eocene and removing the AIS from the modern world. The modern global mean surface temperature anomaly (ΔT) induced by Antarctic Glaciation depends on the background CO2 levels and ranges from −1.22 to −0.18 K. The Eocene ΔT is nearly constant at ~−0.25 K. We calculate an climate sensitivity parameter S[Antarctica] which we define as ΔT divided by the change in effective radiative forcing (ΔQAntarctica) which includes some fast feedbacks imposed by prescribing the glacial properties of Antarctica. The main difference between the modern and Eocene responses is that a negative cloud feedback warms much of the Earth's surface as a large AIS is introduced in the Eocene, whereas this cloud feedback is weakly positive and acts in combination with positive sea-ice feedbacks to enhance cooling introduced by adding an ice sheet in the modern. Because of the importance of cloud feedbacks in determining the final temperature sensitivity of the AIS, our results are likely to be model dependent. Nevertheless, these model results suggest that the effective radiative forcing and feedbacks induced by the AIS did not significantly decrease global mean surface temperature across the Eocene–Oligocene transition (EOT −34.1 to 33.6 Ma) and that other factors like declining atmospheric CO2 are more important for cooling across the EOT. The results illustrate that the efficacy of AIS forcing in the Eocene is not necessarily close to one and is likely to be model and state dependent. This implies that using EOT paleoclimate proxy data by itself to estimate climate sensitivity for future climate prediction requires climate models and consequently these estimates will have large uncertainty, largely due to uncertainties in modelling low clouds.

scholarly journals Dynamics of Muddy Rain of 15 June 2018 in Nepal

Atmosphere ◽  
2020 ◽  
Vol 11 (5) ◽  
pp. 529
Author(s):  
Ashok Kumar Pokharel ◽  
Tianli Xu ◽  
Xiaobo Liu ◽  
Binod Dawadi
Keyword(s):  
Dust Emission ◽  
Dust Storms ◽  
Strong Wind ◽  
Hysplit Model ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Upper Level ◽  
Terra Satellite ◽  
Negative Buoyancy

It has been revealed from the Modern-Era Retrospective analysis for Research and Applications MERRA analyses, Moderate Resolution Imaging Spectroradiometer MODIS/Terra satellite imageries, Naval Aerosol Analysis and Prediction System NAAPS model outputs, Cloud –Aerosol Lidar and Infrared Pathfinder Satellite Observations CALIPSO imageries, Hybrid Single Particle Lagrangian Integrated Trajectory HYSPLIT model trajectories, atmospheric soundings, and observational records of dust emission that there were multiple dust storms in the far western parts of India from 12 to 15 June 2018 due to thunderstorms. This led to the lifting of the dust from the surface. The entry of dust into the upper air was caused by the generation of a significant amount of turbulent kinetic energy as a function of strong wind shear generated by the negative buoyancy of the cooled air aloft and the convective buoyancy in the lower planetary boundary layer. Elevated dust reached a significant vertical height and was advected towards the northern/northwestern/northeastern parts of India. In the meantime, this dust was carried by northwesterly winds associated with the jets in the upper level, which advected dust towards the skies over Nepal where rainfall was occurring at that time. Consequently, this led to the muddy rain in Nepal.

scholarly journals Towards Reliable Probabilistic Time-Series Projections of Global Mean Surface Temperature

2021 ◽  
Author(s):  
Philip G. Sansom ◽  
Donald Cummins ◽  
Stefan Siegert ◽  
David B Stephenson
Keyword(s):  
Time Series ◽  
Global Warming ◽  
Surface Temperature ◽  
Climate Model ◽  
Future Projections ◽  
Perfect Model ◽  
Global Mean Surface Temperature ◽  
Time Series Approach ◽  
Energy Balance Models ◽  
Global Mean

Abstract Quantifying the risk of global warming exceeding critical targets such as 2.0 ◦ C requires reliable projections of uncertainty as well as best estimates of Global Mean Surface Temperature (GMST). However, uncertainty bands on GMST projections are often calculated heuristically and have several potential shortcomings. In particular, the uncertainty bands shown in IPCC plume projections of GMST are based on the distribution of GMST anomalies from climate model runs and so are strongly determined by model characteristics with little influence from observations of the real-world. Physically motivated time-series approaches are proposed based on fitting energy balance models (EBMs) to climate model outputs and observations in order to constrain future projections. It is shown that EBMs fitted to one forcing scenario will not produce reliable projections when different forcing scenarios are applied. The errors in the EBM projections can be interpreted as arising due to a discrepancy in the effective forcing felt by the model. A simple time-series approach to correcting the projections is proposed based on learning the evolution of the forcing discrepancy so that it can be projected into the future. This approach gives reliable projections of GMST when tested in a perfect model setting. When applied to observations this leads to projected warming of 2.2 ◦ C (1.7 ◦ C to 2.9 ◦ C) in 2100 compared to pre-industrial conditions, 0.4 ◦ C lower than a comparable IPCC anomaly estimate. The probability of staying below the critical 2.0 ◦ C warming target in 2100 more than doubles to 0.28 compared to only 0.11 from a comparably IPCC estimate.

scholarly journals The decreasing albedo of Zhadang glacier on western Nyainqentanglha and the role of light-absorbing impurities

2014 ◽  
Vol 14 (9) ◽  
pp. 13109-13131 ◽  
Author(s):  
B. Qu ◽  
J. Ming ◽  
S.-C. Kang ◽  
G.-S. Zhang ◽  
Y.-W. Li ◽  
...  
Keyword(s):  
Mass Balance ◽  
Atmospheric Aerosols ◽  
Radiative Forcing ◽  
Large Change ◽  
In Situ Data ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Glacier Ablation ◽  
Terra Satellite

Abstract. The large change in albedo has a great effect on glacier ablation. Atmospheric aerosols (e.g. black carbon (BC) and dust) can reduce the albedo of glaciers and thus contribute to their melting. In this study, we investigated the measured albedo as well as the relationship between albedo and mass balance in Zhadang glacier on Mt. Nyanqentanglha associated with MODIS (10A1) data. The impacts of BC and dust in albedo reduction in different melting conditions were identified with SNow ICe Aerosol Radiative (SNICAR) model and in-situ data. It was founded that the mass balance of the glacier has a significant correlation with its surface albedo derived from Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Terra satellite. The average albedo of Zhadang glacier from MODIS increased with the altitude and fluctuated but overall had a decreasing trend during 2001–2010, with the highest (0.722) in 2003 and the lowest (0.597) in 2009 and 2010, respectively. The sensitivity analysis via SNICAR showed that BC was a major factor in albedo reduction when the glacier was covered by newly fallen snow. Nevertheless, the contribution of dust to albedo reduction can be as high as 58% when the glacier experienced strong surficial melting that the surface was almost bare ice. And the average radiative forcing (RF) caused by dust could increase from 1.1 to 8.6 W m−2 exceeding the forcings caused by BC after snow was deposited and surface melting occurred in Zhadang glacier. This suggest that it may be dust rather than BC, dominating the melting of some glaciers in the TP during melting seasons.

scholarly journals Variations in Winter Surface Temperature of the Purog Kangri Ice Field, Qinghai–Tibetan Plateau, 2001–2018, Using MODIS Data

2020 ◽  
Vol 12 (7) ◽  
pp. 1133
Author(s):  
Yufan Qie ◽  
Ninglian Wang ◽  
Yuwei Wu ◽  
An’an Chen
Keyword(s):  
Tibetan Plateau ◽  
Surface Temperature ◽  
Land Surface ◽  
Remote Sensing Data ◽  
Warming Trend ◽  
Data Set ◽  
Moderate Resolution ◽  
Ice Field ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Meteorological Observations

In the context of global warming, the land surface temperature (LST) from remote sensing data is one of the most useful indicators to directly quantify the degree of climate warming in high-altitude mountainous areas where meteorological observations are sparse. Using the daily Moderate Resolution Imaging Spectroradiometer (MODIS) LST product (MOD11A1 V6) after eliminating pixels that might be contaminated by clouds, this paper analyzes temporal and spatial variations in the mean LST on the Purog Kangri ice field, Qinghai–Tibetan Plateau, in winter from 2001 to 2018. There was a large increasing trend in LST (0.116 ± 0.05 °C·a−1) on the Purog Kangri ice field during December, while there was no evident LST rising trend in January and February. In December, both the significantly decreased albedo (−0.002 a−1, based on the MOD10A1 V6 albedo product) on the ice field surface and the significantly increased number of clear days (0.322 d·a−1) may be the main reason for the significant warming trend in the ice field. In addition, although the two highest LST of December were observed in 2017 and 2018, a longer data set is needed to determine whether this is an anomaly or a hint of a warmer phase of the Purog Kangri ice field in December.

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