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Global Climate Response to Anthropogenic Aerosol Indirect Effects

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Presentation on theme: "Global Climate Response to Anthropogenic Aerosol Indirect Effects"— Presentation transcript:

1 Global Climate Response to Anthropogenic Aerosol Indirect Effects
John H. Seinfeld California Institute of Technology AMS Annual Meeting January 13, 2009

2 Offline vs. Online Approach in Global Models
Offline aerosols Obtain aerosol mass and properties a priori Online aerosols Compute aerosols during the climate simulation Climate Sensitive Emissions Aerosols Climate (radiation, temp., winds, clouds) Anthropogenic Emissions Emissions Aerosols Climate (radiation, temp., winds, clouds)

3 Transient vs. Equilibrium Climate
Transient Climate Start from initial state (e.g. pre-industrial) Specified annually varying forcing changes Temporal evolution of the response is of interest Time Time Forcing (Anth. CO2 levels) Response (Ts)

4 Transient vs. Equilibrium Climate
Transient Climate Start from initial state (e.g., pre-industrial) Specified annually varying forcing Temporal evolution of the response is of interest Time Time DTs Forcing (Anth. CO2 levels) Response (Ts)

5 Transient vs. Equilibrium Climate
Forcing is constant during the course of simulation Integrate until equilibrium is reached Relative changes in final response vs. changes in forcing Time Time Forcing (Anth. CO2 levels) Response (Ts)

6 Transient vs. Equilibrium Climate
Forcing is constant during the course of simulation Integrate until equilibrium is reached Relative changes in final response vs. changes in forcing Time DF(2-1) Time DTs,(2-1) Forcing (Anth. CO2 levels) Response (Ts)

7 The CACTUS Unified Model
Chemistry, Aerosol, and Climate: Tropospheric Unified Simulation (CACTUS) [ Liao et al., 2004]

8 The CACTUS Unified Model
Chemistry, Aerosol, and Climate: Tropospheric Unified Simulation (CACTUS) [ Liao et al., 2004]

9 The CACTUS Unified Model
Chemistry, Aerosol, and Climate: Tropospheric Unified Simulation (CACTUS) [ Liao et al., 2004]

10 The CACTUS Unified Model
Chemistry, Aerosol, and Climate: Tropospheric Unified Simulation (CACTUS) (no AIE) [ Liao et al., 2004]

11 Aerosol Indirect Climatic Effects
Cloud Albedo Effect (–0.2 to –1.9 W m-2) Cloud Lifetime Effect (Clean) (Polluted) Total AIE forcing: –0.3 to –2.4 W m-2

12 Aerosol Indirect Climatic Effects
Size-resolving microphysics? Parameterization? (Clean) (Polluted)

13 Aerosol Indirect Climatic Effects using GISS III
Chen, W.-T., H. Liao, A. Nenes, P. Adams, and J. H. Seinfeld (JGR submitted)

14 Derive Aerosol-Nc Correlations
TOMAS microphysics model within GISS II’ Conserve both aerosol mass and number concentrations Present-day sulfate and sea salt (internal mixing) Derive CCN spectra for each grid

15 Derive Aerosol-Nc Correlations
TOMAS microphysics model within GISS II’ Conserve both aerosol mass and number concentrations Present-day sulfate and sea salt (internal mixing) Derive CCN spectra for each grid With the predicted CCN spectra, apply the CCN activation parameterization in Fountoukis and Nenes [2005] In each grid, vary aerosol mass between 0.05 and 5 times the average concentration, and compute the corresponding Nc

16 Derive Aerosol-Nc Correlations
TOMAS microphysics model within GISS II’ Conserve both aerosol mass and number concentrations Present-day sulfate and sea salt (internal mixing) Derive CCN spectra for each grid With the predicted CCN spectra, apply the CCN activation parameterization in Fountoukis and Nenes [2005] In each grid, vary aerosol mass between 0.05 and 5 times the average concentration, and compute the corresponding Nc Fit Nc with the molar concentrations of total soluble ions (mi), (formulation proposed by Boucher and Lohmann [1995]) Coefficients A and B are computed for each grid cell and for each month to account for geographic and seasonal variations of aerosol-cloud interactions

17 Derive Offline Nc for Climate Simulations
Use aerosol mass predicted by the Unified Model; apply the above correlations to derive consistent offline Nc Use sea salt for 20C in all conditions When converting aerosol mass to soluble ions: sulfate, ammonium, and nitrate are fully soluble, POA and SOA are 80% soluble, BC is insoluble Set a lower limit of 20 cm–3 for Nc; interpolate from 9 to 23 layers

18 Modify Warm Stratiform Clouds in GISS III
In standard GISS III, cloud droplet size (rv) and autoconversion rates of stratiform clouds only depend on liquid water density (m) and liquid water mixing ratio (ql), not explicitly related to Nc

19 Modify Warm Stratiform Clouds in GISS III
In standard GISS III, cloud droplet size (rv) and autoconversion rates of stratiform clouds only depend on liquid water density (m) and liquid water mixing ratio (ql), not explicitly related to Nc Del Genio et al. [1996] Our Modifications rv (in c and droplet evaporation rates)  m assuming constant Nc (60 cm–3 over ocean; 170 cm–3 over land)  (m/ Nc) Offline Nc imported to each grid autoconversion rates  ql[1–exp(–m similar to Sundqvist et al. [1989]  qlNc–1.79 Khairoutdinov and Kogan [2000]

20 Adjustment to the New Autoconversion Rate
For the same liquid water density, K&K >> Sundqvist parameterization (20 to100 x) Increase in liquid water path and “drift” toward a cooler climate compare to standard GISS III

21 Adjustment to the New Autoconversion Rate
For the same liquid water density, K&K >> Sundqvist parameterization (20 to100 x) Increase in liquid water path and “drift” toward a cooler climate compare to standard GISS III Proper adjustment to the new autoconversion rates is needed Scale the K&K autoconversion with a tuning parameter (): Testing values between 10 and 100. For each value of , carry out one year of simulation with modified stratiform cloud scheme; diagnose the TOA radiation fluxes Compare to standard GISS III for present day equilibrium climate Optimum value 40

22 Equilibrium Simulations with GISS III
Two 100- year equilibrium simulations using standard GISS III Use the final year from each simulation as I.C. Simulation GHG ADE AIE Years of Integration GD20C 20C — 100 GD21C 21C

23 Equilibrium Simulations with GISS III
Two 100- year equilibrium simulations using standard GISS III Use the final year from each simulation as I.C. Four 20-year equilibrium simulations using modified GISS III Perturbations only in Nc (PI to 20C and 20C to 21C) Perturbations in GHG, ADE, and Nc (20C to 21C) Simulation GHG ADE AIE Years of Integration GD20C 20C — 100 GD21C 21C GD20CI20C 20 GD20CIPI PI GD20CI21C GD21CI21C

24 Equilibrium Simulations with GISS III
Two 100- year equilibrium simulations using standard GISS III Use the final year from each simulation as I.C. Four 20-year equilibrium simulations using modified GISS III Perturbations only in Nc (PI to 20C and 20C to 21C) Perturbations in GHG, ADE, and Nc (20C to 21C) Simulation GHG ADE AIE Years of Integration Instant. Forcing (w.r.t. 20C) GD20C 20C — 100 GD21C 21C +6.59 (GHG=+6.47,ADE=+0.12) GD20CI20C 20 GD20CIPI PI –1.81 GD20CI21C –0.84 GD21CI21C +5.75

25 Perturbation of Nc -- from PI to 20C
Stream function Large Nc increase and negative TOA SW cloud forcing over 30–60oN (especially in JJA) TOA net cloud forcing = –1.32 W m–2 Ts = –0.95 K; stronger cooling in NH ITCZ: southward shift Precip = –3% Nc at surface Nc TOA CF Ts Precip.

26 Perturbation of Nc -- from 20C to 21C
Nc increase is smaller than perturbation from PI to 20C Nc increase peaks around 30oN (DJF > JJA) Nc decreases in northern high latitudes owing to predicted sulfate reduction TOA net cloud forcing = –0.42 W m–2 Ts = –0.25 K Precip. = –1% Stream function Nc at surface Nc TOA CF Ts Precip.

27 Perturbation of GHG, ADE and AIE from 2000 to 2100
Patterns dominated by GHG warming Ts = K; amplified warming in the Arctic Broadened Hadley Cell with strengthened ascending branches in convection zone near the Equator Precip. = +8% Stratiform precipitation decreases over 45oS–45oN Ts (Precip.-Evap.) Strat. Precip.

28 Effects of Including AIE in GISS III
Modified GISS III (GHG+ADE+AIE) Standard GISS III (GHG+ADE) Similar change in Ts (+4.61 K vs K) and circulation Smaller increase in total precip. (+8% vs. +11%) Decrease vs. increase of stratiform precip. (–3% vs. +5%), especially in lower latitudes Ts (Precip.-Evap.) Strat. Precip.

29 Summary Perturbation of Nc from PI to 20C
Large Nc increase over 30–60oN AIE forcing = –1.81 W m-2, Ts = –0.95 K, Precip. = –3.0 % Southward shift of ITCZ Perturbation of Nc from 20C to 21C Smaller Nc increase (peaks at 30oN) AIE forcing = –0.84 W m-2, Ts = –0.25 K, Precip. = –1.0 % No significant change in circulation Compare modified version (GHG+ADE+AIE) with standard version (GHG+ADE) Decrease in stratiform precip.; smaller precipitation increase Similar change in Ts and circulation

30 Stepwise vs. Fully Coupled Simulations
The previous studies on ADE and AIE adopted a “stepwise” method No feedback of climate on the offline aerosols and Nc Liao et al. [2008] (submitted) Study the effects of full coupling on predicted future climate and future O3 and aerosols

31 Stepwise vs. Fully Coupled Simulations
The previous studies on ADE and AIE adopted a “stepwise” method No feedback of climate on the offline aerosols and Nc Liao et al. [2008] (submitted) Study the effect of full coupling on predicted future climate and future O3 and aerosols The CACTUS Unified Model SRES A2 scenario: include GHG, tropospheric O3, and aerosols ADE only, no AIE Equilibrium simulations: compare results between “stepwise” and “fully coupled” methods

32 Stepwise vs. Fully Coupled Simulation
Effects of full coupling on predicted aerosols in 21C Reductions of all aerosol burdens in mid to high latitudes in NH Increases in JJA aerosol burdens over populated and biomass burning areas Increases in aerosol burdens over the topics

33 Stepwise vs. Fully Coupled Simulation
Effects of full coupling on predicted aerosols in 21C Reductions of all aerosol burdens in mid to high latitudes in NH Increases in JJA aerosol burdens over populated and biomass burning areas Increases in aerosol burdens over the topics Effects of full coupling on predicted climate in 21C A stronger global warming (an additional 0.4 K increase) Weaker convection and precipitation

34 Stepwise vs. Fully Coupled Simulation
Effects of full coupling on predicted aerosols in 21C Reductions of all aerosol burdens in mid to high latitudes in NH Increases in JJA aerosol burdens over populated and biomass burning areas Increases in aerosol burdens over the topics Effects of full coupling on predicted climate in 21C A stronger global warming (an additional 0.4 K increase) Weaker convection and precipitation Positive feedback between ADE forcing and aerosol burdens aerosol concentrations  boundary-layer height and wet deposition of aerosols  aerosol concentrations in lower tropospheric 

35 Climate & Hydrological Sensitivities (20C to 21C)
Climate Sensitivity (K m2 W-1) Hydrological Sensitivity (% K-1) GHG only (GISS II’) +0.81 +2.19 ADE only (GISS II’) +0.78 -7.34 AIE only (modified GISS III) +0.30 +4.18

36 Climate & Hydrological Sensitivities (20C to 21C)
Climate Sensitivity (K m2 W-1) Hydrological Sensitivity (% K-1) GHG only (GISS II’) +0.81 +2.19 ADE only (GISS II’) +0.78 -7.34 AIE only (modified GISS III) +0.30 +4.18 GHG+ADE (standard GISS III) +0.74 +2.27 GHG+ADE+AIE (modified GISS III) +0.80 +1.78 (fully coupled, Unified model) --- +1.5

37 Acknowledgement NASA IDS US EPA

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