STOTEN-13524; No of Pages 14
Science of the Total Environment xxx (2012) xxx–xxx

Contents lists available at SciVerse ScienceDirect

Science of the Total Environment
journal homepage: www.elsevier.com/locate/scitotenv

Regional projections of North Indian climate for adaptation studies
Camilla Mathison a,⁎, Andrew Wiltshire a, A.P. Dimri b, Pete Falloon a, Daniela Jacob f, Pankaj Kumar f,
Eddy Moors c, Jeff Ridley a, Christian Siderius c, Markus Stoffel d, e, T. Yasunari ba

Met Ofﬁce, FitzRoy Road, Exeter, Devon, EX1 3PB, UK
Hydrospheric Atmospheric Research Center, Nagoya University, Furo-ch, Chikusa-ku, Nagoya,464-8601, Japan
ESS-CC, Alterra Wageningen UR, P.O. Box 47, 6700 AA Wageningen, The NetherlandsdInstitute for Environmental Sciences, University of Geneva, 7, Route de Drize, 1227 Carouge, Geneva, SwitzerlandeDepartment of Geography, University of Zurich, Irchel Winterthurerstrasse 190, 8057 Zürich, Switzerlandf
Max-Planck-Institute für Meteorologie, Bundesstrasse 53, 20146 Hamburg, Germanybca r t i c l e

i n f oArticle history:
Received 6 March 2012
Received in revised form 25 April 2012
Accepted 25 April 2012
Available online xxxx
Keywords:
MonsoonClimateRegional
ModelsGangesBrahmaputraa b s t r a c t
Adaptation is increasingly important for regions around the world where large changes in climate could havean impact on populations and industry. The Brahmaputra–Ganges catchments have a large population, amain industry of agriculture and a growing hydro-power industry, making the region susceptible to changes
in the Indian Summer Monsoon, annually the main water source. The HighNoon project has completed four
regional climate model simulations for India and the Himalaya at high resolution (25 km) from 1960 to 2100
to provide an ensemble of simulations for the region. In this paper we have assessed the ensemble for thesecatchments, comparing the simulations with observations, to give credence that the simulations provide a realistic representation of atmospheric processes and therefore future climate. We have illustrated how thesesimulations could be used to provide information on potential future climate impacts and therefore aid
decision-making using climatology and threshold analysis. The ensemble analysis shows an increase in temperature between the baseline (1970–2000) and the 2050s (2040–2070) of between 2 and 4 °C and an increase in the number of days with maximum temperatures above 28 °C and 35 °C. There is less certainty
for precipitation and runoff which show considerable variability, even in this relatively small ensemble, spanning zero. The HighNoon ensemble is the most complete data for the region providing useful information on awide range of variables for the regional climate of the Brahmaputra–Ganges region, however there are processes not yet included in the models that could have an impact on the simulations of future climate. We havediscussed these processes and show that the range from the HighNoon ensemble is similar in magnitude topotential changes in projections where these processes are included. Therefore strategies for adaptation must
be robust and ﬂexible allowing for advances in the science and natural environmental changes.
© 2012 Published by Elsevier B.V.

1. Introduction
Carbon dioxide emissions from anthropogenic sources are now
widely considered very likely to have contributed to observedchanges in climate (Meehl et al., 2007). The Intergovernmental
Panel on Climate Change (IPCC AR4, 2007) also highlight in its fourth
assessment report (AR4) that inertia in the climate system, the atmospheric lifetime of carbon dioxide and feedbacks within the climate
system mean that previous emissions will continue to have an impact
into the future. We are therefore already committed to some level ofclimate change at least to the middle of the 21st century which meansthat adaptation to a changing climate will be a necessity around theworld in the next few decades, increasing in priority for more

⁎ Corresponding author. Tel.: +44 1392 886695.
E-mail address: camilla.mathison@metofﬁce.gov.uk (C. Mathison).

vulnerable populations. Often the change in climate is discussed interms of global mean temperature, for example Betts et al. (2011) focuses on the potential for breaching the threshold of 4 °C in global
mean temperature. However, regionally, a global mean temperaturechange can translate to a much bigger or smaller regional or local
temperature change; therefore for adaptation purposes, analysis ofclimate change for individual regions is important.
The EU funded HighNoon project focuses on how water resources
may change in the future for the region of South Asia, speciﬁcally Northern India; the region comprises the Indian Himalayas and its foothills,
home to an estimated 500 million people. The economy is predominantly
rural, highly dependant on climate sensitive sectors such as the agricultural and horticultural industry and characterised by a large demand forwater resources. In India agricultural use comprises 80 or 90% of humanwater extraction with almost 60% of this from groundwater. This large demand is part of an increasing trend with groundwater withdrawals increasing by 113-fold between 1950 and 1985 (Douglas et al., 2006).

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Products using a combination of models with satellite observations (during the period 2002 to 2008) from GRACE (NASA Gravity Recovery andClimate Experiment Satellites) have shown that this trend has continued
suggesting a mean depletion in groundwater of 4.0±1.0 cm yr− 1 (Rodell
et al., 2009). This increased demand and use of previously unavailable
water resource has resulted in a rapid rate of economic development
and has helped alleviate poverty in the region. However as a result,groundwater stores have declined at a rate of 20 cm per year in as
many as 15 Indian states. In most of these states the resource from groundwater is projected to dry out as soon as 2025 (Douglas et al., 2006, 2009).
On the other hand, most Himalayan glaciers, the largest ice pack outside
the poles, have been retreating over the past decades (Bolch et al.,2011). The relative contribution of snow and glacier melt to river ﬂows
decreases with increasing distance from the mountains and may varyfrom 10% or less annually (Immerzeel et al., 2010) to 12–38% during themonths of March, April and May (MAM), outside the Indian summer
monsoon (ISM deﬁned as extending from June to September, JJAS,
Goswami and Xavier, 2005) when other sources of water are scarce.
This contribution is likely to decrease with increasing glacier wasting(Immerzeel et al., 2010; Siderius et al., submitted for publication). Irrigation and drinking water supplies are thus likely to decrease and become
seasonal if water deﬁcits are not compensated for by increased
precipitation.
The ISM has been a critical source of water for this region in termsof ground water recharge, river basin and river ﬂow (Rodell et al.,2009) with the HighNoon region (deﬁned by 25°N79°E − 32°N88°E
and illustrated in Fig. 1, centre) receiving up to 70% of its annual precipitation during this period based on APHRODITE data from 1979 to2007 (Yatagai et al., 2009). The ISM could become even more important in the future as other sources of water for agriculture and domestic use could become more scarce and inter-sectoral competition forwater may increase. Future changes in precipitation amounts andtiming could therefore be of critical importance to the region.
Apart from the changes in the ISM other fundamental climaticchanges impacting Northern India could be many and varied, someexamples are given here:• Increases in temperature could mean certain crops may no longer
be viable at lower elevations, while rendering mountain areas at
higher elevations suitable for horti-agricultural purposes. Kumar
et al. (2011b) highlights the potential losses in wheat production
with rising temperatures (4–5 million tonnes with every 1 °C increase in temperature throughout the growing period with current
land use). However, in the case of irrigated rice, the negative impacts of increasing temperature may be adequately compensatedby high CO2 concentrations (Kumar et al., 2011b).
• Sea level rise could lead to salt water intrusion into highly productive deltas rendering large areas of agricultural land no longer tenable potentially causing population displacement (Dasgupta et al.,2007);• Altered patterns of snow and glacial melt changing the availabilityof water for spring and summer irrigation or possibly causing sporadic ﬂooding (Dasgupta et al., 2007).
• Changes in weather extremes, leading among others to cloud bursts
or ﬂash ﬂoods could also put additional stresses on critical infrastructure such as roads, rail and energy supply networks. Such networks are already under stress from population growth, increased
urbanisation, resource use, and regionally imbalanced economic
growth. This could critically impair economic growth by disrupting
connectivity or damaging basic service installations such as watersupply and drainage (Naswa and Garg (2011).
Acting to adapt to altered availability of resources and potentialchanges in extreme weather is therefore an urgent need for this region, especially if the economic growth it has experienced in recent
years is to be sustained. In order to adapt a population needs tomake informed decisions both in terms of policy and as individuals.
In recent years through the publication of the IPCC reports, climate
models have become more accessible as tools to aid decisions for adaptation, for example, through methods such as climatology assessments and threshold analysis.
Climatology assessments are important in developing a general
understanding of regional climate change using standard climate parameters such as temperature and precipitation. They are often used
for presenting key generic messages including uncertainties for policy
makers, for example in the IPCC AR4. Threshold analysis, on the other
hand, is suited to more speciﬁc adaptation applications, such as therail and power distribution network (Thornton et al., 2011; McColl
et al., 2012) making climate information directly useable by, and relevant for, speciﬁc industries.
Many industries (see Table 1), for example agriculture, areinﬂuenced directly by weather and climate on a wide range of timescales (e.g. precipitation, temperature and CO2 concentrations) andindirectly (e.g. by changes in the occurrence of pests and diseases,or changing market forces). Adaptation in the agricultural industry
tends to be inﬂuenced by knowledge of the local climate and individual experience (Gornall et al., 2010). Changes to the local climate
could threaten established farming methods; however adaptation toclimate change could provide the mechanism through which thesemethods could be improved. Hydroelectric power is another important industry in India with the Brahmaputra catchment and Ganges
highlighted by the World Development Bank as key areas with potential for development (Ramanathan and Abeygunawardena, 2007).
The beneﬁts and problems from such developments need to be evaluated using understanding of the likely future changes to precipitation and runoff in order to ensure that facilities are developed in arobust and resilient way.
The use of scientiﬁc information in policy making is often complicated due to the uncertainties inherent in the climate system, which
in reality can never be entirely eliminated (Webster, 2003), andFig. 1. Precipitation climatology for India with the sea masked out to show grid box coverage over land for India, in the global model (left) and the regional model (centre). Climatology of APHRODITE observations are also shown (right).

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3

Table 1
Summary of sectors and the information needed from climate models (†Gornall et al., 2010, §Dutt et al., 1992; Sirohi and Michaelowa, 2007, ‡Naswa and Garg, 2011, ΩBOM: http://
www.bom.gov.au/info/thermal_stress/).
SectorClimate variables

Timescales known to be important for sector

Examples of important thresholds

Agriculture

Temperature

Annual — important for established farming practises
Seasonal — high growing season temperatures can affect crop
productivity by bringing forward harvest times. Even hightemperatures for only a short period can have severe
detrimental effects to some crops if they occur at crucial times
e.g. ﬂowering in rice†.
Seasonal — low rainfall during the growing season can have a
detrimental impact on productivity (e.g. Ganges), conversely
high rainfall can lead to ﬂooding in some areas (e.g.
Brahmaputra basin). Excess water from prolonged periods ofrain can lead to soil water logging, anaerobicity and reduced
plant growth†.
Seasonal — changes to seasonal ﬂows could affect theavailability of water for irrigation and domestic use
particularly when ﬂows are very low or even cease ﬂowing
completely during the dry season†.
Seasonal — rising temperatures could impact energy demand,
particularly during warmer periods of the year, this could
mean a greater demand for energy for cooling. This could
translate into a high demand during periods with low ﬂow
and therefore reduced supply‡.
Annual — changing patterns in precipitation and runoff could
mean hydro-power is more or less viable‡.
Seasonal — changes in seasonal cycles of precipitation andglacial melt could result in large changes in river ﬂows withsome rivers completely drying up in the low ﬂow months andothers very high during peak ﬂows, thus having implications
for hydropower‡.
Seasonal — rising temperatures could affect the population
directly through heat stress or indirectly through changes tothe spread of pests and diseases‡†.

Max temperature during rice ﬂowering
affecting crop yield > 35 °C
Upper limit of max temperature for milk
production is 27 °C§

Precipitation

RunoffHydro-power

Temperature

Precipitation and runoff

Health and housing

Temperature

Minimum ﬂows needed to sustain a healthy
river ecosystem are different for each river.

Heat stress threshold: daily maximum
temperature > 28 °C although the effect of hightemperatures can be reduced or exacerbated bydifferent levels of humidityΩ.

those due to scientiﬁc understanding, which has seen rapid development in recent years. One of the overarching issues is uncertainty inthe size and, for some regions, direction of future projected changes
in water-related quantities. For example, the IPCC AR4 climate
models do not agree on the sign of annual/seasonal precipitation
changes for some key regions, including South Asia — particularly
for December January February (DJF). In other regions and seasons
there may be greater consensus on the sign of future changes, but awide range of magnitudes may be projected across the models.
These uncertainties are potentially challenging for assessing thescale and nature of adaptation options required.
In this paper we describe the latest high-resolution (25 km) ensemble of regional climate model (RCM) simulations run for the
HighNoon project for India and the Himalayas. These simulations covering the whole of India (See Fig. 1, centre) are described and evaluated in detail in Pankaj et al. (in preparation). We brieﬂy verify model
performance in simulating the current climate of the speciﬁc region of
the Ganges and Brahmaputra catchments (Section 2). This is then extended to assess the use of the ensemble to provide information on
climate change impacts on adaptation timescales (i.e. to the middleof the 21st century; IPCC AR4, 2007), providing information for adaptation decisions for these catchments using the methods describedabove (climatology assessment and threshold analysis, Section 3.1).
We also discuss the main sources of uncertainty and the potential
limitations of these model simulations (Section 4).

al., 2000). Two global models that represent the monsoon well for thecurrent climate; The Third Version of the Met Ofﬁce Hadley Centre Climate Model (HadCM3; Pope et al., 2000; Gordon et al., 2000 — A versionof the Met Ofﬁce Uniﬁed Model, MetUM) and ECHAM5 (3rd realisation,
Roeckner et al., 2003) provide the boundary conditions for the two regional climate models; REMO (Jacob, 2001, 2009) and HadRM3 (Joneset al., 2004). This is illustrated by the ﬂow chart shown in Fig. 2,
which shows the inputs, the main processes and outputs of a typical
RCM. The conﬁguration of HadRM3 includes representation of land surface sub-grid scale heterogeneity provided by version 2.2 of the Met Ofﬁce Surface Exchange Scheme (MOSES 2, Essery et al., 2003). The
combination of two RCMs each driven by two GCMs provides an ensemble of four high resolution model simulations for the HighNoon domain(see Fig. 1).
The inﬂuence of horizontal resolution on the simulations is illustrated in Fig. 1 which shows three plots of June precipitation climatologies;
a global-scale HadCM3 simulation (left), a regional-scale 25 km resolution HadRM3 simulation (centre), the latter driven with HadCM3 dataat the boundaries and APHRODITE observations (right). Fig. 1 (left)
shows clearly visible grid cells with limited detail around the coastlines,
over orography or the Indo-Gangetic plains. In contrast, the 25 km RCM
is more detailed with the coastlines and other features represented in
more detail. Comparing the observed June APHRODITE climatology(Fig. 1, right) with the RCM illustrates the improvements in the representation of current climate by using high resolution RCM simulations.

2. HighNoon simulations

2.2. Assessment of HighNoon simulations

2.1. Experiment design

Future climate projections may be treated with more credibility if agiven model can realistically simulate the physical and dynamical processes of the current climate since it may adequately capture the fundamental processes and therefore project a plausible future climate (Lianget al., 2008). In this section the outputs from the HighNoon ensemble

The HighNoon regional climate model simulations provide data at a
25 km resolution for the HighNoon domain (See [Fig. 1, centre) for theperiod the 1960 to 2100 using the SRES A1B scenario (Nakićenović etPlease cite this article as: Mathison C, et al, Regional projections of North Indian climate for adaptation studies, Sci Total Environ (2012),
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C. Mathison et al. / Science of the Total Environment xxx (2012) xxx–xxx

InputsRegional Model Processe

Boundary conditions from
GCM

Atmospheric Physics

e.g. winds,
surface pressuremoisture,temperature,aerosols

Outputs

A wide range of self-consistentvariables on a range oftimescales relating to all
aspects of atmosphereand land-surface

Atmospheric Dynamics

e.g. Typical variables:
Precipitation,
Temperature,
RunoffLand-surface Processes

Regional scale
Ancillary datasets

Parameterizations e.g.
convection, cloude.g. orography,
vegetation,
sea surface temperature,ozone,
aerosols

Other variables usefulfor example in agriculture andwater resources
include soil moisture,
net primary productivity,
leaf area index,
net downward surface radiation

Processes driven by Orographic
and coastline features.
(highly resolution dependant)

Fig. 2. Flow chart showing the inputs, main processes and example outputs for a regional climate model.

using the baseline period 1970–2000 of temperature (top row) and precipitation (bottom row) for both global (left column) and the regional
25 km data (right column). The shaded region represents 2-standard deviations in the 30-year means of a 150-year control global HadCM3 run.
Natural climate variability is commonly represented by ±2 standard deviations of a long control climate model simulation (Collins et al., 2006;

are compared with observations to verify that the simulations capture
the main features of the current climate of the Ganges Brahmaputra region in order to provide conﬁdence in the analysis of the future periodof 2040–2070 presented in Section 3.
Fig. 3 shows the annual cycle of 30-year climatologies, for the Ganges
and Brahmaputra catchment (highlighted by the black outline in Fig. 4)

Northern India: Ganges/Brahmaputra catchments
Temp

25

25

20

20

15

15

Deg C

Deg C

Northern India: Ganges/Brahmaputra catchments
Global Temp

10

10

5

5

0

0
J

F

M

A

M

J

J

A

S

O

N

D

J

F

M

A

M

J

J

A

S

O

N

D

Northern India: Ganges/Brahmaputra catchments
Precip

10

10

8

8

mm per day

mm per day

Northern India: Ganges/Brahmaputra catchments
Global Precip

6
4
2

6
4
2

0

0
J

F

M

A

M

J

J

A

Observations CRU-T/CMAP-P
Observations GPCP
Global-HadCM3
Global-ERA-int

S

O

N

D

J

F

M

A

M

J

J

A

S

O

N

D

Observations Aphrodite
HadCM3-HadRM3P
Echam5-HadRM3P
ERA-int-HadRM3P
HadCM3-REMO
Echam5-REMO

Fig. 3. Annual cycle of temperature (1st row) and precipitation (2nd row) 30-year climatologies for global resolution data (left) and 25 km resolution data (right) with the 2 standard deviation envelope for 30-year global HadCM3 control run (shaded).

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5Fig. 4. 30-year JJAS temperature climatology for observations (CRU — 1st row, left) and each HighNoon model run for Ganges/Brahmaputra catchment. Left to right along each row
HadCM3–HadRM3, ECHAM5–HadRM3, HadCM3–REMO and ECHAM5–REMO.

Cowling et al. 2009). Hence changes outside this range may be considered
as a signal outside the “noise” of natural climate variability. Excludingchanges within ±2 standard deviations of the control simulation is approximately equivalent to a signal:noise ratio of 2 (Hawkins and Sutton,2011). The global models show a larger annual cycle of temperaturethan that shown in the global observation dataset (CRU temperatures:
Mitchell and Jones, 2005). The ensemble of regional model simulations
has an improved range although one model (ECHAM5-REMO) shows ashift in phase of the annual cycle — earlier by one month; this could be associated with the way that the REMO land-surface soil properties are
speciﬁed see Section 4.3.3 for discussion of the REMO soil properties
used in the HighNoon ensemble.
The annual cycle of precipitation (Fig. 3, bottom row) shows that
there is more variability in simulations of precipitation than temperature, with HadCM3 having a strong annual cycle and ERA-Interim(Simmons et al., 2007, 2010) a weak cycle compared to observations(CMAP Precipitation, Xie and Arkin, 1997). The global models (Fig. 3,
bottom left) diverge during JJAS tending to underestimate the rainfall
during this period compared to observations. The regional models(Fig. 3, bottom right) generally compare well against the
APHRODITE observations, but have longer wet seasons such thatthey overestimate the observed precipitation between January andJune. This difference between the model and observations should
not be over-interpreted as the large spread in the precipitation observations mean it is difﬁcult to robustly attach error bars to the observations for this region, particularly given the relative scarcity ofobservations in the mountains (this is also illustrated in Fig. 6a).
The spatial pattern of the observed and model temperature andprecipitation climatologies are shown in Figs. 4 and 5 respectively
for the wet season (JJAS). The temperature observations shown in
Fig. 4 (top row, left) are CRU observations and are therefore oflower resolution than the regional models. This considered, themodel climatologies all show a reasonable approximation to the observed climatology; a more in depth analysis of the models for thewhole HighNoon domain is completed in Pankaj et al. (inpreparation). Fig. 5 shows three observed precipitation climatologies(ﬁrst row), of these APHRODITE (Fig. 5, ﬁrst row, centre) is thehighest resolution and shows the region of highest rainfall along theHimalayan foothills, although observations are sparse in this region.
The regional model climatologies show the region of maximum

rainfall to be slightly further north. In other seasons there is a generalover estimation in the models of the precipitation in the east of thecatchments, particularly in the pre-monsoon season (MAM — notshown).
Fig. 6 shows a comparison of the observed; GPCP (Adler et al.,2003), CRU, GPCC (Beck et al., 2005; Rudolf et al., 2003; Rudolf andSchneider, 2005; Rudolf et al., 2005), and APHRODITE JJAS precipitation, averaged across the HighNoon domain, with two regional simulations which use ERA-Interim global re-analysis data to provide
boundary conditions. Fig. 6a shows a decreasing trend in the observations (except APHRODITE), and for the ERA-Interim forced models;
HadRM3 shows a trend of increasing precipitation while REMO
shows no trend but strong decadal variability. There is some agreement across the observational datasets on seasonal precipitation extremes; the models show a range of interannual variability (IAV) in
agreement with the APHRODITE data. The correlation of the IAVfrom APHRODITE and the RCMs, shown in Fig. 6b, is 0.42 for REMO
and 0.38 for HadRM3. Such low correlations may be related to theweak annual cycle in the ERA-Interim precipitation, (Fig. 3) themodel representation of convection (Pal et al., 2007), and the model
ability to recycle precipitation through evaporation (Giorgi and Bi,2000).
Though the number of models in the HighNoon ensemble is limited and therefore spread in the HighNoon ensemble is relatively small,in general, the models do represent the current climate in terms of
the seasonality of temperature and precipitation, and therefore provide useful information about the Brahmaputra and Ganges region.
This is reﬂected in the analysis presented in Pankaj et al. (inpreparation) which analyses the RCM ensemble (using ERA-Interim
boundary conditions) for the whole of India. The model simulations
capture the major rainfall regimes over India compared to observations (Indian Meteorological Department, IMD). The temperaturepatterns are also well simulated though they showed a schematic
cold bias for country as a whole, also reported in IPCC 2007.
3. Climate projections in adaptation studies
The assessment of the ensemble members (Section 2.2) demonstrates that the models in the HighNoon ensemble simulate the important processes of the current climate and therefore should

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Fig. 5. 30-year JJAS precipitation climatology for observations (CMAP, APHRODITE, GPCP — 1st row, left to right) and each HighNoon model run for Ganges/Brahmaputra catchment.
Left to right along each row HadCM3–HadRM3, ECHAM5–HadRM3, HadCM3–REMO and ECHAM5–REMO.

simulate a realistic future climate for the Ganges and Brahmaputra regions (Liang et al., 2008). The climate projections provide information
on a range of timescales, however at very short timescales the naturalvariability masks the climate signal and therefore we use 30-yearmeans to analyse future changes in climate.
In this section we explore how the output from RCMs can be useddirectly in studies of adaptation focussing on the 30-year period
centred on the 2050s (2040–2070) as this timescale is appropriate
to inform adaptation policies (IPCC AR4, 2007). This is mainly due
to the inertia in the climate system which means that some change
in climate is inevitable, and will be evident by the 2050s given theemissions already in the atmosphere (IPCC AR4, 2007).
3.1. Using the climate projections
The analysis of the HighNoon RCM ensembles presented here suggests two possible approaches for general analysis; the ﬁrst considers
the 30-year seasonal means and the differences between the projectionof the future period (here the 2040–2070) and the estimate of the current climate provided by the period 1970–2000 (Section 3.1.1 applies
this method to the HighNoon ensemble); this is a more generic method
which is independent of the end user or the application. The second approach uses known thresholds at which a climate variable is known tohave an impact, for example, if the temperature exceeds 35 °C during
the critical ﬂowering period of a rice crop it can cause infertility of theplant (Yoshida, 1981) and therefore detrimentally impact the crop
yield (Section 3.1.2 applies this method to the members of the
HighNoon ensemble). This requires some knowledge of the end user
as this information is more speciﬁc; however thresholds of standard parameters such as temperature and precipitation could have multiple
applications.

3.1.1. Climatology analysis
The HighNoon ensemble analysis focuses on the Ganges and Brahmaputra catchments for two 30-year periods; the baseline(1970–2000) and future (2040–2070) and three variables; temperature, precipitation and runoff. Fig. 7 (left column) shows the annual
cycle for the temperature, precipitation and runoff across the four member HighNoon ensemble for these two periods. The magnitude of thedifferences between the two periods is shown in Fig. 7 (right column).
Fig. 7 (top row) shows that there is a near constant increase in the regional mean temperature across the four ensemble members by the2050s, in the annual mean of between 2.5 and 3 °C. These values compare with the projections of the 23 member AR4 ensemble whichshows an annual mean temperature rise of 2 ± 1 °C (Kumar et al.,2011a). Thus, the RCM ensemble under-samples the uncertainty, withprojections at the higher range indicated by the AR4 global models.
Across the annual cycle, the RCMs show temperature changes of between 2 and 4 °C, with suggestions that the mean dry season temperature (DJF) could increase more than the mean JJAS temperature.
The spatial distributions of the temperature changes (not shown)are similar across the catchment although during DJF and SON thetemperature rise is greatest in the mountainous regions of the catchment. An increase in temperature at higher elevations could have impacts on the glacial melt and the risk of glacial lake related ﬂoods,
possibly making runoff from glacial melt and snowmelt occur earlier
in the year. Increases in temperature could also mean that regions
previously too cold for crops become more temperate therefore increasing the viability of land at higher elevations for hortiagricultural activities. At lower elevations where temperatures are already at the physiological maxima for crops, the impact of even asmall increase in temperature could be detrimental to crop yields(Gornall et al., 2010). In order to adapt to increasing temperatures,

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C. Mathison et al. / Science of the Total Environment xxx (2012) xxx–xxx(a) Seasonal Area Averaged Prec (mm/d)
Precipitation (mm/d)

15
12
9
APHR
GPCC
GPCP
CRU
REMO
HadRM3

6
3
0
1980

1985

1990

1995

2000

2005

7

precipitation both in terms of sign of change and spatial patternmeans that adaptation strategies should be both robust and ﬂexible(e.g. Hay, 2007; Hertzler, 2007). In addition advances in modellingto include important aspects of the local environment such as aerosols and their transport, irrigation and land use change may alter
the projections presented here (see Sections 4.3.1 and 4.3.2). Adaptation strategies such as improving the efﬁciency of water storage andinfrastructure are positive changes in any climate. For example, in2006 Singapore aimed to meet 15% of its water needs using reclaimed
wastewater, a process with a low energy requirement (Tortajada,
2006), however Singapore's national water agency indicate thatreclaimed water now accounts for 30% of the nations water needs(PUB, Singapore's national water agency, 2011).

Time(b) Prec Anomaly (mm/d)2

Precipitation (mm/d)

1.6
1.2
0.8
0.4
0
-0.4
-0.8
-1.2
APHR
REMO
HadRM3

-1.6
-2
1980

1985

1990

1995

2000

2005

Time
Fig. 6. (a) Interannual Variability in seasonal (JJAS) averaged precipitation (mm/day)over HighNoon region with various observational ﬁelds (GPCP, GPCC, CRU and
APHRODITE (APHR)) and (b) Seasonal (JJAS) precipitation (mm/day) anomaly in
APHRODITE and regional models (HadRM3 and REMO) driven by ERA-Interim boundary conditions.

a shift in the growing season to avoid the highest temperatures might
be necessary which could itself have an additional impact on the regional climate by altering irrigation demands (see Section 4.3.2).
There is considerable uncertainty within the ensemble with regard to changes in wet season precipitation illustrated in Fig. 7 (middle row). Precipitation also has a much more variable spatial
distribution. These are highlighted in Fig. 8, which shows the difference between 2040–2070 and 1970–2000 climatologies of total precipitation for JJAS for the Ganges Brahmaputra catchment(highlighted with the black outline) for the HighNoon ensemble.
March, April and May (MAM, not shown) also showed distinct spatialdifferences, in particular, there was an increase in all four ensemble
members to the east of the catchment and a reduction in the west.
The annual cycle in runoff is shown for those members of the
HighNoon ensemble for which data were available, Fig. 7 (bottomrow). In general the two HadRM3 simulations show an increase in therunoff through most of the year, with the REMO run driven by
ECHAM5 showing a slight reduction during certain months of the year
(e.g. April, August November and December). The spatial distribution
is not uniform across the catchment; this is illustrated in the difference(2040–2070 minus 1970–2000) plots of runoff shown Fig. 9.
In both precipitation and runoff the ensemble members span zeroand therefore even given the similarities between these models, there
are considerable differences in their projections of future climate. Thishighlights that projections of rainfall and runoff are more variable andtherefore less certain than those for temperature, where all themodels in this analysis agree on sign if not magnitude. This ishighlighted in the summary given in Table 2. The uncertainty in precipitation is supported in the AR4 model ensemble which indicates asmall, 5%, increase in monsoon season rainfall (by 2050), but with anuncertainty of 20% (Kumar et al., 2011a). The variability in runoff and3.1.2. Threshold analysis
The understanding of the vulnerability of a population, sector orindustry to current weather and climate is an essential precursor tobeing able to adapt to potential changes to the climate in the future.
Often this understanding is through critical thresholds above which
there are impacts on a population or industry, for example, a totalmonthly rainfall value which could affect infrastructure or a temperature which is signiﬁcantly warmer than the average temperatureand could therefore cause health problems or crops to fail (seeTable 1).
The main industry in the Brahmaputra–Ganges catchment is agriculture and in this industry there are many fundamental thresholds
that are important. One example of a critical temperature for rice production is a temperature of greater than 35 °C during the crucial
ﬂowering period as this can affect the fertility of the plant (Yoshida,1981). Fig. 10 (bottom row) shows the change in the number ofdays per year with the maximum temperature above 35 °C between
the baseline (1970–2000) and the future (2040–2070) for two of
the HighNoon ensembles. In these two ensembles, there is an increase in the number of days exceeding the 35 °C threshold acrossthe south of the catchments for these two ensembles of 26 to
28 days on average but an increase of 70 days in the east of the region. On the basis that this region is one suitable in the current climate for multiple crops per year, an increased incidence of periods
with temperatures above the 35 °C threshold could increase the likelihood of these temperatures coinciding with crops ﬂowering. Further
analysis would be necessary on temperature, water availability andother interactions for example the inﬂuence of rising CO2 to diagnose
the full regional impact on crop yield.
Another factor important to India is population health and energy
demand. Anecdotally a temperature threshold of 28 °C has been used(gathered from various online sources, see web references 1; 2 and 3)
to represent a threshold at which energy demand increases; due tothe population using energy to operate cooling mechanisms such as
air conditioning to remain comfortable. Sherwood and Huber(2010) also highlight the importance of humidity and how this exacerbates the effect of high temperatures, while the UK Heat Healthwatch 2 recognises the importance of cooler nights which allow a period of recovery. Fig. 10 (top row) shows the difference between thetwo periods: 2040–2070s and the baseline (1970–2000) in the mean
number of days per year that are projected by these two ensembles to
exceed 28 °C. Both ensembles indicate an increase in the south of theGanges–Brahmaputra catchment, with a mean increase of 25 to
30 days for these two ensembles, with a maximum of 80 days in theeast of the region.
This large range in the difference between the numbers of days
with a maximum temperature that exceeds particular thresholds forthe 2050s compared to the baseline is due to the complex terrain of
the region which varies from high mountainous regions to lowlying deltas. Analysis of lower thresholds would be more appropriate
for regions of high elevation where the mean temperature is lower; inaddition, different temperature thresholds for both health and energy

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C. Mathison et al. / Science of the Total Environment xxx (2012) xxx–xxx

Northern India: Ganges/Brahmaputra catchments
Delta Temp

Northern India: Ganges/Brahmaputra
catchments Temp
4

25
20

Deg C

Deg C

3
15
10
5

2

1

0
J

F

M

A

M

J

J

A

S

O

N

J

D

F

M

A

M

HadCM3-REM O
Echam5-REMO
HadCM3-HadRM3P
Echam5-HadRM3P
J J A S O N

DNorthern India: Ganges/Brahmaputra catchments
Delta Precip

Northern India: Ganges/Brahmaputra
catchments Precip
2
10
1

mm per day

mm per day

8
6
4

0

2
-1
0
J

F

M

A

M

J

J

A

S

O

N

D

J

Northern India: Ganges/Brahmaputra
catchments Runoff

F

M

A

M

J

J

A

S

O

N

D

Northern India: Ganges/Brahmaputra catchments
Delta Runoff
1.5

6

mm per day

mm per day

1.0
4

2

0.5

0.0

0

-0.5
J

F

M

A

M

J

J

A

S

O

N

D

J

F

M

A

M

J

J

A

S

O

N

D

Fig. 7. Annual cycle in temperature (top row), precipitation (middle row) and runoff (bottom row) for the future period: 2040–2070 (dotted lines) and baseline: 1970–2000 (solidlines, left column) climatologies and the differences between the annual cycles for the two periods (right column) for the Ganges Brahmaputra catchment HighNoon ensemble
members.

demand may apply to different locations since local infrastructure
and local populations may differ in their current adaptation to heat.

the South Asia region. This uncertainty needs to be accounted for if adaptation related decision making is to be robust.
4.1. Sources of uncertainty

4. Discussion: uncertainty and adaptation
The model simulations from the HighNoon project are the mostcomplete set of data available for South Asia and are therefore a usefuland important resource for the types of climate analyses illustrated in
Section 3.1. However there are uncertainties in the models and understanding of atmospheric processes at both global and regional scales,
the implications of which are not yet fully understood and yet could signiﬁcantly change the regional projections if included in climate models.
In this section we describe sources of potential uncertainty in climate
models and speciﬁc processes that could be particularly relevant for

Climate models are a physical representation of a highly complex
system based on mathematical equations that simplify complicatedphysical processes. In deﬁning a climate the complex system is thewhole Earth system, including oceans, atmospheric processes, the land
surface and ecosystems and atmospheric chemistry. While parts of thissystem can be well represented by mathematical relationships, models
cannot provide a ‘perfect’ representation, due to limitations in scientiﬁcunderstanding of the large number of different processes, dependencies
and feedbacks and due to limitations in computing resources. Climate
models and their projections are therefore affected by many aspects

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C. Mathison et al. / Science of the Total Environment xxx (2012) xxx–xxx

9Fig. 8. Difference between 2040–2070 and 1970–2000 climatologies of total precipitation for the JJAS season for the Ganges Brahmaputra catchment (highlighted) for the fourHighNoon ensemble members.

such as the choice of numerical method to solve the dynamical equations;the processes that are included and the parameterizations that are used;
as well as any assumptions that are made. All of these potential
differences between models mean that every climate model could produce different projections creating a spread of results; this is known as
structural uncertainty (Sexton et al., 2011; Sexton and Murphy, 2011).
In addition to uncertainty from the structure and composition of themodel, different settings of key parameters within the parameterizationsof one model can also lead to a spread in projection results; this is parametric uncertainty (Sexton et al., 2011; Sexton and Murphy, 2011). For
example an uncertain physical parameter relevant to water and agriculture is the relationship between carbon dioxide concentration in the atmosphere and plant stomatal closure, in this example the uncertainty
has considerable implications for water resources (Wiltshire et al.,submitted for publication).

Model and parametric uncertainties are mainly due to the limitationsof the current modelling system; however there are other sources of
uncertainty that are more related with the chaotic nature of the naturalenvironment; this is referred to as natural internal variability. This typeof variability in the climate system occurs from regional scales to largescale interactions between the atmosphere and oceans such as the El
Niño/La Niña-Southern Oscillation (ENSO). Natural external variability
includes factors that can inﬂuence the climate but are not part of theclimate system, such as volcanoes and the solar cycle. Currently, there is
no way of predicting how the solar radiation reaching the Earth willchange in the future; therefore this is not included in models. There isalso no way of predicting when, on climate timescales, a volcano might
erupt in the future nor how much ash it will emit into the atmosphere,
therefore this is not modelled in any climate projections. Though bothof these types of variability are missing in the climate models used inFig. 9. Difference between 2040–2070 and 1970–2000 climatologies of total runoff for the JJAS season for the Ganges Brahmaputra catchment (highlighted) for the HighNoon ensemble members (the units of river ﬂow given in mm/day).

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Table 2
Summary of differences future − baseline for temp, precip and runoff.
Differences future − baseline
across ensemble

Annual
Min

Mean

Max

Min

Mean

Max

Temperature (°C)
Precipitation (mm/day)
Runoff (mm/day)

2
−1
− 0.2

2.6
0.2
0.2

3.7
1
1

2
−1
0

2.4
0.4
0.45

2.9
1
1

Seasonal (JJAS)

the analysis in this paper, on the 30-year timescales (used in Section 3)
their effects are considered unlikely to have a large long-term impact.
The third major uncertainty in climate projections is associated withhow human activities will inﬂuence and interact with the environment.
This is dependent on many factors across a broad spectrum from economic and policy driven aspects to social behaviour; making it difﬁcult
to predict how the population could change or how future populations
might consume resources. In the IPCC AR4, the Special Report on Emissions Scenarios (SRES) representative pathways were used to depict a
range of plausible future emissions (Nakićenović et al., 2000); however
none of these scenarios represent a mitigation scenario and therefore
no projection of the impact of future mitigation is currently available.
These scenarios often include assumptions about future changes in population, land use and other socio-economic factors.
The relative roles of these different sources of uncertainty depend on
the time scales under consideration. On decadal timescales, the climatechange signal is small compared to natural variability, such that uncertainty caused by initial conditions and natural forcing dominates(Hawkins and Sutton, 2009, 2011). This timescale is often consistent
with the timescales of adaptation of infrastructure. Research on decadal
climate predictions is just emerging (e.g., Collins et al., 2006; Smith etal., 2007; Keenlyside et al., 2008), and no regional climate predictions
on decadal scales currently exist. However, decadal and seasonal prediction may signiﬁcantly improve our understanding of adaptation requirements over the next few decades (Betts et al., 2009).
The HighNoon RCM ensemble is comprised of four simulations andtherefore it is unlikely to capture the full range of uncertainty of a larger
ensemble for example, from the IPCC AR4 (IPCC AR4, 2007). The IPCC

AR4 ensemble showed a global mean annual temperature change of between 1.7 and 4.4 °C for the A1B scenario, regionally for South Asia thechange was between 2 and 5 °C. Precipitation for South Asia is much
more variable in the IPCC ensemble, showing changes in annual precipitation of between −15 and +20% for the A1B scenario. Though the analysis
showed here does show a similar range in temperatures and large variation in precipitation, similar to the AR4 ensemble, this analysis is for amuch smaller area (just the Ganges–Brahmaputra catchments) than the
IPCC South Asia region so the two are not directly comparable.
In general the implications of using an ensemble of future climate
projections that captures the uncertainty in the climate system sufﬁciently may include the following:• Strong agreement on sign and magnitude of changes: more conﬁdent
adaptation decisions can be made (for example, on both the need toincrease water storage, and on the range of capacity required).
• Agreement on the sign, but not magnitude of changes: adaptation decisions will need to be more ﬂexible (for example, a broader rangeof water storage capacity).
• Disagreement on the sign of changes: adaptation plans may need tobe ﬂexible to changes in both directions (for example, a verybroad range of water storage capacity or hedging by planting
mixes of different crop types).
4.2. Large scale processes
Global model simulations supply the large scale information at theboundaries of the RCMs and the correct representation of key large-

Fig. 10. The ﬁgure shows the difference between the number of days per year for the future period 2041–2070 minus the baseline (1971–2000) where the maximum daily temperatures exceed 28 °C (top row) and 35 °C (bottom row) for the two HadRM3 simulations driven at the boundaries by ECHAM5 (left) and HadCM3(right).

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scale processes is therefore extremely important in constraining the
RCMs (Lucas-Picher et al., 2011). An RCM is ﬁrst-order sensitive tothe driving lateral boundary conditions (Liang et al., 2008) and therefore reduction of the errors in the large-scale circulation will mainly
be possible through development of the driving GCM. Therefore it isimportant to improve representation of physical processes at the
global scale as these could have an impact on regional models, forexample ensuring the large scale correlations between ﬂows at
remote locations; referred to as teleconnections (James, 1994) arerepresented properly. The El-Niño Southern Oscillation (ENSO) is awell known teleconnection pattern which is considered to contributeto the total interannual variance over much of the globe (James,
1994). This is particularly important for this region as the interaction
between ENSO and the inter-annual and inter-decadal Hadley circulation has been shown to signiﬁcantly affect the South Asian Monsoon(Krishnamurthy and Goswami, 2000).
The ISM is a key driver of the climate in South Asia providing themain source of water for populations in India, Bangladesh, Myanmar
and Nepal. Therefore understanding the processes, global or more
localised, that feedback and affect the intensity of the monsoon
rains is important for representing the climate of the region.
The RCMs resolve other processes that are important for the regional
climate but occur on a resolution too small for global models to resolve
such as precipitation enhanced by local orography, coastlines or the effect of land use. Gaining a better understanding of these processes occurring on a regional scale should improve the model representationof the regional climate system and ultimately could affect the range of
uncertainty in model projections of future climate.
4.3. Local inﬂuences on regional climate
As previously discussed, the inertia in the global climate is sensitive, partly to the long-lived nature of atmospheric carbon dioxide.
Therefore even if atmospheric concentrations are stabilised theglobe will continue to warm indicating a commitment to adaptation.
However, on the regional scale, local inﬂuences can play a substantial
role in the local climate on comparatively shorter timescales. For instance the atmospheric lifetime of black carbon is approximately
1 week. Similarly, irrigation has been shown to alter the climate ofSouth Asia. In the remainder of this section we consider the processes
that are not currently represented in the regional model simulations
presented here, but could be important; such as black carbon andthe occurrence of Atmospheric Brown Clouds, land use, irrigationand soil properties. In some cases these localised processes could substantially alter the climate over the next few decades signiﬁcantly altering the needs of adaptation.
4.3.1. Atmospheric brown clouds and black carbon
Asia is a region that relies on coal powered energy generation andtherefore emissions of black carbon are relatively high in this region,such that UNEP have identiﬁed this region as an Atmospheric Brown
Clouds (ABC) hotspot. ABCs are regional scale plumes of air pollution
consisting of particles (referred to as primary aerosols), black carbon
and pollutant gases such as nitrogen oxides (NOx), carbon monoxide(CO), sulphur dioxide (SO2), ammonia (NH3) and other organic gases
and acids. ABCs have been shown to have signiﬁcant impacts on theregional climate of India especially the monsoon (Meehl et al.,2008; Wang et al., 2009); such is the concern over ABCs that a comprehensive impact assessment report on ABCs with particular focus
on Asia was commissioned by the United nations Environment Programme (UNEP, 2008).
Black carbon particles are typically small in size and chemicallyinert and therefore remain in the atmosphere for up to a week before
they are precipitated or deposited out. This relatively long lifetime in
the atmosphere means that these particles can be transported considerable distances away from the emission source and therefore the11effects of black carbon can extend well beyond the regions where
the concentration is highest (Wang et al., 2007). Black carbon causes
radiation to be absorbed higher in the atmosphere therefore
preventing radiation from reaching the surface, simultaneously
warming the troposphere and cooling the surface thereby making
the atmosphere more stable. The combined effect of this change indistribution of energy through the troposphere and the reduced
evaporation from a cooler surface suppresses convective precipitation(Ramanathan et al., 2005, 2007a, 2007b). Black carbon also absorbs
radiation reﬂected from the surface, hence reducing the amount of radiation that is reﬂected back into space, thus reinforcing its positive
radiative forcing effects.
Black carbon could also affect the lifetime of clouds in the atmosphere and therefore have an impact on large-scale precipitation patterns. The varied composition of ABCs is likely to mean that theireffect on precipitation and radiative forcing is likely to vary considerably from region to region. This will mean some areas experiencing
increases and others decreases in precipitation. The deposition of
black carbon has also been shown to affect snowmelt and glacial
melt by reducing its albedo (Qiu, 2010). The effects of black carbon
and ABCs on the atmosphere are therefore complex and diverse making them difﬁcult to represent fully in climate models. However giventhat black carbon emissions have increased by three times in South
Asia between 1950 and 2000 together with an observed decrease in
ISM precipitation of approximately 5–7% for the same period, ABCs
are an increasingly important feature of the Asian climate. In the
HighNoon simulations there is no representation of the aerosol species in ABCs or their transport; however it is possible that their impact
on future projections could be large and have a signiﬁcant impact on
surface energy and moisture ﬂuxes.
4.3.2. Land use and irrigation
As the population of India grows, demand for food and water could
potentially drive changes in land use either to increase the amount ofagricultural land or increase the productivity of the land already being
farmed. Land use change has been identiﬁed as a key driver of climatechange not only through absorption or emission of greenhouse gases
but also by modifying the physical properties of the land surface(Betts et al., 2006). For example changing the land cover from dark forest to agriculture will mean more exposed bare soils which will affectthe surface roughness and albedo as well as the ﬂuxes of moisture andheat (Betts and Ball, 1997). The effects of changes in land use dependon the region and the conditions at a particular location.
There is growing evidence that human activities that change theland surface can have an impact on regional climate. Irrigation is an
important modiﬁcation to the land-surface, essential for the successof agriculture in dry climates such as the Ganges Brahmaputra region.
Irrigation has important effects on the hydrological cycle at a numberof different stages. Extraction of water from rivers and ground water
for irrigation reduces their ﬂow, transferring moisture to the soils andplants, thus modifying the land-surface moisture ﬂuxes throughevaporation and transpiration. The changes in vegetation distributions and the increased water vapour present in the atmosphere affect the heat and moisture ﬂuxes at the surface, in turn this has aneffect on the generation and lifetime of clouds, which affects precipitation. An example of this is given by Lohar and Pal (1995), they report a reduction in mean monthly rainfall over West Bengal (forexample the March rainfall reduced from 50–80 mm between 1973
and 1982 to 20–40 mm between 1983 and 1992); a region which
has experienced a rapid expansion of summer paddy crop agriculture
along the coast (with an increase of more than 3-fold between 1980
and 1990). The high moisture content of paddy ﬁelds has reduced
the temperature difference between the sea and land, thus reducing
the strength of the sea breeze and therefore convection. Douglas etal. (2009) show that the effect of moving to irrigated croplands varies
regionally but for the Ganges–Brahmaputra basins there is a

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reduction in the rainfall of between 20 and 60 mm for a 5-day period
during the ISM. Douglas et al. (2009) demonstrated that by modifying
the moisture ﬂux over a large enough area and reducing the Convective Available Potential Energy (CAPE) the mesoscale convection patterns are modiﬁed therefore affecting the rainfall patterns acrossIndia. Douglas et al. (2006) reported regional differences in latent
heat ﬂux across India; with increases in the northern regions due tochanges in land-use from drier soils to irrigated agriculture. These reductions in precipitation will have direct consequences on the regeneration of groundwater and the ﬂow of the rivers in the region.
Saeed et al. (2009) demonstrate the impact of including an irrigation scheme in an RCM on the modelled South Asian Monsoon using
the REMO RCM driven by ERA-40 data (Uppala et al., 2005). In partsof India such as the Indus and Ganges basins, which are highly irrigated regions, there is a warm bias in the model which can be as much as
5 °C. Including irrigation in the REMO model reduces the warm bias in
the model which weakens the westerly winds from the Arabian Sea.
This allows the advection of moist air into western India and Pakistan
removing the low precipitation bias also in evidence in these regions.
Irrigation also improves the evapotranspiration in these regions byrouting water more effectively.
In the RCM runs for HighNoon there is no accounting for the effectof land use change or irrigation and therefore their impact on the regional climate is not captured. Though it is possible to use ofﬂine
land-surface simulations to route the rivers more effectively thiswould not capture the feedbacks of the more efﬁcient routing and improve the evapotranspiration on larger scales. Representing physicalprocesses important in the vicinity of steep orography, leading to enhanced ascent of air, affecting cloud and precipitation formation andresulting in increased moisture along the upslope (Yasunari, 1976;
Singh et al., 1995) are likely to improve the precipitation simulations
for the region. Also, the important mountainous physical processes
such as snow drift/accumulation, liquid to solid precipitation formation, and rain shadow effect need explicit driving mechanisms inthe model physics (Leung and Ghan, 1995). Incorporation of suchphysical processes will enhance the model reliability towards adaptation issues associated with the extremes of precipitation issues suchas changing agriculture patterns, population migration and health.
4.3.3. Soil properties
The important inﬂuence of soil properties on the climate, particularly the coupling between soil moisture and rainfall have been identiﬁed by a number of studies (Kendon et al., 2010; Koster et al., 2004,
2006). Koster et al. (2006) identify South Asia as a region with particularly strong coupling which results in a warm bias in models over
the region; this is referred to as a hotspot. Though this regime has
been identiﬁed in several models, a sensitivity study for Europe(Anders and Rockel, 2009) demonstrated that this warm bias is affected by soil type.
In order to isolate the effect of different soil properties two simulations using the 25 km REMO RCM using ERA-Interim boundary data
for the HighNoon domain were performed; one using the standard
soil hydrology and soil thermal characteristic (baseline) and theother using a modiﬁed soil thermal characteristic (test). Initial analysis of these simulations suggests that soil properties could be
important.
REMO uses the Dümenil and Todini (1992) soil hydrology scheme
which uses a bucket type soil module, where each grid box is represented by a single soil water reservoir i.e. the depth of the bucket. Therefore
the water available for evaporation is deﬁned by the rooting depth ofthe plants and the soil texture properties. The comparison of the current
climate from the baseline simulation with CRU observation data sets indicates a warm bias in the surface temperature of as large as 8 °C insome parts of northern regions. In REMO, thermal diffusivity and capacity are parameterized as a function of soil moisture, using external constants as reference values independent of soil moisture. These reference

values represent values of a medium moist soil for a mid-latitude rainy
climate; however the climate over South Asia is mainly dry categorised
as arid or semi-arid climate. Thus in the test simulation the soil thermal
characteristic is modiﬁed to be more representative of dry soil bychanging the thermal diffusivity and the conductivity (Gordon, 2002).
The comparison of the test simulation with CRU observation data setsshow a reduction of the warm bias observed in the baseline simulationof between 1.5 and 4 °C.
4.3.4. Summary of missing processes
The list of processes presented here, though not exhaustive, illustrates the considerable uncertainty that arises from these missing
processes in the model simulations. For instance, the inclusion of irrigation can reduce the simulated temperature biases by approximately the same magnitude as the range presented by the ensemble
members. Black carbon and other aerosols in the atmosphere, bothin terms of their chemistry and transport, have been observed tohave an increasing effect on the regional climate acting to reduce precipitation during the ISM, however these observed precipitation
changes are currently within the range of uncertainty of the
HighNoon ensemble.
5. Conclusions
We have completed four simulations for the HighNoon project
covering India and the Himalaya using two different regional models(REMO and HadRM3) with boundary data supplied from two global
models (ECHAM5 and HadCM3). Analysis of the ensemble members
has shown that they represent the general processes and climate of
the region although the observed patterns in rainfall and temperature
are not replicated exactly. This ensemble is the most complete(1960–2100) high resolution (25 km) data set available for the region
providing data across a wide range of model variables. This ensemble
provides useful information on the potential future changes in temperature (e.g. increasing mean temperatures of between 2 and 4 °C)
and precipitation (though precipitation is more variable with arange of ±1 mm/day across the ensemble). Analysis of the model
data using known critical thresholds can provide useful information
for industry, population and infrastructure; for example the change(between 1970–2000 and 2040–2070) in the number of days withmaximum temperatures exceeding 28 °C is between 25 and 30 days.
In using this information it is therefore important to balance therisks versus beneﬁts of the adaptation policy, for example what are
the costs of adaptation against the costs of no action, while considering the potential cost of adaptation in response to incomplete science.
In this situation there is a requirement for adaptation pathways that
do not restrict the future ability to adapt and are therefore resilient
to both future advances in science and natural changes in the environment. Therefore the projections discussed here should be viewed
as providing scoping information rather than supporting detailed adaptation plans.
Regional climate impacts are often strongly dependent on the climate data and scenarios used and the assumptions made, suggesting
the need for application of a wider set of common scenarios (including both climate and socio-economic factors), and a more in-depth
assessment of assumptions and factors considered to enable a better
comparison across studies. This also supports the need for a riskbased approach which considers model skill, conﬁdence, and uncertainties in future projections more comprehensively. In light of themany sources of potential uncertainty discussed above, Hay (2007)
and Hertzler (2007) suggest that the development of robust ways ofapplying uncertain climate information to agricultural decision making (e.g. hedging, foreclosing options, creating new options and diversiﬁcation) will be critical in planning resilient future land/water
management options for agriculture. Similar approaches will also be
important for adaptation planning for other impacts sectors.

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C. Mathison et al. / Science of the Total Environment xxx (2012) xxx–xxx

Acknowledgements
This work was supported by the EU FP6 HighNoon Programme
and the Joint DECC/Defra Met Ofﬁce Hadley Centre Climate Programme (GA01101).
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