Climate models: What they show us and how they can be used in

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FUTURE
CLIMATE
FOR
AFRICA
GUIDE
December 2016
Climate models: What they show us and how they can
be used in planning
Weather and climate: what’s the difference?
Weather is the state of the atmosphere at a particular time, for example in terms of temperature, rainfall, wind speed and
humidity. It is what we experience each day.
“Chichiri at 8 am on 29 September: 22°C, relative humidity 64%, light easterly winds.”
Climate can be thought of as the average weather over a long period of time. For example, at a given location the climate can
be characterised by the average temperature and rainfall, or the average number of very hot days in the dry season, taken from
many years of data.
“Malawi has a subtropical climate, which is relatively dry and strongly seasonal. The warm-wet season stretches from November
to April, during which 95% of the annual precipitation takes place.”1
Some people say “climate is what you expect, weather is what you get.”
What are global climate
models?
The climate conditions that we
experience are the result of complex
interactions between processes
occurring in the atmosphere and in
the oceans. These processes operate
at global and local scales and are
influenced by other factors, including
the land surface, polar ice sheets and
the sun. This is why different parts
of the world experience different
climates. Global Climate Models
(GCMs) are computer models that
attempt to capture and simulate all
these processes, based on our current
knowledge (Figure 1).
About FCFA
Future Climate for Africa (FCFA) aims to
generate fundamentally new climate science
focused on Africa, and to ensure that this
science has an impact on human development
across the continent.
www.futureclimateafrica.org
Figure 1: Atmosphere, ocean and land surface processes simulated in GCMs
(source: US National Climate Assessment 2014)2
How do GCMs work?
Global Climate Models are run on
supercomputers at a number of
centres around the world, including
the Max Planck Institute in Germany,
the UK Met Office Hadley Centre,
and the National Oceanic and
Atmospheric Administration in the
USA. The models use physical laws and
mathematical equations that reflect
our understanding of atmospheric and
oceanic processes.
How do we know if GCMs are
reliable?
To test how well GCMs capture climate
processes, they can be validated in
various ways. This might be done by
testing based on what we know about
past climate patterns, for example
from recorded observations on
temperature and rainfall conditions.
If a model can accurately predict
historical climate trends (e.g. trends
in temperature and rainfall), then
we will have more confidence that
the model can accurately represent
relevant atmospheric and oceanic
processes. We can also test how
well the model simulates key largescale weather patterns. If models are
able to do this reliably, we will have
further confidence that they can
be used to predict, with reasonable
certainty, what climates are possible
in the future. The poor availability
of historical weather observations
in some parts of Africa limits our
understanding of how reliable these
models are.
How do GCMs project future
climate?
No predictions of the future can be
made with 100% certainty. We know
that the amounts of greenhouse
gases in the atmosphere affect the
Africa’s climate: Helping decision-makers make sense of climate information
More information on what we know, and still need to know, about central and
southern Africa’s climate can be found in the report Africa’s climate.3 The report
includes the following relevant sections:
ŸŸSouthern Africa: Tools for observing and modelling climate – an overview of the
various data sources we use to understand climate, and how what we want to
know determines which data source to use.
ŸŸCentral Africa’s climate system – an overview of why the Congo Basin plays such a
key role in affecting the climate of central and southern Africa.
ŸŸSouthern Africa: Studying variability and future change – what we know and what
we still need to find out about the current climate system of southern Africa.
ŸŸCentral and Southern Africa: Burning questions for climate science – what scientists
need to know to better understand the climate of central and southern Africa to
inform modelling of future climate conditions.
climate, and that human activities
are contributing to increases in
these greenhouse gases. To predict
the future climate, we need to have
an idea of what the future levels
of atmospheric greenhouse gases
are likely to be. If, for example, we
continue on a carbon-intensive
economic growth path burning high
quantities of fossil fuels, there will be
higher concentrations of greenhouse
gases in the atmosphere. In that
case, the effect on climate is likely to
be greater than if society decides to
reduce emissions (for example through
the United Nations Framework
Convention on Climate Change).
To take these possible futures into
account, scenarios – or plausible socioeconomic futures – are used. Models
are typically run under different
scenarios to give a range of potential
future climate conditions.
As well as the uncertainty in human
activities, our understanding of the
smaller-scale processes that affect
climate is incomplete. For example,
although the Congo Basin plays a key
role in determining the climate system
of central and southern Africa, the
2
lack of data means we have limited
understanding about the processes
involved. There are also likely to be
thresholds and tipping points in the
system. For example, warmer air holds
more moisture so, as temperatures
increase, more water vapour will be
held in the atmosphere. Water vapour
is itself a greenhouse gas, and so more
moisture in the atmosphere may,
in turn, exacerbate the increase in
temperature.
What can GCMs tell us about
the future climate?
GCMs model processes and
interactions at global scale. As this is a
complex task, the models are built to
emphasise particular trends (averages
of weather over longer-term time
periods). Examples of what a climate
model can feasibly tell us include
that spring temperatures are likely to
increase (and approximately by how
much); or that rainfall in the early
summer is likely to decrease.
Because they work at global scale,
the resolution of GCM projections
is typically coarse. As computing
How are climate projections different from weather forecasts?
The models that predict both weather and climate are similar in structure. But the
predictions made using weather models are always being tested: for example, in the
case of a 5-day forecast, we can see if the prediction was correct within those 5 days. In
contrast, climate models predict a future we have yet to experience, so we test GCMs
differently.
We talk about projections of future climate (as opposed to predictions of weather).
Projections give us a description of the future in the long term, and are dependent on
the future evolution of a number of atmospheric processes.
When we look at the short term, we can be more certain and we can predict weather
based on what we know for sure today. For example, if there is a high-pressure
anticyclone, that particular atmospheric condition is likely to give rise to clear, sunny
weather tomorrow – so we can predict clear, sunny weather.
power has increased, the detail and
resolution has improved over time.
In the 1970s the first GCMs had grid
cells of 700 × 500 km, whereas today
that has improved to 100 × 100 km
(Figure 2).
Figure 2: Improved resolution of
GCMs from the First (1992) to Fourth
(2007) Assessment Reports of the
Intergovernmental Panel on Climate
Change, reflecting the greater layer
of complexity that can now be
modelled (source: IPCC, 2007)4
Another key consideration with regard
to GCMs is that temperature is easier
to project than rainfall. There are
various reasons for this. Systems that
affect rainfall are more localised than
those which affect temperature (e.g.
presence of mountains and their role
in influencing cloud cover; major forest
basins such as the Congo). Models are
unable to skilfully model the El Niño
Southern Oscillation phenomenon,
which is also a major driver of rainfall in
southern Africa.
Projections of future climate have
many uses for planning over medium
to long time frames (e.g. 5–40 years).
Infrastructural interventions have a
long lifespan and therefore require
consideration of both current and future
conditions. When designing an irrigation
scheme, for example, it is useful to know
what rainfall might be in 40 years’ time
to inform design, manage operations,
and ensure the intended benefits will be
sustainable. The location of the dam may
need to change, and its design could
be less costly if it will need to hold less
water.
❝All models are wrong,
but some are useful ❞
– British mathematician George Box6
The projections generated by GCMs can
be displayed as a map, or as a graph
(see the box on evaluating outputs of
GCMs).
How can we use GCMs for
planning?
When we plan for the future, we
take a variety of information into
account. This might include population
projections, for example, or anticipated
demand based on economic growth
trajectories. In order to take the impact
of climate change into account, we
first need to understand what aspects
of climate might affect planning
decisions. With that understanding we
can move to the second stage, which is
to seek the relevant information in the
projections.
AR5: 70 km maximum horizontal
resolution; up to 90 layers in the
atmosphere and over 60 in the
ocean.
3
If the onset of the rainy season is going
to continue to be later than in the past,
a 5-year agricultural plan may wish to
consider a strategic move to promote
switching to early maturing varieties or
crops that can withstand lower water
availability. Our understanding of
current weather patterns can be used to
contextualise future projections. Current
weather patterns will vary throughout
a country. If a high rainfall area were
projected to become drier, the crops
that could grow successfully would
be different from those that could be
sustained in a low-rainfall area if the
region were projected to experience a
later onset to the rainy season.
Evaluating outputs of GCMs
If someone tells you that the global temperature will increase by 4°C by 2080, two questions that you should definitely ask are:
ŸŸaccording to which model?
ŸŸaccording to which scenario?
Figure 3 shows the modelled change in global temperature based on two scenarios for Representative Concentration Pathways
(RCPs) for greenhouse gas concentrations, as reported in the most recent assessment report by the IPCC.5 RCP8.5 is a scenario where
greenhouse gas emissions continue to rise rapidly, whereas RCP2.6 is a scenario where active management causes emissions to level off.
As no model can be perfect, more
confidence is added if projections
from several models under the same
scenarios can be used (known as an
ensemble of models). In Figure 3, 39
models were run under the RCP8.5
scenario and 32 under the RCP2.6
scenario. The differences in what each
model projects for the future under
each scenario are represented by the
pink and blue areas, respectively. For
the RCP8.5 scenario, for example, the
temperature increase by 2100 is likely
to be 2.6–4.8°C (this is the range of
temperature increase projected by the
39 models). Having such an “envelope”
of projected change is much more
robust than a single figure.
Medium-term planning can take into
account the effect of likely future
conditions based on projections, in
conjunction with current knowledge
of weather conditions and how they
vary from place to place. This enables
identification of robust strategies
for what crops to promote and what
farming techniques to investigate.
The specifics of decisions – in terms
of exactly what to plant and when in
different places – can be part of shortterm planning (e.g. annual), and can
be informed by weather information
such as seasonal forecasts. However,
knowing the likely longer-term
situation can enable adaptive planning
to meet national strategic goals, such as
long-term food security and livelihood
resilience. Longer-term decisions
that may result from such planning
Figure 3: Global average surface temperature change (source: IPCC, 2014)7
include training staff in the use of
early maturing crops and the practices
required to farm them optimally,
informing seed-breeding activities and
technology development, and setting
supply chains in place.
The nature of planning decisions that
we make over the medium term is
different from those that we make in
the short term. So what we require
from climate projections is different
from what we need from shorter-term
weather predictions. The resolution
provided by GCMs is useful to inform
medium- to long-term planning
decisions. Over these time frames, for
example, whether the temperature will
increase by 2.1°C or 2.2°C is not usually
as important as knowing that there will
be an increase in temperature.
4
Decision-making in Malawi and
Tanzania
For more information on the actual
and potential uses of weather and
climate information in Malawi and
Tanzania – how weather and climate
information is currently used in
public sector decision-making in
each country, and how to improve its
use – see Africa’s climate.8
Why high resolution doesn’t necessarily give better climate projections
A common myth is that high-resolution or
“downscaled” climate projections are better Figure 4: The cascade of uncertainty in projecting future climate
than the coarser projections from GCMs.
(source: RiskChange)9
As noted above, expectations of climate
projections cannot be the same as for
weather forecasts. Climate projections take
into account large-scale processes that affect
Uncertainty in
weather systems. Because they are projecting
future society
far into the future, they are driven by scenarios
(of greenhouse gas emissions) that are never
Uncertainty in climate
forcing scenarios
likely to represent exactly what will unfold
in the future. As with any model, there is an
Uncertainty in Global
element of uncertainty.
Climate Models
Although there are various methods of
downscaling, they are all contingent on
starting with outputs from GCMs. When model
data are further manipulated in another
modelling process, there is a risk that the
uncertainty can increase (see Figure 4). The
outputs of downscaled models appear to be
more precise as they show differences on a
smaller scale – but that can give a false level of
confidence. The likely future states projected
by GCMs are typically sufficient to inform the
kinds of planning decisions that are made over
the same medium to long time frames.
Uncertainty in Regional
Climate Models
Uncertainty in statistical
downscaling
Uncertainty in impact
modelling
Uncertainty in
adaptation response
For more information see the section Southern Africa: Climate science and refining the models in the Africa’s climate report.10 This
section describes the three main tiers of modelling (global, downscaled and impacts models), where they work, and where the
gaps are.
5
Further information on GCMs and generating climate projections
ŸŸEducational Global Climate Modeling: http://edgcm.columbia.edu/
This site, targeted at students, provides access to a GCM and enables the user to undertake model experiments and manage
aspects of working with a GCM.
This site provides easily accessible information on climate change, how we know about it, and how GCMs work and can be
evaluated (www.climatechangeinaustralia.gov.au/en/climate-campus/modelling-and-projections/).
ŸŸClimate Systems Analysis Group (CSAG) e-learning: www.csag.uct.ac.za/elearning
This series of modules provides an introduction to climate science and its use. It includes modules on climate models, future
projections and downscaling.
Endnotes
1 Ministry of Natural Resources, Energy and
Environment: Climate of Malawi. www.
metmalawi.com/climate/climate.php
2 National Climate Assessment: Full report.
http://nca2014.globalchange.gov/report
3 FCFA (2016) Africa’s climate: Helping
decisionmakers make sense of climate
information. Cape Town: Future Climate for
Africa. www.futureclimateafrica.org/wpcontent/uploads/2016/11/africas-climatefinal-report-4nov16.pdf
4 IPCC (2007) Climate Change 2007: The Physical
Science Basis. Contribution of Working Group
I to the Fourth Assessment Report of the
Intergovernmental Panel on Climate Change
[Solomon, S., Qin, D., Manning, M., Chen,
Z., Marquis, M., Averyt, K.B., Tignor, M. and
Miller, H.L., eds]. Cambridge, UK: Cambridge
University Press. www.ipcc.ch/publications_
and_data/ar4/wg1/en/contents.html
5 IPCC (2014) Climate Change 2014: Synthesis
Report. Summary for Policymakers. www.ipcc.
ch/pdf/assessment-report/ar5/syr/AR5_SYR_
FINAL_SPM.pdf
6 Box, G.E.P. and Draper, N.R. (1987) Empirical
model-building and response surfaces. Oxford:
Wiley, p. 424.
7 IPCC (2014) Op. cit.
8 FCFA (2016) Op. cit.
9 RiskChange: Work Package 2: Downscaling
and uncertainty estimation. www.riskchange.
dhigroup.com/activities/wp2_downscaling_
and_uncertainty_estimation.html
10 FCFA (2016) Op. cit.
About Future Climate for Africa
Future Climate for Africa (FCFA) aims to generate fundamentally new climate science focused on Africa, and to ensure that this science has an
impact on human development across the continent. Members of the UMFULA and FRACTAL research teams, covering central and southern Africa,
contributed jointly to writing this guide. You can find out more about their work under ‘research teams’ on www.futureclimateafrica.org
Funded by:
FUTURE
CLIMATE
FOR
AFRICA
www.futureclimateafrica.org
e: [email protected]
t: +2721 4470211
This document is an output from a project funded by the UK Department for International Development (DFID) and the Natural Environment Research Council (NERC) for the benefit of
developing countries and the advance of scientific research. However, the views expressed and information contained in it are not necessarily those of, or endorsed by DFID or NERC, which
can accept no responsibility for such views or information or for any reliance placed on them. This publication has been prepared for general guidance on matters of interest only, and does not
constitute professional advice. You should not act upon the information contained in this publication without obtaining specific professional advice. No representation or warranty (express or
implied) is given as to the accuracy or completeness of the information contained in this publication, and, to the extent permitted by law, the Climate and Development Knowledge Network’s
members, the UK Department for International Development (‘DFID’), the Natural Environment Research Council (‘NERC’), their advisors and the authors and distributors of this publication do
not accept or assume any liability, responsibility or duty of care for any consequences of you or anyone else acting, or refraining to act, in reliance on the information contained in this publication
or for any decision based on it.
Copyright © 2016, Climate and Development Knowledge Network. All rights reserved.
Front cover photo: © GTS Productions / shutterstock.com | Editing, design and layout: Green Ink (www.greenink.co.uk)
ŸŸClimate Change in Australia: www.climatechangeinaustralia.gov.au/en/

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