atmospheric dynamics feedback · 1. calculating the atmospheric dynamics feedback 2. dynamics...

Atmospheric dynamics feedback: concept, simulations and climate

implicationsMichael P. Byrne1,2 & Tapio Schneider3

1. Imperial College London 2. ETH Zürich 3. California Institute of Technology

Example of a potential atmospheric dynamics feedback: Tropical iris effect

0 30 latitude

Current climate

weak cooling

[e.g. Pierrehumbert (1995), Lindzen et al. (2001), Mauritsen &

Stevens (2015), Bony et al. (2016)]

strong cooling

0 30 latitude

Future climate

[e.g. Pierrehumbert (1995), Lindzen et al. (2001), Mauritsen &

Stevens (2015), Bony et al. (2016)]

Example of a potential atmospheric dynamics feedback: Tropical iris effect

weak cooling

strong cooling

Outline

1. Calculating the atmospheric dynamics feedback

2. Dynamics feedback in coupled climate models

3. Impact of atmospheric circulation changes on global climate: Inferences from simple theory and idealised simulations

Dynamic/thermodynamic decomposition of top-of-atmosphere radiative anomalies: Bony et al. (2004)

• Premise: Large-scale atmospheric circulation is a strong control on top-of-atmosphere radiation

Bony et al. (2004); Byrne & Schneider (submitted)

Dynamic/thermodynamic decomposition of top-of-atmosphere radiative anomalies: Bony et al. (2004)

• Premise: Large-scale atmospheric circulation is a strong control on top-of-atmosphere radiation

• Bin all-sky TOA fluxes as a function of mid-tropospheric vertical velocity

ω [hPa day−1]

All−

sky

rad.

effe

ct [W

m−2

]

−100 −75 −50 −25 0 25 50 75−80

−60

−40

−20

0

ω [hPa day−1]

Area

PD

F [%

]

−100 −75 −50 −25 0 25 50 750

5

10

15

R(!) A(!)

� = [�30�, 30�]

Bony et al. (2004); Byrne & Schneider (submitted)

Dynamic/thermodynamic decomposition of top-of-atmosphere radiative anomalies: Bony et al. (2004)

• Premise: Large-scale atmospheric circulation is a strong control on top-of-atmosphere radiation

• Bin all-sky TOA fluxes as a function of mid-tropospheric vertical velocity

R(!) A(!)

R =

Z 1

�1R(!)A(!)d!

ω [hPa day−1]

All−

sky

rad.

effe

ct [W

m−2

]

−100 −75 −50 −25 0 25 50 75−80

−60

−40

−20

0

ω [hPa day−1]

Area

PD

F [%

]

−100 −75 −50 −25 0 25 50 750

5

10

15

R(!) A(!)

� = [�30�, 30�]

Bony et al. (2004); Byrne & Schneider (submitted)

Dynamic/thermodynamic decomposition of top-of-atmosphere radiative anomalies: Bony et al. (2004)

�R =

dynamicz }| {Z 1

�1R(!)�A(!)d!+

thermodynamicz }| {Z 1

�1�R(!)A(!)d!+

nonlinearz }| {Z 1

�1�R(!)�A(!)d!

Bony et al. (2004); Byrne & Schneider (submitted)

Dynamic/thermodynamic decomposition of top-of-atmosphere radiative anomalies: Bony et al. (2004)

�R =

dynamicz }| {Z 1

�1R(!)�A(!)d!+

thermodynamicz }| {Z 1

�1�R(!)A(!)d!+

nonlinearz }| {Z 1

�1�R(!)�A(!)d!

ITCZ narrowing, convective aggregation, Hadley cell widening, tropical slowdown, jet shift…

Bony et al. (2004); Byrne & Schneider (submitted)

Dynamic/thermodynamic decomposition of top-of-atmosphere radiative anomalies: Bony et al. (2004)

• Simulations: Use CMIP5 abrupt4xCO2 and piControl runs (27 models) • Method: Perform decomposition at each latitude individually, and for all-

sky fluxes (not only cloud-radiative effect)

�R =

dynamicz }| {Z 1

�1R(!)�A(!)d!+

thermodynamicz }| {Z 1

�1�R(!)A(!)d!+

nonlinearz }| {Z 1

�1�R(!)�A(!)d!

Bony et al. (2004); Byrne & Schneider (submitted)

ITCZ narrowing, convective aggregation, Hadley cell widening, tropical slowdown, jet shift…

Atmospheric dynamics feedback vs lat: Smaller than thermodynamic feedbacks but shapes tropical cloud response

All-sky radiative effect Cloud radiative effect

Latitude [deg]

−δR

(clo

ud) [

W m

−2] ×

cosφ

−60 −30 0 30 60−1

0

1

2

3

4 TotalThermodynamicDynamic + Nonlinear

Latitude [deg]

δR (a

ll sk

y) [W

m−2

] × c

osφ

−60 −30 0 30 60

−6

−5

−4

−3

−2

−1

0

1

(multimodel mean, averaged over 100 years following 4xCO2)

warming

cooling

Byrne & Schneider (submitted)

Influence of circulation changes on global radiative balance is negligible

Surface temperature response [K]

Glo

bal r

ad. a

nom

aly

[W m

−2]

1 2 3 4 5

−7−6−5−4−3−2−1

01

TotalThermodynamicDynamic + NonlinearFit

CCSM4 model

estimated equilibrium temperature change

Byrne & Schneider (submitted)

Global dynamics feedback is small & positive: Increases temperature response by 0.2K (3% of total warming)

CCSM4 model

Surface temperature response [K]

Glo

bal r

ad. a

nom

aly

[W m

−2]

1 2 3 4 5

−7−6−5−4−3−2−1

01

FitFit (thermo. only)

estimated equilibrium temperature change

(thermodynamic only)

Byrne & Schneider (submitted)

Why is the global atmospheric dynamics feedback small?

Byrne & Schneider (submitted); see also Wyant et al. (2006)

• A simple explanation with two ingredients: Mass budget + linearity of R(ω)

1. Mass budget: “what goes up must come down”

2. Assume TOA radiation depends linearly on ω

R(!) = a+ b!

Why is the global atmospheric dynamics feedback small?

• A simple explanation with two ingredients: Mass budget + linearity of R(ω)

upward fluxz }| {Z 0

�1!A(!)d! = �

Z 1

0!A(!)d!

| {z }downward flux

Byrne & Schneider (submitted); see also Wyant et al. (2006)

1. Mass budget: “what goes up must come down”

2. Assume TOA radiation depends linearly on ω

upward fluxz }| {Z 0

�1!A(!)d! = �

Z 1

0!A(!)d!

| {z }downward flux

R(!) = a+ b!

Why is the global atmospheric dynamics feedback small?

• A simple explanation with two ingredients: Mass budget + linearity of R(ω)

Byrne & Schneider (submitted); see also Wyant et al. (2006)

1. Mass budget: “what goes up must come down”

2. Assume TOA radiation depends linearly on ω

Why is the global atmospheric dynamics feedback small?

ω [hPa day−1]

All−

sky

rad.

effe

ct [W

m−2

]

−100 −75 −50 −25 0 25 50 75−80

−60

−40

−20

0

area PDF of ω

• A simple explanation with two ingredients: Mass budget + linearity of R(ω)

upward fluxz }| {Z 0

�1!A(!)d! = �

Z 1

0!A(!)d!

| {z }downward flux

R(!) = a+ b!

Byrne & Schneider (submitted); see also Wyant et al. (2006)

1. Mass budget: “what goes up must come down”

2. Assume TOA radiation depends linearly on ω

upward fluxz }| {Z 0

�1!A(!)d! = �

Z 1

0!A(!)d!

| {z }downward flux

R(!) = a+ b!

dynamic comp.z }| {Z 1

�1R(!)�A(!)d! =

a

Z 1

�1�A(!)d! + b

Z 1

�1!�A(!)d! = 0

Why is the global atmospheric dynamics feedback small?

• A simple explanation with two ingredients: Mass budget + linearity of R(ω)

Byrne & Schneider (submitted); see also Wyant et al. (2006)

1. Mass budget: “what goes up must come down”

2. Assume TOA radiation depends linearly on ω

A strong constraint on ability of atmospheric dynamics feedbacks to influence global climate

=0 by definition =0 by mass balance

upward fluxz }| {Z 0

�1!A(!)d! = �

Z 1

0!A(!)d!

| {z }downward flux

R(!) = a+ b!

dynamic comp.z }| {Z 1

�1R(!)�A(!)d! =

a

Z 1

�1�A(!)d! + b

Z 1

�1!�A(!)d! = 0

• A simple explanation with two ingredients: Mass budget + linearity of R(ω)

Byrne & Schneider (submitted); see also Wyant et al. (2006)

1. Mass budget: “what goes up must come down”

2. Assume TOA radiation depends linearly on ω

A strong constraint on ability of atmospheric dynamics feedbacks to influence global climate

=0 by definition =0 by mass balance

upward fluxz }| {Z 0

�1!A(!)d! = �

Z 1

0!A(!)d!

| {z }downward flux

R(!) = a+ b!

dynamic comp.z }| {Z 1

�1R(!)�A(!)d! =

a

Z 1

�1�A(!)d! + b

Z 1

�1!�A(!)d! = 0

• A simple explanation with two ingredients: Mass budget + linearity of R(ω)

Difficult for atmospheric circulation changes to create

large TOA anomalies

Byrne & Schneider (submitted); see also Wyant et al. (2006)

Influence of hypothetically large dynamics feedbacks on global climate? Test using idealised simulations

• BUT… Processes not captured by global models could produce a large dynamics feedback [e.g. strongly nonlinear R(ω), convective aggregation]

• Investigate using an idealised GCM

Byrne & Schneider (submitted)

Latitude [deg]

TOA

forc

ing

[W m

−2]

−60 −30 0 30 60

0

5

10

Tropical forcingExtratropical forcing

• Slab-ocean aquaplanet with simplified radiative transfer [see Frierson et al. (2006), Frierson (2007), O’Gorman & Schneider (2008)]

• Impose two stylised longwave top-of-atmosphere forcings: tropical and extratropical

• Motivated by work on ocean heat uptake at different latitudes [e.g. Armour et al. (2013), Rose et al. (2014)]

Forcings

• BUT… Processes not captured by global models could produce a large dynamics feedback [e.g. strongly nonlinear R(ω), convective aggregation]

• Investigate using an idealised GCM:

Influence of hypothetically large dynamics feedbacks on global climate? Test using idealised simulations

Byrne & Schneider (submitted)

Tropical forcing ineffective at changing global temperature -> difficult for iris-type dynamic feedbacks to influence climate

Byrne & Schneider (submitted); see Rose et al. (2014) for ocean uptake analogy

• Tropical TOA anomalies less than half as effective at changing global surface temperature

Latitude [deg]

TOA

forc

ing

[W m

−2]

−60 −30 0 30 60

0

5

10

Tropical forcingExtratropical forcing

Forcings

Latitude [deg]

Surfa

ce te

mpe

ratu

re re

spon

se [K

]

−60 −30 0 30 60

−0.5

0

0.5

1

1.5

Tropical forcingExtratropical forcing

Responses

Tropical forcing ineffective at changing global temperature -> difficult for iris-type dynamic feedbacks to influence climate

Byrne & Schneider (submitted); see Rose et al. (2014) for ocean uptake analogy

• Tropical TOA anomalies less than half as effective at changing global surface temperature

Latitude [deg]

TOA

forc

ing

[W m

−2]

−60 −30 0 30 60

0

5

10

Tropical forcingExtratropical forcing

Forcings

Latitude [deg]

Surfa

ce te

mpe

ratu

re re

spon

se [K

]

−60 −30 0 30 60

−0.5

0

0.5

1

1.5

Tropical forcingExtratropical forcing

ResponsesTropical TOA anomalies inefficient at changing global

temperature

Summary• Atmospheric dynamics feedback calculated for coupled climate

models

• Dynamics feedback smaller than thermodynamic feedbacks at all latitudes, but relatively important in the tropics

• Two reasons why iris-type mechanisms unlikely to strongly influence global climate:

1. Mass balance + quasi-linear R(ω) constrain dynamics feedback to be small on large scales

2. Tropical TOA anomalies (e.g. iris) are relatively inefficient at changing global temperature