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Climate problems

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Understanding the changing state of the global climate poses some very interesting and challenging environmental problems.

1) How to characterize trends in temperature time series?

Observed surface air temperatures vary on all possible time-scales from daily, monthly, to seasonal and longer. There has been much debate about the possible increasing trends in mean temperatures expected from man-made global warming. However, trends can also occur in quantities other than annual and seasonal mean values such as daily temperature extremes. A recent investigation of cold extremes in daily mean Central England Temperatures revealed that cold extremes exhibit clearer recent increasing trends than those seen in monthly and seasonal averages (Antoniadou et al. 2000).

The Central England Temperature series was originally constructed by the late Professor Gordon Manley, and is now routinely updated by the Hadley Centre. The monthly mean surface air temperatures, for a region representative of the English Midlands, are expressed in degrees Celsius for the period from 1659 to the present. The data are discussed in Manley (1973). The monthly mean data is freely available from

http://www.cru.uea.ac.uk/~mikeh/datasets/uk/cet.htm.

A time series of daily mean temperature (defined as the arithmetic mean of the min and max temperatures) is also available for the shorter period of 1772-now. They are described in the article by Parker et al. (1992) and have been used to study extreme cold events by Antoniadou et al. (2000).

Both data sets (monthly and daily data) are available from  http://www.badc.rl.ac.uk/data/cet

2) Estimating the probability of atmospheric circulation regimes ?

The climate is a non-linear dynamical system having many degrees of freedom. From chaos theory for systems with a small number of degrees of freedom, one expects the system to remain longer in certain regions of state space. These more persistent states are referred to as "regimes" and can be discerned as local maxima in estimates of the probability density for the leading principal components (Corti et al. 1999). The existence (or not!) of such regimes is important for our understanding of climate dynamics and predictability.

Corti et al. (1999) presented evidence for regimes in an analysis of the gridded geopotential height of the 500mb pressure surface over the period 1949-94. Geopotential height at 500mb is a good indicator of the flow patterns in the troposphere. The monthly mean data set consists of height values on a 2.5 degree grid covering the northern hemisphere (p=144x36=5184 variables). Corti et al. (1999) reduced the dimension by projecting onto the first two leading principal components and then examined the p.d.f. in this PCA reduced state space. A Gaussian kernel method was used to make smooth estimates of the p.d.f.

It is not at all clear that this is the best way to reduce this large data set in order to search for persistent regime modes. New methods need to be developed capable of working with the large gridded data sets commonly used in climate research. It is also not clear whether or not the regimes are merely artifacts of poor sampling or are in fact significant structures in state space.

This data is available at  http://www.met.rdg.ac.uk/cag/  under 'Data Analysis'.

Problems provided by :

Dr. David B. Stephenson

Head of Climate Analysis Group

University of Reading

E-mail: D.B.Stephenson@reading.ac.uk

http://www.met.rdg.ac.uk/cag/

References

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T. Antoniadou, P. Besse, A.-L. Fougeres, C. Le Gall, and D.B. Stephenson, (2000), Balancier atmospherique (NAO) et climat en Atlantique nord, Revue de Statistique Appliquee, (submitted Jan 2000).

S. Corti, F. Molteni, and T.N. Palmer, (1999), Signature of recent climate change in frequencies of natural atmospheric circulation regimes, Nature, 398, 799-802.

G. Manley, 'Central England Temperatures: monthly means 1659 to 1973', Quarterly Journal of the Royal Meteorological Society, 1974, vol. 100, pp. 389--405.

D.E. Parker, T.P. Legg and C.K. Folland, 'A new daily Central England Temperature series, 1772--1991', International Journal of Climatology, 1992, vol. 12, pp. 317--42.

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An oceanographic problem

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Finding Rossby waves in 2-d and 3-d data

When the ocean is perturbed, because of the rotation and shape of the Earth, a wave is generated which travels slowly (a few km per day) approximately westward. These waves (called Rossby waves) are long (typically a few hundred km) and are very difficult to detect with conventional measurements but they can be detected from satellites as they increase/decrease the sea surface height by a few cm.

In theory Rossby waves travel westward so you can consider the problem as one of looking for signals in a 2-D field, but there are good reasons for thinking that particularly when they cross topography (such as an mid-ocean ridge) they deviate from westward propagation so a full 3-D analysis would be preferable. It should be noted that these waves are not necessarily periodic. Oceanographically we are interested in detecting their speed, their acceleration (if any), the point where they are generated, where they die and any deviation from westward propagation (in the 3-D analysis). Two datasets are provided. The first is sea surface height anomaly averaged in 10 day periods and 1 degree along 34 degrees north in the North Atlantic (234 x 71 grid points). The second is a global 3-d data set of average sea surface height anomaly on a 1 degree by 1 degree by 10 day grid (360x140x234 grid points). There are more details at

http://www.soc.soton.ac.uk/JRD/SAT/Rossby

The data can be found at the above web address as rss.html.

The problem was provided by:

Peter Challenor
Southampton Oceanography Centre
University of Southampton and NERC

P.Challenor@soc.soton.ac.uk
 
 

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EMEP

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The Co-operative Programme for Monitoring and Evaluation of the Long Range Transmission of Air Pollutants in Europe (known as EMEP) has run a model prediction deposition of various pollutants for around 20 years. Until recently deposition was predicted on a 150km square grid across the whole of Europe, although in the last few years this has been replaced by a new model on a 50km grid. The actual deposition is also measured at around 80 monitoring sites. For the purposes of this meeting the 150km modelled data, and the observations from the measurement stations, have been made available for total wet deposition of N and S, for the years 1985 to 1995.

The modelled data can be regarded as estimates of the average deposition over some area around the centre of each square, although how big this area is, is not clear. The measured data are point values. One aim of the EMEP project is to assess how much damage is being done by air pollution, and one means of quantifying this is by estimating the area over which critical loads are being exceeded. Critical loads are annual deposition values calculated for all areas of natural or semi-natural land. It is assumed that damage is done if deposition is higher than these critical values.

In order to estimate the exceedance of critical loads, it is necessary to estimate the deposition on a scale much finer than the 150km squares. Both critical loads and deposition can be expected to vary considerably in such a large area. It is also reasonable to assume that there is some error in the EMEP model, though no external estimates are available for this. The measurement station observations are averages of a large number of individual measurements, and so measurement error should be fairly small, but the location of these stations is definitely not random. They are found predominately in central and northern Europe, and it has been suggested that the stations in Germany at least have been sited to measure ‘background’ values, ie lower than the average values for the EMEP square in which they are located. The history of monitoring stations in general would suggest that most if not all have been sited in locations that are in some way ‘interesting’.

Some of the questions that can be asked of this data are therefore:

1. How can the two sources of data best be combined?
2. Although it is unreasonable to estimate the actual deposition at locations other than monitoring stations, can the mean and variance of the deposition within a square be estimated?
3. Are there any temporal trends in the mean deposition? It should be borne in mind that the measurement stations are not the same every year. Also the model has been changed over time.
4. Does the local variation in deposition change spatially (or temporally)? This could be critical in estimating exceedance of critical loads.
5. Is there any systematic error in the EMEP model?
6. Can some measure of uncertainty be attached to the EMEP model errors?

One approach to using this data to estimate critical load exceedance has been published in they July 2000 edition of Atmospheric Environment (Hirst,D.J., Kåresen,K., Høst,G. and Posch,M: Estimating the exceedance of critical loads in Europe by considering local variability in deposition)

The data are available at the Norwegian Computing Center ftp server

ftp://ftp.nr.no/pub/david  in the file rssdata.zip

Problem provided by:

David Hirst

Norsk Regnesentral (Norwegian Computing Center)

email david.hirst@nr.no
 
 

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Lead concentrations in river water

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Water quality is measured in thousands of sites in rivers, at a few of the sites, a wide range of determinands are measured, including heavy metals. The challenge is to use the more extensive data at selected sites to estimate national trends. Difficulties include outliers, partially available data (i.e. the true values lie below the limit of chemical detection) and missing data. The data come from the Harmonised (water) Monitoring Scheme (HMS).

Focussing on the limits of chemical detection problem, many pollutants exist at only very low concentrations and may fall below the limit of detection (LOD) of the techniques used to analyse the samples. The challenge is to use such data in the calculation of trends and the difficulty is further compounded by the fact that advances in laboratory techniques have led to the lowering of LOD’s with time.

Data on the River Blackwater is provided and is available by e-mailing Simon_Lunn@detr.gsi.gov.uk (telephone 020 7944 6505).

Problem originally provided by:

John Custance
DETR
London

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Derivation of Baseline Data and Spatial Variability

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Data Provided: Copper concentrations in stream sediments (mg/kg) for the UK

Note. BGS G-BASE data is normally provided under license to academic and commercial users. For the purpose of participation in this exercise the data provided has been transformed by application of a constant weighting factor. Users interested in accessing un-transformed data should discuss access and potential uses with the G-BASE data manager Dr Peter Dunkley, British Geological Survey, Keyworth, Nottingham, NG12 5GG

Overview of Problem

Land is an important resource that fulfils a number of essential functions including:

• providing a structural base on which to build;
• maintaining habitats and biodiversity;
• providing a reserve of potable water and raw materials;
• acting as a support medium for plant growth and a protective filter for groundwater resources; and
• providing settings for recreation and tourism.

Soil surveys have been undertaken in the context of agricultural and mineral exploration programmes for at least 100 years. However, it is only recently that national soil surveys have been undertaken with a view to assess the environmental implications of land quality.

The concentrations of contaminants in soil vary considerably throughout the UK, according to regional differences in geology and historical land uses. The background concentration of a contaminant comprises the ‘normal’ or characteristic concentration resulting from both natural sources and non-natural diffuse sources such as atmospheric deposition. The background in (naturally) mineralised areas may exceed those present in urban environments which are considered to be contaminated.

Many data sets exist at varying resolutions that may be used to infer background concentrations of inorganic substances and heavy metals at a regional or national scale in the UK. However, given the varied industrial history and mineralisation of the UK and associated urban environments, the derivation of a national background level might not be meaningful

Objectives of Case Study

• Is deriving a national background level appropriate or meaningful from a statistical standpoint

• If so, how can this parameter be defined and how can its variability be best conveyed

• If deriving a national background is inappropriate how can regionally specific baselines be best defined

The problem was provided by Dr Barry Smith, BGS (bsmi@bgs.ac.uk).