8Incorporating climate change withinasset managementRalph Rayner Professor, Centre for the Analysis of Time Series, LondonSchool of Economics and Political ScienceA description of established techniques for deriving environmental criteria forthe management of assets is presented, followed by a review of the principalissues surrounding the practical application of uncertain knowledge of thefuture climate. A case study of the Thames Barrier provides an example ofbest practice.1 IntroductionPhysical assets such as buildings, oﬀshore structures and transportationsystems operate in a dynamic environment where they are exposed toshort, medium and long-term variability in ambient environmentalconditions. An important input to asset management is an adequateunderstanding of this variability. This typically includes the estimationof environmental conditions that can be expected over the life of anasset (e.g. an oﬀshore structure) or a system of assets (e.g. a transportation system). Engineers and asset managers employ these environmentalcriteria as the basis for understanding the impact of the environment onproposed or existing physical assets. Criteria may be derived on an assetspeciﬁc basis or through the application of predetermined codes.Environmental criteria are used as inputs to the design and constructionof an asset, to the planning of operations and to gain an understandingof through-life maintenance requirements.Environmental criteria usually take the form of a statistical view ofthe variability of conditions within which the asset must operate: forexample, wind speed variability to determine the wind loading on abuilding, wave height variability to determine the loading on an oﬀshorestructure, or air temperature variability as an input to the design ofrailways or roads.For operational planning, daily, weekly, monthly or seasonal variability of an environmental parameter which has an impact is oftenrequired: for example, the likely exceedance of a threshold limitingcondition for the operation of a port or airport. To understandthrough-life maintenance costs, an understanding of the relationshipAsset management – Whole-life management of physical assets978-0-7277-3653-6# Thomas Telford 2010All rights reserved
162Asset management – Whole-life management of physical assetsbetween environmental factors and the deterioration of the asset isneeded, such as the impact of continuous wave loading on thedeterioration of key structural components of an oﬀshore structure.A critical input to design is an analysis of the most extreme environmental conditions that an asset must be designed to withstand: forexample, the maximum wind speed that a building must withstand orthe extremes of air temperature under which a road or railway must beable to operate. Design codes and asset-speciﬁc design studies typicallyprovide an estimation of the magnitude of the most extreme event thatmight be expected to occur at least once in a speciﬁed return period.The selection of return periods is based on the expected life of theasset and the economic, safety and environmental risks associated withdamage or structural failure. The selection of diﬀerent return periodspermits trade-oﬀs between economic, safety and environmental impactsand the costs of construction to be taken into account. For example,oﬀshore oil and gas production facilities have typically been designedfor a 100 year return period, and coastal nuclear power stations for areturn period of 10 000 years or more. Steven Male talks more aboutthe importance of climate change in long-life asset management inChapter 3, ‘The challenges facing public sector asset management’.2 Use of time historiesThe fundamental basis for meeting the environmental information needsof asset management is the analysis of time histories of environmentalvariables. Time histories can be derived in a number of ways. Theymay be calculated (e.g. astronomical tidal elevation) or measureddirectly (e.g. location-speciﬁc measurements of wind speed and direction). Empirical formulae are frequently employed to compute thevalue of a variable at a location which diﬀers from where it was observed;for example, wind speeds measured at one elevation are often convertedto values at a diﬀerent elevation based on empirical factors derived fromexperiment. For spatial interpolation, numerical modelling techniquesare frequently employed. Numerical modelling also provides a basisfor deriving time histories of one variable from knowledge of another;for example, ocean wave characteristics derived from surface windsbased on the knowledge of the physics of wave generation. Manydiﬀerent types of model are widely used to describe data and processes.For determining extremes, the length of available time histories isalmost always signiﬁcantly less than the design return period, soprobabilistic techniques are used to extrapolate to longer periods.
Section 3: Incorporating climate change within asset management163Generalised extreme value distributions such as Weibull, Fisher Tippettand Gumbel (Kotz and Nadarajah, 2000) or generalised Paretodistributions (Falk et al., 2004) are used for this purpose.3 Uncertainty in environmental criteriaSources of time history data and the tools used to analyse or extrapolatethem are subject to a range of generic uncertainties.For measured data there are uncertainties associated with the accuracyand precision of measurement devices. Empirical factors for inferringvariation in an environmental parameter at a location diﬀerent to whereit was measured are generalised approximations. Numerical modelsintroduce uncertainty through inadequate mathematical representationof processes, errors in parameterisation, omission of important processes,and spatial and temporal smoothing across model grids and time steps.In Chapter 5, ‘Asset management strategy: leadership and decisionmaking’, Penny Burns highlights the risk of basing future strategy onhistorical information alone. A key source of uncertainty is the assumption that past time histories will be statistically representative of thefuture. Time histories must be long enough to capture annual variability,but how long do they need to be to adequately capture inter-annualvariability?It has long been understood that the natural climate is not stationary.This is self-evidently the case over geological timescales, where we knowthat climatic conditions, such as temperature, precipitation and sea level,lay well outside of anything observed during human history. Even overthe period of human history, climate is known to have varied considerably. As climate science has advanced, natural climate cycles have beenidentiﬁed which operate on multi-year scales.The best known of these is the El Niño-Southern Oscillation (ENSO).Driven by large-scale sea surface temperature ﬂuctuations in the tropicalEastern Paciﬁc, this natural climate cycle is associated with ﬂoods,droughts and other disturbances at a range of locations around theworld. ENSO is the most prominent known driver of inter-annualvariability in weather and climate around the world, and has a periodof between 3 and 8 years (Glantz, 2000).A number of other long-period natural cycles which have far-reachingeﬀects have also been identiﬁed. Examples include the North AtlanticOscillation (NAO), which is responsible for much of the variability ofweather in the North Atlantic region during the November to Aprilperiod, aﬀecting wind speed and wind direction, temperature and
164Asset management – Whole-life management of physical assetsmoisture distribution and the intensity, number and track of storms(Hurrell and Loon, 1997), and the Indian Ocean Dipole (IOD), whichinﬂuences climate throughout the Indian Ocean region, including eﬀectson the magnitude of Indian monsoons (Saji et al., 1999).Established techniques for deriving environmental criteria are allbased on the assumption that the use of a long enough time history ofpast variability will capture inter-annual variability suﬃciently toensure that it is incorporated into statistical summaries and projectionsof extreme events.This implicit assumption of stationarity is no longer valid when thereis a cycle longer than the length of the time history or if a single eventfrom a class of events, which are absent in the observed or modelledpast, occurs; for example, the occurrence of a hurricane at a locationwhere no hurricanes have previously been observed. A cycle or type ofevent not captured in the time history will mean that the statisticsof the past will not be representative of the future. Most signiﬁcantlyin the context of climate change, non-linear long-term trends whichmay extend far into the future are explicitly excluded.4 Climate changeThere is now a high level of conﬁdence that our climate is changing dueto human activity and especially due to emissions of greenhouse gases.The ‘greenhouse eﬀect’ is an essential component of maintaining ahabitable planet. Naturally occurring greenhouse gases (especiallycarbon dioxide, methane and nitrous oxide) eﬀectively trap part of theheat radiated from the earth’s surface. Were these natural greenhousegases absent, the earth would be too cold to support life as we know it.Direct measurements of carbon dioxide in the atmosphere show thatits concentration has been progressively increasing over the last 50 years.Indirect measurements based on analysis of gas bubbles trapped in icecores show a carbon dioxide increase of approximately one-third sincethe start of the industrial revolution. The majority of the increase froma pre-industrial level of 280 parts per million to a level of 387 partsper million in 2009 has been ﬁrmly attributed by the IntergovernmentalPanel on Climate Change (IPCC) to the burning of fossil fuels andchanges in land use (IPCC, 2007a). Current atmospheric concentrationsof carbon dioxide far exceed the natural range of the last 650 000 years(IPCC, 2007a).The eﬀects of increased greenhouse gases and land use changes onglobal climate are determined by the analysis of a wide variety of
Section 3: Incorporating climate change within asset management165observations and measurements and the use of mathematical modelswhich aim to provide insight into future change. The most sophisticatedof these are the so called atmosphere–ocean general circulation models(AOGCMs), which aim to reproduce many of the processes throughwhich greenhouse gases inﬂuence the earth’s climate.The Fourth Assessment Report of the Intergovernmental Panel onClimate Change (IPCC, 2007a) provides a synthesis of observed andprojected results of anthropogenic climate change. In summary:.The IPCC report ﬁnds with a very high conﬁdence that the globallyaveraged net eﬀect of human activities since 1750 has been one ofwarming. Global mean temperatures have been rising over the lastcentury with a more rapid rise since 1970. Average global loweratmosphere temperatures have increased by 0.748C, with most ofthis increase having occurred in the last 50 years. Climate modelsused to estimate temperature changes all project that it will bewarmer in the future, with global average warming of about0.48C expected during the next 20 years. Over the longer term,these models project average global temperature increases rangingfrom 1.18C to 6.48C by the end of the 21st century withconsiderable regional variation. Extreme temperatures are alsoexpected to increase. These projections are the result of integratingthe results from a range of global climate models under a variety ofscenarios for future economic activity and energy use. Over the last50 years, the frequency of cold days and nights has declined and thefrequency of hot days, hot nights and heat waves has increased.The number of days with temperatures above 328C and 388Chas been increasing since 1970, as has the intensity and length ofperiods of drought. The report ﬁnds it virtually certain thatwarmer and more frequent hot days and nights will occur overmost land areas during the next century.Over the past century, precipitation has increased in several regionswhile drying has been observed in others, notably in Africaand Asia. During the 21st century, increases in the amount ofprecipitation are very likely in high latitudes while decreases arelikely in most subtropical land regions. While the average levelsof precipitation will vary by region, the incidence of extremeprecipitation events is expected to increase.The IPCC reports that the globally averaged rise in sea level duringthe 20th century was 0.17 m and that average sea level rose at a rateof 1.8 mm per year between 1961 and 2003, with the majority ofthis rise being due to the thermal expansion of seawater. Excluding
166.Asset management – Whole-life management of physical assetsthe eﬀects of rapid changes in ice ﬂow from the polar ice sheets,model-based projections for global sea level rise over the nextcentury across multiple socio-economic scenarios are in the range0.18–0.59 m. These estimates are being re-examined in the lightof new evidence that glaciers and ice sheets could experiencemore rapid melting, leading to a signiﬁcantly larger global meansea level rise by the end of the century (Pfeﬀer et al., 2008).It is likely that future tropical cyclones will become more intense,with higher peak wind speeds and heavier precipitation. There iscurrently insuﬃcient evidence to clearly identify trends for otherstorm phenomena.5 Uncertainty in climate observations and projectionsObservations of climate change are subject to a range of measurementand analysis uncertainties. It remains the case that the earth is sparselymonitored, and this is especially true for the oceans. The inadequacyof the observational base, measurement accuracy and analytical techniques all contribute to uncertainty in global and regional measures ofobserved climate change.The cascade of uncertainty surrounding projecting future climatebegins with forcing uncertainties; to predict future
asset management Ralph Rayner Professor, Centre for the Analysis of Time Series, London School of Economics and Political Science A description of established techniques for deriving environmental criteria for the management of assets is presented, followed by a review of the principal issues surrounding the practical application of uncertain knowledge of the future climate. A case study of .
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