Dataset Information

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

ACCESS DATA

Publications

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

Similar Datasets

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.

Dataset Information

Statistically downscaled climate dataset for East Africa.

Publications

Statistically downscaled climate dataset for East Africa.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets