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Statistically downscaled climate dataset for East Africa.

ABSTRACT: For many regions of the world, current climate change projections are only available at coarser spatial resolution from Global Climate Models (GCMs) that cannot directly be used in impact assessment and adaptation studies at regional and local scale. Impact assessment studies require high-resolution climate data to drive impact assessment models. To overcome this data challenge, we produced a station based climate projection (precipitation and maximum and minimum temperature) for Ethiopia, Kenya, and Tanzania using observed daily data from 211 stations obtained from the National Meteorological Agency of Ethiopia and international databases. Moreover, 26 large-scale climate variables derived from the National Centers for Environmental Prediction reanalysis data (1961-2005) and second generation Canadian Earth System Model (CanESM2, 1961-2100) are used. Statistical Down-Scaling Model (SDSM) is used to produce the required high-resolution climate projection by developing a statistical relationship between the large- and local-scale climate variables. The predictors are analysed more than 16458 times and we provided 20 ensembles for the current (1961-2005) and future (2006-2100, under RCP2.6, RCP4.5, and RCP8.5) climate.

SUBMITTER: Gebrechorkos SH

PROVIDER: S-EPMC6472408 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

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Statistically downscaled climate dataset for East Africa.

Gebrechorkos Solomon H SH Hülsmann Stephan S Bernhofer Christian C

Scientific data 20190415 1

For many regions of the world, current climate change projections are only available at coarser spatial resolution from Global Climate Models (GCMs) that cannot directly be used in impact assessment and adaptation studies at regional and local scale. Impact assessment studies require high-resolution climate data to drive impact assessment models. To overcome this data challenge, we produced a station based climate projection (precipitation and maximum and minimum temperature) for Ethiopia, Kenya ...[more]

PMID: 30988412

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Project description:Climate change impact studies need climate projections for different scenarios and at scales relevant to planning and management, preferably for a variety of models and realizations to capture the uncertainty in these models. To address current gaps, we statistically downscaled (SD) 3-7 CMIP6 models for five key indicators of marine habitat conditions: temperature, salinity, pH, oxygen, and chlorophyll across European waters for three climate scenarios SSP1-2.6, SSP2-4.5, and SSP5-8.5. Results provide ensemble averages and uncertainty estimates that can serve as input data for projecting the potential success of a range of Nature-based Solutions, including the restoration of habitat-forming species such as seagrass in the Mediterranean and kelp in coastal areas of Portugal and Norway. Evaluation of the ensemble with observations from four European regions (North Sea, Baltic Sea, Bay of Biscay, and Mediterranean Sea) indicates that the SD projections realistically capture the climatological conditions of the historical period 1993-2020. Model skill (Liu-mean efficiency, Pearson correlation) clearly improves for both surface temperature and oxygen across all regions with respect to the original ESMs demonstrating a higher skill for temperature compared to oxygen. Warming is evident across all areas and large differences among scenarios fully emerge from the background uncertainties related to internal variability and model differences in the second half of the century. Scenario-specific differences in acidification significantly emerge from model uncertainty and internal variability leading to distinct trajectories in surface pH starting before mid-century (in some cases starting from present day). Deoxygenation is also present across all domains, but the climate signal was significantly weaker compared to the other two indicators when compared to model uncertainty and internal variability, and the impact of different greenhouse gas trajectories is less distinct. The substantial regional and local heterogeneity in these three abiotic indicators underscores the need for highly spatially resolved physical and biogeochemical projections to understand how climate change may impact marine ecosystems.

Project description:Understanding major climate risks, adaptation strategies, and factors influencing the choice of those strategies is crucial to reduce farmers' vulnerability. Employing comprehensive data from 2822 farm households in Ethiopia and Kenya (East Africa; EA) and 1902 farm households in Bangladesh, India, and Nepal (South Asia; SA), this study investigates the main climate risks that farmers faced and the adaptation strategies they used. Among others, excessive rainfall and heightened crop pest/disease incidence are commonly observed climate-induced risks in all study areas, while cyclones and salinity are unique to Bangladesh. Drought is prevalent in Ethiopia, India, Kenya, and Nepal. Farmers in those countries responded with strategies that include change in farming practices, sustainable land management, reduce consumption, sell assets, use savings and borrowings, seek alternative employment and assistance from government or NGO. In general, farmers faced several multiple climate risks simultaneously and they responded with multiple adaptation strategies. Therefore, this study used a multivariate probit (MVP) approach to examine the factors influencing the adoption of adaptation strategies. Unlike other studies, we also tested and corrected for possible endogeneity in model estimation. All the countries mentioned have low adaptive capacity to address climate change, which is further weakened by inadequate governance and inefficient institutions. We observed significant differences in the choice of adaptation strategies between male-headed households (MHHs) and female-headed households (FHHs), as well as across countries. Generally, MHHs are more likely to seek additional employment and change agricultural practices, while FHHs and households headed by older persons tend to reduce consumption and rely on savings and borrowings. Institutional support for adaptation is much less in EA compared to SA. Training on alternative farming practices, enhancing non-farm employment options, better institutional support, and social security for older farmers are crucial for climate change adaptation in both regions.

Project description:Located across the equator, the East Africa region is among regions of Africa which have previously known the severe vegetation degradation. Some known reasons are associated with the climate change events and unprofessional agricultural practices. For this purpose, the Advanced Very High Resolution Radiometer (AVHRR) version 3 NDVI (NDVI3g) and Climate Research Unit (CRU) datasets for precipitation and temperature were used to assess the impact of climate factors on vegetation dynamics over East Africa from 1982 to 2015. Pearson correlation of NDVI and climate factors were also explored to investigate the short (October - December) rainy seasons. The phenological metrics of the region was also extracted to understand the seasonal cycle of vegetation. The results show that a positive linear trend of 14.50 × 10-4 for mean annual NDVI before 1998, where as a negative linear trend of -9.64 × 10-4 was found after 1998. The Break Point (BP) was obtained in 1998, which suggests to nonlinear responses of NDVI to climate and non-climate drivers. ENSO-vegetation in El-nino years showed a weak teleconnection between ENSO and vegetation growth changes of croplands. Also, the analyzed correlations on NDVI data resulted to the higher correlation between NDVI and precipitation than that with temperature. The Hurst exponent result showed that about, 18.63% pixels exhibited a behavior, typical of random walk (H = 0.5) suggesting that NDVI growth changes may eventually persist, overturn or fluctuate randomly in the future depending on the drivers. Vegetation trends with sustainable (unsustainable) trends were 36.8% (44.6%). Strikingly, about 20% of the total vegetated area showed unsustainable trend from degradation to amelioration. More so, results reveal that the vegetation of the croplands (non-croplands) over East Africa changed insignificantly by 6.9 × 10-5/yr (5.16 × 10-4/yr), suggesting that non-croplands are fast getting reduced Nonetheless, the NDVI growth responses to monthly and seasonal changes in climate were adjudged to be complex and dynamic. Seasonally, the short rainy season showed the higher variability in NDVI than the long rainy season. Also, the DJF, MAM and SON seasons are strongly driven by precipitation variation effect of ENSO versus NDVI series.

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Statistically downscaled climate dataset for East Africa.

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Statistically downscaled climate dataset for East Africa.

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