scholarly journals Surface and top-of-atmosphere radiative feedback kernels for CESM-CAM5

2017 ◽  
Author(s):  
Angeline G. Pendergrass ◽  
Andrew Conley ◽  
Francis Vitt
Keyword(s):  
Water Vapor ◽  
Greenhouse Gases ◽  
Surface Temperature ◽  
Radiative Forcing ◽  
Surface Albedo ◽  
Large Ensemble ◽  
Radiative Fluxes ◽  
Radiative Feedback ◽  
Ensemble Simulations ◽  
Temperature Water

Abstract. Radiative kernels at the top of atmosphere are useful for decomposing changes in atmospheric radiative fluxes due to feedbacks from atmosphere and surface temperature, water vapor, and surface albedo. Here we describe and validate radiative kernels calculated with the large-ensemble version of CAM5, CESM 1.1.2, at the top-of-atmosphere and the surface. Estimates of the radiative forcing from greenhouse gases and aerosols in RCP8.5 in the CESM large ensemble simulations are also included. As an application, feedbacks are calculated for the CESM large ensemble. The kernels are freely available at https://zenodo.org/record/997902, and accompanying software can be downloaded from https://github.com/apendergrass/cam5-kernels.

scholarly journals Surface and top-of-atmosphere radiative feedback kernels for CESM-CAM5

2018 ◽  
Vol 10 (1) ◽  
pp. 317-324 ◽  
Author(s):  
Angeline G. Pendergrass ◽  
Andrew Conley ◽  
Francis M. Vitt
Keyword(s):  
Water Vapor ◽  
Greenhouse Gases ◽  
Surface Temperature ◽  
Radiative Forcing ◽  
Surface Albedo ◽  
Large Ensemble ◽  
Radiative Fluxes ◽  
Radiative Feedback ◽  
Ensemble Simulations ◽  
Temperature Water

Abstract. Radiative kernels at the top of the atmosphere are useful for decomposing changes in atmospheric radiative fluxes due to feedbacks from atmosphere and surface temperature, water vapor, and surface albedo. Here we describe and validate radiative kernels calculated with the large-ensemble version of CAM5, CESM1.1.2, at the top of the atmosphere and the surface. Estimates of the radiative forcing from greenhouse gases and aerosols in RCP8.5 in the CESM large-ensemble simulations are also diagnosed. As an application, feedbacks are calculated for the CESM large ensemble. The kernels are freely available at https://doi.org/10.5065/D6F47MT6, and accompanying software can be downloaded from https://github.com/apendergrass/cam5-kernels.

scholarly journals Reply to: "Tropical cirrus and water vapor: an effective Earth infrared iris feedback?"

2002 ◽  
Vol 2 (2) ◽  
pp. 99-101 ◽  
Author(s):  
M.-D. Chou ◽  
R. S. Lindzen ◽  
A. Y. Hou
Keyword(s):  
Boundary Layer ◽  
Optical Properties ◽  
Water Vapor ◽  
Surface Temperature ◽  
Climate Sensitivity ◽  
Physical Meaning ◽  
Radiative Forcing ◽  
Sea Surface ◽  
Boundary Layer Clouds ◽  
High Level

Abstract. In assessing the iris effect suggested by Lindzen et al. (2001), Fu et al. (2002) found that the response of high-level clouds to the sea surface temperature had an effect of reducing the climate sensitivity to external radiative forcing, but the effect was not as strong as LCH found. The approach of FBH to specifying longwave emission and cloud albedos appears to be inappropriate, and the derived cloud optical properties may not have real physical meaning. The cloud albedo calculated by FBH is too large for cirrus clouds and too small for boundary layer clouds, which underestimates the iris effect.

scholarly journals Effects of Surface Albedo on Cloud and Radiation Processes in Cloud-Resolving Model Simulations

2010 ◽  
Vol 67 (5) ◽  
pp. 1474-1491 ◽  
Author(s):  
Sunwook Park ◽  
Xiaoqing Wu
Keyword(s):  
Radiative Forcing ◽  
Surface Albedo ◽  
Critical Value ◽  
Cloud Base ◽  
Radiative Fluxes ◽  
Cloud Properties ◽  
Cloud Resolving Model ◽  
Albedo Effect ◽  
Evolving Surface ◽  
Radiation Processes

Abstract The relationship among the surface albedo, cloud properties, and radiative fluxes is investigated for the first time using a year-long cloud-resolving model (CRM) simulation with the prescribed evolving surface albedo. In comparison with the run using a fixed surface albedo, the CRM with the observed surface albedo represents the shortwave radiative budget closer to the observations in the winter. The greater surface albedo induces weaker instability in the low troposphere so that the amount of low clouds decreases during the winter. This reduces the shortwave and longwave cloud radiative forcing at the surface. The analysis of the CRM simulations with the evolving surface albedo reveals that there is a critical value (0.35) of the surface albedo. For albedos greater than the critical value, the upward shortwave flux at the top of the atmosphere (TOA) is positively proportional to the surface albedos when optically thin clouds exist, and is not much affected by reflection on the cloud top. If optically thick clouds occur and the surface albedo is greater than the critical value, the upward shortwave flux at the TOA is significantly influenced by the reflection of cloud top, but not much affected by the surface albedo. In addition, for albedos larger than the critical value, the downward shortwave flux at the surface is primarily influenced by the surface albedo and the reflection from the cloud base if optically thick clouds occur. However, the downward shortwave flux at the surface is not significantly affected by the surface albedo when optically thin clouds exist because the reflection on the cloud base is weak. When surface albedos are less than the critical value, those relationships among surface albedo, shortwave flux, and cloud properties are not obvious. The surface albedo effect on shortwave flux increases as solar zenith angle (SZA) decreases, but its dependence on the SZA is negligible when optically thick clouds exist.

A Climatology of Midlatitude Continental Clouds from the ARM SGP Central Facility. Part II: Cloud Fraction and Surface Radiative Forcing

2006 ◽  
Vol 19 (9) ◽  
pp. 1765-1783 ◽  
Author(s):  
Xiquan Dong ◽  
Baike Xi ◽  
Patrick Minnis
Keyword(s):  
Water Vapor ◽  
Radiative Forcing ◽  
Cloud Cover ◽  
Surface Albedo ◽  
Cloud Fraction ◽  
Total Cloud Cover ◽  
Cloud Radiative Forcing ◽  
Low Clouds ◽  
High Clouds ◽  
Central Facility

Abstract Data collected at the Department of Energy Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) Central Facility (SCF) are analyzed to determine the monthly and hourly variations of cloud fraction and radiative forcing between January 1997 and December 2002. Cloud fractions are estimated for total cloud cover and for single-layered low (0–3 km), middle (3–6 km), and high clouds (>6 km) using ARM SCF ground-based paired lidar–radar measurements. Shortwave (SW) and longwave (LW) fluxes are derived from up- and down-looking standard precision spectral pyranometers and precision infrared radiometer measurements with uncertainties of ∼10 W m−2. The annual averages of total and single-layered low-, middle-, and high-cloud fractions are 0.49, 0.11, 0.03, and 0.17, respectively. Both total- and low-cloud amounts peak during January and February and reach a minimum during July and August; high clouds occur more frequently than other types of clouds with a peak in summer. The average annual downwelling surface SW fluxes for total and low clouds (151 and 138 W m−2, respectively) are less than those under middle and high clouds (188 and 201 W m−2, respectively), but the downwelling LW fluxes (349 and 356 W m−2) underneath total and low clouds are greater than those from middle and high clouds (337 and 333 W m−2). Low clouds produce the largest LW warming (55 W m−2) and SW cooling (−91 W m−2) effects with maximum and minimum absolute values in spring and summer, respectively. High clouds have the smallest LW warming (17 W m−2) and SW cooling (−37 W m−2) effects at the surface. All-sky SW cloud radiative forcing (CRF) decreases and LW CRF increases with increasing cloud fraction with mean slopes of −0.984 and 0.616 W m−2 %−1, respectively. Over the entire diurnal cycle, clouds deplete the amount of surface insolation more than they add to the downwelling LW flux. The calculated CRFs do not appear to be significantly affected by uncertainties in data sampling and clear-sky screening. Traditionally, cloud radiative forcing includes not only the radiative impact of the hydrometeors, but also the changes in the environment. Taken together over the ARM SCF, changes in humidity and surface albedo between clear and cloudy conditions offset ∼20% of the NET radiative forcing caused by the cloud hydrometeors alone. Variations in water vapor, on average, account for 10% and 83% of the SW and LW CRFs, respectively, in total cloud cover conditions. The error analysis further reveals that the cloud hydrometeors dominate the SW CRF, while water vapor changes are most important for LW flux changes in cloudy skies. Similar studies over other locales are encouraged where water and surface albedo changes from clear to cloudy conditions may be much different than observed over the ARM SCF.

Discrepancy in radiative feedbacks between models and observations tied to models inability to reproduce historical surface temperature patterns over the tropical Indo-Pacific.

2020 ◽  
Author(s):  
Cristian Proistosescu ◽  
Yue Dong ◽  
Malte Stuecker ◽  
Kyle Armour ◽  
Robb Wills ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Climate Sensitivity ◽  
Climate Models ◽  
Atmospheric Model ◽  
Natural Variability ◽  
Forced Response ◽  
Large Ensemble ◽  
Radiative Feedback ◽  
Wide Range ◽  
Radiative Feedbacks

<p>How much Earth warms in response to radiative forcing is determined by the net radiative feedback, which quantifies how much more energy is radiated to space for a given increase in surface temperature.  Estimates from present day observations of temperature and earth's energetic imbalance yield a strongly negative radiative feedback, or, equivalently, a very low climate sensitivity, which lies outside the range of climate sensitivity in coupled climate models. This discrepancy in radiative feedbacks can be linked to discrepancies between models and observations in the pattern of historical sea-surface temperature (SST) anomalies driving tropical atmospheric circulation and radiative damping.  Indeed, we find that an atmospheric model (CAM5) forced with observed SSTs yields a net feedback that is consistent with observational estimates, but up to three times more negative than that from the same period (2000-2017) in historical simulations where the same atmospheric model is coupled to a dynamical ocean model (CESM1). </p><p>To understand the role natural variability can play in this discrepancy, we compare the radiative feedbacks generated by the observed pattern of SSTs to those within the CESM1 large ensemble over the same period. The large ensemble produces a wide range of feedbacks due to internal variability alone. Yet, global radiative feedbacks (cloud feedbacks in particular) generated by observed warming patterns are far outside the range of natural variability in the large ensemble. Using both a Green's function approach, as well as a simple metric based on the East-West tropical pacific gradient, we show that none of the control simulations of CMIP5 climate models can generate sufficiently large natural variability to explain the discrepancy between models and observations. We conclude that the discrepancy in SST patterns, and the resulting discrepancy in radiative feedbacks, is caused by an deficiency in models' ability to simulate either natural variabilty or the forced response over the recent historical period. We will also show preliminary analysis from CMIP6 simulations.</p>

scholarly journals Radiative and Dynamical Forcing of the Surface and Atmospheric Temperature Anomalies Associated with the Northern Annular Mode

2013 ◽  
Vol 26 (14) ◽  
pp. 5124-5138 ◽  
Author(s):  
Yi Deng ◽  
Tae-Won Park ◽  
Ming Cai
Keyword(s):  
Water Vapor ◽  
North America ◽  
Surface Temperature ◽  
Surface Albedo ◽  
Temperature Response ◽  
Atmospheric Temperature ◽  
Atmospheric Dynamics ◽  
Surface Dynamics ◽  
Northern Annular Mode ◽  
Temperature Anomalies

Abstract On the basis of the total energy balance within an atmosphere–surface column, an attribution analysis is conducted for the Northern Hemisphere (NH) atmospheric and surface temperature response to the northern annular mode (NAM) in boreal winter. The local temperature anomaly in the European Centre for Medium-Range Weather Forecasts (ECMWF) Interim Re-Analysis (ERA-Interim) is decomposed into partial temperature anomalies because of changes in atmospheric dynamics, water vapor, clouds, ozone, surface albedo, and surface dynamics with the coupled atmosphere–surface climate feedback–response analysis method (CFRAM). Large-scale ascent/descent as part of the NAM-related mean meridional circulation anomaly adiabatically drives the main portion of the observed zonally averaged atmospheric temperature response, particularly the tropospheric cooling/warming over northern extratropics. Contributions from diabatic processes are generally small but could be locally important, especially at lower latitudes where radiatively active substances such as clouds and water vapor are more abundant. For example, in the tropical upper troposphere and stratosphere, both cloud and ozone forcings are critical in leading to the observed NAM-related temperature anomalies. Radiative forcing due to changes in water vapor acts as the main driver of the surface warming of southern North America during a positive phase of NAM, with atmospheric dynamics providing additional warming. In the negative phase of NAM, surface albedo change drives the surface cooling of southern North America, with atmospheric dynamics providing additional cooling. Over the subpolar North Atlantic and northern Eurasia, atmospheric dynamical processes again become the largest contributor to the NAM-related surface temperature anomalies, although changes in water vapor and clouds also contribute positively to the observed surface temperature anomalies while change in surface dynamics contributes negatively to the observed temperature anomalies.

scholarly journals Reply to: “Tropical cirrus and water vapor: an effective Earth infrared iris feedback?"

2002 ◽  
Vol 2 (1) ◽  
pp. 173-180 ◽  
Author(s):  
M.-D. Chou ◽  
R. S. Lindzen ◽  
A. Y. Hou
Keyword(s):  
Boundary Layer ◽  
Optical Properties ◽  
Water Vapor ◽  
Surface Temperature ◽  
Climate Sensitivity ◽  
Physical Meaning ◽  
Radiative Forcing ◽  
Sea Surface ◽  
Boundary Layer Clouds ◽  
High Level

Abstract. In assessing the iris effect suggested by Lindzen et al. (2001), Fu et al. (2001, 2002) found that the response of high-level clouds to the sea surface temperature had an effect of reducing the climate sensitivity to external radiative forcing, but the effect was not as strong as Lindzen et al. (2001) found. The approach of Fu et al. (2001, 2002) to specifying longwave emission and cloud albedos appears to be inappropriate, and the derived cloud optical properties may not have real physical meaning. The cloud albedo calculated by Fu et al. (2001, 2002) is too large for cirrus clouds and too small for boundary layer clouds, which underestimates the iris effect.

Using the Radiative Kernel Technique to Calculate Climate Feedbacks in NCAR’s Community Atmospheric Model

2008 ◽  
Vol 21 (10) ◽  
pp. 2269-2282 ◽  
Author(s):  
Karen M. Shell ◽  
Jeffrey T. Kiehl ◽  
Christine A. Shields
Keyword(s):  
Water Vapor ◽  
Radiative Forcing ◽  
Surface Albedo ◽  
Atmospheric Model ◽  
Cloud Feedback ◽  
Climate Feedback ◽  
Time Step ◽  
Global Average ◽  
Clear Sky ◽  
Vapor Temperature

Abstract Climate models differ in their responses to imposed forcings, such as increased greenhouse gas concentrations, due to different climate feedback strengths. Feedbacks in NCAR’s Community Atmospheric Model (CAM) are separated into two components: the change in climate components in response to an imposed forcing and the “radiative kernel,” the effect that climate changes have on the top-of-the-atmosphere (TOA) radiative budget. This technique’s usefulness depends on the linearity of the feedback processes. For the case of CO2 doubling, the sum of the effects of water vapor, temperature, and surface albedo changes on the TOA clear-sky flux is similar to the clear-sky flux changes directly calculated by CAM. When monthly averages are used rather than values from every time step, the global-average TOA shortwave change is underestimated by a quarter, partially as a result of intramonth correlations of surface albedo with the radiative kernel. The TOA longwave flux changes do not depend on the averaging period. The longwave zonal averages are within 10% of the model-calculated values, while the global average differs by only 2%. Cloud radiative forcing (ΔCRF) is often used as a diagnostic of cloud feedback strength. The net effect of the water vapor, temperature, and surface albedo changes on ΔCRF is −1.6 W m−2, based on the kernel technique, while the total ΔCRF from CAM is −1.3 W m−2, indicating these components contribute significantly to ΔCRF and make it more negative. Assuming linearity of the ΔCRF contributions, these results indicate that the net cloud feedback in CAM is positive.

scholarly journals Why Does Aerosol Forcing Control Historical Global-Mean Surface Temperature Change in CMIP5 Models?

2015 ◽  
Vol 28 (17) ◽  
pp. 6608-6625 ◽  
Author(s):  
Leon D. Rotstayn ◽  
Mark A. Collier ◽  
Drew T. Shindell ◽  
Olivier Boucher
Keyword(s):  
Greenhouse Gases ◽  
Surface Temperature ◽  
Radiative Forcing ◽  
Dominant Factor ◽  
Cmip5 Models ◽  
Surface Temperature Change ◽  
Enhanced Sensitivity ◽  
Aerosol Forcing ◽  
Global Mean Surface Temperature ◽  
Global Mean

Abstract Linear regression is used to examine the relationship between simulated changes in historical global-mean surface temperature (GMST) and global-mean aerosol effective radiative forcing (ERF) in 14 climate models from CMIP5. The models have global-mean aerosol ERF that ranges from −0.35 to −1.60 W m−2 for 2000 relative to 1850. It is shown that aerosol ERF is the dominant factor that determines intermodel variations in simulated GMST change: correlations between aerosol ERF and simulated changes in GMST exceed 0.9 for linear trends in GMST over all periods that begin between 1860 and 1950 and end between 1995 and 2005. Comparison of modeled and observed GMST trends for these time periods gives an inferred global-mean aerosol ERF of −0.92 W m−2. On average, transient climate sensitivity is roughly 40% larger with respect to historical forcing from aerosols than well-mixed greenhouse gases. This enhanced sensitivity explains the dominant effect of aerosol forcing on simulated changes in GMST: it is estimated that 85% of the intermodel variance of simulated GMST change is explained by variations in aerosol ERF, but without the enhanced sensitivity less than half would be explained. Physically, the enhanced sensitivity is caused by a combination of 1) the larger concentration of aerosol forcing in the midlatitudes of the Northern Hemisphere, where positive feedbacks are stronger and transient warming is faster than in the Southern Hemisphere, and 2) the time evolution of aerosol forcing, which levels out earlier than forcing from well-mixed greenhouse gases.

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