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  • Author or Editor: Mark A. Saunders x
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Budong Qian
and
Mark A. Saunders

Abstract

Motivated by an attempt to predict summer (June–August) U.K. temperatures, the time-lagged correlations between summer U.K. and European temperatures and prior snow cover, North Atlantic sea surface temperatures (SSTs), and the North Atlantic Oscillation (NAO) are examined. The analysis centers on the 30-yr period 1972–2001 corresponding to the interval of reliable satellite-derived land snow cover data. A significant association is found between late winter Eurasian snow cover and upcoming summer temperatures over the British Isles and adjacent areas, this link being strongest with January–March snow cover. Significant links are also observed between summer temperatures and the preceding late winter NAO index and with a leading principal component of North Atlantic SST variability. The physical mechanisms underlying these time-lagged correlations are investigated by studying the associated variability in large-scale atmospheric circulation over the Euro–Atlantic sector. Seasonal expansion in the Azores high pressure system may play an important role in the time-lagged relationships. The potential seasonal predictability of summer U.K. temperatures during the period 1972–2001 is assessed by cross-validated hindcasts and usable predictive skill is found. However, the presence and cause of temporal instability in the time-lagged relationships over longer periods of time requires further investigation.

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Christopher J. Merchant
and
Mark A. Saunders

Abstract

The presence of stratospheric aerosol can bias the results of infrared satellite retrievals of sea surface temperature (SST) and total precipitable water (TPW). In the case of linear SST retrieval using the Along Track Scanning Radiometer (ATSR), on the ESA European remote-sensing satellites, constant coefficients can be found that give negligible bias (less than 0.1 K) over a wide range of aerosol amount (11-μm optical thickness from 0.0 to 0.022). For TPW retrieval, in contrast, the biases associated with stratospheric aerosol are less satisfactory (2 kg m−2 or greater across a range of 11-μm optical thickness of 0.0–0.01). However, the authors show how to find optimal aerosol-dependent retrieval coefficients for any stratospheric aerosol distribution from knowledge of the mean and variance of that aerosol distribution. Examples of SST and TPW retrieval using simulated ATSR brightness temperature data are given.

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Christopher G. Fletcher
and
Mark A. Saunders

Abstract

Recent proposed seasonal hindcast skill estimates for the winter North Atlantic Oscillation (NAO) are derived from different lagged predictors, NAO indices, skill assessment periods, and skill validation methodologies. This creates confusion concerning what is the best-lagged predictor of the winter NAO. To rectify this situation, a standardized comparison of NAO cross-validated hindcast skill is performed against three NAO indices over three extended periods (1900–2001, 1950–2001, and 1972–2001). The lagged predictors comprise four previously published predictors involving anomalies in North Atlantic sea surface temperature (SST), Northern Hemisphere (NH) snow cover, and an additional predictor, an index of NH subpolar summer air temperature (T SP). Significant (p < 0.05) NAO hindcast skill is found with May SST 1900–2001, summer/autumn SST 1950–2001, and warm season snow cover 1972–2001. However, the highest and most significant hindcast skill for all periods and all NAO indices is achieved with T SP. Hindcast skill is nonstationary using all predictors and is highest during 1972–2001 with a T SP correlation skill of 0.59 and a mean-squared skill score of 35%. Observational evidence is presented to support a dynamical link between summer T SP and the winter NAO. Summer T SP is associated with a contemporaneous midlatitude zonal wind anomaly. This leads a pattern of North Atlantic SST that persists through autumn. Autumn SSTs may force a direct thermal NAO response or initiate a response via a third variable. These findings suggest that the NH subpolar regions may provide additional winter NAO lagged predictability alongside the midlatitudes and the Tropics.

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Benjamin Lloyd-Hughes
,
Mark A. Saunders
, and
Paul Rockett

Abstract

A prime challenge for ENSO seasonal forecast models is to predict boreal summer ENSO conditions at lead. August–September ENSO has a strong influence on Atlantic hurricane activity, Northwest Pacific typhoon activity, and tropical precipitation. However, summer ENSO skill is low due to the spring predictability barrier between March and May. A “consolidated” ENSO–climatology and persistence (CLIPER) seasonal prediction model is presented to address this issue with promising initial results. Consolidated CLIPER comprises the ensemble of 18 model variants of the statistical ENSO–CLIPER prediction model. Assessing August–September ENSO skill using deterministic and probabilistic skill measures applied to cross-validated hindcasts from 1952 to 2002, and using deterministic skill measures applied to replicated real-time forecasts from 1900 to 1950, shows that the consolidated CLIPER model consistently outperforms the standard CLIPER model at leads from 2 to 6 months for all the main ENSO indices (3, 3.4, and 4). The consolidated CLIPER August–September 1952–2002 hindcast skill is also positive to 97.5% confidence at leads out to 4 months (early April) for all ENSO indices. Optimization of the consolidated CLIPER model may lead to further skill improvements.

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Matthew S. Jones
,
Mark A. Saunders
, and
Trevor H. Guymer

Abstract

The Along Track Scanning Radiometer (ATSR) was launched in July 1991 on the European Space Agency's first remote sensing satellite ERS-1. ATSR has the potential to measure sea surface temperature (SST) to a precision of 0.3 K, which is more than double the accuracy of any previously flown infrared radiometer. A key factor limiting ATSR's performance is remnant cloud contamination. Examination of the 0.5° spatially averaged ATSR SST data (version 500) from the South Atlantic for the whole of 1992 and 1993 shows the presence of regional cloud contamination in the night SST measurements. The authors establish a figure of 5.7% as a lower limit for this nighttime cloud contamination. The contamination leads to differences between day and night mean SSTs and to poor comparisons with in situ thermosalinograph SST data. A new cloud filtering process designed for postprocessing of the data is proposed to remove the contamination. The algorithm presented here relies on assumptions that the day data are less cloud contaminated than the night data and that a large proportion of the SST variability can he explained by an annual and semiannual model. Testing the filtering algorithm shows that differences between the day and night SST signals are substantially reduced and that comparisons with the thermosalinograph SST data improve by a factor of 3 in rms scatter and by 0.3 K in the mean difference.

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Sarah J. Doherty
,
Stephan Bojinski
,
Ann Henderson-Sellers
,
Kevin Noone
,
David Goodrich
,
Nathaniel L. Bindoff
,
John A. Church
,
Kathy A. Hibbard
,
Thomas R. Karl
,
Lucka Kajfez-Bogataj
,
Amanda H. Lynch
,
David E. Parker
,
I. Colin Prentice
,
Venkatachalam Ramaswamy
,
Roger W. Saunders
,
Mark Stafford Smith
,
Konrad Steffen
,
Thomas F. Stocker
,
Peter W. Thorne
,
Kevin E. Trenberth
,
Michel M. Verstraete
, and
Francis W. Zwiers

The Fourth Assessment Report (AR4) of the Intergovernmental Panel on Climate Change (IPCC) concluded that global warming is “unequivocal” and that most of the observed increase since the mid-twentieth century is very likely due to the increase in anthropogenic greenhouse gas concentrations, with discernible human influences on ocean warming, continental-average temperatures, temperature extremes, wind patterns, and other physical and biological indicators, impacting both socioeconomic and ecological systems. It is now clear that we are committed to some level of global climate change, and it is imperative that this be considered when planning future climate research and observational strategies. The Global Climate Observing System program (GCOS), the World Climate Research Programme (WCRP), and the International Geosphere-Biosphere Programme (IGBP) therefore initiated a process to summarize the lessons learned through AR4 Working Groups I and II and to identify a set of high-priority modeling and observational needs. Two classes of recommendations emerged. First is the need to improve climate models, observational and climate monitoring systems, and our understanding of key processes. Second, the framework for climate research and observations must be extended to document impacts and to guide adaptation and mitigation efforts. Research and observational strategies specifically aimed at improving our ability to predict and understand impacts, adaptive capacity, and societal and ecosystem vulnerabilities will serve both purposes and are the subject of the specific recommendations made in this paper.

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