Detecting spatial patterns with the cumulant function. Part II: An application to El Nino

The spatial coherence of a measured variable (e.g. temperature or pressure) is often studied to determine the regions where this variable varies the most or to find teleconnections, i.e. correlations between specific regions. While usual methods to find spatial patterns, such as Principal Components Analysis (PCA), are constrained by linear symmetries, the dependence of variables such as temperature or pressure at different locations is generally nonlinear. In particular, large deviations from the sample mean are expected to be strongly affected by such nonlinearities. Here we apply a newly developed nonlinear technique (Maxima of Cumulant Function, MCF) for the detection of typical spatial patterns that largely deviate from the mean. In order to test the technique and to introduce the methodology, we focus on the El Nino/Southern Oscillation and its spatial patterns. We find nonsymmetric temperature patterns corresponding to El Nino and La Nina, and we compare the results of MCF with other techniques, such as the symmetric solutions of PCA, and the nonsymmetric solutions of Nonlinear PCA (NLPCA). We found that MCF solutions are more reliable than the NLPCA fits, and can capture mixtures of principal components. Finally, we apply Extreme Value Theory on the temporal variations extracted from our methodology. We find that the tails of the distribution of extreme temperatures during La Nina episodes is bounded, while the tail during El Ninos is less likely to be bounded. This implies that the mean spatial patterns of the two phases are asymmetric, as well as the behaviour of their extremes.Comment: 15 pages, 7 figure

An empirical parameterization of subsurface entrainment temperature for improved SST anomaly simulations in an intermediate ocean model

An empirical model for the temperature of subsurface water entrained into the ocean mixed layer (Te) is presented and evaluated to improve sea surface temperature anomaly (SSTA) simulations in an intermediate ocean model (IOM) of the tropical Pacific. An inverse modeling approach is adopted to estimate Te from an SSTA equation using observed SST and simulated upper-ocean currents. A relationship between Te and sea surface height (SSH) anomalies is then obtained by utilizing a singular value decomposition (SVD) of their covariance. This empirical scheme is able to better parameterize Te anomalies than other local schemes and quite realistically depicts interannual variability of Te, including a nonlocal phase lag relation of Te variations relative to SSH anomalies over the central equatorial Pacific. An improved Te parameterization naturally leads to better depiction of the subsurface effect on SST variability by the mean upwelling of subsurface temperature anomalies. As a result, SSTA simulations are significantly improved in the equatorial Pacific; a comparison with other schemes indicates that systematic errors of the simulated SSTAs are significantly small—apparently due to the optimized empirical Teparameterization. Cross validation and comparisons with other model simulations are made to illustrate the robustness and effectiveness of the scheme. In particular it is demonstrated that the empirical Te model constructed from one historical period can be successfully used to improve SSTA simulations in another

On Sub-ENSO Variability

Multichannel singular spectrum analysis (MSSA) of surface zonal wind, sea surface temperature (SST), 20° isotherm depth, and surface zonal current observations (between 1990 and 2004) identifies three coupled ocean–atmosphere modes of variability in the tropical Pacific: the El Niño–Southern Oscillation (ENSO), the annual cycle, and a mode with a 14–18-month period, which is referred to as sub-ENSO in this study. The sub-ENSO mode accounts for the near 18-month (near annual) variability prior to (following) the 1997/98 El Niño event. It was strongest during this El Niño event, with SST anomalies exceeding 1°C. Sub-ENSO peak SST anomalies are ENSO-like in structure and are associated with eastward propagating heat content variations. However, the SST anomalies are preceded by and in near quadrature with relatively strong remotely forced westward propagating zonal current variations, suggesting the sub-ENSO mode arises from the zonal-advective feedback. The sub-ENSO mode is found to exist also in an intermediate complexity model (ICM) of the tropical Pacific. A heat budget analysis of the model’s sub-ENSO mode shows it indeed arises from the zonal-advective feedback. In the model, both ENSO and sub-ENSO modes coexist, but there is a weak nonlinear interaction between them. Experiments also show that the observed changes in sub-ENSO’s characteristics may be explained by changes in the relative importance of zonal and vertical advection SST tendencies

Bringing Statistical Learning Machines Together for Hydro-Climatological Predictions - Case Study for Sacramento San Joaquin River Basin, California

Study region: Sacramento San Joaquin River Basin, California Study focus: The study forecasts the streamflow at a regional scale within SSJ river basin with largescale climate variables. The proposed approach eliminates the bias resulting from predefined indices at regional scale. The study was performed for eight unimpaired streamflow stations from 1962–2016. First, the Singular Valued Decomposition (SVD) teleconnections of the streamflow corresponding to 500 mbar geopotential height, sea surface temperature, 500 mbar specific humidity (SHUM500), and 500 mbar U-wind (U500) were obtained. Second, the skillful SVD teleconnections were screened non-parametrically. Finally, the screened teleconnections were used as the streamflow predictors in the non-linear regression models (K-nearest neighbor regression and data-driven support vector machine). New hydrological insights: The SVD results identified new spatial regions that have not been included in existing predefined indices. The nonparametric model indicated the teleconnections of SHUM500 and U500 being better streamflow predictors compared to other climate variables. The regression models were capable to apprehend most of the sustained low flows, proving the model to be effective for drought-affected regions. It was also observed that the proposed approach showed better forecasting skills with preprocessed large scale climate variables rather than using the predefined indices. The proposed study is simple, yet robust in providing qualitative streamflow forecasts that may assist water managers in making policy-related decisions when planning and managing watersheds

Causal networks for climate model evaluation and constrained projections

Global climate models are central tools for understanding past and future climate change. The assessment of model skill, in turn, can benefit from modern data science approaches. Here we apply causal discovery algorithms to sea level pressure data from a large set of climate model simulations and, as a proxy for observations, meteorological reanalyses. We demonstrate how the resulting causal networks (fingerprints) offer an objective pathway for process-oriented model evaluation. Models with fingerprints closer to observations better reproduce important precipitation patterns over highly populated areas such as the Indian subcontinent, Africa, East Asia, Europe and North America. We further identify expected model interdependencies due to shared development backgrounds. Finally, our network metrics provide stronger relationships for constraining precipitation projections under climate change as compared to traditional evaluation metrics for storm tracks or precipitation itself. Such emergent relationships highlight the potential of causal networks to constrain longstanding uncertainties in climate change projections. Algorithms to assess causal relationships in data sets have seen increasing applications in climate science in recent years. Here, the authors show that these techniques can help to systematically evaluate the performance of climate models and, as a result, to constrain uncertainties in future climate change projections