An official website of the United States government
Abstract The El Niño‐Southern Oscillation (ENSO) phenomenon—the dominant source of climate variability on seasonal to multi‐year timescales—is predictable a few seasons in advance. Forecast skill at longer multi‐year timescales has been found in a few models and forecast systems, but the robustness of this predictability across models has not been firmly established owing to the cost of running dynamical model predictions at longer lead times. In this study, we use a massive collection of multi‐model hindcasts performed using model analogs to show that multi‐year ENSO predictability is robust across models and arises predominantly due to skillful prediction of multi‐year La Nina events following strong El Niño events.
more »
« less
Seasonality of Pacific Decadal Oscillation Prediction Skill
Abstract We investigate coupled climate model initialized predictions of the Pacific Decadal Oscillation (PDO) prediction skill in the Community Earth System Model (CESM) Seasonal to Multi Year Large Ensemble (SMYLE). The PDO is predictable up to a year in advance in SMYLE; however, the predictability depends on verification month, with skill degrading most rapidly in boreal spring for all initializations. To examine the role of teleconnections from El Niño–Southern Oscillation (ENSO) in the prediction skill of the PDO, we use a multi‐linear regression model. The linear model shows that initial value persistence explains most of the PDO prediction skill in SMYLE. In addition, the PDO prediction skill's seasonal dependence is fully reproduced only when ENSO is included as a predictor. These results suggest that ENSO has a strong influence on the seasonality of PDO predictions.
more »
« less
- PAR ID:
- 10626794
- Publisher / Repository:
- American Geophysical Union
- Date Published:
- Journal Name:
- Geophysical Research Letters
- Volume:
- 52
- Issue:
- 14
- ISSN:
- 0094-8276
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
An analysis of crop yields for the state of Missouri was completed to determine if an interannual or multidecadal variability existed as a result of the El Niño Southern Oscillation (ENSO) and the Pacific Decadal Oscillation (PDO). Corn and soybean yields were recorded in kilograms per hectare for each of the six climate regions of Missouri. An analysis using the Mokhov “method of cycles” demonstrated interannual, interdecadal, and multidecadal variations in crop yields. Cross-spectral analysis was used to determine which region was most impacted by ENSO and PDO influenced seasonal (April–September) temperature and precipitation. Interannual (multidecadal) variations found in the spectral analysis represent a relationship to ENSO (PDO) phase, while interdecadal variations represent a possible interaction between ENSO and PDO. Average crop yields were then calculated for each combination of ENSO and PDO phase, displaying a pronounced increase in corn and soybean yields when ENSO is warm and PDO is positive. Climate regions 1, 2, 4, and 6 displayed significant differences (p value of 0.10 or less) in yields between El Niño and La Niña years, representing 55–70 % of Missouri soybean and corn productivity, respectively. Final results give the opportunity to produce seasonal predictions of corn and soybean yields, specific to each climate region in Missouri, based on the combination of ENSO and PDO phases.more » « less
-
Observations show that the teleconnection between the El Niño‐Southern Oscillation (ENSO) and the Asian summer monsoon (ASM) is non‐stationary. However, the underlying mechanisms are poorly understood due to inadequate availability of reliable, long‐term observations. This study uses two state‐of‐the‐art data assimilation‐based reconstructions of last millennium climate to examine changes in the ENSO–ASM teleconnection; we investigate how modes of (multi‐)decadal climate variability (namely, the Pacific Decadal Oscillation, PDO, and the Atlantic Multidecadal Oscillation, AMO) modulate the ENSO–ASM relationship. Our analyses reveal that the PDO exerts a more pronounced impact on ASM variability than the AMO. By comparing different linear regression models, we find that including the PDO in addition to ENSO cycles can improve prediction of the ASM, especially for the Indian summer monsoon. In particular, dry (wet) anomalies caused by El Niño (La Niña) over India become enhanced during the positive (negative) PDO phases due to a compounding effect. However, composite differences in the ENSO–ASM relationship between positive and negative phases of the PDO and AMO are not statistically significant. A significant influence of the PDO/AMO on the ENSO–ASM relationship occurred only over a limited period within the last millennium. By leveraging the long‐term paleoclimate reconstructions, we document and interrogate the non‐stationary nature of the PDO and AMO in modulating the ENSO–ASM relationship.more » « less
-
The El Niño–Southern Oscillation (ENSO) provides most of the global seasonal climate forecast skill, yet, quantifying the sources of skilful predictions is a long-standing challenge. Different sources of predictability affect ENSO evolution, leading to distinct global effects. Artificial intelligence forecasts offer promising advancements but linking their skill to specific physical processes is not yet possible, limiting our understanding of the dynamics underpinning the advancements. Here we show that an extended nonlinear recharge oscillator (XRO) model shows skilful ENSO forecasts at lead times up to 16–18 months, better than global climate models and comparable to the most skilful artificial intelligence forecasts. The XRO parsimoniously incorporates the core ENSO dynamics and ENSO’s seasonally modulated interactions with other modes of variability in the global oceans. The intrinsic enhancement of ENSO’s long-range forecast skill is traceable to the initial conditions of other climate modes by means of their memory and interactions with ENSO and is quantifiable in terms of these modes’ contributions to ENSO amplitude. Reforecasts using the XRO trained on climate model output show that reduced biases in both model ENSO dynamics and in climate mode interactions can lead to more skilful ENSO forecasts. The XRO framework’s holistic treatment of ENSO’s global multi-timescale interactions highlights promising targets for improving ENSO simulations and forecasts.more » « less
-
Abstract Seasonal‐to‐decadal climate prediction is crucial for decision‐making in a number of industries, but forecasts on these timescales have limited skill. Here, we develop a data‐driven method for selecting optimal analogs for seasonal‐to‐decadal analog forecasting. Using an interpretable neural network, we learn a spatially‐weighted mask that quantifies how important each grid point is for determining whether two climate states will evolve similarly. We show that analogs selected using this weighted mask provide more skillful forecasts than analogs that are selected using traditional spatially‐uniform methods. This method is tested on two prediction problems using the Max Planck Institute for Meteorology Grand Ensemble: multi‐year prediction of North Atlantic sea surface temperatures, and seasonal prediction of El Niño Southern Oscillation. This work demonstrates a methodical approach to selecting analogs that may be useful for improving seasonal‐to‐decadal forecasts and understanding their sources of skill.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript
- Journal Article:
- https://doi.org/10.1029/2025GL116122
