On the nonlinear interaction between global teleconnection patterns

As leading modes of the planetary-scale atmospheric circulation in the extratropics, the Northern Hemisphere (NH) annular mode (NAM) and Southern Hemisphere (SH) annular mode (SAM) are important components of global circulation, and their variabilities substantially impact the climate in mid−high latitudes. A 35-yr (1979−2013) simulation by the climate system model developed at the Chinese Academy of Meteorological Sciences (CAMS-CSM) was carried out based on observed sea surface temperature and sea ice data. The ability of CAMS-CSM in simulating horizontal and vertical structures of the NAM and SAM, relation of the NAM to the East Asian climate, and temporal variability of the SAM is examined and validated against the observational data. The results show that CAMS-CSM captures the zonally symmetric and out-of-phase variations of sea level pressure anomaly between the midlatitudes and polar zones in the extratropics of the NH and SH. The model has also captured the equivalent barotropic structure in tropospheric geopotential height and the meridional shifts of the NH and SH jet systems associated with the NAM and SAM anomalies. Furthermore, the model is able to reflect the variability of northern and southern Ferrel cells corresponding to the NAM and SAM anomalies. The model reproduces the observed relationship of the boreal winter NAM with the East Asian trough and air temperature over East Asia. It also captures the upward trend of the austral summer SAM index during recent decades. However, compared with the observation, the model shows biases in both the intensity and center locations of the NAM’s and SAM’s horizontal and vertical structures. Specifically, it overestimates their intensities.

The chaotic behavior in the global climate system of the Earth and the nonlinear interaction between some teleconnection patterns during different epochs of the twenty century are studied. To study the influence of low frequency variations, the wavelet decomposition is applied. We use the cross-redundancy and Granger causality for detailed components of the wavelet decomposition.

Western Patagonia harbors unique and sparsely studied terrestrial ecosystems that are threatened by land use changes and exposure to basin-scale climatic variability. We assessed the performance of two satellite vegetation indices derived from MODIS–Terra, EVI (Enhanced Vegetation Index) and NDVI (Normalized Difference Vegetation Index), over the northern and southern sectors of the Chiloé Island System (CIS) to advance our understanding of vegetation dynamics in the region. Then we examined their time-varying relationships with two climatic indices indicative of tropical and extratropical influence, the ENSO (El Niño–Southern Oscillation) and the Antarctic Oscillation (AAO) index, respectively. The 17-year time series showed that only EVI captured the seasonal pattern characteristic of temperate regions, with low (high) phenological activity during Autumn-Winter (Spring–Summer). NDVI saturated during the season of high productivity and failed to capture the seasonal cycle. Temporal patterns in productivity showed a weakened seasonal cycle during the past decade, particularly over the northern sector. We observed a non-stationary association between EVI and both climatic indices. Significant co-variation between EVI and the Niño–Southern Oscillation index in the annual band persisted from 2001 until 2008–2009; annual coherence with AAO prevailed from 2013 onwards and the 2009–2012 period was characterized by coherence between EVI and both climate indices over longer temporal scales. Our results suggest that the influence of large-scale climatic variability on local weather patterns drives phenological responses in the northern and southern regions of the CIS. The imprint of climatic variability on patterns of primary production across the CIS may be underpinned by spatial differences in the anthropogenic modification of this ecosystem, as the northern sector is strongly modified by forestry and agriculture. We highlight the need for field validation of satellite indices around areas of high biomass and high endemism, located in the southern sector of the island, in order to enhance the utility of satellite vegetation indices in the conservation and management of austral ecosystems.

The interannual variability of the northern northeast Brazil (NNB) rainfall is directly affected by the dynamics of large-scale systems through atmospheric circulation. In this context, a hidden Markov model (HMM) was used to describe the daily rainfall occurrence and intensity at four meteorological stations of the Metropolitan Region of Fortaleza (MRF) in the NNB, and the global teleconnection patterns that influence precipitation regimes during the rainy season (February, March, April and May) from 1975 to 2013. An HMM with 4 states was set up and interpreted as: very rainy (1), rainy (2), less rainy (3), and dry (4) weather conditions. They agree satisfactorily with the interannual variability of the rainy season in this region. State 2 is the only one which showed a statistical trend, indicating a probable decrease of precipitation occurrence in the rainy season. Results also show that the meteorological weather associated with both states 2 and 4 is strongly related to the El Nino-Southern Oscillation (ENSO), the North Atlantic tripole and are intrinsically conditioned to the large-scale atmospheric teleconnections of the Northern Hemisphere. All those mechanisms modulate the shift of the Intertropical Convergence Zone (ITCZ) southward (state 2) or northward (state 4), thus affecting the precipitation occurrence over the NNB. State 1 is distinguished by the influence of the Atlantic Meridional Mode (AMM) and the ITCZ displacement further south, while state 3 is identified by the absence of teleconnection patterns.

Monthly mean summer (DJF) temperature and precipitation from Global Historical Climate Network (GHCN-V3) for the period of 1870-2011, are analyzed to assess the role of teleconnections on climate of Darwin, Australia. Indices of El Nino-Southern Oscillation (ENSO), Antarctic Oscillation (AAO), Pacific Decadal Oscillation (PDO), North Atlantic Oscillation (NAO), Arctic Oscillation (AO), and Pacific North American Oscillation (PNA) are extracted from monthly means and compared with climatic data of Darwin. Most of these climate modes are shown to have a strong influence on the monthly mean summer temperature and precipitation. ENSO is shown to have a positive relationship with the amount of precipitation received and a negative relationship with the temperature. Where an El Nino event produces warmer drier conditions and a La Nina event produces colder wetter conditions. The AAO is shown to cause cold and dry conditions during the positive phase and warm and wet conditions during the negative phase. The PDO is shown to cause El Nino like condition during the positive phase causing warmer, drier weather, and La Nina like conditions during the negative phase causing cooler, wetter weather. Through the analysis it is also shown that the NAO, AO, and PNA have little effect on the temperature and precipitation patterns of Darwin.

A new approach is used to detect atmospheric teleconnections without being bound by orthogonality (such as empirical orthogonal functions). This method employs negative correlations in a global dataset to detect potential teleconnections. One teleconnection occurs between the Tasman Sea and the Southern Ocean. It is related to El Niño–Southern Oscillation (ENSO), the Indian Ocean dipole (IOD), and the southern annular mode (SAM). This teleconnection is significantly correlated with SAM during austral summer, fall, and winter, with IOD during spring, and with ENSO in summer. It can thus be described as a hybrid between these modes. Given previously found relationships between IOD and ENSO, and IOD’s proximity to the teleconnection centers, correlations to IOD are generally stronger than to ENSO. Increasing pressure over the Tasman Sea leads to higher (lower) surface temperature over eastern Australia (the southwestern Pacific) in all seasons and is related to reduced surface temperature over Wilkes Land and Adélie Land in Antarctica during fall and winter. Precipitation responses are generally negative over New Zealand. For one standard deviation of the teleconnection index, precipitation anomalies are positive over Australia in fall, negative over southern Australia in winter and spring, and negative over eastern Australia in summer. When doubling the threshold, the size of the anomalous high-pressure center increases and annual precipitation anomalies are negative over southeastern Australia and northern New Zealand. Eliassen–Palm fluxes quantify the seasonal dependence of SAM, ENSO, and IOD influences. Analysis of the dynamical interactions between these teleconnection patterns can improve prediction of seasonal temperature and precipitation patterns in Australia and New Zealand.

In view of the key factor in regional hydrological processes and water resource management, the temporal patterns of precipitation anomalies and oscillations were detected by the Quantile Perturbation Method (QPM) and the Singular Spectrum Analysis (SSA) Method, and the spatial patterns were identified using the Principal Component Analysis (PCA) Method. In addition, the teleconnections and lagged influence with large-scale climate oscillations in the Yangtze River Delta (YRD) of China from 1957 to 2016 were also analyzed. Results showed that, temporally, the main oscillations of precipitation were all found to be 2, 7–11 and 3–4 years in the annual and seasonal scales. Precipitation quantiles are subject to strong temporal oscillations at (multi-)decadal time scales, with high and low anomalies at specific periods. Spatially, the whole region could be divided into two main sub-regions in annual and seasonal scales, respectively. Among the selected large-scale climate oscillations in this study, the Pacific Decadal Oscillation (PDO) has a stronger influence on precipitation in March, July and September, but significant correlations were detected in more than 18% of the total stations. These stations were mainly in the southeast regions. The North Pacific index (NP) controlled the precipitation in February (13.95% of the total stations) and October (37.21% of the total stations) in the north region. Generally, the indicators of the Southern Oscillation Index (SOI) and Oceanic Niño 4 SST Index (ONI) had the strongest influence in regional precipitation variations, but significant correlations were detected in more than 20% of the total stations in March, September, October and November. Also, large-scale climate oscillations have a delayed way on precipitation. Among the oscillations, the Arctic Oscillation (AO) and North Atlantic Oscillation (NAO) showed that significant cross-correlations on precipitation were 0 and 3–5 months, respectively. NP showed significant cross-correlations with precipitation in many stations when the lag time was 0–3 months. Generally, the PDO, SOI and ONI have a greater influence in the south region, mainly with the lag time of 0–3, 2–3 and 1–5 months, respectively. The results will provide a basis for taking relevant measures to deal with problems of meteorological disaster and water supplement under climate change.

The Cape Point wave record, extreme events and the role of large-scale modes of climate variability

Sensitivity Difference in the Extratropical Atmosphere to Two Types of El Niño Events

Recent studies have shown that the Madden–Julian Oscillation (MJO) impacts the leading modes of intraseasonal variability in the northern hemisphere extratropics, providing a possible source of predictive skill over North America at intraseasonal timescales. We find that a k-means cluster analysis of mid-level geopotential height anomalies over the North American region identifies several wintertime cluster patterns whose probabilities are strongly modulated during and after MJO events, particularly during certain phases of the El Nino-Southern Oscillation (ENSO). We use a simple new optimization method for determining the number of clusters, k, and show that it results in a set of clusters which are robust to changes in the domain or time period examined. Several of the resulting cluster patterns resemble linear combinations of the Arctic Oscillation (AO) and the Pacific/North American (PNA) teleconnection pattern, but show even stronger responses to the MJO and ENSO than clusters based on the AO and PNA alone. A cluster resembling the positive (negative) PNA has elevated probabilities approximately 8–14 days following phase 6 (phase 3) of the MJO, while a negative AO-like cluster has elevated probabilities 10–20 days following phase 7 of the MJO. The observed relationships are relatively well reproduced in the 11-year daily reforecast dataset from the National Centers for Environmental Prediction (NCEP) Climate Forecast System version 2 (CFSv2). This study statistically links MJO activity in the tropics to common intraseasonal circulation anomalies over the North American sector, establishing a framework that may be useful for improving extended range forecasts over this region.

The Tianshan Mountains are known as the “water tower” in the arid region of Central Asia. Change in precipitation amount and pattern can have a profound impact on regional civilization and life supporting ecosystems. For this study, a systematic analysis of long-term precipitation data for the eastern Tianshan Mountains was conducted to investigate the influence of climate teleconnections on annual and intra-annual precipitation using data collected between 1951 and 2014 from 39 meteorological stations. Annual precipitation has increased during the past six decades at an average rate of 6.7 mm/10 years largely due to the increase in precipitation during the intra-annual wet period (May–October). The annual precipitation and its rate of increase were higher in the northwestern region. Annual precipitation was found to be most strongly correlated with index of Indian Summer Monsoon (ISM), and partially correlated with indices of Pacific Decadal Oscillation (PDO), Pacific North American Teleconnection Pattern (PNA), Arctic Oscillation (AO), El Nino–Southern Oscillation (ENSO), and North Atlantic Oscillation (NAO). ISM was positively correlated with the precipitation in almost the entire region during the intra-annual wet period, while it showed positive correlations in the northern slope and the alpine region, and negative correlations in the southern slope during the intra-annual dry period (November to April). PDO had much weaker influence both in spatial scale and strength and primarily affected low elevations on the southern slopes of the middle and western regions. The impacts of PNA and AO on precipitation were weak and localized. ENSO and NAO indices were almost not correlated with annual precipitation observation in the eastern Tianshan Mountains.

In this paper, the authors quantitatively investigated the joint impact of the Arctic Oscillation (AO) and El Niño‐Southern Oscillation (ENSO) on vegetation net primary productivity (NPP) over Indo‐Myanmar in boreal winter from 1981 to 2018, and found that there is a significant in‐phase variation between them. When the warm ENSO co‐occurs with the positive AO, the NPP in more than 80% of the grids in the study region significantly increases by approximately 24 gC m−2. For the cold ENSO plus the negative AO, the regionally averaged NPP anomalies are approximately −10 gC m−2, and the local minimum is as low as −60 gC m−2. The combination of AO and ENSO can explain approximately 36% of the total variance of NPP in Indo‐Myanmar. The AO/ENSO linkages to the NPP are very likely due to regional precipitation anomalies through atmospheric circulation. In association with the positive (negative) AO, the Rossby wave, propagating eastward along the subtropical jet stream, brings an anomalous cyclone (anticyclone) over Indo‐Myanmar. This enhances (weakens) Indo‐Myanmar Trough, and resulting in more (less) regional precipitation. During the warm (cold) ENSO, through the Gill‐type response, an anomalous high (low)‐pressure appears in the lower and middle troposphere over Philippine‐South China Sea. The Walker circulation is also weaker (stronger) than normal. These are conducive to anomalous southerly (northerly) winds in Indochina. As a result, in the warm ENSO plus positive AO winters, the mean precipitation anomaly in Indo‐Myanmar increased by 27% as averaged for five data sets. In contrast, when the cold ENSO co‐occurs with negative AO, the precipitation is significantly reduced where the largest anomaly exceeds 100 mm in the ERA5. The precipitation changes are consistent with the local NPP anomalies, whereas the temperature does not seem to be a dominant limiting factor.

This study examines associations of diurnal temperature range (DTR) changes in observations at the global, hemispheric, subcontinental, and grid box scales with five leading climate variability modes, including the Arctic Oscillation (AO), hemispheric Pacific–North America (PNA)–like mode, Pacific Decadal Oscillation, El Niño Southern Oscillation (ENSO), and Antarctic Oscillation (AAO) during the Northern Hemisphere winter season (Jan–Mar). Winter DTR variability in most subcontinental regions is significantly related to variations in either the AO, or hemispheric PNA‐like mode, or ENSO index in the Northern Hemisphere. In the Southern Hemisphere, the DTR variability appears closely coupled with variations in the ENSO and AAO. From 1951 to 2000, variations in the circulation patterns account for a significant fraction of the DTR increase at all scales although the strength of these associations varies geographically. After removing the linearly congruent component of leading climate variability modes from the total wintertime DTR trends in the observations, statistically significant residual trends in DTR are still found at the global, hemispheric, and most subcontinental regions. Ensemble mean multimodel averaged DTR trends to major anthropogenic and natural forcing are significantly smaller than not only observed total DTR trends but also residual trends at these large scales. The implication of changes in the leading climate variability modes on the detection of regional DTR trends is discussed. We find that the detection of the regional response to combined anthropogenic and natural forcing is robust to the exclusion of trends related to changes of the five modes considered here.

Analysis of recent rainfall trends and links to teleconnection patterns in California (U.S.)

<p>The teleconnection patterns are an important feature influencing the variability of moisture transport toward the continent. This work analyses the influence of the Arctic Oscillation, Antarctic Oscillation, Pacific North America, and ENSO on the moisture transport from major oceanic and continental moisture sources in the month of higher precipitation. The Pacific North America higher influence is observed over North America with an increased contribution to the western region from the Pacific and lower over the eastern region from the Atlantic in the positive phase. The moisture transport during Arctic Oscillation events seems to be modulated by the Mediterranean Sea and North Atlantic, increasing from the Mediterranean in the positive phase and decreasing from the Atlantic. The Antarctic Oscillation shows its most relevant influence over Australia and Eastern Africa, with increased moisture contribution from eastern regions on the positive phase. Finally, ENSO events show influence in moisture transport over different areas in the world. El Niño events are associated with increased transport from the Atlantic region over western Europe and from the Pacific over North America. In South and Central America, the moisture contribution decreased over the regions closer to the equator, while the opposite occurs over southern South America. Over eastern Africa and Southern Asia, moisture inflow from the Indian Ocean seems to be affected by the pattern. The result suggests the influence of the moisture contribution on the precipitation pattern in association with main teleconnection patterns.</p>