Atmospheric processes

Abstract:

Radiation in the atmosphere provides the energy that drives atmospheric dynamics and physics on all scales, from cloud particle growth to global weather and climate. Radiation schemes in global weather and climate models have to simplify the complex interaction of radiation with the Earth system. Capturing the interactions of gases and clouds with radiation is particularly challenging, since gas effects are extremely wavelength-dependent, while clouds vary strongly on small spatial and temporal scales, and they both interact strongly with radiation. Uncertainties in the radiation scheme and the cloud, aerosol and gas and inputs lead to uncertainties in weather and climate processes, such as energy balance, cloud development and dynamics. The radiation scheme ecRad (Hogan & Bozzo 2018) has been operational in the IFS model at ECMWF since 2017 and in ICON at Deutscher Wetterdienst (DWD) since 2021 and will be the next radiation scheme in the operational numerical weather prediction models AROME and ARPEGE, the climate model ARPEGE-Climat and the regional research model Méso-NH at Météo-France. As a modular scheme, ecRad provides the opportunity to vary parametrisations and assumptions individually. Several options are available for the radiation solver, cloud vertical overlap and horizontal inhomogeneity treatment and cloud hydrometeor optical property parametrisations. The solver SPARTACUS is the only radiation solver in a global model that can treat 3D radiative effects. The new gas optics model ecCKD can improve both precision and cost of the gas optics calculation, as can recent code optimisations. We will present the status of and future plans for implementation in the Météo-France models, and show first evaluation results for radiation, energy balance and clouds on various scales scales. We will also investigate the impact of cloud and aerosol input and search for the best settings for radiation balance, model energy and physics and forecast performance. Finally, we will present future plans for radiation work in the Météo-France models.   Reference: Hogan, R. J., & Bozzo, A. (2018), A flexible and efficient radiation scheme for the ECMWF model. Journal of Advances in Modeling Earth Systems, 10, 1990-2008. https://doi.org/10.1029/2018MS001364

Abstract:

A machine learning method is used to identify sources of long-term ENSO predictability in the ocean (sea surface temperature (SST) and heat content) and the atmosphere (near-surface zonal wind (U10)). Tropical SST represents the primary source of predictability skill. While U10 does not increase the skill when associated with SST, our analysis suggests U10 alone has a predictive skill comparable to that of SST between 11 and 21 months in advance, from late fall up to late spring. The long-lead signal originates from coupled wind-SST interactions across the Indian Ocean (IO) and propagates across the Pacific via an atmospheric bridge mechanism. A linear correlation analysis supports this mechanism, suggesting a precursor link between anomalies in SST in the western and wind in the eastern IO. Our results have important implications for ENSO predictions beyond 1 year ahead and identify the key role of U10 over the IO.

Abstract:

The role of internal variability in generating an apparent link between autumn Barents‐Kara sea (BKS) ice and the winter North Atlantic Oscillation (NAO) has been intensely debated. In particular, the robustness and causality of the link has been questioned by showing that BKS‐NAO correlations exhibit nonstationarity in both reanalysis and climate model simulations. We show that the lack of ice observations means nonstationarity cannot be confidently assessed using reanalysis prior to 1961. Model simulations are used to corroborate an argument that forced nonstationarity could result from ice edge changes due to global warming. Consequently, the observed change in BKS‐NAO correlations since 1960 might not be purely a result of internal variability and may also reflect that the ice edge has moved. The change could also reflect the availability of more accurate ice observations. We discuss potential implications for analysis based on coupled climate models, which exhibit large ice edge biases.