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Alison C. Renshaw, David P. Rowell, and Chris K. Folland

Abstract

A study of the impact of ENSO in the Hadley Centre’s atmospheric climate model HADAM1 is presented, with emphasis on the North Pacific–American (NPA) sector. The study is based both on observational data and an ensemble of six integrations for the period 1949–93, forced with observed global sea-ice and sea surface temperature data. The model is shown to reproduce most of the known features of the worldwide atmospheric response to ENSO in boreal winter (January–March).

Focusing on the NPA sector, the leading modes of low-frequency weather variability in the winter season are identified on their natural timescales for both the modeled and observed atmospheres. These modes are analyzed via rotated EOF analysis of daily 500-hPa height data, filtered to remove synoptic timescale variations. The model gives a reasonably skillful simulation of the main features of the four leading modes in the NPA region:the Pacific–North American (PNA), the west Pacific (WP), the east Pacific (EP), and the North Pacific (NP) modes. The sensitivity of these modes to SSTs is investigated. In particular, sensitivity to SSTs associated with ENSO is analyzed in terms of the shift in frequency of occurrence of the opposing phases of a mode between warm event (El Niño) and cold event (La Niña) years. Three of the observed modes show such a sensitivity: the PNA, WP, and NP modes. Of the corresponding model modes, only the PNA responds significantly to ENSO (but too strongly in warm event years), which is clearly illustrated by changes in both the frequency and duration of PNA episodes between warm and cold event years. The EP mode shows no sensitivity to ENSO, in either model or observed atmospheres. Finally, although the model is able to reproduce the pattern of decadal anomalies seen in the North Pacific in the years 1977–87, which is related to the prevalence of the positive phase of the PNA in this period, it does so with a much reduced amplitude; possible reasons for this discrepancy are discussed.

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C. K. Folland, M. J. Salinger, N. Jiang, and N. A. Rayner

Abstract

An analysis of temperature variability and trends in the South Pacific, mainly in the twentieth century, using data from 40 island stations and optimally interpolated sea surface and night marine air temperature data is presented. The last-named dataset is new and contains improved corrections for changes in the height of thermometer screens as ships have become larger. It is shown that the South Pacific convergence zone plays a pivotal role in both variability and trends in all three datasets. Island, collocated sea surface temperature, and night marine air temperature time series for four large constituent regions are created and analyzed. These have been corrected for artificial changes in variance due to changes in the availability of constituent island stations whose intrinsic variance varies from station to station. The method is described in detail. Objective estimates of uncertainty in the sea surface temperature data are also provided. The results extend previous work, showing that annual and seasonal surface ocean and island air temperatures have increased throughout the South Pacific. Variations in trends in the island and marine data show reasonable consistency, with distinctly different patterns of multidecadal change in the four regions. However, a notable inconsistency is the recent lack of warming in night marine air temperature in one of the tropical regions relative to sea surface temperature, with signs of this effect in a second tropical region. Another tropical region near the South Pacific convergence zone shows recent strong warming in the island data but not in the marine data.

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C. K. Folland, R. W. Reynolds, M. Gordon, and D. E. Parker

Abstract

This study results from recommendations made by a 1984 WMO Expert Committee on Ocean-Atmosphere Interaction Relevant to Long-Range Forecasting. The committee suggested that comparisons be carried out between monthly sea surface temperature (SST) analyses routinely made in several different countries in near real time. Emphasis was placed on the improvement of such analyses for use in operational long-range forecasting, especially for initializing dynamical long-range forecasting models. Six different monthly averaged SST analyses have been compared. The extent to which the analyses agree on several space scales and for regions covering the global oceans is shown, together with estimates of the magnitude of various types of errors. Independent estimates of SST obtained from expendable bathythermogmphs indicate that the monthly mean Meteorological Office (UKMO), Climate Analysis Center (CAC) in situ, and CAC blended analyses showed small differences (biases) from the expendable bathythermograph data. The differences were near to or below the margins of statistical significance over the Northern Hemisphere and the Southern Hemisphere tropics. Apparent negative biases in the analyses were noted, however, in the extratropical Southern Hemisphere.

The authors finish with a discussion of recent improvements to the accuracy and scope of SST analyses for both long-range forecasting and climate studies. These improvements include an integrated analysis of ice limit, in situ and satellite SST data, and the developing use of optimum interpolation as a method of SST analysis.

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D. R. Fereday, J. R. Knight, A. A. Scaife, C. K. Folland, and A. Philipp

Abstract

Observed atmospheric circulation over the North Atlantic–European (NAE) region is examined using cluster analysis. A clustering algorithm incorporating a “simulated annealing” methodology is employed to improve on solutions found by the conventional k-means technique. Clustering is applied to daily mean sea level pressure (MSLP) fields to derive a set of circulation types for six 2-month seasons. A measure of the quality of this clustering is defined to reflect the average similarity of the fields in a cluster to each other. It is shown that a range of classifications can be produced for which this measure is almost identical but which partition the days quite differently. This lack of a unique set of circulation types suggests that distinct weather regimes in NAE circulation do not exist or are very weak. It is also shown that the stability of the clustering solution to removal of data is not maximized by a suitable choice of the number of clusters. Indeed, there does not appear to be any robust way of choosing an optimum number of circulation types. Despite the apparent lack of preferred circulation types, cluster analysis can usefully be applied to generate a set of patterns that fully characterize the different circulation types appearing in each season. These patterns can then be used to analyze NAE climate variability. Ten clusters per season are chosen to ensure that a range of distinct circulation types that span the variability is produced. Using this classification, the effect of forcing of NAE circulation by tropical Pacific sea surface temperature (SST) anomalies is analyzed. This shows a significant influence of SST in this region on certain circulation types in almost all seasons. A tendency for a negative correlation between El Niño and an anomaly pattern resembling the positive winter North Atlantic Oscillation (NAO) emerges in a number of seasons. A notable exception is November–December, which shows the opposite relationship, with positive NAO-like patterns correlated with El Niño.

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Carsten S. Frederiksen, David P. Rowell, Ramesh C. Balgovind, and Chris K. Folland

Abstract

Australian rainfall variability and its relationship with the Southern Oscillation index (SOI) and global sea surface temperature (SST) variability is considered in both observational datasets and ensembles of multidecadal simulations using two different atmospheric general circulation models forced by observed SSTs and sea ice extent. Monthly and seasonal time series have been constructed to examine the observed and modeled relationships.

The models show some success in the Australian region, largely reproducing the observed relationships between rainfall, the SOI, and global SSTs, albeit better in some seasons and geographical regions than others. A partition of the rainfall variance into components due to SST forcing and internal variability, suggests that both models have too much internal variability over the central eastern half of the continent, especially during austral winter and spring. Consequently, the strength of the SOI and SST relationships tend to be underestimated in this region. The largest impact of SST forcing is seen over the tropical and western parts of the continent.

A principal component analysis reveals two dominant rotated modes of rainfall variability that are very similar in both the observed and modeled cases. One of these modes is significantly correlated with SST anomalies to the north-northwest of Australia (in the case of the models) and the SST gradient between the Indonesian archepelago and the central Indian Ocean (in the observed case). The other mode is significantly correlated with the typical SST anomaly pattern associated with the El Niño–Southern Oscillation. Correlative maps between the principal component time series and the modeled MSLP, 700-hPa, and 300-hPa geopotential heights are used to explore the underlying physical processes associated with these statistical relationships.

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J. M. Potts, C. K. Folland, I. T. Jolliffe, and D. Sexton

Abstract

The most commonly used measures for verifying forecasts or simulators of continuous variables are root-mean-squared error (rmse) and anomaly correlation. Some disadvantages of these measures are demonstrated. Existing assessment systems for categorical forecasts are discussed briefly. An alternative unbiased verification measure is developed, known as the linear error in probability space (LEPS) score. The LEPS scare may be used to assess forecasts of both continuous and categorical variables and has some advantages over rmse and anomaly correlation. The properties of the version of LEPS discussed here are reviewed and compared with an earlier form of LEPS. A skill-score version of LEPS may be used to obtain an overall measure of the skill of a number of forecasts. This skill score is biased, but the bias is negligible if the number of effectively independent forecasts or simulations is large. Some examples are given in which the LEPS skill score is compared with rmse and anomaly correlation.

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N. A. Rayner, P. Brohan, D. E. Parker, C. K. Folland, J. J. Kennedy, M. Vanicek, T. J. Ansell, and S. F. B. Tett

Abstract

A new flexible gridded dataset of sea surface temperature (SST) since 1850 is presented and its uncertainties are quantified. This analysis [the Second Hadley Centre Sea Surface Temperature dataset (HadSST2)] is based on data contained within the recently created International Comprehensive Ocean–Atmosphere Data Set (ICOADS) database and so is superior in geographical coverage to previous datasets and has smaller uncertainties. Issues arising when analyzing a database of observations measured from very different platforms and drawn from many different countries with different measurement practices are introduced. Improved bias corrections are applied to the data to account for changes in measurement conditions through time. A detailed analysis of uncertainties in these corrections is included by exploring assumptions made in their construction and producing multiple versions using a Monte Carlo method. An assessment of total uncertainty in each gridded average is obtained by combining these bias-correction-related uncertainties with those arising from measurement errors and undersampling of intragrid box variability. These are calculated by partitioning the variance in grid box averages between real and spurious variability. From month to month in individual grid boxes, sampling uncertainties tend to be most important (except in certain regions), but on large-scale averages bias-correction uncertainties are more dominant owing to their correlation between grid boxes. Changes in large-scale SST through time are assessed by two methods. The linear warming between 1850 and 2004 was 0.52° ± 0.19°C (95% confidence interval) for the globe, 0.59° ± 0.20°C for the Northern Hemisphere, and 0.46° ± 0.29°C for the Southern Hemisphere. Decadally filtered differences for these regions over this period were 0.67° ± 0.04°C, 0.71° ± 0.06°C, and 0.64° ± 0.07°C.

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