1. Putting the Clouds Back in Aerosol-Cloud Interactions
Gettelman (NCAR) with H. Morrison (NCAR), A. Seifert (DWD, MPI-Met)
Summary
Conclusions
Aerosols affect climate through direct effects of absorption or scattering, and Aerosol-Cloud Interactions (ACI). ACI change the number of cloud drops and resulting microphysical interactions. This study uses idealized and global tests of a cloud microphysics scheme used in global climate models to show that cloud microphysical processes are critical to ACI. Uncertainties in cloud microphysical processes cause uncertainties of up to -35% to +50% in ACI, stronger than uncertainties due to natural aerosol emissions (-20 to +30%). Precipitation processes are critical for understanding ACI and uncertain cloud lifetime effects are 1/3 of simulated ACI.
ACI are most sensitive to Cloud microphysics: -35% to +50% (Figure 8). ACI are also sensitive to emissions (-18% to +31%). ACI are correlated with LWP (Figure 7). Lifetime effects are about 1/3 of ACI in CAM5.3 (Figure 6) and in idealized tests (Figure 4). Lifetime effects are ‘prescribed’ due to autoconversion (Figure 4). The mechanism is through cloud cover and LWP changes (Figure 5).
Results (A) Idealized Cases
Albedo terms (Fig 3) are estimated using differentials between pairs of simulations for changes in albedo (dA), drop number (dNc), Liquid Water Path (dLWP) and cloud cover (dC).
LWC
Rain Rate
Motivation
Aerosol
(CCN
Number)
RWC a
Microphysical Process rates interact with aerosols by Activation, Autoconversion and Accretion (Fig 1). This will strongly affect ACI.
qc, Nc
Cloud Droplets
(Prognostic)
Activation
Autoconversion
Accretion Au Ac
Au = f(qc,Nc-2)
Ac = f(qr,qc)
Activation (CCN) = f(RH,w) W at cloud scale is critical
Autoconversion (loss process) is a function of Nc-2 (=ACI)
Accretion depends on qr
qr, Nr
Rain
Figure 3: (A) change in albedo with LWP for different fixed drop number cases. This is used to derive terms in (B) for albedo change, colors indicate different pairs of simulations
Figure 2: Idealized Warm Rain Case, oscillating updraft (A) LWC, (B) Rain Rate, (C) Rain Water Content, (D) Albedo, (E) Autoconversion Rate, (F) Accretion Rate. Different colors represent different drop numbers
Sedimentation
Figure 1: Schematic of warm rain formation
‘Lifetime’ effects are due to autoconversion (Fig 4),not sedimentation. Auto-conversion schemes matter. In this case: through cloud cover.
Different Cases: LWP matters in weaker/decaying updrafts for lifetime effects (Fig 5).
Methods
A) Single
A) Lifetime a Terms
B) Autoconv a Terms
A 2-moment bulk microphysics scheme is used to examine ACI in (A) Idealized tests and (B) Global Climate Simulations
Figure 5: Albedo terms with cases as in Figure 4A (removing lifetime effects) for other updraft cases: (A) Single, (B) Oscillating decaying and (C) Weak and shallow oscillating updrafts.
Idealized Tests
Bulk 2-moment microphysics (Gettelman & Morrison 2015) is run in an idealized model (Shipway & Hill 2012) with different prescribed drop numbers (Nc). Look at changes in albedo (A) due to: (1) Nc, (2) LWP, (3) Cloud Coverage (C):
B) Oscillating Decaying
C) Weak & Shallow
Figure 4: Albedo terms for different cases (A) removing lifetime effects (Autoconversion, Sedimentation, Combination) and (B) different autoconversion schemes (Khairoutdinov & Kogan 2000, Seifert & Behang 2001, Kogan 2013) using an oscillating updraft.
Using a simplified albedo:
Results (B) GCM Sensitivity Experiments
(B) Global Sensitivity Tests
The Community Atmosphere Model (CAM) version 5.3 includes an aerosol model, the same cloud microphysics and aerosol activation of cloud drops. Sensitivity tests perturb either cloud microphysics or emissions.
ACI and DMicrophysics: Changing Cloud Microphysics changes ACI significantly (Fig 6). Lifetime effects (NoLif) are a large impact.
ACI are strongly correlated with DLWP (not DNc.), Fig7. Not correlated with cloud fraction(DCLD) and size (DRe) (not shown).
Figure 7: ACI (change in cloud radiative effect, DCRE) v. Change in
LWP (%) for sensitivity tests. Green range is due to autoconversion
ACI Metric: Change in cloud radiative effects (DCRE) between simulations with 2000 and 1850 aerosol emissions.
Autoconversion
Figure 6: (A) Zonal mean ACI and (B) zonal mean change in LWP from different sensitivity tests in Table 1
Table 1: CAM sensitivity tests. Green are changes in microphysics, yellow changes in emissions
ACI Sensitivity
Figure 8: ACI sensitivity (% change in radiative effects) with different sensitivity experiments. Categories: Emissions & Microphysics. Then: Mixed Phase, Autoconversion/Lifetime, Regimes, Prognostic Precip, Activation
ACI Sensitvity by category (Fig 8).
Emissions sensitivity: -18% to +31% (Similar to Carslaw et al., 2013)
Cloud microphysics changes = -35% to +50%. Autoconversion schemes, mixed phase and different regimes (shallow cumulus) are key
References
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Shipway, B. J., and A. A. Hill. Diagnosis of Systematic Differences between Multiple Parametrizations of Warm Rain Microphysics Using a Kinematic Framework. Quart. Journal of the Royal Met. Soc. 138, no. 669 (2012): 2196–2211. doi: /qj.1913.
Regimes