Details

Autor(en) / Beteiligte

• Sane, Aakash
• Fox‐Kemper, Baylor
• Ullman, David S.

Titel

Internal Versus Forced Variability Metrics for General Circulation Models Using Information Theory

Ist Teil von

• Journal of geophysical research. Oceans, 2024-05, Vol.129 (5), p.n/a

Ort / Verlag

Washington: Blackwell Publishing Ltd

Erscheinungsjahr

2024

Link zum Volltext

Quelle

Wiley Online Library All Journals

Beschreibungen/Notizen

• Ocean model simulations show variability due to intrinsic chaos and external forcing (air‐sea fluxes, river input, etc.). It is important to estimate their contributions to total variability for attribution. Using variance to estimate variability might be unreliable due to non‐Gaussian higher statistical moments. We show the use of non‐parametric information theory metrics, Shannon entropy and mutual information, for measuring internal and forced variability in ocean models. These metrics are applied to spatially and temporally averaged data. The metrics delineate relative intrinsic to total variability in a wider range of circumstances than previous approaches based on variance ratios. The metrics are applied to (a) a synthetic ensemble of random vectors, (b) ocean component of a global climate (GFDL‐ESM2M) large ensemble, (c) ensemble of a realistic coastal ocean model. The information theory metric qualitatively agrees with the variance‐based metric and possibly identifies regions of nonlinear correlations. In application (2)–the climate ensemble–the information theory metric detects higher temperature intrinsic variability in the Arctic region compared to the variance metric illustrating that the former is robust in a skewed probability distribution (Arctic sea surface temperature) resulting from sharply nonlinear behavior (freezing point). In application (3)–coastal ensemble–variability is dominated by external forcing. Using different selective forcing ensembles, we quantify the sensitivity of the coastal model to different types of external forcing: variations in the river runoff and changes in wind product do not add information (i.e., variability) during summer. Information theory enables ranking how much each forcing type contributes across multiple variables. Plain Language Summary It is important in models to distinguish variability caused by external forces versus variability that arises within the system. Within the ocean component of a climate model, disturbances from the atmosphere such as wind, solar heating and cooling, and anthropogenic emissions are external disturbances and variations due to swirling motions (eddies) are internal chaotic disturbances. We use statistical concepts to quantify the amount of variations in these models. Here, we study multiple simulations made using a coastal ocean model and a climate model. We find that measuring internal and external variability depends on the statistical method you choose. Some statistical methods make a priori assumptions about the data, some do not. Choosing the method that makes less assumptions is better for estimating variability, particularly when considering temperature in the Arctic and salinity in an estuary. We show that for the estuarine model simulations with altered forcings, analyzing the experiments with statistical methods that use fewer assumptions about the underlying data helps prioritize parts of the design of a forecast model. Key Points We use metrics from information theory to estimate intrinsic and extrinsic variability in ensemble global and coastal model In the Arctic region, metric reveals high intrinsic variability and low to moderate extrinsic forcing for temperature In the coastal model experiments with altered forcings, metrics quantified their effects on temperature and salinity for forecasting application