Best Practice

WA BiCC Countries

This section examines climate analyses in the National Adaptation Program of Action (NAPs) reports for Sierra Leone, Liberia, Guinea, Côte d’Ivoire, Ghana, and Togo, hereafter referred to as “focus countries.” This information is further compared to those of more information-rich countries (e.g., Kenya, Tanzania, South Africa, and United States). Particular attention is paid to data sources, indicators, and types of climate analyses. The quality of climate analyses presented in NAPAs may not accurately reflect the state of climate research and capacity building effort within a country or region as whole. However, since NAPAs are national-level adaptation planning reports, they should aim to achieve the highest caliber climate information and analysis. Finally, a summary list of recommended climate analyses for adaptation assessments is provided, as are climate information resources relevant to adaptation practitioners in West Africa.

To demonstrate and provide greater detail on climate analyses identified in the natural hazards/impacts section above, country-level climate adaptation planning reports are investigated. Best practices, as exemplified by developed and more information-rich countries, are included as are potentially flawed climate analyses from NAPAs.

Key Takeaways

Based on the findings of the present review and critique of climate analyses in national-level planning reports, it is clear that the majority of focus countries can benefit from improved explanations and analyses of their climate information. Quantitatively robust analysis is needed to accurately and truthfully assess the state of the climate, make projections, and understand natural hazards/impacts. Supplementing local knowledge and input  with data and statistics will prove valuable. The following steps can be taken to ensure robust and thorough analysis of climate information:

For each focus country:

  1. Create a time series histogram of the number of met stations in operation.
  2. Determine the percentage of missing data for each met station, from the earliest consistently recorded measurement to the present, for temperature and precipitation.
  3. If percentage of missing data is below 20% (ideal percentage), perform some or a variation of the following climate analyses based on what is deemed most important:

Historical Climate

  • Monthly mean temperature and rainfall climatology for various locations within the country
  • Time series of average annual temperature and rainfall anomalies, showing 11-year running means and either a change-point analysis or linear trend with test for statistical significance for various regions as well as the national level
  • Trend test for high and low temperature extremes, possible defined as the annual and seasonal number of days above the 90th percentile maximum temperature and the 10th percentile minimum temperature for each station
  • Trend test for changes in heavy precipitation, defined as the heaviest 1% of all daily events for each year over a long time series
  • Discussion and analysis of the drivers of rainfall variability at intra-seasonal and inter-annual time scales. Some examples include ENSO, AAO, IOS, MJO, and SOI

Monitoring & Early Warning Systems:

  • Seasonal probabilistic temperature forecast
  • Heatwave forecast
  • Seasonal probabilistic total rainfall forecast
  • Standardized Precipitation Index (SPI)
  • Forecast of total seasonal rainfall divided by number of rainy days.
  • 10-Day Climate Bulletin: Decadal probabilistic rainfall forecast.

Climate Projections

  • Projected changes in average annual and seasonal temperature and rainfall generated by GCMs or downscaling. For larger countries, an ensemble of GCMs may be sufficient. However, at the local level, downscaling via an ensemble of RCMs or statistical methods is needed.
  • Projected changes in extreme temperature and rainfall.
  • For all projections, various emissions scenarios should be used as well as shorter and longer time horizons.
  • For stations with >20% of missing data, perform as many of the above mentioned climate analyses as possible, with identified caveats and constraints.

A review of the climate analyses in NAPAs suggests that focus countries can improve climate analysis capabilities. Climate data exploration tools provide access to additional climate information in the region, and can be used to supplement locally or nationally available data. The use of these resources, and attention to the guidelines laid out in this manual, should greatly improve the quality of NAPA climate analyses.

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