Arguez, A., O'Brien, J. J., & Smith, S. R. (2009). Air temperature impacts over Eastern North America and Europe associated with low-frequency North Atlantic SST variability.
Int. J. Climatol., 29(1), 1–10.
Briggs, K. (2006).
ENSO Event Reproduction: A Comparison of an EOF vs. A Cyclostationary (CSEOF) Approach. Master's thesis, Florida State University, Tallahassee, FL.
Abstract: In past studies, El Niño-Southern Oscillation (ENSO) events have been linked to devastating weather extremes. Climate modeling of ENSO is often dependent on limited records of the pertinent physical variables, thus longer records of these variables is desirable. Noisy signals, such as monthly sea surface temperature, are good candidates for reproduction by several existing auto-regression techniques. Through auto-regression, influential principal component modes are broken down into a series of time points that are each dependent upon an optimal weighting of the surrounding points. Using these unique numerical relationships, a noisy signal can be reproduced by thus processing the leading modes and adding an artificial record of properly distributed noise. Statistical measures of important ENSO regions suggest that the nature of oceanic and atmospheric anomalous events is cyclic with respect to certain timescales; for example, the monthly timescale. To detect ENSO signals in the presence of a varying background noise field, the detection method should take into account the signal's strong phase-locking with this nested variation. Cyclostationary Emperical Orthogonal Functions (CSEOFs) are built upon the idea of nested cycles, unlike traditional EOFs, which incorporate a design that is better detailed for stationary processes. In this study, both EOF and CSEOF modes of a 50-year Pacific SST record are processed using an auto-regression technique, and several sets of artificial SST records are constructed. Appropriate statistical indices are applied to these artificial time series to ensure an acceptable consistency with the real record, and then artificial data is produced using the artificial time series. In all cases, the cyclostationary approach produces more realistic warm ENSO events with respect to timing, strength, and other traits than does the stationary approach. However, both methods produce only a fair representation of cold events, suggesting that further study is necessary for improvement of La Niña modeling. Shorter records of variables such as sea level height and Pacific wind stress anomalies can hinder the usefulness of auto-regression, owing to time point dependence on surrounding points. Using a regression technique to find an evolutionary consistency (i.e. physically consistent patterns) between one of these variables and a variable with a longer record (such as SST) can eliminate this problem. Once a regression relationship is found between two variables, the variable with the shorter record can be re-written to match the time evolution of the variable with the longer record. Here regression, both EOF and CSEOF, is performed on both sea surface temperature and sea level height (a 20-year record), and sea surface temperature and wind stress (a 39-year record). Once the regression relationships are found, artificial SST time series are incorporated in place of the original time series to produce several artificial 50-year SLH and wind stress data sets. 5 Pacific regions are chosen, and statistics and behavior of the artificial sets within these regions are compared to those of the original data. Once again the cyclostationary approach fares better than the stationary. In particular the EOF assumption of cross correlational symmetry fails to capture the direction-dependence of ENSO evolution, causing inconsistent ENSO behavior. This renders an EOF method insufficient for climate modeling and prediction, and implies that a better aim is to incorporate physical cyclic features via a cyclostationary method.
Hu, X., Cai, M., Yang, S., & Wu, Z. (2018). Delineation of thermodynamic and dynamic responses to sea surface temperature forcing associated with El Niño.
Clim Dyn, 51(11-12), 4329–4344.
Abstract: A new framework is proposed to gain a better understanding of the response of the atmosphere over the tropical Pacific to the radiative heating anomaly associated with the sea surface temperature (SST) anomaly in canonical El Niño winters. The new framework is based on the equilibrium balance between thermal radiative cooling anomalies associated with air temperature response to SST anomalies and other thermodynamic and dynamic processes. The air temperature anomalies in the lower troposphere are mainly in response to radiative heating anomalies associated with SST, atmospheric water vapor, and cloud anomalies that all exhibit similar spatial patterns. As a result, air temperature induced thermal radiative cooling anomalies would balance out most of the radiative heating anomalies in the lower troposphere. The remaining part of the radiative heating anomalies is then taken away by an enhancement (a reduction) of upward energy transport in the central-eastern (western) Pacific basin, a secondary contribution to the air temperature anomalies in the lower troposphere. Above the middle troposphere, radiative effect due to water vapor feedback is weak. Thermal radiative cooling anomalies are mainly in balance with the sum of latent heating anomalies, vertical and horizontal energy transport anomalies associated with atmospheric dynamic response and the radiative heating anomalies due to changes in cloud. The pattern of Gill-type response is attributed mainly to the non-radiative heating anomalies associated with convective and large-scale energy transport. The radiative heating anomalies associated with the anomalies of high clouds also contribute positively to the Gill-type response. This sheds some light on why the Gill-type atmospheric response can be easily identifiable in the upper atmosphere.
Hu, Z. - Z., Huang, B., Kinter, J. L., Wu, Z., & Kumar, A. (2012). Connection of the stratospheric QBO with global atmospheric general circulation and tropical SST. Part II: interdecadal variations.
Clim Dyn, 38(1-2), 25–43.
Huang, B., Hu, Z. - Z., Kinter, J. L., Wu, Z., & Kumar, A. (2012). Connection of stratospheric QBO with global atmospheric general circulation and tropical SST. Part I: methodology and composite life cycle.
Clim Dyn, 38(1-2), 1–23.
Hughes, P. J. (2014).
The Influence of Small-Scale Sea Surface Temperature Gradients on Surface Vector Winds and Subsequent Impacts on Oceanic Ekman Pumping. Tallahassee, FL: Florida State University.
Misra, V., Mishra, A., & Li, H. (2016). The sensitivity of the regional coupled ocean-atmosphere simulations over the Intra-Americas seas to the prescribed bathymetry.
Dynamics of Atmospheres and Oceans, 76, 29–51.
Nyadjro, E. S., Jensen, T. G., Richman, J. G., & Shriver, J. F. (2017). On the Relationship Between Wind, SST, and the Thermocline in the Seychelles-Chagos Thermocline Ridge.
IEEE Geosci. Remote Sensing Lett., 14(12), 2315–2319.
Venugopal, T., Ali, M. M., Bourassa, M. A., Zheng, Y., Goni, G. J., Foltz, G. R., et al. (2018). Statistical Evidence for the Role of Southwestern Indian Ocean Heat Content in the Indian Summer Monsoon Rainfall.
Sci Rep, 8(1), 12092.
Abstract: This study examines the benefit of using Ocean Mean Temperature (OMT) to aid in the prediction of the sign of Indian Summer Monsoon Rainfall (ISMR) anomalies. This is a statistical examination, rather than a process study. The thermal energy needed for maintaining and intensifying hurricanes and monsoons comes from the upper ocean, not just from the thin layer represented by sea surface temperature (SST) alone. Here, we show that the southwestern Indian OMT down to the depth of the 26 degrees C isotherm during January-March is a better qualitative predictor of the ISMR than SST. The success rate in predicting above- or below-average ISMR is 80% for OMT compared to 60% for SST. Other January-March mean climate indices (e.g., NINO3.4, Indian Ocean Dipole Mode Index, El Nino Southern Oscillation Modoki Index) have less predictability (52%, 48%, and 56%, respectively) than OMT percentage deviation (PD) (80%). Thus, OMT PD in the southwestern Indian Ocean provides a better qualitative prediction of ISMR by the end of March and indicates whether the ISMR will be above or below the climatological mean value.
Wallcraft, A. J., Kara, A. B., Hurlburt, H. E., Chassignet, E. P., & Halliwell, G. H. (2008). Value of bulk heat flux parameterizations for ocean SST prediction.
Journal of Marine Systems, 74(1-2), 241–258.