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

Allan, Rob; Reason, C. J. C.; Carroll, P.I.; Jones, Phil

doi:10.1002/joc.678

Cited by 22 publications

(18 citation statements)

References 28 publications

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“…Work on developing long, high-quality MSLP stations series can be very manually intensive and time consuming [e.g., Madras, Chennai, India, see Allan et al 2002;Nagasaki, Japan, see Konnen et al 2003;Quebec, Canada, see Slonosky 2003]. Unlike the studies cited above, which have focused on specific individual series, the number of stations requiring quality control in HadSLP2 necessitated that a more automated quality control procedure be setup.…”

Section: A Quality Control-terrestrial Datamentioning

confidence: 94%

A New Globally Complete Monthly Historical Gridded Mean Sea Level Pressure Dataset (HadSLP2): 1850–2004

2006

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An upgraded version of the Hadley Centre's monthly historical mean sea level pressure (MSLP) dataset (HadSLP2) is presented. HadSLP2 covers the period from 1850 to date, and is based on numerous terrestrial and marine data compilations. Each terrestrial pressure series used in HadSLP2 underwent a series of quality control tests, and erroneous or suspect values were either corrected, where possible, or removed. Marine observations from the International Comprehensive Ocean Atmosphere Data Set were quality controlled (assessed against climatology and near neighbors) and then gridded. The final gridded form of HadSLP2 was created by blending together the processed terrestrial and gridded marine MSLP data. MSLP fields were made spatially complete using reduced-space optimal interpolation. Gridpoint error estimates were also produced.HadSLP2 was found to have generally stronger subtropical anticyclones and higher-latitude features across the Northern Hemisphere than an earlier product (HadSLP1). During the austral winter, however, it appears that the pressures in the southern Atlantic and Indian Ocean midlatitude regions are too high; this is seen in comparisons with both HadSLP1 and the 40-yr ECMWF Re-Analysis (ERA-40). Over regions of high altitude, HadSLP2 and ERA-40 showed consistent differences suggestive of potential biases in the reanalysis model, though the region over the Himalayas in HadSLP2 is biased compared with HadSLP1 and improvements are required in this region. Consistent differences were also observed in regions of sparse data, particularly over the higher latitudes of the Southern Ocean and in the southeastern Pacific. Unlike the earlier HadSLP1 product, error estimates are available with HadSLP2 to guide the user in these regions of low confidence.An evaluation of major phenomena in the climate system using HadSLP2 provided further validation of the dataset. Important climatic features/indices such as the North Atlantic Oscillation, Arctic Oscillation, North Pacific index, Southern Oscillation index, Trans-Polar index, Antarctic Oscillation, Antarctic Circumpolar Wave, East Asian Summer Monsoon index, and the Siberian High index have all been resolved in HadSLP2, with extensions back to the mid-nineteenth century.

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Section: A Quality Control-terrestrial Datamentioning

confidence: 94%

A New Globally Complete Monthly Historical Gridded Mean Sea Level Pressure Dataset (HadSLP2): 1850–2004

2006

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“…Finally, for south Asia, we chose a long sea-level pressure (SLP) data set for Madras (now known as Chennai), India. We used the annual Sep-Oct mean value, as this may serve as an index of summertime all-India rainfall associated with the monsoon (Allan et al 2002).…”

Section: Datamentioning

confidence: 99%

A non-uniqueness problem in the identification of power-law spectral scaling for hydroclimatic time series

Fleming¹

2013

Hydrological Sciences Journal

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Power-law spectral scaling violates assumptions of standard analyses such as statistical change detection. However, hydroclimatic data sets may be too short to differentiate the spectra of 1/f α vs low-order linear memory processes, an ambiguity exacerbated by the ubiquity of both process types. We explore this non-uniqueness problem by applying a heuristic tool to four examples from each of four hydroclimatic data types: circulation indices, station climate, river and aquifer conditions, and glacier mass balance. This selection spans much of the globe and includes some of the longest instrumental data sets available. The most common outcome is that power-law scaling is apparent, but the record is insufficiently long to discriminate between underlying mechanisms. The use of palaeoclimatic data to extend the instrumental record was investigated, but produced mixed results. Conversely, a balance-of-evidence approach, additionally incorporating physical process considerations, may help us recognize variate classes for which 1/f α scaling can be concluded. Practical recommendations are offered.Key words spectrum; persistence; streamflow; groundwater; glacier; palaeoclimate Un problème de non-unicité dans l'identification de la scalance spectrale des lois de puissance pour des séries chronologiques hydroclimatiques Résumé La scalance spectrale d'une loi de puissance ne satisfait pas les hypothèses des analyses classiques telles que la détection des changements statistiques. Les ensembles de données hydroclimatiques peuvent cependant être trop courts pour différencier les processus de spectres en 1/fα des processus linéaires à mémoire courte, ambiguïté exacerbée par l'omniprésence de ces deux types de processus. Nous avons exploré ce problème de non-unicité en appliquant un outil heuristique à quatre exemples de chacun de quatre types de données hydroclimatiques suivants: indices de circulation, climat d'une station, caractéristiques d'une rivière et d'un aquifère et bilan de masse d'un glacier. Cette sélection est répartie sur une grande partie du globe et comprend certaines des plus longues séries disponibles de données instrumentales. Le premier résultat est que la scalance selon une loi de puissance est évidente, mais que les séries ne sont pas suffisamment longues pour distinguer entre les mécanismes sous-jacents. L'utilisation des données paléoclimatiques pour étendre la durée des séries instrumentales a été étudiée, mais n'a fourni que des résultats mitigés. Inversement, une approche par bilan des informations, intégrant en outre des considérations de processus physiques, peut nous aider à reconnaître les classes de variables pour lesquelles on peut conclure à une scalance en 1/fα. Des recommandations pratiques sont proposées.

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“…According to McAfee (1981), a weather journal with comparable detail for this period anywhere in the world is a rare find. For the Indo-Australian region, Dawes weather journal is only matched by the English East India Company observations made in Madras (Chennai), India, from 1796 onwards (Allan et al 2002).…”

Section: Introductionmentioning

confidence: 99%

A climate reconstruction of Sydney Cove, New South Wales, using weather journal and documentary data, 1788-1791

Gergis¹,

Karoly²,

Allan³

2009

AMOJ

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

Cited by 22 publications

References 28 publications

A New Globally Complete Monthly Historical Gridded Mean Sea Level Pressure Dataset (HadSLP2): 1850–2004

A New Globally Complete Monthly Historical Gridded Mean Sea Level Pressure Dataset (HadSLP2): 1850–2004

A non-uniqueness problem in the identification of power-law spectral scaling for hydroclimatic time series

A climate reconstruction of Sydney Cove, New South Wales, using weather journal and documentary data, 1788-1791

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