Climate processes

Abstract:

Clouds are a key player in the global climate system, affecting the atmospheric water and energy budgets, and they are strongly coupled to the large-scale atmospheric circulation. Here, we examine the co-variability of the atmospheric energy and water budget imbalances in three different global model configurations–radiative-convective equilibrium, aqua-planet, and global simulations with land. The gradual increase in the level of complexity of the model configuration enables an investigation of the effects of rotation, meridional temperature gradient, land-sea contrast, and seasonal cycle on the co-variability of the water and energy imbalances. We demonstrate how this co-variability is linked to both the large-scale tropical atmospheric circulation and to cloud properties. Hence, we propose a co-variability-based framework that connects cloud properties to the large-scale tropical circulation and climate system and is directly linked to the top-down constrains on the system—the water and energy budgets. In addition, we examine how the water and energy budget imbalances co-variability depends on the temporal averaging scale, and explain its dependency on how stationary the circulation is in the different model configurations. Finally, we demonstrate the effect of an idealized global warming and convective aggregation on this co-variability.

Abstract:

Anthropogenic aerosols effect on clouds remains a persistent source of uncertainty in future climate predictions. The evolution of the environmental conditions controlling cloud properties is affected by the clouds themselves. Hence, aerosol-driven modifications of cloud properties can affect the evolution of the environmental thermodynamic conditions, which in turn could feed back to the cloud development. Here, by comparing many different cloud resolving simulations conducted with different models and under different environmental condition, we show that this feedback loop is strongly affected by the representation of the boundary conditions in the model. Specifically, we show that the representation of boundary conditions strongly impacts the magnitude of the simulated response of the environment to aerosol perturbations, both in shallow and deep convective clouds. Our results raise doubts about the significance of previous conclusions of aerosol-cloud feedbacks made based on simulations with idealised boundary conditions.

Abstract:

<p>Clouds remain a core uncertainty in quantifying Earth’s climate sensitivity due to their complex dynamical and microphysical  interactions with multiple components of the Earth system. Therefore it is pivotal to observationally constrain possible cloud changes in a changing climate in order to evaluate our current generation of Earth system models by a set of physically realistic sensitivities. We developed a novel observational regime framework from over 15 years of MODIS satellite observations, from which we have derived a set of regimes of cloud controlling factors. These regimes were established using the relationship strength, as measured by using the weights of a trained, simple machine learning model. We apply these as observational constraints on the ​​r1i1p1f1 and r1i1p1f3 historical runs from various CMIP6 models to test if CMIP6 climate models can accurately represent key cloud controlling factors.. Within our regime framework, we can compare the observed environmental drivers and sensitivities of each regime against the parameterization-driven, modeled outcomes. We find that, for almost every regime, CMIP6 models do not properly represent the global distribution of occurrence, raising into question how much we can trust our range of climate sensitivities when specific cloud controlling factors are so badly represented by these models. This is especially pertinent in southern ocean and marine stratocumulus regimes, as the changes in these clouds’ optical depths and cloud amount have increased the ECS from CMIP5 to CMIP6. Our results suggest that these uncertainties in CMIP6 cloud parameterizations propagate into derived cloud feedbacks and ultimately climate sensitivity, which is evident from a regimed based analysis of cloud controlling factors.</p>

Abstract:

<p>The long-lived and widespread nature of smoke, coupled with its ability to perturb the atmosphere simultaneously via aerosol-cloud and aerosol-radiation interactions, has proven a challenge to observe and simulate; as such, the impact of smoke on regional and global scales remains uncertain.</p><p>In this study we use an 18-year climatology from multiple instruments onboard AQUA and TERRA satellites to identify and characterise the relationships between aerosol-optical-depth (AOD) and the large-scale properties of the clouds, precipitation, and top-of-atmosphere radiation over the Amazon rainforest during the biomass burning season.</p><p>Our analysis provides robust evidence that localised smoke production drives widespread modification to the cloud regime over the region: in the morning (TERRA) cloud liquid water path increases with AOD, whereas in the afternoon (AQUA) convective activity is initially enhanced then supressed when AOD exceeds 0.4. During both time periods there is an increasingly pronounced presence of high-altitude, optically thin, clouds.</p><p>The result is a sharp contrast in the cloud-field properties and vertical distribution between low-AOD days and high-AOD days and a pronounced top-of-atmosphere radiative effect of -50 Wm­<sup>-2</sup> (for AOD = 1.4), which persists throughout the day.</p>

Abstract:

<p>Exploration of future emissions scenarios mostly relies on one-dimensional impulse response models, or simple pattern scaling approaches to approximate the physical climate response to a given scenario. Such approaches are unable to reliably predict climate variables which respond non-linearly to emissions or forcing (such as precipitation) and must rely on heavily simplified representations of e.g., aerosol, neglecting important spatial dependencies.</p><p>Here we present ClimateBench - a benchmark dataset based on a suite of CMIP, AerChemMIP and DAMIP simulations performed by NorESM2, and a set of baseline machine learning models that emulate its response to a variety of forcers. These surrogate models can skilfully predict annual mean global distributions of temperature, diurnal temperature range and precipitation (including extreme precipitation) given a wide range of emissions and concentrations of carbon dioxide, methane and spatially resolved aerosol. We discuss the accuracy and interpretability of these emulators and consider their robustness to physical constraints such as total energy conservation. Future opportunities incorporating such physical constraints directly in the machine learning models and using the emulators for detection and attribution studies are also discussed. This opens a wide range of opportunities to improve prediction, consistency and mathematical tractability.</p><p>We hope that by defining a clear baseline with appropriate metrics and providing a variety of baseline models we can bring the power of modern machine learning techniques to bear on the important problem of efficiently and robustly sampling future climates.</p>