Climate processes

Abstract:

From 2017 to 2019, vast swathes of eastern Australia were affected by the severe and devastating Tinderbox Drought. Here, we present the first extended drought climatology for New England, spanning 1880 to 2022, and explore trends in drought characteristics over the past 142 years. We use newly recovered historical temperature and rainfall observations, the latest version of the Australian Bureau of Meteorology’s gridded rainfall dataset and a global gridded extreme dataset to assess changes in precipitation signatures and temperature events during droughts. Our analysis identifies 32 meteorological droughts from 1880 to 2022, lasting from 7 months to over 7 years. The climatology also reveals a change in the nature of drought, with a shift from events characterised by warm season rainfall deficiencies to events with greater rainfall reduction in the cool half of the year. Despite this shift, we also find a significant decrease in the number of cold extremes occurring during droughts, and an increase in hot extremes. Droughts in New England have been associated with a greater than average frequency of cold nights and frost days, but this relationship has weakened over recent decades. Conversely, they are generally associated with a greater than average frequency of hot days, a relationship that has increased over time. The Tinderbox Drought was the second-most extreme meteorological drought for New England in terms of rainfall deficit and drought severity, and was associated with the highest number of extreme warm temperature events. The new drought climatology for New England can now be used to provide regional drought information for decision makers and the community.

Abstract:

We developed a cloud microphysics parameterization for the icosahedral nonhydrostatic modeling framework (ICON) model based on physics-informed machine learning (ML). By training our ML model on high-resolution simulation data, we enhance the representation of cloud microphysics in Earth system models (ESMs) compared to traditional parameterization schemes, in particular by considering the influence of high-resolution dynamics that are not resolved in coarse ESMs. We run a global, kilometer-scale ICON simulation with a one-moment cloud microphysics scheme, the complex graupel scheme, to generate 12 days of training data. Our ML approach combines a microphysics trigger classifier and a regression model. The microphysics trigger classifier identifies the grid cells where changes due to the cloud microphysical parameterization are expected. In those, the workflow continues by calling the regression model and additionally includes physical constraints for mass positivity and water mass conservation to ensure physical consistency. The microphysics trigger classifier achieves an F1 score of 0.93 on classifying unseen grid cells. The regression model reaches an score of 0.72 averaged over all seven microphysical tendencies on simulated days used for validation only. This results in a combined offline performance of 0.78. Using explainability techniques, we explored the correlations between input and output features, finding a strong alignment with the graupel scheme and, hence, physical understanding of cloud microphysical processes. This parameterization provides the foundation to advance the representation of cloud microphysical processes in climate models with ML, leading to more accurate climate projections and improved comprehension of the Earth’s climate system.

Abstract:

Earth system modeling is currently undergoing an exciting transformation, thanks to new technical capabilities that allow for significant spatial refinement. For the first time, these capabilities allow us to explicitly simulate extreme precipitation and its effects on climate-relevant timescales on a global scale. Thus, new Earth system data from high-resolution modeling approaches offer an exciting foundation for new analyses and research. In our study, we examine the distribution and changes in extreme precipitation from global simulations. We obtained this data from the ICON Earth system model simulations conducted within the nextGEMS project, which aims to create future projections up to the year 2050 with a grid spacing of approximately 5 km. Our analysis focuses on the portion of precipitation contributing to the top ten percent of globally accumulated precipitation. Using the open-source tool tobac we identify and track the resulting precipitation cells over time. Our analysis reveals that warming causes the most extreme precipitation cells to become more intense. At the same time, the data shows a significant decrease in the total number of cells, resulting in fewer, more intense extremes. Finally, we discuss these findings in relation to changes in the spatial distribution of the cells and changed environmental conditions.

Abstract:

The microphysical impacts of aerosol particles on scattered isolated deep convective cells near Houston, Texas on 19 June 2013, are examined using multiple cloud-system resolving model (CRM) simulations initialized with vertical profiles of low and high concentrations of cloud droplet nucleating aerosols. These simulations formed part of the Model Intercomparison Project (MIP) conducted by the Deep Convective Working Group of the Aerosol, Cloud, Precipitation and Climate (ACPC) initiative. Each CRM generated a field of convective cells representing those observed during the case study with varying degrees of accuracy. The Tracking and Object-Based Analysis of Clouds (tobac) cell tracking algorithm was applied to each MIP CRM simulation to track relatively long-lived convective cells (20–60 minutes). Most of the CRMs produced similar aerosol loading impacts on the warm-phase of tracked cell properties with reduced autoconversion and accretion growth of rain, increased cloud water, reduced rainfall, and reduced near-surface evaporation of rain. The sign of aerosol impacts on the warm-phase properties of the convective cells was also quite consistent over cell lifetimes with the greatest magnitude of influence in the first half of the lifecycle in most CRMs. In contrast, the ice-phase response to aerosol loading was highly variable amongst CRMs and included increases or decreases in ice amounts at inconsistent stages of cell lifecycle and mid-level vs upper-level changes in ice. This inter-model variability in ice is indicative both of the complex indirect interactions between aerosols and ice-phase processes in deep convection and their associated parameterizations.

Abstract:

Anthropogenic aerosols are a primary source of uncertainty in future climate projections. Changes to aerosol concentrations modify cloud radiative properties, radiative fluxes, and precipitation from the micro- to the global scale. Due to computational constraints, we have been unable to explicitly simulate cloud dynamics in global-scale simulations, leaving key processes, such as convective updrafts, parameterized. This has significantly limited our understanding of aerosol impacts on convective clouds and climate. However, new state-of-the-art climate models are capable of representing these scales. In this study, we used the kilometer-scale Icosahedral Nonhydrostatic (ICON) earth system model to explore the global-scale rapid response of clouds and precipitation to an idealized distribution of anthropogenic aerosol via aerosol-cloud interactions (ACI) and aerosol-radiation interactions (ARI). In our simulations over 30 days, we find that the aerosol impacts on clouds and precipitation exhibit strong regional dependence. The impact of ARI and ACI on clouds in isolation shows some consistent behavior, but the magnitude and additive nature of the effects are regionally dependent. Some regions are dominated by either ACI or ARI, whereas others behaved nonlinearly. This suggests that the findings of isolated case studies from regional simulations may not be globally representative; ARI and ACI cannot be considered independently and should both be interactively represented in modelling studies. We also observe pronounced diurnal cycles in the rapid response of cloud microphysical and radiative properties, which suggests the usefulness of using polar-orbiting satellites to quantify ACI and ARI may be more limited than presently assumed. The simulations highlight some limitations that need to be considered in future studies. Isolating kilometerscale aerosol responses from internal variability will require longer averaging periods or ensemble simulations. It would also be beneficial to use interactive aerosols and assess the sensitivity of the conclusions to the cloud microphysics scheme.