Editing IPCC:AR6/WGII/Chapter-9 (section)

=== 9.5.1 Climate Hazards in Africa ===

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Temperature increases due to human-caused climate change are detected across Africa and many regions have warmed more rapidly than the global average (Figure 9.13a; [[#Ranasinghe--2021|Ranasinghe et al., 2021]] ). A signal of increased annual heatwave frequency has already emerged from the background natural climate variability over the whole continent (Figure 9.14; [[#Engdaw--2021|Engdaw et al., 2021]] ). However, detection of statistically significant rainfall trends is evident in only a few regions (Figure 9.13b), and in some regions different observed precipitation datasets disagree on the direction of rainfall trends ( [[#Panitz--2013|Panitz et al., 2013]] ; [[#Sylla--2013|Sylla et al., 2013]] ; [[#Contractor--2020|Contractor et al., 2020]] ). The uncertainty of observed rainfall trends results from a number of sources, including high interannual and decadal rainfall variability, different methodologies used in developing rainfall products, and the lack of and poor quality of rainfall station data (Figure 9.15; [[#Gutiérrez--2021|Gutiérrez et al., 2021]] ).

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'''Figure 9.13 |''' '''Temperature increases due to human-caused climate change are detected across Africa and many regions have warmed more rapidly than the global average.''' Mean observed trends in '''(a)''' average temperature (°C per decade) and '''(b)''' average precipitation in (mm per decade) for 1980–2015. Trends were calculated with respect to the climatological mean over 1980–2015. The Climate Research Unit Time Series data (CRU TS) are used to compute temperature trends using 2-m temperature and the Global Precipitation Climatology Centre data (GPCC) precipitation trends. Regions with no cross-hatching indicate statistically significant trends over this period and regions in grey indicate insufficient data. The figures are derived from [[#Gutiérrez--2021|Gutiérrez et al. (2021)]] .

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'''Figure 9.14 |''' '''Summary of confidence in the direction of projected change in climate impact drivers (CIDs) in Africa.''' Projected changes represent the aggregate changes characteristic for mid-century for a range of scenarios, including: medium emission scenarios RCP4.5, SSP3-4.5, Scenario A1B from Special Report on Emissions Scenarios (SRES), or higher emissions scenarios (e.g., RCP8.5, SSP5-RCP8.5), within each AR6 WGI region (inset map) approximately corresponding to global warming levels between 2°C and 2.4°C (for CIDs that are independent of sea level rise). CIDs are drivers of impacts that are of climatic origin (that is, physical climate system conditions including means and extremes) that affect an element of society or ecosystems. The table also includes the assessment of observed or projected time-of-emergence of the CID change signal from the natural interannual variability if found with at least ''medium confidence'' (dots). Emergence of a climate change signal or trend refers to when a change in climate (the ‘signal’) becomes larger than the amplitude of natural or internal variations (the ‘noise’). The figure is a modified version of Table 12.3 in [[IPCC:Wg2:Chapter:Chapter-12|Chapter 12]] of WGI ( [[#Ranasinghe--2021|Ranasinghe et al., 2021]] ), please see this chapter for definitions of the various climate impact drivers and the basis for confidence levels of the assessment. Please note these WGI regions do not directly correspond to the regionalisation in this chapter nor do we assess climate risks for Madagascar.

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'''Figure 9.15 |''' '''Large regions of Africa lack regularly reporting and quality-controlled weather station data.''' This figure shows stations in Africa with quality-controlled station data used in developing the Rainfall Estimates on a Gridded Network (REGEN) interpolated rainfall product ( [[#Harrison--2019|Harrison et al., 2019]] ). '''(a)''' A spatial representation of stations across the continent since 1950 shown as black dots and red crosses, where red crosses represent stations that were still active in 2017. '''(b)''' The decline in operational stations or stations with quality-controlled data since ''circa'' 1998, which is largely a function of declining networks in a subset of countries. Figure is derived from [[#Carter--2020|Carter et al. (2020)]] .

With increased GHG emissions, mean temperature is projected to increase over the whole continent, as are temperature extremes over most of the continent (Figure 9.16a, b). Increased mean annual rainfall is projected over the eastern Sahel, eastern east Africa and central Africa (Figures 9.14; 9.16c). In contrast, reduced mean annual rainfall and increased drought (meteorological and agricultural) are projected over southwestern southern Africa and coastal north Africa, with drought in part as a result of increasing atmospheric evaporative demand due to higher temperatures (Figure 9.16e; [[#Ukkola--2020|Ukkola et al., 2020]] ; [[#Ranasinghe--2021|Ranasinghe et al., 2021]] ; [[#Seneviratne--2021|Seneviratne et al., 2021]] ). The frequency and intensity of heavy precipitation are projected to increase across most of Africa, except northern and southwestern Africa (Figures 9.14; 9.16d).

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'''Figure 9.16 |''' '''Projected changes of climate variables and hazards at 1''' '''.''' '''5''' '''°''' ''', 2''' '''°''' '''and 3''' '''°''' '''of global warming above the pre-industrial period (1850–1900).'''

Changes shown here are relative to the 1995–2014 period. Rows are

'''(a)''' Mean temperature change (°C);

'''(b)''' Change in the number of days per year above 35°C (days);

'''(c)''' Mean annual rainfall change (%);

'''(d)''' Heavy precipitation change represented by annual maximum 5-day precipitation (%);

'''(e)''' Change in drought represented by the six-month standardised precipitation index (SPI) (%) – negative changes indicate areas where drought frequency, intensity and/or duration is projected to increase and positive changes show the opposite;

'''(f)''' Mean sea surface temperature change (°C). All figures are derived from the WGI Interactive Atlas and show results from between 26 to 33 CMIP6 (Coupled Model Intercomparison Project) global climate models depending on the climate variable. CMIP6 models include improved representations of physical, biological, and chemical processes as well as higher spatial resolutions compared to previous CMIP5 models ( [[#Eyring--2021|Eyring et al., 2021]] ). Robustness of the projected change signal is indicated by hatching – no overlay indicates high model agreement, where at least 80% of models agree on sign of change; diagonal lines (/) indicate low model agreement, where fewer than 80% of models agree on sign of change. NOTE: Model agreement is computed at a gridbox level and is not representative of regionally aggregated results over larger regions ( [[#Gutiérrez--2021|Gutiérrez et al., 2021]] ).

Most African countries are expected to experience high temperatures unprecedented in their recent history earlier in this century than generally wealthier, higher latitude countries ( ''high confidence'' ). As low latitudes have lower internal climate variability (e.g. low seasonality), the low-latitude African countries are projected to be exposed to large increases in frequency of daily temperature extremes (hotter than 99.9% of their historical records) earlier in the 21st century compared to generally wealthier nations at higher latitudes ( [[#Harrington--2016|Harrington et al., 2016]] ; [[#Chen--2021|Chen et al., 2021]] ; [[#Doblas-Reyes--2021|Doblas-Reyes et al., 2021]] ; [[#Gutiérrez--2021|Gutiérrez et al., 2021]] ). Although higher warming rates are projected over high latitudes during the first half of this century, societies and environments in low-latitude, low-income countries are projected to become exposed to unprecedented climates before those in high latitude, developed countries ( [[#Frame--2017|Frame et al., 2017]] ; [[#Harrington--2017|Harrington et al., 2017]] ; [[#Gutiérrez--2021|Gutiérrez et al., 2021]] ). For example, beyond 2050, in central Africa and coastal west Africa, 10 months of every year will be hotter than any month in the period 1950–2000 under a high emissions scenario (RCP8.5) ( [[#Harrington--2017|Harrington et al., 2017]] ; [[#Gutiérrez--2021|Gutiérrez et al., 2021]] ). Ambitious, near-term mitigation will provide the largest reductions in exposure to unprecedented high temperatures for populations in low-latitude regions, such as across tropical Africa ( [[#Harrington--2016|Harrington et al., 2016]] ; [[#Frame--2017|Frame et al., 2017]] ).

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==== 9.5.1.1 Station Data Limitations ====

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Sustained station observation networks (Figure 9.15) are essential for the long-term analysis of local and regional climate trends, including for temperature and rainfall, as well as: the calibration of satellite-derived climate products; development of gridded climate datasets using interpolated and blended station–satellite products that form the baseline from which climate change departures are measured; development and running of early warning systems; climate projection and impact studies; and extreme event attribution studies ( [[#Harrison--2019|Harrison et al., 2019]] ; [[#Otto--2020|Otto et al., 2020]] ).

However, production of salient climate information in Africa is hindered by limited availability of and access to weather and climate data, especially in central and north Africa (Figure 9.15; [[#Coulibaly--2017|Coulibaly et al., 2017]] ; [[#Hansen--2019a|Hansen et al., 2019a]] ). Existing weather infrastructure remains suboptimal for development of reliable early warning systems ( [[#Africa%20Adaptation%20Initiative--2018|Africa Adaptation Initiative, 2018]] ; [[#Krell--2021|Krell et al., 2021]] ). For example, it is estimated only 10% of the world’s ground-based observation networks are in Africa, and that 54% of Africa’s surface weather stations cannot capture data accurately ( [[#Africa%20Adaptation%20Initiative--2018|Africa Adaptation Initiative, 2018]] ; [[#World%20Bank--2020d|World Bank, 2020d]] ). Some programmes are trying to address this issue, including the trans-African hydro-meteorological observatory ( [[#van%20de%20Giesen--2014|van de Giesen et al., 2014]] ), the West African Science Service Centre on Climate Change and Adaptive Land Management (WASCAL) ( [[#Salack--2019|Salack et al., 2019]] ), the Southern African Science Service Centre for Climate Change, Adaptive Land Management (SASSCAL) ( [[#Kaspar--2015|Kaspar et al., 2015]] ) and the AMMA-CATCH National Observation Service and Critical Zone Exploration Network ( [[#Galle--2018|Galle et al., 2018]] ). However, the sustainability of observation networks beyond the life of these programmes is uncertain as many African National Meteorological and Hydrology Services experience structural, financial and technical barriers to maintaining these systems ( [[#9.4.5|Section 9.4.5]] ).

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