Interdecadal Pacific Oscillation Research Articles

Performance of CMIP5 and CMIP6 models in reproducing the Interdecadal Pacific Oscillation and its global impacts

AbstractThis study assessed the capability of the historical simulations of phase 5 and 6 of the Coupled Model Intercomparison Project (CMIP5/6) in reproducing the temporal and spatial characteristics of the Interdecadal Pacific Oscillation (IPO) and its impact on global surface air temperature (SAT), surface equivalent potential temperature (Thetae_sfc) and precipitation. The IPO index time series simulated by CMIP5/6 models deviated from observations and struggled to capture the phase evolution characteristics of the IPO. Nevertheless, CMIP5/6 models successfully captured the horseshoe‐shaped sea surface temperature anomaly in the Pacific. Additionally, the CMIP5/6 models were able to simulate the IPO's 10–30‐year period. Notably, the simulated IPO index exhibited a statistically significant upward trend, which was absent in observations. Additionally, the IPO‐related global land SAT, Thetae_sfc and precipitation simulated by CMIP5/6 models performed differently in boreal winter and boreal summer. Furthermore, the IPO‐related global land SAT performed better in CMIP5/6 models during boreal winter than that in boreal summer. In CMIP6 models, it improved during both boreal winter and summer compared to CMIP5 models. In terms of the IPO‐related global land Thetae_sfc, CMIP5/6 models also performed better during boreal winter than in boreal summer. However, CMIP5 models outperformed CMIP6 models during the boreal summer. In terms of the IPO‐related global land precipitation, CMIP5/6 models performed better during boreal summer compared to boreal winter. Moreover, the IPO‐related global land precipitation in CMIP6 models improved significantly in boreal winter, but almost the same in boreal summer, compared to CMIP5 models. Further studies showed that the enhancements in simulating IPO's spatial pattern did not correspond to improvements in the model's ability to simulate IPO's global teleconnections.

Decadal Thermal Variability of the Upper Southern Ocean: Zonal Asymmetry

Abstract As the major sink of anthropogenic heat, the Southern Ocean has shown quasi-symmetric, deep-reaching warming since the mid-twentieth century. In comparison, the shorter-term heat storage pattern of the Southern Ocean is more complex and has notable impacts on regional climate and marine ecosystems. By analyzing observational datasets and climate model simulations, this study reveals that the Southern Ocean exhibits prominent decadal (>8 years) variability extending to ∼700-m depth and is characterized by out-of-phase changes in the Pacific and Atlantic–Indian Ocean sectors. Changes in the Pacific sector are larger in magnitude than those in the Atlantic–Indian Ocean sectors and dominate the total heat storage of the Southern Ocean on decadal time scales. Instead of heat uptake through surface heat fluxes, these asymmetric variations arise primarily from wind-driven heat redistribution. Pacemaker and preindustrial simulations of the Community Earth System Model version 1 (CESM1) suggest that these variations in Southern Ocean winds arise primarily from natural variability of the tropical Pacific, as represented by the interdecadal Pacific oscillation (IPO). Through atmospheric teleconnection, the positive phase of the IPO gives rise to higher-than-normal sea level pressure and anticyclonic wind anomalies in the 50°–70°S band of the Pacific sector. These winds lead to warming of 0–700 m by driving the convergence of warm water. The opposite processes, involving cyclonic winds and upper-layer divergence, occur in the Atlantic–Indian Ocean sector. These findings aid our understanding of the time-varying heat storage of the Southern Ocean and provide useful implications on initialized decadal climate prediction.

Following the Rain: Climates of Opinion

This article foregrounds La Niña’s role in establishing climate optimism in the southeastern states of Australia in the 1860s and 1870s. It examines the perceptions of climate as influenced by the cycles of the El Niño Southern Oscillation (ENSO), underscored by the decadal cycles of the Interdecadal Pacific Oscillation (IPO) and the influence in some areas of the intensity of the subtropical ridge. It finds an abundance of very wet La Niña years coincided with a key period of Australian immigration and the expansion of pastoralism and agriculture. The article concludes that this period of productivity and expansion substantially contributed to a false sense of optimism. It suggests nineteenth-century ideas about climate can illuminate contemporary cultural understandings.

Large-Scale Climate Modes Drive Low-Frequency Regional Arctic Sea Ice Variability

Abstract Summer Arctic sea ice is declining rapidly but with superimposed variability on multiple timescales that introduces large uncertainties into projections of future sea ice loss. To better understand what drives at least part of this variability, we show how a simple linear model can link dominant modes of climate variability to low-frequency regional Arctic sea ice concentration (SIC) anomalies. Focusing on September, we find skillful projections from global climate models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) at lead times of 4-20 years, with up to 60% of observed low-frequency variability explained at a 5-year lead time. The dominant driver of low-frequency SIC variability is the Interdecadal Pacific Oscillation (IPO) which is positively correlated with SIC anomalies in all regions up to a lead time of 15 years, but with large uncertainty between GCMs and internal variability realization. The Niño 3.4 Index and Atlantic Multidecadal Oscillation have better agreement between GCMs of being positively and negatively related, respectively, with low-frequency SIC anomalies for at least 10-year lead times. The large variation between GCMs and between members within large ensembles indicate the diverse simulation of teleconnections between the tropics and Arctic sea ice, and the dependence on initial climate state. Further, the influence of the Niño 3.4 Index was found to be sensitive to the background climate. Our results suggest that, based on the 2022 phases of dominant climate variability modes, enhanced loss of sea ice area across the Arctic is likely during the next decade.

Competing impacts of tropical Pacific and Atlantic on Southern Ocean inter-decadal variability

The observed Southern Ocean sea surface temperature (SST) has experienced prominent inter-decadal variability nearly in phase with the Inter-decadal Pacific Oscillation (IPO), but less associated with the Atlantic Multidecadal Variability (AMV), challenging the prevailing view of Pacific-Atlantic synergistic effects. Yet, the mechanisms of distinct trans-hemispheric connections to the Southern Ocean remain indecisive. Here, by individually constraining the observed cold-polarity and warm-polarity IPO and AMV SSTs in a climate model, we show that the IPO is influential in initiating a basin-wide Southern Ocean response, with the AMV secondary. A tropical Pacific-wide cooling triggers a basin-scale Southern Ocean cold episode through a strong Rossby wave response to the north-to-south cross-equatorial weakened Hadley circulation. By contrast, due to the competing role of tropical Pacific cooling, an Atlantic warming partly cools the Southern Ocean via a weak Rossby wave response to the south-to-north cross-equatorial enhanced Hadley circulation. Conversely, tropical Pacific warming leads to a warm Southern Ocean episode. Our findings highlight that properly accounting for the tropical Pacific SST variability may provide a potential for skillful prediction of Southern Ocean climate change and more reliable estimates of climate sensitivity, currently overestimated by the misrepresented Southern Ocean warming.

Quantifying Contributions of External Forcing and Internal Variability to Arctic Warming During 1900–2021

AbstractArctic warming has significant environmental and social impacts. Arctic long‐term warming trend is modulated by decadal‐to‐multidecadal variations. Improved understanding of how different external forcings and internal variability affect Arctic surface air temperature (SAT) is crucial for explaining and predicting Arctic climate changes. We analyze multiple observational data sets and large ensembles of climate model simulations to quantify the contributions of specific external forcings and various modes of internal variability to Arctic SAT changes during 1900–2021. We find that the long‐term trend and total variance in Arctic‐mean SAT since 1900 are largely forced responses, including warming due to greenhouse gases and natural forcings and cooling due to anthropogenic aerosols. In contrast, internal variability dominates the early 20th century Arctic warming and mid‐20th century Arctic cooling. Internal variability also explains ∼40% of the recent Arctic warming from 1979 to 2021. Unforced changes in Arctic SAT are largely attributed to two leading modes. The first is pan‐Arctic warming with stronger loading over the Eurasian sector, accounting for 70% of the unforced variance and closely related to the positive phase of the unforced Atlantic Multidecadal Oscillation (AMO). The second mode exhibits relatively weak warming averaged over the entire Arctic with warming over the North American‐Pacific sector and cooling over the Atlantic sector, explaining 10% of the unforced variance and likely caused by the positive phase of the unforced Interdecadal Pacific Oscillation (IPO). The AMO‐related changes dominate the unforced Arctic warming since 1979, while the IPO‐related changes contribute to the decadal SAT changes over the North American‐Pacific Arctic.

Sources of low-frequency variability in observed Antarctic sea ice

Abstract. Antarctic sea ice has exhibited significant variability over the satellite record, including a period of prolonged and gradual expansion, as well as a period of sudden decline. A number of mechanisms have been proposed to explain this variability, but how each mechanism manifests spatially and temporally remains poorly understood. Here, we use a statistical method called low-frequency component analysis to analyze the spatiotemporal structure of observed Antarctic sea ice concentration variability. The identified patterns reveal distinct modes of low-frequency sea ice variability. The leading mode, which accounts for the large-scale, gradual expansion of sea ice, is associated with the Interdecadal Pacific Oscillation and resembles the observed sea surface temperature trend pattern that climate models have trouble reproducing. The second mode is associated with the central Pacific El Niño–Southern Oscillation (ENSO) and the Southern Annular Mode and accounts for most of the sea ice variability in the Ross Sea. The third mode is associated with the eastern Pacific ENSO and Amundsen Sea Low and accounts for most of the pan-Antarctic sea ice variability and almost all of the sea ice variability in the Weddell Sea. The third mode is also related to periods of abrupt Antarctic sea ice decline that are associated with a weakening of the circumpolar westerlies, which favors surface warming through a shoaling of the ocean mixed layer and decreased northward Ekman heat transport. Broadly, these results suggest that climate model biases in long-term Antarctic sea ice and large-scale sea surface temperature trends are related to each other and that eastern Pacific ENSO variability is a key ingredient for abrupt Antarctic sea ice changes.

Impacts of internal variability on winter temperature fluctuations over the Tibetan Plateau

The climate comprises an externally forced component and internal variability within the climate system. This variability arises from interactions among climate subsystems, significantly impacting climate change, particularly over the Tibetan Plateau (TP). Therefore, understanding the mechanisms governing TP temperature responses to internal variability is crucial. In this study, winter temperature components over the TP in response to internal variability and external forcing are distinguished using reconstructed monthly temperature data and large ensemble simulations from 11 CMIP6 coupled models spanning 1901 to 2014. Analysis reveals two cold periods (1901–1931, 1956–1999) and two warm periods (1932–1955, 2000–2014) within the internal-variability component from 1901 to 2014. These periods are significantly associated with the Atlantic Multi-decadal Oscillation (AMO) and Interdecadal Pacific Oscillation (IPO), explaining 38.9% of the winter temperature's internal-variability component over the TP. Manipulating AMO+ (AMO-) without IPO influence or IPO- (IPO+) without AMO impact intensifies the winter warm (cold) anomaly across the entire TP. This suggests that AMO and IPO contribute to the temperature's internal-variability component over the TP. Specifically, in winter, both AMO+ (AMO-) and IPO- (IPO+) induce a “− + −+” (+ − +−) Rossby wave train over Eurasia, influencing the East Asian trough, East Asian jet stream, Ural Blocking high, and the Siberian high, resulting in a warm (cold) anomaly over the TP. Moreover, our findings indicate that AMO and IPO collaboratively determine the cold or warm anomaly and its intensity over the TP in winter.

Salinity effect-induced ENSO amplitude modulation in association with the interdecadal Pacific Oscillation

Salinity effect-induced ENSO amplitude modulation in association with the interdecadal Pacific Oscillation

Impact of the North Pacific Meridional Mode on the Tropical Pacific Modulated by the Interdecadal Pacific Oscillation

Abstract The North Pacific meridional mode (NPMM) peaking in boreal spring influences El Niño–Southern Oscillation (ENSO) properties in the ensuing winter. Whether the precursory impact of NPMM on the spatial diversity of ENSO has decadal variation remains unknown. Using long-term reanalysis datasets, we find that the interdecadal Pacific oscillation (IPO) significantly modulates the NPMM forcing on two types of ENSO. During the positive IPO (+IPO) phase, a strengthened background Aleutian low and southward-shifted storm track, in comparison to the negative IPO (−IPO) phase, produce stronger basin-scale negative geopotential height tendency anomalies over the North Pacific through synoptic-scale eddy–mean flow interaction. Such strong background negative tendency facilitates an Aleutian low–like pressure monopole rather than a North Pacific Oscillation (NPO)-like pressure dipole in boreal spring, leading to a weak NPMM that cannot effectively promote development of either a central Pacific (CP) or an eastern Pacific (EP) ENSO. By contrast, the NPO-like dipole enhances in boreal spring during −IPO, corresponding to stronger and more frequently occurring NPMM events that induce a robust CP-ENSO-like response in boreal winter. Moreover, the −IPO-related tropical Pacific mean states and the associated positive feedbacks cause a strong decrease in mixed layer temperature variance in the equatorial eastern Pacific, but a slight increase in the central Pacific, thus further contributing to the enhanced correlation between NPMM and CP-ENSO. Therefore, −IPO has played a role in the stronger impact of NPMM on CP-ENSO since the 1990s, and the modulation effects of IPO should be considered in understanding the extratropical–tropical climatic connection and ENSO spatial diversity.

A hybrid statistical downscaling prediction model with timescale decomposition for summer precipitation over Hainan Island, China

AbstractBy observing well‐known sea surface temperature (SST) indices and outputs from Climate Forecast System version 2 (CFSv2) hindcasts and predictions, in this study, we developed a hybrid statistical downscaling prediction model (HSDPM) based on a timescale decomposition approach, which effectively improved the forecasting ability of summer (June–August) precipitation over Hainan Island (HNSP) in China; this precipitation presented multiple obvious timescale variabilities. We found that the interannual variability of the HNSP is closely related to the observed North Atlantic tripole SST anomalies in the preceding winter and the summer sea level pressure over the South China Sea by CFSv2 predicted in March. On the other hand, the interdecadal variability in the HNSP is linked to the prewinter Pacific Interdecadal Oscillation (PDO) according to the observations, the CFSv2 predictions of the summer surface air temperature over the tropical central‐east Pacific. On this basis, both the interannual and interdecadal components of the HNSP are effectively predicted using the corresponding predictors via multiple regression; thus, the HSDPM is ultimately established by combining the above two components. Compared with the original CFSv2 model, the HSDPM model achieves a considerable improvement in performance in predicting the HNSP. Specifically, the temporal correlation coefficient, ratio of root‐mean‐square error and anomaly sign consistency rate between the observed and HSDPM‐predicted HNSPs are 0.70, 17.4% and 80.5%, respectively, which are significantly greater than the above three forecasting index values of 0.23, 38.3% and 48.8%, respectively, obtained from the original CFSv2 predictions. The application of the HSDPM may be beneficial for drought and flood prevention and mitigation in Hainan Island.

An underlying factor of increasing early winter precipitation in the Hokuriku region of Japan in recent decades

AbstractUsing a reanalysis dataset and large‐ensemble simulation results, this study examines a possible factor of increasing trend in early winter precipitation in recent decades in the Hokuriku region of Japan. Monthly precipitation in December has a significant increasing trend after the early 1990s, which is different from those in January and February. The increasing precipitation in December is related to that in the sea surface upward latent heat flux due to intensified winter monsoon circulation and warming sea surface temperatures (SSTs) over the Sea of Japan. December averaged SSTs show a trend pattern in recent decades that is similar to the negative phase of the interdecadal Pacific oscillation (IPO), accompanied by positive trends from the eastern Indian Ocean to the western tropical Pacific. The enhanced trend of convection over the Bay of Bengal is seen; suggesting a combined effect of climatologically high SSTs and IPO‐related warmed SSTs over the region. Trends in recent decades of an upper‐level wavy pattern from South Asia to near Japan along the subtropical jet associated with enhanced convection near the Bay of Bengal and the related pressure drop from Japan to the north are seen, which contribute to intensified winter monsoon circulation.

Seasonal predictability of SST anomalies and marine heatwaves over the Kuroshio extension region in the Copernicus C3S models

The warm sea surface temperature anomalies (SSTAs) and marine heatwaves (MHWs) in the Kuroshio Extension (KE) region have profound impacts on local and surrounding ecological and climatic systems. This study evaluates the seasonal prediction skills of KE-SSTAs and KE-MHWs based on six dynamical models from the Copernicus Climate Change Service (C3S) using different observational datasets for verification and further investigates the main sources of predictability. The results show that current dynamical models can provide reliable predictions for KE-SSTAs for up to about 4 months, but they are challenging to accurately predict the occurrence of KE-MHWs. Compared with single models, the C3S multi-model ensemble mean is usually more skillful in predicting KE-SSTAs and KE-MHWs at most lead times. With lead time increasing, the dynamical models tend to underestimate the mean intensity and annual frequency of the KE-MHWs and overestimate their mean duration. The performance of models in predicting KE-SSTAs is largely dependent on their ability to predict the Pacific Decadal Oscillation, Interdecadal Pacific Oscillation, and El Niño–Southern Oscillation which all significantly influence the KE-SSTAs. The results indicate that these three climate modes are the main sources of seasonal predictability for KE-SSTAs and KE-MHWs. These results provide a deeper understanding of the dynamical seasonal predictability of SSTAs and MHWs in the KE region.

The role of interdecadal climate oscillations in driving Arctic atmospheric river trends

Atmospheric rivers (ARs), intrusions of warm and moist air, can effectively drive weather extremes over the Arctic and trigger subsequent impact on sea ice and climate. What controls the observed multi-decadal Arctic AR trends remains unclear. Here, using multiple sources of observations and model experiments, we find that, contrary to the uniform positive trend in climate simulations, the observed Arctic AR frequency increases by twice as much over the Atlantic sector compared to the Pacific sector in 1981-2021. This discrepancy can be reconciled by the observed positive-to-negative phase shift of Interdecadal Pacific Oscillation (IPO) and the negative-to-positive phase shift of Atlantic Multidecadal Oscillation (AMO), which increase and reduce Arctic ARs over the Atlantic and Pacific sectors, respectively. Removing the influence of the IPO and AMO can reduce the projection uncertainties in near-future Arctic AR trends by about 24%, which is important for constraining projection of Arctic warming and the timing of an ice-free Arctic.

Influence of the Atlantic Multidecadal Oscillation and Interdecadal Pacific Oscillation on Antarctic surface air temperature during 1900 to 2015

Influence of the Atlantic Multidecadal Oscillation and Interdecadal Pacific Oscillation on Antarctic surface air temperature during 1900 to 2015

Precipitation changes in High Mountain Asia driven by the combined contributions of anthropogenic greenhouse gases, aerosols, and interdecadal Pacific oscillation

Precipitation changes in High Mountain Asia driven by the combined contributions of anthropogenic greenhouse gases, aerosols, and interdecadal Pacific oscillation

Near-term projection of Amazon rainfall dominated by phase transition of the Interdecadal Pacific Oscillation

The Amazon basin experienced a prolonged drought condition during the 2010s, leading to a large-scale forest degradation destructive to ecosystems and human society. Elusive are issues as to whether the decadal drought is driven by external forcing or internal variability, and whether the drought will continue or recover soon. Using large ensemble simulations from a state-of-the-art climate model, here we find a negative-to-positive phase transition of the Interdecadal Pacific Oscillation (IPO) explains ~45% (~40–49%) of the observed decadal drought of Amazon rainfall since 2010, much greater than the role of external forcing (~12%). Constraining future IPO phase transition reduces the uncertainty by ~38% from a range of −0.73 to + 0.31 mm day−1 decade−1 to a range of −0.42 to + 0.23 mm day−1 decade−1, of the near-term Amazon rainfall projection before 2040 under a mid-intensity emission scenario. Thus, the IPO plays a crucial role in the post-2010 drying and the near-term rainfall projection.

Roles of External Forcing and Internal Variability in Global Marine Heatwaves Change During 1982–2021

AbstractAs discrete prolonged extreme warm water events, marine heatwaves (MHWs) have become more frequent, stronger and longer‐lasting during the past several decades. The relative contributions of external forcing and internal variability to these changes and their underlying drivers remain unclear. Here, analyses of 90 simulations in CESM2 reveal that external forcing dominates the increasing frequency by causing the mean warming of sea surface temperature (SST), accounting for 82% of the observed trends. Both the mean warming and increased variance of SST contribute to the longer‐lasting MHWs during 1982–2021, with external forcing contributing 38% of the increase in the SST variance for global average. Internal variability, especially the Inter‐decadal Pacific Oscillation (IPO), is closely associated with regional MHW changes. The observed negative IPO trend during 1982–2021 is related to increasing, strengthening and longer‐lasting MHW over Kuroshio Extension, but decreasing and shorter‐lasting MHW over the Northeast Pacific Coast.

Spatiotemporal Variability of Tropical Cyclone Genesis Density in the Northwest Pacific

Abstract The small sample size of tropical cyclone (TC) genesis in the observations prevents us from fully characterizing its spatiotemporal variations. Here we take advantage of a large ensemble of 60-km-resolution atmospheric simulations to address this issue over the northwest Pacific (NWP) during 1951–2010. The variations in annual TC genesis density are explored separately on interannual and decadal time scales. The interannual variability is dominated by two leading modes. One is characterized by a dipole pattern, and its temporal evolution is closely linked to the developing ENSO. The other mode features high loadings in the central part of the basin, with out-of-phase changes near the equator and date line, and tends to occur during ENSO decay years. On decadal time scales, TC genesis density variability is primarily controlled by one mode, which exhibits an east–west dipole pattern with strong signals confined to south of 20°N and is tied to the interdecadal Pacific oscillation–like sea surface temperature anomalies. Further, we investigate the seasonal evolution of the ENSO effect on TC genesis density. The results highlight the distinct impacts of the two types of ENSO (i.e., eastern Pacific vs central Pacific) on TC genesis density in the NWP during a specific season and show the strong seasonal dependency of the TC genesis response to ENSO. Although the results from the observations are not as prominent as those from the simulations because of the small sample size, the high consistency between them demonstrates the fidelity of the model in reproducing TC statistics and variability in the observations.

Prediction of seasonal sea surface temperature based on temperature and salinity of subsurface ocean using machine learning

AbstractThe sea surface temperature (SST) is not only a crucial external factor in the evolution of the atmosphere, but also a primary factor and premonition signal used in climate prediction. It is challenging to obtain a precise SST for generating accurate initial and boundary conditions in numerical models. This study employs a machine learning approach, that is, a convolutional neural network (CNN) algorithm, to predict SST on a seasonal scale. In particular, the subsurface ocean temperature (OT) and ocean salinity (OS) at depths of 5.02, 15.08, 25.16, 35.28, 45.45 and 76.55 m were used as training factors in developing a CNN prediction model. The results indicate that subsurface OT and OS can persist for 6 months or longer, with a maximum persistence of up to 12 months. Using the CNN prediction model, the SST can be reliably predicted 6 months in advance in most cases. The predicted SST has a mean bias of approximately 0–0.8 K on the globe. The bias is small (below 0.5 K) in the open ocean. The root mean square errors (RMSEs) of hindcasting for Interdecadal Pacific Oscillation, North Atlantic Oscillation (NAO) and Atlantic Multidecadal Oscillation indices are all less than 1.0 K. Specifically, the RMSE for El Niño prediction is less than 0.5 K. This study provides a viable method for establishing initial and boundary conditions for climate prediction.