Forest landscape management for climate change resilience in West and Central Africa L. V. Verchot, K. Fernandes, D.J. Sonwa, W. Baethgen, and M. Pinedo-Vasquez Our Common Future, Paris, July 2015
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Forest landscape management for climate change resilience in West and Central Africa
L. V. Verchot, K. Fernandes, D.J. Sonwa, W. Baethgen, and M. Pinedo-Vasquez
Our Common Future, Paris, July 2015
Cameroon Policy responses • Forest-Environment Sector Programme document
(FESP) does not mention CC
• 1st communication to the UNFCCC deals with sustainable
forest management, but proposes no changes to meet
CC challenge
• Poverty reduction strategy paper (PRSP) does not
mention CC
Why climate adaptation is poorly reflected in national development planning documents
• Poor data on adaptation options
• Limited awareness of adaptation among
stakeholders and the population
• Low staff capacity for planning, monitoring and
evaluation
• Lack of mechanisms for information sharing and
management across sectors
• Inadequate institutional capacity
• Lack of commitment and incentives to enforce forest
law
• Lack of information about climate patterns
Climatology
The PERSIANN dataset has the advantage of extending back to 1983 and is ¼ degree resolution. Combines satellite and gauge information.
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Central Africa domain Climatology from PERSIANN
JJA DJF
PERSIANN precipitation data in Central Africa 1983-2014.
C. Africa domain averaged ppt (mm month-1)
JJA trends of TRMM precipitation (left) and PERSIANN precipitation (right). Period 2000-2014.
JJA trends in EVI (left), VHI (right). Period 2000-2014.
Vegetation indices (above) indicate a decrease in greenness since 2000. The precipitation trends (left) are much more modest and spotty.
JJA-VHI JJA-EVI
JJA-TRMM
JJA- PERSIANN
Correlation between the vegetation indices (columns) and precipitation datasets (rows) for JJA. The correlations are calculated for the common period 2000-2014, constrained by EVI availability.
As reported in Zhou 2014, the correlations between concurrent precipitation and Veg. indices are not high over the rainforest. Positive correlation between VHI and precipitation seem to perform better than EVI. The areas over the rainforest show a poorer relationship perhaps due to cloudiness.
JJA-VHI JJA-EVI
MAMJJA TRMM
MAMJJA- PERSIANN
Changes in the northern sector are minimal, as JJA vegetation would respond to concurrent wet season precipitation (JJA) as previous months are very dry towards the Sahel.
JJA: dry season in southern sector of the domain,
Rainforest core is never really dry
Correlations improve with a longer period (MAMJJA), especially in the south.
Cumulative effect of precipitation over a 6 mo. is more relevant to veg response during JJA.
The point of the last 4 slides is to establish that TRMM and PERSIANN data are consistent and that trends found in TRMM can be found in PERSIANN.
We want to use PERSIANN to see if the trends
extend further back in time than what Zhou et al. (2014) found.
Correlation between JJA VHI and PERSIANN MAMJJA precipitation (a). Trends in VHI (b), PERSIANN JJA precipitation (c) and MAMJJA precipitation (d) for the period 1985-2014.
Message: Long time-series are needed for trend analysis. The shorter period shown previously (2000-2014) shows decrease in vegetation indices and no consistent precipitation trend, whereas a longer time series show less significant vegetation trends, but better agreement with precipitation trends in the northern sector.
(d) (c) (b) (a)
MAMJJA precipitation trends. (a) PERSIANN 1985-2014, (b) GPCC 1985-2014 and, (c) GPCC 1955-1984. Unit: mm/month per year.
(a) (b) (c)
The plot (a) is the same as figure (d) in the previous slide. Plot b is for the same period, but the dataset is GPCC rain gauge only data. The spatial distribution of trends are consistent: patches of positive trends in the Sahel and negative in Central Africa. Plot (c) is GPCC MAMJJA trends but for last 30 years, showing opposite Sahelian trend.
MAMJJA SPI Trends 1955-1984 MAMJJA SPI Trends 1985-2014
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SPI 1985-2014
Top: Same plots (b) and (c) from previous slide. Bottom: Bar plot is MAMJJA SPI calculated for the domain marked with a box in the map (5N-15N, 8E-34E).
Equatorial (western) Africa
Equatorial Africa for the purpose of this analysis is the area 5S-5N and 8E-30E.
Monthly mean precipitation for the domain 5S-5N and 8E-30E. Precipitation in the Congo follows a bimodal pattern with dry seasons (JJA and DJF) and 2 wet seasons (MAM and SON).
Climatology 1940-2014
Annual
SON JJA
MAM DJF
Precipitation Trends (1940-2014)
Annual
MAM DJF
JJA SON
The trends shown are 90% significant. Units are mm/month per year. Note the marked negative trend in MAM and DJF that reflect in the annual trend above.
Timescales of variability- Domain 5S-5N, 8E-30E
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Annual Trend
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Annual precipitation anomalies (mm/month). Note the predominance of dry years beginning in the 1970s and only recently recovering (with a vengeance)
Timescales of variability- Domain 5S-5N, 8E-30E Dry Seasons
Precipitation anomalies (mm/month) for DJF and JJA.
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DJF Trend DecV IntAnn
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JJA Trend DecV IntAnn
Timescales of variability- Domain 5S-5N, 8E-30E Wet Seasons
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MAM Trend DecV IntAnn
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SON Trend DecV IntAnn
Precipitation anomalies (mm/month) for MAM and SON.
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Annual Trend
Correlation between unfiltered annual Equatorial W. Africa precipitation anomalies(grey bars in top figure) and annual SSTs anomalies. Studies have linked trends and variability of African climate to differential north-south ocean warming. This might be the cause of trend.
Correlation between unfiltered MAM Equatorial W. Africa precipitation anomalies(grey bars in top figure) and MAM SSTs anomalies.
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MAM Trend DecV IntAnn
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