Volume 3 Part 4 Climate Change Scenarios MetOffice

Volume 3 Part 4 Climate Change Scenarios MetOffice - 1 – © Crown copyright 2012

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Volume 3, Part 4 Climate Change Scenarios May 2012

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ENVIRONMENTAL AND SOCIAL IMPACT ASSESSMENT REPORT STRUCTURE Section Report Title Volume 1 Main Environmental and Social Impact Assessment (ESIA) Report Volume 2 Legal and Administrative Framework Volume 3 Physical Environment:

Baseline Conditions (Supplement to Phase 1 Baseline Only) and Impact Assessments

Volume 3, Part 1.1 Geo-Mapping Volume 3, Part 1.2 Geology, Soils and Land Use Volume 3, Part 1.3 Assessment of No Way Camp Disaster and General Considerations for Nimba

Western Area Iron Ore Concentrator Mining Project Volume 3, Part 1.4 Review of Slope Stability and Drainage Conditions During Phase 1 Operations at the

Mine Sites Volume 3, Part 1.5 Review of Slope Stability and Erosion Along the Railway Volume 3, Part 2 Groundwater Baseline and Impact Assessment Volume 3, Part 3.1 Hydrology Baseline and Impact Assessment Volume 3, Part 3.2 Geochemistry and Water Quality (Focusing on The Potential for Acid

Rock Drainage (ARD) Formation Arising From Ore, Tailings and Waste Rock Materials)

Volume 3, Part 4 Climate Change Scenarios by Met Office (UK) Volume 3, Part 5 Air Quality Impact Assessment Volume 3, Part 6 Noise and Vibration Impact Assessment Volume 3, Part 7 Landscape Character and Visual Amenity Impact Assessment Volume 4 Biological Environment:

Baseline Conditions (Supplement to Phase 1 Baseline Only) and Ecological Impact Assessment

Volume 4, Part 1.1 Forest Botanical Impact Assessment Volume 4, Part 1.2 Grassland Botanical Impact Assessment Volume 4, Part 2 Zoological Impact Assessment, Terrestrial and Coastal and Marine Volume 4, Part 3 An assessment of freshwater fish and crustacean consumption in Northern Nimba,

Liberia Volume 4, Part 4 Bushmeat and Biomonitoring Studies in the Northern Nimba Conservation Area by

Conservation International Volume 5 Socio-economic Environment:

Baseline Conditions (Supplement to Phase 1 Baseline Only) and Social Impact Assessment

Volume 5, Part 1 Socio-Economic Baseline for Buchanan, Greenhill Quarry and Areas in Nimba that will be Affected by TMF Operations.

Volume 5, Part 2 Social Impact Assessment, and Framework Social Management Plan Volume 5, Part 3 Cultural Heritage Assessment ARCELORMITTAL LIBERIA LTD ENVIRONMENTAL AND SOCIAL MANAGEMENT PLANNING DOCUMENTATION STRUCTURE Volume 6 Environmental Management Planning Volume 6, Part 1 Framework Resettlement Action Plan for Phase 2 Volume 6, Part 2 ArcelorMittal Liberia Environmental Standards Manual Volume 6, Part 3.1 Overall Environmental Management Plan for Phase 2 Volume 6, Part 3.2 Environmental Management Plan: Construction Works near Mount Tokadeh Volume 6, Part 3.3 Environmental Management Plan: Operation of Quarries Volume 6, Part 3.4 Environmental Management Plan: Rehabilitation of Facilities at the Port of BuchananVolume 6, Part 3.5 Environmental Management Plan: Operation of the Buchanan-Tokadeh Railway Volume 6, Part 3.6 Environmental Management Plan: Operation of the Port of Buchanan, including

Offshore Transhipment Volume 6, Part 3.7 Hazardous Materials and Waste Management Plan for Phase 2 Volume 6, Part 3.8 Townships Management Plan Volume 7 Framework of the Proposed Mine and Infrastructure Closure Plan for Phase 2 Volume 8 Framework of the Proposed Environmental Offset Programme for Phase 2 SUPPLEMENTARY INFORMATION TO PHASE 2 ENVIRONMENTAL AND SOCIAL IMPACT ASSESSMENT REPORT Volume 9 Assessment of Legacy Environmental Issues in the Former LAMCO Mines and

Industrial Areas

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Contents Executive summary ...........................................................................................................5 The Current Status of Climate Projections over West Africa...............................................8

1 Introduction 2 The west African climate 3 Overview of climate projections 4 Conclusion

Analysis of extreme precipitation over the Nimba region and its surrounds.....................40 1 Introduction 2 Analysis of extreme events 3 Results 4 Conclusion 5 Recommendations for further work

Appendix ......................................................................................................................... 60 Acronyms and glossary ...................................................................................................61 References.......................................................................................................................71 Legal disclaimer: The Met Office aims to ensure that the content of this document is accurate and consistent with its best current scientific understanding. However, the science which underlies meteorological forecasts and climate projections is constantly evolving. Therefore, any element of the content of this document which involves a forecast or a prediction should be regarded as our best possible guidance, but should not be relied upon as if it were a statement of fact. Use of the content of this document is entirely at the reader’s own risk. The Met Office makes no warranty, representation or guarantee that the content of this document is error free or fit for your intended use and before taking action based on the content of this document, the reader should evaluate it thoroughly in the context of his/her specific requirements and intended applications.

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Executive summary Summary of previous study: Aspects of climate for iron ore extraction in northern Nimba, Liberia A previous study, ArcelorMittal (2010), has analysed the limited available data to assess the climate of Northern Nimba, Liberia, where iron ore mining activity is proposed. The study addresses, as far as is possible at this stage, the following specific statistical requirements: (a) guideline 30-year averages, maxima and minima of monthly and annual rainfall and temperature (b) guideline rainfall intensities (in mm/hr and mm/day) corresponding to average return periods of 1-year, 10-years, 50-years and 100-years (c) frequency of gusts exceeding 17 m/s at 10m and 15m above the ground and the likely duration of these episodes. The statistics are required for proposed mining operations on Mount Tokadeh and the twin peaks of Mounts Gangra/Yuelliton and for the rail loading site at the foot of Mt Tokadeh. Items (b) and (c) are also required for the port of Buchanan.

For Tokadeh rail loading site, monthly and annual averages, maxima and minima have been derived for rainfall (standard error ± 10%), daily maximum temperature (standard error ±1ºC) and daily minimum temperature (standard error ± 1ºC). Daily maximum and minimum temperatures at the ridge-top sites are estimated to be, respectively, 4ºC and 2ºC lower than those for Tokadeh rail loading site.

Estimates of daily rainfall amounts corresponding to 1, 2, 10, 50 and 100-year return periods have been derived for the Tokadeh rail loading site, with a standard error of ±20%.

At Tokadeh rail-loading site and Buchanan, gusts exceeding 17 m/s seem likely to be confined to short-lived line squalls lasting, usually, no more than an hour.

For mountain top sites, the 17 m/s threshold may be exceeded for longer durations, especially in the dry season and mid wet season. However, it is as yet unclear whether Mounts Tokadeh, Gangra and Yuelliton are high enough for these longer-duration episodes to occur.

There is insufficient evidence to derive reliable rainfall statistics for the ridge top sites, though annual average rainfall seems likely to be between 2256 and 2728 mm.

As a ‘safe’ design/planning guide, daily rainfall amounts derived for Tokadeh rail loading site for different return periods could be doubled for the ridge top sites and also Buchanan. For extreme hourly rainfall amounts, even for the rail loading site, there is nothing to better the values adopted by ArcelorMittal (2010) based on a Nigerian study (Oyebande (1982)).

It is recommended that consideration is given to installing more weather stations/rain gauges and that high priority is given to archiving records of good quality. At present there is no monitoring at on the 1,000m a.m.s.l. Tokadeh, Gangra or Yuellition peaks. The Met Office could provide advice and procurement support for optimum observation equipment and siting.

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The current status of climate projections over west Africa This note summarises the current status of climate projections for precipitation in the region surrounding the Nimba Mountains in Liberia over the course of the twenty-first century. The amount and distribution of precipitation is governed by the West Africa Monsoon (WAM) system and a robust simulation of this system is essential if we are to have confidence in future projections of precipitation in this region. It is, however, extremely challenging to model the variability of precipitation since there are many competing processes, which interact in a complex way. The report focuses on three sets of climate projections:

• The projections produced by phase 3 of the Coupled Model Intercomparison Projection (CMIP3) for the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. This is a multi-model ensemble of coupled global climate projections, contributed by many different climate modelling centres.

• Projections from the Quantifying Uncertainty in Model Predictions (QUMP) ensemble. This is a perturbed-physics ensemble of projections based on the Met Office Hadley Centre Coupled Model Version 3 (HadCM3) global climate model.

• Projections from the Ensembles-based Predictions of Climate Changes and Their Impacts (ENSEMBLES) and African Monsoon Multidisciplinary Analyses (AMMA) projects, created from a variety of regional climate models, driven by one of two global climate models.

The report shows that, although much progress has been made in modelling the West African climate, many of the major climate models fail to capture the key features of the WAM over the course of the twentieth century. Moreover, a successful simulation of the twentieth century behaviour of the WAM may not necessarily imply a more reliable projection for the twenty-first century, since there are indications that the dominating processes will change as the global climate changes. The available projections also differ significantly from each other. We therefore conclude that the change in precipitation in the Nimba Range over the course of this century is uncertain. Given these limitations, it is still possible to draw some conclusions from the CMIP3 projections. For example, Biasutti and Sobel (2009) found that, despite the huge differences in the seasonal rainfall, analysing the projections on a month-by-month basis showed that there was an robust agreement that the rainy season in the Sahel would be shifted to later in the year. Also that most of the ENSEMBLES-AMMA runs do show a dry bias over Liberia and the Nimba Mountains, with respect to the GPCC observation.

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Analysis of extreme precipitation over the Nimba region and its surrounds There is now overwhelming evidence to support the relationship between climate change and anthropogenic activity. Observational evidence continues to accumulate which links human activity to a wide range of indicators of a changing climate, both at the regional and global scale. This report describes the methodology that the Met Office has used to analyse extreme precipitation for the Nimba mountain region and its surrounds. It also describes the assumptions and uncertainties associated with this study and how they might affect the conclusions which are drawn herein. The main findings of this report are: The many different sources of uncertainty associated with this analysis do not allow a robust

estimate of the return levels associated with extreme precipitation events for 2021 to 2050. Sources of uncertainty include, but are not limited to: - emissions uncertainty - uncertainty in the future evolution of greenhouse gas emissions, - modelling uncertainty - uncertainty arising from (a) the differences in formulation between

climate models from different modelling centres and (b) the extent to which the extreme events derived from our statistical model and the regional climate model data represent the observed extreme precipitation events for this region,

- sampling uncertainty – that is the uncertainty associated with observed extreme rainfall events in this region.

Our extreme value analysis uses data from two different regional climate models or RCMs for

the Nimba mountain area and its surrounds. Our analysis suggests that return levels are drier for the period 2021-2050 compared to 1961-1990 in the region north of ~7.5°N which covers the Nimba mountain range. However, the region under investigation is comparitively small, 350km × 350km, and it is possible that this perceived drying in the north of the region is just due to natural variability. Even if this were not the case, a further crucial caveat is that these two RCMs were driven by the same global circulation model or GCM, and as such, we cannot rule out the possibility that the return levels derived from using different GCMs to drive these RCMs – or use of different RCMs – could result in return periods which are wetter in the future.

The main conclusion of the study is that at present scientists do not have any way to assess

whether extreme events in the regions are likely to become more or rather less intense in the future. This is not an artefact of the way the data is analysed but it rather reflects the current limited understanding of the climate of the region. It may be possible to develop a robust methodology to for estimating the value of the ‘pessimistic’ case i.e. over 600mm for a 1 in 100 year 24 hour event - but this would require further work (e.g. only very few models were available for this work).

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The current status of climate projections over west Africa Authors: Karina Williams, Carlo Buontempo, Kate Brown Reviewed: Wilfran Moufouma-Okia

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1. Introduction

Climate models are essential tools for climate adaptation and impact studies. Simulations of the Earth’s climate are extremely sophisticated and used in many regions of the globe to ensure that current climate variability and anticipated future climate changes are taken into consideration in strategic planning activities. However, climate projections are also subject to a wide number of inherent limitations which arise from the lack of understanding of the future emissions of greenhouse gases, uncertainty in the description of key physical processes that occur at the scales smaller than the model spatial resolution and the mechanisms responsible for the interannual and decadal variations of the climate. The limitations of general circulation models (GCMs) are particularly striking in West Africa where the current models poorly capture the key features of the present-day climate, notably its associated precipitation pattern and temporal variability. These limitations will necessarily have an impact on the suitability and reliability of the future climate projections for the Nimba Range. This report examines the current status of climate model projections over West Africa, particularly in the region of the Fouta Djallon Massif and the Nimba Mountains. Section 2 summarises the main characteristics of the West African climate, which are expected to be captured by a climate model, including the monsoon dynamics and the spatial and temporal distribution of precipitation. Section 3 provides an overview of the available climate projections for West Africa with a discussion on their general features and limitations. We will focus on three different collections of climate model projections and examine their behaviour in the region around Liberia. With this in mind, the report concludes with a discussion of the precipitation changes which the Nimba Range may experience over the course of this century.

2. The west African climate

Figure 1: Mean annual precipitation for the period 1901-2009 using the CRU observational data (Mitchell and Jones, 2005).

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Figure 2: Number of years in which one or more stations in the grid box contributed precipitation data to the CRU observational data (Mitchell and Jones, 2005) (the range of the CRU data is 1901-2009). The Nimba Mountains are located near the south-western flank of the West African coast. This area is under the south-westerly wind regime between the surface and an altitude of approximately 800 hPa and under the easterly wind regime above 800 hPa. Precipitation in this region is governed by the seasonal migration of the Inter-Tropical Convergence Zone (ITCZ), a quasi-zonal1 band of moist convection associated with high values of precipitation. As the ITCZ migrates northwards in spring, a low latitude large-scale circulation pattern develops from the meridional2 boundary layer gradient of dry and moist static energy3 between the warm sub-Saharan continent and the tropical Atlantic Ocean, which is known as the West Africa Monsoon (WAM) system. This is a thermally direct land-ocean-atmosphere coupled circulation which combines several wind components: the south-westerly monsoon flow in the lower troposphere, the African easterly jet (AEJ) in the mid-troposphere, and the tropical easterly jet (TEJ) in the upper troposphere. The WAM is a result of interations between moist convection in the ITCZ, dry convection in the transverse circulation associated with the Saharan heat low, easterly waves, the African easterly jet, the tropical easterly jet and moisture flux convergence (Lafore et al., 2011). Due to strong gradients of energy and humidity, the convection is organised and maximised in the ITCZ. Fast-moving mesoscale convective systems (MCSs) also play an important role. MSCs account for about half of the rainfall in the wetter Guinea coast and Soudanian regions (~ 5º-12ºN) and for most of the rainfall over the Sahel region (~ 12º-20ºN).

1 The zonal direction is parallel to lines of latitude. 2 The meridional direction is parallel to lines of longitude. 3 The dry and moist static energy of an air parcel are thermodynamic properties which depend on

variables such as its temperature, height above sea level and (in the case of moist static energy) its water vapour content.

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Map of the Fouta Djallon massif in Guinea-Conakry (A). This area is known as the ‘water tower of West Africa’ because of its high rainfall. The main towns are Mamou, Labé and Dalaba. Liberia (unmarked on the map) is south east of Sierra Leone (source: French Wikipedia and Googlemaps) Figure 1 shows the distribution of annual rainfall across West Africa averaged from Jan 1901 to December 2009. It uses the CRU TS 2.1 data set (CRU), which is a database of monthly climatic variables constructed from observational data (Mitchell and Jones, 2005) and interpolated to a 0.5ºgrid. Due to the relative scarcity of observational stations in West Africa compared to Europe (the location of stations contributing to the CRU precipitation data set is shown in Figure 2), local precipitation characteristics are not well represented. However, the CRU data set is very useful for looking at large scale and long-term precipitation patterns over the course of the twentieth century. Figure 1 shows two coastal regions with particularly high annual rainfall, one centred on the coastal areas of Sierra Leone and Liberia, and the other on the coastal areas of Cameroon. These two coastal regions are located near to the main reliefs of West Africa, notably the Fouta Djallon Massif in Guinea (map above) and the Adamaoua Massif in Cameroon. The Fouta Djallon Massif is known as the ‘water tower’ of West Africa and is the primary source of the West African hydrographic network. There is a steep rainfall gradient south west of the Fouta Djallon Massif. Also visible in Figure 1 is the transition zone between the arid Sahara desert and the wetter climate of tropical Africa Sahel region, known as the Sahel, which stretches across Africa at approximately 12ºN to 20ºN.

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Figure 3: Mean precipitation in the months of June, July and October for the period 1901-2009 using the CRU observational data (Mitchell and Jones, 2005).

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Figure 4: Hovmöller diagram showing the zonal pattern of precipitation using the GPCP 1-Degree Daily observational data (Huffman et al., 2001). It has been produced by averaging the daily precipitation at each 1ºlatitude interval over 20ºW-20ºE for each day in the years 1996-2007. It illustrates the migration of the rain band over the course of the year, including the shift in the maximum from ~5ºN in May-June to ~10ºN in July-August. The precipitation pattern across West Africa shows strong seasonal variability. The climate of the coastal areas near the Fouta Djallon Massif is wet equatorial with a dry season from November to March and a rainy season from April to October, providing an average annual rainfall height higher than 2,500 mm. The coastal rainy season moves steadily northward until May and is characterised by a progressive increase of the moist air from the ocean into the continent, associated with the seasonal migration of the ITCZ (Lebel et al., 2003). In May-June, the rain band typically shifts abruptly from a quasi-stationary position at 5ºN to another quasi-stationary location at 10ºN in July-August, before retreating south in September-October (Sultan and Janicot, 2000). This seasonal migration of rainfall with the latitude is demonstrated in Figure 3, which shows the average precipitation during the months of June, July and October in the CRU data set. The abrupt jump of rainfall maxima in mid-June is illustrated by the time-latitude Hovmöller plot in Figure 4. This characteristic pattern is due to a combination of factors, including a westward travelling monsoon depression (Grodsky and Carton, 2001), the local orography (Drobinski et al., 2005), surface albedo (Ramel et al., 2006), the dynamics of the Saharan heat low (Sultan and Janicot, 2003; Sijikumar et al., 2006), the development of the oceanic cold tongue in the Gulf of Guinea (Nguyen et al., 2011).

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Figure 5: Left: Mean annual precipitation anomaly in 1970-1989, calculated by subtracting the climatological value (defined using the 1901-2009 data shown in Figure 1) from the mean annual precipitation in 1970-1989. Right: The relative anomaly, calculated by dividing the mean annual precipitation anomaly in 1970-1989 (as given in the left plot) by the climatological value. Both plots use the CRU observational data (Mitchell and Jones, 2005). Comparison with Figure 2 shows that the local maximum on Liberian coast occurs in the grid box containing the observational stations, which indicates that this could be an artefact produced by the scarcity of CRU observations in the rest of Liberia. In addition to evaluating climate models based on their ability to capture the structure and spatial characteristics of precipitation in West Africa in a typical year, it also illustrative to examine their ability to reproduce the strong variation which is observed on interannual and decadal time-scales, such as the severe drought which was experienced in the Sahel in the late 1960s to late 1980s. As shown in Figure 5 (left), parts of Liberia experienced the largest absolute decrease in annual precipitation seen in the region. In Figure 5 (right), the drought is expressed in terms of the decrease in precipitation as a fraction of the climatological value, defined here using the CRU data for 1901-2009, which emphasizes the effect of the drought in the Sahel region. Although this region experienced a lower drop in absolute terms as compared to Liberia, this drop represented a larger proportion of the total rainfall received. There is a strong consensus that anomalies in sea surface temperatures (SSTs) play an important role in the variations of West African precipitation on interannual and decadal time-scales. The north-south SST contrast in the tropical Atlantic is an important factor, with anomalously large SSTs in the Gulf of Guinea associated both with wetter conditions in the Guinea coasts and drier conditions further north in the Sahel (Vizy and Cook, 2001; Hastenrath, 1990). The SSTs in the Indian ocean and their relation to the SST anomalies in the Pacific are also a contributing factor (Palmer, 1986; Shinoda and Kawamura, 1994; Rowell, 2001) as are other SST patterns in the Pacific, such as those associated with the El Niño-Southern Oscillation4 (Janicot et al., 1996; Rowell, 2001) and SST anomalies in the Mediterranean (Rowell, 2003; Jung et al., 2006). Recently, a study by Ackerley et al. (2011), has provided further evidence for the argument that the change in sea surface temperatures responsible for the Sahelian drought (Folland et al., 1986; Giannini et al., 2003) is linked to the emission of sulphate aerosols by industrial countries. Other mechanisms, such as land-surface feedback Charney et al. (1975); Zeng et al. (1999) and desert dust (Prospero and Lamb, 2003; Yoshioka et al., 2007) are also thought to be relevant. Our knowledge of the dynamics of the WAM is restricted by a lack of upper air meteorological observations. In this context, meteorological reanalyses, such as the ERA-interim reanalysis by ECMWF (Dee et al., 2011), the Modern Era Retrospective-Analysis for Research and Applications by NASA (Rienecker et al., 2011) and the Japanese 25-year ReAnalysis (Onogi et al., 2007) can provide a useful surrogate. In a reanalysis, past observations are assimilated consistently into a forecast model, to produce a data set representing the status of the atmosphere system at regular time intervals in the past (although, since these reconstructions

4 The El Niño-Southern Oscillation is a coupled atmosphere-ocean interaction, linking SST anomalies in the central and eastern tropical Pacific (with sustained warming (El Niño events) occurring at intervals of approximately 2 to 7 years) with the sea level pressure contrast in the eastern tropical Pacific and western tropical Pacific-Indian Ocean.

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have been mediated by a model, they will inherit any model biases). For example, Cook and Vizy (2006) used the three dimensional data on wind speed and direction in the NCEP-NCAR reanalysis (Kalnay et al., 1996) to identify three southward wind maxima over West Africa during the monsoon season, which they then looked for when evaluating the representation of the WAM in model simulations. In addition, as we will discuss in Section 3.4, meteorological reanalyses can be used to drive regional climate models (RCMs) when investigating the ability of the RCM to simulate the past and present-day climate.

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3 Overview of climate projections This section describes the general features and limitations of current climate models, before providing more detail on the three different ensembles of climate projections used in our analysis. It discusses the extent to which the models are able to capture the past and present climate variability in West Africa and whether there is any consensus in their projections for the twenty-first century climate in the region around Liberia. 3.1 Introduction to climate modelling A climate model is a mathematical representation of the climate system. It consists of a set of equations modelling the various physical and dynamical processes, such as radiative transfer, atmospheric and ocean circulation, cloud formation and precipitation, and the interaction and feedbacks between these processes and other factors, such as changes in atmospheric composition (including the concentration of greenhouse gases), vegetation and ice sheets. This set of equations is solved at finite intervals in space and time. The size of these intervals (resolution) is limited by the available computing power. Many important processes, such as cloud formation and the impact of local topography, occur at scales smaller than the grid resolution and so these processes have to be parametrised within the model. Climate models are subject to certain inherent uncertainties: Forcing uncertainty Changes in the radiative balance of the atmosphere (‘forcing’) arise from, for example, changes in the incident solar radiation, changes in the concentration of greenhouse gases, ozone and aerosols in the atmosphere and land-use changes and can be natural or human induced. It is not possible to accurately predict the forcing which will be experienced in the future. An important element of forcing is the increase in the concentration of greenhouse gases as a result of human activity5. The IPCC produced a Special Report on Emission Scenarios (SRES, Nakicenovic et al., 2000), which identified a number of possible ‘storylines’ which took into account driving forces such as demographic, social, economic, technological, and environmental developments. Possible emission ‘scenarios’ were defined, based on these storylines. These are described in more detail in Appendix 1. These scenarios have been widely use by the climate modelling community when producing climate projections. There are additional uncertainties when relating the future emissions to the future concentrations in the Earth’s atmosphere. Uncertainty arising from natural variability The climate has many internal modes of variability, arising from natural cycles such as the El Niño Southern Oscillation, which has a period of 2-7 years. The global weather experienced in any particular year therefore depends not only on the long-term trend in the climate, such as greenhouse gas-induced warming, but also on the timing of these natural cycles. In order to study the long-term trend in the climate, it is useful to average the results over a time period (typically 30 years) which is greater than most of the natural cycles. However, it should be noted that there are known cycles that act over larger time-scales and that additional natural oscillations are still being identified in observations. Model uncertainty An additional source of uncertainty arises from the representation of key climate processes within the model. No model is an exact representation of the real world and different modelling centres make use of different formalisms and approximations. For example, the parametrisation of cloud formation varies from institution to institution. The parametrisations also involve parameters which have been fixed based on relations derived from observational measurements taken at a specific locations at a specific times, which are then extrapolated for more general use. Therefore the parameters themselves will also have an associated uncertainty. As we will see, in West Africa, 5 “Global atmosphere concentrations of carbon dioxide, methane and nitrous oxide have

increased markedly as a result of human activities since 1750, and now far exceed pre-industrial values ... Most of the observed increase in global average temperatures since the mid-20th century is very likely due to the observed increase in anthropogenic greenhouse gas concentrations” (Alley et al., 2007) In the IPCC 4th Assessment Report, ‘very likely’ indicates > 90 % probability.

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the model uncertainty is larger than both the forcing uncertainty and the uncertainty arising from the internal variability of the climate system. Model validation, where climate models are tested to see whether they can reproduce characteristics of the current and past climate, is very important way of exploring model uncertainty as it tests the effectiveness of the model parametrisations. However, as we will discuss in more detail later, if a model is able to reproduce known climatic features well, this does not guarantee that its projections are also reliable. The other main technique for quantifying and dealing with model uncertainty is the use of ‘ensembles’, which are collections of model runs. These are analysed with the aim of identifying common characteristics and robust trends. There are two types of model ensembles, with complementary strengths and weaknesses. The first type of ensemble is compiled using climate models from a wide variety of institutions (a ‘multi-model ensemble’). These models have been developed largely independently and will therefore sample a wide variety of structural differences. The second type of ensemble is compiled by running one model with many different values for the parameters describing key processes (a ‘perturbed-physics ensemble’). The parameters are varied within plausible ranges, based on expert opinion. Perturbed-physics ensembles or PPE are an effective way of sampling the uncertainties within a single model. Perturbed-physics ensembles can also lend themselves better to probabilistic interpretations as they allow more control over exactly what is being varied across the ensemble. However, if a climate process is inadequately captured in the model being perturbed, this will be inherited by all the PPE members. Both types of ensembles are used extensively in climate impact and mitigation studies. However, it should be noted that it is possible that there are climate mechanisms or feedbacks which are inadequately represented in all the climate models which are currently available. 3.2 CMIP3 Projections The CMIP3 multi-model data set was produced by phase 3 of The Coupled Model Intercomparison Project, in order to inform the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4). It is a co-ordinated set of atmosphere-ocean general circulation model (AOGCM) experiments, organized by the World Climate Research Programme (WCRP) Climate Variability and Predictability (CLIVAR) Working Group on Coupled Models (WGCM) Climate Simulation Panel (Meehl et al., 2007), with 16 modelling groups from 11 countries contributing to the original data set, using 23 models. The experiments included simulations of the twentieth century climate and projections for the twenty-first century climate, based on the emission scenarios described in Appendix 1. This data set has been made freely available for analysis by the international community and remains one of the largest and most comprehensive multi-model experiments ever attempted.

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Figure 6: “Temperature anomalies with respect to 1901 to 1950 for four African land regions for 1906 to 2005 (black line) and as simulated (red envelope) by Multi-Model Data set or MMD models incorporating known forcings; and as projected for 2001 to 2100 by MMD models for the A1B scenario (orange envelope). The bars at the end of the orange envelope represent the range of projected changes for 2091 to 2100 for the B1 scenario (blue), the A1B scenario (orange) and the A2 scenario (red). The black line is dashed where observations are present for less than 50% of the area in the decade concerned.” Figure and caption reproduced from the IPCC AR4 Christensen et al. (2007).

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Figure 7: “Temperature and precipitation changes over Africa from the MMD- A1B simulations. Top row: Annual mean, DJF6 and JJA temperature change between 1980 to 1999 and 2080 to 2099, averaged over 21 models. [...] Bottom row: number of models out of 21 that project increases in precipitation.” Figure and caption reproduced from IPCC AR4 Christensen et al. (2007). Figure 6 has been reproduced from Christensen et al. (2007) and shows the projected temperature changes in 2091 to 2100 (with respect to 1901 to 1950) for the models in the CMIP3 data set, for the B1 (blue), the A1B (orange) and the A2 (red) emission scenarios (see Appendix for scenario definitions). The projected temperature depends on the emission scenario used, but in all regions is expected to increase by between approximately 1ºC and 7ºC. The width of the coloured bar represents the 5 to 95% confidence range of the model results; as we can see the models give a very consistent picture for temperature. The ‘Sahara’ sub-region (defined as 35ºS, 10ºE to 12ºS, 52ºE) is projected to have slightly higher temperature increases than the ‘West Africa’ sub-region (defined here as 12ºS, 20ºW to 22ºN,18ºE) for all three emission scenarios. The top plots in Figure 7 (also reproduced from Christensen et al. (2007)) gives further regional detail for the A1B scenario and show that the interior of West Africa has a higher projected temperature increase than the coast. The model consensus for precipitation over Africa in the twenty-first century is much poorer, with a large spread between projections from different model integrations for the same emission scenario (Figure 7 illustrates this for the A1B scenario). In such a situation, it is useful to look at the ability of individual model integrations in representing the climate of the twentieth century, with the aim of reducing the spread of results by discarding projections from models with a particularly poor representation of key physical processes. Fig 7 is taken from the IPCC 4th assessment report and uses CMIP3 models. To create Fig. 7 (lower), no assumption was made about what constitutes a 'significant level' of precipitation. The colour bar indicates the number of models at each point in space which project an increase in precipitation (i.e. very small increases are still considered as increases). Cook and Vizy (2006) note that, in about a third of the model integrations (including both ECHAM5/MPI-OM and the Met Office Hadley Centre Coupled Model (UKMO-HadCM3) 7), the precipitation maximum remains off the coast. In addition, they note that many simulations fail to reproduce the pattern of total precipitation seen in Figure 1 of a maximum on the west coast and on the east side of the Guinean Coast. Some models miss the former maximum, some miss the latter and some add in extra maxima. In a subset of the models, the onshore flow is too deep, extending into the middle troposphere. They concluded that a selection of the models are able to

6 DJF: December, January, February; JJA: June July, August. 7 We use the naming scheme defined at http://pcmdi.llnl.gov/ipcc/model_documentation/ipcc_model_documentation.php

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capture the three southerly maxima in the vertical structure of zonal wind (which should correspond to the southerly monsoon flow, the African easterly jet, and the tropical easterly jet, which occur at approximately 925hPa, 600hPa, 200hPa respectively in the reanalyses) successfully. However, this includes simulations which place the centre of monsoon circulation in the southern hemisphere instead of the North, so that there is rising instead of sinking motion over the Gulf. In addition, Cook and Vizy (2006) were able to show that a number of the models were able to reproduce some of the characteristics of the dipole structure between precipitation in the Sahel and the Guinean Coast and the Sea Surface Ttemperature or SST anomalies in the Gulf of Guinea. Cook and Vizy (2006) use the above criteria (i.e. the location of the precipitation maxima, the circulation pattern and the dipole structure) to identify GFDL-CM2.0, MIROC3.2(medres) and MRI-CGCM2.3.2 as the three most successful simulations and examine their projections for the A2 and B1 scenarios8. These three model simulations project a dramatically different West African climate by the end of the twenty-first century, as illustrated in Figure 8. GFDL-CM2.0 projects strong drying both the Guinean Coast and the Sahel, due to pronounced surface warming north of 12N, leading to a strong temperature gradient, which strengthens the African easterly jet. The MIROC3.2(medres) projection show a dramatically wetter Sahel and drier Guinean Coast. This is due to large increases in the sea surface temperature in the Gulf of Guinea, which breaks the monsoon and results in westerly flow enhancements across much of West Africa, with an additional northerly flow enhancement on the Guinean Coast. Compared to the GFDL-CM2.0 and MIROC3.2(medres) projections, the precipitation changes projected by the MRI-CGCM2.3.2 model are more modest. For both the A2 and B1 scenarios, the model projects a small increase in precipitation in the Guinean Coast. The projection under the A2 scenario shows noticeable drying in the Sahel, which does not occur for the B1 scenario. Unlike the GFDL-CM2.0 and MIROC3.2(medres) projections, the twenty-first century West Africa monsoon system in the MRI-CGCM2.3.2 integrations retains many of its current characteristics. It is interesting to note from Figure 8 that the projected relative change in precipitation in the grid-box over the Nimba Mountains do not show the rather extreme spread which these three models give for the Sahel, although, as we will see, this greatly underestimates the spread of the CMIP3 precipitation projections as a whole in this region.

Other authors have used different criteria to determine which of the CMIP3 simulations are most successful at reproducing the climate of West Africa in the twentieth century. For example, Joly et al. (2007) focusses on the ability of model integrations to capture the relation between SST anomalies and the WAM system using a maximum covariance analysis in 12 CMIP3 models. They look particularly at the ENSO teleconnection (i.e. the correlation with SSTs in the Pacific Ocean) and the Gulf of Guinea SST anomalies. The Pacific teleconnection is judged realistic in five of the models considered (including MIROC3.2(medres), MRI-CGCM2.3.2 and UKMO-HadCM3) and found to be absent in two of the models studied (including GISS_ER) and to exist with the wrong sign in five of the models (including ECHAM5/MPI-OM and GFDL-CM2.0). Only one model (GFDL-CM2.0) is found to capture a teleconnection with the Gulf of Guinea SST independent of the Pacific. The response of West African precipitation to the inter-hemispheric SST pattern was found to be captured in only five of the twelve models examined, including MIROC3.2(medres), ECHAM5/MPI-OM, MRI-CGCM2.3.2, UKMO-HadCM3. (continued on page 22)

8 The GISS_EH simulation also performs successfully by this criteria, but had no data available

for the A2 and B1 scenarios.

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Figure 8: Projected precipitation change (2070-2100 with respect to a baseline of 1961-1990) for the A2 scenario as produced by three of the CMIP3 models: GFDL-CM2.0 (top row), MIROC3.2(medres)(middle row) and MRI-CGCM2.3.2(bottom row). The plots in the left column show the absolute difference between 2070-2100 and the baseline values whereas the right column show the relative difference i.e. the absolute difference divided by the baseline values. In the MIROC3.2(medres) relative difference plot, white is used to indicate a value above one.

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Figure 9: The region defined as the ‘Liberia region’ in this report, which stretches from 3.75ºN to 11.25ºN and 5.625ºW to 13.125ºW (chosen to correspond to 6 grid boxes in UKMO-HadCM3). The colour bar indicates height of the orography with respect to sea level, with a spatial resolution of approximately 50km in the longitude and latitude directions. This orography data was used in the SMHIRCA ENSEMBLES-AMMA runs and can be downloaded at http://ensemblesrt3.dmi.dk/.

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Figure 10: Blue crosses: the projected precipitation change versus projected projected temperature change for 2070-2100 with respect to a baseline of 1961-1990 for the A1B scenario for 17 of the CMIP3 models in the Liberia region (as defined in Figure 9) for each month. The blue box plots indicate the mean and the spread of the runs. The points for GFDL-CM2.0, MIROC3.2(medres) and MRI-CGCM2.3.2 are highlighted with black, red and green circles respectively.

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Another study, Lau et al. (2006), evaluated the CMIP3 model runs based on their ability to capture the Sahelian drought. They found that only eight of the models were able reproduce the Sahelian drought, with GFDL-CM2.0 judged the best, which requires the correct coupling between West African precipitation and Indian Ocean SST and Atlantic Ocean SST anomalies (they also found land surface feedback to be important). Seven models were found to produce excessive rainfall over Sahel during the observed drought period (including MIROC3.2(medres), ECHAM5/MPI-OM, UKMO-HadCM3 and MRI-CGCM2.3.2), and four models showed no significant deviation from normal. It is clear that many CMIP3 models still have deficiencies in their modelling of the twentieth-century West African climate and that the future projections of models which perform relatively well in the present day climate can differ dramatically. This has been investigated in detail by Biasutti et al. (2008), who found that, while the sensitivity of the twentieth century Sahelian rainfall to sea surface temperatures was broadly captured by the CMIP3 simulations, the difference in their rainfall projections for the twenty-first century could not be explained simply by differences in the projected sea surface temperatures. If other mechanisms become important in the twenty-first century, the successful reproduction of the twentieth century climate by a model would not determine how well it could simulate the twenty-first century climate. It should also be noted that the majority of the CMIP3 models, including UKMO-HadCM3, did not include the effect of land-cover changes, which could arise from human activity such as agriculture, shifting cultivation, pasture, urbanization, and transport infrastructure or as a natural response to climate change in the region. Land-use change is thought to have an important influence on precipitation in tropical Africa (Paeth et al., 2009). Given these limitations, it is still possible to draw some conclusions from the CMIP3 projections. For example, Biasutti and Sobel (2009) found that, despite the huge differences in the seasonal rainfall, analysing the projections on a month-by-month basis showed that there was an robust agreement that the rainy season in the Sahel would be shifted to later in the year. Biasutti and Sobel 20099, who identified the delay in the Sahel rainy season in the CMIP3 models by the end of the twenty-first century, link this delay to global phase shifts in the seasonal cycles of both sea surface temperature and precipitation by the end of the twenty-first century. They also discuss a possible link with sea ice loss. Figure 10 shows the change in average annual precipitation against the change in temperature for 17 of the CMIP3 models10 for the Liberia region (defined in Figure 9) projected for 2070-2100 compared to 1961-1990 11 for the A1B scenario, including GFDL-CM2.0 (black circle), MIROC3.2(medres) (red circle) and MRI-CGCM2.3.2 (green circle). As we would expect from Figure 6 and Figure 7, all models show a projected increase in temperature. However, the projected precipitation diverges markedly from model to model, with a maximum spread in July. It is interesting to note that, out of the three projections which we have discussed in detail (GFDL-CM2.0, MIROC3.2(medres) and MRI-CGCM2.3.2), GFDL-CM2.0 and MRI-CGCM2.3.2 are not outliers. This is perhaps surprising in the case of GFDL-CM2.0, since, as we have discussed, it projects severe drying in the Sahel. It is therefore important that these three models are not taken as an approximation of the spread in CMIP3 projections in the Liberia region, as would possible for the Sahelian region. MIROC(medres), as expected, shows a tendency towards a lower precipitation projection in the Liberia region than the ensemble mean, particularly in July and September, when it projects more drying than any other CMIP3 model run shown.

9 http://www.agu.org/pubs/crossref/2009/2009GL041303.shtml 10 Where multiple runs are available for a model, we take the first run 11 This standard WMO climatological baseline (World Meteorological Organization, 1988) was recommended in Carter et al. (1994) for use in climate impacts and adaptation studies.

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3.3 QUMP The QUMP projections were developed under the Quantifying Uncertainty in Model Predictions program run by the Met Office Hadley Centre. Whereas the CMIP3 data set utilises a multi-model ensemble, the QUMP projections are from a perturbed-physics ensemble (Murphy et al., 2004; Stainforth et al., 2005), where one model is run with many different values for the parameters controlling key physical and bio-geochemical processes. The central values and the ranges of these parameters are chosen, based on the current scientific knowledge. The QUMP (Murphy et al., 2007; Collins et al., 2011) ensemble has 17 members and uses the UKMO-HadCM3 coupled ocean-atmosphere global climate model (which was also used in CMIP3) but for QUMP it was run with adjustments to the heat and water fluxes to correct for biases in the sea surface temperature and salinity (we will denote the unperturbed QUMP run by ‘HadCM3Q0’). These flux adjustments cause significant differences in the projections over West Africa. Figure 12 illustrates the difference in annual precipitation between the HadCM3 run used in CMIP3 (left) and unperturbed QUMP run HadCM3Q0 (right) for the A1B scenario (2070-2100 with reference to 1961-1990). The strong influence of the SSTs leads to two very different projected patterns of precipitation across West Africa. This was also found by McSweeney et al. (2012) for South East Asia, who concluded that, while flux adjustments may give a more realistic baseline, it is unclear whether they produce more reliable projections. For many regions, the range of climate futures projected by the QUMP ensemble is greater than or equivalent to the range of the CMIP3 projections (Collins et al., 2011). For these cases, QUMP is extremely useful for investigating the uncertainties of the projections. However, for the Sahel and the Liberia region, the range of the QUMP ensemble projections does not encompass the CMIP3 projections, as can be seen in Figure 11, which is similar to Figure 10, but also includes the QUMP ensembles members (black). The unperturbed QUMP run HadCM3Q0 and the HadCM3 run used in CMIP3 are highlighted by red circles. It is interesting to note that the QUMP ensemble fails to lie within the range of CMIP3 temperature projections, particularly in the first half of the year, where the QUMP ensemble show a preference for larger temperature increases as compared to the CMIP3 ensemble. For some months, such as August, the QUMP ensemble extends the range of projected precipitation seen in the CMIP3 projections, which could be a valuable indication that the CMIP3 range in this case does not sufficiently represent the current model uncertainty in climate projections over the Liberia region. The QUMP ensemble exhibits a preference for a reduction in precipitation in February-September and an increase in October to January, implying a delayed rainy season, which is especially interesting in the context of the trend towards a delayed rainy season in the Sahel which was identified by Biasutti and Sobel (2009) in the CMIP3 projections.

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Figure 11: Projected precipitation change versus projected projected temperature change for 2070-2100 with respect to a baseline of 1961-1990 for the A1B scenario for the CMIP3 runs (blue, as in Figure 10) and QUMP runs (black) in the Liberia region(as defined in Figure 9) for each month. The box plots indicate the mean and the spread. The points corresponding to the unperturbed QUMP run HadCM3Q0 and the HadCM3 run used in CMIP3 are highlighted by red circles.

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Figure 12: Projected precipitation change (2070-2100 with respect to a baseline of 1961-1990) produced for the A1B scenario by the HadCM3 runs in CMIP3 ensemble (left) and the HadCM3 runs used in the QUMP ensemble (right). The projections differ because the HadCM3 runs used in the QUMP ensemble include flux adjustments. 3.4 ENSEMBLES-AMMA Global climate models are limited by their relatively coarse resolution. One method of deriving more detailed regional climate information is by using ‘nested’ regional climate models (RCMs), which are driven at the boundaries by information from a global climate model. The limited spatial extent of an RCM enables it to be run at a higher resolution that the global models and are thus able to simulate small-scale structure missed by the GCM. This is a particularly important consideration when modelling the climate of the Fouta Djallon and the Nimba Mountains region since, as we discussed in Section 2, this area experiences strong orographic forcing (recall that UKMO-HadCM3 covered the ‘Liberia region’ defined in Figure 9 with six grid boxes). RCMs also allow the flexibility of choosing the values of the internal model parameters to be those most suited to the particular region under study, rather than requiring that the choice of value is suitable for all regions of the globe (see e.g. (Rockel and Geyer, 2008)). On the other hand, if there are large-scale processes which are not captured well by the driving GCM, this will be inherited by the RCM. There has been considerable recent progress in understanding and modelling the current West African climate using individual regional climate models (see Paeth et al. (2011). The first multi-model ensemble of RCM runs over West Africa was coordinated by the Projections from the Ensembles-based Predictions of Climate Changes and Their Impacts (ENSEMBLES) and African Monsoon Multidisciplinary Analyses (AMMA) projects (Van der Linden and Mitchell, 2009)12. The global climate model used to set the boundary conditions was either HadCM3Q0 or ECHAM5-r3. The ENSEMBLES-AMMA resolution is 50km and A1B was chosen as the emission scenario. Figure 9 shows the orography used by SMHIRCA, as an example of the level of orographic detail which can be resolved at ENSEMBLES-AMMA resolution13. Additional ENSEMBLES-AMMA runs were also carried out which were driven by the ERA-interim reanalysis rather than a GCM, over the period 1990-2007, in order to evaluate the ability of each RCM to capture various aspects of the current West African climate. As discussed in Paeth et al. (2011), despite the common boundary conditions, the individual ENSEMBLES-AMMA members runs produced significantly different climatologies for 1990-2007, with different spatial distributions of errors when compared to observations from the Global Precipitation Climatology Centre (GPCC). These error distributions were also markedly different from those of the ERA-interim analysis with respect to the GPCC observations, which implies that the dominating biases

12 The African Monsoon Multidisciplinary Analyses Project (Redelsperger et al., 2006) was set

up to improve the understanding of the West African monsoon system and its societal impacts by facilitating multidisciplinary research, including the coordination of a multiyear field campaign over West Africa and the tropical Atlantic.

13 A description of the RCMs used in ENSEMBLES-AMMA can be found at http://ensemblesrt3.dmi.dk/.

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in this case are those of the individual RCMs, rather than the biases in the driving conditions at the region boundaries. However, most of the ENSEMBLES-AMMA runs do show a dry bias over Liberia and the Nimba Mountains, with respect to the GPCC observations. Paeth et al. (2011) suggest that this is due to the convection scheme in off-shore grid boxes being incited too strongly by orography in the coastal grid boxes, which could possibly be solved by increasing the resolution of the simulations. It is interesting to note that the ERA-interim reanalysis has a predominantly wet bias in this region. In the ENSEMBLES-AMMA (2009) report, the ability of a selection of the ERA-interim driven RCMs to reproduce the seasonal cycle of the WAM is examined. The three phases of the monsoon (as discussed in Section 2) are reproduced by the models but significant discrepancies in the magnitude and timing of the WAM are identified. In particular, many models (including HadRM3P, which is the Met Office RCM used in ENSEMBLES-AMMA) have a wet bias at 5ºN in the first phase of the monsoon system, as discussed above. The ENSEMBLES-AMMA (2009) report also identifies common biases in the timing of the phases of WAM system, such as an early end to the first phase, which it suggests are inherited from the ERA-interim boundary conditions. Similarly, Figure 20 and Figure 2 in the Appendix show zonal plots of the ERA-interim driven ENSEMBLES-AMMA runs for the period 1996-2007 (averaged over 20ºW to 20ºE), which can be compared to the GPCP observations shown in Figure 4. METNOHIRHAM, MPI-M-REMO, DMI-HIRHAM5 and GKSS-CCLM4.8 are shown to particularly overestimate the precipitation at 5ºN. The projections of the ENSEMBLES-AMMA ensembles for precipitation over West Africa in the 2001-2050 period for the A1B scenario show substantial variation, both between different ensemble members and spatially for individual ensemble members (Paeth et al., 2011), with projections ranging from sizeable increases to sizeable decreases. In Figure 15, Figure 16 and Figure 17 we show the projected precipitation in 2030-2050 and in Figure 18 and Figure 19 we show the projected change in precipitation between the time periods 2030-2050 and 1990-2010 (the choice of time period is determined by the availability of the data). Since there are large differences between ensemble members driven by the same GCM, this suggests that these characteristics are not simply inherited from the boundary conditions. In Figure 13 we show the ENSEMBLE-AMMA projections for the Liberia region in 2030-2050 with respect to 1990-2010 for the A1B scenario, with ensemble members driven by HadCM3Q0 in orange crosses and ECHAM5-r3 in orange diamonds. The runs which will be used in our extreme value analysis, HadRM3P and SMHIRCA3, are highlighted in red and blue respectively. The bar plots show the mean and spread for the entire ENSEMBLES-AMMA ensemble (i.e. they are calculated from RCM runs driven by both HadCM3Q0 and ECHAM5-r3). It can be seen that these projections do not show a consensus over whether the precipitation will increase or decrease over this area in this time period. In addition, the independence of the projection from the driving GCM is also illustrated. Figure 14 shows a comparison between the CMIP3 and QUMP projections for the Liberia region for the A1B scenario for 2070-2100 (with reference to a baseline of 1961-1990) and the three ENSEMBLES-AMMA runs which have data available for these time periods (HadRM3P, INMRCA3 and SMHIRCA, which are all driven by HadCM3Q0). The temperature projections for these three runs are within the range of the CMIP3 and QUMP temperature projections. However, these three runs do not all have precipitation projections which lie within the ranges given by CMIP3 and QUMP. This implies that using simply the CMIP3 and QUMP ranges of precipitation projections may be an underestimate of the current model uncertainty.

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Figure 13: Projected precipitation change versus projected projected temperature change for 2030-2050 with respect to a baseline of 1990-2010 for the A1B scenario for the ENSEMBLES-AMMA runs driven by HadCM3Q0 (orange crosses) and ECHAM5-r3 (orange diamonds) in the Liberia region (as defined in Figure 9) for each month. The box plots indicate the mean and the spread. The HadRM3P and SMHIRCA3 projections are highlighted in red and blue respectively.

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Figure 14: Projected precipitation change versus projected projected temperature change for 2070-2100 with respect to a baseline of 1961-1990 for the A1B scenario for three of the ENSEMBLES-AMMA (orange crosses) in the Liberia region (as defined in Figure 9) for each month. The box plots indicate the mean and the spread of the CMIP3 projections (blue) and QUMP projections (black). The ENSEMBLES-AMMA runs shown in Figure 13 did not include the effect of land-cover changes. When REMO was re-run with assumed land-cover assumptions based on the A1B and B1 storylines for future population growth and urbanisation in Africa, a large and extensive drying trend was generated in the twenty-first century projection in both scenarios (Paeth et al., 2011, 2009).

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Additional ENSEMBLES-AMMA plots

Figure 15: Mean precipitation 1990-2010 (left) and 2030-2050 (right) in the GCM-driven ENSEMBLES-AMMA runs. Note: While the 2 periods above would appear very similar, a difference can be seen by eye in, for example, the lower plots in fig 15 around Bioko Island. However, the two columns are indeed very similar and one of the reasons choosing this format is to emphasize that the spatial variation for each model run and the variation between different model runs are both much greater than the temporal variation. The temporal variation is plotted explicitly in fig 18 and fig 19.

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Figure 16: Mean precipitation 1990-2010 (left) and 2030-2050 (right) in the GCM-driven ENSEMBLES-AMMA runs.

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Figure 17: Mean precipitation 1990-2010 (left) and 2030-2050 (right) in the GCM-driven ENSEMBLES-AMMA runs.

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Figure 18: Change in annual precipitation 2030-2050 with respect to 1990-2010 in the GCM-driven ENSEMBLES-AMMA runs.

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Figure 19: Change in annual precipitation 2030-2050 with respect to 1990-2010 in the GCM-driven ENSEMBLES-AMMA runs.

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Figure 20: Hovmöller diagram showing the zonal pattern of precipitation in the ERA-interim ENSEMBLES-AMMA runs. It has been produced by averaging the daily precipitation at each latitude interval over 20W-20E for each day in the years 1996-2007. Results for ERA-interim driven CHMIALADIN_CY28 and INMRCA3_CTL runs were not available for this period. (Continued on following page).

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Figure 20: (Continued from previous page) Hovmöller diagram showing the zonal pattern of precipitation in the ERA-interim ENSEMBLES-AMMA runs. It has been produced by averaging the daily precipitation at each latitude interval over 20W-20E for each day in the years 1996-2007. Results for ERA-interim driven CHMIALADIN_CY28 and INMRCA3_CTL runs were not available for this period.

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4 Conclusion This report uses three different ensembles of climate projections to investigate the projected changes in precipitation in the region surrounding the Liberian section of the Nimba Mountain Range. By considering a multi-model ensemble of Global Climate Model runs (CMIP3), a perturbed-physics ensemble of GCM runs (QUMP) and an multi-model ensemble of Regional Climate Models driven by one of two GCMs (ENSEMBLES-AMMA), we sample a wide range of sources of model uncertainty. We find that, within each ensemble, there is a large range of projected precipitation for a given greenhouse-gas and sulphur emission pathway, with a lack of consensus even on the sign of the change. It is therefore difficult to derive a robust and reliable signal with sufficient regional detail to be able to draw conclusions about the future precipitation changes over the Nimba region. Currently, climate modelling centres across the world are producing results for phase 5 of the Coupled Model Intercomparison Project14 (CMIP5), which will inform the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5). The CMIP5 archive currently holds results from 39 state-of-the-art global climate models from 24 modelling centres, and, when complete, is expected to contain a couple of orders of magnitude more data than the CMIP3 archive. It uses Representative Concentration Pathways (RCPs), which are scenarios for how radiative forcing will evolve in future, as opposed to using the SRES emissions scenarios (which were used in CMIP3). In addition, the progress made by the African Monsoon Multidisciplinary Analysis (Lafore et al., 2011), including observations of the monsoon taken by the during their intensive field campaign in 2006 (Janicot et al., 2008), will be very valuable for tuning model parametrisations and providing a reference for model validation. The West Africa Monsoon Modelling and Evaluation project (Xue et al., 2010) (WAMME) is currently using AMMA data to evaluate the performance of the state-of-the-art GCMs in simulating the key features of the WAM system and investigate the fundamental physical processes involved. There is also ongoing research into integrating other important influences into the model projections, such as land-cover changes. The immediate successor to the ENSEMBLES-AMMA project will be A COordinated Regional climate Downscaling EXperiment (CORDEX) Giorgi et al. (2009), sponsored by the World Climate Research Programme (WCRP), which will create an ensemble of dynamical and statistical downscaling models, driven by the CMIP5 GCMs. The initial focus will be on a 50km resolution grid over Africa. Therefore, while the projected twenty-first century climate in the Nimba region is, as yet, uncertain, the climate of West Africa is the subject of much current work, which will hopefully lead to a greater understanding of model projections in the next few years. Given these limitations, it is still possible to draw some conclusions from the CMIP3 projections. For example, Biasutti and Sobel (2009) found that, despite the huge differences in the seasonal rainfall, analysing the projections on a month-by-month basis showed that there was an robust agreement that the rainy season in the Sahel would be shifted to later in the year. Also that most of the ENSEMBLES-AMMA runs do show a dry bias over Liberia and the Nimba Mountains, with respect to the GPCC observation.

14 The name CMIP4 was skipped, in order to get the numbering in step with the IPCC

Assessment Report numbering.

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Acknowledgement We acknowledge the modeling groups, the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and the WCRP’s Working Group on Coupled Modelling (WGCM) for their roles in making available the WCRP CMIP3 multi-model dataset. Support of this dataset is provided by the Office of Science, U.S. Department of Energy. The 1DD data were provided by the NASA/Goddard Space Flight Center’s Laboratory for Atmospheres, which develops and computes the 1DD as a contribution to the GEWEX Global Precipitation Climatology Project. The ENSEMBLES data used in this work was funded by the EU FP6 Integrated Project ENSEMBLES (Contract number 505539) whose support is gratefully acknowledged.

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Analysis of extreme precipitation over the Nimba region and its surrounds Author: Kate Brown Reviewed: Erasmo Buonomo

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1 Introduction This report describes the methodology and results which we have used to predict extreme precipitation events over Liberia. It should be read in conjunction with its partner report “The current status of Climate Projections over West Africa” hereafter referred to as CPWA2012. CPWA2012 describes the uncertainties associated with climate projections for the West Africa region. Results from this analysis of extreme values should be considered within the context of these uncertainties. Using data from regional climate models (RCMs) the return levels for two different 30-year periods, 1961-1990 and 2021-2050, were investigated. The aim was to see whether the return levels associated with particular return periods are likely to change under a changing climate. In addition to estimating return levels out to the 1 in 1,000-year event, this report will highlight sources of uncertainty and incorporate them, where possible, into the calculation of return levels. 2 Analysis of extreme events

2.1 Introduction

There are two approaches which are typically used to analyse extreme events: cumulative frequency analysis or extreme value analysis. The first method uses the data to construct cumulative frequency distributions of yearly maximum daily rainfall events. However, with this approach it is impossible to estimate the probability of an extreme event greater in magnitude than the maximum in the data series. If the time series is short then it is possible that the series may not be a representative sample of extreme events. Here we consider two different thirty year periods, 1961-1990 and 2021-2050. The length of these periods is inadequate to directly determine the 1 in 100-year events let alone the 1 in 1,000-year return periods. An approach whereby the two thirty year periods are combined together could be considered; however, this assumes that climate change would not affect the precipitation amounts – an assumption that is questionable. Extreme value analysis is less constrained by these limitations – therefore we have chosen to use this methodology for this analysis. It is described in more detail in the next section.

2.2 Extreme value analysis methodology

Extreme value analysis (EVA) is a statistical methodology used to estimate the probability and severity of an extreme event. A significant advantage of using EVA is its ability to predict return levels for probability beyond those present in the data set. That is, it is possible with 20 years’ worth of observations to estimate extreme events over the next 100 years (Coles, 2001). An event with a return period of 1 in N years will on average occur once in a N-year period. However, this does not rule out the possibility of having more than one 1 in N-year event within the N-year period or indeed of having an event larger than a 1 in N-year event occurring with the N years. It is also important to remember that the uncertainty in the projected extreme events increases as the return period approaches the length of the record, and increases still further as the return period exceeds the length of the record. For example, if the record length is 60 years, the uncertainty for the 1 in 50-year extreme events will be greater than the 1 in 10-year event as we could expect on average six 1 in 10-year events in a sixty year period but only one 1 in 50-year event. When we extrapolate, using our statistical model beyond the length of the data record, we make the assumption that the extreme value distribution that we have fitted is still an appropriate distribution to use and that the physical system we are modelling will still behave in a similar manner. This assumption increases the uncertainty and so the uncertainty for a 1 in 100-year event will be larger than the 1 in 50-year event and will increases further as we approach the 1 in 1,000-year events. 2.3 Extreme value distribution – Generalised Extreme Value (GEV) distribution The data sets that we have available are for the daily maximum rainfall from three different regional climate models; SMHIRCA, INMRCA3 and HadRM3P. As described in Section 3.4 of

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CPWA2012, these three regional climate models are driven at their boundaries by a single global circulation model (GCM), HadCM3Q0. There are a number of statistical distributions that can be used to model extreme events. Extreme rainfall events can occur in clusters – that is, one day with a large rainfall event is likely to follow a previous day with a large rainfall event, especially during the monsoon season. Extreme value distributions generally assume that each day is independent of the previous day. Violations of this assumption can have significant effects upon the analysis, in particular on the calculation of confidence intervals around return levels. To avoid clustering in the daily rainfall data it is usual to block the data into periods of time and extract the largest value from within each block. In this analysis the rainfall was grouped into years and the maximum daily rainfall in each year was extracted. This avoids any issues associated with dependency between rainfall events. When using blocked data the generalised extreme value (GEV) distribution is considered an appropriate distribution to model extreme events and its cumulative distribution is given by:

0,/expexp

0,0/1,1exp,,;

/1

x

xx

xG

Equation 1: The cumulative distribution of the GEV distribution A GEV distribution has three parameters: location (µ), scale (σ > 0) and shape (ξ). The location parameter is analogous to the mean of a normal distribution in that an increase in the location parameter causes the entire distribution to shift to higher values, resulting in higher return levels. However, the actual shape of the distribution remains unchanged. The scale and the shape parameter together measure the rate at which the magnitudes alter as the return period lengthens. Figure 1 illustrates the effect that changing the parameters of an extreme value distribution has on the return level curve.

Figure 1: Effect on return level curves of changing the parameters of an extreme value distribution. Circles represent return levels derived from the data, the solid black line is the fitted values using the derived extreme value distribution with associated 5-95% confidence intervals (lighter solid lines). Non-solid lines represent return-level curves where the distribution parameters are adjusted as described in the legend. Stationarity Usually with extreme value analysis it is assumed that the data are stationary – that is, the underlying statistical distribution does not change systematically with time. With climate change this is unlikely to be true. However, one advantage of using the GEV distribution is that it is

R

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vel

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possible to include extra parameters that allow the location, scale and shape parameters to depend on covariates15. In this analysis we use an indicator variable (a variable consisting of 1s and 0s) where 1 represents a maximum rainfall amount from the period 2021-2050 and 0 represents a rainfall amount from the period 1961-1990. Two GEV models were considered; one with stationary parameters (i.e. no distinction between the two thirty year periods), and the other model using the indicator variable and thus accounting for the differences between the two thirty year periods for the location parameter– see Section 0 for model formulation. Differences between these two GEV models are assessed using the ratio of likelihoods which follows the chi-square distribution. Hence, by comparing the differences in the likelihood to the chi-square distribution we can judge whether a stationary model or a model which accounts for the two different 30-year periods is a better fit to the data. Goodness-of-fit statistics In addition, we should assess whether the GEV distribution is an appropriate fit to the data using statistical goodness-of-fit tests. However, as we use estimates of the GEV parameters derived from the data, goodness-of-fit test statistics are not available. Instead, we can use bootstrap samples to derive critical values. A bootstrap sample is one where the original data is sampled with replacement to create a number of new datasets. GEV distributions are then fitted to each of these new datasets and goodness-of-fit statistics calculated. If enough new datasets are generated it is possible to derive the critical values associated with the desired significance level (usually significance levels of 1%, 5% or 10% are chosen). Three goodness-of-fit tests were used: the Anderson-Darling test, the Kolmogorov-Smirnov test and the Cramer-von-Mises test. These statistical tests all assess different aspects of the GEV distribution. The Kolmogorov-Smirnov test uses the GEV distribution derived from the data, and hypothesises that the data come from a GEV distribution. The alternative hypothesis is that the data are not from a GEV distribution. This alternative hypothesis does not specify how the data differ from the GEV distribution; they could have different location parameters, scale parameters or shape parameters, or come from a completely different distribution. The test statistic is the greatest single difference between the cumulative density function (CDF) for a GEV distribution and that derived from the data. The Anderson-Darling and Cramer-von-Mises tests, rather than looking for the single greatest difference between the empirical and theoretical distribution, look at the differences between the empirical and theoretical GEV distributions across the range of the data. The Anderson-Darling test places more weight on observations that are in the tail of the distributions. The Cramer-von-Mises test and Anderson-Darling test are considered to be more robust than the Kolmogorov-Smirnov test.

15 Covariate: any of two or more random variables exhibiting correlated variation (Merriam-Webster)

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Field significance The region selected from the RCMs consists of 49 climate model grid points centred on the Nimba mountains and their surrounds. Each grid point has a resolution of 50km giving a coverage of 350km × 350km. The elevation of this region increases broadly from the south to the north, see Figure 2, and climatology of the region surrounding the immediate mountains is described in “Aspects of climate for iron ore extraction in Western Nimba, Liberia”, hereafter referred to as CIWN2010.

Figure 2: Topography of the Nimba mountains and surrounding regions. The red square represents the approximate extent of the 350km × 350km as extracted from RCMs (equivalent to 7 × 7 grid points) Ideally we would like to assess if there is any change which is happening across the whole region. This would require complex statistical modelling which is beyond the scope of this current analysis. Instead, we assess if there is any change which is happening across the region by using field significance tests. Statistical tests are usually calculated for each individual grid point separately and the assumption made that the grid points are independent of one another – that is, that the results obtained from one grid point are not likely to be more similar, or dissimilar, to each other compared to grid points that are further away – this will be discussed in more detail later.

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Generally, statistical tests have a null hypothesis which is rejected in favour of an alternative hypothesis if the test statistic (and its associated probability (p-value) assuming the null hypothesis is true) falls within a pre-defined critical region. If the probability falls below a pre-defined significance level – typical significance levels are 1%, 5% or 10% – we reject the null hypothesis. Naively, we may consider that with approximately 50 grid points we should expect to see by chance alone approximately 2 or 3 significant values using a 5% significance level. Anything more than 2-3 significant values would lead to rejecting the null hypothesis. However, we can calculate the probability of obtaining significant values at r or more grid points by chance alone, using the binomial distribution.

44.020

19

20

1491)valuestsignificanmoreorr(

49

0

ii

r

i ip

Equation 2: probability of at least r significant events out of 49 calculated from the binomial distribution where r is the number of significant values and in the above example is set to 3. Therefore, assuming the null hypothesis is true, the probability of obtaining 3 or more significant grid points (out of a set of 49 grid points) is 0.44. It is not until we see 5 or more significant grid points that the overall probability falls below the 5% significant level (p=0.03) and we reject the null hypothesis that these significant values have occurred by chance. This result relies on the assumption that the grid points are independent of one another. However, as described by Wilks (2006), tests performed on spatial data usually have positive correlation due to the underlying physical processes, and this produces statistical dependence among the individual tests. Wilks goes on to describe that dependence between rainfall events at two neighbouring boxes will result in the test statistics also being dependent. So if one grid point has a small p-value its neighbouring grid points are also more likely to have small p-values. This means that, assuming that the underlying null hypothesis is true, but by chance alone we obtain a small p-value leading to a rejection of the null hypothesis, then we are also more likely to see rejection of the null hypothesis at neighbouring grid points, due to the underlying correlation between the grid points. This clustering of false rejections of the null hypotheses can lead to the erroneous conclusion of differences in the data when there are none. If we imagine that, rather than N correlated grid points (N=49 grid points in this analysis), we had N’ independent grid points – where N’ < N and i’ < i independent significant grid points where i’ has been scaled in a similar manner to N’ then the p-value for the field significance test associated with N’ independent points of which i’ are significant will be larger than that we obtained from our dependent grid points still with i significant points. Consequently, if a p-value is not significant for the correlated data then it would still not be significant even if we were to adjust the tests to take the correlation into account. Therefore, p-values for field significance obtained from correlated data can be considered as a lower bound for p-value for field significance.

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3 Results The results are presented in three sections. Section 0 summarises the rainfall data by means of plots of minimum, mean and maximum of the annual maximum daily rainfall (mm / day). In Section 0, GEV distributions are fitted to the data and two different methods for parameterising the rainfall are considered. The models are assessed and the most appropriate model is selected. Finally, Section 0 presents the return level plots associated with different return periods and discusses these results.

3.1 Minimum, maximum and mean annual maximum daily precipitation

Plots are presented below for the following:

- minimum annual maximum daily precipitation - mean annual maximum daily precipitation - maximum annual maximum daily precipitation

Each plot contains information for the 1961-1990 and 2021-2050 periods. These plots are useful as they provide an initial guide as to what we can expect from an extreme value analysis and can be used to help verify the fitted GEV models.

Figure 3: Minimum annual maximum daily precipitation (mm / day) for three regional climate models; SMHIRCA, INMRCA3 and HadRM3P. The top row is for the thirty year period 1961-1990 and the bottom row is for the thirty year period 2021-2050. Figure 3 shows the minimum annual maximum precipitation reported for the two thirty year periods; 1961-1990 (top row) and 2021-2050 (bottom row). The minimum annual maximum precipitation is approximately the 1 in 1-year return period: that is, an event of this magnitude can be expected approximately once in a year. It is noticeable that the SMHIRCA model appears wetter than either of the INMRCA3 or HadRM3P regional models. Comparing the minimum annual maximum daily precipitation for the 30-year period 2021-2050 to 1961-1990 solely for the SMHIRCA region model the northern region appears to be drier in 2021-2050 and wetter in the central south-eastern region. This drying in the northern region is harder to see in the INMRCA3 and HadRM3P models.

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Figure 4: Mean annual maximum daily precipitation (mm / day) for the three regional climate models; SMHIRCA, INMRCA3 and HadRM3P. The top row is for the thirty year period 1961-1990 and the bottom row is for the thirty year period 2021-2050. Figure 4 shows the mean of the annual maximum precipitation for the two thirty year periods. Similar to Figure 3 we observe that SMHIRCA is a wetter model for the mean annual maximum precipitation and that on average annual maximum precipitation events in 2021-2050 are less extreme than those in 1961 to 1990 in the northern region and possibly slightly wetter in the far south-east and south-west. The drying of extreme events in the north is harder to discern for the INRCA3 regional model and there is no noticeable increase in the intensity of extreme events in the south. HadRM3P shows a drying of extreme events in the north and south-east.

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Figure 5: Maximum annual maximum daily precipitation (mm / day) for the three regional climate models; SMHIRCA, INMRCA3 and HadRM3P. The top row is for the thirty year period 1961-1990 and the bottom row is for the thirty year period 2021-2050. Figure 5 shows the maximum annual maximum daily precipitation for the three regional climate models for the two thirty year periods. This is approximately the equivalent to a 1 in 30-year return period. As discussed in the extreme value analysis methodology, Section 0, when analysing the data directly, it is not possible to have results more extreme than those present in the data. In addition it is not possible to ascertain whether the two 30-year samples are a representative sample – that is, whether they contain at least one 1 in 30-year event. However, we observe that SMHIRCA still appears as a wetter model and shows more signs of the intensity of extreme events lessening in the future in the north and east with a possible increase in intensity in the far west. We note that the differences between the period 1961-1990 and 2021-2050 appear smaller than the differences between the different RCMs and this is observed across Figure 3 to Figure 5. This implies that the uncertainties associated with the RCMs will dominate the differences between the two 30-year periods. Although both the SMHIRCA and HadRM3 models show a consistent reduction in the intensity of extreme rainfall events in 2021-2050 in the north further investigations would be required to ascertain whether this north-south divide was modelling a real physical process or whether it was just inherent variability due to the small region selected from the RCMs. That is, if a larger region was selected and the analysis repeated, would this pattern still be discernable compared to the background variability across the grid points? An alternative explanation for the north-south divide is that both SMHIRCA and HadRM3P use the same driving GCM, HadCM3Q0, and it could be the manner in which HadCM3Q0 models the large scale synoptic features that is causing the north-south differences in the RCMs. Using different GCMs to drive the RCMs would help to verify whether the north-south differences represent a real physical process, not just a process inherent to HadCM3Q0. 3.2 Fitting GEV distribution to the maximum annual daily precipitation To assess the impact of the two thirty year periods and to estimate return periods beyond that present in the data it is necessary to fit extreme value distributions. The extreme value distribution that we used was the GEV distribution. As stated above in Section 0, two separate models for the GEV distributions were considered:

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Model 1: combined the two different periods Model 2: accounted for the two different 30-year periods using an indicator variable

A single GEV distribution was fitted to Model 1 which combined the two thirty year periods. Model 2 accounted for any differences in the two thirty year periods by using a separate location parameter to distinguish between data from 1961 to 1990 and data from 2021 to 2050. The parameters of these two GEV distributions were formulated as:

*0

*0

20502021*0

0

0

0

2modelEV1modelGEV

yearI

G

Equation 3: Parameters for the GEV distribution for model 1 and model 2. where Iyear is 1 if the year is between 2021 and 2050 else Iyear is 0, and µ, σ, and γ are the location, scale and shape parameters of the GEV distribution.

Figure 6: p-values from the chi-square distribution for the likelihood ratio test. The darker the colour, the smaller the p-value and the more likely we are to reject H0(1) – see text. Note: the scale is not regular. Figure 6 shows the probabilities from the chi-square distribution for the likelihood ratio statistic derived from comparing Model 1 with Model 2. The null hypothesis is: The smaller the p-value, the less we believe H0(1). Using the binomial distribution, we can calculate the field significance for 49 grid points with 10, 17 and 11 significant grid points for SMHIRCA, INMRCA3 and HadRM3P respectively and a significance level of 10%.

iisigno

i ivaluestsignificanmoreorip

491_

0 10

9

10

1491)(

Equation 4: Calculates field significance for 49 grid points and a 10% significance level. where no_sig is the number of significant grid points for each of the RCMs. This gives a field significance of 0.02, <0.01, 0.01 for each of the regional climate models. However, the plots show that significant grid points tend to cluster – that is a significant grid box is more likely to have a neighbouring grid point that is also significant. This clustering of significant points could falsely lead us to reject H0(1). A very simplistic approach to adjust the field significance tests is to consider only grid points that do not share either a horizontal or vertical border – analogous to picking only the white squares on a chess board. This effectively reduces the number of grid points by two and the number of significant points by two.

H0(1): there is no difference between the two 30-year periods.

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Using this approach we have approximate 5, 8 and 5 significant grid points out of twenty five grid points, which gives field significances of 0.097, <0.01, and 0.097 respectively and results in a rejection of H0(1) using a 10% significance level. More sophisticated methods are available that can quantify the field significance using re-sampling techniques, but they are beyond the scope of this current project.

Figure 7: Shows the p-value associated with the goodness-of-fit statistics. Green represents a grid point where we have no evidence to reject the null hypothesis that the data fits the GEV distribution. A yellow grid point represents a probability of between 0.05 and 0.1 that the data does not fit the GEV distribution and red represents a probability of less than 0.05 that the data fit the GEV distribution. Each row represents a different regional climate model (top – SMHIRCA, middle – INMRCA3, bottom – HadRM3P) and each column a different goodness-of-fit statistic. Figure 7 examines how well the data fit Model 2, the GEV distribution with a covariate that distinguishes between the two thirty-year periods. Green squares have a probability greater than 0.1, yellow squares have a probability ranging between 0.05 and 0.1 and red squares have a probability less than 0.05.

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The null hypothesis here is:

We should again consider field significance before deciding whether the GEV distribution is a good fit for the data. The field significance values estimated using Equation 2 for the goodness-of-fit statistics using the SMHIRCA and HadRM3P models are all above 20% which implies that there is not enough evidence to reject H0(2) for all three goodness-of-fit tests using a 10% significance level. Spatial correlation should still be considered. Removing alternate grid squares to reduce the effect of spatial correlation provided little evidence to suggest that the GEV is not an appropriate model. However, the conclusion from the INMRCA3 model (second row in Figure 7), where over 20 grid boxes report significant values using a 10% significance level, provides stronger evidence (p <0.01) using field significance tests for rejection of H0(2). Hence we concluded that the GEV distribution does not fit INMRCA3 data well. We therefore exclude the INMRCA3 regional model from subsequent analysis of extreme values.

Figure 8: Plot of µ2021-2050 (mm / day) from GEV model 2. This is the difference in location parameter for the thirty year period 2021-2050 compared to 1961-1990. Figure 8 is a plot of µ2021-2050 for GEV model 2 as described by Equation 3. µ2021-2050 represents the difference in the location parameter between the thirty year period 2021-2050 and 1961-1990. Positive values correspond to a shift of the entire extreme value distribution to higher values – the extremes become more intense for each return period. Negative values represent a shift of the extreme value distribution towards lower values – the extremes become less intense for each return period. Figure 8 also highlights the differences between the north and south of the region. In the north, both regional models estimate a decrease in the extreme rainfall events for 2021-2050. This decrease is stronger in SMHIRCA than in HadRM3P. In the southern region the intensity of extreme rainfall event increases for the thirty year period 2021-2050 for SMHIRCA. The sign and magnitude of the change is not as clear for HadRM3P. These results agree with results obtained from Figure 3 to Figure 5 but are also subject to the caveats concerning the north-south increase / decrease in intensity previously mentioned in Section 0. That is further investigations would be required to ascertain whether this north-south divide was modelling a real physical process or whether it was just inherent variability due to the small region selected from the RCMs. If a larger region was selected and the analysis repeated, would this pattern still be

H0(2): the data fit the GEV distribution with a covariate that distinguishes between the two

thirty year periods (Model 2).

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discernable compared to the background variability across the grid points? An alternative explanation for the north-south divide is that both SMHIRCA and HadRM3P use the same driving GCM, HadCM3Q0, and it could be the manner in which HadCM3Q0 models the large scale synoptic features that is causing the north-south differences in the RCMs. Using different GCMs to drive the RCMs would help to verify whether the north-south differences represent a real physical process, not just a process inherent to HadCM3Q0. 3.3 Return levels Figure 9 to Figure 13 are the return level plots and 95% confidence limits associated with the 2, 5, 10, 50 and 100 year return periods. Note that the scales differ on these plots. Confidence intervals are used to quantify the uncertainty associated with data that has comparatively short record lengths. They give upper and lower values of a range in which we can be X% confident (X=95% for this analysis) that the true parameter, that is the population parameter, lies. Note that in this analysis the true parameter is the return level associated with the return period for a particular RCM. It does not express any confidence in how well the climate model matches reality. Confidence intervals were calculated, using the method of profile likelihood as described by Coles (2001), as this gives more realistic estimate of the uncertainty compared to the delta method. Figure 9 shows the projected return levels associated with the 1 in 2-year maximum daily rainfall events. Similar to the maximum annual maximum precipitation plot and minimum annual maximum precipitation plot, SMHIRCA produces wetter return periods than HadRM3P. In the north, the 2-year return periods for 2021-2050 appear to be drier than the 1961-1990 2-year return periods. This pattern of SMHIRCA appearing wetter than HadRM3P and the return levels being drier in the north for the 2021-2050 compared to the 1961-1990 is seen for all return periods out to the 1 in 10-year events. Beyond the 1 in 10-year return period, the scale of the plots – which are important in highlighting the differences between the upper and lower 95% confidence intervals across RCMs and thirty year periods – masks any north-south differences. The drying is more difficult to see in HadRM3P but again this is probably due to the scale of the plots and the comparatively small differences between 2021-2050 and 1961-1990 (see Figure 8) compared to HadRM3P. As expected, as the return period lengthens so the corresponding return level increases, and the associated upper and lower 95% confidence range widens.

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Figure 9: 2-year return levels for maximum precipitation (mm / day) for 1961-1990 and 2021-2050 for SMHIRCA and HadRM3P regional climate models. Top row is the lower 95% confidence limit, middle row is the maximum likelihood and bottom row is the upper 95% confidence limit.

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Figure 12: 50-year return levels for maximum precipitation (mm / day) for 1961-1990 and 2021-2050 for SMHIRCA and HadRM3P regional climate models. Top row is the lower 95% confidence limit, middle row is the maximum likelihood and bottom row is the upper 95% confidence limit. As discussed in Section 0, the differences between the period 1961-1990 and 2021-2050 appear smaller than the differences between the different RCMs and this is also observed in the return level plots (Figure 9 to Figure 12). This implies that the uncertainties associated with the RCMs will dominate the differences between the two thirty year periods. In addition, as only a single GCM is used to drive the RCMs,the use of other driving GCMs should be considered in order to enhance the assessment of the uncertainties associated with this analysis. Figure 13 shows the 100-year return levels and confidence interval for the SMHIRCA model only. We have restricted the 1 in 100-year return levels and confidence intervals to SMHIRCA only, as some of the grid points for the 1 in 100 year events for HadRM3P had upper confidence limits which were in excess of 3000 mm a day. To put this in context, the maximum observed rainfall recorded in a day is 1854 mm in La Reunion Island. In addition, neighbouring grid points for HadRM3P showed values of between 200 and 500 mm a day; this reduces our confidence in the consistency of the results. Further, the maximum upper 95% confidence limit for the wetter SMHIRCA is 600mm a day. Hence an upper confidence limit in excessive of 3000 mm a day would appear inconsistent with (i) neighbouring grid points in HadRM3P, (ii) the projected SMHIRCA 1 in 100-year upper 95% confidence limit and (iii) the observed maximum rainfall ever recorded in a day. This led to a lack of confidence in the upper 95% confidence limit for the 1 in 100-year event derived from the GEV model fitted to the HadRM3P rainfall, which is why, return levels for the 1 in 100-year event are shown for the SMHIRCA model only. Similar problems arose when the upper 95% confidence limits for the 1 in 1,000-year events were calculated for both SMHIRCA and HadRM3P and as a consequence these results are not presented in this report due to a lack of confidence in the robustness of the results. This lack of confidence is due to the small sample size (only sixty years in total) and the fact that extreme

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precipitation events have “heavy tails” – that is, there is no upper bound to the GEV distribution when the shape parameter is greater than zero. We have seen in Section 0 that we have no evidence to suggest a lack of fit for the HadRM3P and SMHIRCA GEV distributions given the sixty years of data that we have available. However, once we go beyond the length of the data record, the uncertainties associated with larger return periods become larger. Even though the upper 95% confidence limit for SMHIRCA for the 1 in 100-year event appears plausible at 600 mm / day, we have less confidence in this being the true upper 95% confidence limit than we would for the 1 in 50-year upper 95% confidence limit.

Figure 13: 100-year return levels for maximum precipitation (mm / day) for 1961-1990 and 2021-2050 for SMHIRCA regional climate model. Top row is the lower 95% confidence limit, middle row is the maximum likelihood and bottom row is the upper 95% confidence limit.

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Table 1 shows the range of the confidence intervals across all 49 grid points for each return period, 30-year period and regional climate model. It is interesting to note that the range across all 49 grid points is very similar regardless of the thirty year period studied. This could suggest that although model 2 is statistically significant compared to model 1, in reality this difference between the models is comparatively small compared to the natural variation across the region and the increasing uncertainty associated with lengthening return periods.

Table 1: Range of the confidence intervals for annual maximum daily rainfall across all 49 grid points for each return period, 30-year period and regional climate model. In an earlier report for ArcelorMittal, CIWN2010, estimated return levels of daily rainfall were derived for N’Zerekore in SE Guinea and Tokadeh rail loading site. These sites were chosen as the closest sites with a long record length. (N’Zerekore is approximately 40km to the northwest of Tokadeh.) The return levels and associated return periods are reproduced in Table 2. Return period (years) Daily rainfall amount for

N’Zerekore (mm) Daily rainfall amount at

Tokadeh rail loading site (mm) 1 ~62 ~76 2 83 102 10 109 133 50 129 158

100 138 169 Table 2: Return periods and their estimated return levels for daily rainfall amounts at N’Zerekore, SE Guinea and Tokadeh rail loading site from CIWN2010. The 95% confidence intervals calculated from SMHIRCA contain the estimates of rainfall for N’Zerekore and Tokadeh for all return periods. However for HadRM3P the estimated 2-year return period does not include the 2-year return levels for N’Zerekore and Tokadeh. This may suggest that the extreme climatology as represented by HadRM3P is too dry for this particular region. However, we are comparing grid box values, which are representative of a 2,500km2 area, with localised station sites. The grid box values will tend to be an average of what we can expect for each 2,500km2 area. This then could suggest that SMHIRCA actually overestimates the rainfall amount. Further research and long time series of actual observations would be required to help quantify how well the grid boxes model site-specific observations.

Return period

RCM 1961-1990 2021-2050

Lower 95% confidence limit (mm / day)

Upper 95% confidence limit (mm / day)

Lower 95% confidence limit (mm / day)

Upper 95% confidence limit (mm / day)

2-year SMHIRCA 52 107 54 102 HadRM3P 16 58 16 55




100-year SMHIRCA 117 605 116 600 HadRM3P – – – –

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4 Conclusion Sources of uncertainty There are a number of sources of uncertainty that should be discussed. These include sampling, emissions and modelling uncertainty. Sampling uncertainty This extreme value analysis has considered the uncertainty associated with having a comparatively short record length – sampling uncertainty. It has been incorporated into this analysis by producing return levels with associated confidence intervals, Section 0. These confidence intervals quantify the uncertainty associated with using RCM data of comparatively short duration, but do not necessarily account for the uncertainty associated with any multi-decadal climatic variations. Emissions uncertainty The GCM used to drive the RCMs used the SRES A1B (medium) emission scenario (Nakicenovic and Swart, 2000)16. However, uncertainty in future emissions is dependent on future socio-economic pathways and is not yet predicted with any confidence. Other emission scenarios are available including higher and lower emission scenarios. Currently, there is no mechanism to allocate likelihood to particular future emission scenarios. By using data derived from climate models that use the medium emission scenario, we do not imply that this scenario is a more likely pathway than other emission scenarios. Climate model uncertainty Climate model uncertainty is the uncertainty associated with the way in which meteorological processes occurring on a sub-grid scale are parameterised within a single climate model. It is possible to have a range of parameterisation schemes that still give plausible climate model projections. The uncertainty associated with using different driving GCMs for the RCMs has not been explored with this analysis and the uncertainty associated with using different RCMs could be further quantified by analysis of RCMs in addition to HadRM3P and SMHIRCA Structural model uncertainties There are different ways in which a climate model can be formulated. These different formulations can give different results that are still plausible and realistic. Confidence in the sign of the projected changes in precipitation (i.e. increase or decrease) across different models over the region of West Africa is low, as shown by Figure 7 in CPWA2012. This implies that if this analysis was repeated for the same RCMs but driven by different GCMs, it is likely that return levels could become wetter for the period 2021-2050, rather than drier as we have observed. In addition, this investigation of return levels associated with return periods out to the 1 in 1,000-year using climate simulations makes certain assumptions about the climate system of the future. We have assumed that there is no extreme volcanic activity, no melting of the ice-sheets and no collapse of the thermohaline circulation. (Changes in the thermohaline circulation are modelled by many GCMs; some of the projections from these GCMs are for a slowing of the circulation, but no model projects a complete collapse.) These events could lead to severe climate disruption and their associated uncertainty has not been incorporated into this analysis. In addition there may be multi-decadal variations that are not captured due to the short duration of the data.

16 See Appendix 1 for explanations on the different scenarios

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Summary of results We would recommend that the results from this analysis are treated cautiously for the following reasons:

There are few long time series of observations for the Nimba region so we are unable to verify how well the extreme events derived from GEV models fitted to the regional climate model data compared to actual observations.

As discussed in Section 0 and in the earlier CPWA2012 report, the RCMs are driven at the boundaries by GCMs. Currently different GCMs do not agree on the sign of the projected change in precipitation over West Africa, let alone the magnitude of the change. In this study, both the SMHIRCA and HadRM3P RCMs are driven by the same GCM, HadCM3Q0, and it is probable that if SMHIRCA and HadRM3P had been driven by other GCMs, different results would have been obtained.

This analysis only considered one emission scenario – the medium emission scenario (A1B). As discussed in Section 0 there is currently no mechanism to allocate a probability to a particular future emission scenario.

The climate models are run with an unaltered topography – no account has been taken on how mining in the Nimba mountains would change the local topography and the possible consequences of such a change on the local climatology.

Return periods that approach and exceed 30 years, the length of the periods under consideration, implicitly assume that the climate is stationary – that is, if we calculate a 50-year return level for the period 2021-2050 we are calculating what a 1 in 50-year event would look like should it occur within the period 2021-2050. The return level is not the probability of a 1 in 50-year event occurring from 2021, this would assume that the climate change reaches a steady state after 2021 – whereas the climate is projected to continue changing after this date.

The return levels are calculated for individual grid boxes for each RCM concerned. These RCMs have a resolution of 50km. The return level is predicted for this 2,500km2 grid box which will have a smoothed topography. In reality the intensity of the rain could be considerably greater for the mountains.

To summarise there is a large amount of uncertainty associated with the analysis of extreme events for the Nimba region and its surrounds. Whilst this analysis may suggest that return levels are projected to become drier in the north for the period 2021-2050 compared to 1961-1990, further work would be required. Currently, we cannot rule out the possibility that the return levels in reality will be wetter.

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5 Recommendations for further work To further reduce the uncertainties associated with this analysis further the following could be considered:

Repeat this analysis using different GCMs to drive the RCMs. This would help provide clarification on whether the perceived increase in intensity of extreme rainfall events in the north and decrease in intensity in the south is due to a physical process.

Investigate the possibility of deriving the theoretical maximum precipitation. This could help to quantify physical upper limits of extreme rainfall events.

Should it be desirable to study the effect on the climatology of removing approximately 150 metres from the mountain ridges it may be possible to develop a high resolution model to explicitly model the effect of the topography and any change as a result of mining activities on the Nimba region.

Acknowledgement The ENSEMBLES data used in this work was funded by the EU FP6 Integrated Project ENSEMBLES (Contract number 505539) whose support is gratefully acknowledged.

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Appendix: Emission Scenarios The SRES scenarios (Nakicenovic et al., 2000) predict greenhouse gases emissions based on different economic, technological, and social ‘storylines’. There are six illustrative scenarios, belonging to four families: A1, A2, B1, B2. We will outline the three scenarios used in this report. A1B The major themes of the A1 storyline are convergence among regions, capacity building and increased cultural and social interactions. It describes very rapid economic growth, with the rapid introduction of new and more efficient technologies, a global population which peaks in the middle of the twenty-first century and a substantial reduction in regional differences in per capita income.

The A1B scenario assumes that the technological change will lead to a balance between fossil-intensive and non-fossil energy sources. A2 The major themes of the A2 storyline is self-reliance and preservation of local identities. It has slower and more fragmented economic development, per capita economic growth and technological change and a continuously increasing population. B1 The B1 storyline describes a world which rapidly moves towards a service and information economy, with an emphasis on global solutions to economic, social and environmental sustainability, a reduction in material intensity and the introduction of clean and resource-efficient technologies. It has the same population growth as the A1 storyline. B2 The B2 storyline and scenario family describes a world in which the emphasis is on local solutions to economic, social, and environmental sustainability. It is a world with continuously increasing global population at a rate lower than A2, intermediate levels of economic development, and less rapid and more diverse technological change than in the B1 and A1 storylines. While the scenario is also oriented toward environmental protection and social equity, it focuses on local and regional levels.

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Acronyms and glossary

A1B

One of the SRES (Special Report on Emissions Scenarios (IPCC))emission scenarios – see climate scenarios below

Scenario A1B: Best estimate temperature rise of 2.8 °C with a likely range of 1.7 to 4.4 °C (5.0 °F with a likely range of 3.1 to 7.9 °F). Sea level rise likely range [21 to 48 cm] (8 to 19 inches)

A2

Scenario A2: Best estimate temperature rise of 3.4 °C with a likely range of 2.0 to 5.4 °C. Sea level rise likely range [23 to 51 cm]

AEJ

The African easterly jet, is a region of the lower troposphere over West Africa where the seasonal mean wind speed is maximum and easterly. Forming due to the temperature contrast between the Sahara and the Gulf of Guinea, maximum wind speeds are located at a height of 3 kilometres to the north of the monsoon trough. The jet marches northward from its southern location in January, reaching its most northerly latitude in August, and its strongest winds in September while shifting back towards the equator. (Wikipedia)

AMMA

African Monsoon Multidisciplinary Analysis is an international project to improve our knowledge and understanding of the West African Monsoon (WAM) and its variability with an emphasis on daily-to-interannual timescales. AMMA is motivated by an interest in fundamental scientific issues and by the societal need for improved prediction of the WAM and its impacts on West African nations

AOGCM

Atmospheric Ocean General Circulation Model: A General Circulation Model (GCM) is a mathematical model of the general circulation of a planetary atmosphere or ocean and based on the Navier–Stokes equations on a rotating sphere with thermodynamic terms for various energy sources (radiation, latent heat). These equations are the basis for complex computer programs commonly used for simulating the atmosphere or ocean of the Earth. Atmospheric and Oceanic GCMs (AGCM and OGCM) are key components of Global Climate Models along with sea ice and land-surface components. GCMs and global

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climate models are widely applied for weather forecasting, understanding the climate, and projecting climate change. (Wikipedia)

AR4

The Fourth Assessment Report (AR4) of the United Nations Intergovernmental Panel on Climate Change (IPCC), is the fourth in a series of reports intended to assess scientific, technical and socio-economic information concerning climate change, its potential effects, and options for adaptation and mitigation. The report is the largest and most detailed summary of the climate change situation ever undertaken, produced by thousands of authors, editors, and reviewers from dozens of countries, citing over 6,000 peer-reviewed scientific studies. (Wikipedia)

CLIVAR

CLIVAR (climate variability and predictability) is a component of the World Climate Research Programme. Its purpose: to describe and understand climate variability and predictability on seasonal to centennial time-scales, identify the physical processes responsible for climate change and develop modeling and predictive capabilities for climate modelling. (Wikipedia)

Climate scenarios

Climate Change 2007, the Fourth Assessment Report (AR4) of the United Nations Intergovernmental Panel on Climate Change (IPCC), the 4th in a series of reports is intended to assess scientific, technical and socio-economic information concerning climate change, its potential effects, and options for adaptation and mitigation. (Wikipedia)

CMIP3

Coupled Model Intercomparison Projection for the Fourth Assessment Report:Climate Change 2007, the Fourth Assessment Report (AR4) of the United Nations Intergovernmental Panel on Climate Change (IPCC), is the fourth in a series of reports intended to assess scientific, technical and socio-economic information concerning climate change, its potential effects, and options for adaptation and mitigation.

CORDEX

Coordinated Regional climate Downscaling Experiment (Danish Meteorological Institute)

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CRU

CRU TS 2.10 data was created on 23rd January 2004 by Tim Mitchell, formerly based at the Tyndall Centre, UEA, Norwich. The data are on a 0.5 degree grid covering global land areas for the years 1901 to 2002 inclusive. Note that in areas of low data density, gridded values may be relaxed towards climatology. Variables available are: Diurnal Temperature Range (DTR) Precipitation (daily total) Daily mean temperature Daily maximum temperature Daily minimum temperature (source: Met Office)

DJF

December - January – February

ECHAM 5

ECHAM is a Global Climate Model developed by the Max Planck Institute for Meteorology, one of the research organisations of the Max Planck Society. It was created by modifiying global forecast models developed by ECMWF to be used for climate research. The model was given its name as a combination of its origin (the 'EC' being short for 'ECMWF') and the place of development of its parameterisation package, Hamburg. The default configuration of the model resolves the atmosphere up to 10 hectopascals (primarily used to study the lower atmosphere), but it can be reconfigured to 0.01 hPa for use in studying the stratosphere and lower mesosphere.[1]

Different versions of ECHAM, primarily different configurations of ECHAM5, have been the basis of many publications, listed on the ECHAM5 website (Wikipedia)

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Ensemble

Ensemble forecasting is a numerical prediction method that is used to attempt to generate a representative sample of the possible future states of a dynamical system. Ensemble forecasting is a form of Monte Carlo analysis: multiple numerical predictions are conducted using slightly different initial conditions that are all plausible given the past and current set of observations, or measurements. Sometimes the ensemble of forecasts may use different forecast models for different members, or different formulations of a forecast model. The multiple simulations are conducted to account for the two usual sources of uncertainty in forecast models: (1) the errors introduced by the use of imperfect initial conditions, amplified by the chaotic nature of the evolution equations of the dynamical system, which is often referred to as sensitive dependence on the initial conditions; and (2) errors introduced because of imperfections in the model formulation, such as the approximate mathematical methods to solve the equations. Ideally, the verified future dynamical system state should fall within the predicted ensemble spread, and the amount of spread should be related to the uncertainty (error) of the forecast. (Wikipedia)

ERA 40 and ERA Interim

The European Centre for Medium term Weather Forecasting (ECMWF) re-analysis project is a meteorological reanalysis project.

The first reanalysis product, ERA-15, generated re-analyses for approximately 15 years, from December 1978 to February 1994. The second product, ERA-40 (originally intended as a 40-year reanalysis) begins in 1957 (the International Geophysical Year) and covers 45 years to 2002. A new reanalysis product, ERA-Interim, is being produced, to cover the period from 1989 to present as a precursor to a revised extended reanalysis product to replace ERA-40.

GEV

Generalised Extreme Value

GCM

A General Circulation Model (GCM) is a mathematical model of the general circulation of a planetary atmosphere or ocean and based on the Navier–Stokes equations on a rotating sphere with thermodynamic terms for various energy sources (radiation, latent heat). These equations are the basis for complex computer programs commonly

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used for simulating the atmosphere or ocean of the Earth. Atmospheric and Oceanic GCMs (AGCM and OGCM) are key components of Global Climate Models along with sea ice and land-surface components. GCMs and global climate models are widely applied for weather forecasting, understanding the climate, and projecting climate change.

GFDL-CM2.0

GFDL CM2.X (Geophysical Fluid Dynamics Laboratory Coupled Model, version 2.X) is a coupled atmosphere-ocean general circulation model (AOGCM) developed at the NOAA Geophysical Fluid Dynamics Laboratory in the United States. (Wikipedia)

GPCP

Global Precipitation Climatology Project. A NASA project http://precip.gsfc.nasa.gov/

HadCM3

The Met Office Hadley Centre main climate model: HadCM3 is a coupled model incorporating an ocean with resolution 1.25 x 1.25 degrees and the HadAM3 atmosphere. It uses the thermodynamic ice model with simple advection. The land sea masks match exactly and although the grids are non-congruent the coupling process does conserve.

IPCC

The United Nations Intergovernmental Panel on Climate Change: One of the main IPCC activities is the preparation of comprehensive assessment reports about the state of scientific, technical and socioeconomic knowledge on climate change, its causes, potential impacts and response strategies.

ITCZ

Inter Tropical Convergence Zone. Near the equator, from about 5° north and 5° south, the northeast trade winds and southeast trade winds converge in a low pressure zone known as the Inter tropical Convergence Zone or ITCZ.

JJA

June - July - August

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MCS

MesoScale convective Systems: A mesoscale convective system (MCS) is a complex of thunderstorms that becomes organized on a scale larger than the individual thunderstorms but smaller than extratropical cyclones, and normally persists for several hours or more. A mesoscale convective system's overall cloud and precipitation pattern may be round or linear in shape, and include weather systems such as tropical cyclones, squall lines, lake-effect snow events, polar lows, and Mesoscale Convective Complexes (MCCs), and generally form near weather fronts. (Wikipedia)

MIROC3.2 (medres)

Medium and high resolution Atmospheric Ocean General Circulation Models: MIROC3.2 - Med: T42L20 AGCM, 1.4 deg. lon., 0.5-1.6 deg. lat. 44 levels. Developped in Japan: www.ccsr.u-tokyo.ac.jp (Met Office)

MMD

Multi-Model dataset of the IPCC Fourth Assessment Report

MPI-OM

The Max- Planck- Institute (Germany) ocean model (MPIOM) is the ocean- sea ice component of the Max- Planck- Institute climate model (Roeckner et al., 2006; Jungclaus et al., 2006). http://www.mpimet.mpg.de/en/science/models/mpiom.html

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MRI-CGM2.3.2

Meteorological Research Institute (Japan). The Japan Meteorological Agency (JMA) operates a Coupled ocean-atmosphere General Circulation Model (CGCM) as an El Niño prediction tool.

The oceanic part of the model is known as MRI.COM (Ishikawa et al., 2005), and is identical to the ocean general circulation model used in the Multivariate Ocean Variational Estimation/Meteorological Research Institute Community Ocean Model-Global (MOVE/MRI.COM-G) system. The atmospheric component is a lower-resolution version of the Global Spectral Model (GSM0603) used by JMA for operational numerical weather prediction (JMA, 2007). To improve the features of heat, momentum and fresh water fluxes on the sea surface, several physical parameterizations in the atmospheric component were modified from those in the higher-resolution model. (source Japanese Meteorological Agency)

NCEP-NCAR re-analysis

The NCEP/NCAR Reanalysis data set is a continually updating gridded data set representing the state of the Earth's atmosphere, incorporating observations and numerical weather prediction (NWP) model output dating back to 1948. It is a joint product from the National Centers for Environmental Prediction (NCEP) and the National Center for Atmospheric Research (NCAR). (Wikipedia)

PPE

Perturbed Physics Ensemble (PPE), Ensemble obtained through a modification of the parameters decribing the sub-grid processed in the model for seasonal predictions are shown to generally improve global hindcast skill when compared to Initial Condition Ensembles (ICE) for Precipitation and Mean Sea-Level Pressure (source: Met Office)

PCMDI

Program for Climate Model Diagnosis and Inter-comparison

QUMP

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Quantifying Uncertainty in Model Predictions

REMO

REMO: Regional Model of MPI (in cooperation with DKRZ, DWD and GKSS) http://www.remo-rcm.de/The-Regional-Model-REMO.1267.0.html

SMHIRCA

SMHIRCA: SMHI Rossby Centre Regional Atmospheric Model (RCA) in Sweden

SRES

Special Report on Emissions Scenarios (IPCC), see emissions scenario

SST

Sea surface temperatures

TEJ

Tropical Eastern Jet

UKMO – HadCM3

The Met Office Hadley Centre climate model used in the IPCC AR4 report.

WAM

West African Monsoon

WCRP

World Climate Research Programme: To achieve its objectives, WCRP adopts a multi-disciplinary approach, organizes large-scale observational and modelling projects and provides the international forum to align efforts of thousands of climate scientists to provide the best possible climate information.

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WGCM Working Group on coupled-modelling: To achieve its objectives, WCRP adopts a multi-disciplinary approach, organizes large-scale observational and modelling projects and provides the international forum to align efforts of thousands of climate scientists to provide the best possible climate information.

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