Clouds and Aerosols

Boucher, Oliviér; Randall, David A.; Artaxo, P.; Bretherton, Christopher S.; Feingold, Christopher; Forster, Piers M.; Kerminen, Veli‐Matti; Kondo, Yutaka; Liao, Hong; Lohmann, Ulrike; Rasch, P.; Satheesh, S. K.; Sherwood, Steven C.; Stevens, Björn; Zhang, Xiaoye

doi:10.1017/cbo9781107415324.016

Cited by 780 publications

(495 citation statements)

References 656 publications

(850 reference statements)

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“…2a, 3a). Strong aerosol cooling is supported by Rotstayn et al (2015), who found that ACCESS1.3 showed a large globalmean aerosol effective radiative forcing over the historical period of −1.56 W m −2 , which is much larger than the IPCC best estimate (−0.9 W m −2 ) (Boucher et al, 2013) but still within the uncertainty range.…”

Section: Land Temperature and Precipitationmentioning

confidence: 91%

The carbon cycle in the Australian Community Climate and Earth System Simulator (ACCESS-ESM1) – Part 2: Historical simulations

et al. 2017

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Abstract. Over the last decade many climate models have evolved into Earth system models (ESMs), which are able to simulate both physical and biogeochemical processes through the inclusion of additional components such as the carbon cycle. The Australian Community Climate and Earth System Simulator (ACCESS) has been recently extended to include land and ocean carbon cycle components in its ACCESS-ESM1 version. A detailed description of ACCESS-ESM1 components including results from pre-industrial simulations is provided in Part 1. Here, we focus on the evaluation of ACCESS-ESM1 over the historical period in terms of its capability to reproduce climate and carbon-related variables. Comparisons are performed with observations, if available, but also with other ESMs to highlight common weaknesses. We find that climate variables controlling the exchange of carbon are well reproduced. However, the aerosol forcing in ACCESS-ESM1 is somewhat larger than in other models, which leads to an overly strong cooling response in the land from about 1960 onwards. The land carbon cycle is evaluated for two scenarios: running with a prescribed leaf area index (LAI) and running with a prognostic LAI. We overestimate the seasonal mean (1.7 vs. 1.4) and peak amplitude (2.0 vs. 1.8) of the prognostic LAI at the global scale, which is common amongst CMIP5 ESMs. However, the prognostic LAI is our preferred choice, because it allows for the vegetation feedback through the coupling between LAI and the leaf carbon pool. Our globally integrated land-atmosphere flux over the historical period is 98 PgC for prescribed LAI and 137 PgC for prognostic LAI, which is in line with estimates of land use emissions (ACCESS-ESM1 does not include land use change).The integrated ocean-atmosphere flux is 83 PgC, which is in agreement with a recent estimate of 82 PgC from the Global Carbon Project for the period 1959-2005. The seasonal cycle of simulated atmospheric CO 2 is close to the observed seasonal cycle (up to 1 ppm difference for the station at Mace Head and up to 2 ppm for the station at Mauna Loa), but shows a larger amplitude (up to 6 ppm) in the high northern latitudes. Overall, ACCESS-ESM1 performs well over the historical period, making it a useful tool to explore the change in land and oceanic carbon uptake in the future.

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Section: Land Temperature and Precipitationmentioning

confidence: 91%

The carbon cycle in the Australian Community Climate and Earth System Simulator (ACCESS-ESM1) – Part 2: Historical simulations

et al. 2017

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“…Due to the perturbation of the radiation fields by dust aerosols, the energy budget both at the surface and in the atmosphere is modified and the signal of these impacts is evident in atmospheric stability or instability conditions associated with cloud development and precipitation. These rapid adjustments, which have been referred to earlier as semi-direct effects (Hansen et al, 1997), are induced by the dust REari on surface energy budget and atmospheric profile (Boucher et al, 2013) contributing to the effective radiative forcing (ERFari). Moreover, dust aerosols, due to their ability to serve as cloud condensation nuclei (CCN) and ice nuclei (IN), modify the physical (Twomey, 1974;Albrecht, 1989) and optical properties of clouds (Pincus and Baker, 1994), which consist of the major regulators of the Earth-atmosphere system's radiation budget (Lohmann and Feicher, 2005).…”

Section: Introductionmentioning

confidence: 99%

“…The induced perturbation of the radiation fields by dust particles, the so-called dust radiative effect, takes place through three processes of increasing complexity affecting the energy budgets at the surface, in the atmosphere and at the top of the atmosphere (TOA). The first one, known as direct radiative effect (DRE) and referred as REari (aerosolradiation interactions) in the latest report of the Intergovernmental Panel on Climate Change (IPCC, Boucher et al, 2013), is caused by the absorption and scattering of the SW radiation (Sokolik et al, 2001) and the absorption and reemission of the LW radiation by mineral particles (Heinold et al, 2008). Due to the perturbation of the radiation fields by dust aerosols, the energy budget both at the surface and in the atmosphere is modified and the signal of these impacts is evident in atmospheric stability or instability conditions associated with cloud development and precipitation.…”

Section: Introductionmentioning

confidence: 99%

Direct radiative effects during intense Mediterranean desert dust outbreaks

et al. 2018

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Abstract. The direct radiative effect (DRE) during 20 intense and widespread dust outbreaks, which affected the broader Mediterranean basin over the period March 2000-February 2013, has been calculated with the NMMB-MONARCH model at regional (Sahara and European continent) and shortterm temporal (84 h) scales. According to model simulations, the maximum dust aerosol optical depths (AODs) range from ∼ 2.5 to ∼ 5.5 among the identified cases. At midday, dust outbreaks locally induce a NET (shortwave plus longwave) strong atmospheric warming (DRE ATM values up to 285 W m −2 ; Niger-Chad; dust AODs up to ∼ 5.5) and a strong surface cooling (DRE NETSURF values down to −337 W m −2 ), whereas they strongly reduce the downward radiation at the ground level (DRE SURF values down to −589 W m −2 over the Eastern Mediterranean, for extremely high dust AODs, 4.5-5). During night-time, reverse effects of smaller magnitude are found. At the top of the atmosphere (TOA), positive (planetary warming) DREs up to 85 W m −2 are found over highly reflective surfaces (Niger-Chad; dust AODs up to ∼ 5.5) while negative (planetary cooling) DREs down to −184 W m −2 (Eastern Mediterranean; dust AODs 4.5-5) are computed over dark surfaces at noon. Dust outbreaks significantly affect the mean regional radiation budget, with NET DREs ranging from −8.5 to 0.5 W m −2 , from −31.6 to 2.1 W m −2 , from −22.2 to 2.2 W m −2 and from −1.7 to 20.4 W m −2 for TOA, SURF, NETSURF and ATM, respectively. Although the shortwave DREs are larger than the longwave ones, the latter are comparable or even larger at TOA, particularly over the Sahara at midday. As a response to the strong surface day-time cooling, dust outbreaks cause a reduction in the regional sensible and latent heat fluxes by up to 45 and 4 W m −2 , respectively, averaged over land areas of the simulation domain. Dust outbreaks reduce the temperature at 2 m by up to 4 K during day-time, whereas a reverse tendency of similar magnitude is found during nighttime. Depending on the vertical distribution of dust loads and time, mineral particles heat (cool) the atmosphere by up to 0.9 K (0.8 K) during day-time (night-time) within atmospheric dust layers. Beneath and above the dust clouds, mineral particles cool (warm) the atmosphere by up to 1.3 K (1.2 K) at noon (night-time). On a regional mean basis, negative feedbacks on the total emitted dust (reduced by 19.5 %) and dust AOD (reduced by 6.9 %) are found when dust interacts with the radiation. Through the consideration of dust radiative effects in numerical simulations, the model positive and negative biases for the downward surface SW or LW radiation, respectively, with respect to Baseline Surface Radiation Network (BSRN) measurements, are reduced. In addition, they also reduce the model near-surface (at 2 m) nocturnal cold biases by up to 0.5 K (regional averages), as well as the model warm biases at 950 and 700 hPa, where the dust concentration is maximized, by up to 0.4 K. However, improvements are relatively small and do not happen in all ...

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“…A precise understanding of their properties and processes therefore is necessary to properly address current uncertainties of climate change estimates (Boucher et al, 2013). In particular, the impact of ice clouds on the Earth's 20 radiation budget is recognized as being substantial (e.g.…”

Section: Introductionmentioning

confidence: 99%

Ice crystal number concentration estimates from lidar-radar satellite remote sensing. Part 1: Method and evaluation

Sourdeval¹,

Gryspeerdt²,

Krämer³

et al. 2018

Preprint

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Abstract. The number concentration of cloud particles is a key quantity for understanding aerosol-cloud interactions and describing clouds in climate and numerical weather prediction models. In contrast with recent advances for liquid clouds, few observational constraints exist on the ice crystal number concentration (N i ). This study investigates how combined lidar-radar measurements can be used to provide satellite estimates of N i , using a methodology that constrains moments of a parameterized particle size distribution (PSD). The operational liDAR-raDAR (DARDAR) product serves as an existing base for this method, 5 which focuses on ice clouds with temperatures T c < -30°C.Theoretical considerations demonstrate the capability for accurate retrievals of N i , apart from a possible bias in the concentration in small crystals when T c -50°C, due to the assumption of a monomodal PSD shape in the current method. This is verified by comparing satellite estimates to co-incident in situ measurements, which additionally demonstrates the sufficient sensitivity of lidar-radar observations to N i . Following these results, satellite estimates of N i are evaluated in the context of a 10 case study and a preliminary climatological analysis based on 10 years of global data. Despite of a lack of other large-scale references, this evaluation shows a reasonable physical consistency in N i spatial distribution patterns. Notably, increases in N i are found towards cold temperatures and, more significantly, in the presence of strong updraughts, such as those related to convective or orographic uplifts. Further evaluation and improvements of this method are necessary but these results already constitute a first encouraging step towards large-scale observational constraints for N i . Part two of this series uses this new 15 dataset to examine the controls on N i .

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Clouds and Aerosols

Cited by 780 publications

References 656 publications

The carbon cycle in the Australian Community Climate and Earth System Simulator (ACCESS-ESM1) – Part 2: Historical simulations

The carbon cycle in the Australian Community Climate and Earth System Simulator (ACCESS-ESM1) – Part 2: Historical simulations

Direct radiative effects during intense Mediterranean desert dust outbreaks

Ice crystal number concentration estimates from lidar-radar satellite remote sensing. Part 1: Method and evaluation

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