Abstract This study assesses the predictive skill of eight North American Multimodel Ensemble (NMME) models in predicting the Indian Ocean dipole (IOD). We find that the forecasted ensemble-mean IOD–El Niño–Southern Oscillation (ENSO) relationship deteriorates away from the observed relationship with increasing lead time, which might be one reason that limits the IOD predictive skill in coupled models. We are able to improve the IOD predictive skill using a recently developed stochastic dynamical model (SDM) forced by forecasted ENSO conditions. The results are consistent with the previous result that operational IOD predictability beyond persistence at lead times beyond one season is mostly controlled by ENSO predictability and the signal-to-noise ratio of the Indo-Pacific climate system. The multimodel ensemble (MME) investigated here is found to be of superior skill compared to each individual model at most lead times. Importantly, the skill of the SDM IOD predictions forced with forecasted ENSO conditions were either similar or better than those of the MME IOD forecasts. Moreover, the SDM forced with observed ENSO conditions exhibits significantly higher IOD prediction skill than the MME at longer lead times, suggesting the large potential skill increase that could be achieved by improving operational ENSO forecasts. We find thatmore »
Monthly Modulations of ENSO Teleconnections: Implications for Potential Predictability in North America
Abstract Using a high-resolution atmospheric general circulation model simulation of unprecedented ensemble size, we examine potential predictability of monthly anomalies under El Niño Southern Oscillation (ENSO) forcing and back-ground internal variability. This study reveals the pronounced month-to-month evolution of both the ENSO forcing signal and internal variability. Internal variance in upper-level geopotential height decreases (∼ 10%) over the North Pacific during El Niño as the westerly jet extends eastward, allowing forced signals to account for a greater fraction of the total variability, and leading to increased potential predictability. We identify February and March of El Niño years as the most predictable months using a signal-to-noise analysis. In contrast, December, a month typically included in teleconnection studies, shows little-to-no potential predictability. We show that the seasonal evolution of SST forcing and variability leads to significant signal-to-noise relationships that can be directly linked to both upper-level and surface variable predictability for a given month. The stark changes in forced response, internal variability, and thus signal-to-noise across an ENSO season indicate that subseasonal fields should be used to diagnose potential predictability over North America associated with ENSO teleconnections. Using surface air temperature and precipitation as examples, this study provides motivation to pursue ‘windows more »
- Publication Date:
- NSF-PAR ID:
- 10300566
- Journal Name:
- Journal of Climate
- Page Range or eLocation-ID:
- 1 to 71
- ISSN:
- 0894-8755
- Sponsoring Org:
- National Science Foundation
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El Niño–Southern Oscillation (ENSO) peaks in boreal winter but its impact on Indo-western Pacific climate persists for another two seasons. Key ocean–atmosphere interaction processes for the ENSO effect are investigated using the Pacific Ocean–Global Atmosphere (POGA) experiment with a coupled general circulation model, where tropical Pacific sea surface temperature (SST) anomalies are restored to follow observations while the atmosphere and oceans are fully coupled elsewhere. The POGA shows skills in simulating the ENSO-forced warming of the tropical Indian Ocean and an anomalous anticyclonic circulation pattern over the northwestern tropical Pacific in the post–El Niño spring and summer. The 10-member POGA ensemble allows decomposing Indo-western Pacific variability into the ENSO forced and ENSO-unrelated (internal) components. Internal variability is comparable to the ENSO forcing in magnitude and independent of ENSO amplitude and phase. Random internal variability causes apparent decadal modulations of ENSO correlations over the Indo-western Pacific, which are high during epochs of high ENSO variance. This is broadly consistent with instrumental observations over the past 130 years as documented in recent studies. Internal variability features a sea level pressure pattern that extends into the north Indian Ocean and is associated with coherent SST anomalies from the Arabian Sea to the westernmore »
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Abstract Identifying the origins of wintertime climate variations in the Northern Hemisphere requires careful attribution of the role of El Niño–Southern Oscillation (ENSO). For example, Aleutian low variability arises from internal atmospheric dynamics and is remotely forced mainly via ENSO. How ENSO modifies the local sea surface temperature (SST) and North American precipitation responses to Aleutian low variability remains unclear, as teasing out the ENSO signal is difficult. This study utilizes carefully designed coupled model experiments to address this issue. In the absence of ENSO, a deeper Aleutian low drives a positive Pacific decadal oscillation (PDO)-like SST response. However, unlike the observed PDO pattern, a coherent zonal band of turbulent heat flux–driven warm SST anomalies develops throughout the subtropical North Pacific. Furthermore, non-ENSO Aleutian low variability is associated with a large-scale atmospheric circulation pattern confined over the North Pacific and North America and dry precipitation anomalies across the southeastern United States. When ENSO is included in the forcing of Aleutian low variability in the experiments, the ENSO teleconnection modulates the turbulent heat fluxes and damps the subtropical SST anomalies induced by non-ENSO Aleutian low variability. Inclusion of ENSO forcing results in wet precipitation anomalies across the southeastern United States, unlikemore »
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Abstract Forecasting the El Niño-Southern Oscillation (ENSO) has been a subject of vigorous research due to the important role of the phenomenon in climate dynamics and its worldwide socioeconomic impacts. Over the past decades, numerous models for ENSO prediction have been developed, among which statistical models approximating ENSO evolution by linear dynamics have received significant attention owing to their simplicity and comparable forecast skill to first-principles models at short lead times. Yet, due to highly nonlinear and chaotic dynamics (particularly during ENSO initiation), such models have limited skill for longer-term forecasts beyond half a year. To resolve this limitation, here we employ a new nonparametric statistical approach based on analog forecasting, called kernel analog forecasting (KAF), which avoids assumptions on the underlying dynamics through the use of nonlinear kernel methods for machine learning and dimension reduction of high-dimensional datasets. Through a rigorous connection with Koopman operator theory for dynamical systems, KAF yields statistically optimal predictions of future ENSO states as conditional expectations, given noisy and potentially incomplete data at forecast initialization. Here, using industrial-era Indo-Pacific sea surface temperature (SST) as training data, the method is shown to successfully predict the Niño 3.4 index in a 1998–2017 verification period out tomore »
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Abstract Regional ocean–atmospheric interactions in the summer tropical Indo–northwest Pacific region are investigated using a tropical Pacific Ocean–global atmosphere pacemaker experiment with a coupled ocean–atmospheric model (cPOGA) and a parallel atmosphere model simulation (aPOGA) forced with sea surface temperature (SST) variations from cPOGA. Whereas the ensemble mean features pronounced influences of El Niño–Southern Oscillation (ENSO), the ensemble spread represents internal variability unrelated to ENSO. By comparing the aPOGA and cPOGA, this study examines the effect of the ocean–atmosphere coupling on the ENSO-unrelated variability. In boreal summer, ocean–atmosphere coupling induces local positive feedback to enhance the variance and persistence of the sea level pressure and rainfall variability over the northwest Pacific and likewise induces local negative feedback to suppress the variance and persistence of the sea level pressure and rainfall variability over the north Indian Ocean. Anomalous surface heat fluxes induced by internal atmosphere variability cause SST to change, and SST anomalies feed back onto the atmosphere through atmospheric convection. The local feedback is sensitive to the background winds: positive under the mean easterlies and negative under the mean westerlies. In addition, north Indian Ocean SST anomalies reinforce the low-level anomalous circulation over the northwest Pacific through atmospheric Kelvin waves. Thismore »
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Accepted Manuscript1.0
- Publisher's Version of Record
- https://doi.org/10.1175/JCLI-D-20-0391.1
