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Understanding the Greenhouse Effect: Earth's Climate Model

Explore the greenhouse effect in Earth's climate system through energy balance models, feedback mechanisms, and climate response time. Learn about the impact of greenhouse gases on global warming and temperature changes.

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Understanding the Greenhouse Effect: Earth's Climate Model

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  1. Chapter 6The 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 6.4 The water vapor feedback 6.5 Snow/Ice feedback 6.6 Cloud feedbacks 6.7Other feedbacks in the physical climate system 6.8 Climate response time in transient climate change Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

  2. 6.1 The greenhouse effect in Earth’s current climate Recap (Chap. 2): Pathways of energy transfer in a global average Recap: Figure 2.8 Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

  3. 1 2 1 Neglect for now! 1 2 • For a layer with a single temperature Ta: IR­atm=IR¯atm 2 6.1a Global energy balance A one-layer, globally averaged energy balance model • Repeat essential aspects but in a 1-layer atm Figure 6.1 Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

  4. Radiative fluxes: simplified cases where atmosphere absorbs all infrared For balance, IR­atm has to be 235 (=net solar); IR¯atmis same; so sfc has to be warm enough to emit 470, double the solar Some solar absorbed: IR­sfc has to balance net solar at sfc + IR¯atm : 168+ 235=403 Figure 6.2 • Solutions just by energy balance ·Large impact of greenhouse effect Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

  5. For the 1-layer atm, energy balance solutionswhen 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 1 235 Top of atmosphere 1 168 1 At surface Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

  6. For the 1-layer atm, energy balance solutionswhen atmosphere only partly absorbing: • With IR­atm = IR¯atm(Eq. 6.4), add the 2 eqns to elimIR­atm 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, 2011. Climate Change and Climate Modeling, Cambridge UP

  7. 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 ÞDea • gives temperature change DTs Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

  8. Get temperatures from sTs4 = IR­sfcEq. 6.6 easTa4= IR­atm Eq. 6.7 gives Ts = 283.5 K = 10.4 C The atmospheric temperature is considerably colder: Ta = 249.7 K = -23.5 C Compare to no atm case (same albedo) sTs4 = 235 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! Temperatures from the one-layer energy model Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

  9. 1B 2A 2B 1A 3A 3B 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 4 • Steps in conceptual sequence Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

  10. 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 Figure 6.4 Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

  11. 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 atmG (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) *Forcing: Something that causes an effect. Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

  12. 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 = DeasTs4- DeasTa4 Increased IR absorption (labeled 1B in fig 6.3) Reduced a bit by increased emissivity. Smaller since Ta < Ts 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, 2011. Climate Change and Climate Modeling, Cambridge UP

  13. 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. • aTcorresponds to inverse slope of linear approx. in fig 6.4, about 2.2 (Wm-2K-1) • aTapprox. independent of G • Eq. 6.10 is for equilibrium (time dependence of warming in section 6.8) Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

  14. 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 • larger G balanced by larger DTs; • aTdetermines how much larger • “basic greenhouse effect” Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

  15. 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 Þglobal 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, 2011. Climate Change and Climate Modeling, Cambridge UP

  16. 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, 2011. Climate Change and Climate Modeling, Cambridge UP

  17. ¶S ¶IR­atm _  = ¶Ts ¶Ts 6.3a Climate feedback parameter (cont.) • Contributions to are approx. additive • = T + H2O + ice + cloud • H2Ocontribution of water vapor feedback, icecontribution of snow/ice feedback, cloudnet 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 Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

  18. 6.3b Contributions of climate feedbacks to global avg temp response a = T + H2O + ice + cloud e.g., a= 4.0 – 1.8 -0.2 -0.6* = 1.4 Wm-2K-1 ÞDTs = G/a = 3.1 K for G = 4.3 Wm-2 • Compare to if aH2O = aice = acloud = 0 a= aT = 4.0 ÞDTs = 1.1 K • global average warming increases substantially when positive feedbacks are included • but these feedbacks include complex parts of climate system Þdifferent models give a range of values! *Note: positive feedbacks tend to amplify. But have a negative contribution to a Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

  19. Table 6.1 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, 2011. Climate Change and Climate Modeling, Cambridge UP

  20. 6.3b Climate sensitivity Table 6.2 Mean, standard deviation, and range of doubled-CO2 climate sensitivity for a number of 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-CO2response. 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, 2011. Climate Change and Climate Modeling, Cambridge UP

  21. 6.4 The water vapor feedback Water vapor feedback in the greenhouse effect 2B 3 2A 1 6 4 5 Figure 6.5 Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

  22. Water vapor (as measured by vapor pressure)versus temperature Figure 6.6 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, 2011. Climate Change and Climate Modeling, Cambridge UP

  23. 6.5 Snow/Ice feedback Snow/ice feedback in the global energy balance Figure 6.7 5B 3 5A 2 4 1 Complications: e.g., cloud cover, albedo of aging snow/ice,… Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

  24. 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. Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

  25. 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 • Effects of cloud fraction increases (for given cloud type) Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

  26. Cloud top feedback Figure 6.9 Warming tendency • 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, 2011. Climate Change and Climate Modeling, Cambridge UP

  27. 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, 2011. Climate Change and Climate Modeling, Cambridge UP

  28. 6.8 Climate response time in transient climate change Schematic of an equilibrium response experiment Figure 6.10 Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

  29. Schematic of greenhouse warming with time-dependent forcing Figure 6.11 Warming if equilibrated with forcing Transient response experiment Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

  30. Greenhouse warming with time-dependent forcing: ensemble of simulations Ensemble: runs with different natural climate variability Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

  31. Greenhouse warming with time-dependent forcing: ensemble of simulations Ensemble: runs with different natural climate variability Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

  32. 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!) Temperature was less than equilibrium due to lag so continues to rise for several decades Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

  33. Figure 6.13 6.8b A doubled-CO2 equilibrium response experiment 6.8c The role of oceans in slowing warming Annual average surface air temperature response from an earlier version of the GFDL climate model comparing equilibrium response to time-dependent response Equilibrium temperature response Years 60-80 of time-dependent temperature response Ratio of time-dependent response to equilibrium response After Manabe et al., 1991, J. Climate. Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

  34. ∂Ts ∂t C + Ts = G Ocean heat storage IR to space due to Ts increase Radiative forcing (GHG) 6.8 Climate sensitivity in transient climate change • Extend simple globally averaged energy balance model of Eq. 6.11 to include heat storage in ocean surface layer of depth, H • Heat capacity per unit area C = cwHwhere  - 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, 2011. Climate Change and Climate Modeling, Cambridge UP

  35. G  Ts = in equilibrium 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 High sensitivity model (smaller ) Low sensitivity model Figure 6.14 Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

  36. ∂Ts ∂t C + Ts = G Ocean heat storage IR to space due to Ts increase Radiative forcing (GHG) g(t - )  *to see this try Ts = in Eq. 6.15 using G = gt G  Ts = in equilibrium C  *= lag due to ocean, depends on  A transient response experiment by climate models of different climate sensitivities to forcing Initially small High sensitivity model (smaller ) Hard to distinguish high  from low initially Low sensitivity model Heat storage balances GHG initially Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

  37. C  *= 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 High sensitivity model (smaller ) Hard to distinguish high  from low initially Low sensitivity model Heat storage balances GHG initially Neelin, 2011. Climate Change and Climate Modeling, Cambridge UP

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