Climate Variability in the Context of Climate Change: El Niño and Other Oscillations

Monsoon and its teleconnection with earth system internal processes affect the spatiotemporal distribution of precipitation and water resources. In this paper, the wavelet coherence analysis has been utilized, a time and frequency domain methodology for comparing the spectral features of two independent time series superior to linear approaches. This technique is used to capture the significant modes of variabilities in the Indian Summer Monsoon Index (ISMI) and large‐scale climate indices (CIs) between ocean–atmosphere oscillations, like Indian Ocean Dipole (IOD), El Niño–Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), Southern Oscillation Index (SOI), North Atlantic Oscillation (NAO), Atlantic Multidecadal Oscillation (AMO), and Arctic Oscillation (AO) over Pakistan. Precipitation time series during 1960–2016 revealed significant interannual coherences with ISMI, whereas the remaining CIs (IOD, ENSO, PDO, SOI, NAO, AMO, and AO) revealed interannual, decadal and interdecadal coherences. However, AO revealed strongest coherences in R‐II, III, and VI at interdecadal scales among all CIs. Overall, the interannual cycles on ISMI are 2.8 years, IOD 1–5.3 years, PDO 0–5.3 years, SOI 1–5.3 years, NAO 0–5 years, AO 0–5 years, and AMO 0–8.3 years. Whereas, the remaining CIs shared interdecadal coherences over particular regions. The ISMI displayed coherences (except in the UIB) with the large‐scale CIs over various homogenous regions on an interannual scale. The dominant influence of ISMI is observed in R‐II and III; the significant coherences in R‐II ranged from ~8 to 32 months (~0.8–2.8 years). The IOD and NAO have major coherences than the remaining large‐scale CIs ranging from ~16 to 64 months (1.3–5.3 years). The AO has the most significant coherences observed in R‐II, III, and VI on the decadal/interdecadal scale from 128 months and above (almost 10–15 years). On a 1.0‐year timescale, all homogenous regions demonstrated strong intermittent coherence with ISMI, IOD, ENSO, PDO, SOI, NAO, AMO, and AO. These findings have substantial implications for decision‐makers and scientists in Pakistan looking to enhance water resource planning and operations in the face of future climate uncertainties.

<p>The acquisition of time-lapse satellite gravity measurements during the GRACE and GRACE Follow On (FO) missions revolutionized our understanding of the Earth system, through the accurate quantification of the mass transport at global and regional scales. Largely related to the water cycle, along with some geophysical signals, decadal trends and seasonal cycles dominate the mass transport signals, constituting about 80 % of the total variability measured during GRACE (FO) missions. We focus here on the interannual variability, constituting the remaining 20 % of the signal, once linear trends and seasonal signals have been removed. Empirical orthogonal functions (EOFs) highlight the most prominent signals, including short-lived signals triggered by major earthquakes, interannual oscillations in the water cycle driven by the El Nino Southern Oscillation (ENSO) and significant decadal variability, potentially related to the Pacific Decadal Oscillation (PDO). The interpretation of such signals remains however limited due to the arbitrary nature of the statistical decomposition in eigen values. To overcome these limitations, we performed a LASSO (Least Absolute Shrinkage and Selection Operator) regression of eight climate indices, including ENSO, PDO, NPGO (North Pacific Gyre Oscillation), NAO (North Atlantic Oscillation), AO (Arctic Oscillation), AMO (Atlantic Multidecadal Oscillation), SAM (Southern Annular Mode) and IOD (Indian Ocean Dipole). The LASSO regularization, coupled with a cross-validation, proves to be remarkably successful in the automatic selection of relevant predictors of the climate variability for any geographical location in the world. As expected, ENSO and PDO impact the global water cycle both on land and in the ocean. The NPGO is also a major actor of the global climate, showing similarities with the PDO in the North Pacific. AO is generally favored over NAO, especially in the Mediteranean Sea and North Atlantic. SAM has a preponderant influence on the interannual variability of ocean bottom pressures in the Southern Ocean, and, in association with ENSO, modulates the interannual variability of ice mass loss in West Antarctica. AMO has a strong influence on the interannual water cycle along the Amazon river, due to the exchange of moisture in tropical regions. IOD has little to no impact on the interannual water cycle. All together, climate modes generate changes in the water mass distribution of about 100 mm for land, 50 mm for shallow seas and 15 mm for oceans. Climate modes account for a secondary but significant portion of the total interannual variability (at maximum 60% for shallow seas, 50 % for land and 40% for oceans). While such processes are insufficient to fully explain the complex nature of the interannual variability of water mass transport on a global scale, climate modes can be used to correct the GRACE (FO) measurements for a significant part of the natural climate variability and uncover smaller signals masked by such water mass transports.</p>

We compare the performance of several modes of variability across six U.S. climate modeling groups, with a focus on identifying robust improvements in recent models [including those participating in phase 6 of the Coupled Model Intercomparison Project (CMIP)] compared to previous versions. In particular, we examine the representation of the Madden–Julian oscillation (MJO), El Niño–Southern Oscillation (ENSO), the Pacific decadal oscillation (PDO), the quasi-biennial oscillation (QBO) in the tropical stratosphere, and the dominant modes of extratropical variability, including the southern annular mode (SAM), the northern annular mode (NAM) [and the closely related North Atlantic Oscillation (NAO)], and the Pacific–North American pattern (PNA). Where feasible, we explore the processes driving these improvements through the use of “intermediary” experiments that utilize model versions between CMIP3/5 and CMIP6 as well as targeted sensitivity experiments in which individual modeling parameters are altered. We find clear and systematic improvements in the MJO and QBO and in the teleconnection patterns associated with the PDO and ENSO. Some gains arise from better process representation, while others (e.g., the QBO) from higher resolution that allows for a greater range of interactions. Our results demonstrate that the incremental development processes in multiple climate model groups lead to more realistic simulations over time.

CR Climate Research Contact the journal Facebook Twitter RSS Mailing List Subscribe to our mailing list via Mailchimp HomeLatest VolumeAbout the JournalEditorsSpecials CR 75:65-80 (2018) - DOI: https://doi.org/10.3354/cr01507 Influence of climate oscillations on urban and rural temperature variability in the Kanto region of Japan M. X. C. Seow1,*, M. Roth2 1Department of Earth and Planetary Science, Graduate School of Science, The University of Tokyo, Tokyo 113-0033, Japan 2Department of Geography, National University of Singapore, 117570, Singapore *Corresponding author: xcmarvin@eps.s.u-tokyo.ac.jp ABSTRACT: Climate oscillations of air and sea-surface temperatures are known to influence surface temperature and precipitation variability. Intraseasonal (4 to 12 mo) and interannual (>12 to 120 mo) variability is investigated using monthly mean temperature data from 4 urban and 3 rural weather stations located in the Kanto region of Japan, during the period January 1973-August 2015. Indices of 6 climate oscillations (El Niño-Southern Oscillation, Indian Ocean Dipole, Quasi-Biennial Oscillation, Arctic Oscillation, Pacific North American Pattern and West Pacific Pattern) known to influence Japan’s climate are investigated using wavelet analysis to identify oscillations in temperature data and climate mode indices at various periods. Continuous wavelet transform analysis shows that temperature variability is more pronounced at the intraseasonal than interannual timescale and urbanisation has no influence on temperature variability. Using the wavelet transform coherence analysis, all climate oscillations except El Niño-Southern Oscillation significantly covary with the temperature at certain periods and years, with the West Pacific Pattern having the highest coherence on average across the whole study period and analysed timescales. On average across the study period, using the multiple linear regression technique, only the Arctic Oscillation and West Pacific Pattern are found to significantly contribute to both intraseasonal and interannual temperature variability. KEY WORDS: Climate variability · Air temperature · Urban-rural stations · Wavelet analysis · Japan’s climate Full text in pdf format PreviousNextCite this article as: Seow MXC, Roth M (2018) Influence of climate oscillations on urban and rural temperature variability in the Kanto region of Japan. Clim Res 75:65-80. https://doi.org/10.3354/cr01507 Export citation RSS - Facebook - Tweet - linkedIn Cited by Published in CR Vol. 75, No. 1. Online publication date: April 18, 2018 Print ISSN: 0936-577X; Online ISSN: 1616-1572 Copyright © 2018 Inter-Research.

Climatic variability has profound effects on the distribution, abundance and catch of oceanic fish species around the world. The major modes of this climate variability include the El Nino-Southern Oscillation (ENSO) events, the Pacific Decadal Oscillation (PDO) also referred to as the Interdecadal Pacific Oscillation (IPO), the Indian Ocean Dipole (IOD), the Southern Annular Mode (SAM) and the North Atlantic Oscillation (NAO). Other modes of climate variability include the North Pacific Gyre Oscillation (NPGO), the Atlantic Multidecadal Oscillation (AMO) and the Arctic Oscillation (AO). ENSO events are the principle source of interannual global climate variability, centred in the ocean–atmosphere circulations of the tropical Pacific Ocean and operating on seasonal to interannual time scales. ENSO and the strength of its climate teleconnections are modulated on decadal timescales by the IPO. The time scale of the IOD is seasonal to interannual. The SAM in the mid to high latitudes of the Southern Hemisphere operates in the range of 50–60 days. A prominent teleconnection pattern throughout the year in the Northern Hemisphere is the North Atlantic Oscillation (NAO) which modulates the strength of the westerlies across the North Atlantic in winter, has an impact on the catches of marine fisheries. ENSO events affect the distribution of tuna species in the equatorial Pacific, especially skipjack tuna as well as the abundance and distribution of fish along the western coasts of the Americas. The IOD modulates the distribution of tuna populations and catches in the Indian Ocean, whilst the NAO affects cod stocks heavily exploited in the Atlantic Ocean. The SAM, and its effects on sea surface temperatures influence krill biomass and fisheries catches in the Southern Ocean. The response of oceanic fish stocks to these sources of climatic variability can be used as a guide to the likely effects of climate change on these valuable resources.

Teleconnections between El Niño/Southern Oscillation (ENSO), North Atlantic Oscillation (NAO), Indian Ocean Dipole (IOD) and Pacific Decadal Oscillation (PDO) and seasonal precipitation regimes over the Yangtze River basin have been analysed based on the rotated empirical orthogonal functions. Results show that ENSO is the leading driver of seasonal precipitation variability over the Yangtze River basin, and the spring precipitation has been influenced by the PDO and ENSO, the summer and autumn precipitation has been influenced by the ENSO and IOD, the winter precipitation has been influenced by the ENSO, IOD and NAO. Furthermore, changes for the seasonal occurrence and intensity of wet days linked to the ENSO, NAO, IOD and PDO indices have also been investigated to discover which is the dominant mechanism driving seasonal precipitation changes. And results indicated that the influences of ENSO, NAO, IOD and PDO on the seasonal occurrence and intensity of precipitation events are complex, such as that the negative PDO event at the same year tends to increase the spring occurrence of precipitation events in the southwestern part of the Yangtze River basin while the positive ENSO event a year earlier tends to increase the spring intensity of precipitation events in the east part of the Yangtze River basin.

Understanding linkages between global climate indices and terrestrial water storage changes over Africa using GRACE products

Evapotranspiration is a key factor in regional hydrological processes and water resources management. Long‐term variation of evapotranspiration affects the regional climate wet/dry tendency and agricultural production profoundly. Thus, reference evapotranspiration (ET0) values for 46 meteorological stations in the Yangtze River Delta (YRD) were calculated for 1957–2014 using the FAO‐Penman–Monteith (FAO‐PM) method. The variation patterns of ET0 values were determined based on the principal component analysis (PCA) method. In addition, the methods of cross wavelet transform (CWT), Kendall tau‐b correlation coefficient determination and cross‐correlation method were applied in the assessment of the correlation between ET0 values and large‐scale climate oscillations, such as the North Atlantic Oscillation (NAO), the Pacific Decadal Oscillation (PDO), the Oceanic Niño3.4 Sea Surface Temperature Index (NINO) and the Indian Ocean Dipole (IOD). Annual ET0 patterns for three dominant geographic subregions of the YRD (the southeastern, northwestern and mid‐eastern) were determined. There were only several discontinuous lower timescale bands between the three change patterns of annual ET0 and climate oscillations. In seasonal scale, the temporal patterns of ET0 changed simultaneously with the NAO, NINO and PDO in spring, the PDO in summer and the NAO in winter. The monthly ET0 was mostly influenced by the NAO and IOD in January, the IOD in February, the IOD and NINO in March, the NINO in June, the PDO in July to October, the NINO, IOD and NAO in October and the NINO in December. The lag times for the ET0 changes were about 0–5 months for the NAO and NINO, 1–2 months for the PDO and 4 months for the IOD.

The intensity and frequency of droughts in Poyang Lake Basin have been increasing due to global warming. To properly manage water resources and mitigate drought disasters, it is important to understand the long-term characteristics of drought and its possible link with large-scale climate indices. Based on the monthly meteorological data of 41 meteorological stations in Poyang Lake Basin from 1958 to 2017, the spatiotemporal variations of drought were investigated using the standardized precipitation evapotranspiration index (SPEI). Ensemble empirical mode decomposition (EEMD) methods and the modified Mann–Kendall (MMK) trend test were used to explore the spatiotemporal characteristics and trends of drought. Furthermore, to reveal possible links between drought variations and large-scale climate indices in Poyang Lake Basin, the relationships between SPEI and large-scale climate indices, such as North Atlantic Oscillation (NAO), El Niño–Southern Oscillation (ENSO), Arctic Oscillation (AO), Indian Ocean Dipole (IOD) and Pacific Decadal Oscillation (PDO) were examined using cross-wavelet transform. The results showed that the SPEI in Poyang Lake Basin exhibited relatively stable quasi-periodic oscillation, with approximate quasi-3-year and quasi-6-year periods at the inter-annual scale and quasi-15-year and quasi-30-year periods at the inter-decadal scale from 1958 to 2017. Moreover, the Poyang Lake Basin experienced an insignificantly wetter trend as a whole at the annual and seasonal scales during the period of 1958–2017, except for spring, which had a drought trend. The special characteristics of the trend variations were markedly different in the basin. The areas in which drought was most likely to occur were mainly located in the Poyang Lake region, northwest and south of the basin, respectively. Furthermore, relationships between the drought and six climate indices showed that the drought exhibited a significant temporal correlation with five climate indices at restricted intervals, except for IOD. The dominant influences of the large-scale climate indices on the drought evolutions shifted in the Poyang Lake Basin during 1958–2017, from the NAO, Niño 3.4, and the Southern Oscillation Index (SOI) before the late 1960s and early 1970s, to the AO and PDO during the 1980s, then to the NAO, AO and SOI after the early 2000s. The NAO, AO and SOI exerted a significant influence on the drought events in the basin. The results of this study will benefit regional water resource management, agriculture production, and ecosystem protection in the Poyang Lake Basin.

<p>The Indonesian and South China Sea throughflows play an important role in global ocean circulation as the only low-latitude pathway for the exchange of heat and salt between the Pacific and Indian oceans. This transport is modulated by different climate systems including the El Niño Southern Oscillation (ENSO), the Pacific Decadal Oscillation (PDO) and the East Asian Monsoon. The interactions of these climate systems across the Southeast Asian region are still being understood, particularly the role of sea surface salinity (SSS) in inhibiting flow from the Makassar Strait into the Indian Ocean.</p><p>Reconstructions of SSS from corals provide an opportunity to study long-term trends in climate and ocean circulation. Coral records from north and south of the Luzon Strait, the Makassar Strait, and Lombok Strait for the period 1926 to 2010 are examined to evaluate their shared variability. Principal component analysis synthesizes these records for the boreal winter (December to March) and boreal summer (June to September). The first and second principal components or empirical orthogonal functions (EOF) describe over 55% of the shared variance in both seasons. In the winter, the EOF of both modes correlates to PDO and the first EOF correlates to the Indian Ocean Dipole (IOD). A high-pass filter of the first EOF for <10 years per cycle for the winter and summer significantly correlates to ENSO and IOD respectively. While several sites individually correlate with ENSO and PDO, no individual SSS record correlates to the IOD. This consistent relationship of the IOD to the winter EOF indicates a regional influence on salinity variance that is not identified locally. One hypothesis to explain IOD’s regional influence is that the interaction of the IOD and ENSO through the atmospheric bridge or the Madden Julian Oscillation (MJO) is influencing the region. Spectral analysis, and climatic and oceanographic models will be used to further investigate this connection.</p>

The precipitation variability, trends, and teleconnections are studied over six administrative regions of Pakistan (Gilgit-Baltistan or GB, Azad Jammu and Kashmir or AJK, Khyber Pakhtoonkhawa or KPK, Punjab, Sindh, and Balochistan) on multiple timescales for the period of recent 38 years (1976–2013) using precipitation data of 42 stations and circulation indices datasets (Indian Ocean Dipole [IOD], North Atlantic Oscillation [NAO], Arctic Oscillation [AO], El Nino Southern Oscillation [ENSO], Pacific Decadal Oscillation [PDO], Atlantic Multidecadal Oscillation [AMO], and Quasi-Biennial Oscillation [QBO]). The summer monsoon season received the highest precipitation, amounting to 45%, whereas the winter and pre-monsoon (post-monsoon) seasons contributed 30 and 20% (5%), respectively, of the annual total precipitation. Positive percentile changes were observed in GB, KPK, Punjab, and Balochistan regions during pre-monsoon season and in Balochistan region during post-monsoon season in second half as compared to first half of 38-year period. The Mann-Kendall test revealed increasing trends for the period of 1995–2013 as compared to period of 1976–1994 for entire Pakistan during monsoon season and on annual timescale. A significant influence of ENSO was observed in all the four seasons in Balochistan, KPK, Punjab, and AJK regions during monsoon and post-monsoon seasons. This study not only offers an understanding of precipitation variability linkages with large-scale circulations and trends, but also it contributes as a resource document for policy makers to take measures for adaptation and mitigation of climate change and its impacts with special focus on precipitation over different administrative regions of Pakistan.

This chapter focused on major modes of variability which serve the key role in controlling the regional climate. In terms of tropospheric variability, it defined and discussed ENSO (El Nino Southern Oscillation), NAO (North Atlantic Oscillation), AO and AAO (Arctic Oscillation, Antarctic Oscillation), Indian Monsoon, Indian Ocean Dipole (IOD), PDO (Pacific Decadal Oscillation) and AMO (Atlantic Multidecadal Oscillation). Later it attended stratosphere variability; this constitutes QBO (quasi-biennial oscillation) and SSW (stratospheric sudden warming). Main characteristic features of each of these modes were elaborately discussed.

In arid to semi-arid regions, vulnerability to climate change combined with the overexploitation of water resources is jeopardizing food security. In the Souss-Massa region in central Morocco, the rural population relies on growing olives for a living. The management of these orchards is mostly traditional under rainfed irrigation, which induces a high level of dependence on climate variability. In the present study, we investigate the long-term trends of the relationship between the observed olive yields and global climate patterns during the period 1973–2014. We apply lagged Spearman’s correlations and cross-wavelet analysis to detect the potential influence of El Niño-southern oscillation (ENSO), the Indian Ocean Dipole (IOD), North Atlantic oscillation (NAO) and Pacific decadal oscillation (PDO) on the yield variability of olive orchards. The results of a Mann-Kendall test show a statistically significant decreasing trend in olive yields during the studied period. Statistically significant negative correlations were observed for (lag = −1) with spring and summer NINO 3.4 and with summer and autumn PDO. No statistically significant correlations between olive yields and NAO and IOD were observed. The results of wavelet coherence between annual olive yields and PDO and ENSO revealed that the highest values of power spectrum coherence occurred during the (lag = 0) spring PDO and (lag = −1) spring ENSO, both with an antiphase relationship. During the studied period, the extreme events of El Niña and El Niño years corresponded to below average yields.

Using multiple approaches, the modulation of climate variability on global ocean waves from 1981 to 2020 is investigated based on a wave hindcast model forced by ERA5 winds. These climate variabilities include El Niño–Southern Oscillation (ENSO), Antarctic Oscillation (AAO), Arctic Oscillation (AO), Pacific Decadal Oscillation (PDO), Atlantic Multidecadal Oscillation (AMO), and Indian Ocean Dipole. Linear regression and composite analysis results indicate that El Niño‐induced low‐pressure systems in December–February (DJF) generate energetic waves in the Pacific Ocean. Positive AAO events are associated with the southward movement of westerlies in the Southern Hemisphere and thus produce long‐period Southern Ocean swell that impacts the South Pacific Ocean. In the Northern Hemisphere, positive AO is associated with strengthening winds which generate larger waves around the Icelandic region. An empirical orthogonal function approach was further undertaken to study interannual variability of significant wave height (Hs) anomalies. In DJF, mode 1 marks the impacts of ENSO and PDO in the Pacific Ocean and AO in the North Atlantic Ocean. Mode 2 is controlled by AO, which is characterized by a dipole pattern in the North Atlantic Ocean. Mode 3 describes the AAO footprints in the Southern Ocean and the eastern Pacific Ocean. In June–August, the leading mode is influenced by AAO and AMO. Mode 2 indicates the importance of ENSO and PDO which exhibit opposite patterns in the Pacific Ocean sector of the Southern Ocean. Finally, a wavelet coherence analysis is conducted on the principal components and climate indexes, which shows their respective evolutions over the time–frequency domain.

Oscillations in global modes of variability (MoVs) form global teleconnections that affect regional climate variability and modify the potential for severe and damaging weather conditions. Understanding the link between certain MoVs and regional climate can improve the ability to more accurately predict environmental conditions that impact human life and health. In this study, we explore the connection between different MoVs, including the Arctic oscillation (AO), Eurasian teleconnection, Indian Ocean dipole (IOD), North Atlantic oscillation (NAO), and El Niño southern oscillation (Nino34), with winter and summer climates in the High Mountain Asia (HMA) region, including geopotential height at 250 hPa (z250), 2 m air temperature (T2M), total precipitation (PRECTOT), and fractional snow cover area (fSCA). Relationships are explored for the same monthly period between the MoVs and the climate variables, and a lagged correlation analysis is used to investigate whether any relationship exists at different time lags. We find that T2M has a negative correlation with the Eurasian teleconnection in the Inner Tibetan Plateau and central China in both winter and summer and a positive correlation in western China in summer. PRECTOT has a positive correlation with all MoVs in most regions in winter, especially with the IOD, and a negative correlation in summer, especially with the Eurasian teleconnection. Snow cover in winter is positively correlated with most indices throughout many regions in HMA, likely due to wintertime precipitation also being positively correlated with most indices. Generally, the AO and NAO show similar correlation patterns with all climate variables, especially in the winter, possibly due to their oscillations being so similar. Furthermore, the AO and NAO are shown to be less significant in explaining the variation in HMA climate compared to other MoVs such as the Eurasian teleconnection. Overall, our results identify different time windows and specific regions within HMA that exhibit high correlations between climate and MoVs, which might offer additional predictability of the MoVs as well as of climate and weather patterns in HMA and throughout the globe.