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

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

On the relationship between reliability diagrams and the ‘signal-to-noise paradox’

Geophysical Research Letters American Geophysical Union 50:14 (2023) e2023GL103710

Authors:

Kristian Strommen, Molly MacRae, Hannah Christensen

Abstract:

The ‘signal-to-noise paradox’ for seasonal forecasts of the winter NAO is often described as an ‘underconfident’ forecast and measured using the ratio-of-predictable components metric (RPC). However, comparison of RPC with other measures of forecast confidence, such as spread-error ratios, can give conflicting impressions, challenging this informal description. We show, using a linear statistical model, that the ‘paradox’ is equivalent to a situation where the reliability diagram of any percentile forecast has a slope exceeding 1. The relationship with spread-error ratios is shown to be far less direct. We furthermore compute reliability diagrams of winter NAO forecasts using seasonal hindcasts from the European Centre for Medium-range Weather Forecasts and the UK Meteoro logical Office. While these broadly exhibit slopes exceeding 1, there is evidence of asymmetry between upper and lower terciles, indicating a potential violation of linearity/Gaussianity. The limitations and benefits of reliability diagrams as a diagnostic tool are discussed.

Robustness of the stochastic parameterization of sub-grid scale wind variability in sea-surface fluxes

Monthly Weather Review American Meteorological Society 151:10 (2023) 2587-2607

Authors:

Kota Endo, Adam H Monahan, Julie Bessac, Hannah Christensen, Nils Weitzel

Abstract:

High-resolution numerical models have been used to develop statistical models of the enhancement of sea surface fluxes resulting from spatial variability of sea-surface wind. In particular, studies have shown that the flux enhancement is not a deterministic function of the resolved state. Previous studies focused on single geographical areas or used a single high-resolution numerical model. This study extends the development of such statistical models by considering six different high-resolution models, four different geographical regions, and three different ten-day periods, allowing for a systematic investigation of the robustness of both the deterministic and stochastic parts of the data-driven parameterization. Results indicate that the deterministic part, based on regressing the unresolved normalized flux onto resolved scale normalized flux and precipitation, is broadly robust across different models, regions, and time periods. The statistical features of the stochastic part of the model (spatial and temporal autocorrelation and parameters of a Gaussian process fit to the regression residual) are also found to be robust and not strongly sensitive to the underlying model, modelled geographical region, or time period studied. Best-fit Gaussian process parameters display robust spatial heterogeneity across models, indicating potential for improvements to the statistical model. These results illustrate the potential for the development of a generic, explicitly stochastic parameterization of sea-surface flux enhancements dependent on wind variability.

Environmental Precursors to Mesoscale Convective Systems

(2023)

Authors:

Mark Muetzelfeldt, Robert Plant, Hannah Christensen

Abstract:

Mesoscale convective systems (MCSs) are important components of the Earth’s weather and climate systems. They produce a large fraction of tropical rainfall and their top-heavy heating profiles can feedback onto atmospheric dynamics. Understanding the large-scale environmental precursor conditions that cause their formation is normally done as case studies or on a regional basis. Here, we take a global view on this problem, linking tracked MCSs to the environmental conditions that lead to their growth and maintenance. We consider common variables associated with deep convection, such as CAPE, total column water vapour and moisture convergence. We take care to distinguish between conditions associated with deep convection, and conditions associated with MCSs specifically. Furthermore, we pose the question in a way that is useful for the development of an MCS parametrization scheme, by asking what environmental conditions lead to MCS occurrence, instead of locating an MCS and then finding the associated conditions.