Dataset of the suitability of major food crops in Africa under climate change

Understanding the extent and adapting to the impacts of climate change in the agriculture sector in Africa requires robust data on which technical and policy decisions can be based. However, there are no publicly available comprehensive data of which crops are suitable where under current and projected climate conditions for impact assessments and targeted adaptation planning. We developed a dataset on crop suitability of 23 major food crops (eight cereals, six legumes & pulses, six root & tuber crops, and three in banana-related family) for rainfed agriculture in Africa in terms of area and produced quantity. This dataset is based on the EcoCrop model parameterized with temperature, precipitation and soil data and is available for the historical period and until mid-century. The scenarios used for future projections are SSP1:RCP2.6, SSP3:RCP7.0 and SSP5:RCP8.5. The dataset provides a quantitative assessment of the impacts of climate change on crop production potential and can enable applications and linkages of crop impact studies to other socioeconomic aspects, thereby facilitating more comprehensive understanding of climate change impacts and assessment of options for building resilience

The El Niño effect on Ethiopian summer rainfall

While El Niño is known to cause failure of Kiremt (boreal summer) rainfall in Ethiopia, the mechanisms are not fully understood. Here we use the ECHAM5 Atmospheric General Circulation Model to investigate the physical link between Pacific sea surface temperature (SST) anomalies and Kiremt rainfall. We compare ECHAM5 simulations forced with reconstructed SST data, to gauge-based rainfall observations and atmospheric reanalysis for the time period of 1961–2009. We perform composite analysis and sensitivity experiments driven only with equatorial Pacific SST anomalies. Our results show warm SST anomalies in the equatorial Pacific drive a corresponding large-scale circulation anomaly with subsidence over Ethiopia in dry Kiremt seasons. Horizontal wind fields show a slow-down of the whole Indian monsoon system with a weaker Tropical Easterly Jet and a weaker East African Low-Level Jet in these summers. These changes can be seen as an anomalous circulation cell over northern Africa with westerlies at 100–200 hPa and easterlies below 500 hPa. Surface easterlies might reduce the moisture inflow from the Atlantic and Congo basin into Ethiopia. This and the general subsidence over the region could explain the reduction in Kiremt rainfall. Our results suggest up to 50% of the Kiremt rainfall anomalies is driven by equatorial Pacific SST variability.publishedVersio

Seasonal predictability of Kiremt rainfall in coupled general circulation models

The Ethiopian economy and population is strongly dependent on rainfall. Operational seasonal predictions for the main rainy season (Kiremt, June–September) are based on statistical approaches with Pacific sea surface temperatures (SST) as the main predictor. Here we analyse dynamical predictions from 11 coupled general circulation models for the Kiremt seasons from 1985–2005 with the forecasts starting from the beginning of May. We find skillful predictions from three of the 11 models, but no model beats a simple linear prediction model based on the predicted Niño3.4 indices. The skill of the individual models for dynamically predicting Kiremt rainfall depends on the strength of the teleconnection between Kiremt rainfall and concurrent Pacific SST in the models. Models that do not simulate this teleconnection fail to capture the observed relationship between Kiremt rainfall and the large-scale Walker circulation.publishedVersio

Seasonal predictions initialised by assimilating sea surface temperature observations with the EnKF

This study demonstrates that assimilating SST with an advanced data assimilation method yields prediction skill level with the best state-of-the-art systems. We employ the Norwegian Climate Prediction Model (NorCPM)—a fully-coupled forecasting system—to assimilate SST observations with the ensemble Kalman filter. Predictions of NorCPM are compared to predictions from the North American Multimodel Ensemble (NMME) project. The global prediction skill of NorCPM at 6- and 12-month lead times is higher than the averaged skill of the NMME. A new metric is introduced for ranking model skill. According to the metric, NorCPM is one of the most skilful systems among the NMME in predicting SST in most regions. Confronting the skill to a large historical ensemble without assimilation, shows that the skill is largely derived from the initialisation rather than from the external forcing. NorCPM achieves good skill in predicting El Niño–Southern Oscillation (ENSO) up to 12 months ahead and achieves skill over land via teleconnections. However, NorCPM has a more pronounced reduction in skill in May than the NMME systems. An analysis of ENSO dynamics indicates that the skill reduction is mainly caused by model deficiencies in representing the thermocline feedback in February and March. We also show that NorCPM has skill in predicting sea ice extent at the Arctic entrance adjacent to the north Atlantic; this skill is highly related to the initialisation of upper ocean heat content.publishedVersio

Coupled Impacts of Soil Acidification and Climate Change on Future Crop Suitability in Ethiopia

Agricultural sustainability faces challenges in the changing climate, particularly for rain-fed systems like those in Ethiopia. This study examines the combined impacts of climate change and soil acidity on future crop potential, focusing on Ethiopia as a case study. The EcoCrop crop suitability model was parameterized and run for four key food crops in Ethiopia (teff, maize, barley and common wheat), under current and mid-century climate conditions. To assess the impacts of soil acidification on crop suitability, a simulation study was conducted by lowering the soil pH values by 0.5, 1.0 and 1.5 and re-running the suitability model, comparing the changes in the area suitable for each crop. Our evaluation of the model, by comparing the modeled suitable areas with reference data, indicated that there was a good fit for all the four crops. Using default soil pH values, we project that there will be no significant changes in the suitability of maize, barley and wheat and an increase in the suitability of teff by the mid-century, as influenced by projected increases in rainfall in the country. Our results demonstrate a direct relationship between the lowering of soil pH and increasing losses in the area suitable for all crops, but especially for teff, barley and wheat. We conclude that soil acidification can have a strong impact on crop suitability in Ethiopia under climate change, and precautionary measures to avoid soil acidification should be a key element in the design of climate change adaptation strategies

An international laboratory comparison of dissolved organic matter composition by high resolution mass spectrometry: Are we getting the same answer?

High-resolution mass spectrometry (HRMS) has become a vital tool for dissolved organic matter (DOM) characterization. The upward trend in HRMS analysis of DOM presents challenges in data comparison and interpretation among laboratories operating instruments with differing performance and user operating conditions. It is therefore essential that the community establishes metric ranges and compositional trends for data comparison with reference samples so that data can be robustly compared among research groups. To this end, four identically prepared DOM samples were each measured by 16 laboratories, using 17 commercially purchased instruments, using positive-ion and negative-ion mode electrospray ionization (ESI) HRMS analyses. The instruments identified ~1000 common ions in both negative- and positive-ion modes over a wide range of m/z values and chemical space, as determined by van Krevelen diagrams. Calculated metrics of abundance-weighted average indices (H/C, O/C, aromaticity, and m/z) of the commonly detected ions showed that hydrogen saturation and aromaticity were consistent for each reference sample across the instruments, while average mass and oxygenation were more affected by differences in instrument type and settings. In this paper we present 32 metric values for future benchmarking. The metric values were obtained for the four different parameters from four samples in two ionization modes and can be used in future work to evaluate the performance of HRMS instruments

The need for evidence-based climate risk and adaptation assessments:Lessons learned from the AGRICA project

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Did ERA5 Improve Temperature and Precipitation Reanalysis over East Africa?

Reanalysis products are often taken as an alternative solution to observational weather and climate data due to availability and accessibility problems, particularly in data-sparse regions such as Africa. Proper evaluation of their strengths and weaknesses, however, should not be overlooked. The aim of this study was to evaluate the performance of ERA5 reanalysis and to document the progress made compared to ERA-interim for the fields of near-surface temperature and precipitation over Africa. Results show that in ERA5 the climatological biases in temperature and precipitation are clearly reduced and the representation of inter-annual variability is improved over most of Africa. However, both reanalysis products performed less well in terms of capturing the observed long-term trends, despite a slightly better performance of ERA5 over ERA-interim. Further regional analysis over East Africa shows that the representation of the annual cycle of precipitation is substantially improved in ERA5 by reducing the wet bias during the rainy season. The spatial distribution of precipitation during extreme years is also better represented in ERA5. While ERA5 has improved much in comparison to its predecessor, there is still demand for improved products with even higher resolution and accuracy to satisfy impact-based studies, such as in agriculture and water resources. © 2020 by the authors

Did ERA5 Improve Temperature and Precipitation Reanalysis over East Africa?

Reanalysis products are often taken as an alternative solution to observational weather and climate data due to availability and accessibility problems, particularly in data-sparse regions such as Africa. Proper evaluation of their strengths and weaknesses, however, should not be overlooked. The aim of this study was to evaluate the performance of ERA5 reanalysis and to document the progress made compared to ERA-interim for the fields of near-surface temperature and precipitation over Africa. Results show that in ERA5 the climatological biases in temperature and precipitation are clearly reduced and the representation of inter-annual variability is improved over most of Africa. However, both reanalysis products performed less well in terms of capturing the observed long-term trends, despite a slightly better performance of ERA5 over ERA-interim. Further regional analysis over East Africa shows that the representation of the annual cycle of precipitation is substantially improved in ERA5 by reducing the wet bias during the rainy season. The spatial distribution of precipitation during extreme years is also better represented in ERA5. While ERA5 has improved much in comparison to its predecessor, there is still demand for improved products with even higher resolution and accuracy to satisfy impact-based studies, such as in agriculture and water resources