Regional Variations of Moist Static Energy Flux into the Arctic

Turet, Philip; Oort, Abraham H.

doi:10.1175/1520-0442(1996)009<0054:rvomse>2.0.co;2

Cited by 51 publications

(31 citation statements)

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“…The changes in northward atmospheric energy transport in the A2 scenario are plotted in Figure 1a, colored in order of their change at 70N. The change in atmospheric energy transport can be separated into moisture and dry static energy (DSE) transport changes (Figure 1b) (see Overland et al [1996] for equations). All of the models project increasing moisture transport, due to increases in water vapor content as the climate warms [ Held and Soden , 2006; Solomon , 2006].…”

Section: Changes In Poleward Energy Transportmentioning

confidence: 99%

Coupling between Arctic feedbacks and changes in poleward energy transport

Hwang

Frierson

Kay

2011

Geophys. Res. Lett.

186

182

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[1] The relationship between poleward energy transport and Arctic amplification is examined using climate models and an energy balance model. In 21st century projections, models with large Arctic amplification have strong surface albedo and longwave cloud feedbacks, but only weak increases (or even decreases) in total energy transport into the Arctic. Enhanced Arctic warming weakens the equator-to-pole temperature gradient and decreases atmospheric dry static energy transport, a decrease that often outweighs increases from atmospheric moisture transport and ocean heat transport. Model spread in atmospheric energy transport cannot explain model spread in polar amplification; models with greater polar amplification must instead have stronger local feedbacks. Because local feedbacks affect temperature gradients, coupling between energy transports and Arctic feedbacks cannot be neglected when studying Arctic amplification.

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Section: Changes In Poleward Energy Transportmentioning

confidence: 99%

Coupling between Arctic feedbacks and changes in poleward energy transport

Hwang

Frierson

Kay

2011

Geophys. Res. Lett.

186

182

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“…The Arctic is already undergoing a transition toward a considerably warmer and less icy future, with signs of this shift being realized unambiguously in terms of rising temperatures, declining sea ice, earlier snowmelt, and intrusions of exotic biota (Walsh et al 2005;Overland et al 2008;Stroeve et al 2007;Hegseth and Sundfjord 2008). Another key unknown is how clouds will affect the Arctic's future climate trajectory.…”

Section: Introductionmentioning

confidence: 99%

Factors Influencing Simulated Changes in Future Arctic Cloudiness

2011

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This study diagnoses the changes in Arctic clouds simulated by the Community Climate System Model version 3 (CCSM3) in a transient 2 3 CO 2 simulation. Four experiments-one fully coupled and three with prescribed SSTs and/or sea ice cover-are used to identify the mechanisms responsible for the projected cloud changes. The target simulation uses a T42 version of the CCSM3, in which the atmosphere is coupled to a dynamical ocean with mobile sea ice. This simulation is approximated by a T42 atmosphere-only integration using CCSM3's atmospheric component [the Community Atmosphere Model version 3 (CAM3)] forced at its lower boundary with the changes in both SSTs and sea ice concentration from CCSM3's 2 3 CO 2 run. The authors decompose the combined effect of the higher SSTs and reduced sea ice concentration on the Arctic cloud response in this experiment by running two additional CAM3 simulations: one forced with modern SSTs and the projected sea ice cover changes in CCSM3 and the other forced with modern sea ice coverage and the projected changes in SSTs in CCSM3.The results suggest that future increases in Arctic cloudiness simulated by CCSM3 are mostly attributable to two separate processes. Low cloud gains are primarily initiated locally by enhanced evaporation within the Arctic due to reduced sea ice, whereas cloud increases at middle and high levels are mostly driven remotely via greater meridional moisture transport from lower latitudes in a more humid global atmosphere. The enhanced low cloudiness attributable to sea ice loss causes large increases in cloud radiative forcing during the coldest months and therefore promotes even greater surface warming. Because CCSM3's Arctic cloud response to greenhouse forcing is similar to other GCMs, the driving mechanisms identified here may be applicable to other models and could help to advance our understanding of likely changes in the vertical structure of polar clouds.

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“…To calculate the total latent heat flux LH across 70°N in units of W m –2 , we follow the equation given in Overland et al . []:

LH = \iint L [\overset{true¯}{italicvq}] \frac{d x d p}{g},

where the operator [ A ] indicates a zonal mean,

\overset{A}{true}

is a time mean, g is gravity ( g = 9.8 ms –1 ) and L is the latent heat of vaporization ( L = 2.5×10 6 J kg –1 ). After calculating the zonal mean of the time‐averaged meridional moisture flux for each tropospheric level, we integrate the level‐mean values over the latitude band.…”

Section: Poleward Energy Fluxesmentioning

confidence: 99%

“…One such mechanism is the poleward transport of atmospheric moist static energy (MSE), comprising sensible heat, latent heat, and geopotential energy. In the 20 th century climate, this component of the Arctic's energy budget supplied approximately 98% of the net radiation annually exchanged with outer space north of 70°N [ Nakamura and Oort , ; Overland et al ., ]. The annual‐mean contribution from surface exchanges was small, but seasonal variations, alternating from an input to the ocean during summer to a net loss during winter, strongly influenced seasonal changes in Arctic heat storage [ Serreze et al ., , ].…”

Section: Introductionmentioning

confidence: 99%

Drivers of projected change in arctic moist static energy transport

Skific

Francis

2013

JGR Atmospheres

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[1] We explore annual and seasonal changes in moist static energy transport (MSE) into the Arctic over the 21 st century as projected by the National Center for Atmospheric Research Community Climate System Model, version 3. Self-organizing maps are used to assess changes in MSE and its components-the latent heat flux and dry static energy flux (DSE)-across 70 N. These are computed from multilevel fields of specific humidity, meridional wind, geopotential, and temperature spanning periods in the 20 th century (1960 to 1999) and the 21 st century (2070 to 2089). The 21 st century simulation incorporates the Special Report on Emission Scenarios A2 scenario. A strong decrease in tropospheric DSE of about 9% by the end of 21 st century offsets an increase in latent heat flux of about 20% relative to its 20 th century average. The combined changes result in a total annual decrease in tropospheric MSE of about 3% by the late 21 st century. The difference, while statistically not significant, represents a weak negative feedback on Arctic amplification. Selforganizing maps also allow an attribution of changes in MSE to factors related to varying atmospheric dynamics and/or thermodynamics. A positive contribution to the MSE related to more frequent low pressure systems in high latitudes (dynamic factor) occurs in all seasons, particularly in the summer. The decrease in DSE is mainly due to a weakened poleward temperature gradient (thermodynamic factor) during all seasons except summer, which in turn is caused by amplified warming at high latitudes as a result of increased greenhouse gases.

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Regional Variations of Moist Static Energy Flux into the Arctic

Cited by 51 publications

References 0 publications

Coupling between Arctic feedbacks and changes in poleward energy transport

Coupling between Arctic feedbacks and changes in poleward energy transport

Factors Influencing Simulated Changes in Future Arctic Cloudiness

Drivers of projected change in arctic moist static energy transport

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