Download Predicting regional climate change: living with uncertainty

Progress in Physical Geography 23,1 (1999) pp. 57–78
Predicting regional climate change:
living with uncertainty
Timothy D. Mitchell and Mike Hulme
Climatic Research Unit, School of Environmental Sciences, University of East Anglia,
Norwich NR4 7TJ, UK
Abstract: Regional climate prediction is not an insoluble problem, but it is a problem
characterized by inherent uncertainty. There are two sources of this uncertainty: the unpredictability of the climatic and global systems. The climate system is rendered unpredictable by
deterministic chaos; the global system renders climate prediction uncertain through the unpredictability of the external forcings imposed on the climate system. It is commonly inferred from
the differences between climate models on regional scales that the models are deficient, but
climate system unpredictability is such that this inference is premature; the differences are due
to an unresolved combination of climate system unpredictability and model deficiencies. Since
model deficiencies are discussed frequently and the two sources of inherent uncertainty are
discussed only rarely, this review considers the implications of climatic and global system
unpredictability for regional climate prediction. Consequently we regard regional climate
prediction as a cascade of uncertainty, rather than as a single result process sullied by model
deficiencies. We suggest three complementary methodological approaches: (1) the use of
multiple forcing scenarios to cope with global system unpredictability; (2) the use of ensembles
to cope with climate system unpredictability; and (3) the consideration of the entire response of
the climate system to cope with the nature of climate change. We understand regional climate
change in terms of changes in the general circulations of the atmosphere and oceans; so we
illustrate the role of uncertainty in the task of regional climate prediction with the behaviour of
the North Atlantic thermohaline circulation. In conclusion we discuss the implications of the
uncertainties in regional climate prediction for research into the impacts of climate change, and
we recognize the role of feedbacks in complicating the relatively simple cascade of uncertainties
presented here.
Key words: climate change, climate modelling, downscaling, external forcing, regional climate
prediction, uncertainty, unpredictability.
I
Introduction
When a person, city or nation considers climate change, their primary interest is in
changes in the warmth, rain and wind that affect them and their local environments.
© Arnold 1999
0309–1333(99)PP214RA
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Predicting regional climate change: living with uncertainty
Nevertheless, it was the argument that greenhouse gases emitted by humans (‘anthropogenic emissions’) might substantially alter global climate that persuaded the global
community to establish the UN Framework Convention on Climate Change
(UN/FCCC) (Hecht and Tirpak, 1995) with the following objective: ‘Stabilisation of
greenhouse gas concentrations at a level that would prevent dangerous anthropogenic
interference with the climate system’ (UN/FCCC Article 2). The scientific evidence that
prompted countries to sign and ratify the UN/FCCC and then to negotiate the Kyoto
Protocol in 1997 (which sets specific targets for reducing greenhouse gas emissions –
Masood, 1997) has been summarized in a series of influential reports prepared by the
Intergovernmental Panel on Climate Change (IPCC) (Houghton et al., 1990; 1996). The
majority of the scientific evidence contained in these reports is concerned with the global
response of the climate system to anthropogenic forcing, but people, cities and nations
will be affected by the regional1 responses. The IPCC (Watson et al., 1998) was therefore
asked to prepare a special report on the vulnerability of natural environments and
human societies to climate change in different regions of the world, which was to
provide: ‘A common base of information regarding the potential costs and benefits of
climatic change, including the evaluation of uncertainties, to help the COP [Conference
of the Parties to the UN/FCCC] determine what adaptation and mitigation2 measures
might be justified’ (Obasi and Dowdeswell, 1998: vii).
Such accurate and precise information is highly desirable, but the chair of the IPCC
was forced to acknowledge that it could not be provided: ‘Because of the uncertainties
associated with regional projections of climate change, the report necessarily takes the
approach of assessing sensitivities and vulnerabilities of each region, rather than
attempting to provide quantitative predictions of the impacts of climate change’ (Bolin
et al., 1998: x). Underlying this acknowledgement is the belief that ‘models cannot
predict on regional scales’. Examples of this frequently heard refrain may be taken from
a single volume of Progress in Physical Geography (Joubert and Hewitson, 1997: 52;
Schulze, 1997: 118; Wilby and Wigley, 1997: 531) or from Henderson-Sellers’ (1996: 59)
review of the climate research process: ‘Unfortunately, numerical climate models and
the associated tools currently used to predict climate change, have no regional
prediction skill.’ If we regard GCMs (‘general circulation models’ or ‘global climate
models’) as unreliable at regional scales, and if we regard the palaeo-analogue approach
to regional climate prediction as inappropriate (Covey, 1995; Crowley, 1997), then it
appears that regional climate prediction is not yet possible.
In this review we demonstrate that if we equip ourselves with an understanding of
the fundamental role of uncertainty, then the prediction of regional climate change is
reduced from an insoluble to a difficult problem. Since it is commonly assumed that
model deficiencies are responsible for any differences between models at regional
scales, such differences are commonly used as ‘proof’ that ‘models cannot predict on
regional scales’. Having outlined in section II the differences between models at
regional scales, we discuss in section III the potential for downscaling techniques to
overcome the difficulty posed by the differences; we conclude that downscaling
techniques cannot correct for model inaccuracies and so they fail to overcome the
difficulty. In section IV we consider the possibility that model deficiencies are
responsible for the differences between models at regional scales. However, there is an
alternative explanation provided by climate system unpredictability, and in section V
we show that the dependence on initial conditions of a climate simulation means that
Timothy D. Mitchell and Mike Hulme
59
it is premature to infer from the differences between models at regional scales that the
models are deficient. Differences between simulations may (at least partly) be due to the
inherent unpredictability of the climate system.
Therefore, if we consider the problem of predicting regional climate change in more
general terms we find that in addition to the possibility that model deficiencies may
render our predictions uncertain (section IV), there are two sources of inherent
uncertainty in regional climate prediction: climate system unpredictability (section V)
and global system unpredictability (section VI). By ‘global system unpredictability’ is
meant the inherent unpredictability of the external3 forcings imposed on the climate
system, whether anthropogenic (e.g., greenhouse gas emissions) or natural (e.g., solar
variability or volcanic eruptions). The unpredictability of the climatic and global
systems is such that even with a perfect model, uncertainty would remain inherent to
regional climate prediction. Yet the climatic and impacts research conducted thus far
has largely considered uncertainty in terms of model deficiencies. Therefore we
suggest (in section VII) that the problem of regional climate prediction should be
viewed in terms of a cascade of uncertainty that stems from the unpredictability of
the climatic and global systems, instead of a single result process sullied by model
deficiencies.
Given this understanding of the uncertainties involved in regional climate prediction,
in section VIII we suggest three methodological approaches to deal with them: (1) the
use of multiple forcing scenarios to cope with global system unpredictability; (2) the
use of ensembles to cope with climate system unpredictability; and (3) the consideration of the entire response of the climate system to cope with the nature of climate
change. All three approaches apply equally to global and regional climate prediction,
but the entire response of the system to forcing (the third approach) is treated
differently at global and regional scales. So in section IX we show how regional climate
prediction is concerned in particular with the response of the general circulations of
the atmosphere and oceans. In section X we illustrate this discussion by considering
the North Atlantic thermohaline circulation, and by providing some examples of the
use of ensembles. In conclusion (section XI), we consider the implications of the
uncertainty of regional climate prediction for research into the impacts of climate
change, and we point out the role of feedbacks in complicating the relatively simple
cascade of uncertainties presented here.
II
Intermodel differences on regional scales
In the early 1990s there was very little confidence in the regional climate information
extracted from GCM integrations, and the models were held responsible because of
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the equilibrium method of forcing models with CO2-equivalent gases;
the lack of fully coupled atmosphere–ocean GCMs (AOGCMs);
coarse model resolution;
deficiencies in the model physics;
an inability to simulate present-day regional climate features; and
intermodel differences in simulations of regional climate change.
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Predicting regional climate change: living with uncertainty
Since then there have been a number of improvements in the climate models, notably
in the adoption of transient methods of forcing with CO2-equivalent gases, and in the
development of fully coupled AOGCMs.
Despite the improvements in the climate models, intermodel comparisons still reveal
substantial intermodel differences on regional scales. The IPCC Second assessment report
included a comparison of simulations from a number of AOGCMs for five particular
regions (Kattenberg et al., 1996), and more recent work (Kittel et al., 1998) has confirmed
the results reported by the IPCC. The models’ representation of the real world may be
judged by comparing the control simulations4 with observed climatologies; such
comparisons reveal that the biases in the seasonal cycle range between –7 °K and 10 °K.
These recent model experiments exhibit biases in the same range as those in the IPCC
First assessment report for control simulations with older models (Gates et al., 1990). If we
compare the models we find that over most regions the intermodel range of
temperature bias is of the order of 10 °K, and when AOGCMs are forced with
greenhouse gases, the climate ‘scenarios’5 of regional temperature and precipitation
change vary widely from model to model (Kattenberg et al., 1996; Kittel et al., 1998).
Figure 1 illustrates this variety of regional responses to forcing that a collection of
models typically exhibits. Even when one considers a global-scale phenomenon such as
ENSO (El Niño Southern Oscillation), both the model differences from observations,
and the wide range of results among models and between models and observations,
Figure 1 The differences between models on regional scales. Nine
model experiments (represented by different symbols) were reviewed
by Kattenberg et al. (1996) who presented the regional biases in
summer (JJA) temperature and precipitation for seven regions (the
eighth (GL) is a global average). Temperature biases are differences
(control minus observed); precipitation biases are differences as
percentages of the observed. Square brackets about the zero-bias line
span +σ to –σ for temperature, or the coefficient of variation for
precipitation, of observed regional averages
Source: Modified after Kittel et al. (1998)
Timothy D. Mitchell and Mike Hulme
61
remain; a model’s ENSO may be too strong (Tett et al., 1997), too weak (Knutson and
Manabe, 1994; Schneider and Kinter, 1994), or absent (von Storch, 1994). Intermodel
comparisons such as these have reduced our confidence in AOGCM scenarios of
regional climate change to a ‘low’ level (Kattenberg et al., 1996: 339), and the culprit is
generally held to be the AOGCMs’ low horizontal resolution and physical parameterizations (Gates et al., 1996).
III
Can downscaling help?
Intermodel differences have discouraged many researchers from using AOGCM output
on regional scales. Being aware of the acute need for reliable predictions of regional
climate change, some have therefore turned to downscaling techniques in their
attempts to overcome the differences between models on regional scales. Recent
reviews of downscaling techniques may be found in Hewitson and Crane (1996),
Kattenberg et al. (1996) and Wilby and Wigley (1997). Statistical downscaling techniques
commonly develop statistical relationships between local climate variables and model
predictors, and then apply those relationships to model climate scenarios to derive
estimations of localized climate change. Such statistical links may be made directly by
regression (Kim et al., 1984), via circulation patterns (Wilby, 1994) or via stochastic
weather generators (Wilks, 1992). Dynamical downscaling techniques commonly use
output from GCM simulations to provide the boundary and initial conditions for a
‘one-way nested’ regional model with a higher spatial resolution (Giorgi, 1990); a
variant approach is to develop a variable-resolution GCM (Deque and Piedelievre,
1995; Fox-Rabinovitz et al., 1997).
When applied to regional climate change prediction, the one element these
downscaling techniques have in common is their dependence upon the accuracy of the
large-scale information with which they are ‘driven’. This is true whether the relationship between the predictor and predictand is statistical or dynamical; both assume that
the large-scale information from the GCM is ‘correct’ and on that basis they attempt to
estimate the corresponding local change in climate. If the large-scale changes in the
GCMs are inaccurate, then the derived local changes merely add misleading precision
to the GCM results, and the downscaling techniques fail to add any predictive skill.
Figure 2 illustrates how closely a downscaled variable depends on the GCM variable
from which it is derived. We find that if the GCM variable is an accurate representation
of the corresponding observed variable then the downscaled variable is also an accurate
representation of the observed variable; conversely, where the GCM variable is
inaccurate, so is the downscaled variable. We conclude that a prerequisite of
downscaling is an accurate GCM to ‘drive’ the downscaling technique. Leonard
Bengtsson (1995), former Director of the Max Planck Institute and ECMWF, concurs:
‘Regional climate prediction requires first and for all [sic] good global prediction by
coupled climate models – without it regional simulations are useless.’ The development
of downscaling techniques does not overcome the lack of confidence in AOGCMs at
regional scales; it merely accentuates the need for the inherent regional uncertainties
to be understood.
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Predicting regional climate change: living with uncertainty
Figure 2 The dependence of downscaled results on the large-scale
information supplied by the GCM for three variables: maximum daily
temperature in degrees Celsius (TMX), fog in units 0–1 (FOG) and
snow height in cm (SNW). The observed data (OBS), the GCM data
(CTL) and the downscaled data (SCA) are presented for the weather
station at Potsdam, Germany
Source: Modified after Bürger (1996)
IV
AOGCM deficiencies
Any attempt to explain the wide range of AOGCM outcomes at regional scales
described in section II must consider a number of competing potential reasons, the first
of which is provided by model deficiencies. Most scientists accept that it is unreasonable to suggest that AOGCMs simply have poor physics and are unrepresentative of
reality. If one allows for the coarse resolution of AOGCMs then the models are
remarkably good, simulating the seasonal cycles in temperature, precipitation rates and
mean sea-level pressure (Gates et al., 1996).
However, it is possible that the parameterizations, coarse resolution and unrepresented feedbacks that limit AOGCMs may generate the intermodel variety exhibited in
AOGCM simulations at regional scales. This may particularly apply to hydrological
variables, whose key processes (evaporation, convection, condensation and precipitation) are all highly discontinuous in space and time and are heavily parameterized in
AOGCMs. Such model inaccuracies are a commonly recognized source of uncertainty
in climate prediction. However, in practice it is difficult to judge the effect of such
model weaknesses in isolation from another source of uncertainty that provides a
further potential reason for the wide range of model outcomes at regional scales:
climate system unpredictability.
Timothy D. Mitchell and Mike Hulme
V
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Climate system unpredictability
We may generalize the problem of regional climate prediction to all natural systems and
state that if the natural system bears the characteristics of a chaotic system6 then if (in a
hypothetical situation) it evolved a number of times from slightly different initial
conditions, it would exhibit different characteristics in each evolution. The different
outcomes might be described in terms of a frequency distribution of possible outcomes.
If we model such a natural system then we expect (and hope) that successive
evolutions, with slightly different initial conditions, will also exhibit different characteristics. Our evaluation of the worth of the model will not be on the grounds of
whether or not the model possesses a frequency distribution of possible outcomes, but
of whether or not the model’s distribution corresponds to the distribution of the natural
system.
Since the climate system is commonly regarded as a chaotic system (Lorenz, 1963;
1993) we expect that multiple integrations of an AOGCM, with slightly different initial
conditions, will yield a frequency distribution of possible outcomes. Figure 3 displays
four separate evolutions of a single model where only the initial conditions varied
between evolutions; the wide divergence between the outcomes illustrates the unpredictability inherent to the climate system. So we are not surprised at the occurrence of
intermodel differences at regional scales; we would expect differences to occur even if
the AOGCMs were identical.
It follows that in the presence of such unpredictability, the argument that ‘there is a
wide range of model outcomes, therefore the simulations are inaccurate’ (often with the
thought that this is due to parameterizations or coarse resolutions) is a non sequitur. An
unexplored possibility in such an argument is that the unpredictability of the climate
system may support a frequency distribution of possible outcomes, and that the wide
range of model outcomes may be due to accurate modelling of this distribution, rather
than to inaccurate AOGCMs diverging from a single solution. Therefore it is premature
to state that ‘models cannot predict on regional scales’ merely from the evidence
reviewed in section II. Rather, we must ask whether the model distribution of outcomes
is a good representation of the distribution of outcomes possessed (theoretically) by the
natural system or whether it has been corrupted by poor parameterization schemes, by
coarse AOGCM spatial resolution or by unrepresented external phenomena (such as
vegetation feedbacks or solar variability). The extent to which the wide range of model
outcomes is due to the unpredictability inherent to the climate system over against
AOGCM deficiencies is still an open question.
VI
Global system unpredictability
A second source of inherent uncertainty in regional climate prediction is ‘global system
unpredictability’, which encompasses all the unpredictabilities bound up in the future
external forcings imposed on the climate system. These external forcings may be
anthropogenic or natural; some of the most important include anthropogenic
greenhouse gas emissions, solar variability and volcanic eruptions. Any accurate
climate prediction must take into account the effect of such external forcings on the
climate system, and our second source of inherent uncertainty in regional climate
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Predicting regional climate change: living with uncertainty
Figure 3 The unpredictability inherent in the climate system. The
time evolution (left to right) of overlapping 30-year mean climate
anomalies for four ensemble members, relative to observations. The
final years of the model periods are 2070–99; the last observed period
is 1967–96. The model used is HadCM2, forced with historical changes
in compounded greenhouse gases until 1990, and with increases of 1%
per annum thereafter. The anomalies are of summer mean temperature
and precipitation for the UK, using an average from three grid boxes
centred over the UK
Source: Modified after Hulme and Brown (1998)
prediction is introduced by the fact that such forcings may themselves be unpredictable.
In order to show the magnitude of this source of uncertainty, each of the three external
forcings mentioned above is considered in turn.
We cannot predict with certainty the future course of atmospheric greenhouse gas
concentrations. The growth rate of concentrations (Keeling et al., 1995; Schimel et al.,
1995) is unpredictable, despite the seasonal regularity of the trend in the concentration
of atmospheric CO2. It is particularly the unpredictabilities of the global carbon cycle
and the difficulties in constraining the carbon budget (reviewed by Joos, 1996) that
make concentrations unpredictable. Yet even with a perfect understanding of the
carbon cycle, we could not predict concentrations with certainty because of the unpredictability of anthropogenic greenhouse gas emissions; the IPCC describe the
magnitude of this uncertainty in very strong terms: ‘It is our view that there is no
objective method of assessing the likelihood of all the key assumptions that will
influence future emission estimates . . . Thus we do not attempt to identify the most
likely emission scenario.’ (Alcamo et al., 1995: 258).
We have reasonable reconstructions of historical variations in anthropogenic
greenhouse gas emissions, but not for solar variability; we are uncertain how the
Timothy D. Mitchell and Mike Hulme
65
magnitude of solar forcing has varied in the past. The minimum magnitude of change
we can conceive is the irradiance change (0.15%) in the 11-year Schwabe cycle that has
been directly observed from satellites (Willson and Hudson, 1991) over the past two
decades. The maximum conceivable magnitude was estimated by Reid (1997) to be
0.65%, by assuming that all the estimated global temperature variability since the
Maunder Minimum of the seventeenth century may be attributed solely to solar
variability and to anthropogenic greenhouse gas emissions. More conservative reconstructions of solar variability have been published by Hoyt and Schatten (1993) and by
Lean et al. (1995), who calculate the difference in irradiance between the Maunder
Minimum and the present to be 0.30% and 0.24%, respectively. If the historical
variations in solar forcing are so uncertain – Reid describes the reconstructions as
instances of ‘a highly speculative activity’ (1997: 392) – then it is difficult to evaluate the
effects of solar variability on the climate system; indeed, Hoyt and Schatten admit on
the cover of their work entitled The role of the sun in climate change (1997) that they cannot
answer the question posed by the title. However, a succession of model experiments
(Rind and Overpeck, 1993; Crowley and Kim, 1996) has suggested that solar variability
is sufficient to force global climate changes of several tenths of a degree over centennial
timescales. This succession has culminated in AOGCM experiments (Cubasch et al.,
1997; Tett et al., 1998) that confirm the potential importance of solar variability, but
suggest that solar forcing changes are insufficient by themselves to explain the
temperature record of the twenthieth century. Since the historical record of solar
variability is so uncertain, and since the historical effects of solar variability on the
earth’s radiation budget are unknown, the solar forcing of climate in the future must be
regarded as unpredictable.
Although the climatic effects of certain volcanic eruptions are well known, the
climatic forcing from volcanoes is unpredictable. Observations of the eruption plume of
Mount Pinatubo in the Philippines (15 June 1991) demonstrated the atmospheric effects
of a major eruption in a remarkable way (McCormick et al., 1995); the albedo changes to
the atmosphere resulted in a peak decrease in August 1991 of 8 Wm–2 in radiative
forcing between 5 °S and 5°N, and of 3 Wm–2 globally (Minnis et al., 1993). Despite the
global cooling, this eruption – like others – produced a pronounced warming in the
Northern Hemisphere (Robock and Mao, 1992; Parker et al., 1996) that illustrates the
contrast in climatic effects that may appear between global and subglobal scales.
Although Mount Pinatubo was well observed, it is difficult to reconstruct the history of
volcanic eruptions; even recent volcanoes may escape observation (e.g., Sedlacek et al.,
1981; Mroz et al., 1983) and we are reliant upon proxy measurements for identifying the
dates of major eruptions prior to the commencement of satellite observations (e.g.,
Briffa et al., 1998). It is even more difficult to reconstruct the radiative forcing exerted
upon the atmosphere by an eruption. The radiative forcing depends upon the nature of
the injection of aerosols and aerosol precursors into the stratosphere, and upon the
spatial and temporal patterns of their residence in the atmosphere. The location,
altitude and period of injection all contribute to the former, and the particulate microphysics, particulate chemistry and atmospheric circulation all contribute to the latter.
Whereas there is a comparatively wide range of sources of information for identifying
the dates of eruptions, the scope for identifying the characteristics of the plumes from
individual eruptions is severely limited by the requirement for a wide range of evidence
(e.g., de Silva and Zielinski, 1998). Bradley and Jones (1992: 618) conclude their
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Predicting regional climate change: living with uncertainty
evaluation of volcanic forcing reconstructions as follows: ‘The record of large explosive
eruptions since AD 1500 is probably quite incomplete, making it difficult to assess their
overall impact on climate.’ Predicting the characteristics of future volcanic eruptions is
even more difficult than reconstructing those of the past, and therefore the volcanic
forcing of climate in the future is inherently unpredictable.
VII
The cascade of uncertainty
The uncertainty concerning future regional climate change that results from climate
model inaccuracies is commonly recognized. Yet the two sources of uncertainty that are
inherent to regional climate prediction – the unpredictability of the climatic and global
systems – have not been adequately dealt with in regional climate scenarios thus far,
either in scenario construction, or in considering the environmental effects of the
scenarios. Much of the research concerning the environmental effects of future climatic
change may be categorized into the ‘timeless’ and the ‘single scenario’ approaches. The
research summarized by the IPCC First assessment report (Houghton et al., 1990), and
much of that summarized by the IPCC Second assessment report (Watson et al., 1996),
considered the environmental impacts of a single physical event – such as the doubling
of atmospheric concentrations of CO2 – in a ‘timeless’ approach that made no attempt
to attach any time to the physical event. The introduction of transient perturbed climate
change experiments has permitted a time dimension to be introduced to impact studies,
which is incorporated into some of the Second assessment report and the majority of the
IPCC Regional report (Watson et al., 1998). However, such impact studies are commonly
limited to a ‘single scenario’, considering the environmental impacts at a number of
points in time of only a single climate realization produced by forcing only a single
AOGCM with only a single emissions scenario.
Thus our understanding of the regional consequences of climate change is marked by
the omission of any consideration of the uncertainties introduced by the unpredictability of the climatic and global systems. This neglect has permitted a ‘single result’
approach to prediction to permeate the climate change impacts literature, an approach
that is condemned by Henderson-Sellers (1996: 60): ‘Since prediction is not a single
result process, all predictions are (un)certain, contain and/or are based on, incomplete
information and are debatable.’ The unpredictability of the climatic and global systems
introduces a cascade of uncertainty to regional climate prediction (Figure 4) that cannot
be squeezed to the single point of the ‘single result’ approach to prediction.
VIII
Three methodological responses
Since unpredictability is a system characteristic, even with a perfect model the problem
of regional climate prediction would still be characterized by uncertainty. To live with
this uncertainty demands that we make three specific methodological responses: (1)
the use of multiple forcing scenarios to cope with global system unpredictability; (2)
the use of ensembles to cope with climate system unpredictability; and (3) the consideration of the entire response of the system to cope with the nature of climate change.
We will comment briefly on each of these in turn.
Timothy D. Mitchell and Mike Hulme
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Figure 4 The magnitude of external radiative forcing is uncertain and
so is presented as a range of possibilities in the top triangle. Two of
these possibilities are selected as the starting points from which is
presented the uncertainty concerning the magnitude of the climatic
response. Thus we find that the cascade of uncertainties ultimately
presents us with a wide variety of possible environmental futures at
the base of the lowest triangles
1
Multiple scenarios
Since the future course of anthropogenic emissions is inherently unpredictable, those
responsible for the IS92 emissions scenarios made no attempt to identify one scenario
as more likely than another, and made a specific recommendation to the contrary:
‘Given the degree of uncertainty about future emissions, we recommend that analysts
use the full range of IS92 Scenarios rather than a single scenario as input to
atmosphere/climate models’ (Alcamo et al., 1995: 255). (Since the anthropogenic
emission of greenhouse gases is only one of a number of unpredictable external
forcings, the full range of possible climatic forcings is even wider.) Yet – with a few
exceptions – this clear recommendation has not been followed in climate prediction
studies, and modellers have concentrated on a single emissions scenario, typically a 1%
per annum increase in compounded greenhouse gases that corresponds approximately
to the IS92a emissions scenario (Kattenberg et al., 1996: Table 6.3). Partly because of their
dependence upon the limited range of model experiments, impacts researchers have
taken their lead from the climate modellers and have also concentrated on the environmental consequences of a single emissions scenario: ‘Most commonly, the estimates are
based upon changes in equilibrium climate that have been simulated to result from an
equivalent doubling of carbon dioxide (CO2) in the atmosphere.’ (Watson et al., 1998: 4).
Our uncertainty about climatic forcings is such that AOGCMs must be perturbed with
multiple forcing scenarios to represent a wide range of possible forcings. Only then may
we proceed to evaluate with any confidence the regional climatic effects of any future
external forcing of the climate system.
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2
Predicting regional climate change: living with uncertainty
Ensembles
The inherent unpredictability of the climate system is such that our understanding of
what it means to ‘predict’ climate is somewhat different from the popular perception of
prediction.7 Even if we were able to force a perfect model with a perfectly specified
external forcing scenario, the model would not follow a single trajectory through time
that might be calculated beforehand, in the manner of a frictionless pendulum. Rather,
the dependence of the climate system on initial conditions means that inaccuracies in
the initial state grow through time, so that there is a multiplicity of future states that are
possible, even under a perfect forcing specification (Lorenz, 1963). This characteristic
imposes limits of a few days on numerical weather prediction (James, 1994: 292–93), as
our ability to forecast weather is dependent, not merely upon observational accuracy,
computational power and the accuracy of the model, but more fundamentally upon the
‘chaotic’ nature of the system. For this reason an operational meteorologist will
construct an ‘ensemble’ of simulations when making a forecast. The single GCM is
integrated a number of times, the only difference between each ensemble ‘member’
being the initialization conditions. The aim of constructing the ensemble is to sample
the full probability distribution of the future course of the weather. Likewise, if we are
making predictions about future climate then we must accept that the climate system is
unpredictable and that ensemble techniques should be used to sample the ‘full’
probability distribution of the future course of climate. An examination of Figure 3
illustrates the importance of the ensemble approach. If only one simulation had been
performed with HadCM2 rather than four, then only a single climate scenario would
have been available and a misleading impression would have been gained of the
regional climatic response to the perturbation.
3
Entire response of the system
We have addressed the two sources of uncertainty that are inherent to regional climate
prediction, but we have not yet considered the nature of the regional climate changes
that we expect to find. Such climate changes are conceptually different from daily
changes in weather and so require a different kind of prediction. A change in the
weather from Wednesday to Thursday may be understood in terms of a trajectory (the
weather) in a fixed attractor8 (the coupled atmosphere–ocean system). A weather
forecast is an instance of what Lorenz (1975) described as predictions ‘of the first kind’.
It is an initial value problem, where we are given a set of initial conditions from
Wednesday and we must calculate from them the future behaviour of the atmosphere
in order to derive Thursday’s conditions. In contrast, we may understand a change in
radiative forcing (perhaps from increased greenhouse gas concentrations or from
changes in solar irradiance) as effecting a change in the attractor. We do not expect the
attractor of the climate system to change in response to forcing in a linear manner, such
as in a linear shift of the time-averaged state against an unchanged variability
background (Figure 5); rather, we expect the system response to perturbation to depend
upon the convolution of the forcing with the ‘natural’ variability of the system (Figure
6). A climate prediction is an attempt to predict how the attractor will change (a
prediction ‘of the second kind’9 – Lorenz, 1975); in this sense any climate prediction
must consider the entire response of the system.
Timothy D. Mitchell and Mike Hulme
69
Figure 5 An erroneous linear-thinking perspective might suppose
that a forcing (F) imposed on the Lorenz model (a) would produce an
impact similar to (b)
Source: Palmer (1993)
IX
Characterizing the entire response of the system
Climate system predictions of the second kind may be examined from global or
regional perspectives. These perspectives characterize the entire response of the system
in different ways. It is worth considering this contrast between the global and the
regional, not merely to avoid confusion, but to illustrate what is meant by considering
the ‘entire response of the system’.
In global climate analyses we often consider a single change characterized – in a
highly simplified manner – as a linear shift. We treat the variability as ‘noise’, we treat
the shift as ‘signal’ and we use noise reduction techniques to increase the signal-tonoise ratio so that we may claim ‘detection’ of a statistically significant shift (Santer et
70
Predicting regional climate change: living with uncertainty
Figure 6 If a forcing (F) is imposed on the Lorenz model along the
positive T-axis, then the (counterintuitive) impact is a decrease in T.
This can be understood by noting that near the region of maximum
instability, positive T forcing increases the probability that the system
will evolve towards the regime whose centroid has the smaller value
of T
Source: Palmer (1993)
al., 1996). One particular group of ‘detection studies’ adopting this approach (Santer et
al., 1994; Hegerl et al., 1996; 1997) perturbed an AOGCM with increased atmospheric
greenhouse gas concentrations, employed noise reduction methods to extract a pattern
of response from the model output, and compared the pattern with patterns derived
from observations. Such ‘fingerprint studies’ culminated in the tentative claim
(Houghton et al., 1996) that anthropogenically induced climate change had been
detected. The motive behind these detection studies was to encourage the adoption of
mitigation policies by quantifying and confirming the conclusion that may be drawn
from a simple consideration of the radiative properties of the global climate system
(Arrhenius, 1896a; 1896b).
Since the detection studies described above consider a single global response to
external forcing, it is only at the continental, hemispheric and global scales that they
may be expected to achieve the identification of a response (Stott and Tett, 1998).
However, the information required for adaptive responses to climate change must be on
regional scales. If we consider the response to radiative forcing of a particular region
then we can no longer treat changes in the general circulations of the atmosphere and
oceans as ‘noise’ to be disregarded in favour of a single linear shift, because that
region’s climate is a result of those general circulations. It is through the general circulations that energy is redistributed in such a way as to maintain in equilibrium the
zones of permanent radiation surplus and deficit, and any radiative forcing of the
climate system ‘reaches’ a particular region through these dissipative mechanisms. So
Timothy D. Mitchell and Mike Hulme
71
we are required to understand the behaviour of, and the effects of radiative forcing
upon, the general circulations if we are to understand the consequent climatic changes
in a particular region. In short, we need to understand the ‘entire response of the
system’ in more detail than at the global scale.
This explanation of regional climate change in terms of changes in the general circulations of the atmosphere and oceans is a ‘geographical’ equivalent to the ‘mathematical’ explanation, given above, in terms of a change in the climate system attractor. The
general circulation changes correspond to the changes in the attractor. The general circulations and the attractor incorporate a natural variability that extends from seconds
to millennia, and the trajectory through the attractor is inherently unpredictable. The
regional climate change problem is – assuming that the radiative forcing is perfectly
specified – to elucidate how the general circulations will change or, expressed alternatively, to elucidate how the shape of the climate system attractor will change. It is in this
sense that we need to understand the ‘entire response of the system’. However, on the
interdecadal timescales of interest for climate prediction we hardly understand the
current behaviour of the general circulations – Latif (1998) provides a good review – let
alone the effects of external forcing upon them. The position is summed up well by
Anderson and Willebrand (1996: v) in their work on decadal climate variability: ‘The
causes of natural variability of the climate system on decadal time scales are presently
not well known; understanding the mechanisms is however a prerequisite for any
climate prediction of changes on these time scales.’
X
Some examples
The discussion in this review has necessarily been of a rather theoretical nature, and it
is appropriate to introduce some examples.
A brief consideration of the thermohaline circulation (THC) of the North Atlantic
serves to illustrate a number of points. In 1961 Stommel reported a very simple
experiment with profound results that was forgotten until the 1980s. He considered the
convective behaviour between two interconnected reservoirs forced by density
differences maintained by salt and heat transfers and found that two distinct stable
regimes were possible. Building on this work, Welander (1986) later presented an
assortment of thermohaline oscillators and multiple steady states applicable to the
world’s oceans. So multiple steady states are conceivable under natural variability.
More recently, researchers have shown that anthropogenic external forcing may affect
the behaviour of the THC to the extent of inducing a switch between one climatic state
and another. Manabe and Stouffer (1993) chose two different emissions scenarios with
which to force a coupled model, and examined the response in the North Atlantic THC;
they found two distinct stable regimes, the occupancy of which depended on the
magnitude of the equilibrium change in atmospheric CO2 concentration. In a similar
experiment, Stocker and Schmittner (1997) showed that the stable regime obtained is
also dependent upon the rate of change of atmospheric CO2 concentration. Rahmstorf
(1995) conducted a more general examination of the hysteresis behaviour of the North
Atlantic THC and found a number of forms of nonlinear behaviour. Schiller et al. (1997)
considered the stability of the North Atlantic THC against meltwater input and found
that four elements of the climate system exhibited feedbacks with the THC: oceanic heat
72
Predicting regional climate change: living with uncertainty
transport, precipitation and runoff, the atmospheric circulation and the wind-driven
circulation.
This nonlinear behaviour of the North Atlantic THC on decadal and centennial
timescales illustrates the unpredictability of the coupled ocean–atmosphere system that
was discussed in section V. It suggests that climate predictions for regions dependent
upon the North Atlantic THC (principally Europe) must be made on the understanding
that an external forcing may result in a number of possible outcomes (section VII). Since
the behaviour of the North Atlantic THC depends on the magnitude and rate of
external forcing, any predictions should be made on the basis of AOGCM simulations
in which the model is forced with multiple forcing scenarios (section VIII 1). Since the
behaviour of the North Atlantic THC depends on the initial conditions, an ensemble
experiment should be performed for each forcing scenario (section VIII 2). Since any
external forcing affects the North Atlantic THC through the atmosphere and continental
ice sheets, and since the THC itself influences the atmosphere and continental ice
sheets, the North Atlantic THC response to forcing can only be understood in terms of
the entire response of the system to forcing (sections VIII 3 and IX).
Our second example concerns the application of ensemble methods that (as we
suggested in section VIII 2) allow us to deal with climate system unpredictability in
regional climate prediction. There are not yet many instances of ensemble techniques
being used in the climate change problem, chiefly because of the expense of performing
multicentury AOGCM experiments. (Therefore it is also worth considering the use of
ensemble techniques in the seasonal prediction problem – Dix and Hunt, 1995; Rowell,
1998.) The Max-Planck Institute (Cubasch et al., 1997) have performed a two-member
ensemble experiment that is relevant to the climate change problem, in which ECHAM3
was forced with an estimated historical record of solar variability. The Hadley Centre
have performed a wider range of four-member ensemble experiments in which
HadCM2 was forced with estimated historical and future atmospheric CO2 and
sulphate concentrations (Mitchell et al., 1999), and with estimated historical records of
solar and volcanic variability (Tett et al., 1998). An example of the uses to which the
Hadley Centre ensembles have been put is provided by the analysis on the Northern
Hemisphere storm tracks in general (Carnell and Senior, 1998), on the North Atlantic
Oscillation in particular (Osborn et al., 1999), and on water and crop responses to
climate change in Europe (Hulme et al., 1999).
XI
Conclusion
This discussion of the uncertainty bound up with regional climate prediction has many
implications for research, both of the climate system itself and of the impacts of climate
change. It has been shown that even with a perfect model, uncertainty would remain
inherent to regional climate prediction, because of the inherent unpredictabilities of
external forcing – both anthropogenic and natural – and of the climate system itself.
Only if we were able to alter the fundamental natures of the climate and global systems
to make them predictable might we hope to predict with certainty. It seems that if we
are to be successful in our aim of predicting regional climate change then we must
understand that uncertainty must arise from systemic unpredictability.
The first implication of this insight is that to avoid misunderstanding we must adopt
Timothy D. Mitchell and Mike Hulme
73
the precise terminological language that contrasts unpredictability (a system characteristic) with uncertainty (what we have to live with as a consequence of unpredictability),
and that treats ‘prediction’ in the context of inherent uncertainty. A further implication
is that we must not treat any model output as the one true climate prediction, but must
take the different – and equally probable – climate scenarios illustrated in Figure 3 and
use them as equally probable inputs when researching the impacts of climate change.
The uncertainties involved complicate the communication of vital information,
making scientists instinctively wary of making any assessment of the changes that may
follow anthropogenic forcing. So when pressed for information, scientists are faced
with a classic dilemma: predict and risk being wrong, or remain silent and risk failing
to release useful information. Fowler and Hennessy (1995: 284) discuss the problem for
the specific issue of extreme precipitation events and are convinced of the need for
scientists to be vocal:
Although there are good reasons for scientific reticence concerning possible changes . . . there is also a pressing
need for information . . . In the absence of contrary advice from atmospheric scientists, the norm will prevail of
assuming that past experience is a reliable guide. Given that the implicit assumption of a stationary climate
underlying such an approach contradicts expectations of a rapidly changing climate over the next several
decades, clearly there is an onus on atmospheric scientists to provide as much information as possible.
The inherent uncertainty in regional climate prediction is such that scientists must
counter the damaging norm of assuming a stationary climate. In the past, conventional
understanding has adopted a 30-year mean as the one true climate and this human
artifact has been used as the basis upon which return periods are calculated, infrastructure is designed and policy decisions are reached. Thus humans are only adapted
to the latest 30-year mean, rather than to true levels of natural variability. To be in a
position to adapt to the climatic changes that will follow any future anthropogenic
forcing of the climate system, we must first be better adapted to the levels of variability
present in an unperturbed climate system. Achieving this requires a better understanding both of climatic variability and of the sensitivity of natural environments and
human societies to that climatic variability.
The problem for researchers is to close the gap between the demand for, and supply
of, reliable information on regional climate change and its impacts. At this point it is
helpful to make a distinction between the information necessary for mitigation rather
than for adaptation by policy decisions. Since CO2 emissions are rapidly mixed in the
atmosphere, the climate system response to CO2 emissions is spatially global in scale.
So mitigation policy, which seeks to reduce CO2 emissions, requires information about
the global-scale response to CO2 emissions (the existence of a ‘shift’ in the climate
system attractor); the ‘detection’ work described in section IX has sufficiently supplied
that requirement for the global community to sign the UN/FCCC and the Kyoto
Protocol. In contrast, adaptive responses to climate change require information on
regional scales. Yet regional climate prediction is still in its infancy, so a large gap
remains between the demand for, and supply of, reliable information. There is also a
contrast between the objects of the mitigative and adaptive responses. The adaptive
strategies require that a response be made to the climate system’s behaviour, whether
the system is being forced by humans or by natural phenomena. Mitigative strategies,
however, can only deal with the anthropogenic forcing of the climate system – unless
we are so bold as to contemplate altering solar variability or volcanic eruptions.
74
Predicting regional climate change: living with uncertainty
This review has presented the cascade of uncertainties (Figure 4) as the reality with
which our methodologies must live. None the less we recognize that there are two
problems with limiting the regional climate prediction problem to the resolution of this
relatively simple cascade of uncertainties. The first problem is the presence of feedbacks
between most of the nonhuman elements of the cascade. This is well exemplified by the
exchanges between the coupled atmosphere–ocean–cryosphere system (simulated by
AOGCMs) and the land surface, such as the posited vegetation feedbacks in the Sahel
(reviewed by Nicholson, 1988) that may be partly responsible for the prolonged
droughts of recent decades (Eltahir and Gong, 1996). Another example refers to the
potential, described in section X, for multiple steady states in the thermohaline
circulation. Rahmstorf (1995) has shown with a coupled model that it is conceivable that
precipitation changes may change freshwater inputs into the oceans, triggering
convective instability, and thus inducing transitions between different equilibrium
states of the thermohaline circulation, with substantial climatic effects.
The second problem is the lack of attention to humans. The human world is unpredictable; only the most extreme reductionist can envisage the predictability of human
thought and behaviour. Since humans affect the climate system and the consequences
in turn affect humans, we cannot completely account for all the interactions that are of
relevance to regional climate prediction. Of course, we try to include all the possibilities
of human influence on the climate system through the emissions scenarios, but this
neglects the feedbacks between humans and their environment.
Integrated assessment (Peck and Teisberg, 1992; Nordhaus, 1994; Hasselmann et al.,
1997) is another venture still in its infancy, but in the long run it may make an
invaluable contribution to unravelling the web of feedbacks and to managing global
change. Whatever the contribution made by integrated assessment, we must still
however carefully examine each environmental system for its response to regional
climate change, with a keen awareness of the inherent uncertainties involved. If we are
to adapt sensitively to climate change then we require information now; the many
uncertainties involved should not stop us from providing the best information possible.
The heart of the task of regional climate prediction lies in bringing to light the different
possibilities the future may hold for us; we must elucidate the climatic and environmental changes that are possible and enlighten nonscientists in ways that communicate
the inherent uncertainties of regional climate change.
Acknowledgements
Tim Mitchell is supported by a grant from the Natural Environment Research Council
(GT 04/97/81/MAS) in conjunction with the UK Meteorological Office (Met 1b/2437),
but the views expressed in this article are solely those of the authors, not of NERC or
UKMO. We are grateful to Dr Declan Conway for his comments on a draft of this article,
and to Dr John Mitchell for his support and constructive discussions.
Notes
1. In this review ‘regional’ refers to subcontinental and local scales.
2. In current terminology ‘adaptation’ measures are adaptive responses to an actual climate
Timothy D. Mitchell and Mike Hulme
75
change, past, present or future; ‘mitigation’ measures are responses to a perceived potential future
climate change that attempt to reduce the likelihood of that change occurring. An example of the
former is building more coastal defences; and an example of the latter is reducing greenhouse gas
emissions.
3. In this review any agent or system beyond the scope of the atmosphere, oceans and cryosphere
is considered to be ‘external’ to the climate system. Thus external forcings on the climate system
include both anthropogenic and natural forcings, exemplified by CO2 emissions and solar variability,
respectively.
4. A control simulation is conducted without any ‘perturbation’ from humans, the sun or any other
external agent.
5. The term ‘scenario’ has been much abused (see Henderson-Sellers, 1996). In this review an
‘emissions scenario’ refers to a description of a possible future temporal pattern of anthropogenic
emissions of greenhouse gases; a ‘climate scenario’ refers to a description of a possible future climate.
6. An introduction to deterministic chaos is beyond the scope of this review, and the reader is
referred to Hall’s (1991) introduction to chaos and Lorenz’s (1993) introduction to chaos with its
particular reference to the atmosphere.
7. Palmer (1993) provides a good introduction to these predictability concepts.
8. The climate system ‘attractor’ may be loosely defined as the complete set of possible instantaneous states of the climate system, or more precisely as ‘a limit set that is not contained in any larger
limit set, and from which no orbits emanate’ (Lorenz, 1993: 206).
9. These two ‘kinds’ of prediction – discussed at greater length by Palmer (1996) – are fundamentally different, and it is crucial to understand this difference when interpreting GCM output, especially
since the same GCM may be used for both kinds of prediction (Cullen, 1993).
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