Atmospheric processes

Research group

Research theme

Sub department

About

Multifractal analysis for evaluating the representation of clouds in global km-scale models

(2024)

Authors:

Lilli Freischem, Philipp Weiss, Hannah Christensen, Philip Stier

Abstract:

Clouds are one of the largest sources of uncertainty in climate predictions. Emerging next-generation km-scale climate models need to simulate clouds and precipitation accurately to reliably predict future climates. To isolate issues in their representation of clouds, and thereby facilitate their improvement, km-scale models need to be thoroughly evaluated via comparisons with observations. Traditionally, climate models are evaluated using spatio-temporally averaged observations. However, aggregated evaluation loses crucial information about underlying physical processes, such as convective updrafts, and the resulting cloud macrophysical structures. We postulate that a novel spatio-temporal evaluation strategy using satellite observations provides direct constraints on physical processes. Here, we introduce multifractal analysis as a method for evaluating km-scale simulations. We apply it to top-of-atmosphere outgoing longwave radiation (OLR) fields to investigate structural differences between observed and simulated clouds in the tropics. For this purpose, we compute structure functions from OLR fields to which we fit scaling exponents. We then parameterise the scaling exponents to compute scaling parameters. The parameters compactly characterise OLR variability and can be compared across simulations and observations. We use this method to evaluate the ICON-Sapphire and IFS-FESOM simulations run for cycle 3 of the nextGEMS project via comparison with data from the geostationary satellite GOES-16. We find that clouds in both models exhibit multifractal scaling from 50 to 1000km. However, the scaling parameters are significantly different between ICON and IFS, and neither match observations. In the ICON model, multifractal scaling exponents are lower than in observations whereas in IFS, they are larger. The observed differences indicate how the modelling approaches in ICON and IFS impact the organisation of clouds. More specifically, the deep convection scheme in ICON is switched off completely whereas it is still active in IFS, which could explain the difference in scaling behaviour we observed. Our results show that spatio-temporal analysis is a promising new way to constrain global km-scale models. It can provide key insights into model performance and shed light on issues in the representation of clouds.
More details from the publisher

Understanding the atmospheric kinetic energy spectrum

(2024)

Authors:

Salah Kouhen, Benjamin Storer, Hussein Aluie, David Marshall, Hannah Christensen

Abstract:

The Kinetic Energy spectrum of the atmosphere in the mesoscales (10-500 km) is poorly understood. Aircraft measurements in the eighties first revealed that there was a kink in the spectrum, a transition from a slope of -3 to a slope of -5/3, that occurred at scales below around 400 km (Nastrom et al. [1984]). Since that time many possible mechanisms have been posited for the transition but there has been no consensus. We will present a new way of analysing the local scaling laws of geophysical data using coarse-graining, extending the work of Sadek and Aluie [2018]. Our technique allows for the creation of spatial maps of spectral slope, as well as conditioned spectra that can be used to analyse the relationship between different meteorological variables and the atmospheric kinetic energy power spectrum. This enables us to explore causes for the observed shallower slope. We observe shallower spectral slopes in regions of greater convective activity, as well as shallowing in regions of high orographic variability and interesting latitudinal effects. The important implications of our work for the celebrated Nastrom and Gage spectrum (Nastrom et al. [1984]) will be discussed.   References:  GD Nastrom, KS Gage, and WH Jasperson. Kinetic energy spectrum of large-and mesoscale atmospheric processes. Nature, 310(5972):36–38, 1984.   Mahmoud Sadek and Hussein Aluie. Extracting the spectrum of a flow by spatial filtering. Physical Review Fluids, 3(12):124610, 2018.
More details from the publisher

Improving and Assessing Organized Convection Parameterization in the Unified Model

(2024)

Authors:

Zhixiao Zhang, Hannah Christensen, Mark Muetzelfeldt, Tim Woollings, Bob Plant, Alison Stirling, Michael Whitall, Mitchell Moncrieff, Chih-Chieh Chen

Abstract:

Improving weather and climate prediction cannot avoid accurately representing organized convection, as its convective and stratiform components distinctly reshape large-scale circulations via redistributing momentum and heat. For latent heating, the stratiform heating in organized convection shifts to higher altitudes compared to convective regions, presenting a significant challenge for representation in models across scales. The Multiscale Coherent Structural Parameterization (MCSP), introduced by Moncrieff et al. (2017), offers a promising solution by generating the top-heavy profile from convective heating in slantwise layer overturning scenarios. As part of the MCS: PRIME project, the PRIME-MCSP implementation by Zhang et al. (submitted, 2024) couples MCSP with the CoMorph-A convection scheme in the UK Met Office Unified Model with the following improvements: 1) CoMorph permits unstable air to rise from any height, diverging from the conventional CAPE trigger for deep convection, thereby enhancing continuity and facilitating storm tracking. 2) We activate MCSP selectively for deep mixed-phase clouds, recognizing the limited ability of shallow clouds to produce a stratiform component. 3) We configure the global model runs to include both a fixed convective-stratiform heating fraction and a fraction proportional to cloud top temperature. MCS tracks in ensembles of weather runs show that PRIME-MCSP suppresses cloud deepening and reduces precipitation areas by dampening low-level updrafts. 20-year climate simulations show that PRIME-MCSP improves the precipitation seasonal cycle over the Indian Ocean, while increasing the warm-season wet bias over the Western Pacific. Additionally, PRIME-MCSP intensifies the Madden Julian Oscillation (MJO). The model run using a variable convective-stratiform fraction more accurately represents the MJO frequency and aligns better with reanalysis. Future plans focus on the stochastic representation of stratiform effects, steered by insights from data assimilation increments.
More details from the publisher

Predictable decadal forcing of the North Atlantic jet speed by sub-polar North Atlantic sea surface temperatures

Weather and Climate Dynamics Copernicus Publications 4:4 (2023) 853-874

Authors:

Kristian Strommen, Tim Woollings, Paolo Davini, Paolo Ruggieri, Isla R Simpson
More details from the publisher

Using probabilistic machine learning to better model temporal patterns in parameterizations: a case study with the Lorenz 96 model

Geoscientific Model Development Copernicus Publications 16:15 (2023) 4501-4519

Authors:

Raghul Parthipan, Hannah M Christensen, J Scott Hosking, Damon J Wischik
More details from the publisher