ALBERT

Description: This climate risk profile provides an overview of projected climate parameters and related impacts on different sectors in Jordan until 2080, under different climate change scenarios provided (called Represent- ative Concentration Pathways, RCPs). RCP2.6 repre- sents a low emissions scenario that aims to keep global warming below 2 °C above pre-industrial temperatures; RCP6.0 represents a medium to high emissions scenario. Model projections do not account for effects of future socioeconomic impacts.

Description: This profile provides an overview of projected climate parameters and related impacts on different sectors in Iraq until 2080 under different climate change scenarios provided by the IPCC¹ (called Representative Concentra- tion Pathways, RCPs). RCP2.6 represents a low emissions scenario that aims to keep global warming below 2 °C above pre-industrial temperatures; RCP6.0 represents a medium to high emissions scenario. Model projections do not account for effects of future socioeconomic impacts.

Description: Southern Africa has been identified as one of the hotspot areas of climate extremes increasing, at the same time many communities in the region are dependent on rain-fed agriculture, which is vulnerable to these rainfall and temperature extremes. The aimof this study is to understand changes in extreme indices during the agricultural season under climate change and how that affect the modeling of maize suitability in Southern Africa. We analyze the changes in rainfall and its extreme indices (consecutive dry days, heavy rain events and prolonged rainfall events), and temperature and its extreme indices (hot night temperatures, hot day temperatures and frequency of very hot days) from the past (1986–2014) to the future (2036–2064) and integrate these into a maize suitability model. Temperature extremes are projected to increase in both duration and intensity, particularly in the eastern parts of the region. Also, consecutive dry days are projected to increase over larger areas during the agricultural season, while rainfall will be less in sums, heavier in intensity and less prolonged in duration. Including extreme climate indices in maize suitabilitymodeling improves the efficiency of themaize suitabilitymodel and showsmore severe changes in maize suitability over Southern Africa than using season-long climatic variables. We conclude that changes in climate extremes will increase and complicate the livelihood-climate nexus in Southern Africa in the future, and therefore, a set of comprehensive adaptation options for the agricultural sector are needed. These include the use of heat, drought and high-intensity rainfall tolerant maize varieties, irrigation and/or soil water conservation techniques, and in some cases switching from maize to other crops.

Description: This profile provides an overview of projected climate parameters and related impacts on different sectors in Eastern Africa until 2080 under different climate change scenarios (called Representative Concentration Pathways, RCPs). RCP2.6 represents the low emissions scenario that aims to keep global warming likely below 2 °C above pre-industrial temperatures. RCP6.0 repre- sents a medium to high emissions scenario that is likely to exceed 2 °C. Model projections do not account for effects of future socio-economic impacts, unless indicated otherwise.

Description: Reliable information on climate impacts can support planning processes to make the agricultural sectorwhich has cascading effects on food security, livelihoods, and the security situation – more resilient. Subsequently, uncertainties in past and future climate data need to be decreased and better understood. In this study, we analysed the quality and limitations of different past and future climate data sets to be used for agricultural impact assessments in West Africa. The high differences between the three analysed past climate data sets underline the high observational uncertainty in West Africa and show the influence of selecting the observational data set for the bias-adjustment of climate model data. The ten CMIP6 (Coupled Model Inter-comparison Project Phase 6) models show regional and model-dependent biases with similar systematic biases as have been observed in earlier CMIP 40 versions. Although the bias-adjusted version of this data (ISIMIP3b - Inter-Sectoral Impact Model Intercomparison Project) aligns overall well with observations, we could detect some regional strong deviations from observations for some agroclimatological indices. The use of the multi-model 43 ensemble mean has resulted in an improved agreement of CMIP6 and the bias-adjusted ISIMIP3b data with observations. Choosing a sub-ensemble of bias-adjusted models could only improve the performance of the ensemble mean locally but not over the whole region. Therefore, our results 46 suggest the use of the whole model ensemble for agricultural impact assessments in West Africa. While averaging the impact results over all climate models can serve as a best guess, the spread of the results over all models should be considered to give insights into the uncertainties. This study can support agricultural impact modelling in quantifying climate risk hotspots as well as suggesting suitable adaptation measures to increase the resilience of the agricultural sector in West Africa.

Description: This profile provides an overview of projected climatic changes and related impacts on different sectors in Southern Africa until 2080 under different climate change scenarios (called Representative Concentration Pathways, RCPs). RCP2.6 represents the low emissions scenario that aims to keep global warming likely below 2 °C above pre-industrial temperatures. RCP6.0 represents a medium to high emissions scenario that is likely to exceed 2 °C. Model projections do not account for effects of future socio-economic impacts, unless indicated otherwise.

Description: This profile provides an overview of projected climate parameters and related impacts on different sec- tors in Pakistan until 2080 under different climate change scenarios (called Representative Concentration Pathways, RCPs). RCP2.6 represents the low-emissions scenario, which aims to keep global warming likely be- low 2 °C compared to pre-industrial temperatures, and RCP6.0 represents a medium / high emissions scenario. RCP6.0 was selected to follow a middle-of-the-road pathway, while RCP2.6 was added as the best-case sce- nario. We avoid to add the worst-case scenario RCP8.5, as the energy behaviour leading to such emissions has been deemed not realistic. Model projections do not account for effects of future socioeconomic impacts.

Description: Zambia has a high socio-economic dependency on agriculture which is strongly influenced by weather-related factors and highly vulnerable to climate change. To address current and future climate-related risks in the agricultural sector, this study provides a comprehensive climate risk analysis and evaluates suitable adaptation options to promote climate-resilient agricultural intensification in Zambia. Driven by ten global climate models under two climate change scenarios, SSP 1-RCP2.6 and SSP 3-RCP7.0, we used impact models to analyse future trends in climatic conditions and impacts on agriculture. As part of our adaptation analysis, we consider aspects of risk mitigation potential, cost-effectiveness, financing and gender. The results have been complemented and cross-checked by expert and literature-based assessments and two stakeholder workshops. Climate models project a robust trend towards increasing temperatures all over Zambia ranging between 2 °C and 2.7 °C until mid-century, with the south-western regions showing the strongest increase. Projections of mean precipitation indicate high spatial variations within the country. The drought-prone southern and central parts of the country are projected to experience a decrease in precipitation with ongoing climate change. Overall, there is a shift towards more intense climatic conditions both in terms of dry as well as wet conditions. Climate change will have various impacts on agriculture, for example, a decrease in sorghum yields. Mean sorghum yields for the whole country are projected to decrease by 5.8 to 12.2 % by mid-century with spatial and temporal disparities. The decreases are, however, only about half of the projected decrease in maize yields. This confirms that sorghum is indeed a more resilient crop compared to other cereals. Climate change also affects the extent and distribution of suitable areas for crop production in Zambia. Areas suitable for maize and sorghum production will decrease between 28 and 37 % by mid-century and move northwards within Zambia. A case study in the Kafue Catchment and parts of the Zambezi Catchment shows an increase in water demand and a decrease in water availability – leading to an overall reduction in the climate-related irrigation potential in future. The negative climate impacts on agriculture in Zambia underline the need for strong adaptation efforts. The study analyses two adaptation options, which were selected based on stakeholder priorities: Conservation agriculture and early warning systems. Conservation agriculture is a farming system that promotes minimum soil disturbance, maintenance of a permanent soil cover and diversification of plant species. It can buffer climate impacts in the near term and even increase sorghum yields by 25 to 31 % in drought-prone areas in Zambia. It can play a vital role in adapting to increasingly extreme and dry climatic conditions in Zambia in the near future. Early warning systems have a high potential for anticipating climate risks and thus improving food and nutrition security. In our analysis, we focus on a participatory approach for climate and agricultural extension services that integrates climate information and weather forecasts to inform livelihood decisions of farmers – called PICSA (Participatory Integrated Climate Services for Agriculture). The results show that the initial investment needed to employ PICSA already becomes economically beneficial after one year with returns increasing in the future. Each USD invested in PICSA generates between 3.6 and 3.8 USD in benefits depending upon the climate scenario considered. This suggests that employing PICSA is a highly cost-effective investment that constitutes an important variable in safeguarding farmers’ long-term livelihood. Generally, a combination of different adaptation options entails additional benefits. Active stakeholder engagement as well as participatory, gender-sensitive approaches are needed to ensure the feasibility and long-term sustainability of adaptation options. The findings of this study can help to inform national and local adaptation and agricultural development planning and investments in order to strengthen the resilience of the agricultural sector and especially of smallholder farmers against a changing climate.

Description: Here, we present BASD-CMIP6-PE, a high-resolution (1d, 10 km) climate dataset for Peru and Ecuador based on the bias-adjusted and statistically downscaled CMIP6 climate projections of 10 GCMs. This dataset includes both historical simulations (1850–2014) and future projections (2015–2100) for precipitation and minimum, mean, and maximum temperature under three Shared Socioeconomic Pathways (SSP1-2.6, SSP3-7.0, and SSP5-8.5). The BASD-CMIP6-PE climate data were generated using the trend-preserving Bias Adjustment and Statistical Downscaling (BASD) method. The BASD performance was evaluated using observational data and through hydrological modeling across Peruvian and Ecuadorian river basins in the historical period. Results demonstrated that BASD significantly reduced biases between CMIP6-GCM simulations and observational data, enhancing long-term statistical representations, including mean and extreme values, and seasonal patterns. Furthermore, the hydrological evaluation highlighted the appropriateness of adjusted GCM simulations for simulating streamflow, including mean, low, and high flows. These findings underscore the reliability of BASD-CMIP6-PE in assessing regional climate change impacts on agriculture, water resources, and hydrological extremes.

Description: The new climate dataset, BASD-CMIP6-PE, for Peru and Ecuador is based on bias-adjusted and statistically downscaled CMIP6 climate projections from 10 GCMs. It addresses the need for reliable high-resolution (1d, 10km) climate data covering Peru and Ecuador. This dataset includes historical simulations (1850-2014) and future projections (2015-2100) for precipitation and minimum, mean, and maximum temperature under three Shared Socioeconomic Pathways (SSPs:SSP1-2.6, SSP3-7.0, and SSP5-8.5). The BASD-CMIP6-PE climate data were generated using the trend-preserving Bias Adjustment and Statistical Downscaling (BASD) method (Lange, 2019, 2021) and data from regional observational datasets such as RAIN4PE (Fernandez-Palomino et al., 2021a,b) for precipitation and PISCO-temperature (Huerta et al., 2018) for temperatures as reference data. The reliability of the BASD-CMIP6-PE was evaluated using observational data and through hydrological modeling across Peruvian and Ecuadorian river basins in the historical period. The evaluation demonstrated the dataset’s reliability in describing spatial patterns of atmospheric variables and streamflow simulation, including mean, low, and high flows. This suggests the usefulness of the new dataset for assessing regional climate change impacts on agriculture, water resources, and hydrological extremes. The BASD-CMIP6-PE data are available for the domain covering Peru and Ecuador, located between 19°S and 2°N and 82°W to 67°W, with a spatial resolution of 0.1° and a daily temporal resolution. The unit for precipitation is millimeters (mm), and for temperature, it is degrees Celsius (°C). The BASD-CMIP6-PE dataset is organized within a "daily" folder, denoting its availability at a 1 daily temporal resolution. Within this directory, four subfolders are present: "historical" containing historical data, "ssp126" for SSP1-2.6, "ssp370" for SSP3-7.0, and "ssp585" for SSP5-8.5. Each of these subfolders further includes ten distinct folders, corresponding to different GCMs: CanESM5, IPSL–CM6A–LR, UKESM1–0–LL, CNRM–CM6–1, CNRM–ESM2–1, MIROC6, GFDL–ESM4, MRI–ESM2–0, MPI–ESM1–2–HR, and EC–Earth3. These folders store the data in the NetCDF format arranged by model, model member, experiment, variable, temporal resolution, and subset period, resulting in file names like "canesm5_r1i1p1f1_ssp126_pr_daily_2015_2020.nc". For a detailed description of the BASD-CMIP6-PE development and evaluation, readers are advised to read Fernandez-Palomino et al. (2023), for which this dataset is supplementary material.