Periodic Reporting for period 4 - RECAP (constRaining the EffeCts of Aerosols on Precipitation)
Periodo di rendicontazione: 2021-11-01 al 2023-04-30
Precipitation is of fundamental importance so it is vital to understand its response to man-made perturbations. Aerosols, small particles suspended in air (air pollution), have been proposed to significantly affect precipitation. However, despite major research efforts, evidence for a systematic aerosol effect on precipitation remained “ambiguous”.
The vast majority of prior research has taken a bottom-up (process-driven) approach: trying to infer aerosol effects on precipitation through modelling / observing the chain of microphysical processes: from aerosols acting as cloud condensation / ice nuclei via cloud microphysics to precipitation formation of individual clouds or cloud systems. However, this relies on a complete understanding of a very complex and uncertain process chain. In addition, there exist no clear strategies to scale the response of individual clouds or cloud systems to regional or global scales, incorporating the numerous potential feedback and buffering processes. Together, this explains the limited progress in this area.
RECAP aimed to break this deadlock, introducing a radically different approach to aerosol effects on precipitation: globally, precipitation is energetically controlled, as the associated release of latent heat needs to be balanced by radiative cooling or surface flux changes. However, global mean energetics does not constrain shorter temporal or smaller spatial scales because divergence of dry static energy can locally balance excess input of latent heat from precipitation. At some scale, henceforth denoted the breakdown scale, the compensating divergence of dry static energy becomes insufficient to balance the input of latent heat from precipitation and energetic constraints determine the maximum mean precipitation change. We therefore expected the local (cloud) scale to be process-controlled while large-scale precipitation obeys energetic control.
The primary objective of RECAP is to systematically constrain the energetic control of aerosol effects on precipitation across scales (top-down) and unite this approach with the prevailing process-driven approach (bottom-up).
The vast majority of prior research has taken a bottom-up (process-driven) approach: trying to infer aerosol effects on precipitation through modelling / observing the chain of microphysical processes: from aerosols acting as cloud condensation / ice nuclei via cloud microphysics to precipitation formation of individual clouds or cloud systems. However, this relies on a complete understanding of a very complex and uncertain process chain. In addition, there exist no clear strategies to scale the response of individual clouds or cloud systems to regional or global scales, incorporating the numerous potential feedback and buffering processes. Together, this explains the limited progress in this area.
RECAP aimed to break this deadlock, introducing a radically different approach to aerosol effects on precipitation: globally, precipitation is energetically controlled, as the associated release of latent heat needs to be balanced by radiative cooling or surface flux changes. However, global mean energetics does not constrain shorter temporal or smaller spatial scales because divergence of dry static energy can locally balance excess input of latent heat from precipitation. At some scale, henceforth denoted the breakdown scale, the compensating divergence of dry static energy becomes insufficient to balance the input of latent heat from precipitation and energetic constraints determine the maximum mean precipitation change. We therefore expected the local (cloud) scale to be process-controlled while large-scale precipitation obeys energetic control.
The primary objective of RECAP is to systematically constrain the energetic control of aerosol effects on precipitation across scales (top-down) and unite this approach with the prevailing process-driven approach (bottom-up).
In WP1, we constrained the precipitation response to aerosol perturbations top down through energy and water budget constraints. Through a hierarchy of simulations of increasing complexity, we demonstrated for the first time a strong regional dependence of the precipitation susceptibility to aerosol perturbation, which we could underpin with a physical framework of budgetary and dynamical constraints. We analysed the atmospheric water budget to elucidate the spatial scale of precipitation changes and combine energy and water budgets to further strengthen the constraint on the precipitation response under climate change climate change. We transitioned our idealized setup to more realistic climate model configurations to probe the global precipitation susceptibility and systematically test the regional dependence. We further separated the contrasting behaviour of scattering and absorbing aerosol. Together, RECAP publications provide an authoritative body of literature energy and water budget constraints on regional and global precipitation.
In WP2 we investigated drivers of aerosol effects on precipitation bottom up employing and developing a hierarchy of advanced high-resolution atmospheric models of increasing complexity. We demonstrated a strong sensitivity of convective ice to variations in cloud droplet numbers (as proxy for aerosol perturbations) over the North Atlantic – with moderate precipitation responses. We used large-scale high-resolution atmospheric simulations with idealised aerosol perturbation to isolate smoke impacts on cloud and precipitation processes over the Amazon, highlighting the importance of aerosol-radiation interactions and the complex diurnal response of cloud systems. We capitalized on the emergence of global high-resolution km-scale climate models to investigate global aerosol-cloud-precipitation interactions and set out to develop a novel reduced complexity aerosol module HAM-Lite.
In WP3, we developed novel observational constraints, capitalizing on emerging opportunities and advancements in the field, in particular of Artificial Intelligence (AI) and Machine Learning (ML). We derived a novel framework for satellite data analysis to provide the first observational evidence that aerosols enhance cloud lifetime and brightness. We developed a novel framework for the detection of convective cores and anvils in satellite data that we have applied to derive the first full climatology of the convective lifecycle and aerosol effects thereon. We seized the opportunities provided by opportunistic experiments, such as pollution tracks from ships, to provide novel constraints on aerosol-cloud-precipitation interactions. We also developed a new ML framework for the definition of regimes of cloud controlling factors for cloud forcing and feedback studies.
In WP4, we synthesized aerosol effects on precipitation. Internationally, we co-led the first intercomparison project of cloud resolving models to study aerosol effects on convection and precipitation under the umbrella of the Aerosols, Clouds, Precipitation and Climate (ACPC) initiative and have been invited to co-lead the World Climate Research Programme’s GEWEX Aerosol Precipitation (GAP) initiative – pushing the frontiers through a proposed global km-scale model intercomparison of aerosol effects. We held three expert RECAP workshop at Oxford providing authoritative overview papers of key RECAP topics and activities: the first RECAP expert workshop on aerosol effects on precipitation under the umbrella of GAP; a second RECAP / GAP expert workshop, synthesizing our understanding of satellite-based assessments of aerosol effects on precipitation; and the third RECAP expert workshop on cloud tracking, synthesizing science opportunities and tools and serving as key dissemination route for our tobac cloud tracking tool developed under RECAP.
In WP2 we investigated drivers of aerosol effects on precipitation bottom up employing and developing a hierarchy of advanced high-resolution atmospheric models of increasing complexity. We demonstrated a strong sensitivity of convective ice to variations in cloud droplet numbers (as proxy for aerosol perturbations) over the North Atlantic – with moderate precipitation responses. We used large-scale high-resolution atmospheric simulations with idealised aerosol perturbation to isolate smoke impacts on cloud and precipitation processes over the Amazon, highlighting the importance of aerosol-radiation interactions and the complex diurnal response of cloud systems. We capitalized on the emergence of global high-resolution km-scale climate models to investigate global aerosol-cloud-precipitation interactions and set out to develop a novel reduced complexity aerosol module HAM-Lite.
In WP3, we developed novel observational constraints, capitalizing on emerging opportunities and advancements in the field, in particular of Artificial Intelligence (AI) and Machine Learning (ML). We derived a novel framework for satellite data analysis to provide the first observational evidence that aerosols enhance cloud lifetime and brightness. We developed a novel framework for the detection of convective cores and anvils in satellite data that we have applied to derive the first full climatology of the convective lifecycle and aerosol effects thereon. We seized the opportunities provided by opportunistic experiments, such as pollution tracks from ships, to provide novel constraints on aerosol-cloud-precipitation interactions. We also developed a new ML framework for the definition of regimes of cloud controlling factors for cloud forcing and feedback studies.
In WP4, we synthesized aerosol effects on precipitation. Internationally, we co-led the first intercomparison project of cloud resolving models to study aerosol effects on convection and precipitation under the umbrella of the Aerosols, Clouds, Precipitation and Climate (ACPC) initiative and have been invited to co-lead the World Climate Research Programme’s GEWEX Aerosol Precipitation (GAP) initiative – pushing the frontiers through a proposed global km-scale model intercomparison of aerosol effects. We held three expert RECAP workshop at Oxford providing authoritative overview papers of key RECAP topics and activities: the first RECAP expert workshop on aerosol effects on precipitation under the umbrella of GAP; a second RECAP / GAP expert workshop, synthesizing our understanding of satellite-based assessments of aerosol effects on precipitation; and the third RECAP expert workshop on cloud tracking, synthesizing science opportunities and tools and serving as key dissemination route for our tobac cloud tracking tool developed under RECAP.
The single most important achievement of RECAP is its contribution to our physical understanding of the precipitation response to air pollution and climate changes, which has important implications for a future world under air pollution mitigation and increasing global warming. We for the first time demonstrated, a strong regional dependence of the precipitation susceptibility to aerosol perturbation, which we could underpin with a physical framework of budgetary and dynamical constraints: the latitudinal dependence con be explained by the contrasting ability of the atmosphere to diverge excess dry static energy in low and higher latitudes: atmospheric heating can be easily diverged leading to an increase in precipitation, while the excess energy from heating in the extratropics is constrained due to the Coriolis force, causing the precipitation decrease and we provided dynamical explanation of the dependence and interaction with regional atmospheric dynamics.
Two developments in RECAP were to some degree unexpected:
The rapid advancement of global km-scale climate simulations, in particular also of the ICON model used in RECAP, exceeded our expectations and allowed us to conduct simulations and conduct research avenues we would not have held possible when writing the proposal.
The availability of scalable methodologies as well as the rapid transformation of climate science by AI and ML were not foreseeable.
Two developments in RECAP were to some degree unexpected:
The rapid advancement of global km-scale climate simulations, in particular also of the ICON model used in RECAP, exceeded our expectations and allowed us to conduct simulations and conduct research avenues we would not have held possible when writing the proposal.
The availability of scalable methodologies as well as the rapid transformation of climate science by AI and ML were not foreseeable.