scholarly journals Troubles with teleconnections

2021 ◽  
Author(s):  
Radan Huth ◽  
Martin Hynčica ◽  
Vladimír Piskala ◽  
Lucie Pokorná
Keyword(s):  
Atmospheric Circulation ◽  
Climate Models ◽  
Low Frequency ◽  
Principal Component ◽  
Covariance Structures ◽  
Correct Interpretation ◽  
Sea Level Pressure ◽  
Different Seasons ◽  
Time Periods ◽  
Positive Correlations

<p>Rotated principal component analysis (RPCA) is a commonly used tool to detect modes of low-frequency atmospheric circulation variability, also referred to as teleconnections. Teleconnections manifest themselves as distant areas of high negative or positive correlations in sea level pressure, geopotential height, or another variable describing atmospheric circulation. For outputs of RPCA to be valid representations of teleconnections, their spatial patterns (loadings) must correspond to underlying correlation / covariance structures, that is, be in agreement with autocorrelation maps.</p><p>When comparing teleconnections identified in different datasets (e.g., between reanalyses, between outputs of climate models, between different periods, between different seasons), the spatial similarity of loadings is evaluated and quantified; if it is low, the datasets are said to disagree in the representation of a particular teleconnection. However, things appear to be less straightforward: It may happen that although the loadings pertaining to the same teleconnection differ, the maps of correlations with the action centres (i.e., points with highest positive or negative loadings) are identical. This may suggest that while the autocorrelation structures are the same in the two datasets, they appear with different weight (intensity). This issue appears to be unrelated to uncertainty due to the number of principal components to rotate; it typically occurs for various reasonable numbers of components.</p><p>In our contribution, we (i) introduce the above described issue on several examples (RPCA of different reanalyses, of sliding time periods, and of sliding 93-day seasons), (ii) discuss what is a correct interpretation of such cases (should we consider the teleconnections to be equal or different when the autocorrelation maps agree but the loadings disagree?), and (iii) suggest possible ways out of it (to use oblique instead of orthogonal rotation, to return back to autocorrelation maps).</p>

scholarly journals Global multi-year O<sub>3</sub>-CO correlation patterns from models and TES satellite observations

2011 ◽  
Vol 11 (12) ◽  
pp. 5819-5838 ◽  
Author(s):  
A. Voulgarakis ◽  
P. J. Telford ◽  
A. M. Aghedo ◽  
P. Braesicke ◽  
G. Faluvegi ◽  
...  
Keyword(s):  
Climate Models ◽  
Central Africa ◽  
Global Scale ◽  
Tropospheric Chemistry ◽  
Different Seasons ◽  
Northern Pacific ◽  
Positive Correlations ◽  
The Tropics ◽  
Model C ◽  
Single Figure

Abstract. The correlation between measured tropospheric ozone (O3) and carbon monoxide (CO) has been used extensively in tropospheric chemistry studies to explore the photochemical characteristics of different regions and to evaluate the ability of models to capture these characteristics. Here, we present the first study that uses multi-year, global, vertically resolved, simultaneous and collocated O3 and CO satellite (Tropospheric Emission Spectrometer) measurements, to determine this correlation in the middle/lower free troposphere for two different seasons, and to evaluate two chemistry-climate models. We find results that are fairly robust across different years, altitudes and timescales considered, which indicates that the correlation maps presented here could be used in future model evaluations. The highest positive correlations (around 0.8) are found in the northern Pacific during summer, which is a common feature in the observations and the G-PUCCINI model. We make quantitative comparisons between the models using a single-figure metric (C), which we define as the correlation coefficient between the modeled and the observed O3-CO correlations for different regions of the globe. On a global scale, the G-PUCCINI model shows a good performance in the summer (C=0.71) and a satisfactory performance in the winter (C=0.52). It captures midlatitude features very well, especially in the summer, whereas the performance in regions like South America or Central Africa is weaker. The UKCA model (C=0.46/0.15 for July–August/December–January on a global scale) performs better in certain regions, such as the tropics in winter, and it captures some of the broad characteristics of summer extratropical correlations, but it systematically underestimates the O3-CO correlations over much of the globe. It is noteworthy that the correlations look very different in the two models, even though the ozone distributions are similar. This demonstrates that this technique provides a powerful global constraint for understanding modeled tropospheric chemical processes. We investigated the sources of the correlations by performing a series of sensitivity experiments. In these, the sign of the correlation is, in most cases, insensitive to removing different individual emissions, but its magnitude changes downwind of emission regions when applying such perturbations. Interestingly, we find that the O3-CO correlation does not solely reflect the strength of O3 photochemical production, as often assumed by earlier studies, but is more complicated and may reflect a mixture of different processes such as transport.

scholarly journals Temperature extremes in Europe: overview of their driving atmospheric patterns

2012 ◽  
Vol 12 (5) ◽  
pp. 1671-1691 ◽  
Author(s):  
C. Andrade ◽  
S. M. Leite ◽  
J. A. Santos
Keyword(s):  
Atmospheric Circulation ◽  
Large Scale ◽  
Principal Component ◽  
Temperature Extremes ◽  
Cold Night ◽  
Sea Level Pressure ◽  
Atmospheric Flow ◽  
Atlantic Sector ◽  
Driving Mechanisms ◽  
Large Scale Atmospheric Circulation

Abstract. As temperature extremes have a deep impact on environment, hydrology, agriculture, society and economy, the analysis of the mechanisms underlying their occurrence, including their relationships with the large-scale atmospheric circulation, is particularly pertinent and is discussed here for Europe and in the period 1961–2010 (50 yr). For this aim, a canonical correlation analysis, coupled with a principal component analysis (BPCCA), is applied between the monthly mean sea level pressure fields, defined within a large Euro-Atlantic sector, and the monthly occurrences of two temperature extreme indices (TN10p – cold nights and TX90p – warm days) in Europe. Each co-variability mode represents a large-scale forcing on the occurrence of temperature extremes. North Atlantic Oscillation-like patterns and strong anomalies in the atmospheric flow westwards of the British Isles are leading couplings between large-scale atmospheric circulation and winter, spring and autumn occurrences of both cold nights and warm days in Europe. Although summer couplings depict lower coherence between warm and cold events, important atmospheric anomalies are key driving mechanisms. For a better characterization of the extremes, the main features of the statistical distributions of the absolute minima (TNN) and maxima (TXX) are also examined for each season. Furthermore, statistically significant downward (upward) trends are detected in the cold night (warm day) occurrences over the period 1961–2010 throughout Europe, particularly in summer, which is in clear agreement with the overall warming.

Observed and simulated (CMIP5 and CMIP6) early- to late-winter evolution of North Atlantic atmospheric variability and links to the ocean

2020 ◽  
Author(s):  
Thomas Bracegirdle
Keyword(s):  
Atmospheric Circulation ◽  
North Atlantic ◽  
Climate Models ◽  
Decadal Variability ◽  
Low Frequency ◽  
Late Winter ◽  
Atmospheric Variability ◽  
Sst Variability ◽  
Early Results ◽  
Depth Analysis

&lt;p&gt;Research to date has shown strong multi-decadal variability of the North Atlantic Oscillation (NAO) in late winter, particularly in March when correlations to North Atlantic (NA) ocean variability (Atlantic multi-decadal variability (AMV)) are particularly strong. This late-winter low-frequency atmospheric variability appears too weak in the majority of climate models across a range of indices of North Atlantic large-scale atmospheric circulation. It appears that models do not successfully reproduce responses to either (or both) proximal sea-surface temperature (SST) variability at mid-latitudes or teleconnections to SST variability in the sub tropics.&amp;#160;&lt;/p&gt;&lt;p&gt;Here, an in-depth analysis of the winter evolution of multiple indices of North Atlantic mid-latitude atmospheric circulation will be presented based on both re-analysis data and historical simulations of coupled climate models (CMIP5 and CMIP6). The atmospheric indices assessed will include the NAO, speed and latitude of the NA eddy driven jet and lower-tropospheric westerly wind strength in a region of maximum variability to the west of the UK. Results so far indicate that the CMIP6 models do not exhibit a clear change from CMIP5 in terms of the representation of low-frequency late-winter atmospheric variability. To diagnose in more detail possible origins of differences between observed and simulated variability, a detailed evaluation of early- to late-winter evolution in variability of the above indices will be presented, with an initial focus on observations (re-analysis and SST re-constructions) and incorporating the following questions: &amp;#160;&lt;br&gt;- Are there significant differences in the relative strength of linkages to tropical and extra-tropical SST variability across the different atmospheric indices?&amp;#160;&lt;br&gt;- Is the observed late-winter maximum in correlations between NA atmospheric indices and North Atlantic SSTs still apparent at sub-decadal timescales?&lt;br&gt;Initial results indicate that there are stronger tropical linkages for jet speed and that at sub-decadal timescales late winter is does not dominate in terms of correlations between atmospheric and SST variability. Updates on these early results will be presented along with implications of the results for differences between observed and simulated variability.&amp;#160;&lt;/p&gt;

Modes of Atmospheric Circulation Variability in the Northern Extratropics: A Comparison of Five Reanalyses

2020 ◽  
Vol 33 (24) ◽  
pp. 10707-10726
Author(s):  
Martin Hynčica ◽  
Radan Huth
Keyword(s):  
Atmospheric Circulation ◽  
Low Frequency ◽  
Principal Component ◽  
Reanalysis Data ◽  
Insufficient Amount ◽  
The Pacific ◽  
Correlation Structures ◽  
Southern Half ◽  
Grid Points ◽  
The Impact

AbstractModes of low-frequency circulation variability in the Northern Hemisphere extratropics are compared between five reanalyses. Circulation modes are detected by rotated principal component analysis (PCA) of monthly mean 500-hPa geopotential heights between 1957 and 2002, separately for individual seasons. The quantification of differences between reanalyses is based on the percentage of grid points (approximately corresponding to the percentage of area) where the spatial representations of a mode (loadings) significantly differ between reanalyses. The differences between surface-input reanalyses (20CRv2c, ERA-20C) and full-input reanalyses (NCEP-1, ERA-40, JRA-55) are larger than differences within the reanalysis groups in all seasons except for autumn. The causes of the differences are of two kinds. First, the differences may be inherent to PCA: namely, the spatial structure of the modes may be sensitive to the number of components rotated. This concerns only a few modes. Second, the differences may reflect real correlation structures in reanalysis data. We demonstrate that the differences concentrate in three or fewer modes in each season. The reanalysis most different from the rest is 20CRv2c, with the differences concentrating over the southern half of Asia and in the subtropical belt over the Pacific and adjacent southwestern North America. The 20CRv2c reanalysis disagrees from other reanalyses there predominantly before the 1980s, which points to the impact of insufficient amount of assimilated observations. On the contrary, ERA-20C exhibits a higher agreement with full-input reanalyses, which is why we recommend it for studies of atmospheric circulation over the entire twentieth century.

scholarly journals Global multi-year O<sub>3</sub>-CO correlation patterns from models and TES satellite observations

2011 ◽  
Vol 11 (2) ◽  
pp. 5079-5125 ◽  
Author(s):  
A. Voulgarakis ◽  
P. J. Telford ◽  
A. M. Aghedo ◽  
P. Braesicke ◽  
G. Faluvegi ◽  
...  
Keyword(s):  
Climate Models ◽  
Central Africa ◽  
Global Scale ◽  
Tropospheric Chemistry ◽  
Different Seasons ◽  
Northern Pacific ◽  
Positive Correlations ◽  
The Tropics ◽  
Model C ◽  
Single Figure

Abstract. The correlation between measured tropospheric ozone (O3) and carbon monoxide (CO) has been used extensively in tropospheric chemistry studies to explore the photochemical characteristics of different regions and to evaluate the ability of models to capture these characteristics. Here, we present the first study that uses multi-year, global, vertically resolved, simultaneous and collocated O3 and CO satellite (Tropospheric Emission Spectrometer) measurements, to determine this correlation in the middle/lower free troposphere for two different seasons, and to evaluate two chemistry-climate models. We find results that are fairly robust across different years, altitudes and timescales considered, which indicates that the correlation maps presented here could be used as benchmarks in future studies. The highest positive correlations (around 0.8) are found in the Northern Pacific during summer, which is a common feature in the observations and the G-PUCCINI model. We make quantitative comparisons between the models using a single-figure metric (C), which we define as the correlation coefficient between the modeled and the observed O3-CO correlations for different regions of the globe. On a global scale, the G-PUCCINI model shows a good performance in the summer (C=0.71) and a satisfactory performance in the winter (C=0.52). It captures midlatitude features very well, especially in the summer, whereas the performance in regions like South America or Central Africa is weaker. The UKCA model (C=0.46/0.15 for July–August/December–January on a global scale) performs better in certain regions, such as the tropics in winter, and it captures some of the broad characteristics of summer extratropical correlations, but it systematically underestimates the O3-CO correlations over much of the globe. It is noteworthy that the correlations look very different in the two models, even though the ozone distributions are similar. This demonstrates that this technique provides a powerful global constraint for understanding modeled tropospheric chemical processes. We investigated the sources of the correlations by performing a series of sensitivity experiments. In these, the sign of the correlation is, in most cases, insensitive to removing different individual emissions, but its magnitude changes downwind of emission regions when applying such perturbations. Interestingly, we find that the O3-CO correlation does not solely reflect the strength of O3 photochemical production, as often assumed by earlier studies, but is more complicated and reflects a mixture of different processes such as transport.

scholarly journals Multidecadal variability in seasonal mean sea level along the North Sea coast

2018 ◽  
Author(s):  
Thomas Frederikse ◽  
Theo Gerkema
Keyword(s):  
Sea Level ◽  
North Sea ◽  
Large Scale ◽  
Low Frequency ◽  
Principal Component ◽  
Sea Level Pressure ◽  
Mean Sea Level ◽  
Level Pressure ◽  
The North ◽  
The North Sea

Abstract. Seasonal deviations from annual-mean sea level in the North Sea region show a large low-frequency component with substantial variability at decadal and multi-decadal time scales. In this study, we quantify low-frequency seasonal variations from annual-mean sea level and look for drivers of this variability. The amplitude, as well as the temporal evolution of this multi-decadal variability shows substantial variations over the North Sea region, and this spatial pattern is similar to the well-known pattern of the influence of winds and pressure changes on sea level on higher frequencies. The largest low-frequency signals are found in the German Bight and along the Norwegian coast. We find that the variability is much stronger in winter and autumn than in other seasons, and that this winter and autumn variability is predominantly driven by wind and sea-level pressure anomalies which have their cause in large-scale atmospheric patterns. For the spring and summer seasons, only a small fraction of the observed variability can be explained by local and large-scale atmospheric changes. Large-scale atmospheric patterns have been derived from a principal component analysis of sea-level pressure. The first principal component of sea-level pressure over the North Atlantic Ocean, which is linked to the North Atlantic Oscillation (NAO), explains the largest fraction of winter-mean variability for most stations, while for some stations, the variability consists of a combination of multiple principal components. The low-frequency variability in season-mean sea level can manifest itself as trends in short records of seasonal sea level. For multiple stations around the North Sea, running-mean 40-year trends for autumn and winter sea level often exceed the long-term trends in annual mean sea level, while for spring and summer, the seasonal trends have a similar order of magnitude as the annual-mean trends. Removing the variability explained by atmospheric variability vastly reduces the seasonal trends, especially in winter and autumn.

scholarly journals Phenotypic Characterisation for Growth and Nut Characteristics Revealed the Extent of Genetic Diversity in Wild Macadamia Germplasm

Agriculture ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 680
Author(s):  
Thuy T. P. Mai ◽  
Craig M. Hardner ◽  
Mobashwer M. Alam ◽  
Robert J. Henry ◽  
Bruce L. Topp
Keyword(s):  
Genetic Diversity ◽  
Growth Traits ◽  
Principal Component ◽  
Conservation Strategies ◽  
Wild Germplasm ◽  
Phenotypic Characterisation ◽  
Wide Range ◽  
Genetic And Environmental Factors ◽  
Positive Correlations ◽  
Wild Accessions

Macadamia is a recently domesticated Australian native nut crop, and a large proportion of its wild germplasm is unexploited. Aiming to explore the existing diversity, 247 wild accessions from four species and inter-specific hybrids were phenotyped. A wide range of variation was found in growth and nut traits. Broad-sense heritability of traits were moderate (0.43–0.64), which suggested that both genetic and environmental factors are equally important for the variability of the traits. Correlations among the growth traits were significantly positive (0.49–0.76). There were significant positive correlations among the nut traits except for kernel recovery. The association between kernel recovery and shell thickness was highly significant and negative. Principal component analysis of the traits separated representative species groups. Accessions from Macadamia integrifolia Maiden and Betche, M. tetraphylla L.A.S. Johnson, and admixtures were clustered into one group and those of M. ternifolia F. Muell were separated into another group. In both M. integrifolia and M. tetraphylla groups, variation within site was greater than across sites, which suggested that the conservation strategies should concentrate on increased sampling within sites to capture wide genetic diversity. This study provides a background on the utilisation of wild germplasm as a genetic resource to be used in breeding programs and the direction for gene pool conservation.

scholarly journals Complex Principal Component Analysis of Antarctic Ice Sheet Mass Balance

2021 ◽  
Vol 13 (3) ◽  
pp. 480
Author(s):  
Jingang Zhan ◽  
Hongling Shi ◽  
Yong Wang ◽  
Yixin Yao
Keyword(s):  
Principal Component Analysis ◽  
Ice Sheet ◽  
Low Frequency ◽  
Principal Component ◽  
Equatorial Pacific ◽  
Mass Change ◽  
Frequency Variation ◽  
Periodic Signal ◽  
Time Frequency ◽  
The Antarctic

Ice sheet changes of the Antarctic are the result of interactions among the ocean, atmosphere, and ice sheet. Studying the ice sheet mass variations helps us to understand the possible reasons for these changes. We used 164 months of Gravity Recovery and Climate Experiment (GRACE) satellite time-varying solutions to study the principal components (PCs) of the Antarctic ice sheet mass change and their time-frequency variation. This assessment was based on complex principal component analysis (CPCA) and the wavelet amplitude-period spectrum (WAPS) method to study the PCs and their time-frequency information. The CPCA results revealed the PCs that affect the ice sheet balance, and the wavelet analysis exposed the time-frequency variation of the quasi-periodic signal in each component. The results show that the first PC, which has a linear term and low-frequency signals with periods greater than five years, dominates the variation trend of ice sheet in the Antarctic. The ratio of its variance to the total variance shows that the first PC explains 83.73% of the mass change in the ice sheet. Similar low-frequency signals are also found in the meridional wind at 700 hPa in the South Pacific and the sea surface temperature anomaly (SSTA) in the equatorial Pacific, with the correlation between the low-frequency periodic signal of SSTA in the equatorial Pacific and the first PC of the ice sheet mass change in Antarctica found to be 0.73. The phase signals in the mass change of West Antarctica indicate the upstream propagation of mass loss information over time from the ocean–ice interface to the southward upslope, which mainly reflects ocean-driven factors such as enhanced ice–ocean interaction and the intrusion of warm saline water into the cavities under ice shelves associated with ice sheets which sit on retrograde slopes. Meanwhile, the phase signals in the mass change of East Antarctica indicate the downstream propagation of mass increase information from the South Pole toward Dronning Maud Land, which mainly reflects atmospheric factors such as precipitation accumulation.

scholarly journals Sensory Profile of Chihuahua Cheese Manufactured from Raw Milk

2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
Sarai Villalobos-Chaparro ◽  
Erika Salas-Muñóz ◽  
Néstor Gutiérrez-Méndez ◽  
Guadalupe Virginia Nevárez-Moorillón
Keyword(s):  
Principal Component Analysis ◽  
Geographical Location ◽  
Principal Component ◽  
Raw Milk ◽  
Component Analysis ◽  
Sensory Profile ◽  
Different Seasons ◽  
Quantitative Descriptive ◽  
Traditional Manufacturing ◽  
Chihuahua Cheese

Chihuahua cheese is a local artisanal cheese traditionally produced from raw milk. When this cheese is produced with pasteurized milk, cheesemakers complain that there are differences in taste and aroma as compared with traditional manufacturing. This work aimed to obtain a descriptive sensory analysis of Chihuahua cheese manufactured with raw milk under traditional conditions. Samples were collected in five cheese dairies at two different seasons (summer and autumn), and a Quantitative Descriptive Sensorial Analysis was done by a panel of trained judges. For aroma descriptors, cooked descriptor showed differences between dairies, and whey was different among dairies and sampling seasons (P<0.01); diacetyl, fruity (P<0.01), as well as free fatty acids, nutty and sulphur (P<0.05) descriptors varied between seasons. For flavour descriptors, bitter perception was different between dairies and seasons (P<0.01). Salty and creamy cheese was also different among dairies (P<0.01). A Principal Component Analysis for differences among dairies and sampling season demonstrated that the first three components accounted for 90% of the variance; variables were more affected by the sampling seasons than by the geographical location or if the dairy was operated by Mennonites. Chihuahua cheese sensorial profile can be described as a semi-matured cheese with a bitter flavour, slightly salted, and with a cream flavour, with aroma notes associated with whey and sour milk. Principal Component Analysis demonstrated season influence on flavour and aroma characteristics.

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