Separating radiative forcing by aerosol–cloud interactions and rapid cloud adjustments in the ECHAM–HAMMOZ aerosol–climate model using the method of partial radiative perturbations

Abstract

Abstract. Using the method of offline radiative transfer modeling within the partial radiative perturbation (PRP) approach, the effective radiative forcing by aerosol–cloud interactions (ERFaci) in the ECHAM–HAMMOZ aerosol climate model is decomposed into a radiative forcing by anthropogenic cloud droplet number change and adjustments of the liquid water path and cloud fraction. The simulated radiative forcing by anthropogenic cloud droplet number change and liquid water path adjustment are of approximately equal magnitude at −0.52 and −0.53 W m−2, respectively, while the cloud-fraction adjustment is somewhat weaker at −0.31 W m−2 (constituting 38 %, 39 %, and 23 % of the total ERFaci, respectively); geographically, all three ERFaci components in the simulation peak over China, the subtropical eastern ocean boundaries, the northern Atlantic and Pacific oceans, Europe, and eastern North America (in order of prominence). Spatial correlations indicate that the temporal-mean liquid water path adjustment is proportional to the temporal-mean radiative forcing, while the relationship between cloud-fraction adjustment and radiative forcing is less direct. While the estimate of warm-cloud ERFaci is relatively insensitive to the treatment of ice and mixed-phase cloud overlying warm cloud, there are indications that more restrictive treatments of ice in the column result in a low bias in the estimated magnitude of the liquid water path adjustment and a high bias in the estimated magnitude of the droplet number forcing. Since the present work is the first PRP decomposition of the aerosol effective radiative forcing into radiative forcing and rapid cloud adjustments, idealized experiments are conducted to provide evidence that the PRP results are accurate. The experiments show that using low-frequency (daily or monthly) time-averaged model output of the cloud property fields underestimates the ERF, but 3-hourly mean output is sufficiently frequent.

Radiative forcing of climate change from the Copernicus reanalysis of atmospheric composition

“…Clouds mask 14 % of carbon dioxide and methane RF. That estimate is similar to the previous estimate of 13 % for carbon dioxide by Myhre et al (1998) but smaller than the estimate of 29 % given for methane by Minschwaner et al (1998). Clouds mask 17 % of tropospheric ozone RF and switch the sign of global-mean stratospheric ozone RF, which, however, remains weak.…”

“…The CAMS estimates of RF by carbon dioxide and methane are based on the three-dimensional distributions of CAMS greenhouse gas flux inversions. Most previous estimates are either based on radiative-transfer calculations that assume a uniform mixing ratio of these gases or use simplified expressions, especially those by Myhre et al (1998), obtained by fitting the calculations of radiative-transfer models of varying spectral resolution. Figure 13 compares the CAMS estimates to calculations using the same methods and input datasets, except that carbon dioxide and methane are now prescribed uniformly as measured by the ESRL and AGAGE networks (see Sect.…”

“…3.1. Also included in the comparison are estimates from the simplified expressions in Table 3 of Myhre et al (1998), calculated using annually averaged mass-weighted atmospheric concentrations from the CAMS greenhouse gas flux inversions. Calculations assume the same pre-industrial concentrations as above and in addition assume a pre-industrial concentration of 270 ppb for nitrous oxide (again from Table 8.2 of Myhre et al, 2013a), which is a required input for the methane forcing calculation.…”

“…Those difficulties are compounded by the lack of consistent and integrated quantifications across forcing agents. In the IPCC Fifth Assessment Report (AR5) (Myhre et al, 2013a), carbon dioxide and methane radiative forcing were derived from fits to line-by-line radiative-transfer models (Myhre et al, 1998) using global-mean changes in surface concentrations as input. Aerosol radiative forcing from interactions with radiation was based on global modelling inter-comparisons (Myhre et al, 2013b;Shindell et al, 2013a) and observationbased estimates (Bond et al, 2013;Bellouin et al, 2013).…”

Coupling aerosols to (cirrus) clouds in the global EMAC-MADE3 aerosol–climate model

“…where p ice is the temperature-dependent saturation vapor pressure over ice, calculated according to Murphy and Koop (2005). To distinguish between cloudy and cloud-free model grid boxes when comparing with the observations, the criterion IWC > 0.5 mg kg −1 is adopted.…”