Atmospheric processes

Abstract:

To study and monitor the Sun and its atmosphere, various space missions have been launched in the past decades. With rapid improvement in technology and different mission requirements, the data products are subject to constant change. However, for such long-term studies as solar variability or multi-instrument investigations, uniform data series are required. In this study, we built on and expanded the instrument-to-instrument translation (ITI) framework, which provides unpaired image translations. We applied the tool to data from the Extreme Ultraviolet Imager (EUI), specifically the Full Sun Imager (FSI) on Solar Orbiter and the Atmospheric Imaging Assembly (AIA) on the Solar Dynamics Observatory (SDO). This approach allowed us to create a homogeneous dataset that combines the two extreme ultraviolet (EUV) imagers in the 174/171 Å and 304 Å channels. We demonstrate that ITI is able to provide image calibration between Solar Orbiter and SDO EUV imagers, independent of the varying orbital position of Solar Orbiter. The comparison of the intercalibrated light curves derived from 174/171 Å and 304 Å filtergrams from EUI and AIA shows that ITI can provide uniform data series that outperform a standard baseline calibration. We evaluate the perceptual similarity in terms of the Fréchet inception distance, which demonstrates that ITI achieves a significant improvement of perceptual similarity between EUI and AIA. The study provides intercalibrated observations from Solar Orbiter/EUI/FSI with SDO/AIA, enabling a homogeneous dataset suitable for solar cycle studies and multi-viewpoint investigations.

Abstract:

Abstract. The Next Generation of Earth Modeling Systems (nextGEMS) project aimed to produce multidecadal climate simulations, for the first time, with resolved kilometer-scale (km-scale) processes in the ocean, land, and atmosphere. In only 3 years, nextGEMS achieved this milestone with the two km-scale Earth system models, ICOsahedral Non-hydrostatic model (ICON) and Integrated Forecasting System coupled to the Finite-volumE Sea ice-Ocean Model (IFS-FESOM). nextGEMS was based on three cornerstones: (1) developing km-scale Earth system models with small errors in the energy and water balance, (2) performing km-scale climate simulations with a throughput greater than 1 simulated year per day, and (3) facilitating new workflows for an efficient analysis of the large simulations with common data structures and output variables. These cornerstones shaped the timeline of nextGEMS, divided into four cycles. Each cycle marked the release of a new configuration of ICON and IFS-FESOM, which were evaluated at hackathons. The hackathon participants included experts from climate science, software engineering, and high-performance computing as well as users from the energy and agricultural sectors. The continuous efforts over the four cycles allowed us to produce 30-year simulations with ICON and IFS-FESOM, spanning the period 2020–2049 under the SSP3-7.0 scenario. The throughput was about 500 simulated days per day on the Levante supercomputer of the German Climate Computing Center (DKRZ). The simulations employed a horizontal grid of about 5 km resolution in the ocean and 10 km resolution in the atmosphere and land. Aside from this technical achievement, the simulations allowed us to gain new insights into the realism of ICON and IFS-FESOM. Beyond its time frame, nextGEMS builds the foundation of the Climate Change Adaptation Digital Twin developed in the Destination Earth initiative and paves the way for future European research on climate change.

Abstract:

Weather regimes and weather patterns, here jointly referred to as circulation types, are used to generate forecasts for a variety of applications, such as energy demand and flood risk. However, there are usually many different choices available for precisely which circulation types to use. Ideally, one would like to use circulation types that are both highly informative for the application and also highly predictable, but in practice, there is often a tradeoff between informativity and predictability. We present a simple, general framework for how to construct a circulation type forecast that optimally balances these factors by segueing between different choices of circulation types at different lead times based on information‐theoretic considerations. As an example, we apply this framework to the case of forecasting energy demand in Great British winters. We compare a set of 30 weather patterns produced by the UK Met Office with the much simpler two‐state framework consisting of a positive and negative North Atlantic Oscillation (NAO) regime and show how to optimally combine the two across a winter season.

Abstract:

Mesoscale Convective Systems (MCSs), with length scales of 100 to 1000 km or more, fall into the "grey zone" of global models with grid spacings of 10s of km. Their under-resolved nature leads to model deficiencies in representing MCS latent heating, whose vertical structure critically shapes large-scale circulations. To address this challenge, we use analysis increments—the corrections applied by Data Assimilation (DA) to the model's prior state—from a 10 km Met Office operational forecast model to inform the development of a stochastic parameterization for MCS latent heating. To focus on errors in MCS feedback rather than errors due to a missing MCS, we select analysis increments from 1037 MCS tracks that the model successfully captures at the start of the DA cycle.A Machine Learning–based Gaussian Mixture Model reveals that the vertical structure of temperature analysis increments is probabilistically linked to the atmospheric environment. Bottom-heavy heating increments tend to occur in low Total Column Water Vapor (TCWV) conditions, suggesting that the model underestimates low-level convective heating in relatively dry environments. In contrast, top-heavy heating increments are linked to a moist layer overturning structure—characterized by high TCWV and strong vertical wind shear—indicating model underestimation of upper-level condensate detrainment in such environments. This probabilistic relationship is implemented in the Met Office operational forecast model as part of the MCS: PRIME stochastic scheme, which corrects MCS-related uncertainties during model integration. By enhancing top-heavy heating, the scheme backscatters kinetic energy from the mesoscale to larger scales, improving predictions of Indian seasonal rainfall and the Madden–Julian Oscillation (MJO). Future work will assess its impact on forecast busts and its potential to extend predictability.

Abstract:

A parameterisation suite is the combination of all parameterisation schemes that is used by a numerical model of the atmosphere. These parameterisation (or “physics”) suites are widely seen as the most uncertain components of atmospheric models.  In MUMIP we compare deterministic parameterisation suites from across different modelling centres under common prescribed large-scale dynamics. In the first MUMIP experiment, these dynamical tendencies have been derived by coarse-graining the convection-permitting ICON DYAMOND simulation to 0.2 degree resolution. We use these realistic spatiotemporal dynamical patterns to drive millions  of single column model simulations over the tropical Indian Ocean with prescribed SSTs. We use this data to estimate the uncertainty from their physics across four models, each using their default convection-parametrised physics suites. The models are: IFS, GFS, RAP and ARPEGE. The distributions of precipitation rate, convective available potential energy (CAPE), convective inhibition (CIN) and level of neutral buoyancy are analysed, as well as individual model tendencies and rate of change of CAPE and CIN as a function of lead time and, for instance, the diurnal cycle . We find notable differences across the physics suites and even more strongly between convection-parameterised physics suites and the convection-permitting ICON DYAMOND benchmark. Furthermore, we relate these diagnostics to biases in temperature and specific humidity. We also develop a framework for the detection of statistical relations among diagnostics and/or their change. The framework may for instance be used to quantify the impact of spin-up compared to persistence ("memory") and randomness within a dataset and to identify similarity in the physics across modelling centres. In this contribution some of the early results of the international MUMIP project will be presented and we hope to encourage other researchers to use and/or complement the data of MUMIP. Please refer to https://mumip.web.ox.ac.uk for details of how to get involved.