Chapter 6 The Greenhouse Effect and Climate Feedbacks

Name: Chapter 6 The Greenhouse Effect and Climate Feedbacks
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Chapter 6 The Greenhouse Effect and Climate Feedbacks
6.1 The greenhouse effect in Earth’s current climate 6.2 Global warming I: example in the global-average energy balance model 6.3 Climate feedbacks cloud.feedback.clr.gif 6.4 The water vapor feedback 6.5 Snow/Ice feedback 6.6 Cloud feedbacks 6.7 Other feedbacks in the physical climate system 6.8 Climate response time in transient climate change Neelin, Climate Change and Climate Modeling, Cambridge UP

Recap (Chap. 2): Pathways of energy transfer in a global average
6.1 The greenhouse effect in Earth’s current climate Recap (Chap. 2): Pathways of energy transfer in a global average global.energy.bal.clr.eps from chap2 Recap: Figure 2.8 Neelin, Climate Change and Climate Modeling, Cambridge UP

A one-layer, globally averaged energy balance model
6.1a Global energy balance A one-layer, globally averaged energy balance model Repeat essential aspects but in a 1-layer atm 1 Figure 6.1 2 globavg.1layerenbal.clr.eps Neglect for now! For a layer with a single temperature Ta: IRatm=IR¯atm 2 Neelin, Climate Change and Climate Modeling, Cambridge UP

Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP
Radiative fluxes: simplified cases where atmosphere absorbs all infrared For balance, IRatm has to be 235 (=net solar); IR¯atm is same; so sfc has to be warm enough to emit 470, double the solar radfluxs.1layer.clr.eps Some solar absorbed: IRsfc has to balance net solar at sfc + IR¯atm : =403 Figure 6.2 Solutions just by energy balance · Large impact of greenhouse effect Neelin, Climate Change and Climate Modeling, Cambridge UP

For the 1-layer atm, energy balance solutions when atmosphere only partly absorbing: Only 10% of IRsfc gets through atm (for “normal” climate, before increasing greenhouse gases), i.e., atm is absorbing 90%. Bulk absorptivity for the atm layer, ea=0.90 1 Energy balance: Input = Output (in Wm-2) Top of the atm 235 (1-ea) IRsfc + IRatm Eq. 6.2 At surface 168+ IR¯atm IRsfc Eq. 6.3 globavg.1layerenbal.clr.eps 235 1 Top of atmosphere 1 168 1 At surface Neelin, Climate Change and Climate Modeling, Cambridge UP

Gives: IRsfc = 403/(2-ea) Eq. 6.5
For the 1-layer atm, energy balance solutions when atmosphere only partly absorbing: Energy balance: Input = Output (in Wm-2) Top of the atm 235 (1-ea) IRsfc + IRatm Eq. 6.2 At surface 168+ IR¯atm IRsfc Eq. 6.3 With IRatm = IR¯atm (Eq. 6.4), add the 2 eqns to elim IRatm Gives: IRsfc = 403/(2-ea) Eq. 6.5 For ea= 0.90, IRsfc = 366; Increased ea gives increased IRsfc [Note (2-ea) = 1.1; 403 = solar absorbed by sfc + IR¯atm ] [Compare to obs budget: IR¯atm bigger because atm warmer near surface. i.e., even larger greenhouse effect that this 1-layer atm.] Neelin, Climate Change and Climate Modeling, Cambridge UP

Surface temperature (C) as a function of absorptivity ea (unitless)
Figure 6.4 One-layer global-average energy balance model IRsfc = 403/(2-ea) Eq. 6.5 sTs4 = IRsfc Eq. 6.6 Use ea =0.9 as “normal” climatology (open circle) Change in GHG Þ Dea gives temperature change DTs TsEpsilona_c.eps Neelin, Climate Change and Climate Modeling, Cambridge UP

Temperatures from the one-layer energy model
Get temperatures from sTs4 = IRsfc Eq. 6.6 easTa4 = IRatm Eq. 6.7 gives Ts = K = 10.4 C The atmospheric temperature is considerably colder: Ta = K = C Compare to no atm case (same albedo) sTs4 = Þ Ts = -19 C versus obs avg sfc temp = 15C: a 34 C difference (i.e., greenhouse effect powerful in current climate) Note Ta similar to Ts in no atm case because atm does most of emitting to space. (If atm absorbs all IRsfc exactly same: the “emission temperature”) Why is 1-layer model Ts colder than obs? In real atm Ta decreases with height! Neelin, Climate Change and Climate Modeling, Cambridge UP

6.2 Global warming I: example in global-avg energy balance model Increased absorption of infrared radiation by greenhouse gases leading to surface warming Figure 6.3 1B 1A 2A 2B inc.abs.IR.clr.eps 3A 3B 4 Steps in conceptual sequence Neelin, Climate Change and Climate Modeling, Cambridge UP

Surface temperature (C) as a function of absorptivity ea (unitless)
6.2b Climate feedback parameter in the one-layer global avg model Surface temperature (C) as a function of absorptivity ea (unitless) Recall: One-layer global-average energy balance model Use ea =0.9 as “normal” climatology (open circle) x-axis also given as the top of the atmosphere greenhouse radiative forcing G in Wm-2 DTs (temperature change) Linear approximation is shown as dashed line TsEpsilona_c.eps Figure 6.4 Neelin, Climate Change and Climate Modeling, Cambridge UP

In a more complex model, equivalent of ea not a single number: absorptivity is calculated at many levels, depends on various greenhouse gases, … … so express radiative change effects in terms of changes in top of atm radiative balance In simple model, increase of greenhouse gas, corresponds to increase in absorptivity Dea (trapping more upgoing IR in atm) Before any temp change occurs there will be a deficit G in the outgoing IR Þ imbalance at top of atm G (in Wm-2) good measure of the greenhouse radiative forcing* (as a change from normal climatology. Note G is independent of the particular gas that does the absorbing) TsEpsilona_c.eps *Forcing: Something that causes an effect. Neelin, Climate Change and Climate Modeling, Cambridge UP

increased emissivity. Smaller since Ta < Ts
Simple model details: Keep Ts, Ta (temporarily) fixed at climatological values (Ts, Ta): reduction in outgoing IR due to change in absorptivity Dea is: G = DeasTs DeasTa4 Increased IR absorption (labeled 1B in fig 6.3) Reduced a bit by increased emissivity. Smaller since Ta < Ts TsEpsilona_c.eps For more complex models similar proceedure: for Ts, Ta, fixed, compute reduction in outgoing IR due to change in GHG Temperature must warm to bring outgoing IR back into balance. Neelin, Climate Change and Climate Modeling, Cambridge UP

G » 4 Wm-2, for doubled CO2; G increases with increasing GHG Linear approximation for sufficiently small DTs aTDTs = G Eq. 6.10 aT climate feedback parameter (Wm-2K-1) that arises only from changes in temperature (without any changes in water vapor, snow ice clouds, etc. (next section!)) DTa can be obtained from DTs aT: increase in outgoing IR at top of atm per unit increase in sfc temp. aT corresponds to inverse slope of linear approx. in fig 6.4, about 2.2 (Wm-2K-1) TsEpsilona_c.eps Slope = [10.3-(-12)]/10 = 2.23 aT approx. independent of G Eq is for equilibrium (time dependence of warming in section 6.8) Neelin, Climate Change and Climate Modeling, Cambridge UP

aTDTs = G G: imbalance in outward IR, (before temperature increases) increased absorption of IR in atm by aTDTs: increase in outward IR to space due to warmer temperatures balances the heating effects of the forcing G negative feedback if forcing disappeared negative feedback would cause temperatures to go back toward normal TsEpsilona_c.eps larger G balanced by larger DTs; aT determines how much larger “basic greenhouse effect” Neelin, Climate Change and Climate Modeling, Cambridge UP

6.3 Climate feedbacks Climate feedbacks modify changes due to basic greenhouse effect Some may amplify warming considerably Main feedbacks: The water vapor feedback: assoc. with increases in water vapor with temp., since water vapor is a GHG The snow/ice feedback: decreases in snow and ice Þ global albedo decreases (less solar radiation reflected) Cloud feedbacks: due to changes in cloud cover, which affect both cloud contribution to the greenhouse effect and to albedo More details on each below. First use framework from global-average energy balance model indicate relative importance of feedbacks Neelin, Climate Change and Climate Modeling, Cambridge UP

6.3a Climate feedback parameter Generalize the climate feedback parameter  Ts = G Eq. 6.11 To calculate G from a complex model: Hold temperature, moisture, clouds, etc. fixed Increase greenhouse gas concentrations & calculate global average changes in outgoing IR To measure the effects of different feedbacks: hold different parts of the climate system constant e.g., T: hold water vapor, ice, snow and clouds fixed but allow temp to vary. Measure increased IR to space as temp increases Then let water vapor vary Þ H2O, … Neelin, Climate Change and Climate Modeling, Cambridge UP

 = T + H2O + ice + cloud
6.3a Climate feedback parameter (cont.) Contributions to  are approx. additive  = T + H2O + ice + cloud H2O contribution of water vapor feedback, ice contribution of snow/ice feedback, cloud net contribution of the cloud feedback [formally, , where S is the net solar flux] In practice, contributions of various feedbacks do not add as neatly but gives sense of contributions Þ some caveats = ¶IRatm ¶Ts _ ¶S  Neelin, Climate Change and Climate Modeling, Cambridge UP

6.3b Contributions of climate feedbacks to global avg temp response a = T + H2O + ice + cloud e.g., a = 4.0 – * = 1.4 Wm-2K-1 Þ DTs = G/a = 3.1 K for G = 4.3 Wm-2 Compare to if aH2O = aice = acloud = 0 a = aT = 4.0 Þ DTs = 1.1 K global average warming increases substantially when positive feedbacks are included but these feedbacks include complex parts of climate system Þ different models give a range of values! *Note: positive feedbacks tend to amplify. But have a negative contribution to a Neelin, Climate Change and Climate Modeling, Cambridge UP

Table 6.1 Feedback Radiative flux to space per degree inc. in Ts [Wm-2K-1] Cumulative climate feedback parameter,  [Wm-2K-1] Cumulative change in equil. temp, Ts [K] Infrared cooling (negative feedback) T 3.7 to 4.4 3.7 to 4.4 T 1.0 to 1.2 Water vapor (positive feedback) H2O  -2.0 to -1.5 2.0 to 2.4 T + H2O 1.8 to 2.1 Sea ice/land snow ice  -0.3 to -0.1 1.7 to 2.3 T + H2O + ice 1.9 to 2.5 Clouds (positive/negative?) cloud  -1.2 to -0.1 0.9 to 1.6 T + H2O + ice + cloud 2.7 to 4.8 Contributions of various feedbacks to climate feedback parameter  and surface temperature increase Ts=G/ (using G=4.3 Wm-2 for doubled CO2) Sign is positive for negative feedback, (i.e., energy loss that opposes warming) Based on 12 models (Soden & Held, 06). Note a range is from actual model values ¹ sum of lowvalues to sum of high values. e.g., aT = 3.7 is not from same model as aH2O = -2.0 Thus, climate feedbacks: amplify the warming increase the uncertainty in the estimate of this warming Neelin, Climate Change and Climate Modeling, Cambridge UP

6.3b Climate sensitivity Mean, standard deviation, and range of doubled-CO2 climate sensitivity for a number of models Table 6.2 Publication Number of models Mean Standard deviation Range IPCC (1996) IPCC (2001) IPCC (2007) 17 15 18 3.8 3.5 3.2 0.8 C 0.9 C 0.7 C 1.9 to 5.2 C 2.0 to 5.1 C 2.1 to 4.4 C The mean, standard deviation, and range of doubled-CO2 climate sensitivity (global averaged surface temperature response) from models included in the two most recent IPCC reports. Range refers to highest and lowest values from among all models. Different approach: define a standardized experiment: Double CO2 & run the simulation to new equilibrium climate state. Change in the long term average defines doubled-CO2 response. Global-average surface temperature response DT2x used as a measure of climate sensitivity: doubled-CO2 climate sensitivity.** [**Terminology alert: sometimes a-1 is also called climate sensitivity] Neelin, Climate Change and Climate Modeling, Cambridge UP

Water vapor feedback in the greenhouse effect
6.4 The water vapor feedback Water vapor feedback in the greenhouse effect 2B 3 2A inc.water.vapor.clr.eps 1 6 4 5 Figure 6.5 Neelin, Climate Change and Climate Modeling, Cambridge UP

Water vapor (as measured by vapor pressure) versus temperature
Figure 6.6 sat.vapor.pres.vsT.clr.eps As T increases in atm, if* RH range similar, water vapor increases * RH limited by evap. near surface, condensation, but can depend on circulation Neelin, Climate Change and Climate Modeling, Cambridge UP

Snow/ice feedback in the global energy balance
Figure 6.7 5B 3 snow_ice.feedback.clr.eps 2 5A 4 1 Complications: e.g., cloud cover, albedo of aging snow/ice,… Neelin, Climate Change and Climate Modeling, Cambridge UP

Cloud feedback challenges:
6.6 Cloud feedbacks Cloud feedback challenges: Small scale motions. Average effects at the grid size must be parameterized Opposing effects in infrared and solar contributions to energy budget Several cloud properties can affect radiation, e.g.: cloud fraction, cloud top height, cloud water and ice content. Different cloud types thus have different net feedback. cloud.feedback.clr.eps Neelin, Climate Change and Climate Modeling, Cambridge UP

Effects of cloud amount in the global energy balance
6.6 Cloud feedbacks Effects of cloud amount in the global energy balance Figure 6.8 Tend to cancel Cooling tendency Warming tendency Net Cooling tendency IR Increased reflection of solar solar IR Small cloud.feedback.clr.eps Effects of cloud fraction increases (for given cloud type) Neelin, Climate Change and Climate Modeling, Cambridge UP

Cloud top feedback Figure 6.9 Warming tendency cloud.top.ht.effect.clr.jpg Cloud top feedback: low-level moisture and temperature increase Þ cloud top tends to reach higher Þ IR emissions decrease (from colder cloud top) Neelin, Climate Change and Climate Modeling, Cambridge UP

Stratospheric cooling
6.7 Other feedbacks in the physical climate system Stratospheric cooling Surface and troposphere warm when GHG increase, but stratosphere tends to cool GHG increase in the stratosphere Þ absorption and emission of IR both increase, but emission exceeds absorption Energy budget of layer of temp. Tstrat 2estratT4strat = Qozone + estratIRtrop has to decr. increase about the same Neelin, Climate Change and Climate Modeling, Cambridge UP

Schematic of an equilibrium response experiment
6.8 Climate response time in transient climate change Schematic of an equilibrium response experiment Figure 6.10 forcing_response1.eps Neelin, Climate Change and Climate Modeling, Cambridge UP

Schematic of greenhouse warming with time-dependent forcing
Figure 6.11 forcing_response2.eps Warming if equilibrated with forcing Transient response experiment Neelin, Climate Change and Climate Modeling, Cambridge UP

Greenhouse warming with time-dependent forcing: ensemble of simulations forcing_response2.eps Ensemble: runs with different natural climate variability Neelin, Climate Change and Climate Modeling, Cambridge UP

Greenhouse warming with time-dependent forcing: ensemble of simulations forcing_response2.gif (animation from forcing_response2.psd) Ensemble: runs with different natural climate variability Neelin, Climate Change and Climate Modeling, Cambridge UP

A transient response experiment where greenhouse gas concentrations are capped at time ts, so the forcing stabilizes (upper panel) Figure 6.12 Idealized case: cap GHG at given level (i.e., stop emissions suddenly!) forcing_response3.eps Temperature was less than equilibrium due to lag so continues to rise for several decades Neelin, Climate Change and Climate Modeling, Cambridge UP

6.8b A doubled-CO2 equilibrium response experiment 6.8c The role of oceans in slowing warming Figure 6.13 Equilibrium temperature response Annual average surface air temperature response from an earlier version of the GFDL climate model comparing equilibrium response to time-dependent response Years of time-dependent temperature response manabe_clr.eps Ratio of time-dependent response to equilibrium response After Manabe et al., 1991, J. Climate. Neelin, Climate Change and Climate Modeling, Cambridge UP

6.8 Climate sensitivity in transient climate change
C Ts = G ∂Ts ∂t Ocean heat storage IR to space due to Ts increase Radiative forcing (GHG) Extend simple globally averaged energy balance model of Eq to include heat storage in ocean surface layer of depth, H Heat capacity per unit area C = cwH where  - density; cw- heat capacity of sea water [Details for following example: G reaches 4 W m-2 in 70 years, roughly like 1%/yr CO2 incr.; high/low a from Table 6.1 (1.6, 0.9 Wm-2K-1); r=1000 kg m-3, cw=4200 J kg-1, H=200m gives 17, 30 yr lag] Neelin, Climate Change and Climate Modeling, Cambridge UP

lag due to ocean, depends on 
A transient response experiment by climate models of different climate sensitivities to forcing (upper panel) Simple example: linearly increasing radiative forcing starting in 1970 Fig6.14_DT_t.eps Ts = G  in equilibrium High sensitivity model (smaller ) lag due to ocean, depends on  Low sensitivity model Figure 6.14 Neelin, Climate Change and Climate Modeling, Cambridge UP

IR to space due to Ts increase lag due to ocean, depends on 
A transient response experiment by climate models of different climate sensitivities to forcing Initially small C Ts = G ∂Ts ∂t Ocean heat storage IR to space due to Ts increase Radiative forcing (GHG) *to see this try Ts = in Eq using G = gt g(t - )  *= C Ts = G  in equilibrium Fig6.14_DT_t.eps Full solution DTs=(g/alpha)[t-tau(1-exp(-t/tau))]; Values used: alpha from Table 6.1 alpha = 0.9 to 1.6 cw=4200 J/(kgK), rho=1000 kg/m3, H=200 m. C =8.4e8 so tau = C/alpha gives for low alpha: 8.4e8/.9 =29.6; High alpha: 8.4e8/1.6=16.6 yr G reaches 4 W m-2 in 70 years so g= W m^-2 yr^-1 roughly like 1%/yr CO2 incr. High sensitivity model (smaller ) lag due to ocean, depends on  Hard to distinguish high  from low initially Low sensitivity model Heat storage balances GHG initially Neelin, Climate Change and Climate Modeling, Cambridge UP

lag due to ocean, depends on 
A transient response experiment by climate models of different climate sensitivities to forcing Climate models agree fairly well in the early stages of s increase and predicted warming for next few decades. But doesn’t yet constrain size of eventual warming for given GHG concentrations Fig6.14_DT_t.eps High sensitivity model (smaller ) *= C  lag due to ocean, depends on  Hard to distinguish high  from low initially Low sensitivity model Heat storage balances GHG initially Neelin, Climate Change and Climate Modeling, Cambridge UP

Chapter 6 The Greenhouse Effect and Climate Feedbacks

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