Download 2007-2012 Hadley Centre Climate Programme

2007-2012 Hadley Centre Climate Programme
End of Contract Report
June 28, 2013
Paul Halloran and Jason Lowe
Contents
1 Introduction
2
1.1 How this document works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2
1.2 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3
1.3 Summary of customer focused outcomes . . . . . . . . . . . . . . . . . . . . . . . .
5
1.4 Executive Scientific Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7
1.5 A look to the future . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
9
2 2007-2012 MOHCCP achievements
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2.1 Climate science background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
10
2.2 Modelling the climate system . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
12
2.3 How and why is the Earth System already responding to climate change? . . . . . .
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2.4 What climate change is likely to occur in the coming decades, and which changes
may cause socioeconomic stress? . . . . . . . . . . . . . . . . . . . . . . . . . . . .
33
2.5 Looking beyond the next couple of decades, what climate change is likely to cause
socioeconomic stress? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
40
2.6 How might the Earth System respond to different pathways of anthropogenic activity
over the coming century and how resilient is the system to change? . . . . . . . . .
3 Looking Forward
48
70
4 Annex A: Milestones and Deliverables
107
5 Annex B: Glossary of Acronyms
113
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Chapter 1
Introduction
The 2007-2012 Met Office Hadley Centre Climate Programme (MOHCCP) has responded to both
the evolving state of climate science and the evolving needs of UK Government for climate science
advice. This report aims to summarise the major scientific acheivements during the programme.
This report examines how research from the Met Office Hadley Centre, together with national
and international partners, has pushed forward our understanding of a number of overarching ques­
tions. These questions could be framed in a number of different ways, but in the context of this
report, we consider them to be:
• How and why is the Earth System already responding to climate change?
• What climate change is likely to occur in the coming decades?
• Looking beyond the next couple of decades, what climate change is likely to cause socioeco­
nomic stress?
• How might the Earth System respond to different pathways of anthropogenic activity over the
coming century, and how resilient is the system to change?
It is a testament to the quality and breadth of science being undertaken at the Hadley Centre
that the MOHCCP can make progress on such significant questions. By highlighting the progress
made on these overarching questions, this report places the full spectrum of science occurring at
the Met Office Hadley Centre, from underpinning work to directly applied science, in context. Since
a significant fraction of the programme has focused on improving climate models there is also a
section describing the key model development highlights from the 2007-2012 period.
1.1 How this document works
In addition to the narrative describing the progress in answering the overarching questions, this
report also provides a comprehensive record of milestones and deliverables supplied to DECC and
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Defra during the 2007 to 2012 period (Annex A). For clarity two referencing styles are employed in
this report. 2007-2012 MOHCCP funded results are referenced with a number enclosed in squarebrackets, and non 2007-2012 MOHCCP funded results, where necessary, are referenced by author
and year, for example, (Author et al., 2006). The square bracketed numbers refer to a concise
bibliography at the end of the main document. Top-level summaries of the milestones, deliverables,
and relevant scientific papers are supplied in the supplementary materials, and linked to from the
bibliography at the end of this document.
1.2 Background
The Met Office Hadley Centre (MOHC) was formed in 1990 at the request of the then Prime Minister
to act as a focal point for climate science and projections in the United Kingdom, and to establish
the nation as a world leader in the field. In the early years the MOHC produced numerous scientific
developments to equip the UK to play a major role in international climate negotiations focusing on
emission reductions.
Working with numerous academic partners the MOHC:
• Produced quality controlled observational datasets to monitor climate change
• Played a leading role in establishing much of the methodology demonstrating that many recent
observed changes were unusual and that some of the observed trends could be shown as
having a human derived contribution
• Built on earlier work in the Met Office to construct global and regional climate models for
projecting and understanding possible future conditions.
With the evidence of a changing climate becoming more established in the scientific literature
the MOHC realised that this information would be needed to plan adaptation strategies and took on
a role to produce the climate projections needed for this by the United Kingdom. The first set of UK
Climate Projections were released in 1998, with major updates in 2002 and 2009 providing greater
detail and eventually risk based information.
The quality of the MOHC outputs have been assessed in numerous ways during the 2007-2012
period. In addition to an annual review by its Science Review Group several other external reviews
have taken place. A report by Professor Sir John Lawton1 was commissioned by the Department
for Energy and Climate Change (DECC) and completed in October 2009. This concluded that ’UK
Government continues to require access to a top-flight climate research capability to deliver the
advice and information it needs, and further concludes that this essential capability will be most
reliably and effectively delivered by the MOHC’. Sir John Beddington led a further investigation in
2010 examining the Government’s needs for climate science advice services over the next decade,
1 http://www.bis.gov.uk/assets/goscience/docs/s/2009-sir-john-lawton-review-report.pdf
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and how these could continue to be met. This report was published in 20102 . The Beddington review
stated that ’A key conclusion is to confirm the assessment from Sir John Lawtons 2009 report that
the MOHC provides essential and world-leading climate modelling services to Government, and
that it is uniquely placed to do so. It represents a critical national capability, with a central role of
meeting the Governments requirements for climate evidence and advice’.
The reputation of the MOHC is also recognised widely. It was identified as the world leading
centre in geoscience research within a survey by Times Higher Education 3 . This is reflected in its
journal publication record and citations, and its ability to attract some of the best climate scientists
from around the world. The MOHC’s leading role in climate science is also demonstrated by the
number of MOHC scientists who contributed to the Intergovernmental Panel on Climate Change
(IPCC) process, as authors and reviewers, and by the fact that the MOHC housed the technical
support units for Working Group 1, and later Working Group 2, of the IPCC climate change assess­
ments.
Collaboration has always been a key feature of the MOHC approach and during the 2007-2012
period this has increased in importance. The establishment of the Joint Weather and Climate
Research Programme with NERC (Natural Environmental Research Council) has enabled faster
progress in many areas, with a growing emphasis on sharing the development of new climate and
Earth System Models. The MOHC has also played a central role in many large European climate
projects, such as the major ENSEMBLES project, which have brought benefits to the DECC and
Defra funded climate programme.
2 http://www.bis.gov.uk/assets/goscience/docs/r/10-1290-review-of-climate-science-advice.pdf
3 http://www.timeshighereducation.co.uk/story.asp?storycode=409181
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1.3 Summary of customer focused outcomes
The MOHCCP has provided scientific information through many different channels during the 2007­
2012 programme. MOHC scientists communicate with other researchers through collaborative
projects, contributions to the scientific literature and conferences, and through international efforts
such as the IPCC. Additionally, the MOHC has, and continues to, host visiting scientists from around
the world.
The MOHC provides DECC and Defra with written reports of outputs, short written and oral
briefings on key pieces of work, and by providing scientific information to rapidly help answer adhoc
questions relating to climate science. Additionally the MOHC has provided a supporting role at
numerous workshops and conference events at the request of DECC and Defra, such as the annual
Conference of the Parties to the UNFCCC (CoP). Finally, some outputs, such as the 2009 UK
Climate Projections (UKCP09), have been made publically available so that experts in other fields
can use them to examine impacts on particular sectors.
Key outputs from the 2007-2012 programme include:
• UK National Climate Capability Approximately three quarters of the funding that the MOHC
receives from DECC and Defra is used to deliver the underpinning climate science needed
to maintain the UK’s leading capability in climate science. The MOHC Climate Model has
been adopted by the UK academic community, resulting in major benefits in delivering climate
advice to the UK. These benefits included shared model development, consistency of advice,
and the development of services. The Met Offices Unified Model has also being adopted by a
number of centres internationally.
• IPCC Contribution - As a lead contributor to IPCC assessment reports the MOHC has pro­
vided global leadership in climate science. Successive IPCC assessment reports have been
pivotal in establishing the evidence base to conclude that climate change is a real and pro­
found threat requiring national and international action to both mitigate against and adapt to
inevitable climate change.
• Attribution - Pioneering work on attributing observed changes to both anthropogenic and
natural changes have revealed the human influence on long-term trends and the changing
risks of extreme events as the climate changes, for example increased risk of hot summers and
increasing risks of flooding. The pioneering ACE (Attributing Climate Events) group continues
to take this forward.
• Mitigation Advice - Climate Projections from the MOHC have been central to establishing the
emission targets and pathways written into the Climate Change Act and international agree­
ments. Earth System Model and other simulations have been key in understanding and quan­
tifying what impacts can be avoided through mitigation measures. The Lawton report (2009)
confirmed the significance of the MOHC to the UKs influence in international negotiations’.
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• UKCP09 Scenarios These detailed scenarios are a comprehensive set of climate projections
that provide an unprecedented level of information about climate change at regional and local
scales across the UK. Used by both government and industry, the projections form a consis­
tent basis on which adaptation decisions and planning are made. The projections also formed
the basis for the assessment of future risk in the UK Climate Change Risk Assessment.
• Near Term Climate projections - As society becomes increasingly vulnerable to hazardous
weather and climate extremes, the demand for information at monthly to decadal timescales
is growing rapidly. The development of high resolution climate models has significantly en­
hanced our ability to understand natural climate variations on these timescales and is already
demonstrating significant prediction skill.
• Tailored Climate Communications Tools - The 4◦ C map published by the MOHC drew many
strands of international research together to provide a persuasive and influential summary
of global impacts likely to be experienced if global temperatures increase by four degrees.
It was well received internationally and translated into ten languages. Understanding the
potential impacts of climate change is essential for informing both adaptation strategies and
actions to avoid dangerous levels of climate change. In April 2011, the MOHC was asked
by the Secretary of State for Energy and Climate Change to compile scientifically robust and
impartial information on the physical impacts of climate change for more than 20 countries
to inform negotiations at the 17th Conference of the Parties. A report on the observations,
projections and impacts of climate change was prepared for each of those countries. In the
2012-2015 Programme, engaging fact sheets, summarising these findings, are being supplied
making information even more accessible to a non-technical audience.
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1.4 Executive Scientific Summary
We present here a brief description of some of the highest impact and most important scientific
achievements for the Met Office Hadley Centre Climate Programme for the period 2007-2012. The
work has increasingly involved close collaboration with the academic sector.
Observations are needed to monitor changes in climate, to attribute the causes of climate
changes, and to validate and constrain climate models. The quantification and presentation of
the degree of certainty in observed climate data took a leap forward with the release of the 4th sur­
face temperature product produced jointly between the Hadley Centre and the Climatic Research
Unit (CRU; University of East Anglia), HadCRUT4. This presents the range of certainty as 100
different possible realisations of the temperature record. A further highlight has been the growing
consideration of climate records other than temperature, with the MOHC making a significant con­
tribution to the Bulletin of the American Meteorological Societys State of the Climate report. These
show the diversity of planetary change over recent decades.
Attribution of observed change is the process of identifying which factors, natural or
man-made, have contributed to an observed change in the climate system. Over the 2007­
2012 period the MOHC has been pushing these techniques to smaller temporal and spatial scales,
extending the range of climate components in which change has been attributed. Key developments
focused on starting to examine the change in the probability of extreme events occurring (for exam­
ple, the 2000 summer floods in Southern England) that might result from humankinds greenhouse
gas emissions.
Model development has remained a central Met Office Hadley Centre activity with ad­
vances in three distinct areas. First, increases in High Performance Computing have been ex­
ploited to set up models with greater spatial detail, capable of explicitly representing more climate
processes. Highlights include making significant improvements of how atmospheric blocking is sim­
ulated over the UK. Second, complexity has been increased so that more physical, chemical and
biological processes of the earth system are represented. Many of which have the potential to feed
back on the physical climate, and may impact society in other ways such as through food produc­
tion potential. A major highlight was the development and testing of the HadGEM2-ES earth system
model. Finally, a sizeable effort focused on developing the intellectual and computational framework
needed to run and evaluate large sets of simulations.
The model developments have benefited attribution studies and projection of both nearer-term
and longer-term future climate. For nearer-term projections there have been many advances,
for example, a better understanding of the role of the stratosphere in propagating changes from the
tropics to the latitudes of Europe, the role the subtle changes in the suns output plays in determining
European winter climate, and emerging evidence of the role of loss of Arctic sea ice on European
climate variability. Focusing on longer-term projections, experimental forecasts of the next decade
were begun, showing useful skill in many regions. The UKCP09 probabilistic simulations were
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produced to better understand and measure the uncertainty in multidecadal climate projections
out to the end of the 21st century. They were an important input to the first national climate risk
assessment. Multi-century projections were provided by the HadGEM2-ES earth system model and
form an important part of the CMIP5 model assessment, which will inform the IPCC 5th assessment.
Over the 2007-2012 period new understanding has emerged on potential thresholds in the
large-scale climate system, with significant improvement in the understanding of Arctic sea ice
loss, reversibility of the loss of the Greenland ice sheet, and stability of the Atlantic overturning
circulation. A key output has been to focus on process understanding and consider how early
warning of these changes might be detected.
Throughout this period of research a greater focus has been placed on the communication
and tailoring of MOHC science to make it more useable by DECC and Defra, and their stakehold­
ers. The substantial developments in climate science and delivery have placed the MOHC in an
ideal position on which to build in the new work plan during the period 2012-2015.
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1.5 A look to the future
A new DECC and Defra funded climate programme began in 2012 and is scheduled to run until
March 2015. It builds on the scientific work of the 2007-2012 programme, providing further un­
derpinning research and policy focused outputs. In addition to generating improved information
for mitigation advice and resilience and adaptation planning, the new programme is also focused
on providing information to better understand the capacity for renewable energy supply in the UK.
There is an enhanced emphasis on tailoring the advice from the MOHC so it matches even more
closely the evolving needs for impartial high quality climate science evidence by Government.
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Chapter 2
2007-2012 MOHCCP achievements
2.1 Climate science background
This section contains a brief introduction to the climate science behind the MOHCCP, and explana­
tion of some of the technical ideas and terminology used throughout this document.
Contemporary climate change is occurring in response to change in a number of driving factors.
It is often useful to group all of these different driving factors under a single name - climate forcings.
Climate forcings can be natural (e.g. volcanic emissions or changes in the sun’s output) or man
made (e.g. changes from woodland to pasture, or changing atmospheric carbon dioxide (CO2 )
concentrations). Radiative forcing is used to describe a change in the balance of energy radiating
(traveling in electromagnetic waves, for example as visible light) downwards towards the Earth’s
surface, versus that radiating back out to space - note that of the three ways that heat can travel
(radiation, convection and conduction), only radiation can travel through a vacuum, and therefore in
space, so the balance of radiation coming into/out-of the planet essentially accounts for all of the
energy (heat) entering or leaving the planet. This is called the planet’s energy budget.
The dominant anthropogenic (resulting from human activity) climate forcing agent is CO2 . In
steady state, (having settled to a stable condition in response to constant forcings), the radiation
arriving from the sun and warming the planet, equals that which is emitted by the planet and lost to
space - the earth is a radiator, and the hotter it is, the more heat it gives off. The radiation coming
in from the sun has a high frequency (ultraviolet), and like the sound produced by an opera singer
which shatters a glass, this only resonates with, and therefore is absorbed by, specific things at
specific frequencies. Radiation in these higher frequencies is not absorbed by CO2 . The radiation
emitted by the earth (and largely lost to space), has a lower frequency, it is infrared rather than
visible or ultraviolet radiation. Infrared radiation is absorbed by CO2 , and therefore a component of
this is prevented from escaping to space. The effect of trapping more heat is that the planet warms
up. A warm object gives off more heat, so eventually if CO2 concentrations are stabilised, the global
climate will come to rest at a new, overall warmer, steady state.
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The relationship between increasing CO2 concentrations and global warming is approximately
logarithmic - if the atmospheric CO2 concentration was doubled, then doubled again, the second
doubling would warm the planet by the same amount as the first, despite the magnitude of the
second CO2 increase being twice that of the first1 .
CO2 is not the only important anthropogenic climate forcing. Methane, nitrous oxide and ozone
also act in a similar way to CO2 , absorbing the long-wavelength radiation being emitted by the earth
(but at different wavelengths than those absorbed by CO2 ). These non-CO2 greenhouse gasses
get broken down relatively quickly (days to decades) in the atmosphere by chemical reactions, so
do not build up in the atmosphere in quite the same way as does CO2 .
Another very important anthropogenic climate forcing is that coming from aerosols. Aerosols
are very small particles in the atmosphere. These tiny particles both directly reflect sunlight, and
cause changes in the refection of sunlight by changing the properties of clouds. Like bubbles
in a champagne glass, water droplets in the atmosphere form around imperfections (particles) ­
it requires energy to change from one phase to another (liquid to gas or gas to liquid), and an
imperfection can reduce the energy required to do this. In a very clean atmosphere, adding aerosols
could mean the difference of going from no clouds to having clouds (and would therefore greatly
increase the amount of sunlight reflected to space), but in the present atmosphere, aerosols are
more likely to cause cloud that already exists to be made up of smaller droplets, which makes them
more reflective (brighter to look at), and because the droplets take longer to get to the size of rain­
drops, those clouds remain in the atmosphere for longer, increasing the total amount of reflected
sunlight.
The final important anthropogenic climate forcing is changing land-use. For example leaving
land as forest, removing that forest to open that land up for pasture or crop growth, or building urban
areas. How the land is used is important from a climate perspective because differently used land
has different physical properties. It is clear that (for example) ice reflects more light (and therefore
does not absorb as much heat) as dark green woodland. Similarly, grassland reflects more light
than woodland, and urban areas, less light than grassland. It is not just the proportion of sunlight
which is reflected (the albedo of that surface) which is important in how it affects climate. Woodland
presents a rougher surface than grassland, and therefore wind experiences more friction passing
over woodland than grassland, impacting its behaviour. Similarly, urban areas absorb much less
moisture into the soil than do grasslands, and woodlands extract much more moisture from the soil
and pass this to the atmosphere through evapotraspiration than do grasslands. All of these process
can be important for climate.
1 This relationship occurs because CO is very effective at absorbing the infrared radiation emitted by the Earth. Even
2
at preindustrial CO2 levels, this absorption was 100% efficient over a range of wavelengths of infrared radiation, so as CO2
concentrations continuously increase, more and more of that CO2 is having no effect - more and more wavelengths of infrared
radiation are already absorbed with 100% efficiency. With rising CO2 concentrations, an increasingly small proportion of the
atmospheric CO2 is absorbing infrared radiation - and therefore causing warming. The second CO2 doubling is less effective
at capturing heat than the first.
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2.2 Modelling the climate system
To explore how the forcings and feedbacks described in the previous section operate to cause cli­
mate change, we use physically-based climate models. Three levels of complexity of climate models
are developed and used within the MOHC; Earth System Models (ESMs), General Circulation Mod­
els (GCMs), and Simple Climate Models (SCMs) (figure 2.1). We also use output from Integrated
Assessment Models (IAMs) (figure 2.1), and are beginning to work directly with the developers and
expert users of this type of model.
Earth System Models attempt to simulate all of the first order2 processes within the climate
system which are believed to be important within centennial timescale climate change. These
include chemical and biological processes (which make up - for example - the carbon cycle) as
well as the physical climate processes; which are calculated in the underlying General Circulation
Model. Earth system models are discussed in detail in section 2.6 with reference to our recent
model development work. The GCM is a reduced resolution (explained later) weather model (a high
fidelity model of the atmosphere), linked to an ocean model.
Although we are just beginning to understand, and be capable of simulating the role of the
ocean on weather timescales (typically less than 15 days), the ocean generally changes relatively
slowly, and therefore whilst being of limited importance for weather forecasting, plays a critical role
in storing and transporting heat (and represented in an ESM, carbon) on climate timescales (years
to centuries). Models used to forecast the weather and make projections of the climate work by
subdividing the atmosphere and/or ocean into many adjoining boxes, grid boxes (figure 2.2). Within
the computer code, the average conditions (e.g. heat or salinity) occurring in that boxed section
of the atmosphere or ocean are contained in a list. The model solves the mathematical equations
which describe the movement of these quantities between adjacent grid boxes occurring within
a certain amount of time (the time-step) - typically from a few minutes to an hour in a current
generation climate model. The model can only move through the simulation in small steps, rather
than determining what the climate will look like a century down the line in one go, because if too
much of any quantity is moved from one box to a neighboring box, the value initially used by the
equation working on that neighboring box will no longer be valid. After the equations have been
solved for one time-step, the model then records the solution back in the list, then starting from
those new conditions, takes another mathematical time-step forwards.
The size of the adjoining grid-boxes (or the number of boxes required to surround the whole
planet) in the horizontal plane is called the horizontal resolution, and the number of boxes (rather
than size, because their vertical dimension varies with height/depth) in a column between the sea
surface and sea floor (in the case of the ocean) is called the vertical resolution (or the number of
2 In an area of science as complex as climate modelling, pragmatic decisions have to be made - to simulate every process
which may play an interesting, by tiny role, would be impossible. An important part of our job is to distinguish the important
processes which we should focus our effort and computing power on, from those which are unimportant with respect to what
the models are being used for. The important processes are described as being 1st order (applied rigorously this would
mean processes that affect the first significant figure of a relevant numerical outcome), the less important are described as
second order.
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Earth System Model (ESM)
General Circulation
Model (GCM)
∂U=∂Q+∂W
ac=-2Ω . v
PV=n.R.T
pDv/Dt=-∆p+pf
Aerosols
Greenhouse Gases
Chemistry
Land and Ocean Ecosystems
emission policy
CO2
Surface Ocean
(and biosphere)
Heat
Economic Model
greeh
ou
desig
n
o emulate ES
ed t
Ms
Simple Climate Model
(SCM)
Atmosphere
Heat
(Treatment of
Landuse)
e d to
un
st
Energy Systems
Model
IA M
s us
M
SC
e
sed to d
IA Ms u
sign ae
se gas
rosol em
licy
ission po
ESM projections
Integrated Assessment Model (IAM)
Socioeconomic
Impacts
CO2
Deep Ocean
Figure 2.1: Schematic explaining the different types of models used in the MOHC, and how they
are linked. Earth System Models (ESMs) are the most comprehensive climate modelling tools avail­
able. At the core of an ESM is a General Circulation model (GCM) which deals with the movement
of quantities such as heat or carbon around the atmosphere and ocean. GCMs or ESMs are run
following emissions scenarios to produce projections of future climate changed based on our fun­
damental understanding of physics (and in the case of ESMs our understanding of chemistry and
biology). The climate projections are used to fine-tune Simple Climate Models (SCMs), which are
based on our understanding of the system, but not necessarily fundamental physics - the schematic
representation of an SCM presented here is representative of one of a number of SCM model types.
Once shown to be able to replicate results from ESMs and GCMs, the SCM may be incorporated
into an Integrated Assessment Model (IAM). IAMs are not developed at the MOHC, but we are
increasingly working with experts in this field to help us make use of them. IAMs consider the in­
teraction of climate with human systems. One of the uses of IAMs is to develop greenhouse gas
(and other climate forcings) emissions scenarios. These emissions scenarios are used to drive
ESMs and GCMs to examine (more completely than can be done in an SCM) their potential climate
impact. Note, less common links which are made within IAMs are represented with dashed lines.
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vertical resolution
horizontal resolution
grid box
Figure 2.2: Simplified description of a Global Circulation Model (GCM). GCMs (and therefore ESMs
- figure 2.1) split the atmosphere and ocean into many adjacent grid boxes. The number of grid
boxes in a horizontal plane defines the horizontal resolution, and the number in a vertical plane
defines the vertical resolution. The model works by solving the physical equations describing fluid
flow through each of the six faces of the grid box.
vertical levels) (figure 2.2). In a climate model, processes like eddies (the swirling flow of water
occurring behind an obstacle in a stream, or due to unstable flow in the open ocean - important for
the vertical movement of water), or cloud formation, occur on space-scales smaller than the model’s
grid-boxes. These processes, along with many others, have to be parameterised. Parameterisa­
tions are mathematical equations which allow a process to be described simply without having to
simulate the underlying physics. For example, we know that evaporation from the ocean is driven by
temperature, wind-speed and humidity, so we can describe this in a simple equation (in reality, there
are many important processes such as the breaking of waves, turbulence in the air-sea boundary
etc., but these are considered to be second order).
Parameterisations have to be used in many areas of climate modelling - particularly Earth Sys­
tem Modelling, where for example, the fundamental physical and chemical processes behind bio­
logical processes are too complicated to be simulated, and gaps exist in our understanding. Whilst
we may be able to parameterise these processes, we may only know the range of values which
this parameter might take, rather than the exact value. Real-world experiments might say that a
parameter value lies somewhere between (for example) 0.6 and 0.9, so to take this into account
we can undertake model studies where we do many different simulations, each using a different
number between 0.6 and 0.9. These are called perturbed parameter experiments. Perturbing pa­
rameters within a simple model is one way of exploring the range of possible climatic responses, the
other way is by comparing independently developed models to explore structural uncertainty. Mod­
els that are developed largely independently from each other (i.e. at different international climate
research centres) differ in their structure - the equations used to represent a particular process or
set of processes differ between models (rather than them using the same equation, but different
values in that equation, as one would if following a perturbed parameter approach). By comparing
the results from a set of models like those which have been contributed to the 5th Coupled Model
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Intercomparison Project (CMIP5), we can quantify the range of possible answers - the uncertainty ­
arising from these structural differences.
Understanding and interpreting the behaviour of General Circulation Models or Earth System
Models, or particularly a suite of results from multiple models, can be very difficult. To help with
this, and to explore how GCMs and ESMs may behave under certain conditions without having to
undertake long simulations under those conditions, we can use simple climate models. Simple
climate models come in various forms. One important family of simple models uses simple physical
equations to describe the flow of energy and carbon between different reservoirs on the planet (for
example the deep ocean, surface ocean, atmosphere and land), rather than using complex physics
to actually simulate those energy and carbon transports occurring. This form of simple climate
model is described in figure 2.1. Another family of simple climate models calculates the global
average climate response by breaking the response into a number of components, each statistically
describing the timescale over which a certain important climate process (for example ocean mixing)
operates. When the parameters in the simple climate model equations are adjusted to allow them to
replicate the changes seen in physics-based models (General Circulation Models or Earth System
Models), simple climate models can accurately represent global average quantities, but must only
be used to simulate conditions close to those where they have been shown to produce the same
result as the physics-based model. It is important to make this distinction between models based
on fundamental physical laws, which can (because they are constrained by the same rules as the
Earth is) be used to explore conditions not previously experienced, and empirical models (simple
climate models), which can only provide meaningful information about conditions similar to those
over which they have already been shown to work3 .
Simple climate models form the climate module within Integrated Assessment Models (IAMs).
Integrated Assessment Models are tools which integrate our understanding from different physi­
cal and human systems together in one model to allow feedbacks occurring through, for example,
climate on society then back on climate, to be explored. These models are used to develop anthro­
pogenic CO2 emission scenarios which are consistent with various different potential sociological
and economic pathways [162]. Integrated Assessment Models are required to give rapid answers
to many different questions, and therefore can’t include full Earth System Models to calculate the
climate and carbon cycle changes due to computational limitations (Earth System Models take too
long to run on present-generation supercomputers to be used in this way). The MOHC is increas­
ingly working with external experts to make use of Integrated Assessment Models, but does not
develop this type of model.
3 For
more details about one simple climate model, MAGICC, see http://wiki.magicc.org.
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Model development within the Met Office
Since its conception, the MOHC has had at its core the Met Office’s Unified Model - the computer
code underlying the Met Office weather and climate models. Whist the weather and climate config­
urations of the Unified Model shared the same framework and advances made within the weather
model are generally brought into the climate model (and vice versa), these two configurations were
largely developed in parallel. Over the last MOHCCP, it has been increasingly recognised that there
are many significant advantages to be gained by bringing the weather and climate model develop­
ment processes together into a single development cycle:
• The use of a common model for weather and climate modelling will allow us to provide con­
sistent weather/climate advice across all societally relevant timescales
• Assessment of model performance across a range of timescales provides a tougher scientific
and technical test for new model developments than do isolated tests.
• Testing the seasonal forecasts, started from observations, against extreme recent climate
events helps identify areas for development of the model which will improve longer-term model
projections
• Assessment of simulations starting from observed values, and those running freely will high­
light different positive and negative model characteristics
• Understanding the climate model’s behaviour on weather timescales will help us interpret
climate events meteorologically, rather than considering the meteorology to simply be noise
on top of a time-averaged climate signal
• Common weather and climate model development leads to efficiency savings, and allows the
MOHC to take maximum advantage of its position within the Met Office
• A common and transparent model development process across weather and climate science
will maximise the benefit from collaboration with national and international Unified Model part­
ners working on different timescales
This seamless weather and climate model development process has been put into practice
with the development of a new family of models based around HadGEM3 (The 3rd MOHC Global
Environmental Model). HadGEM3 is the latest climate model being developed within the MOHC,
and consists of an ocean-model jointly developed with UK and European partners, and the latest
configuration of the Unified Model used within the Met Office’s Weather Science area [157]. The
different components of HadGEM3 were coupled together around the start of the last MOHCCP,
and marked the first step in response to the decision to move to a two-level model development
process. Rather than developing a single new model over each Coupled Model Intercomparison
Project (CMIP) cycle, a configuration of the physical climate model was frozen at that stage of the
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development cycle, and used as the basis for development of the HadGEM2-ES Earth System
Model (to contribute to the 5th CMIP), and in parallel a new physical climate model, HadGEM3,
began to be developed. Over the last five years a new system has been formalised to continue
developing, validating and releasing updates to this model, the atmospheric component of which
is now used in the five-day high resolution weather forecast, the 15-30 day suite of probabilistic
forecasts, near-term seasonal and decadal forecasts, regional climate model projections, and will
be used for different horizontal resolution global climate projections as well as acting as the basis for
the MOHC’s next Earth System Model. The atmospheric model development cycle now occurring
across the weather and climate science areas is described in figure 2.3.
Over the last five years this process has been sequentially focused in three important directions.
First, in the MORPH3 (MOdel for improved Regional Prediction - HadGEM3) project, which took
the (at the time) newly constructed, but only lightly validated HadGEM3-ES model [157], validate it,
and develop it to provide useful information at a regional level. Within this project, working groups
were set up across weather and climate science to examine monsoons, linkages between remote
phenomena (e.g. equatorial Pacific temperatures and regional rainfall around the world), ENSO (El
Nino Southern Oscillation), clouds, humidity and radiation, the MJO (Madden Julian Oscillation ­
variability in the tropical atmosphere), differences between the modelled and observed background
climate state, and regionally poor surface ocean validation.
The CAPTIVATE (Climate Processes, Variability And Teleconnections) project continued pro­
gressing a number of these themes, but focused on providing a model capable of producing high
quality near-term climate simulations [111]. Within CAPTIVATE working groups focused on mon­
soons, tropical cyclones, the blocking of extreme weather by stable weather systems, ENSO and
remote weather/climate linkages, the MJO, sea surface temperature offsets from observations, sur­
face ocean circulation, clouds, radiation and light rain, the interaction of rain and topography, and
processes important for African climate. Significant improvements were made to the blocking of ex­
treme weather by stable weather systems, and the representations of climate links to ENSO [111].
The INTEGRATE (ImproviNg model error, TEleconnections, and predictability Globally and Re­
gionally Across Timescales) project began in December 2011. This project is focusing on supplying
a high fidelity physical climate model to act as the basis for the development of a next-generation
joint UK Earth System Model, and developing a model to be used for medium-range weather­
timescales forecasts (15-30 days) which for the first time will bring together an ocean and atmo­
sphere model for use on these short timescales. The results from the INTEGRATE project will be
delivered throughout the 2012-2015 MOHCCP.
The scientific outcomes of the MORPH3, CAPTIVATE and INTEGRATE projects are described
in the context of the overarching questions throughout section 2.3 of this report.
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Figure 2.3: Continuous development of new model components, processes, parameterisations and
code, and continuous model assessment, evaluation and understanding form the basis of the Met
Office’s weather and climate modelling capabilities. These two cycles are now brought together
on an annual cycle through a formalised model implementation process. At the start of the year
this process identifies and incorporates new developments from the model development cycle, im­
plements these changes into a single version of the atmospheric model, tests this model across a
range of timescales and uses, then internally and externally releases a new finalised model version.
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Informing model resolution decisions
An important benefit arising from bringing together the Met Office’s weather and climate models in
a single development framework, has been the ability to consider in a coherent manner the role that
lower than ideal model resolution plays in the poor simulation of some climate system components.
This benefit builds on and extends many years of work developing a hierarchy of models using a
range of horizontal resolutions [290]. The aim of this work has been to answer the question, at what
resolution does the simulation of a certain process no longer continue improving, and therefore
based on our understanding of which processes we must critically represent within our models to
provide advice to different users, what set of model resolutions should we aim for.
This work has helped us understand the sources of poor model performance in simulating trop­
ical Pacific sea surface temperatures [289], tropical variability [289, 23], salt transport into the At­
lantic, regional coastal cloud amounts [332], topography driven rainfall, tropical cyclones and storms
[309], large-scale atmospheric circulation [309], mineral dust emissions from bare soil [353] and
ocean-atmosphere interactions [309]. Many of these results are discussed further as this report
explores the four overarching questions (sections 2.3, 2.4, 2.5 and 2.6).
Models and supercomputing
A key bottleneck on model improvement is the availability of supercomputing resources. As dis­
cussed above, perhaps the most important developments leading to improved climate model fidelity
are increasing the horizontal resolution of models and simulating more, and more complex, Earth
System processes. These changes can not happen without increasing the demand on supercom­
puting. For example, to double a climate model’s horizontal resolution typically requires about an
8-fold increase in available supercomputing, and turning a physical climate model into a (current
generation) Earth System Model demands approximately a three times increase in resources4 .
The 2007-2012 MOHCCP has seen one full, and one mid-life supercomputer upgrade. In 2007­
2008 the Met Office installed a new 141 Teraflop (1.41x1014 calculations per second) IBM super­
computer, moving the Met Office’s supercomputer capacity temporarily into the list of the fastest
100 supercomputers in the world. The mid-life upgrade (to 1.2 Petaflops), occurring in 2012, and
taking advantage of additional resources provided by DECC and Defra, moved the Met Office to
43rd on the June 2012 list of the world’s fastest supercomputers. In parallel to these supercom­
puting upgrades, a sister machine MONSooN (Met Office and Nerc Supercomputing (oo) Nodes)
was installed with NERC funding, to help facilitate JWCRP (the Joint Weather and Climate Re­
search Partnership NERC-Met Office) collaborations. The higher resolution and more complete
(with respect to the simulated processes) climate models which can be run with the increased su­
percomputing resource produce considerably more data than lower resolution and simpler models.
The supercomputing upgrades occuring over the 2007-2012 MOHCCP have therefore been ac­
4 see
section 2.6
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companied by equally important projects to extend the data storage facilities available at the Met
Office.
In line with the recommendations of Sir John Beddington’s review5 , which ’assessed that a stepchange increase in supercomputing capacity6 ’ was required, but also noted that a ’key need is to
extend collaboration with European counterparts’ and that ’Longer term investments in hardware, at
peta/exa-scale level7 , will sensibly be made on a wider European basis’, international collaborations
on high-resolution modelling, already in place with Japan, have been extended. A new project has
recently begun to take advantage of resources at the European PRACE (the Partnership for Ad­
vanced Computing in Europe) supercomputing facility, which is currently running what many would
consider to be a global weather-forecast level model (25km atmospheric horizontal resolution), on
climate timescales. This single project will use resources equivalent to the whole of the MOHC’s
supercomputing over one year. An even more ambitious project is in negotiation with colleagues at
the University of Texas in Austin. If it goes ahead, this study will extend work currently being un­
dertaken on a new Welsh supercomputing facility, simulating detailed (1.5km horizontal resolution)
atmospheric processes over the southern UK to examine rainfall change [204].
5 http://www.bis.gov.uk/assets/goscience/docs/r/10-1290-review-of-climate-science-advice.pdf
6 864
Teraflops from 2011, rising to 8 Peta (1015 ) in 2016
supercomputers (i.e. 1015 Flops, floating point operations per second) are available now. Supercomputer
vendors are currently forecasting that Exa-scale supercomputers (1018 Flops) will be available around 2018).
7 Peta-scale
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2.3 How and why is the Earth System already responding to
climate change?
The compilation of environmental observations from new measurements and archived data-sets
serves three main purposes. Firstly, it provides an ever increasing catalogue of evidence which
shows that the climate is changing, and can be used to identify sensitive components of the climate
system. Secondly, it allows us to validate, calibrate and test our models of the climate system to
give us confidence that they can represent the background climate state, the mechanisms driving
change, and the characteristics of extreme weather. Thirdly, only through observations can we
understand how extreme weather/climate events impact people, and can therefore focus future
climate simulations on providing the information necessary to understand the impacts of climate
change, as they will continue to be felt by human systems.
After the IPCC’s 4th Assessment Report (AR4), and moving beyond the question ‘is climate
change real‘, the observational bases for climate science have been focusing understanding and
interpretation of observed change at increasingly small spatial and temporal scales. Research
has been carefully exploring our levels of confidence in many aspects of observational and mod­
elling systems, to be able to identify and make sense of trends occurring at continental and sub­
continental scales. But at the same time understanding individual past extreme events, and identi­
fying, by quantifying the probability of their contribution, the drivers of those events.
The development of new, and refinement of existing observation-based prod­
ucts
Since its conception, the MOHC has been integral to the development of some of the world’s most
iconic and heavily used climate data sets, with the paper describing the historical sea surface tem­
perature product HadISST (Rayner et al., 2003), being the first of two MOHC papers to reach 1000
citations8 (Rayner et al., 2003, Cox et al., 2000). Not only are these products continuously ingesting
the latest observations and identifying and digitising historical records [211], but they are rapidly
evolving to answer the requirements and questions posed by the public, media, government and
scientists. As a result there have been a number of detailed improvements in the data-set construc­
tion methodology. Despite overwhelming evidence that the heat coming into the Earth system no
longer balances the heat going out, and therefore that the total energy (mainly in the form of heat)
in the system is increasing [146], the last five years have seen unprecedented scrutiny of some of
the most widely used global data-sets, in the wake of the hacking of emails from the University of
East Anglia’s Climatic Research Unit.
The apparent public distrust in climate data-sets stimulated a huge effort to facilitate the release
of data from over 1500 observations sites and the code used to produce our global surface tem­
8 A standard measure of the usefulness of a piece of published science is how many other peer-reviewed publications
refer to it (cite it). Typically a vey good piece of science may be cited a few tens of times.
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Global Average Near-Surface Temperatures 1850- 2011
0.8
Temperature difference (oC)
with respect to 1961-1990
0.6
0.4
0.2
0.0
-0.2
-0.4
-0.6
-0.8
1860
1880
1900
1920
1940
1960
1980
2000
Figure 2.4: Annual global average temperatures and 95% confidence ranges on those values from
the HadCRUT4 surface temperature product [245].
perature data-sets [275, 244]. Whilst requiring a massive scientific, technical and communications
effort [272], this work has highlighted the integrity of Met Office science. As a result there have been
a number of detailed improvements in the data-set construction methodology9 .
In parallel to playing a leading role in making the process of producing these data-sets more
transparent [322], public scrutiny and the ability of the climate modelling community to ask in­
creasingly detailed questions of observational data-sets, has required that uncertainty ranges be
placed on the observational data-sets. The push to quantify measurement and interpolation un­
certainty has led to work identifying differences between different ocean temperature and salinity
profile instruments [132, 235], drifts in surface temperature buoys, techniques to splice together
different satellite data sets [100], work to investigate how to robustly combine data from satellites
and surface observations (which generally record land/sea-surface and air temperatures respec­
tively) [207], and correct for the time of measurement [217]. By firstly understanding the sources
of inaccuracy in measurements [264, 132, 216]; secondly, explicitly assigning uncertainty ranges
to individual measurements in the historic record [209], and finally developing new statistical tech­
niques to combine these uncertainties and interpolate between measurements [245, 206, 207], a
new generation of more robust and more transparent data-sets has been produced [206, 207, 245]
(e.g. figure 2.4).
9 These changes and their negligible impact on the global
http://www.metoffice.gov.uk/hadobs/crutem3/jan 2010 update.html
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temperature
record
can
be
seen
here
Interpreting climate data
Whilst surface temperature data-sets have remained vitally important in communicating climate
science to the public and interested parties [271, 108, 109], scientifically there is often a fairly a
limited amount of information which can be gained from a single data-set. A combined observationmodelling study has shown that over any decade, surface temperatures alone are an inadequate
indicator of changes in the total energy (and therefore potential heat) in the climate system [258]. In
fact we are only just realising the depths in the ocean to which observations may need to be made
to fully budget for the changes in energy (predominantly heat) in the climate system on decadal
timescales [258].
The need to be able to explain the natural and anthropogenic drivers of variability, or lack of
variability, in surface temperature over decadal timescales was recognised early on during the last
MOHCCP, and a study examining variability in models concluded that hiatuses in globally averaged
surface temperatures lasting up to around a decade and a half (at present rates of change of an­
thropogenic forcing) were consistent with the naturally occurring climate variability thought to exist
[214]. But we know there are many more components of change than just natural variability, and we
know that our observing systems are limited. Explaining the recent hiatus in surface temperature
change, and any future short-term particularly rapid or particularly muted changes, will therefore be
an important challenge to our observational and modelling systems over the coming years if we are
to prove that our near-term modelling tools are ready to inform adaptation to near-term changes in
climate.
The MOHC has, over the last MOHCCP, developed, and collaborated with developers of, a
wide range of data-sets both broadening our understanding of the changes in the climate system,
and identifying the potential impacts that climatic events may have on people [47, 54, 75, 73, 240,
105, 212, 213, 263, 265, 3, 326, 6, 233, 232]. The seasonal and annual summaries of the state
of the climate, supplied to government and more recently published in the Bulletin of American
Meteorological Society (BAMS) [11, 12, 144, 267, 208, 31, 238, 342, 227, 344, 345, 343, 346]
have been building up a valuable repository of interpreted climate information across the globe,
complementing the data archived by the MOHC’s National Climate Information Centre (NCIC). This
work has also contributed to the publication of a syntheses of climate indicators (figure 2.5) [208].
Brought together, the separate climate indicators present a much more complete picture about
changing climate than does a single record. This observation-based description is consistent with
our basic understanding of how the climate system will respond to anthropogenic activity. There are
ongoing challenges to convey this more complicated, but more compelling message to the public,
and to make more use of novel data-sets in parallel with traditional data-sets to more-tightly identify
correct climate model behaviour.
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1.0
Land Surface Air Temperature: 4 Datasets
0.6 Tropospheric Temperature:
7 Datasets
0.4
Anomaly (oC)
Anomaly (oC)
0.5
0.0
0.2
0.0
-0.2
-0.4
-0.5
-0.6
20
Sea-surface Temperature: 6 Datasets
0.2
0.0
-0.2
-0.4
Ocean Heat Content (0-700m): 7 Datasets
10
Anomaly (1022J)
Anomaly (oC)
0.4
0
-10
-0.6
0.3
Marine Air Temperature: 5 Datasets
0.0
-0.2
-0.4
0.1
0.0
-0.1
-0.2
-0.6
100
1.5 Stratospheric Temperature: 8 Datasets
Sea Level: 6 Datasets
1.0
Anomaly (oC)
Anomaly (mm)
50
0
-50
-100
0.5
0.0
-0.5
-1.0
-150
-1.5
-200
1850
6
Specific Humidity: 3 Datasets
0.2
0.2
Anomaly (g/kg)
Anomaly (oC)
0.4
1900
1950
2000
1940
Northern Hemisphere (March-April) Snow Cover: 2 Datasets
1960
1980
2000
10 September Arctic Sea-Ice Extent: 3 Datasets
Extent (106km2)
Area (106km2)
4
2
0
-2
8
6
-4
4
-6
1900
1950
2000
Mean Specific
Mass Balance (mm w.e.)
1850
0 Glacier Mass Balance: 4 Datasets
-200
-400
-600
-800
-1000
1940
1960
1980
2000
Figure 2.5: 11 climate indicies compiled by the MOHC for publication in ’The State of the Climate
2009’ [208]. Ongoing work has recently seen global time-series compilations of up to 32 climate
indicators published in the 2011 state of the climate report. Anomalies presented relative to the
1961-1990 average. All values are global averages unless otherwise stated.
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Interpreting climate data using models
There is a huge amount we can learn about the climate system and its response to external forcings
from the observations alone. Statistical relationships between different components of the global
climate system can highlight new mechanisms and teleconnections: e.g. the relationships between
ENSO variability and European climate [115], or East Pacific sea surface temperatures and the
blocking of weather system movement over the Equatorial Pacific [262, 17, 160]. However when
these observations are combined with modelling, we can test those relationships and develop new
mechanism-based understanding. This has been done for the impact of ENSO [169] and solar vari­
ability [168] on European winter temperatures, and to identify a potential role for anthropogenic and
volcanic aerosol emissions in driving recent observed sea surface temperature variability (previously
considered to result from internal variability) [33] (figure 2.6).
Even where limited observations are available, we can, for example, use those observations
to narrow possible interpretations of the land-surface carbon flux [226], or to try and understand
and predict changes in the large-scale ocean circulation in the North Atlantic [237]; in some cases
demonstrating how difficult it will be to confirm or deny past changes have occurred given available
historical observations [288]. A number of such lines of evidence indicate that the strength of this
circulation could have increased since preindustrial times, but also find that this will be hard to verify
observationally [269, 225].
At the forefront of climate change is changing Arctic sea-ice extent. The speed with which seaice extent can change, and its importance in the global energy budget10 and regional weather,
presents a challenge to those trying to observe the system, but also a challenge to how we do cli­
mate science. The rate at which sea-ice extent has reduced over recent decades has necessitated
a reactive rather than preemptive approach, an approach from which we may need to learn a lot of
lessons about how we conduct climate science over the coming decades.
New sea-ice observing techniques have allowed estimates of sea-ice extent to include con­
fidence ranges, critical if we are to be able to understand or forecast change, but we still lack
decent measurements of ice-thickness, and therefore the ice volume and the energy required to
melt that ice. Whilst analysis of our previous generation physical climate model HadGEM1 (the 1st
MOHC Global Environmental Model - which contains the same sea-ice model as 2nd such model
HadGEM2) over the last MOHCCP shows that it was one of very few models at the time to be able to
replicate the rate of decrease of sea-ice extent [197, 198] (figure 2.7), we have found that the pattern
of ice-loss, and therefore potentially the detailed mechanisms of ice-loss, differ from those occurring
10 It is often useful to think about climate change in terms of changes in the earth’s energy budget rather than just changing
surface land and ocean temperatures. For example, we know that if we reflect back to space less of the energy coming
from the sun (in the form of light) because we have less sea-ice (which is highly reflective compared to open water), more
energy must remain in the atmosphere-ocean-land system. Energy exists in various forms - for example heat, chemistry, or
electromagnetic radiation (often light), so a change in the total amount of energy in the earth system can only occur through
a change in one of these things, typically heat, but possibly (for example) a chemical change from ice to water or expansion
of that water. Therefore, without having to measure a change in temperature - which in the Arctic (for example) may be
logistically difficult - we know that melting of sea-ice (for example) will cause the climate to change. We know this because
we can see the change in light being reflected to space.
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(A) ERSST and CMIP3 SSTs
0· 6
0.4
z0
co
(C) CMIP3 Aerosol Complexity
0 ·6
1950-75 Trend:
ERSST= -0.37+/-o.07
0.4
0.2
0.2
-0.0
-0.0
-0.2
-0.2
-0.4
-0.4
-0.6 ........._
I
0
w
~.
f'..
I
LO
f'..
en
Q)
195D-75 Trend:
ERSST= -0.37+/-0.07
. lntl'!lrAN =
__.__........_........________
1880 1900 1920 1940 1960 1980 2000 Year 1880 1900 1920 1940 1960 1980 2000
(B) ERSST and HadGEM2ES Atlantic Response
0.6
0.4
.._
1950-75 Trend:
ERSST = -0.37+/-0.07
Model = -0.43+/-0.08
:::J
ca.._
0.2
Q)
c.
E
Q)
I-0.2
. 1880
1900
1920
1940
1960
1980
2000
Figure 2.6: Annually averaged North Atlantic Sea Surface Temperature (SST) from observations
(black) and HadGEM2-ES (orange). HadGEM2-ES shows remarkable agreement with the observa­
tions over timescales of only a few decades - unlike the previous generation (CMIP3 - 3rd Coupled
Model Intercomparison Project) models (blue and green), although some of these models, those
which contained more advanced representation of aerosol (pollution particles) impacts do show
some of the observed variability (red) [33].
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Figure 2.7: Total Arctic sea-ice extent averaged throughout September of each year, from each
of the available CMIP5 (5th Coupled Model Intercomparison Project) models (gray), including
HadGEM2-ES (blue). HadGEM1 (red) and observations (black).
in reality [93]. We are therefore beginning to understand how to validate our modelled sea-ice in
more detail, and can use this to improve the new sea-ice model being developed in HadGEM3 (the
3rd MOHC Global Environmental Model).
Despite recent work to incorporate observed sea-ice data into our near-term climate forecast
systems [154], sea-ice loss can not yet be forecast with confidence. It is therefore important that
we provide regular updates on the state of the sea-ice [152, 153, 155], look retrospectively at major
sea-ice loss events in the context of contemporaneous meteorological/oceanographic observations
[341, 198, 154] and explore similar rapid ice-loss events identified within our model simulations
[197]. Based on this understanding we can make improved estimates of when summer sea-ice
might disappear [151], and make it clear that recent particularly-rapid periods of ice-loss are not
inconsistent with natural variability simulated within our models, and therefore that a slowing in the
rate of sea-ice loss is still possible [156]. Limited confidence still remains in our short-term and
decadal sea-ice outlook [283]. Increasing this confidence will be an important challenge for the
2012-2015 MOHCCP.
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1961-90
long-term
average
maps
Mean temperature
Daily maximum temp
Daily minimum temp
Days of air frost
Heating Degree Days
Cooling Degree Days
Total precipitation
Days of rain ≥1mm
Sea-level pressure
Relative humidity
Windspeed at 10m
x
x
x
x
x
x
x
x
x
x
1971­
2000
long-term
average
maps
x
x
x
x
x
x
x
x
x
x
x
Difference Climate
maps
change
maps
Regional
decadal
change
maps
Regional
linear
trends
tables
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
Table 2.1: Variables and climatological information presented in UKCP09 ’The climate of the UK
and recent trends’ [107]
Observations within the UK Climate Projections
As the requirements moved over the last MOHCCP from demonstrating that climate is changing to
informing decisions based on climate forecasts and projections, increasingly specific questions are
being asked of the observations. This may require new observations to be collated, or previously
compiled data sets to be differently interpreted. As part of the UK Climate Impacts Programme
(UKCIP), ’The climate of the United Kingdom and recent trends’ report stimulated a push to develop
spatially complete maps of many climate variables across the UK to allow validation of models and
understanding of how the occurrence and magnitude of specific climate events, such a drought,
might change in the future. Monthly and annual climate variables have been reconstructed for the
UK back to the early or middle 20th century [171, 107], daily minimum and maximum temperatures
have been compiled in response to UKCIP user requests and return periods of droughts, extreme
temperature and precipitation events have been characterised. Table 2.1 details the observational
data compiled and presented for the 2009 UK Climate Projections report(UKCP09).
Validating models
By combining the extreme event observations with analysis of the associated meteorological condi­
tions surrounding these events it has been found that in the UK and Europe the occurrence of many
meteorological extremes is primarily driven by large-scale change, rather than local conditions [52].
This increases confidence that if we can recreate the correct background climate state in models,
model projections of extreme climate events are likely to be correct. This work is not just focused
on the UK, but has been extended to many regions around the world to provide evidence to be
presented by the UK Government at the Durban Conference of the Parties (CoP) in 2011 [109] and
to understand changes in heat-stress and temperature related mortality [77].
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For many environmental variables which are critical for the validation and improvement of cli­
mate models, spatially complete ’climate monitoring quality’ data-sets [239] are not available. Oc­
casionally individual time-series stations can be placed in context by, and help validate models (e.g.
figure 2.8), but often only measurements from short observational campaigns (for example ship
cruises) are available - these can at best validate the behavior of our models under specific con­
dition (those experienced during the observational campaign), but are invaluable where we have
limited observational constraints on how components of our models should behave e.g. [140]. To
take full advantage of measurements of cloud properties coming from satellites, computer code has
been incorporated into the latest HadGEM models to output the specific variables that satellites can
observe, and does so in a way consistent with the temporal and spatial distribution of satellite obser­
vations [143, 32] - analysing these results will make up an important component of the 2nd Cloud
Feedback Model Intercomparison Project (CFMIP2) which is being co-led by the MOHC. CFMIP2
will also be taking advantage of the fact that Met Office weather and physical climate models now
form a hierarchy which is traceable across timescales and resolutions [304]. Representing how
cloud will change in the future is key to correctly representing the change in energy coming into and
out of the planet.
This hierarchy means that models using the same physics and parameterisations but different
horizontal spatial resolution can be used to understand cloud problems occurring across weather
and climate time scales and therefore the causes of problems can be identified. For example, a
systematic cloud issue was identified occurring above the Southern Ocean in all of our models. A
new method was developed to link cloud behaviour to conditions where the properties of lowest
level of the atmosphere was dominated by high wind-speeds. This was implemented and initially
tested in our high resolution regional and global weather models to ensure that spurious results
(occurring due to the lower resolution in the climate model) were not obtained, then tested in the
model’s climate configuration before being integrated into the latest version of HadGEM3 [5].
There are numerous examples of where observations have been used to validate the models
which have been in development during the last MOHCCP. Perhaps the most valuable are those
which have been used to constrain the often highly parameterised and complex (and therefore highly
uncertain) earth system processes, to improve our representation of processes like carbon cycling.
Examples include the validation of aerosol simulations against observed weekly variability arising
from patterns of human activity in urban areas, and aerosol concentrations measured during Natural
Environment Research Council (NERC)-Met Office aircraft missions [174, 20, 145, 2, 92, 158, 253,
65, 78, 175, 79], the validation of ozone profiles in the atmosphere at individual measurement sites
[89], the validation of terrestrial vegetation behaviour against atmospheric CO2 seasonal cycles [61],
and the validation of ocean dimethylsulphide emissions against individual ship-based observations
[140]. Numerous additional model validation results can be found within papers published by the
MOHC over the last five years for example [89, 331, 273, 78, 112, 158].
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Figure 2.8: Surface ocean pH measured at the two longest running ocean time-series stations at
Hawaii (a) and Bermuda (b) (black), and simulated at those locations by the HadGEM2-ES model
(red). Models and observations agree on the absolute value, trend, and magnitude of the interannual and intra-annual variably.
Detecting climate change and identifying its drivers
A number of examples have so far been described where observed historical change has been
attributed to anthropogenic activity by identifying, understanding and validating the mechanisms in­
volved. However, given that each of these studies will have required many months, or even years,
of a scientist, or group of scientists time, there is also a need to determine whether observed trends
and events can be attributed to anthropogenic activity relatively quickly by identifying statistical rela­
tionships between model and observational changes [7]. These results can be used to test models,
and to identify where future impacts might come from. The science of climate detection and attri­
bution, the identification of a climate change signal, and demonstrating that signal to be a response
to a specific component of human activity, has been a key component of climate research for many
years. Recently work has begun to attribute individual weather and climate events, identifying to
what extent the risk of a particular event can be attributed to a specific component of human activity
or to natural variability. This work has been developing rapidly over the last five years.
The IPCCs 4th assessment report presented results which attributed most of global mean tem­
perature change since the mid 20th century to anthropogenic activity from increased greenhouse
concentrations [147], but did not characterise the effects of obervational inaccuracies or fully ex­
plore modelling uncertainties (figure 2.9) [147]. More recent work has shown that the AR4 findings
are robust to known model and observational uncertainty [193], has furthered quantified the per­
centage of specific changes to human activity - for example, it was shown that 80% of the increase
in land temperatures since the late 1970s can be attributed to human activity. Improved techniques
to compare models and observations more clearly have also been developed [258, 257].
The attribution of changes has also progressed beyond standard climate variables, by utilising
new compilations of humidity [347] and ocean oxygen concentrations, we have worked with col­
leagues to demonstrate a human driven component of the observed changes [252, 348, 298], and
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Figure 2.9: Observed global continental-scale temperature time-series are only consistent with
climate model results when those model simulations include anthropogenic forcings. At the time
of the IPCC’s 4th assessment report (from which this figure comes) [147], human activity has been
attributed as the cause of centennial timescale temperature change in all continents other than
Antarctica. More recent work by the MOHC has extended this result to all continents.
even quantified the likely human contribution to observed changes in growing season length [74].
The statistical attribution of observed changes has also focused on smaller regions [254, 126], with
the demonstration that the likelihood of the occurrence of recent warmth in all subcontinental regions
other than North America has doubled in response to anthropogenic activity and the understanding
of trends in precipitation [325, 361] (figure 2.10).
In some areas of the science, models and physical understanding are telling us about things
which are likely to be changing, but the required observations are not yet forthcoming. The humidity
of the upper troposphere is one area where improved observations will place much tighter restric­
tions on what we consider to be good model simulations. These measurements will help quantify
the strength of the climate feedback associated with the increased capacity of warm air to hold
moisture, and the greenhouse properties of that moist air. Methods are being developed within the
MOHC to measure upper-tropospheric humidity from satellites [286, 161].
As well as narrowing the spatial-scales over which climate attribution studies are undertaken, the
last MOHCCP has played an important role in the narrowing of the time-scales [127] of detection
and attribution from decades to individual weather events. Consequently we have begun developing
near-real time capabilities which will allow us to determine whether the probability of occurrence of
individual weather events has been increased by anthropogenic activity. So far events that have
been looked at include abnormally warm seasons in Europe, where the probabilities of occurrence
have been shown to have very likely more than doubled or in the case of summer-season temper­
atures, quadrupled [324, 194], and extreme flooding events in the southern UK, the probability
of occurrence of which has been shown to increased [256]. This work was in part stimulated by
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Figure 2.10: Advances in the science used to detect climate change, and attribute it to human or
natural drivers has progressed over the course of the last MOHCCP. We can now not only say that
the observed changes are likely to be caused by human activity, but can also quantify how likely
human activity has been the drive. This is being undertaken at smaller and smaller spacial scales
[325].
the Pitt Review ’Lessons learned from the 2007 summer floods’ to which the MOHC contributed
significantly. A prototype system to quantify the role anthropogenic activity has played in changing
the probability of the occurrence of individual extreme events, in near-real time, is being developed
using the finalised atmospheric component of the HadGEM3 model [327, 76]. An international
working group (ACE - the Attribution of Climate Events group) to look at the use of such systems
has been set-up and co-led by the MOHC [321, 323]. Once this system is complete, it will both
function as a near-real-time model validation tool, but will also play a key role in highlighting the
sorts of weather/climate events that societies and economies will have to increasingly adapt to deal
with as climate change progresses.
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2.4 What climate change is likely to occur in the coming decades,
and which changes may cause socioeconomic stress?
Prior to the IPCC’s 4th Assessment Report, climate science had been asking almost exclusively
what the planet might look like by the end of the 21st century or in response to idealised changes
in forcings (CO2 concentration doubling, or 1% per year CO2 rise) - a time-horizon, or magnitude of
forcing, at which there was confidence that the external forcing (be that idealised or anthropogenic)
would be significant compared to natural variability in the system and chaotic processes. However,
planning within many societal systems occurs on the much shorter timescales - a few decades or
less. Improved scientific understanding, computational resources and significant innovation over
the last five years has allowed us to tackle the challenges posed by natural variability and chaos on
near-term prediction, and begin to fill the gap between weather-forecasting and climate projections
[128].
Whilst the range of climate forcing change implied by plausible future socioeconomic scenar­
ios do not diverge significantly until the 2030’s (figure 2.11), over this interval we will experience
large changes in regional climate forcings (e.g. aerosols) and significant changes in global climate
forcings (e.g. CO2 ) [120]. Over this time period we will also experience the combined effects of
changes within the state of internal variability (e.g. ENSO, NAO11 ) and natural external forcing such
as solar cycle changes [192]. Near term climate fluctuations are as large as many years of anthro­
pogenic change and so it is when these natural factors are superimposed on anthropogenic change
that the risk of extreme or even unprecedented climatic events and impacts at local and regional
scales is highest. These factors will superimpose to produce considerable climate impacts at local,
regional, and perhaps even global scales, which, due to inertia and human and climate systems,
are practically unavoidable over the coming decades12 .
A perfect model, initialised with perfect observations, would be able to predict the probability of
most of these changes occurring [94]. This is what we are working towards. To allow societies
to minimise the impact of these inevitable climate changes, we must: understand where and what
near-term climate forecasts can skillfully predict; explore past high-impact climate events using
models and observations to learn where to target new technical developments; translate modelled
change into climate impacts; and understand how to usefully present this information to those who
can best make use of it.
Decadal forecasting
Near-term climate prediction jumped from being a largely empirical statistical exercise, to a mechanisticallygrounded science at the start of the last MOHCCP with the publication of Smith et al. (2007) [317].
11 ENSO stands for El Nino Southern Oscillation, and NAO for North Atlantic Oscillation. These are two components of
variability that appear to occur naturally in the Earth system.
12 Although coordinated global efforts could potentially bring about significant changes in global radiative forcing by rapidly
reducing emissions of some short-lived climate pollutants - see http://www.unep.org/ccac/
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British Isles decadal mean temperature
Fraction of total variablity (%) y
Global decadal mean temperature
Internal
variability
90
90
Internal
variability
Scenario-dependant
variability
Scenario-dependant
variability
60
60
30
0
30
Model-derived
variability
0
20
40
60
Years from 2000
80
100
0
0
Model-derived
variability
20
40
60
Years from 2000
80
100
Figure 2.11: Figure taken from Hawkins and Sutton (2009), which highlights the different contribu­
tors to variability in the climate system over various time-intervals starting in the year 2000. It can
be seen that natural variability within the climate system is a key component of change on short
timescales. On multi-decade timescales, limitations to our representation of the climate system
within models is the dominant cause for variability in projection. Moving towards the end of the
century, socioeconomic choices about climate forcings dominates the spread of plausible climate
conditions.
This paper showed that by combining observations of external factors and the internal climate state,
a model could skillfully predict global and regional temperature changes years before they occurred
(figure 2.12). This groundbreaking work has spawned a new field, ’decadal forecasting’, within inter­
national climate science [137]. Since the first projections were made, considerable effort has gone
into using, understanding and developing the forecast system [318, 19, 123, 102, 167, 236, 246,
269, 315].
Our decadal prediction system has been used to develop new climatologies (maps of the aver­
age climate state) for the present-day and future decades to assist planning decisions [45, 280], but
also to identify skill at predicting impacts more tangible to societies and individuals than global or
regional temperatures - for example, extreme warmth or lack of rain [278, 349, 313, 135, 133, 314].
Work has focused on how knowledge of the state of the North Atlantic Ocean can provide pre­
dictability in the regions surrounding this ocean, showing that hurricane formation in the tropical
ocean could be predicted a number of years in advance [315]. Rather counterintuitively, the ability
of the model to correctly predict changes in the tropical Atlantic is only achievable when observa­
tions from north of this region were fed into the modelling system [102]. This work gained first prize
in the Lloyd’s of London Science of Risk awards, recognising the value of such information to the
insurance industry and therefore economic adaptation.
The value of different observation types and different observational strategies has been ex­
plored [102], and more recently possible drivers of such variability have been investigated [33]. In
combination with work undertaken to quantify historical changes in extreme climate/weather events
[46, 47, 54, 240, 105, 212, 213, 263, 265, 3, 326, 6], and development of our understanding of
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Figure 2.12: Forecasts of how the climate is likely to change over decadal timescales must take
natural variability in the climate system into account (see figure 2.11) [317]. The black lines show
changes in the observed high-latitude North Atlantic temperature, and the coloured lines the decadal
forecasts started at different years throughout the period using the observed state of the ocean at
that time to inform the model’s starting conditions.
what drives extreme events [302, 57, 116, 228, 3, 75, 73], skillful regional prediction of extreme
temperature events13 has even been shown to be possible out to a number of years [104, 106].
Seasons to decades
Whilst decadal timescale forecasts are the shortest range predictions to be strongly impacted by
what we would generally consider to be climate change drivers, the climate-influenced events that
will impact people will often be much shorter lived - for example wet/dry or hot/cold seasons or runs
of such seasons. It is therefore essential that we are confident that our models can correctly simulate
these events on top of the more slowly changing background climate. To gain this confidence is not
just a matter of showing that models can recreate the present-day occurrence of these events in
a statistical sense, i.e. the number and distribution of these events through time (although this
can still be very useful [279, 104, 336, 340, 1]), because it is quite possible to achieve this without
necessarily having the correct mechanisms in place.
It is also necessary to be able to recreate the specific characteristics of, and understand the
mechanisms driving, numerous individual observed extreme events. It is for this reason, amongst
others, that a large effort has been exerted over the last MOHCCP to make the decadal and longer
time scale physical modelling systems share a common physical model and largely common pa­
rameterisations14 with the seasonal, medium range (up to 15 days) and short range (up to five
13 Some of the work described here takes the definition of an extreme event to be an event encountered on average only
one out of ten times (for an extreme extreme daily rainfall would be the amount of rain experienced on only 10% of days).
These are classed as ’moderate extremes’. In other studies, an extreme event will be one occurring much less frequently.
14 To allow a common set of parameterisations to be used across models, all models are now being developed with a
common spacing between the vertical levels dividing up the atmosphere. This is important because over short vertical
distances atmospheric conditions change much more rapidly than they do over similar distances horizontally, and most
parameterisations define how properties behave within a vertical column of atmosphere - across these sharp gradients.
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days) forecasting systems. The distinction between the models therefore essentially becomes one
of horizontal resolution [215, 84, 14] and therefore model fidelity and computational expense.
By moving the decadal climate prediction system from the HadCM3 (the 3rd MOHC Climate
Model) to the HadGEM3 model, the ability to predict near term climate change has been increased.
This is partly due to the improved representation of processes like aerosol-cloud interactions in
the new model, but also because improved physics means that connections between, for example,
Atlantic sea surface temperature change and Sahel precipitation are better represented [215]. By
sharing a common physical framework, all of our models will benefit from forthcoming advances
in the mathematical representation of the dynamics of the atmosphere (the movement of heat,
moisture and other quantities between grid-boxes within the climate model). The model dynamics
are being improved to take advantage of a new generation computing architecture. This work is
being undertaken in collaboration with NCAR (National Centre for Atmospheric Research) and UK
partners [296] and funded in part by the Science and Technology Facilities Council. Despite the
Met Office’s integrated approach to weather and climate modelling being the envy of many climate
centres across the world, our seamless-prediction system is only in its infancy [299]. However, a
number of key findings of direct relevance to decadal prediction and adaptation in the European
region that can also be used to test and improve the fidelity of our climate models, are already
emerging from the use of the seasonal forecast system to examine past events [302, 169, 229, 230,
168, 301, 1].
Whilst to a large degree sharing the same physical model, our seasonal and decadal prediction
systems still differ in how they use observations as a starting point for forecasts. Because a model’s
equilibrium state (the climate that it simulates after being run for a long time without any changes in
greenhouse gas concentrations (or other climate forcings)), differs from the real world’s equilibrium
state (perhaps the gulf stream crosses the Atlantic too far north, or too much cloud is simulated
in the Arctic), a model simulation started from a state that looked identical to reality would slowly
drift back to the model’s preferred equilibrium state. The similarity between the latest generation
of models and the real world now means that the rate at which this drift occurs is small enough
to be subtracted out and still simulate events recognisable as those occurring in reality. Our sea­
sonal forecast systems can therefore be used to recreate key observed real-world events, such as
the recent run of cold European winters [19, 269] which oppose the long term climate trend and
can therefore confuse policy makers, the public and users, but nonetheless show a useful level of
predictability. When simulations using our current generation of seasonal-forecast (i.e. observation­
initialised) models are extended to a few years, the drift becomes problematic, so it is better to
begin simulations from the model’s equilibrium state (or at least a state close to this), then change
the model’s starting values by an amount equal to the difference between the real-worlds equilibrium
state and its value at the moment of interest. The decadal-forecast system is therefore appropri­
ate for identifying likely patterns and magnitudes of changes in the system rather than predicting
specific meteorological events. Because our decadal and longer-timescale models can’t recreate
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the specific evolution of individual real-world weather/climate events, it is imperative that we use our
seasonal models to do this, and ensure that those components of the seasonal model necessary to
successfully simulate these events, are included in our longer time-scale models.
Forecasts on all these timescales allow identification of likely patterns and magnitudes of changes
in near-term climate and the risk of extremes rather than the exact occurrence of individual events.
It is imperative that we use retrospective seasonal predictions to test our climate models against
recent extreme events. Ongoing work is examining when it will be possible to initialise the seasonal
and decadal systems using a common methodology, and therefore produce truly seamless forecasts
from weather to climate time-scales. The early signs are encouraging, and suggest that we may
be able to do this in the next few years, after which we will be able to present coherent adaptation
advice from days to decades ahead. The combination of recent modelling developments, together
with the possibility of future step-changes in supercomputing capacity, mean that such models ­
models truly capable of providing consistent adaptation across timescales - are now within reach.
Translating short timescale understanding to long-term model improvement
Analysis and modelling of past extreme seasons has led to a number of high profile papers and
an increasingly complete picture of the drivers of such events in the European region. These
simulations have also allowed us to identify the model requirements necessary to capture these
events. Several components of the earth system appear to influence European climate extremes:
correct representation of the tropical Pacific [169, 81], proper representation of the stratosphere
[116, 230, 229, 168]; unbiased North Atlantic sea surface temperatures (figure 2.13) [290, 300],
and the correct representation of sea-ice. Ineson et al. (2011) [168] show that stratospheric warm­
ing/cooling caused by changes in ultraviolet output from the sun, modifies the high latitude tropo­
spheric circulation and European winter temperatures. Marshall and Scaife [230] show that the
quasi-biennial oscillation (QBO), a slowly varying (years) signal in stratospheric winds, again im­
pacts European climate. The correct initialisation of the QBO state could help us to correctly predict
regional temperature extreme events out to a few years ahead, and is likely to be important if we
are to simulate realistic variability and changes in frequency of extremes on longer timescales [59].
More recent work shows that a further important component of European extreme weather is
the Atlantic storm track position and intensity, and that this is strongly determined by North Atlantic
sea surface temperature patterns. Many CMIP3 and CMIP515 models, including all MOHC models,
have been shown to exhibit offsets (when compared with observations) in North Atlantic sea surface
temperatures. Recent work demonstrates that by increasing the number of ocean grid-boxed per
degree latitude/longitude to 16 (quarter of a degree each), the gulf stream path is better simulated
and ocean temperatures are closer to reality which in turn improves the simulation of the storm
track. It is quite possible that changes in storm track position in the future will be a first order
15 Note
that CMIP3, the 3rd climate Model Intercomparison Project preceded CMIP5 - the name CMIP4 was skipped to
bring the project numbers into line with the IPCC’s Assessment Report numbering
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control on European climate change [356] over coming decades. Improved understanding about
the specific benefits of ocean resolution and stratospheric representation, for example, obtained
from seasonal time-scale simulations, will lead to significant improvements in our ability to model
change over adaptation and mitigation timescales [270]. This is discussed in the paper ’Climate
Change Projections and Stratosphere-Troposphere interactions’ [302], awarded a further prize by
Lloyd’s of London for improving our understanding of climate related risks.
A number of Met Office and collaborative projects are ongoing which should lead to improved
model predictions on near-term adaptation timescales. The adoption of the Met Office’s models by
centres in India, Korea and Australia will begin to extend the, so far largely European and African
focused, near-term climate work to many regions around the world [71]. Together with colleagues
at the European Centre for Medium-range Weather Forecasting (ECMWF), we are investigating
the use of multiple models, and the suites of many model simulations with stochastically perturbed
physics (different random numbers chosen from plausible ranges put into equations describing un­
certain processes within the model), to improve probability-based near-term projections.
Finally, work is ongoing at the MOHC to combine the approach so far used to explore the lack of
certainty in centennial timescale projections, with the idea of starting projections from observations
to overcome problems with natural variability [215]. The approach used to explore the impact of
uncertain parameter values is similar to that described for representing uncertainties in the climate
state, whereby, by varying uncertain values within model equations, the full range of future climate
possible within that model design can be simulated - this is the technique used in the MOHC’s QUMP
(Quantifying Uncertainty in Model Predictions) project, which will be discussed later. The use of realworld observations to define the starting conditions for a model simulation is the approach described
above for seasonal and decadal forecasting. By combining these two methods it is anticipated that
forecasts out to the middle of the century would allow adaptation decisions to be made based on
likelihoods, but also that that information takes into account the present state of natural variability,
and therefore spans, as tightly as possible, the real future change. It is likely that such a system
could be used to extend the focus of adaptation tools like the UK Climate Projections (UKCP09)
from the long-term to the more user relevant near-term [88, 248].
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a
b
60oN
0 oN
90oW
0o E
90oW
0o E
Temperature ofset from obs.
-9
-3
0
3
9
0.3
Blocking Frequency
c
observation
high resolution
low resolution
0.2
0.1
0.0
90oW
45oW
0 oE
45oE
Longitude
Figure 2.13: Extreme European weather is strongly influenced by high pressure systems which
block storms coming in from the Atlantic (e.g. when the UK experiences a sustained period of clear
weather). New work has shown that the ability of the model to recreate the observed frequency
and spatial distribution of these blocking events is highly dependent on correctly representing the
sea surface temperature conditions in the high-latitude North Atlantic. The ability of the model
to correctly simulate the sea-surface temperature conditions has also been shown to be highly
dependent on the horizontal resolution of the model. Part a shows the sea surface temperature
offset from that which is observed in the standard resolution model, and part b the (much reduced)
temperature offset in the high-resolution model. The positive impact this has on the number of
blocking events is then presented in part c [300].
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2.5 Looking beyond the next couple of decades, what climate
change is likely to cause socioeconomic stress?
Near-term climate forecasting is still in its infancy, and its real value in providing advice for use
by those planning the adaptation response to climate change, is only likely to be realised in the
coming years as new supercomputing infrastructure allows higher resolution models start to be run
operationally. Moving beyond the next few decades, and beyond the time period where internal
variability dominates regional climate projections (figure 2.11), by carefully taking model-derived
variability, and different socioeconomic pathways into account, we can provide robust, probabilityand scenario-based advice to inform adaptation.
The UK Climate Projections
Whilst UKCP09 has used free-running climate simulations, rather than observation-initialised simu­
lations, and hence the value of its shortest-term projections are likely to be limited by not accounting
for the changing phase of internal variability (figure 2.11) [262, 17] (which can only be accounted
for within the probabilities presented in the UK Climate Projections) [150], the UK Climate Projec­
tions has been widely seen as the most cutting edge climate adaptation tool available. UKCP09
provides probabilistic climate data at 25km by 25km space-scales for the UK, across a number of
variables selected for their relevance to medium-term climate adaptation [247, 48, 46] (figure 2.14).
The original set of environmental variables that were released included summer and winter mini­
mum/mean/maximum temperatures and precipitations, and mean cloud amount and humidities, but
after user feedback and validation of model output, this was extended to also include wind-speed,
fog, lightning and snow.
To make the 2009 UK Climate Projections robust, two very significant methodological leaps had
to be made. Firstly, to consider as full a range of future climates as possible (given the current
understanding within climate science), QUMP-type (perturbed parameter) suites of simulations had
to be combined in a statistically robust way with the outputs from CMIP3 models, and therefore allow
UKCP to sample both model parameter (lack of certainty in the numbers used to solve a problem)
and model structure (different ways of solving the problem) limits to our confidence. Secondly,
this statistically robust probability-based understanding of the climate had to be mechanistically
scaled down to provide information on societally useful space-scales, and take account of local
drivers of climate change. Neither of these tasks had been attempted before, but with additional
funding from the European framework 7 project ENSEMBLES, an approach was developed and
implemented [249, 307, 308] which uses a statistical replica of the HadCM316 model [292, 293]
and pattern scaling techniques (methods to turn globally averaged information coming from the
simple statistical representation of the climate model, into spatial information) [86] to represent
16 A
previous-generation Hadley Centre climate modes computationally ’cheap’ enough to run many times.
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UKCP02
Single Projection
UKCP09
10% probability level
Very likely to be
less than
UKCP09
50% probability level
Central estimate
UKCP09
90% probability level
Very unlikely to be
greater than
✹
✸
✷
✷
✵
✴
-70
❂
Ð
❀
❁
❃
-50
Ð
❂
❁
❄
-30
❅
Ð
❃
-10
❆
Ð
❁
❄
✮
✤
✬
✱
✩
✥
✬
10
❇
❉
❁
❁
Change in precipitation (%)
✕
✛
✜
✩
❄
✳
✥
✛
✥
✧
❊
❁
✤
✧
✥
30
❃
✢
✬
✾
✚
❁
❋
50
●
❂
❁
❃
70
❂
❀
❁
✿
Figure 2.14: Summer season, year 2100 precipitation change from UKCP02 (year 2002) and
UKCP09. This figure highlights three advances made from UKCP02 to UKCP09. Firstly the careful
analysis of probability has allowed projections to be made showing the range of likely changes.
Secondly, the use of high-resolution regional modelling has allowed the results to be provided with
more detailed spatial coverage. Finally, improved modelling and improved use of information from
other models has shifted the results for some variables, so as we see here, the single projection
from UKCP02 is more similar to the ’very likely to be less than’ UKCP09 projection than the UKCP09
central estimate [170].
the full range of outcomes that HadCM3 could have potentially produced given an infinite amount
of time to run simulations with infinite subtle changes to the uncertain equations. Historical data
and data from non-MOHC climate models was then used to adjust the statistical representation
of HadCM3’s results and the final probability-based description of possible global model outcomes
was downscaled to represent the UK in detail using relationships derived from a set of HadRM3
regional model simulations driven by selected members of the HadCM3 suite of global perturbed
parameter experiments.
Much of the methodology leading to the downscaling techniques came out of research done
as part of the ENSEMBLES project. One major conclusion of the ENSEMBLES project was that
outside of regions exhibiting extremely strong local gradients (e.g. mountainous regions) the range
of possible outcomes is more efficiently explored by considering different possible global model
simulations (which provide the values used at the edges of the regional model), than by running
lots of versions of the regional downscaling modelling [200, 97]. Many of the issues relating to
the methodology used within UKCP can be found in a special issue of the Royal Society’s journal
Philosophical Transactions A edited by the MOHC [86].
The UKCP modelling methodology described above was applied, and validated using many of
the newly compiled observations discussed in relation to this report’s first question (section 2.3).
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Having confirmed that the projections and their use of probabilities were robust, the reasons for
wide probability ranges (where found) were explored, projections were released, and ongoing sci­
entific backup for the public facing UKCP help-desk provided [247]. As previously mentioned, user
feedback led to an expansion of the available variables, but in addition, a further product tailored to
users with wide geographical interest was released. A previously unidentified group of users were
those who were interested in spatially coherent probabilistic projections.
The original UKCP09 methodology presented probabilities on a grid-box by grid-box basis, but
users interested in (for example) water resources across whole catchment basins, want to know
if probabilities at specific locations were independent from each other or not. It was important for
these users to know if probabilities at nearby points would vary together or vary independently from
one another, because if they vary independently, over a large enough region they might be expected
to at least partially cancel out; if they vary together, one might need to plan for a much larger range
of possible outcomes. User feedback was taken on board, and the 11 regional model simulations
were scaled to make them consistent with the range of likelihoods obtained for the global climate
model analysis, then released as a set of plausible scenarios [305, 306].
The success of UKCP09 stimulated a large body of work extending some of the results and
techniques to many regions around the world. To assist with DECC’s contribution to the United
Nations Framework Convention on Climate Change (UNFCCC) negotiations in Durban in 2011
(COP17), the MOHC, in collaboration with partners funded through the AVOID project, undertook a
large literature assessment and modelling study to understand possible changes in the climate of
23 countries (Argentina, Australia, Bangladesh, Brazil, China, France, Germany, India, Indonesia,
Italy, Japan, Kenya, Korea, Mexico, Russia, Saudi Arabia, South Africa, Spain, Turkey, UK, USA.
Egypt and Bangladesh). This was done to provide evidence to aid discussions at CoP 17 in Durban
[335, 63]. Whilst the full UKCP09 methodology could not be applied to the 23 different countries
examined within this project for CoP 17, the ideas learned from UKCP09 were applied using the
CMIP3 models in collaboration with the AVOID programme17 [333, 44]. Regional modelling was an
important aspect of UKCP09 which could not be achieved on the timescales required for CoP 17,
regional modelling has still played a key role in both equipping less developed countries with the
tools to plan for climate change and to contribute evidence to the climate negotiation and reporting
(CoP and IPCC) process, but also to extend our understanding by working on regional questions
with local experts [195].
Whilst the breadth of this task did not allow for a full UKCP-like assessment to be undertaken for
each country, the global-model experiments used for UKCP were extended to consider how our lack
of certainty about a wider range of climate components than previously considered, would impact
results [117]. The simulations undertaken for CoP17 were also the first to consider how our knowl­
edge of these uncertain model equations impact the results obtained when simulating a climate
mitigation scenario [63]. Utilising the framework developed through the AVOID project, partners at
17 see
http://www.avoid.uk.net
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the Walker institute used the climate model data to drive climate impacts models, examining the
potential effect of changing climate on drought, crop yield, flooding and ecosystem change in these
countries.
Modelling the climate at regional scales
Regional modelling, the downscaling of output from global climate models to space-scales where
climate really interacts with people [72], is presently, and will be increasingly, important in planning
for how to adapt to climate change. Regional models can be run with considerably higher spatial
resolution than global models, and therefore the fundamental physics calculating the transports
between grid-boxes can simulate small space-scales phenomena (for example local rainfall driven
by topography) [255, 210, 294, 30, 359] which can not be simulated given the lower spatial resolution
we can afford to use in our global models. Considerable technical developments had to be made to
the MOHC’s regional modelling system to allow it to be driven by output from the perturbed physics
experiments underpinning the UK Climate Projections [247]. However, the major technical regional
modelling task occurring over the last MOHCCP has been the move from the HadCM3-based to
HadGEM3-based model system [195, 157], incorporating the new physics, but also new processes
(for example the land surface model).
The MOHCCP has played an important role in funding a component of this technical develop­
ment, but the adaption-relevant nature of regional modelling means that significant funding has also
been attracted from the United Nations Development Programme (UNDP), the Foreign and Com­
monwealth Office (FCO) and the Department for International Development (DFID), adding signifi­
cant value to the work supported by the MOHCCP. To best understand our regional climate projec­
tions, it is important that the regional climate model and global model are based around the same
physics. This way, inconsistencies between the regional model and the conditions specified at its
boundaries are reduced, and spurious results minimised. This presents challenges, but also brings
great benefits, for example, the regional HadGEM3 model (HadGEM3-RA) is now very similar to
the North Atlantic and European model run daily to produce the operational weather forecast [304].
This means that the climate modelling benefits directly from the developments and understanding
achieved from continuously running the weather model and validating it against real world events.
It also means that the very high resolution weather-forecast model, recently developed to simulate
convective rainfall events (rainfall resulting from small pockets of warm rising air, often associated
with thunder storms) in the UK, can be used to explore the impact of climate change on these very
detailed processes [204]. Ongoing simulations taking output from the high-resolution (60km atmo­
spheric and 1/4 degree ocean resolution) HadGEM3 model [290], and downscaling this to 12km
resolution in the atmosphere-only HadGEM3 regional model, are being used to drive the 1.5km
Southern UK region model [199]. These experiments will help us understand changes in detailed
processes such as convective rainfall, and help resolve whether the effects of warming, changing
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circulation, or changes in rainfall type will dominate future UK precipitation [204, 201, 202, 203, 205].
Note that for this work to be undertaken, the regional model had to be extended to include aerosol
processes, allowing it to be driven by the high resolution global simulations [177].
The finalised version of HadGEM3-RA has been shown to validate well against observations,
and to perform significantly better than the previous, HadCM3-based regional model [195]. HadGEM3­
RA is now being used extensively in the COordinated Regional climate Downscaling Experiment
(CORDEX) project, partially led by the MOHC, which is downscaling the CMIP5 model results
across all of the worlds continental regions for use in adaptation and impacts studies [195].
Modelling the climate hazards most relevant to society
Increasingly over the past five years climate impacts information provided by the MOHC has been
going beyond projections of climate variables, to simulating the climate hazards and impacts them­
selves and assessing the likelihood of these within a risk-based framework. This has included
improving understanding, and where possible modelling, of the dominant drivers that affect the cli­
mate hazards, and assessing the likelihood for these hazards to change under future scenarios of
climate and environmental change. The modelling approaches developed have been focused on
those most appropriate for the specific hazards and stakeholder requirements. These have included
simple statistical models, complex statistical emulators, semi-empirical process models, and com­
plex process models. A major focus of the model development has been on advancing JULES, the
Joint UK Land Environment Simulator [82, 24, 337].
JULES has been developed in close collaboration with UK partners, to improve its utility for
modelling impacts. The starting point for JULES was the model originally used to represent the
land-surface type (e.g. vegetation, ice or urban materials), and associated physical properties (e.g.
evaporation or friction), within Met Office weather and climate models. JULES includes more com­
plex climate components (e.g. the land carbon cycle) than the original Met Office system, but also,
river and groundwater flows, wetlands, land-use change, urban environments and crop growth. Be­
cause JULES simulates large-scale river-runoff, which is the standard metric used by the climate
impacts community to investigate water-stress, it is possible within JULES to use direct outputs from
the MOHC’s global and regional models to investigate the impact changes in climate will have on
water availability for human activity [241, 134, 335].
Improvements made to the hydrology scheme now allow the build up of a realistic water table
rather than the loss of water through the deepest soils, and out of the system. Development of
JULES for inclusion in the HadGEM2-ES model [83] has resulted in good agreement between model
outputs and river runoff observations (and improved land surface temperatures [331]) at annual
timescales, although monthly variability still needs to be improved [112]. Techniques have now
been set up to make validation of model output easier and therefore expedite improvements to
JULES in HadGEM3 [28].
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The MOHC have investigated the importance of integrating climate impacts models directly into
climate models. It has been found that when running the same hydrological model fully integrated
into a climate model, and then separately from that climate model but receiving exactly the same
precipitation, the integrated and non-integrated approaches simulated considerably different runoff
change, in some places even differing between positive and negative changes [29] (figure 2.15). It
is likely that the difference between integrated and non-integrated use of the hydrological model is
caused by the different treatments of evaporation between the simulations18 . Our understanding of
the impacts of climate on river-runoff has been applied to the UK [241] but also, through the partly
European funded protect HighNoon, to the Ganges Basin [242]. In the Ganges Basin predicted
changes in precipitation and temperature were shown to be unlikely to lead to significant increase in
water availability by 2050, but that the increased runoff resulting from snow-melt was likely to occur
earlier in year.
By incorporating the impacts model (in this case a hydrology scheme) into the climate model,
one can ensure that the advice provided is consistent across model outputs (for example changes
in humidity and therefore heavy frost occurrence being consistent with water availability - important
for planning in sectors like agriculture), but it also means that understanding climate impacts is not
a two-step process and can therefore occur more quickly, and with better understanding by those
focusing on each area of the strengths and weaknesses in the different model components.
Prior to the development of HadGEM2-ES [89], impacts modelling essentially had to be done
after the climate model simulations were released. This meant that the scientists involved in the
impacts modelling were removed from the development and running of the climate model, which
can increase he risk of poor interpretation of the climate data, as well as reducing the opportunities
for feedback from the climate impacts community to influence how the climate models are run and
developed. Furthermore, the dependency of climate impacts models on the supply of climate data
previously caused delays. With JULES running as an integral part of HadGEM2-ES, climate-impacts
data are generated contemporaneously and consistently with the climate data. The MOHC is now
ahead of the game in impacts modelling, and in an excellent position to direct the work that goes
into the impacts chapters of the IPCC’s 5th assessment report.
Crops are critically dependent on water availability, and therefore vulnerable to changing climate
[69, 63]. Within JULES, climate impacts on crops can be simulated [277, 114]. Furthermore, chang­
ing irrigation requirements and water resources can be investigated [266]. An investigation into win­
ter wheat production in the UK (the UK’s most important food crop) using output from the UK Climate
Projections (2009) showed that in response to climate change, despite elevated atmospheric CO2
concentrations enhancing growth19 , large decreases on yield could be possible in parts of the UK
18 This difference appeared to arise because in the integrated approach the calculation of evaporation was consistent with,
and contributed to the global model’s humidity, but in the non-integrated scheme, no feedback on evaporation could occur
through the climate model’s humidity
19 Plant growth occurs through photosynthesis, and photosynthesis requires CO . Where there are plentiful nutrients and
2
water, CO2 can therefore be the factor that limits plant growth, and increases in atmospheric CO2 concentration can remove
that limitation and enhance plant growth.
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a) Online Hydrological model
b) Offline Hydrological model
-6x10-6
-3x10-6
0
3x10-6
6x10-6
runoff (kg m-2 s-1)
Figure 2.15: Changes in runoff simulated to occur by the end of the century within HadGEM2-ES
(a) and using the hydrological model used in HadGEM2-ES, but driven offline using the meteorolog­
ical output from HadGEM2-ES. Results highlight the importance of incorporating impacts models
directly into climate models.
[70], indicating that changes in sowing regime, fertiliser use or the consideration of irrigation may be
necessary. Whilst this study highlights potentially important issues, there are large uncertainties in
climate projections at this fine-scale. The 25x25km space-scale results used within UKCP09 were
obtained by identifying relationships between large scale climate variables (e.g. globally averaged
temperature) from the global (low resolution) and detailed patterns of variability simulated within the
regional (high resolution) models; then calculating likely patterns of change corresponding to the
full range of possible global variable time-series obtained from a simplified (statistical) version of
the global model. This technique is called pattern scaling, and is used extensively to link results
from integrated assessment models, or simple climate models, to the impacts on people - which will
occur at a regional scale.
Where pattern scaling, rather than regional climate modeling (which is based on physics rather
than statistical relationships) is used to downscale or generate regional results relating to complex
components of the Earth System (e.g. vegetation), problems have been identified [334]. These
problems arise because many components in the earth system do not change in a way directly
proportionate to global climate variables - they may have memory, so behave differently in response
to, for example, increasing temperature than they do to, for example, decreasing temperature, or
their behaviour may depend on the combined effects of different stressors (e.g. temperature and
precipitation) at a regional scale.
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The complexity of the vegetation response to climate change was highlighted in a study exam­
ining the impact of elevated CO2 concentrations. It has been recognised for a long time that higher
atmospheric CO2 levels allow increased terrestrial vegetation growth, but this study showed that be­
cause under high CO2 conditions plants need to open their stomata less widely, less transpiration
of water from the plants takes place, and water availability is increased [26]. This work suggests
that regionally freshwater resources may be less limited than previously assumed under scenarios
of future global warming, but also that the change in growth and transpiration can impact climate
[101, 39].
Marine climate impacts are becoming an increasingly important issue in which the MOHC is
investing significant resources through the 2013-2015 climate programme. One important marine
climate impact question which has been addressed through the 2007-2012 MOHCCP is that of
future storm surge occurrence and height in the UK. This work followed on from an Environment
Agency funded project to examine the likely requirements from the Thames Barrier over the com­
ing century, extending the analysis to the whole of the UK coastline. It was found that driving a
surge model with down-scaled output from a set of HadCM3 climate model simulations, changes in
storm surge event magnitude and frequency in the future were driven primarily by relatively gradual
changes in regional sea-level, rather than changes in atmospheric storminess; a conclusion backed
up by historical observations [287]. However, it should be noted that projections at this scale are
interpreting climate models at the very boundary of this skill, and it is possible that the alleviation of
systematic biases within future climate model mean that these conclusions are revised.
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2.6 How might the Earth System respond to different pathways
of anthropogenic activity over the coming century and how
resilient is the system to change?
Looking beyond the next few decades, many decisions regarding physical infrastructure and the
mitigation of climate change must be thought about in a risk-based context. These decisions must
account for the likelihood and magnitude of various feedbacks, state-changes and commitments to
change within the climate system. Many of the processes through which these non-linear changes
in climate might occur fall outside the scope of physical climate models such as those built for the
IPCC’s 4th Assessment Report, and can only be considered within Earth System Models - models
which, in addition to physical processes, represent the biological and chemical components of the
climate system. Much of the progress made with respect to this question has driven, and been
made possible by, the development and analysis of the MOHC’s first, and possibly the worlds most
sophisticated (Heffernan, Nature 2010) Earth System Model, HadGEM2-ES (the 2nd MOHC Global
Environmental Model, with additional Earth System processes).
Earth System Modelling: HadGEM2-ES development
At the start of the last MOHCCP (2007) the MOHC had available a number of mature tools for
modelling the climate, including HadGEM1, a physical climate model the performance of which
ranked well against other CMIP320 models, and HadCM3-LC (the 3rd MOHC Climate Model - Low
resolution, Carbon cycle), a carbon-cycle capable low resolution climate model which, whilst initially
developed in the year 2000, remained at the forefront of climate-carbon-cycle modelling. The last
MOHCCP (2007-2012) saw these tools combined and extended to produce a model capable of
robustly simulating the key physical climate processes, but also simulating what were considered to
be the first order biological and chemical climate-relevant processes, HadGEM2-ES [89].
The development of the MOHC’s first true Earth System model has been achieved by first de­
veloping the HadGEM2-AO (Atmosphere and Ocean physics only) model from HadGEM1, by: clos­
ing the water cycle [291]; improving Northern Hemisphere continental temperature offsets through
comparison and understanding of the differences between weather and climate simulations [231];
improving tropical sea surface temperature biases and poor variability by modifying mixing in the up­
per ocean [331]; and improving the HadGEM1 aerosol scheme by updating existing aerosol species
and representation of additional species [21, 20]. Secondly, ocean and terrestrial biogeochemical
components were updated to improve validation against observations and scientific validity. Finally,
an additional component, atmospheric chemistry [243, 251], was added to the model, and the links
between model components were made (figure 2.16).
20 Note that CMIP3, the 3rd climate Model Intercomparison Project preceded CMIP5 - the name CMIP4 was skipped to
bring the project numbers into line with the IPCC’s Assessment Report numbering
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Figure 2.16: The components and interactions within the recently developed HadGEM2-ES Earth
System model.
UKCA (the UK Community Aerosol), the atmospheric chemistry component of HadGEM2-ES,
simulates the temporal and spatial evolution of climatically important trace gases (e.g. methane
and tropospheric ozone) and the supply of oxidants (e.g. hydroxyl radical, hydrogen peroxide and
ozone) to the model’s sulphate aerosol scheme. To a first order, it is the availability of oxidants
that determines whether sulphur dioxide gas becomes particles of sulphate aerosols, and therefore
determines the extent to which emissions of anthropogenic aerosol precursor (e.g. sulphur dioxide),
impact the climate through the scattering of light and changes to cloud properties [320].
The availability of oxidants depends on changes in emissions of oxidant precursors (e.g. methane
and carbon monoxide), the rate of sunlight-mediated oxidant formation, and the consumption of ox­
idants (through aerosol forming and non-aerosol forming chemical reactions). Many of the oxidant
formation and consumption reactions are climate dependent, as are the availability of chemicals
involved in these reactions dependent on the climate. It is therefore important to simulate atmo­
spheric chemistry as a fully interactive component of the climate model to be able to quantify the
important role played by aerosols in future climate. This was highlighted by Rae et al. [274] who
showed that the oxidant reduction occurring in a business as usual scenario reduced the amount
of sulphate in the atmosphere by 3%, comparable in magnitude to the 9% reduction occurring in
response to physical effects (e.g. rainfall washing particles out of the atmosphere) on the lifetime of
sulphate in the atmosphere.
The development of UKCA has been a highly collaborative effort, bringing together colleagues
from the Met Office, Cambridge, Leeds and Oxford Universities through NERC funding (NCAS ­
National Centre for Atmospheric Science - and more recently the Joint Weather and Climate Re­
search Programme between the Met Office and NERC), providing a template for the increasing
collaborative approach to model development that as been occurring over the last five years [71].
Collaboration with NERC is continuing to strengthen through (amongst other projects) the develop­
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ment of a jointly owned UK Earth System Model. Technically, the development of HadGEM2-ES
required UKCA to be integrated into the climate model, and working linkages to be made to diverse
components of the climate system [251, 90]. Once incorporated into the HadGEM2-ES model [176],
the implications of climate on changing tropospheric chemistry, and the impact of changing tropo­
spheric chemistry on climate, aerosols and air quality were explored [36, 15, 16, 91, 98, 99, 311, 43].
The important role played by aerosols in climate has been appreciated for some time - and
HadGEM1 already contained an advanced aerosol scheme. Significant improvements were how­
ever made to the aerosol representation module into HadGEM2-ES by adding a new pathway for
sulphur dioxide oxidation (occurring in water droplets) and by the addition of further aerosol species
(mineral dust, fossil-fuel organic carbon, and secondary organic aerosol from biogenic terpene
emissions) [20].
The simulation of ozone in the troposphere, the lower part of the atmosphere (rather than the
stratosphere where ongoing work examines the recovery ozone hole amongst other processes
[243, 329, 56, 124, 125, 58, 59, 60]), is an important part of UKCA. Changing tropospheric ozone
concentrations play an important role across timescales from weather to climate [118], impacting
human health through air-quality issues [66], food production through damage to plants [311, 4,
90, 164], and the climate due to its greenhouse properties and aerosol impacts [18, 98, 85, 122].
The highly reactive nature of ozone results in rapid, cumulative, plant damage, which not only
causes reduced crop yields, but is also likely to be important at the level of global carbon cycling
[311]. Plants limit ozone uptake where ozone concentrations are elevated, by partially closing their
stomata.
A consequence of stomatal closure is that less CO2 is taken up, less photosynthesis can take
place, and less anthropogenic carbon is stored in the terrestrial carbon sink. Sitch et al. [311]
demonstrated that the climate impact of changing ozone through changing terrestrial carbon uptake
was potentially greater than the direct greenhouse effect of ozone (figure 2.17). A further impor­
tant link between tropospheric chemistry and terrestrial vegetation occurs through methane. The
potency of methane as a greenhouse gas, and therefore the large climate impact it has despite
being present in the atmosphere in very low concentrations, makes it of particular importance to
decisions regarding climate mitigation. The bringing together of models capable of examining ter­
restrial methane sources and the behaviour of methane in the atmosphere within HadGEM2-ES
has facilitated work which has examined climate feedbacks relating to wetland extent and the role
that melting permafrost might play in future methane release, and the impact of these processes
on air-quality and climate [250, 42, 350, 85, 51, 190, 281, 37, 55] (figure 2.18). A number of the
potential feedbacks resulting in methane release have been considered in a large assessment of
the current literature [250] (figure 2.19).
The terrestrial biogeochemistry scheme TRIFFID (Top-Down Representation of Interactive Fo­
liage and Flora Including Dynamics) is a component of the land surface scheme JULES (introduced
in the previous section). TRIFFID simulates the carbon uptake, storage, and release within the
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Figure 2.17: Modelled changes in June-July-August averaged ozone (O3 ) concentrations between
the present day and the year 2100 (a-b). The change in Gross Primary Production (GPP), a mea­
sure of carbon storage, simulated over this interval given a low (c) and high (d) vegetation sensitivity
to ozone [311].
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Observations
Model
2080-2090
2080-2090
2080-2090
2080-2090
RCP2.6
RCP4.5
RCP6.0
RCP8.5
extent (%)
Active Layer
Thickness (m)
Extent
1900-1910
0-10
10-50
50-90 90-100
0.6 1.2 1.8
0.6 1.2 1.8
0.6 1.2 1.8
0.6 1.2 1.8
0.6 1.2 1.8
Figure 2.18: Observational validation and future projections of permafrost area and active layer
thickness (or the depth to which the permafrost thaws) derived from the HadGEM2-ES model [96,
50].
terrestrial biosphere, but also interacts with the physical climate through its control on albedo, land
surface roughness (and therefore friction, and winds) and moisture fluxes. In addition to moving
TRIFFID from HadCM3-LC to HadGEM2-ES, improvements have been made over the last five years
to the way in which the progression of primary production throughout the annual cycle is simulated.
This has addressed problems highlighted in the original version of TRIFFID when it was validated
against the seasonal cycle of atmospheric CO2 concentrations [61]. Three new soil carbon reser­
voirs (splitting the original generic reservoir into soil biomass, humus, decompostable (leaf litter)
and resistant (twigs, roots etc.) plant material) were also added to the model to more realistically
simulate the different timescales of carbon storage [84, 89]. Finally, the temperature dependence of
respiration within plant leaves was modified to reflect new understanding about the acclimatisation
of plants to higher temperatures [82]. A technique was then developed to allow the slowly evolving
carbon reservoirs to be brought into equilibrium in a computationally efficient manner.
Limits to our certainty about climate change, stemming from our incomplete understanding of
terrestrial carbon cycle processes [163, 166] has been shown to be comparable in magnitude to the
lack of certainty coming from the imperfect representation of physical processes such as those that
relate to cloud formation [34, 136]. The uncertainty derived from biogeochemical model components
(those that consider spatial variability in biological and chemical processes - such as the carbon cy­
cle) is not surprising, because the processes involved, particularly the biological processes, are
often not fully understood. If they are, are almost certainly too complex to simulate from first prin­
ciples in an Earth System Model. We have therefore attempted to make simulations as realistic as
possible by using techniques such as data assimilation21 [93], comparing different terrestrial carbon
21 Data assimilation is a technique for combining model and observational data. The model is run to produce a forecast,
which can be compared with observations when and where they exist. Where in space and time observations exist, the
forecast is adjusted to minimise the difference between its estimate and the observation, without knocking the model off
balance.
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Figure 2.19: Summary diagram of the relative sizes and time scales associated with methane
sources/feedbacks. Note that the diagram is semiquantitative. BVOCs are biogenic volatile organic
compounds [250].
cycle models [312, 226], and mechanistically understanding how and why our models behave as
they do [107, 131].
At present, the oceans take up a similar quantity of anthropogenic CO2 to the land carbon
cycle. The ocean carbon cycle was represented in HadCM3-LC by the sub-model HadOCC (the
MOHC Ocean Carbon Cycle model). HadOCC represented the transfer of atmospheric CO2 to and
from the ocean, its chemical storage within the seawater, the flux of carbon from the seawater to
phytoplankton through photosynthesis, and carbon flux from phytoplankton back to the seawater in
the ocean’s interior, after plankton death or consumption and excretion by zooplankton. Recognition
that different phytoplankton species move carbon around the ocean differently, and acknowledgment
that low-concentration nutrients, as well as primary nutrients (such as nitrogen) play an important
role in the carbon cycle led to the addition of a new phytoplankton species, and the introduction of
iron cycling, in the new ocean biogeochemical model, diat-HadOCC [89, 159]. A new link between
ocean biogeochemistry and the atmospheric chemistry (and subsequently aerosol formation) was
added by including an interactive scheme to simulate the biological production of dimethylsulphide
(DMS), an important source of sulphur to the atmosphere [140, 355].
The motivation for including an interactive DMS scheme was that a long-proposed feedback
mechanisms (Charlson et al., 1987) suggested that increased temperatures may drive increased
phytoplankton production of DMS, which would cause aerosol production, the brightening of clouds,
and act as a negative feedback on warming. HadGEM2-ES is the first climate model capable
of testing this. Whilst the strength of this feedback mechanism appears to be very weak when
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considered globally [354], regionally, it looks like DMS feedbacks could be of some importance.
However, the major changes in DMS emission within HadGEM2-ES appear to occur in regions with
very stable atmospheric conditions, which prevent the DMS from reaching altitudes where it would
interact with cloud, and become climatically important. The next step is to investigate whether
there is a physical reason within the model for the co-location of regions of DMS emission change
and stable air masses, and if so, whether this result will hold true in the real world [159]. It is
also important the differences between DMS production simulated by different CMIP5 models is
investigated [190]
Pushing forward the MOHC’s supercomputing and IT infrastructure
The benefits derived from the complexity of the HadGEM2-ES model comes with a large computa­
tional cost. Whilst solving the equations relating to the various chemical reactions or component­
to-component interactions is not trivial. The largest computational cost associated with a climate
model is simply solving the equations representing the physics of the ocean and atmosphere, which
transports each of the tracers, the quantities such as heat, aerosol species and chemical species
(or salinity, nutrient concentrations and dissolved carbon in the ocean) around the model’s atmo­
sphere and ocean. In a non-Earth-System model, it is only necessary to transport a small number
of tracers around the model - for example, only temperature, salinity and three-dimensional veloc­
ities, are required in an ocean model. The introduction of Earth System processes dramatically
increases the number of these tracers, for example the MOHC ocean biogeochemical model (diatHadOCC) requires 13 ocean tracers, and the atmospheric chemistry model (UKCA) requires 24
atmospheric tracers. HadGEM2-ES therefore runs about a factor of three times more slowly than
the equivalent climate model (without earth system components) would run, and produces orders
of magnitude more data. The provision of HadGEM2-ES results to CMIP5 in time to maximise the
MOHC’s input into the IPCC’s 5th Assessment Report was therefore contingent on the successful
procurement, installation and transfer of models to a new supercomputer. Furthermore, the archiv­
ing of data from this model, and supply of data to CMIP5, required a new, larger and considerably
faster, data-archive system. As the HadGEM2-ES model was being finalised (2009/2010), a large
Met Office wide project installed a new supercomputer, providing the capability to run most of the
critical CMIP5 simulations simultaneously, with speeds of around three model years per real-world
day.
Once the models were all running smoothly, further work by a team of 21 scientists and IT spe­
cialist had to be undertaken to process and check the 250,000 files which were to be delivered to
CMIP5, and make them suitable for submission to the central data repository. Technical develop­
ments also had to be put in place to transfer the 66 Terrabytes of data to CMIP5 - the success of
the management of the HadGEM2-ES development project, the supercomputer installation, and the
data processing and delivery projects has been demonstrated by the fact that the MOHC was one
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of the first climate centres to deliver any data to CMIP5, and one of the top two centres in terms of
the completeness of the range of simulations delivered.
The final technical challenge, prior to the scientific analysis of CMIP5 data, was the retrieval of
output from other climate centre’s models. Data retrieval from CMIP5 was made possible by the
installation of a direct link into the UK’s super-high-speed academic network. So far 250,000 files
(22 Terrabytes of data) have been downloaded to the Met Office from the CMIP5 archive, by over
35 different users. The supercomputing and data storage advances described here in the context
of the CMIP5 project have been equally, or perhaps in the case of near-term forecasting even more
important, for other aspects of the MOHCCP.
Contributing to the 5th Coupled Model Intercomparison Project
Over centennial timescales, our lack of certainty about feedbacks within the climate system is
thought to be the dominant barrier to accurate prediction. The motivation for building the HadGEM2­
ES model was therefore that we knew that the Earth System had the capacity to change significantly
in a non-linear manner, and that these departures from proportionality between climate forcing and
response have in the past been mediated by feedbacks in ’Earth System’ components such as the
carbon cycle [234]. Feedbacks can either be negative, and therefore pull the system back towards
its original state after a change, or positive, and push the system towards a new state. The MOHC
must understand both types of feedback, but it is particularly important the we understand the lat­
ter if decisions relating to climate mitigation are going to continue to be based on climate model
understanding (figure 2.20).
CMIP3, the intercomparison project focused on contribution to the IPCC’s 4th Assessment Re­
port, involved climate modelling centres running up to 12 different experiment types. These ranged
from free-running simulations to allow quantification of internal variability simulated by models in
a pre-industrial climate, to experiments examining an instantaneous doubling of atmospheric CO2
concentrations to look at the timescales of the climate response. The CMIP5 design specifies 32
different types of key simulation, all of which the MOHC have run, or are currently running in collab­
oration with external partners as is the case for the CMIP5 palaeoclimate simulations. Many more
individual simulations, contributing to a number of the 32 different experiment types, have been sub­
mitted to CMIP5 [187] - resulting in the supply to CMIP5 of 100 times the volume of data supplied
to CMIP3. The increase in the number of types of experiment largely reflects the wider range of
processes now being included in models, and therefore the need for more detailed experiments to
allow us to understand the contribution many of these different processes make to climate change.
The model set up, and the way in which these different experiments were applied within HadGEM2­
ES is described in Jones et al. [184]. In addition to contributing one of the most complete (of all the
international climate centers) set of experiments to CMIP5, we have undertaken a plethora of addi­
tional experiments to allow us to quantify specific feedbacks within our models (e.g. fixed aerosol
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Figure 2.20: A set of simple experiments have been run to help identify and quantify the feedbacks
in HadGEM2-ES. The red line represents the basic HadGEM2-ES global temperature response to
an instant quadrupling of atmospheric CO2 concentrations. Despite all of the extra Earth System
processes incorporated into the Earth System version of the model, this response is very similar
to what is simulated in a version of the model without any Earth System processes (HadGEM2­
Atmosphere and Ocean only - AO) (blue). However, when the Earth system model is run without
the carbon cycle components experiencing the CO2 change, and the CO2 simply impacting tem­
perature (black), the model’s climate is considerably less sensitive to CO2 - similarly if the forcing
is changed by just changing the energy coming in, but keeping CO2 levels constant (green), the
temperature response is lowered. This behaviour occurs because if the terrestrial vegetation only
experiences the climate change, its extent in many areas decreases - there is more bare soil. If
the vegetation experiences more CO2 as well a a changed climate, more photosynthesis can occur
(CO2 fertilisation of the vegetation), and less vegetation disappears. If there is more bare soil, there
is more dust being emitted, and dust reflects large amounts of sunlight back to space, cooling the
planet [10].
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emission simulations to subdivide attribution of change beyond just anthropogenic forcing) [33]; to
contribute to intercomparison projects removed from CMIP5 (e.g. CFC simulations to quantify phys­
ical ocean changes); and to push beyond the science occurring for CMIP5 (e.g. idealised climate
mitigation scenarios to examine Earth System reversibility, or quantify Earth System feedbacks)
[38, 10].
Earth System Science
Probably the most surprising and controversial result to come out of out 1st generation carbon-cycle
capable model, HadCM3-LC, over a decade ago, was that, under a number of different emissions
pathways, by the year 2100 a large proportion of the Amazon Rainforest had died. This became
known as ’Amazon dieback’. Perhaps even more concerning than this original finding was that
the commitment to change may occur long before that change actually happens [191]. Whilst the
terrestrial vegetation schemes in HadCM3-LC and HadGEM2-ES are very similar, improvements
to the physical model mean that on average HadGEM2-ES represents the temperature and pre­
cipitation changes better than does HadCM3-LC. It is ultimately changes in these physical climate
variables that drives terrestrial vegetation change. It has been shown that the climate change sim­
ulated by HadCM3-LC would cause significant vegetation loss in other climate centres’ vegetation
models [312], so based on our present understanding of terrestrial vegetation, it would appear that
the major uncertainty surrounding the future of the Amazon (if we ignore deforestation [360, 276]) is
our representation of the temperature and precipitation in the Amazon region [165]. The complexity
of the vegetation response to different climate forcings, and therefore the need to move away from
simple metrics of climate change (such as radiative forcing) has also been examined [164].
One of the first results to come out of the HadGEM2-ES CMIP5 simulations was that no major
Amazon dieback was seen by the year 2100 under any of the scenarios [188, 107] (figure 2.21). As
previously discussed, the vegetation response is a complicated interaction between climate pres­
sures (e.g. temperature and water availability), anthropogenic pressures (e.g. atmospheric CO2
concentrations or deforestation), and the inertia in slowly changing vegetation components. It is
therefore not trivial to explain why different models are behaving differently. To explain the differ­
ences between the HadCM3-LC and HadGEM2-ES future Amazon Rainforest simulations a sta­
tistical model was developed, which was shown could explain the steady-state (having settled to
a no-longer-changing distribution within a stable climate) tropical forest distribution, based on the
atmospheric CO2 concentration, temperature and dry-season length [131]. This simple statistical
model was then extended and shown to work with HadGEM2-ES. Because this tool replicated the
behaviour of the two models, but did so based on changes in three generalised variables (atmo­
spheric CO2 concentration, temperature and dry-season length), the contribution of each of these
variables to the full change could be quantified. It was found that around 40% of the difference in
forest dieback between the two models could be accounted for by differences in the change of dry
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Figure 2.21: The difference in the proportion of the tropical land surface covered by trees simulated
at high CO2 levels, between our last-generation carbon-cycle capable model (HadCM3-LC), and
our state-of-the-art Earth System Model (HadGEM2-ES) [107].
season length, more minor contributions then arise from other components of the system [107]. It
should however be noted that whilst the dry-season simulation within HadGEM2-ES is more likely
to be correct than that simulated in HadCM3-LC, regional precipitation change is notoriously diffi­
cult to model and validate [165, 328, 172, 202, 218, 219, 297, 319, 295], so the vegetation change
simulated by HadCM3-LC should not be ruled out as a plausible future scenario.
The MOHC’s scientific analysis of HadGEM2-ES and the other CMIP5 models has only recently
started, but a large push was made to ensure that as many key papers as possible were submitted
in time for them to be included in the first order draft of the IPCC’s 5th Assessment Report (Au­
gust 2012). Overview papers from the MOHC have presented: top-level analysis of HadGEM2-ES
future scenario simulations [62]; model descriptions, validation and implementation [89, 184, 331];
and an overview of the decadal timescale experiments started from an observation-based state
[316]. Papers have been published looking at the fundamental sensitivity of the climate simulated
by HadGEM2-ES and other CMIP5 models to anthropogenic activity [9, 10, 196], and high-level
carbon-cycle and cloud feedbacks within the models [13, 339]. Detailed feedbacks, such as the re­
lease of carbon from permafrost [53], the drivers of changed tropical precipitation [67, 221], and the
change in uptake of CO2 by the North Atlantic [139] have been explored (figure 2.22). Stratospheretroposphere interactions have been examined [59] and the impact of possible solar and volcanic
changes on 21st century climate quantified [192].
The working group of the IPCC’s 5th Assessment Report which examines human relevant cli­
mate pressures has later deadlines for paper to be submitted than the physical science working
group (mentioned above). However many studies looking at the directly human relevant climate
changes are underway, and a number of studies, looking, for example, at changing hurricane activ­
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6
QUMP air-sea CO2 flux
(20yr smoothing)
observation-constrained flux
(20yr smoothing)
CO2 flux mol-C m-2 yr-1
4
unsmoothed data range
2
0
Observation based climatology (Takahashi et al., 2009)
Observation based reanalysis (Peters et al, 2007)
-2
1900
1950
2000
2050
year
Figure 2.22: The North Atlantic has been shown in observations to be a highly variable sink for
atmospheric CO2 . The results presented here show that many HadCM3C (carbon cycle capable
version of HadCM3) simulations suggest that the North Atlantic CO2 sink may decrease consider­
ably in the future (from which the gray band and lines have been derived). By tuning a box model
to the HadCM3C results, limitations to the HadCM3C simulation can be removed, and observa­
tions can be used to come up with an improved estimate of North Atlantic CO2 uptake (red). The
observation-based simulations suggest that the North Atlantic CO2 uptake may start to decrease
as soon as the 2030’s, and that the region might even (in the worst case simulations) be a peri­
odic source of CO2 to the atmosphere [139]. Work is ongoing to incorporate data from the CMIP5
simulations and improve our confidence in the observation-based projections.
ity, and the drivers of changing coral growth, have already been submitted for peer-review [53, 103].
More climate hazard/impact work will be completed using the CMIP5 results within the 2012-2015
MOHCCP.
It was decided when designing the CMIP5 experiments that atmospheric CO2 concentrations,
rather than CO2 emissions, would be specified within models for most simulations [330]. This means
that all models experience similar climate forcing (i.e. carbon cycle feedbacks do not cause large
changes in atmospheric CO2 concentrations which render models incomparable). This approach
has been shown to have only a minimal impact on the marine carbon cycle [138]. The emissions
which would have resulted in those atmospheric CO2 concentrations, taking account of the feed­
backs occurring in different models, can be back-calculated from individual models [222]. It has
broadly been found that most of the CMIP5 Earth System models simulate a stronger carbon cycle
feedback than that generated by the Integrated Assessment Model used to produce the high-end
CMIP5 future scenario [121], and that approximately half of the CMIP5 models require negative
emissions (e.g. some form of carbon capture in combination with bio-fuel energy production) to
meet the CMIP5 climate mitigation scenario (Representative Concentration Pathway (RCP) 2.622 )
(figure 2.23) [186].
22 The latest set of IPCC climate forcing scenarios are described by the peak radiative forcing, or year 2100 radiative
forcing (see earlier explanation of radiative forcing) in that scenario. So RCP2.6 means that at 2100, that scenario expects
the disequilibrium in the planet’s energy balance to be 2.6Wm−2 .
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Figure 2.23: The model simulations contributing to the next IPCC Assessment Report have been
designed to all use identical CO2 concentration changes through time. Climate centres with Earth
System Models (which include carbon cycles) can then back-out the CO2 emissions which would
give those CO2 concentrations. Here the CO2 implied emissions are presented as calculated from
the HadGEM2-ES simuations.
While the development of Earth System Models allows us to explore new processes and feed­
backs in the climate system, a large and important component of the work over the last five years
has been the continued understanding and improved representation of so-called physical feed­
backs, for example, those occurring through changing water vapor and cloud distributions/amounts,
and their interaction with radiation. Indeed, the representation of cloud processes continues to pro­
vide one of the most challenging aspects of climate modelling, and feedbacks mediated through
changes in cloud properties remain the primary source of uncertainty in the response of the latest
generation of climate models to a change in specified atmospheric CO2 concentrations [9] (figure
2.24).
Moreover, it is important to examine such physical feedbacks within the new Earth System Model
context as clouds also play a critical role in, for example, anthropogenic aerosol-induced climate
forcing, the hydrological cycle, and the large-scale atmospheric circulation at both global and re­
gional scales. Uncertainties in cloud and moist processes thus have implications for many other
aspects of climate modelling and climate prediction.
It is also important to realise that our understanding of the response of clouds to climate forcings
continues to evolve. For example, a number of studies now suggest that cloud changes also influ­
ence climate through short timescale (i.e. days to weeks), non-feedback atmospheric temperature
and land surface adjustments [8]. These rapid adjustments, which occur before the global mean
surface temperature has had time to change in response to the forcing, can, for example, arise due
to the change in atmospheric heating caused by the increased CO2 concentrations. Cloud feed­
backs are then defined as the cloud changes associated with increases in the global mean surface
temperature and which occur on multi-annual to decadal timescales. Correct separation of cloud
feedbacks from these rapid adjustments is clearly important when quantifying climate change [338],
not least because the adjustments will depend on the nature of the forcing (e.g. greenhouse gas
concentration changes, changes in the sun’s output, etc), and will therefore have different implica­
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tions for climate projections depending on which socioeconomic pathway may be followed.
One particularly valuable set of simulations which were conducted by the MOHC outside of
the CMIP5 framework were the idealised mitigation simulations [38]. A standard climate simulation
used through a number of generations of intercomparison project has been the ’1% CO2 rise’ exper­
iments. These simulations start at preindustrial CO2 concentrations, then increase the atmospheric
CO2 concentration by 1% a year until they reach four times preindustrial atmospheric CO2 concen­
trations. The 1% CO2 rise experiments are useful because they are essentially simple versions
of a business as usual scenario, but also because the continuously increasing rate of change of
atmospheric CO2 essentially offsets the fact that at higher CO2 concentrations a given increase in
CO2 concentration has a smaller warming effect. Therefore the CO2 rise results in an approximately
linear increase in globally averaged temperature with time.
The MOHC extended these simulations by decreasing atmospheric CO2 concentrations at 1% a
year from the points at which atmospheric CO2 concentrations were at 1 times, 2 times, 3 times and
4 times preindustrial concentrations respectively. This allowed us to look at whether it was possible
to reverse changes in the model’s Earth System, and build on a question explored in a previous
paper ’How difficult is it to recover from dangerous levels of global warming?’ [224]. This study
suggested that in the context of HadGEM2-ES and the very simplified experiments that were run,
there were four components of the system which may not recover along the same path as which
they originally changed. These components were: low-level clouds, which displayed a small timelag before recovering [38]; global and regional precipitation [68], nutrient availability in the Southern
Ocean (figure 2.25), and the strength of the large scale Atlantic ocean circulation (the Atlantic
Meridional Circulation - AMOC) [357]. In a related experiment conducted with HadCM3, a lag, but
given time to stabalise, no irreversible change is found in Antarctic sea-ice cover [285]. This result
may however be complicated by slow changes in ocean heat storage and should be investigated
further [284].
HadCM3 CO2 increase then decrease experiments also suggested that the global precipitation
change might continue increasing for a number of years after atmospheric CO2 concentrations
has begun to decline. This behaviour occurs in response to additional heat (and therefore surface
warming) being accumulated in the ocean even after the CO2 had peaked. The heat builds up
because the surface ocean is continuously mixed with cooler waters from below, and therefore
remains colder than the atmosphere for a few years after the CO2 peak [358, 351]. A number of
these issues relating to reversibility of the climate system will be examined within the European
project EMBRACE.
How resilient is the climate system to change?
The idealised mitigation experiments form a small part of the work undertaken over the last five
years to assess resilience in the climate system - i.e. how far the system can be pushed before
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Figure 2.24: Comparison of the equilibrium climate sensitivity (Eqm) - the change in globally aver­
aged temperatures resulting from a long simulation with CO2 concentrations doubled with respect
to their preindustrial values, and net climate feedback - the same property expressed as a change in
the balance of radiation coming into the earth system for a certain change in temperature (top row),
adjusted radiative forcing - the change in radiative balance occurring in response to a doubling of at­
mospheric CO2 concentrations (yellow) or an imposed change in sea surface temperatures (blue),
but accounting for the initial adjustment of the system to this through local adjustments (second
row), the component of the climate feedback occurring through changing cloud properties (third
row) and the feedbacks impacting the long wavelength (largely being lost to space) radiation (blue)
and short wavelength (largely coming from the sun) radiation (yellow) which have nothing to do
with cloud (bottom row). Results are presented for 11 CMIP5 models. The parameters shown are
calculated from experiments in which the atmospheric CO2 concentration in the climate models is
instantaneously increased by four times its pre-industrial value; the models are then run for at least
150 years. The black vertical bars represent the range of values for each model which less than 5%
of that model’s results are expected to be greater than or less than. Units K or Wm−2 K−1 .
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CO2 increasing
CO2 decreasing
-0.050
-0.025
0.00
0.025
0.050
trend in nutrient concentrations (mmol/m/yr)
Figure 2.25: Surface ocean nutrient concentration trends (units of mmol nitrogen m−3 yr−1 ) in the
ramping up (top) and ramping down (bottom) phase of the 1% per year CO2 rise then 1% per
year CO2 decrease experiments. Values have been multiplied by minus one in the lower globe, so
that the two plots would look identical if the system was completely reversible. The red colours in
the lower globe show that the nutrient concentrations continue to increase in the Southern Ocean
through the ramp up and ramp down phase - i.e. the Southern Ocean biogeochemistry may not
quickly return to its previous state after CO2 mitigation.
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committing to a change from which the system can not easily recover [281, 350, 130]. Three
particular areas which we have looked at in detail are terrestrial vegetation, land-ice and large scale
ocean circulation. The possibility of dramatic change in the Amazon Rainforest has already been
discussed, but so far only in the context of progressively increased climate change. An important
finding has been that large forests actually play a role in sustaining their own climate by cycling water
(through uptake from the soil, evapotranspiration, precipitation etc.) further into continental regions
than would occur through physical processes alone [185]. A consequence of this is that if the forest
is degraded by climate change (or deforestation [360, 276]), the ability of the forest to maintain its
own environment can be damaged, both accelerating further forest loss [189], and meaning that the
system will not necessarily recover to its original state if the imposed climate change is removed
[191].
A similar climate-mediated commitment to move to a new state has been suspected to occur
in ice-sheets, because the ice reflects light and elevates the surface - keeping its local climate
’artificially’ cold. A temperature which would have been consistent with the existence of ice while the
climate was warming (and ice existed), may therefore be too warm for ice to form as temperatures
are decreasing from an elevated value. This process was demonstrated by a long set of experiments
undertaken using HadCM3, where the climate model was run for a period, then that climate state
passed to an ice sheet model which was run for a time, then that ice-sheet state put back into
HadCM3 and the climate run forward (etc.) [282]. These experiments identified multiple different
states at which the ice-sheet could be stable, and implied that melting of 10-20% of the ice sheet
could already commit us to (at best) recovering to a lower ice volume than exists at present.
The final component of the climate system that we have been exploring in terms of resilience
is the Meridional Overturning Circulation (the MOC, the large-scale ocean circulation traveling at
the surface up the Atlantic, before sinking and traveling south at depth). The MOC plays an impor­
tant role in transporting heat northwards, helping to keep Europe warmer than it would be if this
circulation did not exist. The MOC also plays an important role in controlling the uptake of CO2 in
the North Atlantic [139]. The MOC is in a sense self-sustaining, because it relies on the transport
of saline waters (which are denser than fresher water) from the evaporative low latitudes up to the
high northern latitudes so that when it cools, water is available which is dense enough to sink ­
and this transport occurs as part of the MOC. If this feedback loop were to be broken, it has been
proposed that the MOC might switch to an ’off state’. Past climate evidence suggests that the MOC
has slowed considerably and rapidly in the Earth’s history [234], exhibiting an ’on’ and ’off’ state and
hysteresis (figure 2.26).
Previously, models with anything other than highly simplified physics have failed to demonstrate
two MOC states. New results obtained in collaboration with NERC partners have shown the exis­
tence of these two states in a low resolution version of HadCM3, and suggested that for this to be
achieved, the balance of salinity flowing into the Atlantic and evaporation from the Atlantic, must be
such that a reduced circulation (and associated reduction in water being brought into the Atlantic),
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causes the Atlantic to become less saline [142, 310]. The real world appears to be in this state,
whereas most climate models appear to be in a more stable MOC regime due to biases in their
simulation of South Atlantic salinity. This idea has been explored in a simple box model which, if it
can be shown to be robust when tested against a number of climate models, suggests that basic
observations of the state of the Atlantic can tell you how close we are to the point at which this
commitment threshold is reached [110, 352] (figure 2.26). Whilst these results have so far only
been demonstrated in very simple climate models, the improved simulation of South Atlantic salinity
simulated in the MOHC’s latest physical climate model HadGEM3 means that we would expect this
two-state MOC to exist within this model. Experiments are ongoing to look for the existence of this
behaviour in HadGEM3, but the required simulations are very slow to run so it is too early to draw
strong conclusions.
Whilst it is clearly important to know how close the climate system may be to moving to a
changed MOC state, even if this point were unlikely to ever be reached, reversible changes in the
MOC would still play an important role in climate change. Work has been undertaken to understand
how damped versions of the feedbacks described above may result in multi-decadal variability in the
MOC [225], which through the role it could play in determining sea surface temperatures [288], may
account for the (or a component of the) observed multi-decadal sea surface temperature variability
in the North Atlantic [80, 33]. Recent results even suggest that past change in the MOC might
not be limited to cyclic variability, perhaps even exhibiting a long-term (preindustrial to presentday) increase in strength [225]. If this result is found to be robust, it could well have important
implications for the future rate of change in the MOC. It is generally accepted that in response to
increased high latitude precipitation and melting of land ice, the waters which sink to form the lower
part of the Meridional Overturning Circulation will become less saline, and therefore more buoyant ­
resulting in a slowing of the MOC. A robust finding from climate models is that warming of the North
Atlantic under increasing greenhouse gases will make the source waters of the MOC less dense,
resulting in a weakening of the MOC. Changes in salinity generally act to moderate this weakening,
but this effect varies widely between models. Following the Quantification of Uncertainty in Model
Predictions (QUMP) approach described in the previous section, the plausible range of rates at
which we expect the MOC to slow down over the coming century is being tested [220]. This work
is feeding into a larger project being undertaken in collaboration with NERC colleagues to run very
large sets (many thousands) of simulations across the internet on volunteering individual’s home
PCs to examine further the range of possible future changes in the MOC [119].
Quantifying uncertainty
The Quantification of Uncertainty in Model Predictions (QUMP) project has featured in various
guises throughout this report, but in addition to contributing to UKCP, future decadal forecasting,
and large MOC investigations (amongst many other studies e.g. [141, 149, 87]), extension of the
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hysteresis
positive feedback
simple response
forcing
response
forcing
Meridional Overturning Circulation strength (Sv)
response
Climate Model (GCM)
20
Box Model
10
0
-10
0.4
0.0
0.4
0.8
freshwater forcing (Sv)
Figure 2.26: Some components of the climate system are likely to display hysteresis, which means
that if they are forced to move to a new state (for example by changing greenhouse gas concen­
trations), just reversing the greenhouse gas changes may not necessarily return the system to its
previous state. This can be visualised by imagining how a ball would travel along a valley. The sim­
ple response would be for you to push the ball, and it travel smoothly along the bottom of a straight
valley - here the ball would be moving away from its starting position at a rate purely dependent
on how fast it is pushed. However, a positive feedback could be imagined by visualising pushing
the ball down a curved valley - although the ball may being pushed at a constant rate, the valley
system forces the ball to move off its straight path, and for a time moves rapidly perpendicular to the
direction it started to move in. To visualise hysteresis, one could think about two adjacent valleys,
one that slowly finishes geographically soon after the other starts - the ball would be pushed down
one valley until that valley gets swallowed up by the adjacent valley, then rolls down into the second
valley. If the ball is then pushed in the opposite direction, it would travel up the valley that it is in,
rather than rolling up the side of that valley to follow its original path, and would only end back up
in the first valley once the second valley was swallowed up by the first [234]. It has been proposed
that the Meridional Overturning Circulation (MOC) would follow this behaviour - where the physical
landscape and gravity, are substituted for other conditions constraining how the system can change.
This MOC behaviour has recently been demonstrated in a GCM for the first time (black dots) [142],
and understood by using a box model (red line in lower part of figure) [110, 1].
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scope of the QUMP project over the last five years has resulted in a number of important findings.
Prior to the last MOHCCP the QUMP project had been examining how our lack of certainty about
how to represent physical processes in the atmosphere contributed to the spread of simulated pos­
sible future climates. Work was also beginning to examine the contribution to this spread in results
coming from our incomplete representation of sulphur-aerosol processes. Over the last MOHCCP,
the QUMP project completed its analysis of anthropogenic aerosol uncertainty [95], began to ex­
amine the consequences of imprecise representation of physical ocean processes [268, 148], and
extended the exploration of uncertain parameters to those in the land carbon cycle [166, 34]. Over
the last couple of years, all of these ideas have been brought together into a large set of experiments
which simultaneously explored the lack of certainty in all of these different components of the sys­
tem to identify where interactions between different processes might influence our understanding
[117].
A number of the important findings which came out of this work have been mentioned elsewhere
in this report, but to highlight a few: it was found that our incomplete understanding of land carbon
cycle processes contributed as much to our lack of certainty about the end of the century climate
as did uncertainty in cloud processes [34]; that given our uncertain understanding of earth system
feedbacks, it is possible that the planet could warm to four degrees above preindustrial temperatures
by the early 2060’s [27]; and that a doubling of atmospheric CO2 concentrations could lead a to 5 to
45% increase in land experiencing drought conditions globally [49]. Whilst the perturbed parameter
approach followed in the QUMP project allows us to explore the range of outcomes simulated by a
set of structurally different23 models (e.g. CMIP5), in a more coherent and therefore more tractable
way than we could by looking at those structurally different models, some variables show smaller
ranges in the QUMP simulations than across structurally different models. This behaviour is seen
when examining sea-level rise. Differences in the changing patterns of sea-level rise between the
two previous-generation (CMIP2 and CMIP3) models are approximately the same. This difference
relates largely to how the ocean circulation changes in different models [260]. However the QUMP
experiments simulate a smaller range of different patterns of sea-level rise than is seen in the multimodel studies.
Providing mitigation relevant advice
Sea-level rise has also been looked at in the context of climate mitigation. Through the European
ENSEMBLES project a new climate mitigation emission scenario was developed called ’E1’, with
the aim of stabilising global temperatures below two degrees Celsius24 [173]. It was found that
achieving this aim would require a 50% reduction in emissions (from 1990’s levels) by 2050, with
23 Models
that are developed largely independently from each other (i.e. at different international climate research centres)
differ in their structure - for example the equations used to represent a particular process or set of processes differ between
models. QUMP models are structurally identical - they use the same equations - but the parameter values in those equations
are varied.
24 The E1 scenario is similar to the newer RCP2.6 mitigation scenario used by IPCC.
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further emissions reduction (possibly even negative emissions) after 2050. Whilst there is consider­
able uncertainty about the contribution of land-ice to future sea-level rise [119], there is an important
sea-level rise component which results from warming of the ocean. Under the E1 mitigation sce­
nario temperature-driven sea-level rise would continue long after the atmosphere stopped warming,
because it takes a long time for mixing within the ocean to allow the cool deep-ocean waters to
experience the warmer surface conditions, and therefore expand. It has however been shown that
around 30% of the (temperature driven) sea-level rise occurring in response to a business as usual
scenario could be avoided (or even reversed [259]) if emissions were cut further by following the
E1 heavy mitigation scenario [261]. The simple climate models used to develop the E1 climate
mitigation scenario are based on a small set of equations which broadly describe the processes
occurring within more complex Earth System Models, and the real world. The parameters in these
simple climate models can be adjusted so that they replicate the global climate behaviour of most
individual Earth System Models, but more recently have been extended to represent the lack of cer­
tainty in these parameters, allowing the range of behaviors simulated across the different available
Earth System Models to be captured probabilistically.
The simple probabilistic models developed by the MOHC have been used by the Committee on
Climate Change to develop mitigation scenarios. The current generation of simple climate models
were developed to replicate the behaviour of the CMIP3 and C4 MIP (Coupled Climate Carbon Cycle
Model Intercomparison Project) models. A preliminary assessment of the behaviour of the latest
generation of Earth System Models suggests that the simple climate modelling tools we currently
use within the MOHC may not need updating to allow them to replicate CMIP5 results [129]. This
finding potentially reflects the fact that many CMIP5 models are essentially a bringing together of
C4 MIP model components with CMIP3 physical models, rather than representing a step-change in
our fundamental understanding.
The capacity in HadGEM2-ES, and increasingly in developmental versions of HadGEM3, to
simulate the behaviour of climatically important non-greenhouse gases and aerosols makes these
tools particularly valuable for answering more complex mitigation questions than those which might
be answered using a simple climate model. Studies have looked a the role of methane removal
within mitigation strategies [36], and the impact of clean air policy to limit black-carbon [35], oxides
of nitrogen and volatile organic compounds [43], and other non-Kyoto pollutants [41] as mitigation
options. The impacts of climate mitigation on temperature and precipitation [64], ocean acidification
[22] and agriculture [266, 113, 25] have also been explored. This information has been delivered to
UK Government customers as and when required [223]. Furthermore, the potential to use agricul­
tural planning as a mitigation tool has been examined, for example by reducing emissions through
deforestation. The complexity of climate-vegetation feedbacks mean that the long-term response to
(for example) deforestation policy may not be as simple as it initially seems [276].
The Earth System capacity developed within HadGEM2-ES over the last MOHCCP means that
we can provide advice about how the Earth System might respond if climate mitigation plans are
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unsuccessful or implemented too late, and the application of geoengineering is considered. Whilst
it could be argued that geoengineering is implicit within the idealised 1% per year CO2 rise then 1%
per year CO2 decrease experiments already discussed [38], a number or more realistic experimental
designs have been considered within the Geoengineering Model Intercomparison Project (GeoMIP)
[179]. These experiments consider the climate impacts of brightening (making more reflective)
marine stratocumulus clouds by spraying sea salt in the regions where these form [178, 182], and
the injection of sulphur dioxide into the stratosphere to form aerosols and directly reflect sunlight
[179, 180, 181]. Further idealised experiments have been undertaken which reduce the sunlight
coming into the planet, without specifying the mechanisms by which this process occurs [303, 183,
10]. Work has also looked at how geoengineering would have to be combined with reduced CO2
emissions to achieve long-term climate stabilisation [40].
In general, where cloud brightening or stratospheric aerosol geoengineering proposals have
been examined, it has been found that the global temperature can be prevented from rising, but that
regional temperatures, and consequently precipitation patterns (the climate change most likely to
impact people), are changed [303, 179].
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69
Chapter 3
Looking Forward
As demonstrated by the breadth and quality of the work highlighted here and published in many
of the best scientific journals, ’the Met Office Hadley Centre provides essential and world-leading
climate modelling services to Government’, and ’represents a critical national capability, with a cen­
tral role of meeting the Government’s requirements for climate evidence and advice’ (Professor Sir
John Beddington, 2010). Whilst much of the work which has been discussed in this report is now
complete, many studies are still ongoing, and will form the basis of what is delivered within the
new 2012-2015 MOHCCP. The 2012-2015 work-plan places increasing emphasis on making our
science usable within, and relevant to, DECC and Defra’s current requirements. Pushing forward
the work undertaken in the 2007-2012 period, a major focus will continue to be placed on provid­
ing information relevant to setting international climate targets and negotiations on the mitigation
of emissions. New work will examine the climate controls on performance of renewable energy,
investigate the regional consequences of climate variability, and understand how best to inform fu­
ture climate risk assessments and national adaptation planning. To aid adaptation planning, the
new MOHCCP contract places more emphasis on (i) monitoring of climate for local and regional
patterns of change and quantitative estimates of the attribution of extremes to human activity and
(ii) regional projections of the biophysical impacts of climate variability and change from near term
out to a few decades.
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70
Acknowledgments and Disclaimer
We thank the MOHC strategic heads and senior staff for sharing their thoughts on the direction
and achievements of the last MOHCCP, to individual scientists for contributing summaries of their
publications to the supplementary material, and to everyone who patiently answered my questions,
provided figures, or corrected my mistakes. We specifically thank Stephen Belcher, Richard Betts,
Mick Carter, Debbie Hemming, Chris Jones, Richard Jones, Carol McSweeney, John Mitchell, Erika
Palin, Mark Ringer, Peter Stott, Cath Senior and Richard Wood for providing detailed thoughts on
and proof reading sections of the report. While every effort has been made to cite published work
appropriately, it has not been possible to read individual papers in any detail. It is therefore inevitable
that there will be a small number of incorrectly cited publications, and for that we apologise.
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106
Chapter 4
Annex A: Milestones and
Deliverables
This annex contains a list of all milestones and deliverables provided to DECC and Defra during
the last MOHCCP. Descriptions of the deliverables are supplied in table B of the supplementary
materials.
Item
Authors
Title
Year
DOI
1
Arribas, A. et al.
Delivery of a new seasonal ensemble prediction system using a new model
2010
A5, Milestone 20
2011
D3.2.1c
derived from the HadGEM family
2
Best, M. et al.
Development , testing and evaluation of the UKCA and JULES models: Re­
lease of JULES v3.0 with driving datasets for historical and future simulations
including the IMOGEN pattern-scaling model
3
Betts, R. et al
RCM-scale glacier model implemented in JULES for Himalayas
2010
A13
4
Betts, R. et al.
Strategy for implementing impacts into HadGEM3 using JULES
2010
D3.2.1 e
5
Boucher, O. et al.
Climate change impacts of non-Kyoto pollutants
2008
M3
6
Boucher, O. et al.
Report on impacts on atmospheric chemistry and climate associated with
2009
D3.1
2012
M5, Milestone 31
large atmospheric releases of methane - Literature review on the climate risk
associated with large atmospheric releases of methane from clathrates and
permafrost.
7
Boucher, O. et al.
Report on the role of aerosols, methane, NOx and VOC emission controls for
climate mitigation policies
8
Boucher, O. et al.
Report on the Climate effects of black carbon
2011
D2.3.6
9
Brookshaw, A. et al.
Future global climatology ’atlas’ for ranges 10, 20 and 30 years
2009
P4, Milestone 12
10
Brown, S. et al.
Plan for coordination of activities on projections of climate extremes, including
2009
A9
specific recommendations for future deliverables in addition to A4.
11
Brown, S. et al.
Projections of extreme characteristics of temperature and precipitation
2011
A4
12
Brown, S. et al.
Report on projected changes in extra-tropical storms in transient runs, includ­
2011
D3.2.8
2011
D3.3.4b
2009
P7.1, Milestone 14
ing exploring mechanisms responsible for driving changes and their uncertain­
ties
13
Brown, S. et al.
Guidance on current developments for prediction of future regional temper­
ature and precipitation extremes with current modelling technology including
an assessment of possible methodologies for obtaining probabilistic predic­
tions to extend the advice provided through UKCP09 and inform options for
the provision of extremes in UKCPnext
14
Bryden, C. et al.
Deliverables listed in proposals for underspend in FY08/09 (delivery and in­
terim report by March 2009) (Final report by July 2009)
15
Bryden, C. et al.
Annual technical report
2009
C5
16
Burke, E. et al.
The potential importance of permafrost thawing as a climate feedback through
2011
D2.3.4,a,
emissions of CO2 and methane
17
Butchart, N. et al.
Influence of the stratosphere on climate projections with HadGEM2
2012
D3.2.2
18
Caesar, J. et al.
Regional benefits of mitigation policy for climate impacts
2012
D2.3.11 M18
19
Carroll, F. et al.
A comprehensive Met Office Hadley Centre website
ongoing C1
20
Carroll, F. et al.
A comprehensive Met Office Hadley Centre website
ongoing C1
21
Carroll, F. et al.
A comprehensive Met Office Hadley Centre website
ongoing C1
22
Carroll, F. et al.
Annual Technical Report 2009-2010
2010
c Crown Copyright 2013
©
107
D1.2.8
23
Carter, M. et al.
Development of a supercomputer strategy
2010
D3.4.2 b
24
Carter, M. et al.
Supercomputer Strategy
2010
D2.1.1 ,a,
25
Carter, M. et al.
JWCRP/MONSooN application support
2011
D3.4.3
26
Chris F. et al.
Annual temperature forecasts
2010
P2
27
Chris, F. et al.
Annual temperature forecast for 2009
2009
P2
28
Chris, G. et al.
Collaboration Strategy
2007
C2.3, Milestone 3
29
Chris, G. et al.
Science and general strategy - Annual April
2008
C2.1, Milestone 8
30
Chris, G. et al.
Science and general strategy - Annual April
2009
C2.1, Milestone 8
31
Chris, J. et al.
2009
D7, Milestone 34
32
Chris, J. et al.
2010
Q9.2, Milestone 15
2010
Q9.2, Milestone 15
2012
D7.2, Milestone 34
2010
E5, Milestone 19
Interim deliverable April 2010: Completion of HadGEM2-ES 1% CO2 and his­
torical simulations for CMIP5/AR5.
33
Chris, J. et al.
Full earth system climate projections with HadGEM2-ES following CMIP5 and
IPCC recommendations for AR5 - Completion of 2 RCP scenarion runs ro
2100.
34
Chris, J. et al.
Assessment of potential and risk of rapid or irreversible ecosystem response
to climate change - Final Report
35
Christidis, N. et al.
Report on attribution of regional extremes to anthropogenic influence including
impacts
36
Christidis, N. et al.
AM3-based attribution stystem for estimating attribution risk of extreme events
2011
D3.1.6 a
37
Christidis, N. et al.
Report on progress in attribution of regional hanges and extremes including
2011
D2.1.5
assessment and validation of rototype HadGAM3 based attribution modelling
system eliverable
38
Collins, B. et al.
Release and evaluation of HadGEM2-ES model
2008
Q1, Milestone 6
39
Collins, B. et al.
Full earth system climate projections with HadGEM2-ES following CMIP5 and
2009
Q9.1, Milestone 15
2012
M6, Milestone 32
2010
D2.3.5
2009
A3, Milestone 13
IPCC recommendations for AR5 - HadGEM2-ES model and experimental
setup frozen
40
Collins, B. et al.
Analysis of biogeochemical climate feedbacks and interactions in HadGEM2­
ES
41
Collins, B. et al.
The Climate impact due to pulse emissions of methane, Nox and VOC in the
context of air quality
42
Collins, M. et al.
Report surveying issues for consideration in the design of future UK Climate
Projections
43
Derrick, R. et al.
Report on the impact of climate change on Defence studies
2008
P3
44
Falloon, P. et al.
Report on implementing relevant physical and biological processes relevant to
2011
3.2.11d
permafrost thawing in the grid-point version of JULES, and on development of
hydrological processes including flooding ,D3.2.11d,
45
Fiona, S. et al.
A comprehensive Met Office Hadley Centre website will be maintained
2007
C1
46
Fiona, S. et al.
A comprehensive Met Office Hadley Centre website
2008
C1
47
Gohar, L. et al.
Report on the quantification of the carbon-climate feedback rapid response
2011
D2.3.1,b,
tools improved
48
Good, L. et al.
Report on development of sample climate information bulletins
2012
D2.1.1 c
49
Good, P. et al.
Progress report on traceability of simple models to GCMs
2011
D3.2.6
50
Gordon, C. et al.
Science and general strategy
2010
C2.1, Milestone 8
51
Gordon, C. et al.
Science and general strategy
2011
C2.1, Milestone 8
52
Gordon, C. et al.
Science and general strategy
2012
C2.1, Milestone 8
53
Graham, R. et al
Global decadal predictions of multi-year temperature averages up to 10-years
2010
P5, Milestone 17
ahead - Annual
54
Graham, R. et al
Global seasonal predictions up to 6-months ahead.
2011
P1
55
Graham, R. et al
Annual temperature forecasts
2011
P2
56
Helene, H. et al.
Evaluation of HadGEM3-OA
2008
Q2,
57
Hemming, D. et al.
Identification of dangerous thresholds in regional climate and other biophysical
2011
D2.4.3
2009
D8
quantities relating to key impacts
58
Hemming, D. et al.
Assess climate impacts in a high end (eg., 4 deg warming) simulation. Prelim­
inary HadCM3-based assessment March 2009 (subject to additional end-FY
ICP funding)
59
Hewitt, H. et al.
Polar Climate Update
2010
D2.2.4 ,a,
60
Hewitt, H. et al.
Polar Climate Update
2010
D3.2.1
61
Hewitt, H. et al.
Polar Climate Update
2011
D3.2.16
62
Hewitt, H. et al.
Annual update on the state of the Arctic sea ice, and report on improved quan­
2011
D2.2.4,a,
2012
D2.2.4,b,
tification of local feedback processes controlling keyArctic predictions. To in­
clude a short appendix detailing the status of theAntarctic Sea Ice.
63
Hewitt, H. et al.
Report on the Assessment of Possibility and Impact of Rapid Climate Change
in the Arctic
64
Hewitt, H. et al.
Assessment of potential for rapid loss of Arctic sea ice - Interim Report
2010
D6.1, Milestone 22
65
Hines, A. et al.
Progress report on ocean biogeochemistry development
2011
D3.2.13
66
John, V. et al.
Analysis of uppe-tropospheric humidity in sub-tropical descent regions using
2011
D3.1.7 a
2012
D3.2.11,b,
2009
M3.1, Milestone 11
observed and modelled radiances
67
Johnson, C. et al.
UKCA merged code implemented and tested ready to form part of version 8.2
of the Unified Model
68
Johnson, C. et al.
The first projections with HadGEM2-ES of climate change in 21st century with
interactive aerosol and chemistry
c Crown Copyright 2013
©
108
69
Johnson, C. et al.
Comparison between UKCA-MODE and CLASSIC aerosol schemes in
2010
M3.2, see comment
HadGEM3
70
Jones, A. et al.
The climate response to high-latitude injection of SO2 into the stratosphere
2010
D2.3.7
71
Jones, A. et al.
Assessment of benefits and dis-benefits of proposed solar radiation manage­
2011
D2.3.8
2009
M8.1
2010
M8.2
ment geoengineering schemes
72
Jones, A. et al.
Report on the climate response to a uniform injection rate of sulphur dioxide
into the stratosphere.
73
Jones, A. et al.
Report on the sensitivity of stratospheric aerosol forcing as a function of region
and height of SO2 injection.
74
Jones, C. et al.
Quantification of the carbon-climate feedback in HadGEM2-ES
2011
D2.3.1,a,
75
Jones, C. et al.
Completion of 1percent CO2 and RCP carbon-uncoupled simulations and ex­
2010
Q9.3, Milestone 15
2012
D2.2.6
2012
D2.3.3
2011
D3.3.1
2011
D3.1.5,b,
2011
A6, Milestone 27
2011
P9
tension of some RCP simulations to 2300
76
Jones, C. et al.
Assessment of potential and risk of rapid or irreversible response of major
ecosystems to climate change
77
Jones, C. et al.
Quantification and reduction of uncertainties on CO2 and non-CO2 biogeo­
chemical feedbacks, and the effects of interactions with physical feedbacks,
in the context of the CMIP5 multi-model and QUMP perturbed-parameter en­
sembles
78
Jones, C. et al.
Delivery of core CMIP5 climate experiments for IPCC AR5: decadal and cen­
tennial scenarios
79
Jones, G. et al.
Development of detection and attribution to a wider range of variables and
to regional scales: report on new attribution estimates using new HadGEM2
model simulations and from CMIP5 archive
80
Jones, R. et al
New projections of detailed regional climate change over the UK and Europe
and other key regions
81
Jones, R. et al
Updated regional climate change information system for use by developing
countries
82
Jones, R. et al
Report on evaluation of HadGEM3-RA against previous model HadRM3P
2011
D3.2.3
83
Keen, A. et al.
Assessment of potential for rapid loss of Arctic sea ice - Annual Update
2009
D6
84
Kendon, L. et al.
Report evaluating the climate simulation of extreme precipitation over South­
2011
D 3.2.4
ern UK in a convection permitting model ,1.5km, and comparison to lower
resolution models
85
Kennedy, J. et al.
Global and regional climate in 2010
2011
D2.1.1 ,b,
86
Kennedy, J. et al.
Indicators of Change for the three month period of October to December 2011.
2012
D2.1.1 ,a
87
Kennedy, J. et al.
Monitor climate in near-real time and provide information on unusual climate
2009
E1, May 09
2009
E1, August 09
2009
E1, November 09
2010
E1, February 2010
2010
E1, Annual
2010
E1, May 10
2010
E1, August 10
2010
E1, November 10
events
88
Kennedy, J. et al.
Monitor climate in near-real time and provide information on unusual climate
events
89
Kennedy, J. et al.
Monitor climate in near-real time and provide information on unusual climate
events
90
Kennedy, J. et al.
Monitor climate in near-real time and provide information on unusual climate
events
91
Kennedy, J. et al.
Monitor climate in near-real time and provide information on unusual climate
events. Global and regional climate in 2009
92
Kennedy, J. et al.
Monitor climate in near-real time and provide information on unusual climate
events
93
Kennedy, J. et al.
Monitor climate in near-real time and provide information on unusual climate
events
94
Kennedy, J. et al.
Monitor climate in near-real time and provide information on unusual climate
events
95
Kennedy, J. et al.
Indicators of Change
2010
D2.1.1 ,a,
96
Kennedy, J. et al.
Indicators of Change
2010
E1
97
Kennedy, J. et al.
Indicators of Change
2010
D2.1.1 ,a,
98
Kennedy, J. et al.
Monitor climate in near-real time and provide information on unusual climate
2011
E1, February 2011
2011
E1, Annual
2011
E1, May 11
2011
E1, August 11
2011
E1, November 11
2012
E1, February 2012
2012
E1, Annual
2007
E1, May 07
2007
E1, Aug 07
2007
E1, Nov 07
events
99
Kennedy, J. et al.
Monitor climate in near-real time and provide information on unusual climate
events. Global and regional climate in 2010
100
Kennedy, J. et al.
Monitor climate in near-real time and provide information on unusual climate
events
101
Kennedy, J. et al.
Monitor climate in near-real time and provide information on unusual climate
events
102
Kennedy, J. et al.
Monitor climate in near-real time and provide information on unusual climate
events
103
Kennedy, J. et al.
Monitor climate in near-real time and provide information on unusual climate
events
104
Kennedy, J. et al.
Monitor climate in near-real time and provide information on unusual climate
events. Global and regional climate in 2011
105
Kennedy, J. et al.
Monitoring climate in real-time and provide information on unusual climate
events
106
Kennedy, J. et al.
Monitor climate in near real-time and provide information on unusual climate
events
107
Kennedy, J. et al.
Monitor climate in near real-time and provide information on unusual climate
events
c Crown Copyright 2013
©
109
108
Kennedy, J. et al.
Annual report on the climate of the UK and Europe
2008
E1, Jan 08
109
Kennedy, J. et al.
Monitor climate in near-real-time and provide information on unusual climate
2008
E1, Feb 08
events
110
Kennedy, J. et al.
Global and regional climate in 2007
2008
E1
111
Kennedy, J. et al.
Monitoring climate in real-time and provide information on unusual climate
2008
E1, May 08
2008
E1, August 08
2008
E1, Nov 08
2009
E1, February 09
events
112
Kennedy, J. et al.
Monitoring climate in real-time and provide information on unusual climate
events
113
Kennedy, J. et al.
Monitoring climate in real-time and provide information on unusual climate
events
114
Kennedy, J. et al.
Monitoring climate in real-time and provide information on unusual climate
events
115
Kennedy, J. et al.
2009
E1, Annual
116
Kettleborough, J. et al.
Supporting Software for AR5 Data Use
2011
D3.4.10
117
Knight, J. et al.
Developing improved initialised systems for near term climate predictions out
2012
D3.3.5
2012
D2.2.2 ,M17,
to a decade ahead, including investigation of a seamless approach for sea­
sonal to decadal forecasts
118
Lowe, J. et al.
Assessment of the risk of rapid sea level rise, including refined estimates of
highest credible mean sea level rise and assessment on the potential irre­
versibility of sea level change
119
Lowe, J. et al.
Implementation plans for provision of mitigation advice
2007
M1.5, Milestone 2
120
Lowe, J. et al.
Report on climate change projections with MAGICC model using multi-gas
2008
M2, Milestone 5
mitigation profiles, accounting for non linear system behaviour and reduction
of non-CO2 species
121
Lowe, J. et al.
Ad hoc advice on mitigation options
2009
M1
122
Lowe, J. et al.
Report on risk of rapid or irreversible regional sea level rise - FINAL REPORT
2012
D5.2, Milestone 29
123
Martin, G. et al.
Progress on quantifying and reducing uncertainty in the large-scale response
2011
D3.2.15
of the water cycle
124
Morice, C. et al.
Indicators of Change for the three month period of April to
2011
D2.1.1 ,a,
125
Morice, C. et al.
Indicators of Change Feb 2011
2011
D2.1.1 ,a,
126
Morice, C. et al.
Indicators of Change May 2011
2011
D2.1.1 ,a,
127
Morice, C. et al.
Indicators of Change for the three month period of July to september 2011
2011
D2.1.1 ,a,
128
Morice, C. et al.
Development of homogenised surface temperature datasets for climate mon­
2011
D3.1.1,b,
itoring, attribution of climate variability and trends and for the improvement of
climate predictions: create ensembles of HadCRUT4, blending HadSST3 and
the latest version,s, of the land temperature record.
129
Murphy, J. et al.
Capability report for future Climate Projections
2011
D 3.3.8
130
Murphy, J. et al.
Production and delivery of data for the UK Climate Projections (UKCP09)
2009
A2, Milestone 10
131
Murphy, J. et al.
Report on options for future UK climate projections,
2010
A8, Milestone 18 SEE Comment
132
N Rayner, N. et al.
Improving quality of historical climate data for the sub-surface ocean: report on
2012
D3.1.3,a,
biases between surface and sub- surface observations and improved version
of ocean database and its objective analysis
133
Nicola, G. et al.
Preliminary representation of irrigation in HadRM3
2009
A12
134
O’ Connor, F. et al.
Report on impacts on atmospheric chemistry and climate associated with
2010
D3.2
2011
D3.2.12
2009
E4, Q
2011
D3.1.2 a
2011
D3.1.2,b,
2011
D3.1.4a
large atmospheric releases of methane - Analysis of the response of atmo­
spheric chemistry to CH4 pulse emissions.
135
O’Connor, F. et al.
Scoping of the HadGEM3-ES project including a selection of suitable Earth
System components and interactions
136
Palmer, M. et al.
Report on ocean heat content trends and ability for climate models to repro­
duce observed trends
137
Parker, D. et al.
Report on progress towards first version of homogenized Integrated Surface
Daily temperature and dew point data for 1973-2008
138
Parker, D. et al.
Extreme European heat waves analysed using in situ and satellite data and
the Climate of the 20th Century Reanalysis
139
Parker, D. et al.
Using the 20th Century Reanalysis and satellite data to detect inhomo­
geneities and to fill gaps in land surface air temperatures
140
Pete, F. et al.
Communications Plan
2007
C2.2, Milestone 1
141
Peter, T. et al.
Report on tropospheric trends in temperature and humidity, and their consis­
2008
E2
tency with model expectations
142
Pope et al
CoP brochure
2010
C4, Milestone 16
143
Pope et al
CoP brochure
2011
C4, Milestone 16
144
Pope et al
Annual headline report - June 08
2008
C4, June 08
145
Pope et al
2008
C4, Sep 08
146
Pope et al
Annual technical report
2008
C5
147
Pope et al
Quarterly ,for Defra, and monthly ,for MoD, highlight Reports
2007
C6
148
Pope et al
Integrated Climate Programme: Stakeholder engagement and Communica­
2008
C2.2, Milestone1 revised
tions Strategy 2007-2012
149
Pope et al
Ad hoc requests
2008
C3
150
Pope et al
Ad hoc requests
2009
C3
151
Pope et al
Quarterly ,for Defra, and monthly ,for MoD,highlight reports
2009
C6
152
Pope et al
Ad hoc requests
ongoing C3
c Crown Copyright 2013
©
110
153
Pope et al
Quarterly ,for DECC, Defra, and monthly highlight reports. The Annual Tech-
ongoing C6
nical Report will subsume the Jan-Mar quarterly report
154
Pope et al
Ad hoc requests
ongoing C3
155
Pope et al
Quarterly Report ,for DECC, Defra, and monthly highlight reports. The Annual
ongoing C6
Technical Report will subsume the Jan-Mar quarterly report
156
Pope et al
Ad hoc requests
ongoing C3
157
Pope et al
Quarterly ,for DECC, Defra, and monthly highlight reports. The Annual Tech-
ongoing C6
nical Report will subsume the Jan-Mar quarterly report
158
Pope, V. et al
CoP 15 report - ’SCIENCE: Driving our response to climate change’
2009
C4, Milestone 16
159
Pope, V. et al.
Annual Technical report
2010
C5
160
Pope, V. et al.
Annual Technical report
2011
C5
161
Rayner, N. et al.
Development of homoegised surface temperature data sets for climate moni­
2010
D3.1.1 a
2011
E6, Milestone 25
2008
M4
toring
162
Rayner, N. et al.
Key global and regional surface temperature analyses, with uncertainties, for
IPCC assessment reports
163
Richard, B. et al.
Risk assessment of the permanence of Reducing Emissions from Deforesta­
tion and Degradation (REDD) under climate change in Amazonia
164
Richard, B. et al.
Preliminary global scale crop model implemented in JULES
2009
A11
165
Richard, B. et al.
Global-scale projections of the impacts of various magnitudes of change in
2012
M7, Milestone 33
climate, atmospheric composition and land use on global water resources,
food supply and natural ecosystems
166
Richard, G. et al.
Global seasonal predictions up to 6 months ahead
2008
P1
167
Richard, G. et al.
Global seasonal predictions up to 6 months ahead
2009
P1
168
Richard, G. et al.
Global decadal predictions of multi-year temperature averages up to 10-years
2009
P5, Milestone 17
ahead - Annual
169
Richard, G. et al.
Global seasonal predictions up to 6-months ahead.
2010
P1
170
Richard, G. et al.
Global decadal predictions of multi-year temperature averages up to 10-years
2011
P5, Milestone 17
ahead - Annual
171
Richard, G. et al.
Climate change impacts ’atlas’ for ranges 10, 20 and 30 years,
2012
A7, Milestone 30
172
Richard, G. et al.
Annual temperature forecasts
2012
P2
173
Richard, G. et al.
Global seasonal predictions up to 6-months ahead. ,RG,
ongoing P1
174
Richard, J. et al.
Strategy for ’technology / capability’ transfer
2008
A1, Milestone 7
175
Richard, J. et al.
Recommendation on downscaling methods, post-processing methods to im­
2009
P8
2009
D2
prove regional climate change information for users
176
Richard, W. et al.
State of the Art report on the resilience of the climate system. (NOTE: This is
an updated Product as it incorporates Product D1)
177
Richard, W. et al.
Assessment of potential for rapid loss of Arctic sea ice - Annual Update
2011
D6.3
178
Ridley, J. et al.
Report on risk of rapid or irreversible regional sea level rise - INTERIM RE­
2010
D5.1 Milestone 29
PORT March 2010 to include rate estimates of glacier contribution, and re­
versibility in Greenland ice sheet.
179
Ringer, M. et al.
Reduction of uncertainty in cloud feedbacks
2011
Q5, Milestone 26
180
Ringer, M. et al.
Progress on work to quantify and reduce uncertainty in cloud feedbacks
2012
D3.2.14
181
Roberts, M. et al.
Delivery of high resolution HadGEM3-AO
2011
D3.2.1
182
Sanchez, C. et al.
HadGEM3 low resolution dynamical limitation and process based traceable
2011
D 3.2.5
solutions.
183
Saunders, R. et al.
Strategy for development of climate quality satellite datasets
2009
E3, Milestone 9
184
Scaife A. et al.
Final Report of the CAPTIVATE model development project
2011
D3.2.7
185
Scaife A. et al.
Predictions out to a decade ahead issued once per year on the web
2011
D3.3.6,b,, including D2.4.5,b,
186
Scaife, A. et al.
Evaluation of HadGEM3-AO.
2010
Q7, Milestone 21
187
Senior, C. et al.
Strategy for traceable science on a range of space and timescales
2008
Q4
188
Senior, S. et al.
Further deliverables for 2008 - 2012
2008
Q3, Milestone 4
189
Sexton, D. et al.
UKCP09: Spatially Coherent Scenarios
2010
D2.4.1 ,a,
190
Sexton, D. et al.
UKCP09: Spatially Coherent Scenarios
2010
D2.4.1 ,b,
191
Smith, D. et al.
Delivery of initialised decadal forecasts extended to beyond 2030 in associa­
2010
A10, Milestone 24
2011
D3.3.2
2011
D2.1.4 a
tion with IPCC AR5.
192
Smith, D. et al.
Methodologies for prediction of climate extremes: assessment of the pre­
dictability of moderate temperature and precipitation extremes out to a decade
ahead
193
Stott, P. et al.
Report on latest state of attribution science with early view of attribution sci­
ence to be reviewed in AR5 of IPCC
194
various authors
(1) UK Climate Projections gridded data download facility
2009
P7.2 part 1
195
various authors
(2) Potential for high-end climate changes (¿4C)
2009
P7.2 part 2
196
various authors
(3) High end climate change (¿4C): Sea level rise
2009
P7.2 part 3
197
various authors
(4) Short review of chemical and biological consequences of ocean acidifica­
2009
P7.2 part 4
tion
198
various authors
(5) Short review on results of acidification modeling study
2009
P7.2 part 5
199
various authors
(6) An assessment of pattern scaling for the AVOID Project
2009
P7.2 part 6
200
various authors
(7) The application of pattern-scaling to climate impacts: the importance of
2009
P7.2 part 7
model uncertainty
201
various authors
Assessment of potential for rapid loss of Arctic sea ice - Final Report
2012
D6.4
202
various authors
Quarterly Highlights Report, July to September 2011
2011
D1.2.2
203
various authors
Met Office Hadley Centre Annual Highlight Report 2010-2011
2011
D1.2.8
c Crown Copyright 2013
©
111
204
various authors
Interim report on assessment of changes in hazardous weather and key bio­
2011
D2.4.6
2012
D4
2012
D3.1.1,c,
2010
Q8, Milestone 23
2012
D2.1.2 a
physical impacts
205
Vellinga, M. et al.
Robust assessment of the risk of rapid or irreversible shutdown of the Atlantic
THC
206
Willett, K. et al.
Development of homogenised surface datasets: report on new surface humid­
ity estimates of the globe
207
Williams, K. et al
Report on mechanisms or processes that limit the capability of using a sin­
gle modelling system from 1-day through seasonal/decadal and centennial
timescales at a range of resolutions.
208
Wood, R. et al
Observations for early warning of changes to vulnerable components of the
climate system
209
Wood, R. et al.
State of the Art report on the resilience of the climate system
2010
D2, Annual update
210
Wood, R. et al.
Assessment of potential for rapid loss of Arctic sea ice - Annual Update
2010
D6.2
211
Wood, R. et al.
Progress in understanding dangerous climate change: Annual update on
2010
D2.2.1 ,a,
2010
D2.2.1,a,
2011
Q6, Milestone 28
baseline 2009 report on the resilience of the climate system
212
Wood, R. et al.
Progress in understanding dangerous climate change: Annual update on the
2010 report on the resilience of theclimate system
213
Wood, R. et al.
Quantify and reduce uncertainty in the large scale response of the hydrological
cycle to climate change
214
Wood, R. et al.
State of the Art report on the resilience of the climate system
2011
D2, Annual update
215
Wood, R. et al.
State of the Art report on the resilience of the climate system. PAPER OR
2011
D2, Papers
2011
D2.2.3
2012
D2.2.5
PAPERS FOR PEER-REVIEW
216
Wood, R. et al.
Assessment of the impacts of GHG stabilisation or overshoot on acceleration
of the global water cycle
217
Wood, R. et al.
Assessment of risk and consequences of rapid or irreversible shutdown of the
Atlantic MOC
c Crown Copyright 2013
©
112
Chapter 5
Annex B: Glossary of Acronyms
MOHC
Met Office Hadley Centre
MOHCCP
Met Office Hadley Centre Climate Programme
IPCC
Intergovernmental Panel on Climate Change
AR3
the 3rd Assessment Report on climate science undertaken by the IPCC
AR4
the 4th Assessment Report on climate science undertaken by the IPCC
CoP
Conference of the Parties - periodic meetings head by the United Nations Framework
Convention on Climate Change (UNFCCC)
DECC
Department of Energy and Climate Change
UKCIP
United Kingdom Climate Impacts Programme
UKCP
United Kingdom Climate Projections
UKCP09
United Kingdom Climate Projections released in 2009
CO2
Carbon dioxide
GCM
Global Circulation Model
ESM
Earth System Model
SCM
Simple Climate Model
IAM
Integrated Assessment model
HadCM3
3rd Hadley Centre Climate Model
HadCM3C
3rd Hadley Centre Climate Model with carbon cycle components
HadCM3LC
Lowe resolution 3rd Hadley Centre Climate Model with carbon cycle components
HadGEM1
1st Hadley Centre General Environmental model
HadGEM2
2nd Hadley Centre General Environmental model
HadGEM2-ES
2nd Hadley Centre General Environmental model with Earth System Components
HadGEM3
3rd Hadley Centre General Environmental model
HadGEM3-ES
3rd Hadley Centre General Environmental model with Earth System Components
CMIP
Coupled Model Intercomparison Project
c Crown Copyright 2013
©
113
MORPH3
MOdel for improved Regional Prediction - HadGEM3 project, part of HadGEM3 devel­
opment
CAPTIVATE
Climate Processes, Variability and Teleconnections project, part of HadGEM3 devel­
opment
INTEGRATE
ImproviNg model error, TEleconnections and predictability Globally and Regionally
Across Timescales project, part of HadGEM3 development
NCIC
National Climate Information Centre
ENSO
El Nino and the Southern Oscillation - an important component of natural climate vari­
ability driven from the Equatorial Pacific
SST
Sea Surface Temperature
CFMIP
Cloud Feedbacks Model Intercomparison project
NERC
Natural Environmental Research Council
ACE
the Attribution of Climate Events group
NAO
North Atlantic Oscillation - an important component of natural climate variability
NCAR
National Centre for Climate Research
QBO
Quasi Biennial Oscillation - an important component of natural climate variability
ECMWF
European Center for Medium Range Weather Forecasting
QUMP
Quantifying Uncertainty in Model Predictions - an internal MOHC project
ENSEMBLES
The name of a European funded project co-lead by the Met Office
EMBRACE
Earth system Model Bias Reduction and assessing Abrupt climate change, the name
of a European funded project co-lead by the Met Office
HadGEM3-RA
Regional version of the HadGEM3 model
UNFCCC
United Nations Framework Convention on Climate Change
CORDEX
COordinated Regional climate Downscaling Experiment
JULES
Joint UK Land Environment Simulator
UKCA
the UK Community Aerosol model
TRIFFID
Top-Down Representation of Interactive Foliage and Flora including Dynamics
HadOCC
Hadley centre Ocean carbon Cycle Model
Diat-HadOCC
Hadley centre Ocean carbon Cycle Model including the Diatom phytoplankton group
DMS
Dimethylsulphide
MONSooN
Met Office and Nerc Supercomputing (oo) Nodes
CFC
Chloro Fluoro Carbons
RCP
Representative Concentration Pathway - the new set of climate forcing scenarios
AMOC
Atlantic Meridional Overturning Circulation
MOC
Meridional Overturning Circulation
4
C MIP
Coupled Climate Carbon Cycle Model Intercomparison Project
PRACE
the Partnership for Advanced Supercomputing in Europe
c Crown Copyright 2013
©
114
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