Influences of the seasonal Indian monsoons, 1790-1993 CE: Sub-annual sea surface temperature and precipitation reconstructed from laminated Pakistan Margin sediments

2020 ◽  
Author(s):  
Tiffany J. Napier ◽  
Lars Wӧrmer ◽  
Jenny Wendt ◽  
Andreas Lückge ◽  
Kai-Uwe Hinrichs
Keyword(s):  
Sediment Core ◽  
Temporal Resolution ◽  
River Runoff ◽  
Southern Oscillation ◽  
Power Spectra ◽  
Principal Component ◽  
Summer Monsoon Rainfall ◽  
Winter Monsoon ◽  
Sea Surface ◽  
Annual Range

<p>Sub-decadal to annual climate oscillations are particularly relevant to human climate perception, including such well-known phenomena as the seasonal monsoons and El Niño-Southern Oscillation (ENSO). To assess the variability of these oscillations in the past, proxies for climate parameters that are influenced by these oscillations (e.g., temperature, precipitation) and geologic materials with a temporal resolution able to record them are both needed. However, even in settings where these two criteria are met, the sample size needed for laboratory analysis can limit temporal resolution.</p><p>We utilize a novel mass spectrometry imaging technique to measure and map distributions of climate-relevant biomarkers (e.g., GDGTs, alkenones) from intact sediment core surfaces in sub-mm increments, unlocking the ability to reconstruct sub-annual paleoclimate. These same sediment sample surfaces are analyzed with micro-XRF mapping to enable congruent examination of complementary elemental- and biomarker-derived paleoenvironmental proxies at ultra-high spatial resolution, both down-core and along-lamination.</p><p>We applied our biomarker and elemental mapping techniques to annually-laminated Pakistan Margin (northeastern Arabian Sea) sediment core SO90-58KG, spanning 1790-1993 CE. Laminated Pakistan Margin marine sediments are excellent archives of past climate and oceanographic conditions that are influenced by the summer (Southwest) and winter (Northeast) monsoons of India. We measured alkenones and GDGTs at 200 µm resolution, and elemental abundances at 50 µm resolution. Reconstructed sea surface temperatures (SSTs) were calculated from alkenone (U<sup>K'</sup><sub>37</sub>) and GDGT (CCaT) ratios, respectively, with sample resolution up to four points per year. Principal component analysis was applied to the elemental measurements. The first principal component (PC1) is associated with siliciclastic elements (Al, Si, K, Ti, Fe), and is used as a proxy for sub-annual precipitation-driven river runoff.</p><p>Reconstructed SSTs for both biomarker proxies contain congruent trends, and align with the annual range of instrumental measurements (23 to 30 °C). The annual cycles in SST, with low temperatures driven by mixing during the winter monsoon, are prominent in the time series and highly significant in their power spectra. Using this annual cycle in SST and our paired elemental measurements, we determine the season(s) of river runoff. PC1 is typically highest when SST is low, suggesting runoff/deposition usually occurs during the winter monsoon, consistent with precipitation from westerly storms. However, some years contain PC1 peaks that occur in-phase with warm SSTs, suggesting expansion of summer monsoon rainfall west of Karachi during these years. This work demonstrates the cutting edge of high-resolution paleoclimate science, and provides new insights into the variability of the Indian monsoon from its sensitive western edge.</p>

scholarly journals Relationships between Southeast Australian Temperature Anomalies and Large-Scale Climate Drivers

2014 ◽  
Vol 27 (4) ◽  
pp. 1395-1412 ◽  
Author(s):  
Alexandre O. Fierro ◽  
Lance M. Leslie
Keyword(s):  
Large Scale ◽  
Southern Oscillation ◽  
Power Spectra ◽  
Warm Season ◽  
Principal Component ◽  
Temperature Variability ◽  
Maximum Temperature ◽  
Past Century ◽  
Tasman Sea ◽  
Australian Continent

Abstract Over the past century, particularly after the 1960s, observations of mean maximum temperatures reveal an increasing trend over the southeastern quadrant of the Australian continent. Correlation analysis of seasonally averaged mean maximum temperature anomaly data for the period 1958–2012 is carried out for a representative group of 10 stations in southeast Australia (SEAUS). For the warm season (November–April) there is a positive relationship with the El Niño–Southern Oscillation (ENSO) and the Pacific decadal oscillation (PDO) and an inverse relationship with the Antarctic Oscillation (AAO) for most stations. For the cool season (May–October), most stations exhibit similar relationships with the AAO, positive correlations with the dipole mode index (DMI), and marginal inverse relationships with the Southern Oscillation index (SOI) and the PDO. However, for both seasons, the blocking index (BI, as defined by M. Pook and T. Gibson) in the Tasman Sea (160°E) clearly is the dominant climate mode affecting maximum temperature variability in SEAUS with negative correlations in the range from r = −0.30 to −0.65. These strong negative correlations arise from the usual definition of BI, which is positive when blocking high pressure systems occur over the Tasman Sea (near 45°S, 160°E), favoring the advection of modified cooler, higher-latitude maritime air over SEAUS. A point-by-point correlation with global sea surface temperatures (SSTs), principal component analysis, and wavelet power spectra support the relationships with ENSO and DMI. Notably, the analysis reveals that the maximum temperature variability of one group of stations is explained primarily by local factors (warmer near-coastal SSTs), rather than teleconnections with large-scale drivers.

scholarly journals Characteristics of African rainfall – An update

MAUSAM ◽  
2021 ◽  
Vol 50 (2) ◽  
pp. 159-176
Author(s):  
R. P. KANE
Keyword(s):  
El Niño ◽  
Monsoon Rainfall ◽  
Indian Summer Monsoon Rainfall ◽  
El Nino ◽  
Southern Oscillation ◽  
Rainfall Variability ◽  
Summer Monsoon Rainfall ◽  
Sea Surface ◽  
Continental Scale ◽  
African Rainfall

Each year during 1901-1990 was characterized as having an El Nino (EN) or Southern Oscillation minimum (SO) or warm (W) or cold (C) waters in east equatorial Pacific sea surface or any combination of these, or none (non-events). In contrast to Indian summer monsoon rainfall which showed a very good association between ENSOW type years and droughts, none of the African regions showed any significant, consistent relationship with any combination, except S. Africa where a slight bias for droughts was observed during El Nino years.   When departures in specific regions were compared, often there was lack of coherence within regions. For years when departures in every region could be classified as positive or negative, all type of teleconnections between W. Africa, E. Africa and S. Africa were seen and no preponderance was observed for continental scale floods or droughts, nor for opposite depart for equator and subtropics.   Five-year running averages indicated long intervals of positive departures preceded or followed by long intervals of droughts, with average spacings of -24 years for W. Africa and E. Africa (but phases not matching) and of -17 years for S. Africa. This seems to be a basis feature of African rainfall variability.

Interannual to Decadal Predictability of Tropical and North Pacific Sea Surface Temperatures

2007 ◽  
Vol 20 (11) ◽  
pp. 2333-2356 ◽  
Author(s):  
Matthew Newman
Keyword(s):  
North Pacific ◽  
Time Scale ◽  
Decadal Variability ◽  
Southern Oscillation ◽  
Power Spectra ◽  
Sea Surface ◽  
Intergovernmental Panel ◽  
Long Memory Process ◽  
Decay Times ◽  
Spectral Peaks

Abstract A multivariate empirical model is used to show that predictability of the dominant patterns of tropical and North Pacific oceanic variability, El Niño–Southern Oscillation (ENSO), and the Pacific decadal oscillation (PDO), is mostly limited to little more than a year, despite the presence of spectral peaks on decadal time scales. The model used is a linear inverse model (LIM) derived from the observed simultaneous and 1-yr lag correlation statistics of July–June-averaged SST from the Hadley Centre Global Sea Ice and Sea Surface Temperature (HadISST) dataset for the years 1900–2002. The model accurately reproduces the power spectra of the data, including interannual and interdecadal spectral peaks that are significant relative to univariate red noise. Eigenanalysis of the linear dynamical operator yields propagating eigenmodes that correspond to these peaks but have very short decay times and, thus, limited predictability. Longer-term predictability does exist, however, due to two stationary eigenmodes that are more weakly damped. These eigenmodes do not strongly correspond to the canonical ENSO and PDO patterns. Instead, one is similar to the 1900–2002 trend and might represent anthropogenic effects, while the second represents multidecadal fluctuations of a pattern that potentially represents natural decadal variability; however, neither attribution can be made unambiguously with the analysis presented in this paper. Predictability of these two stationary eigenmodes is significantly enhanced by tropical–North Pacific coupling. Neither stationary eigenmode is well captured in the control run of any coupled GCM in the CMIP-3 project of the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4), perhaps because in all of the GCMs tropical SST decadal variability is too weak and North Pacific SSTs are too independent of the Tropics. A key implication of this analysis is that the PDO may represent not a single physical mode but rather the sum of several phenomena, each of which represents a different red noise with its own autocorrelation time scale and spatial pattern. The sum of these red noises can give rise to apparent PDO “regime shifts” and seeming characteristics of a long memory process. Such shifts are not predictable beyond the time scale of the most rapidly decorrelating noise, less than two years, although the expected duration of regimes may be determined from the relative amplitudes of different eigenmodes.

Multiscale Variability of the River Runoff System in China and Its Long-Term Link to Precipitation and Sea Surface Temperature

2005 ◽  
Vol 6 (4) ◽  
pp. 550-570 ◽  
Author(s):  
Yongkang Xue ◽  
Jinjun Ji ◽  
Shufen Sun ◽  
Guoxiong Wu ◽  
K-M. Lau ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
River Runoff ◽  
El Niño ◽  
El Nino ◽  
Southern Oscillation ◽  
Sea Surface ◽  
Delayed Response ◽  
Continental Scale ◽  
Runoff Depth

Abstract This is an exploratory study to investigate the spatial and temporal characteristics of east China’s (EC) river runoff and their relationship with precipitation and sea surface temperature (SST) at the continental scale. Monthly mean data from 72 runoff stations and 160 precipitation stations in EC, covering a period between 1951 and 1983, are used for this study. The station river runoff data have been spatially interpolated onto 1° grid boxes as runoff depth based on an extracted drainage network. Comparing runoff depth with precipitation shows that seasonal variation in runoff is consistent with the development of the summer monsoon, including the delayed response of runoff in several subregions. The dominant spatial scales and temporal patterns of summer runoff and precipitation are studied with empirical orthogonal function (EOF) analysis and wavelet analyses. The analyses show interannual, biennial, and longer-term variations in the EOF modes. South–north dipole anomaly patterns for the first two runoff EOF’s spatial distributions have been identified. The first/second runoff principal components (PCs) are highly correlated with the second/first precipitation PCs, respectively. The summer runoff’s EOF PCs also show significant correlations with the multivariate El Niño–Southern Oscillation index (MEI) of the summer and winter months, while the summer precipitation PCs do not. Statistic analysis shows that EOF1 of runoff and EOF2 of precipitation are related to El Niño, while EOF2 of runoff and EOF1 of precipitation are related to a dipole SST anomaly over the northwestern Pacific. The interdecadal relationship between summer runoff, precipitation, and SST variability is further studied by singular value decomposition (SVD) analysis. Pronounced warming (SST) and drying (runoff) trends in first SVD PCs have been identified. These SVDs are used to reconstruct a decadal anomaly pattern, which produces flooding in part of the Chang Jiang River basin and dryness in the northern EC, consistent with observations.

scholarly journals Summer monsoon rainfall over Bangladesh in relation to Multivariate ENSO Index

MAUSAM ◽  
2021 ◽  
Vol 51 (3) ◽  
pp. 255-260
Author(s):  
O. P. SINGH ◽  
TARIQ MASOOD ALI KHAN ◽  
SAZEDUR RAHMAN ◽  
SALAH UDDIN
Keyword(s):  
Monsoon Rainfall ◽  
Southern Oscillation ◽  
Monsoon Season ◽  
Surface Wind ◽  
Surface Air Temperature ◽  
Principal Component ◽  
Complete Information ◽  
Summer Monsoon Rainfall ◽  
Multivariate Enso Index ◽  
Enso Index

The relationship between monthly rainfall over Bangladesh during monsoon season and bi-monthly Multivariate ENSO Index (MEI) pertaining to the period from first week of previous month to first week of the month under consideration, has been investigated. The MEI is calculated as the first Principal Component (PC) of six variables over the tropical Pacific, viz., sea surface temperature, sea level pressure, zonal and meridional components of the surface wind, surface air temperature and total cloudiness fraction of the sky. The MEI values for prognostic purposes are available by the first week of every month. MEI is better for monitoring ENSO than other indices like Southern Oscillation Index (SOI) or various SST indices as it integrates complete information on ENSO and reflects the nature of complete ocean atmosphere system. Positive values of MEI indicate warm ENSO phase (EI-Nino) and negative ones represent cold phase (La-Nina).   The results of the present study show that June rainfall of Bangladesh is adversely affected by the ENSO. But interestingly Bangladesh seems to receive more than normal rainfall during August of ENSO years. ENSO does not seem to have any significant adverse impact on July and September rainfall of Bangladesh. The results of the study may find applications in foreshadowing monsoon rainfall over Bangladesh on a monthly scale.

scholarly journals Seasonal-To-Interannual Variability of Sea-Surface Temperatures in The Inter-Americas Seas: Pattern-Dependent Biases in The Regional Ocean Modeling System

2021 ◽  
Vol 13 (2) ◽  
pp. 20
Author(s):  
Ana Lucia Caicedo Laurido ◽  
Ángel G. Muñoz Solórsano ◽  
Xandre Chourio ◽  
Cristian Andrés Tobar Mosquera ◽  
Sadid Latandret
Keyword(s):  
Surface Temperature ◽  
Southern Oscillation ◽  
Geographical Area ◽  
Principal Component ◽  
Heat Fluxes ◽  
Western Hemisphere ◽  
Ocean Modeling ◽  
Sea Surface ◽  
Regional Ocean Modeling System ◽  
The Caribbean

The Inter-Americas Seas (IAS), involving the Gulf of Mexico, the Caribbean and a section of the eastern tropical Pacific Ocean bordering Central America, Colombia and Ecuador, exhibits very active ocean-land-atmosphere interactions that impact socio-economic activities within and beyond the region, and that are still not well understood or represented in state-of-the-art models. On seasonal-to-interannual timescales, the main source of variability of this geographical area is related to interactions between the Pacific and the Atlantic oceans, involving to anomalous sea-surface temperature (SST) patterns like El Niño-Southern Oscillation (ENSO), and regional features in the Caribbean linked to the bi-modal seasonality of the Caribbean Low-Level Jet. This study investigates seasonal-to-interannual IAS surface-temperature anomalies in observations, and their representation in am eddy-permitting, 1/9o-resolution simulation using the Regional Ocean Modeling System (ROMS), interannually-forced by the Climate Forecast System Reanalysis. Here, rather than analyzing model biases locally (i.e., gridbox-by-gridbox), a non-local SST pattern-based diagnostic was conducted via a principal component analysis. The approach allowed to identify magnitude, variance and spatial systematic errors in SST patterns related to the Western Hemisphere Warm Pool, ENSO, the Inter-American Seas Dipole, and several other variability modes. These biases are mainly related to errors in surface heat fluxes, misrepresentation of air-sea interactions impacting surface latent cooling in the Caribbean, and too strong sub-surface thermal stratification, mostly off the coast of Ecuador and northern Peru.

scholarly journals Interannual Variability and Trends in Sea Surface Temperature, Lower and Middle Atmosphere Temperature at Different Latitudes for 1980–2019

Atmosphere ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 454
Author(s):  
Andrew R. Jakovlev ◽  
Sergei P. Smyshlyaev ◽  
Vener Y. Galin
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Lower Stratosphere ◽  
Middle Atmosphere ◽  
Southern Oscillation ◽  
Lower Troposphere ◽  
Reanalysis Data ◽  
Sea Surface ◽  
The Arctic ◽  
The Impact

The influence of sea-surface temperature (SST) on the lower troposphere and lower stratosphere temperature in the tropical, middle, and polar latitudes is studied for 1980–2019 based on the MERRA2, ERA5, and Met Office reanalysis data, and numerical modeling with a chemistry-climate model (CCM) of the lower and middle atmosphere. The variability of SST is analyzed according to Met Office and ERA5 data, while the variability of atmospheric temperature is investigated according to MERRA2 and ERA5 data. Analysis of sea surface temperature trends based on reanalysis data revealed that a significant positive SST trend of about 0.1 degrees per decade is observed over the globe. In the middle latitudes of the Northern Hemisphere, the trend (about 0.2 degrees per decade) is 2 times higher than the global average, and 5 times higher than in the Southern Hemisphere (about 0.04 degrees per decade). At polar latitudes, opposite SST trends are observed in the Arctic (positive) and Antarctic (negative). The impact of the El Niño Southern Oscillation phenomenon on the temperature of the lower and middle atmosphere in the middle and polar latitudes of the Northern and Southern Hemispheres is discussed. To assess the relative influence of SST, CO2, and other greenhouse gases’ variability on the temperature of the lower troposphere and lower stratosphere, numerical calculations with a CCM were performed for several scenarios of accounting for the SST and carbon dioxide variability. The results of numerical experiments with a CCM demonstrated that the influence of SST prevails in the troposphere, while for the stratosphere, an increase in the CO2 content plays the most important role.

Diagnosing Sources of U.S. Seasonal Forecast Skill

2006 ◽  
Vol 19 (13) ◽  
pp. 3279-3293 ◽  
Author(s):  
X. Quan ◽  
M. Hoerling ◽  
J. Whitaker ◽  
G. Bates ◽  
T. Xu
Keyword(s):  
Air Temperature ◽  
Southern Oscillation ◽  
Surface Air Temperature ◽  
Seasonal Forecast ◽  
Forecast Skill ◽  
Diagnostic Methods ◽  
Sea Surface ◽  
Empirical Methods ◽  
Spatial Coverage ◽  
Sst Anomalies

Abstract In this study the authors diagnose the sources for the contiguous U.S. seasonal forecast skill that are related to sea surface temperature (SST) variations using a combination of dynamical and empirical methods. The dynamical methods include ensemble simulations with four atmospheric general circulation models (AGCMs) forced by observed monthly global SSTs from 1950 to 1999, and ensemble AGCM experiments forced by idealized SST anomalies. The empirical methods involve a suite of reductions of the AGCM simulations. These include uni- and multivariate regression models that encapsulate the simultaneous and one-season lag linear connections between seasonal mean tropical SST anomalies and U.S. precipitation and surface air temperature. Nearly all of the AGCM skill in U.S. precipitation and surface air temperature, arising from global SST influences, can be explained by a single degree of freedom in the tropical SST field—that associated with the linear atmospheric signal of El Niño–Southern Oscillation (ENSO). The results support previous findings regarding the preeminence of ENSO as a U.S. skill source. The diagnostic methods used here exposed another skill source that appeared to be of non-ENSO origins. In late autumn, when the AGCM simulation skill of U.S. temperatures peaked in absolute value and in spatial coverage, the majority of that originated from SST variability in the subtropical west Pacific Ocean and the South China Sea. Hindcast experiments were performed for 1950–99 that revealed most of the simulation skill of the U.S. seasonal climate to be recoverable at one-season lag. The skill attributable to the AGCMs was shown to achieve parity with that attributable to empirical models derived purely from observational data. The diagnostics promote the interpretation that only limited advances in U.S. seasonal prediction skill should be expected from methods seeking to capitalize on sea surface predictors alone, and that advances that may occur in future decades could be readily masked by inherent multidecadal fluctuations in skill of coupled ocean–atmosphere systems.

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