Monthly Mean Sea Level Pressure Research Articles

Characteristics of Ocean Waves in the Southern Ocean and Seasonal Prediction of Difficulty for the Vessels Crossing the Westerlies

In this study, the European Centre for Medium-Range Weather Forecasts reanalysis v5 (ERA5) wave height reanalysis data and sea-level pressure data from two seasonal prediction systems were used to study the wave characteristics of the Southern Ocean and the seasonal prediction of difficulty for the vessels crossing the westerlies. The results show that significant wave height, extreme wave height, and extreme wave processes were all much stronger in austral winter than in austral summer, making it more difficult for vessels to cross the westerlies in winter. Furthermore, the difficulty of crossing the westerlies has increased over the last 40 years, except in areas east of South America. We found that the monthly frequency of crossing the westerlies between 100° E and 75° W could be accurately estimated point by point using the monthly mean sea-level pressure difference between 30° S and 65° S. With a 1-month lead, the multi-model forecasts gave a fairly accurate forecast of such sea-level pressure difference signal between 150° E to 60° W for target months from May to December. Based on these assessments, we could estimate the difficulty of crossing the westerlies in some specific months in advance using a seasonal prediction system. We also found that the seasonal prediction system produced inaccurate predictions of sea-level pressure at high latitudes, which is the main barrier to further accurately estimating the difficulty of crossing the westerlies. These findings may provide support for predicting difficulty for the vessels crossing the westerlies with a 1-month lead and can provide planning for the route and the allocation of material reserves.

An assessment of the atmospheric centers of action in the northern hemisphere winter

In the northern hemisphere, there are six permanent or semi-permanent atmospheric activity centers, namely the Icelandic Low, Aleutian Low, India Low, Mongolia High, North Pacific High, and North Atlantic High. The first four are semi-permanent action centers and the last two are permanent circulation systems. The India Low exists only during the summer. By using 160 years (1850–2009) of monthly mean sea level pressure data from the Hadley Centre in the UK, we conduct a comprehensive study of the five boreal winter atmospheric centers of action (ACAs). Based on a unified definition and a method determined in previous studies, we calculate the indices of areal coverage (S), intensity (P), and position of action center (λ c, φ c) for each of these five ACAs. Through an in-depth analysis of these indices and their relationships with climate variables, we evaluate the indices by describing and explaining areal climate anomalies, particularly precipitation and temperature anomalies in China. We show that (1) ACAs significantly influence the climate anomalies of surrounding areas (2) the influences of oceanic ACAs are larger and the intensity anomalies of ACAs have a greater impact than their location displacement, and (3) ACAs exert more control on temperature than they do on precipitation. For the two ACAs over the north Atlantic, the impacts of their intensities on the anomalies of temperature and precipitation are similar. For the two ACAs over the north Pacific, their influences are almost the opposite. The most influential ACA for climate anomalies in China during the boreal winter is HMO. When HMO is stronger, China has a colder winter and it is wetter in the north. With stronger ACAs in the upstream, i.e., the Icelandic Low and North Atlantic High, northern China has a warmer winter. The ACAs over the north Pacific exert little influence on climate anomalies in China during winter. The analyses presented in this paper provide a set of useful indices for defining and describing ACAs, and they suggest insightful applications for these indices.

A score‐based method for assessing the performance of GCMs: A case study of southeastern Australia

AbstractA multi‐criteria score‐based method is developed to assess General Circulation Model (GCM) performance at the regional scale. Application of the method assessing 25 GCM simulations of monthly mean sea level pressure (MSLP) and air temperature, and monthly and annual rainfall over the southeastern Australia region for 1960/1961–1999/2000 indicate that GCMs usually simulate monthly temperature better than monthly rainfall and mean sea level pressure. For example, the mean observed annual temperature for the study region is 16.7°C while the median and mean values of 25 GCMs are 16.8 and 16.9°C, respectively, and 24 GCMs (except BCC:CM1) can reproduce the annual cycle of temperature accurately, with a minimum correlation coefficient of 0.99. In contrast, the mean observed annual rainfall for the study region is 502 mm, whereas the GCM values vary from 195 to 807 mm, and 12 out of 25 GCMs produce a negative correlation coefficient of monthly rainfall annual cycle. However, GCMs overestimate trend magnitude for temperature, but underestimate for rainfall. The observed annual temperature trend is +0.007°C/yr, while both the median and mean GCM values are +0.013°C/yr, which is almost double the observed magnitude. The observed annual rainfall trend is +0.62mm/yr, while the median and mean values of 25 GCMs are 0.21and 0.36mm/yr, respectively. This demonstrates the advantages of using multi‐criteria to assess GCMs performance. The method developed in this study can easily be extended to different study regions and results can be used for better informed regional climate change impact analysis.

Temperature extremes in Europe: overview of their driving atmospheric patterns

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.

Reconsideration of the True versus Apparent Arctic Oscillation

Abstract The physical reality of the Arctic Oscillation (AO; or northern annular mode) is considered. The data used are mainly the monthly mean sea level pressure (SLP). A schematic figure is first presented to illustrate the relationship between the North Atlantic Oscillation (NAO)–Pacific–North American Oscillation (PNA) system and the AO–negative correlation mode between the Atlantic and the Pacific (AO–NCM) system. Although the NAO–PNA (apparent AO–NCM) and true AO–NCM systems give rise to the same EOFs, the probability density functions for the time coefficients of the two leading modes are different. Therefore, the discrimination of the two systems is possible. Several pieces of evidence indicate that, in the real world, the NAO–PNA and the AO–NCM are located on almost the same plane in phase space. This means that the NAO–PNA and AO–NCM systems have the same variations on the plane in common, implying that when the NAO–PNA system is real, the AO–NCM is unlikely to be real. Simple independent component analysis is carried out to distinguish between the true and apparent AO–NCM systems, indicating that the NAO and PNA are independent oscillations, that is, true ones. The analysis is extended to the winter mean SLP field, for which the EOF shows the NAO–PNA but not the AO–NCM. This may be due to the fact that the winter mean NAO and PNA patterns have little spatial correlation. Calculations using randomly selected samples also indicate that when the NAO and PNA patterns have little spatial correlation, the AO never appears as EOF1. All the preceding results show that almost all characteristics of the AO–NCM can be explained from those of the NAO–PNA. Hence it is concluded that the AO, which is extracted by EOF analysis from the temporarily independent but spatially overlapping variations of the NAO and PNA, is almost apparent.

North Atlantic climate variability from a self‐organizing map perspective

North Atlantic variability in general, and the North Atlantic Oscillation (NAO) in particular, is a long‐studied, very important but still not well‐understood problem in climatology. The recent trend to a higher wintertime NAO index was accompanied by an additional increase in the Azores High not coupled to changes in the Icelandic Low, as shown by a self‐organizing maps (SOMs) analysis of monthly mean DJF mean sea level pressure data from 1957 to 2002. SOMs are a nonlinear tool to optimally extract a user‐specified number of patterns or icons from an input data set and to uniquely relate any input data field to an icon, allowing analyses of occurrence frequencies and transitions complementary to principal component analysis (PCA). SOMs analysis of ERA‐40 data finds a North Atlantic “monopole” roughly colocated with the mean position of the Azores High, as well as the well‐known NAO dipole involving the Icelandic Low and the subtropical high. Little trend is shown in December, but the Azores High increased along with the NAO in January and February over the study interval, with implications for storminess in northwestern Europe. In short, our SOM‐based analyses of winter MSLP have both confirmed prior knowledge and expanded it through the relative ease of use and power with nonlinear systems of the SOM‐based approach to climatological analysis.

Arctic or North Atlantic Oscillation? Arguments based on the principal component analysis methodology

The Arctic Oscillation (AO) appears as the leading unrotated mode of principal component analysis (PCA) of monthly mean sea level pressure anomalies, whereas the North Atlantic Oscillation (NAO) results from rotated PCA, regardless of the number of PCs rotated. Three criteria are employed to decide whether the interpretation in terms of the NAO or AO should be preferred: the degree of simple structure, the similarity between the PC loadings and correlation/covariance maps, and the sensitivity to spatial subsampling. All these criteria favour, to a different extent, the interpretation in terms of the NAO. This is further supported by more general arguments. Therefore, the statistical arguments suggest that in interpreting the Northern Hemisphere circulation variability, the sectorial view, i.e. the NAO, should be preferred to the hemispheric view, i.e. the AO. Our analysis supports the idea expressed in other studies that the AO is rather a statistical artifact.

Relationships between sea‐level pressure and sea‐level height in the Camargue (French Mediterranean coast) by Vincent Moron and Albin Ullmann, pages 1531–1540, Volume 25, Issue 11, 2005

AbstractA statistical study of daily maximum sea‐level height at one station (Grau de la Dent) in the Camargue (Rhône delta, French Mediterranean coast) and daily sea‐level pressure (SLP) at 12 h UTC over the eastern North Atlantic is used to identify the meteorological conditions associated with sea‐level variations in the Camargue for the winters 1974–1975 to 2000–2001. Mean SLP composites during and 5 days before major surge events (defined as those with a daily maximum sea‐level height > 80 cm) suggest the dominant influence of storms, moving northwest to southeast across the North Atlantic and strengthening as they approach the Bay of Biscay. During such storms, strong onshore winds may persist for up to 4–5 days. These winds tend to strengthen from 3 days to 1 day before the surge events. The mean October–March correlation between daily maximum sea‐level height in the Camargue and SLP averaged over the Bay of Biscay (10°W–0° , 40° –50°N) is strong (r = − 0.69). A methodology is developed for assessing the low frequency SLP variability impact on sea‐level height in the Camargue. A cross‐validated linear regression is used to hindcast the interannual and intraseasonal variability of the monthly 75th and 90th percentiles of the daily maximum sea‐level height from the monthly mean SLP over the Bay of Biscay. The linear correlation between the cross‐validated hindcast and observed time series is 0.83 (0.77) for the 75th (90th) percentile over the 1974–1975 to 2000–2001 period. The mean bias error, reflecting systematic errors in predicting the monthly percentiles, is close to zero. Copyright © 2006 Royal Meteorological Society.

Glacier mass balance in southern norway modelled by circulation indices and spring‐summer temperatures ad 1781–2000

By stepwise regression analysis the accumulation, ablation, and equilibrium line altitude (ELA) were modelled by circulation indices and spring‐summer temperature on six Norwegian glaciers (Ålfotbreen, Nigardsbreen, Rembesdalsskåka, Storbreen, Hellstugubreen and Gråsubreen). The circulation indices were derived from a gridded monthly mean sea level pressure (MSLP) data set, whereas temperature series were derived from instrumental and proxy data.Analyses showed that accumulation on the western glaciers was strongly related to western airflow perpendicular to the main mountain range releasing precipitation on the glaciers. No other airflow variable significantly improved the regression. For the continental glaciers, circulating air in connection with low pressure systems was also found to be important. This may explain the lack of synchronicity in the glaciers' development in southern Norway during the Holocene. Accumulation was better modelled using the MSLP data set than by using the North Atlantic Oscillation (NAO) index as predictor.The decadal variations of accumulation, ablation, and ELA were analysed by a Gaussian low pass filter. The well‐known abundant accumulation on Norwegian glaciers during the early 1990s turned out to be unprecedented during the entire series (since 1781), whereas the accumulation of the 1960s has not been lower since the early 19th century according to model results. Ablation increased significantly from the mid‐19th century to the 1930s. The ablation maximum during the 1930s has not yet been exceeded. Also the 1930s show extremely high ELA values.

Predicting the Onset of Australian Winter Rainfall by Nonlinear Classification

Abstract A method for predicting the timing of winter rains is presented, making no assumptions about the functional form of any relationships that may exist. Ideas built on classification and regression trees and machine learning are used to develop robust predictive rules. These methods are applied in a case study to predict the timing of winter rain in five farming towns in the southwest of Western Australia. The variables used to construct the model are mean monthly sea surface temperatures (SSTs) over a 72-cell grid in the Indian Ocean, Perth monthly mean sea level pressure (MSLP), and monthly values of the Southern Oscillation index (SOI). A predictive model is constructed from data over the period 1949–99. This model correctly classifies the onset of the winter rains approximately 80% of the time with SST variables proving to be the most important in deriving the predictions. Further analysis indicates a change point in the mid-1970s, a well-known phenomenon in the region. The prediction rates are significantly worse after 1975. Furthermore, the important region of the Indian Ocean, in terms of SSTs for prediction, moves from the Tropics down toward the Southern Ocean after this date.

Relationship between sea-level pressure and sea-level height in the Camargue (French Mediterranean coast)

A statistical study of daily maximum sea-level height at one station (Grau de la Dent) in the Camargue (Rhone delta, French Mediterranean coast) and daily sea-level pressure (SLP) at 12 h UTC over the eastern North Atlantic is used to identify the meteorological conditions associated with sea-level variations in the Camargue for the winters 1974–75 to 2000–01. Mean SLP composites during and 5 days before major surge events (defined as those with a daily maximum sea-level height >80 cm) suggest the dominant influence of storms, moving northwest to southeast across the North Atlantic and strengthening as they approach the Bay of Biscay. During such storms, strong onshore winds may persist for up to 4–5 days. These winds tend to strengthen from 3 days to 1 day before the surge events. The mean October–March correlation between daily maximum sea-level height in the Camargue and SLP averaged over the Bay of Biscay (10°W–0°, 40–50°N) is strong (r = 0.69). A methodology is developed for assessing the low-frequency SLP variability impact on sea-level height in the Camargue. A cross-validated linear regression is used to hindcast the interannual and intraseasonal variability of the monthly 75th and 90th percentiles of the daily maximum sea-level height from the monthly mean SLP over the Bay of Biscay. The linear correlation between the cross-validated hindcast and observed time series is 0.83 (0.77) for the 75th (90th) percentile on the 1974–75 to 2000–01 period. The mean bias error, reflecting systematic errors in predicting the monthly percentiles, is close to zero. Copyright © 2005 Royal Meteorological Society

A Simplified Linear Framework for Interpreting Patterns of Northern Hemisphere Wintertime Climate Variability

Abstract The principal patterns of variability of the extratropical Northern Hemisphere (NH) wintertime circulation are examined, based on 42 yr of data from the NCAR–NCEP reanalyses. The two-dimensional phase space defined by the two leading PCs of the monthly mean sea level pressure (SLP) field poleward of 20°N is used as a basis for surveying the structure of the geopotential height and surface air temperature (SAT) fields. Together these two patterns account for roughly half the variance of SLP on interannual time scales and longer, and virtually all the planetary-scale SLP trends over the 42-yr period of record. The leading EOF corresponds to the NH annular mode (NAM), and the second EOF resembles the Pacific–North America (PNA) pattern. The leading EOF of the monthly mean geopotential height field at various levels throughout the troposphere and lower stratosphere is well represented by linear combinations of these two SLP patterns, as are the intraseasonal and interannual SLP fields, the NAM, the N...

A reconstruction of Madras (Chennai) mean sea‐level pressure using instrumental records from the late 18th and early 19th centuries

AbstractIn and around the Indian Ocean basin, historical records documenting observations of climatic variables such as rainfall, surface‐air and sea‐surface temperature, and atmospheric pressure can be found in various publications by the European colonial powers of the time. However, it is only the most accessible of these records from the mid‐to‐late 1800s that have been transferred into electronic form and used in any contemporary climatic studies. A longer‐term set of observations from the observatory at Madras (now Chennai), set up by the English East India Company, was found to have the potential to provide useful climatic data back to 1796.In this study, atmospheric pressure data recorded at the Madras Observatory, and at other climate stations in or near Madras, and in India, were recovered and examined for their quality and veracity. From the old observatory publications, pre‐1841 records (that is prior to those documented in standard data compilations) were quality controlled, and the full record from 1796 was then reduced to mean sea level, standard gravity and 24 h means to produce a 205 year set of monthly observations. This monthly mean sea‐level pressure (MSLP) record was correlated with monthly or seasonally stratified instrumental indices of the El Niño southern oscillation (ENSO) phenomenon and all‐India rainfall, and a seasonally stratified palaeoclimatic reconstruction of the southern oscillation index. In general, the Madras MSLP record is shown to be robust over time, and thus a useful long‐term measure of fluctuations in the ENSO phenomenon across the Indian Ocean basin and of the Indian summer monsoon. Copyright © 2002 Royal Meteorological Society.

Winter surface pressure anomalies over Eurasia and Indian summer monsoon

In this study, the spatial relationships between the monthly mean sea level pressure (MSLP) anomalies and Indian summer monsoon rainfall (ISMR) have been examined using the gridded MSLP data (GMSLP2) for 46 years (1949–1994). Winter MSLP anomalies over NW Europe (south Europe) are negatively (positively) correlated with ISMR. During the strong (deficient) monsoon years, there is enhanced (weakened) poleward pressure gradient over Eurasia. The Europe pressure gradient index, which is an indicator of the winter poleward pressure gradient, is significantly and positively correlated with ISMR. This relationship also suggests a link between the North Atlantic Oscillation and ISMR on inter‐annual time scale. The winter pressure gradient over Eurasia has physical linkage with Indian summer monsoon rainfall, through winter snow cover anomalies and springtime mid latitude activities like upper tropospheric blocking highs and stationary wave anomalies.

LONG-TERM FLUCTUATION CHARACTERISTICS OF SOUTHERN OSCILLATION

Southern Oscillation (SO) is an atmospheric see-saw phenomenon in tropical Pacific sea level pressure between the eastern and western hemispheres associated with the El Nino and La Nina oceanographic features. The oscillation can be characterized by Southern Oscillation Index (SOI) which is derived from values of the monthly mean sea level pressure difference between Tahiti and Darwin, Australia. The impacts of SO on climate are widespread and extend far beyond the tropical Pacific, a phenomenon known as teleconnection.In this study, long-term fluctuation characteristics of SO are presented as guidance when using SOI for other analyses. The results show SO intensity (i. e., cross-correlation of Tahiti and Darwin pres sure deviations) is increasing almost constantly since around 1900. The reasons of increase of SO intensity are also investigated statistically in detail.

STATISTICAL CHARACTERISTICS OF SOUTHERN OSCILLATION INDEX AND ITS BAROMETRIC PRESSURE DATA

The impacts of Southern Oscillation (SO) on climate are widespread and extend far beyond the tropical Pacific, a phenomenon known as teleconnection. The oscillation can be characterized by Southern Oscillation Index (SOI) which is derived from values of the monthly mean sea level pressure difference between Tahiti and Darwin, Australia. Its best-known extreme is the El Nifio event.In this study, general statistical characteristics of SOI and the data from which it is derived (i.e. mean sea level pressure data at Tahiti and Darwin) are presented as guidance when using SOI for other analyses.The characteristics include the availability of the barometric pressure data, statistics of monthly pressure data, correlation of SO intensity, frequency analysis of SOI by magnitude and by month (January- December), duration properties of SOI by run analysis.

An appraisal of NCEP/NCAR reanalysis MSLP data viability for climate studies in the South Pacific

Fifty years of monthly mean sea level pressure (MSLP) data from the NCEP/NCAR reanalysis (hereinafter NNR) are validated against station observations in the extratropical South Pacific and West Antarctica. The period for which the NNR may be considered reliable differs markedly between regions. It is only realistic from ∼1970 across West Antarctica. A lack of surface observations entering the model causes the NNR to be poorly constrained prior to the availability of GTS‐based data in 1967: the latter coincides with a significant improvement in the NNR MSLP across the eastern South Pacific. Substantial spurious negative trends occur within the NNR MSLP at high latitudes; although the decrease is greatest prior to the advent of satellite sounder data it continues into the 1990s south of 60°S. In addition, widely used tropical and extra‐tropical circulation indices are poorly represented in the NNR data prior to the 1960s.

Nonseasonal variability of monthly mean sea level pressure and precipitation variability over Europe

The spatial modes of nonseasonal variability in both monthly mean sea level pressure (MSLP) fields over the northeastern Atlantic and western Europe sector and precipitation over Europe are investigated by using principal component analysis (PCA). The relationships between the two fields are revealed by canonical correlation analysis (CCA). The data sets used refer both to the period from 1911 to 1990. The most important spatial mode in MSLP fields is the NAO pattern and its corresponding principal component (PC) is closely related to the NAO index. However, it is interesting that the NAO pattern seems to be responsible only for the second EOF in precipitation while the most important spatial mode of precipitation corresponds to the third EOF of MSLP (North Sea pattern). Furthermore, the second EOF (Scandinavian pattern) of MSLP is highly associated with the third EOF in precipitation. Significant pairs of canonical correlation patterns between the MSLP fields and precipitation were obtained being coherent to the conclusions above referred. The results can be used to assess potential changes of precipitation over Europe based on variability of MSLP simulated by GCMs.

Temperature and precipitation variations in Norway 1900-1994 and their links to atmospheric circulation

The main aim of the present study was to identify to what degree decadal scale variability and long-term trends in temperature and precipitation in Norway can be attributed to variations in the dominating atmospheric circulation patterns. Empirical models were developed and tested on monthly series of temperature and precipitation in different regions in Norway. The monthly mean sea level pressure (SLP) field over the northern North Atlantic and northern Europe was used as a predictor. Principal components (PCs) deduced from this field were used as a basis for stepwise multiple regression analysis. The downscaling models were developed using 1925–1969 as a training period, while 1900–1924 and 1970–1994 were used as validation periods. Model testing revealed that the temperature variability during 1970–1994 in most cases was better simulated than the variability during 1900–1924. The models reproduced most of the observed trends and decadal scale variability from 1940 to present. They also reproduced the precipitation trends in western Norway before 1940. However, the temperature increase observed over all the country in 1900–1940 was not reproduced. Nor was the increased winter precipitation in southeastern Norway during the same period. It is concluded that the temperature and precipitation changes observed in Norway during the last 40 years can mainly be attributed to variations in the SLP field. Variations in the precipitation conditions in the eastern parts of the country, and in temperature all over the country, during 1900–1940 are probably connected to changes in external forcings and/or atmosphere–ocean interactions. Copyright © 2000 Royal Meteorological Society

A monthly circulation climatology for Sweden and its application to a winter temperature case study

Atmospheric circulation is important in determining the surface climate and environment. To quantify its effect, circulation indices or classifications of circulation type are often used. In this study, the classification system developed by Lamb (1950. Quarterly Journal of the Royal Meteorological Society76: 393–438) is applied to obtain circulation information for Sweden on a monthly basis. For that purpose, monthly mean sea-level pressure (MSLP) data from 1873 to 1995 is used to derive six circulation indices and to provide a circulation catalogue with 27 circulation types. The frequency of circulation types over different periods is computed and described. Four major types (cyclonic, C; west, W; southwest, SW; anticyclonic, A) have been identified. The catalogue and the associated indices provide a tool for interpreting the regional climate and for developing statistical downscaling models to derive regional climate change scenarios for Sweden. An example is given to illustrate the application and usefulness of this climatology. It deals with the linkage between the mean regional temperature in southwestern Sweden and the large-scale circulation. It is shown that directional flows with a westerly component clearly favour a positive temperature anomaly, while the anticyclonic condition often induces a negative anomaly. Moreover, it is the westerly or southwesterly flows that accompany extreme positive temperature anomalies, whereas anticyclonic conditions play an important role in creating extreme negative temperature anomalies. Further, the derived indices have a relationship with temperature anomalies, established via a statistical model. Stepwise multiple regression was used, leading to a successful model with only three indices. Using the model, 70% of the total variance in the temperature anomalies between 1887 and 1994 has been reconstructed from the pressure, which leads to the conclusion that the circulation is a critical determinant of the January temperature and that the derived climatology is useful in explaining the temperature anomalies. Copyright © 2000 Royal Meteorological Society