Search

The seasonal variability of precipitation has a profound impact on water demand for India's agricultural and socio‐economic sectors which are associated with continental and surrounding oceanic moisture sources controlled by dynamic and thermodynamic processes. In this study, moisture sources have been tracked by the Lagrangian trajectory approach over four homogeneous regions: Northwest India (NWI), Central India (CEI), Northeast India (NEI) and South Peninsular India (SPI). ERA‐Interim reanalysis data are used throughout the study for three decades from 1989 to 2018. The results indicate that the multidecadal variability of precipitation has strengthened towards the mid‐decade (1999–2008), mainly attributable to moisture source transport from the Arabian Sea. These enhancement patterns of moisture sources have been widespread over Central India for all three decades. Moisture availability in the boundary layer highly affects the precipitation and it is found that 80% of the moisture above the boundary layer contributes >10% of monsoonal precipitation occurrences. Further, the NEI drying trend in monsoonal rainfall is found to be related to the consequent weakening of the land–ocean temperature gradient (LOTG) during all three decades in this region and the absence of low cloud cover over the region throughout the study period. In addition, the moisture sources are regulated by prevailing large‐scale features such as frequency of La Niña, northward shifting of low‐level jet and strengthening of Hadley cell. [ABSTRACT FROM AUTHOR]

The wintertime multidecadal land precipitation (WMLP) variability is obtained using the ensemble empirical mode decomposition, and then the leading spatial pattern and temporal evolution of the WMLP in the northern hemisphere during the period of 1891–2014 are investigated. The time series shows a pronounced enhancement of the variability since the mid‐1970s, coincident with the 1976/1977 climate transition, with the variance increasing from 0.33 in the former period (P1) to 2.57 in the latter period (P2). The spatial structure in P2 resembles that in the entire period. The precipitation anomaly is positive in the North America between 30° and 50°N, South America and southern Europe; while it is negative in the southern North America, majority of Russia, Arabian Peninsula, Iran and South China. However, the spatial pattern in P1 differs from the entire period. An Interdecadal Pacific Oscillation (IPO)‐like sea surface temperature (SST) variability in cold phase and an Atlantic Multidecadal Oscillation (AMO)‐like SST variability in warm phase are associated with the leading mode of WMLP in P2 and the entire period. Yet, the relationship of IPO and AMO on the WMLP is barely found in P1. The in‐phase change of IPO and AMO in P1 may contribute to the smaller amplitude of WMLP variability in P1. In the end, the coupled models from CMIP6 historical runs are evaluated. The good‐models average is superior to any single good model in capturing the observed spatial features of WMLP in both P1 and P2. The poor‐models average performs better than any single poor model. Both good‐ and poor‐models averages are able to reproduce the IPO‐ and AMO‐like SST variabilities in P2. [ABSTRACT FROM AUTHOR]

The Atlantic Meridional Overturning Circulation (AMOC) has an important role in the Earth's climate system, but it remains unclear how the instrumentally observed multidecadal variability of the Earth's climate is related to the AMOC. We carried out a comprehensive evaluation of five representative indicators of the AMOC, including one atmospheric index based on the effect of accumulated atmospheric forcing on the AMOC and four oceanographic indices using surface and subsurface oceanographic variables in the North Atlantic Ocean. We compared reconstructions of the AMOC against measurement records and analysed the relationships with the multidecadal variability of the annual mean surface air temperature anomalies over the North Atlantic, Northern Hemisphere and South Atlantic. All the AMOC indicators show a weakening trend during the most recent decade, which is in good agreement with the RAPID measurement record of the AMOC. Besides, the atmospheric reconstruction shows the best agreement with the observed low‐frequency variation in the AMOC since 2004. The multidecadal variability in hemispheric/regional surface temperatures is closely related to all the AMOC reconstructions and opposite relationships are observed between the North Atlantic and South Atlantic oceans. The multidecadal surface air temperature anomalies associated with the variations in the AMOC are most pronounced for the atmospheric reconstruction index, which leads the variations in the surface air temperature by 4–5 years. Among the oceanographic indicators, the reconstructions using subsurface oceanographic variables performed best in terms of the relationship with the variations in the multidecadal surface air temperature. [ABSTRACT FROM AUTHOR]

ABSTRACT We examine secular changes and multidecadal climate variability on a seasonal scale in northern France over the last 500 years and examine the extent to which they are driven by large-scale atmospheric variability. Multiscale trend analysis and segmentation procedures show statistically significant increases of winter and spring precipitation amounts in Paris since the end of the 19th century. This changes the seasonal precipitation distribution from one with a pronounced summer peak at the end of the Little Ice Age to an almost uniform distribution in the 20th century. This switch is linked to an early warming trend in winter temperature. Changes in spring precipitation are also correlated with winter precipitation for time scales greater than 50 years, which suggests a seasonal persistence. Hydrological modelling results show similar rising trends in river flow for the Seine at Paris. However, such secular trends in the seasonal climatic conditions over northern France are substantially modulated by irregular multidecadal (50-80 years) fluctuations. Furthermore, since the end of the 19th century, we find an increasing variance in multidecadal hydroclimatic winter and spring, and this coincides with an increase in the multidecadal North Atlantic Oscillation ( NAO) variability, suggesting a significant influence of large-scale atmospheric circulation patterns. However, multidecadal NAO variability has decreased in summer. Using Empirical Orthogonal Function analysis, we detect multidecadal North Atlantic sea-level pressure anomalies, which are significantly linked to the NAO during the Modern period. In particular, a south-eastward (south-westward) shift of the Icelandic Low (Azores High) drives substantial multidecadal changes in spring. Wetter springs are likely to be driven by potential changes in moisture advection from the Atlantic, in response to northward shifts of North Atlantic storm tracks over European regions, linked to periods of positive NAO. Similar, but smaller, changes in rainfall are observed in winter. [ABSTRACT FROM AUTHOR]

ABSTRACT Atmospheric and oceanic reanalyses are used widely by the climate science community. These products provide full three-dimensional state fields and gapless time series, along with the confidence of being constrained by observational measurements, for atmospheric scientists and oceanographers to use in analyses of the climate system. However, as ubiquitous as reanalysis data are, it is not often considered how a scarcity of measurements in certain poorly observed regions, or over the course of a long period of time in which the observational system has changed significantly, impacts the realism of the data. This study explores this question using tropical surface pressures from the Twentieth Century Reanalysis to hindcast an index of the Madden-Julian Oscillation ( MJO) over the 20th century. We show that by changing the choice of surface pressure predictor locations, and being aware of the observational measurements that have been assimilated by the reanalysis system, it is possible to control the estimated centennial-scale trend in MJO activity from nearly zero to an increase of 30% over the 20th century. We emphasize that this is an apparent trend as it arises solely from the use of reanalyzed surface pressures from locations that have either been poorly observed or have experienced significant changes in the observing system over the 20th century. This highlights the need to be aware of the observational measurements (or lack of them), particularly their density in space and time, that have been assimilated by a reanalysis system. [ABSTRACT FROM AUTHOR]

ABSTRACT Eastern China suffered a record-breaking heat wave throughout almost all of summer, during 2013. Recent studies found a discernible impact of anthropogenic forcing on this extreme heat wave event. In this study, we investigate the role of multidecadal variability ( MDV) in regulating the likelihood of the 2013 heat wave event. Using the ensemble empirical mode decomposition ( EEMD) method, we decomposed the heat wave index series into three components: (1) a non-linear secular trend ( ST), representing the long-term anthropogenic warming; (2) MDV; and (3) the residual high-frequency variability. For the period 1873-2013, the linear trend of heat wave strength ( HW-S, defined as the July-August mean temperatures) was 0.06 °C per decade, mainly because of the contribution of ST. MDV plays an important role in regulating changes over decades. ST and MDV together form a changing climate background ( CB) for extreme events. The 2013 heat wave event would be almost impossible under the pre-industrial CB but becomes likely under the long-term warming background, with a return period of longer than 103 years. When the positive phase of MDV is added to the secular warming background (the current CB), the return period of a super-heat wave such as the 2013 event becomes about 42 (12-103) years. The Atlantic Multidecadal Oscillation ( AMO) shows significant correlation with the MDV component of the HW-S in Shanghai. The correlation pattern between the AMO and MDV of July-August temperatures over the Northern Hemisphere is analysed to explain the AMO-Shanghai relationship. It is suggested that the likelihood of such an extreme event will increase with further long-term climate warming, modified by low-frequency oceanic variations such as the AMO. [ABSTRACT FROM AUTHOR]

ABSTRACT There is growing evidence of significant changes in components of the Antarctic climate system, an important issue given the influence Antarctica has on global climate. It is important to infer to what extent these regional changes could be attributed to human-induced processes and to what extent to natural variability. Standard methods such as linear trend estimates or piecewise linear trends can be inadequate because they may result in erratic, non-systematic results, particularly if different scales of variability are present in each record and various records are to be compared. The Orcadas Antarctic Station (Argentina), with daily surface meteorological observations since April 1903, provides Antarctica's longest observational record. This study analyses the Orcadas seasonal surface temperature variability. Multidecadal variability and short-term trends are studied to provide an improved assessment of climate evolution and necessary information for the determination of mechanisms driving regional/local change. A combined method using wavelet transform ( WT), non-linear statistical model approaches and derivative of fits is developed. This methodology is also applied for validation and comparison to the Gomez ice core oxygen isotope record for the 1857-2006 and 1903-2006 time intervals. Significant quasi 50-year and quasi 20-year variability bands were obtained, both for the quarterly and seasonal Orcadas temperature records, with warming (cooling) periods detected between 1903-1912, 1927-1961 and 1972-2004 (1912-1927 and 1962-1972). If seasons are considered, the only one with a fairly sustained warming is summer, where actual cooling is observed only at the beginning, prior to the early 1930s. Quasi 50-year variability was also detected in the Gomez record. Long periods are obtained in the model fits, longer than the time series, which varied with window length. Although not representing variability cycles, they could represent the best fit of the non-linear, non oscillating asymptotic stationary component of the series, i.e. a non-linear trend. [ABSTRACT FROM AUTHOR]