Potential impacts of afforestation on climate change ...

Clim Dyn DOI 10.1007/s00382-012-1523-9

Potential impacts of afforestation on climate change and extreme events in Nigeria Babatunde J. Abiodun • Ayobami T. Salami Olaniran J. Matthew • Sola Odedokun

•

Received: 23 March 2012 / Accepted: 6 September 2012 Ó Springer-Verlag 2012

Abstract Afforestation is usually thought as a good approach to mitigate impacts of warming over a region. This study presents an argument that afforestation may have bigger impacts than originally thought by previous studies. The study investigates the impacts of afforestation on future climate and extreme events in Nigeria, using a regional climate model (RegCM3), forced with global climate model simulations. The impacts of seven afforestation options on the near future (2031–2050, under A1B scenario) climate and the extreme events are investigated. RegCM3 replicates essential features in the present-day (1981–2000) climate and the associated extreme events, and adequately simulates the seasonal variations over the ecological zones in the country. However, the model simulates the seasonal climate better over the northern ecological zones than over the southern ecological zones. The simulated spatial distribution of the extreme events agrees well with the observation, though the magnitude of the simulated events is smaller than the observed. The study shows that afforestation in Nigeria could have both positive and negative future impacts on the climate change and extreme events in the country. While afforestation reduces the projected global warming and enhances rainfall over the afforested area (and over coastal zones), it enhances the warming and reduces the rainfall over the north-eastern part of the country. In addition, the afforestation induces more frequent B. J. Abiodun (&) Climate System Analysis Group, Department of Environmental and Geographical Science, University of Cape Town, Cape Town, South Africa e-mail: [email protected] A. T. Salami O. J. Matthew S. Odedokun Climate Change Unit, Institute Ecology, Obafemi Awolowo University, Ile-Ife, Nigeria

occurrence of extreme rainfall events (flooding) over the coastal region and more frequent occurrence of heat waves and droughts over the semi-arid region. The positive and negative impacts of the afforestation are not limited to Nigeria; they extend to the neighboring countries. While afforestation lowers the warming and enhances rainfall over Benin Republic, it increases the warming and lowers the rainfall over Niger, Chad and Cameroon. The result of the study has important implication for the ongoing climate change mitigation and adaptation efforts in Nigeria. Keywords Nigeria Monsoon Climate change Extreme events Afforestation Geo-engineering

1 Introduction The social-economic impacts of climate change and extreme events remain an issue of great concern. Many studies now focus on how to adapt to climate change or mitigate the impacts, especially, in developing countries like Nigeria where the vulnerability is high. Afforestation (or reforestation) is proposed as a major mitigation/adaptation option to the climate change (IPCC 2007). Forest sequesters carbon in the biomass of trees, thereby reducing the concentration of the atmospheric greenhouse gases that induce global warming. Besides, afforestation can alter surface properties relevant for climate, generate favourable atmospheric circulations for precipitation, control ground water, and increase evaporation. Using afforestation to induce favourable climate has been discussed over many years. Brooks (1928) suggested that replacing bare soil with forest would increase local precipitation by 1–2 %. Stebbings (1935) proposed a forest band across West Africa to bind the blowing sand and increase the rainfall

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amount. It has even been suggested that a forest plantation in semiarid regions, initially irrigated by aquifer water, may increase the precipitation so much that it would ultimately eliminate the need for further irrigation (Enger and Tjernstrom 1991). Various afforestation projects are now planned over West Africa with the aim of mitigating the future climate change. The Economic Community of West African States (ECOWAS) may soon agree on a strategic afforestation program within the member countries.1 The Nigerian government has released funds for afforestation over the entire country.2 In fact, the Federal Government of Nigeria has decided to use 60 % of its Ecological Fund to plant more than one billion trees across the country. Nevertheless, the practicality of afforestation (a geoengineering activity) is still under debate. It is not clear if this option would work in reality. The adverse effects of implementing it are not yet fully understood. NAS (1992) recommended that before implementing any geo-engineering option, the direct and potential side effects, the ethical issues, and the risks associated with the option should be well understood. Hence, before embarking on such a massive afforestation activity in Nigeria, there is need to study the influence of the intended afforestation on the climate and the extreme climate events, to guide the policymakers on the pros and cons of the afforestation. Recently, Abiodun et al. (2012a) showed that reforestation in West Africa could have both positive and negative impacts on future climate over the region. While the reforestation could reduce the greenhouse induced warming (and increase rainfall) over reforested area, it could enhance the warming (and decrease rainfall) outside the reforested area because the reforestation slows down the monsoon flow in transporting cool and moisture air north of the reforestation area (Abiodun et al. 2012a). However, the study only considered the impacts of reforestation on mean climate variables, while the impacts on climate extremes, to which people are more vulnerable, remain unstudied. The present study extends the work of Abiodun et al. (2012a) by investigating impacts of afforestation one climate extremes, but with a focus on Nigeria, where reforestation activity is increasing. The study investigates the potential side effects of afforestation in Nigeria, with the aim of assessing how afforestation can modify or alter the projected future climate changes. It uses a series of multi-decadal regional climate simulations to quantify the potential impacts of different afforestation options on the future climate changes and on the associated extreme events in Nigeria.

1 2

www.wildaf-ao.org/eng/IMG/doc/WACSOF_final_report_Dec03.doc. (http://www.punchng.com).

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2 The climate of Nigeria Nigeria, located between 4°–14°N and 3°–15°E on the coast of West Africa, has a total landmass of about 925,796 km2. The climate of Nigeria varies more than those of any other country in West Africa, because of its great length from south to the north (1,100 km), which covers all the climatic belts of West Africa. The climate is dominated by the West African monsoon, which is characterised by fluctuations of tropical maritime air mass (south-westerly wind) and the tropical continental air mass (north-easterly wind). The tropical maritime air mass originates from the southern high-pressure belt located off the Namibian coast, and along its way picks up moisture from over the Atlantic Ocean and is thus wet. The tropical continental air mass has its origin from the high-pressure belt north of the Tropic of Cancer. This air mass is not only dry as a result of its passing over the Sahara desert but also dusty sometimes during harmattan season. The tropical maritime and tropical continental air masses meet along a slanting surface called the intertropical discontinuity (ITD). The ITD in West Africa reaches its southernmost position (about 5°N) in January and northernmost position (about 22°N) in August (Peter and Tetzlaff 1988). The West African monsoon accommodates the rainfall-producing systems during the Northern hemispheric summer months, and provides Nigeria with more than 75 % of their annual rainfall (Omotosho 1985). The rain-producing systems include the African easterly jet (AEJ), tropical easterly jet (TEJ), African easterly waves (AEWs), and mesoscale convective systems (MCSs). These phenomena interact in a complex way with the low-level south-westerly monsoon flow, which transports moisture inland from the Atlantic Ocean providing West Africa with most of its moisture for rainfall (Abiodun et al. 2008). Nigerian climate is humid in the south (with annual rainfall over 2,000 mm/year) and semi-arid in the north (with annual rainfall \450 mm/year). Rainfall commences around March/April from the coast (in the south), spreads through the middle belt between April and May, to eventually get to the northernmost part between May and June. The reverse of the situation also holds for the rainfall retreat period (Ojo 1977). Nigeria can be divided latitudinally into three climatic zones: Guinea (coast–8°N), Savana (8°–11°N) and the Sahel (11°–16°N) (Omotosho and Abiodun 2007; Nicholson and Palao 1993). The country can also be grouped into seven ecological zones, ranging from south to north of the country, namely: Mangrove, Fresh water swamp, Rainforest, Woodland or tall grass savanna, Montane, Short grass savanna, and Marginal savanna (Fig. 1). The Mangrove, Fresh water swamp and Rain forest zones roughly are in Guinea zone; Tall grass and Montane zones are in Savanna zone, while Short grass savanna and Marginal savanna are in Sahel zones.

Potential impacts of afforestation

Fig. 1 Study domain showing the a topography (meters) and b ecological zones in Nigeria with the regions designated as Guinea, Savanna and Sahel

3 Methodology This study used the international centre for theoretical physics (ICTP) regional climate model (version 3; RegCM3) as described in Pal et al. (2007). The model configuration and physics options for the study are identical to those described in Afiesimama et al. (2006) and Pal et al. (2007). Radiative transfer is represented using the package in the National Center for Atmospheric Research (NCAR) community climate model, version 3 (Kiehl et al. 1996). Land surface processes are simulated using the biosphere– atmosphere transfer scheme (BATS) as described by Dickinson et al. (1993). The model uses the Holtslag et al. (1990) counter gradient planetary boundary layer (PBL) scheme. For moist processes, the sub-grid explicit moisture and cloud scheme (SUBEX) developed by Pal et al. (2000) is used. The Grell (1993) mass-flux based cumulus convection scheme, with the Fritsch and Chappell (1980) closure, is used for the convection parameterization. The model domain extends from about 9°S to 29°N and 28°W to 28°E, with horizontal grid resolution of 40 km. In the vertical, the model extends from the surface to 50 hPa with 18 vertical grid points. The domain is large enough to fully capture the primary features that control the annual cycle of the West African monsoon, and to minimize the lateral boundary conditions problems. The region of focus extends from 0°N to 15°N and 0° to 15°E. The study performed nine numerical experiments (Table 1), using eight different land cover patterns as shown in Fig. 2. The characteristics of the land cover are in Table 2. The first two experiments (denoted as PRS and NAF) simulated the present-day (1980–2000) and future (2030–2050) climates, respectively. Both experiments used the ‘‘present-day’’ vegetation (Fig. 2a) as characterized in the United States Geological Survey (USGS) global land cover characterization (GLCC) version 2 data as land cover. The differences in results of PRS and NAF provide the projected future climate change over West Africa based on the increasing GHG concentration (A1B scenario). Note

that the two experiments are similar to those of Sylla et al. (2010b), except that Sylla et al. (2010b) simulated climate change for late-century (2080–2100), while here we simulated climate change for a nearer future (2031–2050). Simulation of the nearer future is more relevant for policymakers, whose interests are more in the near-time climate impacts over West Africa. The simulations are identical to those in Abiodun et al. (2012a), except Abiodun et al. (2012a) considered the entire West Africa domain but the present study focuses on Nigerian domain. The next three experiments, denoted as SAHA, SAVA and GUNA, simulated the impacts of Sahel, Savanna, and Guinea afforestation, respectively, on the future climate. The model set-ups for the experiments were identical to that of NAF, except that in the land cover pattern, we used topical forests to replace the present-day vegetation types in the Sahel zone (i.e. 12°–16°N; Fig. 2b), Savanna zone (8°–12°N; Fig. 2c) and Guinea zone (4°–8°N; Fig. 2d), respectively. The last four experiments (denoted as NGR, NGR75, NGR50, and NGR25) simulated the impacts of random afforestation in Nigeria on the future climate. The model set-ups for the four experiments are identical to that of NAF, except in the land cover pattern; tropical forests randomly replaced 100 % (NGR; Fig. 2e), 75 % (NGR75; Fig. 2f), 50 % (NGR50; Fig. 2g) and 25 % (NGR25; Fig. 2h) of the present-day vegetation in Nigeria. The first year of all the simulations were discarded for spin-up, while the simulation for remaining years (1981–2000 and 2031–2050) were analysed for the study. The initial and lateral boundary conditions data for the nine experiments are from Max Planck Institute for Meteorology GCM, European Center/Hamburg 5 model (ECHAM5) simulations (Roeckner et al. 2003). ECHAM5 is coupled to the MPIOM ocean model (Jungclaus et al. 2006), which provides 6 h sea surface temperature data for present-day and future. The data were interpolated onto the RegCM3 grid and used in the corresponding simulations. That is, the ECHAM5 and MPIOM simulated data for present-day (1980–2000) climate were used in PRS, while

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B. J. Abiodun et al. Table 1 Summary of the experiments used in the study S/no.

Experiment

Initial and boundary condition data

Land cover pattern

1

PRS

Present-day (1980–2000)

Present-day (Fig. 2a)

2

NAF

Future period (2030–2050) A1B scenario

Present-day (Fig. 2a)

3

SAHA


Sahel zone (12o–16oN) reforestation over Nigeria (Fig. 2b)

4

SAVA


Savanna zone (8°–12°N) reforestation over Nigeria (Fig. 2c)

5

GUNA


Guinea zone (4°–8°N) reforestation (Fig. 2d)

6

A100


100 % reforestation over Nigeria (Fig. 2e)

7

A75


75 % random reforestation over Nigeria (Fig. 2f)

8

A50


50 % random reforestation over Nigeria (Fig. 2g)

9

A25


25 % random reforestation over Nigeria (Fig. 2h)

Fig. 2 The distribution of land cover types used in the study: a Present-day (PRS); b Sahel zone afforestation (SAHA); c Savanna zone afforestation (SAVA); d Guinea zone afforestation (GUNA); e 100 % afforestation over Nigeria (A100); f 75 % Random Afforestation over Nigeria (A75); g 50 % Random Afforestation over Nigeria (A50); and h 25 % Random Afforestation over Nigeria (A25)

the future period (2030–2050) simulations were used in the remaining eight experiments (Table 1). The future simulations used the SRES A1B scenario, which lies in the middle of the IPCC SRES ‘‘business-as-usual’’ emission scenarios, with CO2 concentrations of *720 ppm by 2,100 (IPCC 2007). The PRS simulation started from 1980, while the other simulation started from 2030; all simulations ran 21 years. We discarded the first simulated year for spin up, and analyzed the remaining 20 years for the study. The analysis includes calculation of extreme temperature and rainfall events, heat wave, and agricultural drought. The extreme temperature event is defined as the 99.5 percentile of the daily maximum temperature in the past climate (1981–2000), while heat wave is defined as an occurrence of the extreme temperature consecutively for 3 days. The extreme rainfall is defined as the 99.5 percentile of the daily rainfall in the past climate

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(1981–2000). The drought is defined using standard precipitation evapotranspiration index (SPEI), described by (Vicente-Serrano et al. 2010). SPEI is a multi scalar drought index based on simple climatic water balance (i.e. precipitation minus potential evapotranspiration, PET). The calculation of SPEI is similar to that of SPI, except that while SPI is calculated using monthly precipitation data; SPEI is calculated using monthly difference between precipitation and PET. The calculation of SPEI requires precipitation and temperature data; the temperature data is used in calculating PET. Because the SPEI calculation is based on water balance, SPEI can be used to identify a drought caused by a decrease in rainfall or higher water demand (i.e. evaporation) or both. SPEI is also suitable for calculating drought index at different time scales. In the present study SPEI is used to calculate drought at 3 month scale (i.e. agricultural drought); hence, a drought

Potential impacts of afforestation Table 2 Summary of BATS land cover characteristics used in the study Parameter

Land cover Disturbed forest

Tropical forest

Tall grass

Short grass

Swamp

Semi-desert

Desert

Crop/mixed

Max fractional vegetation cover

0.80

0.90

0.80

0.80

0.80

0.35

0.00

0.85

Difference between maximum fractional vegetation cover and fractional vegetation cover at 269 K

0.4

0.3

0.0

0.1

0.0

0.0

0.2

0.6

Roughness length (m)

0.30

2.00

0.10

0.05

0.03

0.10

0.05

0.08

Displacement height (m)

0.0

18.0

0.0

0.0

0.0

0.0

0.0

0.0

Min stomata resistance (s/m)

120

60

60

60

45

150

200

45

Max leaf area index

6

6

6

2

6

6

0

6

Min leaf area index

0.5

5

0.5

0.5

0.5

0.5

0

0.5

Stem (& dead matter area index)

2.0

2.0

2.0

4.0

2.0

2.0

0.5

0.5

Inverse square root of leaf dimension (m-1/2)

5

5

5

5

5

5

5

10

Light sensitivity factor (m2W-1) Upper soil layer depth (mm)

0.02 100

0.06 100

0.02 100

0.02 100

0.02 100

0.02 100

0.02 100

0.02 100

Root zone soil layer depth (mm)

2,000

1,500

1,000

1,000

1,000

1,000

1,000

1,000

Depth of total soil (mm)

3,000

3,000

3,000

3,000

3,000

3,000

3,000

3,000

Soil texture type: from sand (1) to clay (12)

6

8

6

6

6

5

3

6

Soil color type: from light (1) to dark (8)

4

4

4

3

5

2

1

5

Vegetation albedo for wavelengths

\0.7 mm

0.06

0.04

0.08

0.10

0.06

0.17

0.20

Vegetation albedo for wavelengths

[0.7 mm

0.18

0.20

0.03

0.30

0.18

0.34

0.40

See Dickinson et al. (1993) for more a detailed description

is said to occur when the 3 month SPEI (ending in August) is \-1.0.

4 Results and discussions 4.1 Model validation The study validates RegCM3 simulation (PRS) to establish the capability of the model in reproducing essential climate features in temperature, rainfall and wind fields over Nigeria. If a model can simulate these meteorological variables very well, other variables should be adequately simulated. Rainfall, in particular, is an outcome of the complex interaction of variables and processes in the model. The Climate Research Unit gridded data (CRU; Mitchell and Jones 2005), the ECMWF Interim Reanalysis (ERA-Interim; Berrisford et al. 2009), and the Nigerian Meteorological stations data (obtained from Nigerian Meteorological Agency, NIMET) are used as the reference for the validation. CRU provides the monthly surface data (temperature and rainfall), ERA-Interim provides the atmospheric data (wind speed and direction), and NIMET provided daily surface data (temperature and rainfall) used in calculating the rainfall and temperature extreme events.

4.1.1 West African monsoon system The ability of the RegCM3 to predict the seasonal cycle of West African monsoon, the inter-tropical discontinuity (ITD), temperature, and rainfall is demonstrated in Fig. 3. In agreement with observations (Fig. 3a, c) and previous studies (i.e. Le Barbe et al. 2002), the simulated monsoon system exhibits three distinct phases: the onset, the peak, and the retreat of the rainfall (Fig. 3b, c). The onset period is characterized by the northward extension of the rain-belt from the coast to about 7°N, and occurs from March to June in both CRU and RegCM (Fig. 3), though RegCM3 overestimates the rainfall amount in March–May and underestimates it in June. In CRU and RegCM3, the peak of monsoon period is characterized by a northward jump of the rain-belt in June/July and the rain-belt is located at 10°N in August with a termination of rainfall south of 7°N in CRU, but south of 6°N in RegCM3 (Fig. 3); the simulated rain-belt is wider than the observed. The southward retreat of the rain-belt starts in September in the model and observation. The seasonal variation of the simulated and observed monsoon system closely follows the latitudinal movement of solar radiation and the associated the heatlow. The ITD is located over the heat-low, while the rainbelt (corresponding to inter-tropical convergence zone (ITCZ); Nicholson 2008) is located south of the ITD. In

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Fig. 3 The time-latitude cross section of surface (2 m) temperature (°C; upper panels) and rainfall (mm day-1; lower panels), averaged between 8°E and 8°W over West Africa, as observed (CRU and

ERAIM; left panels) and simulated (RegCM3; left panels). The corresponding locations of the ITD (dashed lines) are shown

both RegCM and ERAIM, the ITD advanced to the northernmost position of 22°N in August. The maximum rainfall is about 1 mm/day higher in RegCM3 than in CRU, but the northern limit is about 18°N in both. There is no appreciable rainfall for about 3° latitude south of the ITD. The results are consistent with previous studies (Le Barbe et al. 2002; Omotosho and Abiodun 2007; Sylla et al. 2010a; Abiodun et al. 2012a).

rainfall by about 1 mm day-1. The entire domain is dry except for the light rainfall (1 mm day-1 in CRU; 2 mm day-1 in RegCM) along the coast. In JAS, the simulated ITD is located north of Nigeria and the south-westerly monsoon prevails over the country, bringing cool and moist air from the Atlantic Ocean to the country. The temperature gradient is a reverse of that in DJF, generally increasing from south–west to north–east. The warmest temperature lies over the north-eastern part of Nigeria (Maiduguri), but the coldest temperature is still maintained over the Jos plateau and Cameroon Mountain due to the high topography. The entire county experience rainfall of more than 4 mm/day in JAS, the simulated belt of maximum rainfall lies in around 10°N. All these features agree with CRU observation (Fig. 5), except that RegCM3 underestimates the temperature by 2 °C, overestimates the rainfall amount by 2 mm day-1 over the western part of the rain-belt, and underestimates the maximum rainfall over the Cameroon Mountain and over south-eastern part of Nigeria by 6 mm day-1. The study also compares the simulated extreme temperature and rainfall with the observed (Fig. 6). In both observed and simulated data, the extreme temperature and rainfall event are computed as 99.5 percentile of the daily maximum temperature and rainfall during the period 1981–2000. The model captures the spatial distribution of the extreme temperature, which generally increases from

4.1.2 Horizontal distribution of temperature and rainfall over Nigeria The simulated temperature and rainfall fields over Nigeria in winter (dry season; DJF) and summer (wet season JAS) compare well with those from CRU (Fig. 4). Both CRU and RegCM results show that north-easterly winds prevail over Nigeria in DJF, bringing cold dry air over the country. Hence, temperature generally decreases from south-west to the north-east. The warmest temperature (27 °C in CRU; 25 °C in RegCM3), located along the Guinean coast (4°N), extends northward along the valley of River Niger up to about 8°N. The coldest temperature (20 °C in CRU; 25 °C in RegCM3), located in the north, produces a cold tongue that extend southward over Jos plateau. The model also captures the cold temperature over the Cameroon Mountain and the eastern edge of Nigeria. In general, RegCM3 underestimates the temperature by about 2 °C and the

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Fig. 4 The horizontal distribution of surface (2 m) temperature (°C; top panels) and rainfall (mm day-1; lower panels) as observed (CRU) and simulated (RegCM3) over Nigeria in winter period (DJF). The arrows show the corresponding wind speeds (m s-1) and directions (ERAIM)

Fig. 5 Same as Fig. 4, but for summer period (JAS)

south (i.e. along the coastal region) to the north, with a local minimum over the Jos plateau. However, RegCM underestimates the magnitude of the extreme temperature by about 6 °C. Although, the magnitude of the simulated

extreme rainfall is about half of the observed, the spatial distribution of simulated extreme rainfall agrees well with the observed; for instance, in both observed and simulated fields, the highest values of the extreme rainfall event lie

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Fig. 6 The horizontal distribution of extreme surface (2 m) temperature (°C; 99.5 percentile; top panels) and rainfall (mm day-1; 99.5 percentile; lower panels) as observed and simulated (RegCM3) over Nigeria

Fig. 7 The annual cycle of observed (CRU) and simulated (RegCM3) of surface (2 m) temperature (°C) over the ecological zones in Nigeria

along the coast and along the Cameroon Mountains, and the lowest values occur at the central part of the country. The magnitude of the simulated extreme rainfall is lower

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than the observed because the observations are points measurement at the stations while the simulated values are averages over the model grid area (50 9 50 km), where


Fig. 8 Same as Fig. 6, but for rainfall (mm day-1)

Fig. 9 The simulated future changes in surface (2 m) temperature (°C) over the ecological zones in Nigeria with and without afforestation

convection that produces the extreme rainfall are not explicitly resolved but parameterized. However, for this study, the spatial distribution of the extreme event is more important

than the magnitude, and the model captures the spatial distribution well. This further shows that the model capture important synoptic climate features over Nigeria well.

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Fig. 10 Same as Fig. 9, but for rainfall (mm day-1)

4.1.3 Seasonality over the ecological zones Regional climate model (RegCM3) represents well the seasonal variation of temperature and rainfall over the ecological zones (Fig. 7). The simulated temperature, which shows the best agreement with observation over the savanna zones, shows a cold bias over all other zones in all months, with a largest cold bias of about 3 °C over Fresh water swamp in summer. The model reproduces the differences in the seasonality over the zones as in observation; the largest seasonality occurs over the Marginal Savanna and the lowest over Fresh Water Swamp. In agreement with observation, the model shows that the maximum temperature over the zones occurs during the pre-monsoon months, and a local minimum temperature (associated with increase in cloudiness) occurs during the peak of the monsoon. This implies that the onset of the monsoon flow and rains lowers temperature over the zones. The seasonal cycle of observed and simulated rainfall over all the zones shows that RegCM3 simulates the best seasonal cycle of rainfall over savanna zones (Fig. 8). Over Mangrove and Fresh water swamp, the model overestimates rainfall in February–April and underestimates it in June–September (Fig. 8). 4.2 Projected future changes in climate Here, we discuss the projected future climate changes (with and without afforestation) over Nigeria in 2031–2050

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under the A1B climate scenario. The changes are obtained as the differences between each future climate simulation (with or without afforestation) and the present-day climate simulation (i.e. future minus present). The seasonal distribution of the projected temperature changes shows a warmer climate in the future over the ecological zones in all the months. The greatest warming (about 2.5 °C) occurs over Short Grass Savanna and Marginal Savanna zones (between May and June, before the onset on Monsoon), and the lowest warming (about 1.3 °C) over Mangrove and Fresh Water Swamp zones. This result is consistent with those in previous studies (i.e. Abiodun et al. 2012b; Olusina and Odumade 2012; Sylla et al. 2010b) on climate change over Nigeria. For example, using statistical approach to downscale 9 GCMs simulation over Nigeria, Abiodun et al. (2012b) projects a warming of about 1.5 °C over Mangrove in 2046–2065 under B1 scenario (about 2.0 °C under A2 scenario), and a warming of 1.8 °C over Short Grass Savanna in 2046–2065 under B1 scenario (about 2.5 °C under A2 scenario). However, Fig. 9 shows that afforestation alters the magnitude of the warming (Fig. 9). While the afforestation reduces the warming over some zones, it enhances the warming over other zones. For instance, all afforestation options reduce the warming over Mangrove and Fresh Water Swamp zones (in all months) but increase it over the Marginal Savanna (in April–October). The A100 produces maximum reduction (about 1 °C in April) over Mangrove and Fresh

Potential impacts of afforestation Fig. 11 The simulated future changes in surface (2 m) temperature in summer (MJJ) over Nigeria with and without afforestation

Water Swamp zones, but produces the maximum increase over the Marginal zone (about 2.5 °C in July). In addition, over some zones, the afforestation lowers the warming in some months and enhances it in other months. For example, over Rainforest (Fig. 9c), GUNA enhances the warming by about 0.5 °C in April and reduces it by the same value in October. However, Fig. 9 shows that most of the afforestation options produce their highest warming over the Marginal Savanna (in May–June). In contrast to the temperature projections, rainfall projections show both positive and negative changes over the zones in different months (Fig. 10). The future projection (without afforestation) shows an increase in rainfall over all the zones during the transition period between dry and rainy seasons (i.e. March–April and September–October) with a decrease in rainfall in May. The increase in the rainfall between the transitional months is consistent with the poleward extension of the Hadley circulation (Lu et al. 2007). There is no consensus among previous studies regarding the future changes in rainfall over Nigeria or West Africa (Druyan 2011), but the results of the present study agrees with those in Patricola and Cook (2010), who used dynamic approach (weather research and forecasting model, WRF) to downscale 9 GCMs simulations (with A1B scenario) and project less rainfall over Nigeria in

June-July, but more rainfall towards the end of summer season. This results, however, disagree with those in Abiodun et al. (2012b), who used statistical approach to downscale 9 GCMs simulations (with A1B scenario) and project more rainfall over Nigeria in July. Nevertheless, Fig. 10 shows that most afforestation options would enhance the rainfall increase in March–April, and produce an increase in rainfall in June-July over all zones except over Marginal Savanna. The spatial distribution of the temperature changes in MJJ (Fig. 11) shows that without afforestation the warming generally increases inland from the coast (about 1 °C over the Guinea, 1.5 °C over the Savanna, and 2.0 °C over the Sahel regions). This is because the cool monsoon air from the Atlantic Ocean lowers the warming near the coastal region. However, all the afforestation options alter this distribution. For example, the zonal afforestation options (i.e. SAVA and SAHA) reduce the warming by more than 0.5 °C over the reforested area (and to the south of the area), but increase it by more than 0.6 °C over the northern part of Nigeria. As the area of the random afforestation area increases, the warming reduces over a wider area in Nigeria, but the magnitude of the warming increases over the north-eastern part (i.e. Maiduguri) (Fig. 11). Hence, all afforestation options (except GUNA) enhance warming

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B. J. Abiodun et al. Fig. 12 Same as Fig. 10, but for changes in rainfall (%)

from the elevated GHG over the northern part of Nigeria. Abiodun et al. (2012a) obtained similar results over West Africa and showed that afforestation enhances the global warming because the forest slows down the northward progression of the West Africa monsoon flow during the summer in two ways. First, the cooling over reforested area weakens the temperature gradient that drives the monsoon flow, thus, reduces the speed of the flow. Second, the increase in surface roughness over the reforested area increases the surface drag on the shallow monsoon, retarding the flow over the reforested area. Hence, in summer, the afforestation delays the onset of monsoon flow in transporting cool moist air over the area located downwind (i.e. North–east) of the reforested area; consequently, the area would experience a longer pre-monsoon hot period, thereby enhancing the projected warming over the area (Abiodun et al. 2012a). The impacts of the afforestation are not limited to Nigeria; they extend over the neighboring countries (Fig. 11). While the afforestation lowers the GHG warming over the countries located west of Nigeria (i.e. Benin Republic), it enhances the warming over the countries located east, north, and north–east of Nigeria (i.e. Cameroon, Niger and Chad). As the area of zonal afforestation shifts northward (i.e. from GUNA to SAVA, and SAHA), the area with the reduced warming over Benin Republic

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also shifts northward. Also, as the area of the random afforestation in Nigeria increases, the area with reduced warming over Benin Republic also increases such that with A100 the reduced warming covers the entire Benin Republic. On the other hand, both the zonal and random afforestation enhance the warming by up to 0.5 °C over Niger, Chad and Cameroon. Hence, the positive and negative afforestation effects of the afforestation in Nigeria extend over the neighboring countries. The spatial distribution of the future changes in rainfall without afforestation shows an increase in rainfall over the coastal region but a decrease over the northern region in MJJ (Fig. 12). The increase in the coastal rainfall occurs because, with the increase in temperature, the monsoon air would contain more moisture as it blows over the ocean, and would release more rainfall over the Guinea coast. But after the release, the monsoon would depend on evapotranspiration from the land surface to replenish the moisture before it can produce rainfall further inland. However, with the warmer climate, the monsoon would require more evapotranspiration from land surface to reach saturation. And if the evapotranspiration is not sufficient for the saturation, the monsoon would produce less rainfall further inland, especially at the northern part of Nigeria. However, all afforestation options enhance the drying at the northeastern part of Nigeria, because the decrease in the speed of monsoon flow

Potential impacts of afforestation Fig. 13 Same as Fig. 10, but for changes in extreme temperature events (events per year)

(due to the afforestation) causes a delay in onset of monsoon rainfall, and reduces the amount of moisture transported northeast of the reforested area (Abiodun et al. 2012a). 4.3 Projected future changes in the extreme events Afforestation considerably alters the projected future changes in extreme events. Fig. 13 shows that without afforestation (NAF) the projected number of days with extreme temperature events (i.e. 99.5 percentile of presentday climate, PRS) increases by about 15 days/decade over most part of Nigeria, with a local maximum (20 days/ decade) in the middle-belt, and local minima (15 days) in the north and south. All afforestation options alter this pattern. For instance, SAHA lowers the increase in heat wave occurrence (by 5 days/decade) over the reforested area, but enhances it (by 5 days/decade) north of Nigeria (i.e. over southern part of Niger). SAVA lowers the increase (by 10 days/decade) south of the reforested area, but enhances it (by 10 days/decade) over the north-eastern part of Nigeria. GUNA lowers the increase (by 10 days per decade), but enhance it (by 15 days/decade) in Savanna region. The random afforestation options also lower the increase over some part of Nigeria, and enhances it over other parts, especially over north-eastern part of the country (i.e. Maiduguri). The impacts of the afforestation

on changes in heat wave events (defined as consecutive occurrence of extreme temperature for 3 days) are similar to that of extreme temperature (compare Figs. 13 and 14). Without the afforestation, the maximum increase in heat wave event (about 12 days/decade) occurs in the middle belt; but with afforestation options (except with GUNA), the maximum increase heat wave events (up to 16 days/ decade) shifts to the north-eastern part of the country. With GUNA the maximum increase in the heat wave occurrence remains at the middle-belt but increase to 20 days/decade. Hence, the results imply that while afforestation in Nigeria may lower the frequency of the heat wave events (induced by the global warming) over some areas in Nigeria, it may increase the frequency over other areas. This is because afforestation reduces the transport of cool monsoon air north of afforested area, thus, exposes the northern region to a longer pre-monsoon hot period. The projected changes in future extreme rainfall events are small and relatively uniform (about 4 days/decade) over Nigeria, but with afforestation the changes increase (up to 8 days/decade) along south-western coast of Nigeria (Fig. 15). The random afforestation options increase the extreme rainfall event (by more than 4 days/decade) along the coast in Nigeria and Benin Republic. In addition, A25 increases it (by more than 4 days/decade) over the western part of Nigeria and over the whole of Benin Republic.

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B. J. Abiodun et al. Fig. 14 Same as Fig. 10, but for changes in heat wave events (events per decade)

Fig. 15 Same as Fig. 10, but for changes in extreme rainfall events (event per decade)

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Potential impacts of afforestation Fig. 16 Same as Fig. 10, but for changes in MJJ drought events per decade

Afforestation increases the extreme rainfall along the coastal region because the slower monsoon flow (induced by the afforestation) would produce accumulation of moisture over the coastal region, and that will increase the depth of moisture in the boundary to produce stronger convective rainfall over the region, especially during the onset of monsoon rainfall over the coastal region. On the other hand, Fig. 15 shows that the afforestation (especially, in SAVA and A100) decreases the occurrence of extreme rainfall event over Cameroon (by more than 2 days/decade). The decrease is due to the delay of monsoon flow in transporting moisture over the country (following the afforestation); hence, the boundary layer over the country is drier and produces less intense rainfall. The future projections show an increase in drought event during the MJJ season (Fig. 16). Without afforestation (NAF), the lowest increase (\3 events/decade) occurs over the savanna region, while the highest increase ([5 events/ decade) is over the coastal region and over the northeastern part. All afforestation options alter the pattern consistently with their impacts on the temperature and rainfall changes. The zonal afforestation options lower the increase over the afforested area (and south of the area), but enhances the increase north of the afforested area. A100 produces the most significant alterations; it reduces

increase in drought event over most part of Nigeria (to about 1 event/decade over the savanna region, and \4 events per decade the coastal region) but enhances the increase (to about 7 events/decade) over the north-east. Hence, the results show that the north-eastern part of Nigeria (around the Lake Chad) experiences the greatest negative impact of the afforestation induced droughts (in MJJ) because the afforestation reduces the speed of the monsoon during the northward progression and therefore delays the transport of moisture and cool air to the northeastern region of the country; consequently, the region experiences a delay in onset of rainfall and an extended ‘hot’ pre-monsoon period. The associated decrease in rainfall and increase in evapotranspiration will enhance drought occurrences over the region. Given, that the drought occurrence is the major environmental problem in the north-east, afforestation in Nigeria may further aggravate the environmental problem over the region.

5 Conclusion This study has investigated the impacts of afforestation on future climate and extreme events in Nigeria, using a regional climate model that has the capability to simulate

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the feedbacks between land surface conditions and local climate. The study first evaluated the ability of the regional climate model (RegCM3) in simulating the climate of Nigeria before using the model to simulate the future climate with or without afforestation. The results of the evaluation show that RegCM3 captures the essential climate features over Nigeria, including the dynamics of West African monsoon, spatial distribution of the climatic fields (temperature and rainfall) and the seasonality over the ecological zones. Although the study is not the first to evaluate the RegCM3 performance over Nigeria, unlike the previous evaluations (i.e. Omotosho and Abiodun 2007), the study shows the strength and weakness of the model in simulating the seasonality over each zone in Nigeria, and in simulating the extreme climate events over the country. The model show a very good skill in simulating the seasonality over the northern ecological zones, but show a weak skill in simulating the seasonality over the southern ecological zones. The simulated spatial distribution of the extreme events agrees well with observation, but the magnitudes of the events are smaller than the observed. This information is relevant for further development of RegCM3 in simulating the West African climate and the extreme events. In the afforestation simulations, the impacts of seven afforestation options (see Table 1) on future climate and extreme events over Nigeria (under A1B scenario) were studied. The results show that: •

•

•

•

•

while afforestation in Nigeria reduces the future impacts of the global warming over some zones, it enhances the impacts over other zones; all afforestation options reduce the global warming over the afforested area and over coastal zones (i.e. Mangrove and Fresh Water Swamp), but enhance the warming over the semi-arid zone (i.e. Marginal Savanna); the afforestation enhances the rainfall locally over the reforested area but reduces rainfall over Marginal Savanna zone; both positive and negative effects of the afforestation in Nigeria extend to the neighboring countries; while the afforestation lowers the warming and enhances rainfall over Benin Republic, it increases the warming and lowers rainfall over Niger, Chad and Cameroon; the afforestation encourages more frequent occurrence of heat wave events and droughts over the semi-arid region (i.e. north–east) of the country, and promote more frequent occurrence of extreme rainfall events over the coastal region.

While the study is not the first study to show that afforestation can increase local precipitation and reduce air temperature, it offers new argument that forestation may

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have bigger impacts over Nigeria (and the neighboring countries) than originally thought by previous studies. The information provided by this study has important implication to the ongoing climate change mitigation and adaptation efforts in Nigeria. For robust political decisions and agricultural planning, further reforestation experiments with different GCMs as boundary forcing may be necessary to account for modeling uncertainties and internal climate variability. Nevertheless, the results of such experiments would be probably similar to those obtained in the present study, because Patricola and Cook (2010) used future boundary conditions from nine different AOGCMs in regional model simulations and obtained similar West African precipitation changes for 2081–2100 under the A2 emissions scenario. The relative insensitivity to which AOGCM supplies the boundary conditions in that study supports the use of one AOGCM in this study. Acknowledgments The project was supported by grants from National Research Foundation (NRF, South Africa), the International Science Program (ISP, Sweden), and the Ecological Fund Office (EFO, Nigeria). Computing facility was provided by the Centre for High Performance Computing (CHPC) in South Africa.

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