Identifying extreme event climate thresholds for greater Manchester, UK: examining the past to prepare for the future

METEOROLOGICAL APPLICATIONSMeteorol. Appl. 19: 26–35 (2012)Published online 21 March 2011 in Wiley Online Library(wileyonlinelibrary.com) DOI: 10.1002/met.252

Identifying extreme event climate thresholds for greaterManchester, UK: examining the past to prepare

for the future

Claire Smitha* and Nigel Lawsonb

a College of Science and Engineering, University of Leicester, Bennett Building, University Road, Leicester LE1 7RH, UKb School of Environment and Development, University of Manchester, Oxford Road, Manchester M13 9PL, UK

ABSTRACT: Extreme weather events can have severe consequences for the local environment and population. Projectedfuture changes in climate (e.g. UKCP09) indicate that North West England is likely to experience an increasing frequencyand intensity of meteorological extremes, leading to flooding, heat waves and storms. Consequently, it is important thatthe region enhances its preparedness for such events. This paper explores the possibility of developing quantifiable climaterisk indices for the case study area of Greater Manchester, using a combination of archival research and statistical analysisof past climate data. For extremes which are the function of a single meteorological variable (e.g. heat waves, pluvialflooding and heavy snowfall) the thresholds proved to be reliable and skillful. Days with maximum daily temperaturegreater than or equal to 29.2 °C, daily snowfall amount greater than or equal to 6 cm or maximum gust speed greaterthan or equal to 60 knots are found to be indicative of weather-related impacts which have in the past affected humanhealth/well-being, have caused damage to the urban infrastructure or have severely disrupted services. Extreme eventswhich are the result of a more complex interaction between variables (e.g. drought, freezing conditions) were less wellcaptured by applying the thresholds associated with a single variable in isolation. Such critical threshold indices can be usedin conjunction with future projections of climate change to establish weather-related risk for the future. This risk-basedapproach can subsequently be integrated to climate change adaptation strategies and development planning to ensure futurepreparedness. Copyright 2011 Royal Meteorological Society

KEY WORDS extreme event; temporal trend; threshold; urban; risk; climate change adaptation

Received 17 September 2010; Revised 2 November 2010; Accepted 31 January 2011

1. Introduction

Extreme weather events can have severe consequencesfor the local environment and population, and areaccountable for a disproportionately large amount ofclimate-related risk. In addition to causing extensivephysical damage, these events can have a significantnegative impact on society and the economy (Hulme,2003; McMichael et al., 2006). There is evidence thatsuch weather-related events, at the margins of currentlocal climate distributions, will increase in frequency andintensity in the future (Palmer and Raisanen, 2002; Benis-ton et al., 2007). It is therefore prudent that action is takento ensure that social and physical systems have the capac-ity and capability to absorb and respond to these impacts,such that the structure and function of the systems aremaintained and major disruption is avoided.

Future projections of climate change for the UnitedKingdom (e.g. UKCP09) indicate that the North Westregion, home to the major conurbations of Manchester

* Correspondence to: Claire Smith, College of Science andEngineering, University of Leicester, Bennett Building, UniversityRoad, Leicester LE1 7RH, UK. E-mail: [email protected]

and Liverpool, is likely to experience an increasing fre-quency and intensity of extreme climate events, such asfloods, heat waves and storms (Murphy et al., 2009).The impact of these rapid fluctuations in meteorolog-ical conditions and shifts in the distribution of localclimate variables over time will be felt most acutelyin those areas where the human and environmental sys-tems are already marginal (e.g. heavily populated urbanareas, coastal zones which suffer frequent flooding). Forexample, during the 2003 heat wave when the highesttemperature ever recorded in the United Kingdom wasregistered (38.5 °C at Brogdale, Kent) and average dailytemperatures in Manchester exceeded 20 °C in August oneight near-consecutive days (compared to the 1971–2000daily average of 16.1 °C), unhealthy living and work-ing spaces were experienced in buildings situated in theManchester city region and surrounding suburbs (Wrightet al., 2005). In contrast, the heavy snowfall and extremelow temperatures (below −10 °C during the night) experi-enced during winter 2009/2010 are reported to have costthe local economy £24 m in 24 h (Manchester EveningNews, 2010). Consequently, it is important that the regionenhances its preparedness for such events and implementsappropriate adaptation strategies.

Copyright 2011 Royal Meteorological Society

Extreme event thresholds for greater Manchester, UK 27

Temporal analogues offer a valuable resource to aidunderstanding of how past events have affected the localenvironment and population. Future climate conditionscan then be reported with respect to these historical eventsin terms of the frequency and severity of recurrence,within the scientific limitations of error and uncertaintyassociated with climate forecasting. For example, the2003 European heat wave cited above, which is con-sidered an extreme event (exceeds the 90th percentile)under current climate conditions becomes comparableto median climate conditions for the period 2071–2100(Beniston, 2004; Beniston and Diaz, 2004). For GreaterManchester the 2003 event is equivalent to median sum-mer temperature conditions under the 2050s high emis-sions scenario, and in 9 out of 10 years the warmestday in summer will exceed 28 °C for much of the region(Cavan, 2010). As an alternative example, heavy precip-itation events which have in the past caused detrimentalflooding in Greater Manchester (e.g. flooding in Heywoodin 2004 and 2006; Douglas et al., 2010), may occur asfrequently as one to four times per year by the 2050sunder the high emissions scenario (Cavan, 2010). Suchanalogues are a useful communication tool for policymakers, stakeholders and the general public because theyare based on real events and the likelihood that suchevents will become increasingly common in the future.They can also provide the type of quantitative informa-tion which is required for informed risk evaluation andadaptation planning.

This paper describes the application of a three-stageprocess to determine quantitative climate thresholds,indicative of extreme events, for the case study areaof Greater Manchester, situated in North West Eng-land. In the first phase of the process a local climateimpacts profiling exercise was carried out to identifythe principal extreme weather-related events that haveoccurred in Greater Manchester in the past. The secondphase involved an independent quantitative evaluation ofextreme events using historical meteorological recordsfrom the UK Met Office. In the final stage of the workthe findings from phases 1 and 2 were combined andstatistical methods were employed to extract key mete-orological thresholds which have in the past resulted inimpacts which have affected human health/well-being,have caused damage to the urban infrastructure or haveseverely disrupted services.

2. Greater Manchester climate impacts profile

Greater Manchester offers an interesting case study forinvestigation as it is home to some 2.5 million people.With population densities reaching 3779 people km−2

in central areas (Office for National Statistics, 2008),it is one of the most densely populated regions in theUnited Kingdom. Almost two thirds, 62%, of the regionis characterized as urban (793 km2), which is comprisedof a range of land use types and built forms (Gill et al.,2008). However, the region also encompasses more rural

Table I. Archive sources consulted to construct the database ofpast meteorological events in Greater Manchester, 1961–2009.

Organization Source of data

Association of BritishInsurers

Supplementary information,table VII: major weatherincidents in the UnitedKingdom

Association ofGreater ManchesterAuthorities (AGMA)

Local authorities asked tocollate data from their civilcontingencies, education,highways, social services,drainage, media and archivedepartmentsStrategic flood risk assessmentfor Greater Manchester

Environment Agency Catchment management plansGreater ManchesterFire and RescueService

Flooding call-out records

Local media reports(Manchester EveningNews, BoltonEvening News,Ashton Reporter, Postand Chronicle, WiganObserver, WiganEvening Post)

Available from ManchesterCentral Library Archives andlocal studies unit

North West PublicHealth Observatory

Emergency hospital admissionsdatabase

areas of surrounding farmland with contrasting soil types,and has a significant altitudinal gradient (from 540 mabove sea level in the northeast to less than 100 m in thesouthwest).

A database of weather related events, which havenegatively impacted on human health, human well-being and on infrastructure and housing or have causeddisruption to services in Greater Manchester for theperiod 1961–2009 was constructed through consultationof a variety of archival sources. This formed part of abroader Local Climates Impact Profile which was carriedout for Manchester City Council. The key sources of dataused are listed in Table I.

For the 30 year period 1961–1990, 94 weather-related events were identified across Greater Manchester.Between 1971 and 2000, 112 weather-related events wereexperienced. These are events which manifest as flood-ing, heat stress, storm damage and tree falls, lightningdamage and wild fires, weather related transport damageand delays, ground instability (land slides and subsi-dence), poor air quality due to fog and smog, droughtor extreme cold (hypothermia and ice damage). The risein the number of events for the two 30 year periods doesnot necessarily indicate that extreme events are becomingmore frequent, but may simply be the result of growingmedia interest in climate change related impacts (Boykoffand Rajan, 2007).

Copyright 2011 Royal Meteorological Society Meteorol. Appl. 19: 26–35 (2012)

28 C. Smith and N. Lawson

Figure 1. Proportion of extreme events by type from 1961 to 2009derived from archive searches: flood (horizontal zig-zag), wind/storm(diagonal bold stripe), low temperature (white), fog (grey), high

temperature (black), drought (spot).

The proportional split of different types of extremeevent shows some variation over the period 1961–2009(Figure 1). Summertime events associated with hightemperatures or reduced precipitation amounts do notfeature at all prior to 1971 but now account for ∼10%of extreme events. This is reflective of the significantincreasing temperature trend experienced by the NorthWest between 1961 and 2006 (Jenkins et al., 2008). Inparticular, maximum summer temperatures have risen by1.63 °C during this period, and this has been coupled witha 13.2% decrease in summer precipitation (Jenkins et al.,2008). While the percentage of reported wind events hasfluctuated slightly, the actual number of events between1961 and 2000 has not changed (five per decade in1961–1970; six per decade from 1971 onwards). Thisis in contrast to the suggested UK-wide increase inwindstorms made by Jenkins et al. (2008). However,as Jenkins et al. (2008) state, trends in storminess aredifficult to identify due to the low number of eventsannually. In addition, their analysis at the UK aggregate-level may not be representative of storm trends in theNorthwest. Furthermore, this contradiction may be due to

improved building standards and arboreal services and thereduction in pedestrians on the streets in suburban areasleading to a decline in the severity of the impacts on thelocal population and/or infrastructure, and consequentlya lack of archive evidence of such events.

Flooding is the dominant climate impact within theregion across all decades, and is found to be attributablefor 37–55% of decadal extreme events. There is somesuggestion from the database that flood events are becom-ing more frequent (Figure 2). This would correspond tochanges in precipitation patterns exhibited over the sameperiod, exemplified by both winter precipitation totals andthe frequency and contribution of intense rainfall eventsincreasing (Osborn and Hulme, 2002). However, the timeseries is not sufficiently long enough to draw any robustconclusions. Indeed, the majority of pluvial flood eventsactually occur during the summer months when overallprecipitation totals have declined. This may indicate thesignificance of brief but very intense episodes of rainfallduring the summer and/or changes to land use over time,such as urban infill trends, which increase surface runoffand consequently pressure on drainage systems.

Mapping the location of flood event occurrencethrough time (Figure 3) suggests that flooding in thenorth of the region (Irwell catchment) has become moreprevalent over the past decade. To the south of the region(Mersey catchment) the number of events has remainedcomparatively high throughout the period under investi-gation, but there has been a shift from riverine to pluvialevents. In particular, riverine flooding on the Mersey hasdecreased considerably since the opening of the Sale andDidsbury temporary flood storage basins in the 1970s(Environment Agency, 2009). The decrease in the sever-ity of recent flood events, with respect to the impact onthe health and well-being of the local population, infras-tructural damage and delivery of services as suggestedby the various archive records (Table I), is perhaps areflection of the success of flood prevention measuresthroughout Greater Manchester in recent years.

While the process of collating this evidence basehas been rigorous and has drawn on a broad range ofresources, there are several external factors which maylimit the completeness of the extreme events database.

Figure 2. Number of pluvial (black) and fluvial (grey) flood events per decade from 1961 to 2009 derived from archive searches.



Figure 3. Georeferenced flood events from archive evidence in Greater Manchester from 1961 to 2000.

Namely, other important sources of information on theimpacts of weather-related events in Greater Manchestermay have been overlooked, and those that were consultedmay be incomplete. For example, the press is inconsistentin recording weather related events. As well as dependingon the whims of journalists, the recording of such eventswill also be influenced by the amount of other news-worthy events at the time. Furthermore, it is not alwayspossible to extrapolate the precise location of the eventfrom media reports. For example, an event is invariablyrecorded as having taken place ‘in Salford’, ‘in the IrwellValley’, ‘across north Manchester’. In addition, some ofthe Local Authority officers consulted experienced diffi-culty in obtaining records of past weather related eventsfrom within their respective departments. The recordingof past weather related impacts by Local Authorities ishaphazard, a weakness that should ideally be addressedif climate change adaptation is to be a priority for thefuture.

3. Quantifying extreme events in GreaterManchester

Ringway (Manchester International Airport), offers theonly suitably long, uninterrupted record of observedmeteorological data in Greater Manchester. However,there is a denser network of rainfall monitoring sta-tions, due to the high spatial variability of precipitation(Figure 4(a)). Daily meteorological data for Ringway,along with precipitation data for 14 stations with contin-uous or near continuous rainfall records, were extractedfrom the British Atmospheric Data Centre (BADC) forthe period 1961–2009. Extreme events for a range of cli-mate variables were classified using the Ringway dataset

according to a set of objective criteria (Table II). Theseindicators are based upon previously defined indices(Frich et al., 2002; Alexander et al., 2006) but are nec-essarily more stringent than those that have been appliedat a global scale due to the spatially localized nature ofthis study. The percentiles and other criteria used to clas-sify extreme events were set at a level when impactswhich affect human health/well-being, cause damage tothe urban infrastructure or severely disrupt services ishighly likely to occur.

3.1. Temperature extremes

Extremely high temperature events are quantified asthose days where maximum daily temperature (Tmax)was greater than or equal to 29.2 °C. The frequencyof these events has risen from 0.2 per annum during1961–1970 to greater than 1 per annum over the pasttwo decades (Figure 5). Although the majority of the hightemperature days experienced during the latter decadesoccurred during the summers of 1995 and 2003, the dataexhibit an upward trend in the frequency of hot daysacross all decades, which is consistent with findings fromprevious studies (Jenkins et al., 2008). Over a quarter ofthe interdecadal variation in high temperature extremes isattributable to the increasing tendency toward extremesover time. In general, the days when extreme maximumtemperatures were recorded coincided with days whenTmin was also considered unusually high. However, onover a quarter of occasions when Tmax was high butnot classified as extreme, Tmin was classed as extreme.In relation to human health and well-being it is oftenelevated night time temperatures which are consideredmore serious than the daytime maxima (Kalkstein, 1991;



(a)

(b)

Figure 4. (a) Met Office precipitation stations situated in Greater Manchester shown as graduated symbols indicative of the 99th percentile dailyprecipitation amount in millimetres. (b) Contour map showing the precipitation gradient across Greater Manchester based on the 99th percentile

daily precipitation amount in millimetres.

Hajat et al., 2002), and this must therefore be taken intoaccount when defining critical meteorological thresholdsfor the local population.

The time series of extreme low temperature eventsmirrors that of the high temperature events, displayingan overall decreasing trend across the five decades(Figure 5). As with high temperature extremes, over25% of the changing interdecadal frequency of lowtemperature extremes is accounted for by the time seriestrend. However, the dataset is skewed somewhat bythe unusually cold decade from 1961 to 1970. Indeed,quantifying an extremely low temperature event for theclimatological periods 1961–1990 and 1971–2000 givesa 0.8 °C discrepancy (daily Tmax < −2.4 °C and dailyTmax < −1.6 °C, respectively).

3.2. Precipitation extremes

Extreme precipitation events are classed as days with totalprecipitation exceeding or equal to 24.6 mm, based on theRingway dataset. However, due to the significant NE-SWprecipitation gradient which exists across Greater Manch-ester (Figure 4(b)), there is a 14 mm difference in thedefining threshold for extreme precipitation at meteoro-logical stations situated across the region. Ringway hasthe lowest threshold and Broadhead Noddle (Figure 4(a))the highest (38.6 mm). Moreover, extreme events at thesetwo stations coincide on less than a third of occasions(31% of the time), emphasizing the spatially variantnature of intense precipitation episodes. Consequently,



Table II. Indicators used to identify extreme weather eventsfrom historical Met Office records.

Weather event Extreme events identified as:

High temperature 1961–1990 0.995 percentile(Tmax ≥ 29.2°C; Tmin ≥ 22°C)

Low temperature 1961–1990 0.005 percentile(Tmax ≤ −2.4 °C)

High precipitation 1961–1990 0.995 percentile (Ppn≥24.6 mm)

Low precipitation Consecutive dry days (CDD) ≥15Heavy snowfall 1961–1990 0.995 percentile (snow

amount ≥6 cm)High wind speeds Gale days (mean wind speed reaches or

exceeds 34 km for a period of 10 min)

some form of distance function is required in order toestablish the most appropriate precipitation risk metricfor a particular location, based on the nearest and mostgeographically comparable meteorological station.

There is an absence of a temporal trend in thefrequency of extreme precipitation events across theprecipitation monitoring network. All stations display anapparent oscillatory pattern, switching between decadeswith a high number of extreme rainfall events (>2.2 perannum during 1961–1970, 1981–1990, 2001–2009) andthose with a comparatively low number (<1.5 per annumduring 1971–1980, 1991–2000). Osborn and Hulme(2002) found a seasonal disparity in trends of heavyrainfall events across the United Kingdom, with heavywinter precipitation events increasing in frequency whilstthe number of intense summer rainfall events decreased.To check whether this seasonal difference was beingaveraged over the annual scale data, the data were alsoanalyzed for summer and winter separately. However,there was no evidence of the existence of a trend in eitherthe winter or the summer datasets. The lack of a trend inthe meteorological data, at odds with the archive evidence(Figure 2), is likely to be the result of the daily resolutionof the dataset. The daily total precipitation is too coarseto capture the short duration (maximum 3–4 h), highintensity rainfall episodes that lead to excessive runoff,and cause pluvial flooding.

Heavy snowfall events (daily snowfall ≥6 cm) havedropped off markedly since 1970 and almost threequarters (R2 = 0.74) of the interdecadal variation inthe frequency of extreme snow days is explained bythe temporal trend. Given the lack of trend in theprecipitation dataset it is likely that this decreasing trendis due to the significant winter warming which hasoccurred over the past 50 years. It is also worth notingthat the winter North Atlantic Oscillation index has beenin a positive phase since 1970 (Jenkins et al., 2008).This is associated with warmer and wetter conditions innorthern Europe, which may provide some explanationfor the decline in heavy snowfall events since thistime. There is also evidence that snowfall events arebecoming less extreme. Prior to the heavy snowfall

that occurred during winter 2009/2010 there were nooccurrences of daily snowfall exceeding 10 cm since1984, in spite of events of this magnitude being notuncommon during the 1960s, 1970s and early 1980s.Although the frequency and intensity of extreme snowfallevents are decreasing, the archive evidence suggests thatthe disruptive consequences in the urban environmentmay be increasing due to the changing provision ofmitigation tools, such as snow ploughs and localizedsupplies of salt and grit. It is therefore necessary toconsider the non stationarity of the social, political andphysical environment when considering what constitutesan extreme event in the past and how this can betranslated into the future.

Drought is a complex process to objectively quantifyas it is a function of several physical, biological andsocio-economic factors, and unlike other extreme eventssuch as heat waves or floods, the effects are the resultof a longer term deviation from average climatologicalconditions. For example, the hosepipe ban that came intoeffect in July 2010 in northwest England was the resultof an extensive period of drier than average conditions,with much of the region receiving less than 70% ofthe 1971–2000 average spring rainfall amount (MetOffice, 2010). Here, low precipitation as an indicator fordrought conditions is based on the consecutive numberof dry days (daily precipitation <1 mm) rather than apercentile, following from previous work on extremeclimate indicators (Frich et al., 2002). This is necessaryto capture the aggregation of low rainfall days over timewhich may lead to drought, and because precipitationexhibits a strong negatively skewed distribution deeminga percentile approach inappropriate. The two periods withthe highest number of consecutive dry days occurred inSeptember/October 1986 (CDD = 30) and in March 1993(CDD = 29). There is no evidence that drought incidencehas increased over time, however, the number of summerdroughts has increased consistently decade on decade.This supports previous findings that summer precipitationtotals are decreasing (Osborn and Hulme, 2002; Jenkinset al., 2008). This is potentially a more serious threatfor the local population and environment as it coincideswith the time of year when soil moisture conditions mayalready be stressed and it is more likely that the droughtconditions will be coupled with high temperatures.

3.3. Storminess and wind extremes

The UK Met Office identifies extreme wind events as‘Gale Days’ in their climate records. These are definedas a day when mean wind speed reaches or exceeds34 km (force 8 on the Beaufort scale) over a periodof at least several minutes (10 min in the case of astation equipped with an anemograph). As with extremesnowfall events, wind events have reduced rapidly since1970 and exhibit an overall decreasing trend with time(Figure 5). However, the magnitude of events shows nooverall trend, with incidents of windspeeds exceeding 60



Table III. Contingency table to compare the results of the archive search with the meteorological threshold for extreme hightemperature events derived from Met Office data.

Classified as extreme byMet Office data

Not classified as extremeby Met Office data

Total

Extreme event identified from the archive search a = 19 b = 2 21No extreme event identified from the archive search c = 8 d = 4479 4487Total 27 4481 n = 4508

Figure 5. Frequency of extreme events identified by applying the thresholds from Table II to Ringway Met Office data: flood (horizontal zig-zag),wind/storm (diagonal bold stripe), low temperature (grey), heavy snowfall (white), high temperature (black), drought (spot).

knots occurring in all five decades between 1961 and2009.

4. Meteorological thresholds for adaptive planning

In order to establish meteorological thresholds for GreaterManchester, which are indicative of extreme events,the two sources of temporal analogue data discussedabove, are combined. A comparison is made betweenthe dates of extreme events as classified by the objectiveanalysis of the Met Office data and the dates of weatherrelated events in Greater Manchester which have resultedin impacts on human health/well-being, infrastructureand/or service provision, extracted from the historicalarchive search. This allowed for the construction ofcontingency tables (as an example Table III). These canthen be used to calculate verification statistics whichoffer a useful gauge to establish the robustness ofthe quantitative thresholds as a warning mechanismfor extreme weather event occurrence (Thornes andStephenson, 2001).

The type of information that can be extracted byanalyzing the data in this way includes the percentage oftimes the two methods independently recognize an eventas being extreme:

PercentCorrect = (a + d)

n× 100 (1)

However, due to the intrinsic rarity of extreme events,this measure is likely to be heavily skewed by the largeamounts of days when an extreme event doesn’t occur.Perhaps more important parameters, particularly froma planning for adaptation perspective, are the risks of

Type 1 and Type 2 errors. Type 1 errors (denoted asb in Table III) are defined as days when an extremeevent has been recognized to occur from the archiveevidence, resulting in a negative impact on the localpopulation, infrastructure and/or service delivery, but nocorresponding extreme event has been identified fromthe historical Met Office data. Consequently, if themeteorological thresholds were being used in conjunc-tion with weather forecasts as an alert for an extremeevent occurrence, the days with a Type 1 error wouldbe those days when it is deemed safe to take noaction, assuming an accurate meteorological forecast,when actually the weather conditions do cause harmto the local population or environment. These are dayswhen some form of preparedness would be beneficial butthe conditions appear to decision makers to be of lowrisk.

Type 2 errors, in contrast, occur when the meteorologi-cal conditions suggest that there is likely to be an extremeevent which will impact on the local area, when actuallythe day passes without significant consequence for localinhabitants. Type 2 errors are considered less serious thanType 1 errors. In this instance a Type 2 error would implythat unnecessary action is taken by, for example, thelocal authorities. There may be a cost associated withthis, however this is likely to be less costly than damageto infrastructure, disruption to services and in the worstcase, loss of lives, which may arise due to the result ofinaction during a Type 1 error day.

Other measures of quantifying the reliability, accuracyand skill of climate metrics are discussed by Thornes andStephenson (2001). These include calculating bias (B) as



an indicator for reliability:

B = a + b

a + c(2)

using the Miss (M) and False Alarm Rates (F ) toestablish accuracy:

M = c

a + c, F = b

b + d(3)

and using skill scores such as, the Odds Ratio Skill Score(ORSS):

ORSS = ad − bc

ad + bc(4)

4.1. Temperature extremes

The threshold of 29.2 °C, extracted from the analysisof the Ringway Met Office data, as an indicator ofan extreme high temperature event produces favourableresults. As a metric of negative impact upon the localpopulation, environment or service provision the thresh-old was correct on 99.78% of days. However, there weretwo occasions when a Type 1 error occurred and eightwhen a Type 2 error occurred (Table III). The number ofType 2 errors can be reduced, and even eliminated, bymaking the threshold more stringent. For example, everyday with a maximum temperature greater than 30.9 °Ccoincided with an archive report associated with thenegative consequences of higher temperatures. However,increasing the threshold will not help to reduce Type 1errors, which are arguably the more severe outcome.

As a tool for communication and adaptive planningit may be appropriate to provide the threshold warninginformation along with some indication of the likelihoodof climate risk. For example, a traffic light warningsystem could be used to denote the level of preparednessrequired, given a temperature forecast of a particularmagnitude. To illustrate, for the extreme high temperaturescenario a red light would be used for days wheremaximum temperature is predicted to exceed 30.9 °C,signifying that it is very likely that the local populationand/or environment will suffer negative impacts andtherefore decision makers ought to take action. Formaximum daily temperatures between 29.2 and 30.9 °Can amber warning would be applied, indicating that thereis a likelihood of negative impact and that stakeholdersshould be prepared to take action. For temperaturesbelow this threshold a green light would indicate that thethermal environment falls within the safe boundaries and,therefore, no action is required. This type of informationallows for more informed decision-making and couldbe integrated into a more detailed cost-benefit analysis(Thornes and Stephenson, 2001).

The few ‘heat’ events extracted from the archivesources which were not picked up by the Met Office datawere often indirect consequences of the high tempera-ture/heat wave conditions, for example poor air quality,lightning and water shortages. These are all the result of

a complex interplay between a number of meteorologicalvariables and, therefore, may not lend themselves to iden-tification by assessing a single meteorological variable inisolation.

Low temperature extremes are not adequately capturedby using a low temperature threshold alone. The impactsof low temperatures that prove hazardous are associatedwith ice and snow, and therefore a suitable climate warn-ing system would require additional information aboutprecipitation and atmospheric stability in combinationwith temperature data.

4.2. Precipitation extremes

The precipitation threshold of daily precipitation≥24.6 mm derived from the Ringway data is an unreli-able indicator of an extreme event. The number of Type 1and Type 2 errors are 28 and 47, respectively, and theMiss Rate is greater than 0.5. The number of Type 2errors can be reduced considerably by raising the thresh-old to 38 mm (0.999 percentile). Now, all but three ofthe days exceeding this threshold correspond to reportsof local flooding, with two out of these three occurringin 2003, which may be the result of recent improve-ments to infrastructure and drainage to withstand extremeevents. The large amount of Type 1 errors will still exist,however, as days with precipitation totals much lowerthan this are often reported as flood events. This is aconsequence of the high spatial variability of precipita-tion, which means that events in the north of the region(Bolton, Wigan, Rochdale) could not be identified usingthe Ringway data. Using the Broadhead Noddle data,with the higher threshold calculated for this station, inconjunction with the Ringway data, reduces the numberof Type 1 errors by 50%. Specifically, by incorporatingthis additional constraint the thresholds are now capableof identifying riverine flooding events, which have beenextracted from the archive searches, associated with theRiver Irwell and the River Douglas (Figure 3). Pluvialflooding events, however, remain difficult to identify dueto the temporal resolution (daily total) of the precipitationdata being insufficient (Section 3.2). Other data sources,such as the hourly rain gauge network maintained by theEnvironment Agency, may in future provide a more use-ful means of forecasting flood risk. However, becausethis dataset was not available for the full extent of theperiod under investigation here (1961–2009) and becausethe data may not be freely available to decision-makers,this dataset was not used in the current analysis.

Days with snowfall greater than or equal to 6 cm werefrequently reported as having caused some disruption tothe region, but the Miss Rate using this criterion stillexceeded 0.5. The majority of these unreported eventsoccurred between 1961 and 1970, and this may havebeen because events of this magnitude were not con-sidered particularly unusual at this time. By discountingthe ‘Misses’ during this period, the Miss Rate improvesto 0.19, with the remaining unreported incidents identi-fied as isolated events, where heavy snowfall (6–8 cm)



occurred on an odd day during an otherwise climato-logically average period. There is a slight bias usingthis threshold (0.87), which indicates underforecasting ofextreme events and therefore some risk of a Type 1 erroroccurring. This bias can be reduced by using the consec-utive number of snow days as an additional criterion forextreme event identification.

Very few drought events were extracted from thearchive searches. All of these were matched by eventsin the Met Office database using the 15 consecutive drydays criterion (Table II). On the other hand, there is aconsiderable number of occasions when this criterion isexceeded yet no drought is identified in the archives. Thiscan be attributed to drought being a more complex eventto capture from a single meteorological variable, com-pared to flooding and heat wave events. Similar to thetemperature traffic light warning system described above,it may be that planning for drought should simply be ona ‘be prepared’ basis. Alternatively, further analysis mayreveal drought to be definable through a specific combi-nation of two or more variables.

4.3. Storminess and wind extremes

The more stringent threshold (than that indicated inTable II) of days with a maximum gust speed of greaterthan or equal to 60 knots, as identified from the Ring-way data, were all found to correspond to reports in thearchive sources of negative impacts to the local popula-tion and/or environment. A number of days with lowermaximum gust speeds were also reported (Type 1 errors).These were all recorded as gale days and still experiencedhigh wind speeds but in these instances it may be that thewind speed was high over a longer period of time whichcaused more damage than can be inferred from the max-imum wind speed alone. In addition, some of the stormand gale events which were unidentified by the Ring-way data analysis were located in the north of the regionand therefore may have been subject to differing condi-tions than those recorded at Ringway due to localizedand topographical (particularly altitude) differences.

In light of these Type 1 errors, it may seem sensible tolower the threshold which corresponds to extreme windevents. However, this would then cause the number ofType 2 errors to rise. This trade off between Type 1and Type 2 errors is a weakness of using the objectivequantitative metrics but may in part be overcome byusing the sliding scale of risk indicated by the trafficlight system, a more detailed cost-benefit analysis ofpast events and/or the integration of other meteorologicalparameters. This latter suggestion is borne out by ahigh proportion of Type 1 events which occurred ondays subject to high wind speed conditions (>34 knots)together with heavy rain or snowfall.

5. Conclusions

This paper has examined the variation of extreme eventoccurrence over the past five decades. Extreme events

have been identified using two methodologies. The firstapproach made use of historical archive information inorder to build up a picture of how extreme events haveimpacted upon the population, environment, infrastruc-ture and services of Greater Manchester. The secondapproach involved the quantitative analysis of past mete-orological data from the UK Met Office. The outcome ofthe latter approach was, to some degree, particularly forsome variables, influenced by the altitude and location ofthe Met Office station used (Ringway).

Temperature extremes, with respect to the 1961–1990baseline period, are found to have increased for hightemperatures but decreased for low temperature eventsin both datasets. In line with these trends, extremesnowfall events are also found to have declined overthe past five decades. These findings are consistent withother studies, and are projected to reflect longer termpatterns of meteorological change for Greater Manchesterin the future (Jenkins et al., 2008; Murphy et al., 2009).There was some disagreement between the two datasources in relation to temporal trends in precipitationextremes. While both approaches suggested summerdrought occurrence to be increasing in frequency, onlythe archive database revealed an increasing trend inflood events from intense precipitation episodes, notrend was evident in the meteorological data. This may,however, be the result of the daily precipitation totaldata being too coarse to effectively resolve the shortduration, high intensity rainfall episodes responsible forpluvial flooding. Storms, and in particular wind extremes,are difficult to capture. There is some evidence of adecreasing trend in the frequency of high wind speedevents: however, the magnitude of the maximum gustspeed associated with extremes has remained stable.

An attempt was made to define quantifiable thresholdsindicative of extreme events by combining the qualitativearchive evidence with the quantitative records of meteo-rological variables. Such thresholds offer a useful meansof evaluating the risk to the local population and/or envi-ronment for local stakeholders and decision makers whenpresented with weather forecast information. They aredesigned as a guidance measure to be used in conjunc-tion with other relevant information in order to supportinformed decision making as oppose to being used toillicit an ‘Action vs No Action’ response. This latterresponse, as demonstrated in Section 4, could potentiallyprove hazardous.

The thresholds had varying degrees of success. Forextremes which are the function of a single meteoro-logical variable (e.g. heat waves, pluvial flooding, heavysnowfall) the thresholds proved to be reliable and skillful.However, caution should be used when applying them tohighly spatially variant parameters, such as precipitation.For these variables it may be necessary to ensure that thethresholds are based on a meteorological station a mini-mum distance away or which is geographically similar, orderived from spatially interpolated climate datasets (e.g.Perry and Hollis, 2005). Extreme events which are theresult of a more complex interaction between variables



(e.g. drought, freezing conditions) were less well cap-tured by applying the thresholds associated with a singlevariable in isolation. This shortcoming of the thresholdapproach could be overcome through further analysis andthe integration of multiple meteorological parameters intoa single extreme metric. Another weakness of the currentapproach is the trade off between Type 1 and Type 2errors which is required when assigning the quantitativethresholds. The use of a sliding scale of climate risk (traf-fic light approach) when communicating the thresholds,and a more comprehensive cost-benefit analysis approachmay assist with this issue.

In general, the threshold approach for the identificationof extreme events outlined here offers a valuable tool forthe region’s decision-makers, stakeholders and popula-tion. The quantitative and objective means of identifyingan extreme event allows for informed decision makingand preparedness, and will inevitably assist in reducingcosts and minimizing risk to the local population andenvironment. When used in conjunction with projectionsof future climate change (e.g. UKCP09), the thresholdswill help to determine the probabilities of extremes occur-ring in the future and identify the elements or areas mostat risk. This information can subsequently be used to pri-oritize and target climate adaptation strategies, with theimportant caveat that extraneous factors are nonstation-ary (Wilby et al., 2009). For example, improvements toadaptive capacity (e.g. changes to surface cover proper-ties, policy interventions) may mean that society and theenvironment become increasingly resilient to extreme cli-mate events, and therefore the thresholds may have to bere-evaluated and adjusted accordingly.

Acknowledgements

Claire Smith gratefully acknowledges the support ofManchester Geographical Society for their contribu-tion to this work. Nigel Lawson was funded by theEcoCities project. Thanks also to all of the peoplewho assisted with the collation of data on past extremeevents: Dave Hodcroft (Bury MBC), Jonathan Mayo(Bolton MBC), Georgina Brownridge (Oldham MBC),Barry Simons/Jonathan Kershaw (Rochdale MBC), AndyWilliams (Stockport MBC), Christina Sexton (Tame-side MBC), James Noakes (Wigan MBC), Paul Need-ham/John Thompson (Environment Agency) and KenBrown (GM Fire and Rescue Service). The Met Officedata were accessed via the British Atmospheric DataCentre.

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Identifying extreme event climate thresholds for greater Manchester, UK: examining the past to prepare for the future