Dr David Macleod

(Translated he/him)

2026

• Mena, R. et al., 2026. Syria’s disaster risk profile and opportunities for disaster governance: from crisis to capacity. In: Alam, E. ed. Disaster, Crisis and Emergency Management in the Middle East and North Africa. Cham, Switzerland: Springer. , pp.155-176. (10.1007/978-3-032-24436-9_7)
• Ansell, T. , Mena, R. and MacLeod, D. 2026. Co-creating community-based disaster risk reduction in conflict settings: evidence from North-West Syria. Journal of Hazards Risk and Resilience 1 (1) 18. (10.53941/jhrr.2026.100018)
• O'Reilly, C. H. et al., 2026. Evaluating seasonal forecast improvements over the past two decades. Quarterly Journal of the Royal Meteorological Society 152 (774) e70036. (10.1002/qj.70036)

2025

• Chappell, A. , He, P. and MacLeod, D. 2025. Vignette 10.1: Challenging fieldwork with problem-based learning. In: France, D. , Batty, L. and Swanton, D. eds. Teaching Fieldwork in Geography, Earth and Environmental Sciences. Geography, Planning and Tourism 2025 Cheltenham: Edward Elgar Publishing. , pp.293-295. (10.4337/9781035322398.00056)
• Daron, J. et al., 2025. SIMBOL: A method to co-produce impact-based seasonal outlooks. Climate Services 38 100579. (10.1016/j.cliser.2025.100579)
• Odongo, R. A. et al., 2025. Drought impacts and community adaptation: perspectives on the 2020-2023 drought in East Africa. International Journal of Disaster Risk Reduction 119 105309. (10.1016/j.ijdrr.2025.105309)
• Antonio, B. et al., 2025. Postprocessing East African rainfall forecasts using a generative machine learning model. Journal of Advances in Modeling Earth Systems 17 (3) e2024MS004796. (10.1029/2024ms004796)

2024

• Cocking, K. et al. 2024. Locally defined seasonal rainfall characteristics within the Horn of Africa drylands from rain gauge observations. Journal of Hydrometeorology 25 (12), pp.1845-1861. (10.1175/jhm-d-23-0228.1)
• Kolstad, E. W. et al., 2024. Beyond the regional average: Drivers of geographical rainfall variability during East Africa's short rains. Quarterly Journal of the Royal Meteorological Society 150 (764), pp.4550-4566. (10.1002/qj.4829)
• Mwangi, E. et al., 2024. Variability of rainy season onsets over East Africa. International Journal of Climatology 44 (10), pp.3357-3379. (10.1002/joc.8528)
• MacLeod, D. et al. 2024. Sensitivity of rainfall extremes to unprecedented Indian Ocean Dipole events. Geophysical Research Letters 51 (5) e2023GL105258. (10.1029/2023gl105258)
• Antonio, B. et al., 2024. Postprocessing East African rainfall forecasts using a generative machine learning model. [Online].Authorea. (10.22541/au.170967780.04179996/v1)Available at: https://doi.org/10.22541/au.170967780.04179996/v1.
• Antonio, B. et al., 2024. Postprocessing East African rainfall forecasts using a generative machine learning model. Presented at: EGU General Assembly 2024 Vienna, Austria 14-19 April 2024. EGU General Assembly 2024 Abstracts. Copernicus. (10.5194/egusphere-egu24-2897)

2023

• Steinke, J. et al., 2023. Seasonal seed scenario planning: co-design of a generic framework for matching seed supply and demand using seasonal climate forecasts. Climate Services 32 100410. (10.1016/j.cliser.2023.100410)
• Macleod, D. et al. 2023. Translating seasonal climate forecasts into water balance forecasts for decision making. PLOS Climate 2 (3) e0000138. (10.1371/journal.pclm.0000138)

2022

• Mwangi, E. et al., 2022. Mainstreaming forecast based action into national disaster risk management systems: experience from drought risk management in Kenya. Climate and Development 14 (8), pp.741–756. (10.1080/17565529.2021.1984194)
• Kolstad, E. W. and MacLeod, D. 2022. Lagged oceanic effects on the East African short rains. Climate Dynamics 59 , pp.1043–1056. (10.1007/s00382-022-06176-6)
• White, C. J. et al., 2022. Advances in the application and utility of subseasonal-to-seasonal predictions. Bulletin of the American Meteorological Society 103 (6), pp.E1448–E1472. (10.1175/BAMS-D-20-0224.1)
• Muktadir, M. et al., 2022. Links between land cover change and climate in the Horn of Africa. Presented at: EGU General Assembly 2022 Vienna, Austria 23–27 May 2022. (10.5194/egusphere-egu22-10577)
• Otieno, D. et al., 2022. Climate predictions in a forecast based action (FbA) pilot within the Greater Horn of Africa; Experiences from ForPAc and Down2Earth projects. Presented at: EGU General Assembly 2022 Vienna, Austria 23–27 May 2022. (10.5194/egusphere-egu22-2129)
• Boult, V. L. et al., 2022. Towards drought impact-based forecasting in a multi-hazard context. Climate Risk Management 35 100402. (10.1016/j.crm.2022.100402)
• MacLeod, D. et al. 2022. Translating seasonal climate forecasts into decision-relevant water security forecasts. Presented at: EGU General Assembly 2022 23–27 May 2022. (10.5194/egusphere-egu22-2804)

2021

• MacLeod, D. et al. 2021. Verification of forecasts for extreme rainfall, tropical cyclones, flood and storm surge over Myanmar and the Philippines. Weather and Climate Extremes 33 100325. (10.1016/j.wace.2021.100325)
• Singer, M. B. et al. 2021. Hourly potential evapotranspiration at 0.1˚ resolution for the global land surface from 1981-present. Scientific Data 8 224. (10.1038/s41597-021-01003-9)
• Guigma, K. H. et al., 2021. Prediction skill of Sahelian heatwaves out to subseasonal lead times and importance of atmospheric tropical modes of variability. Climate Dynamics 57 (1-2), pp.537–556. (10.1007/s00382-021-05726-8)
• Kolstad, E. W. , MacLeod, D. and Demissie, T. D. 2021. Drivers of subseasonal forecast errors of the East African short rains. Geophysical Research Letters 48 (14) e2021GL093292. (10.1029/2021GL093292)
• MacLeod, D. A. et al. 2021. Drivers and subseasonal predictability of heavy rainfall in Equatorial East Africa and relationship with flood risk. Journal of Hydrometeorology 22 (4), pp.887–903. (10.1175/JHM-D-20-0211.1)
• de Andrade, F. M. et al., 2021. Subseasonal precipitation prediction for Africa: forecast evaluation and sources of predictability. Weather and Forecasting 36 (1), pp.265–284. (10.1175/WAF-D-20-0054.1)
• MacLeod, D. et al. 2021. Causal pathways linking different flavours of ENSO with the Greater Horn of Africa short rains. Atmospheric Science Letters 22 (2) e1015. (10.1002/asl.1015)
• MacLeod, D. et al. 2021. Are Kenya Meteorological Department heavy rainfall advisories useful for forecast-based early action and early preparedness?. Natural Hazards and Earth System Sciences 21 , pp.261-277. (10.5194/nhess-21-261-2021)
• MacLeod, D. , Kniveton, D. R. and Todd, M. C. 2021. Playing the long game: anticipatory action based on seasonal forecasts. Climate Risk Management 34 100375. (10.1016/j.crm.2021.100375)
• Weisheimer, A. et al., 2021. How confident are predictability estimates of the winter North Atlantic Oscillation?. Presented at: EGU General Assembly 2021 Online 19–30 Apr 2021. EGU General Assembly 2021 Proceedings. (10.5194/egusphere-egu21-2928)

2020

• MacLeod, D. et al. 2020. Subseasonal forecasts for humanitarian decision-making in Kenya: understanding forecast skill and the latest results from the S2S ForPAc real-time pilot study. Presented at: EGU General Assembly 2020 4–8 May 2020. (10.5194/egusphere-egu2020-3533)

2019

• MacLeod, D. and Caminade, C. 2019. The moderate impact of the 2015 el niño over East Africa and its representation in seasonal reforecasts. Journal of Climate 32 (22), pp.7989–8001. (10.1175/JCLI-D-19-0201.1)
• MacLeod, D. 2019. Seasonal forecasts of the East African long rains: insight from atmospheric relaxation experiments. Climate Dynamics 53 (7-8), pp.4505–4520. (10.1007/s00382-019-04800-6)

2018

• Kilavi, M. et al., 2018. Extreme rainfall and flooding over Central Kenya including Nairobi city during the long-rains season 2018: causes, predictability, and potential for early warning and actions. Atmosphere 9 (12) 472. (10.3390/atmos9120472)
• Hosking, J. S. et al., 2018. Changes in European wind energy generation potential within a 1.5 C warmer world. Environmental Research Letters 13 (5) 054032. (10.1088/1748-9326/aabf78)
• MacLeod, D. et al. 2018. Flow dependent ensemble spread in seasonal forecasts of the boreal winter extratropics. Atmospheric Science Letters 19 (5) e815. (10.1002/asl.815)

2017

• MacLeod, D. et al. 2017. Transforming climate model output to forecasts of wind power production: how much resolution is enough?. Meteorological Applications 25 (1), pp.1-10. (10.1002/met.1660)
• White, C. J. et al., 2017. Potential applications of subseasonal-to-seasonal (S2S) predictions. Meteorological Applications 24 (3), pp.315-325. (10.1002/met.1654)
• Leutbecher, M. et al., 2017. Stochastic representations of model uncertainties at ECMWF: state of the art and future vision. Quarterly Journal of the Royal Meteorological Society 143 (707), pp.2315-2339. (10.1002/qj.3094)
• O'Reilly, C. H. et al., 2017. Variability in seasonal forecast skill of Northern Hemisphere winters over the twentieth century. Geophysical Research Letters 44 (11), pp.5729-5738. (10.1002/2017GL073736)
• Torralba, V. et al., 2017. Seasonal climate prediction: a new source of information for the management of wind energy resources. Journal of Applied Meteorology and Climatology 56 (5), pp.1231–1247. (10.1175/JAMC-D-16-0204.1)
• Weisheimer, A. et al., 2017. Atmospheric seasonal forecasts of the twentieth century: multi-decadal variability in predictive skill of the winter North Atlantic Oscillation (NAO) and their potential value for extreme event attribution. Quarterly Journal of the Royal Meteorological Society 143 (703), pp.917-926. (10.1002/qj.2976)

2016

• MacLeod, D. et al. 2016. Evaluating uncertainty in estimates of soil moisture memory with a reverse ensemble approach. Hydrology and Earth System Sciences 20 (7), pp.2737–2743. (10.5194/hess-20-2737-2016)
• MacLeod, D. A. et al. 2016. Improved seasonal prediction of the hot summer of 2003 over Europe through better representation of uncertainty in the land surface. Quarterly Journal of the Royal Meteorological Society 142 (694), pp.79-90. (10.1002/qj.2631)

2015

• MacLeod, D. A. et al. 2015. Demonstration of successful malaria forecasts for Botswana using an operational seasonal climate model. Environmental Research Letters 10 (4) 044005. (10.1088/1748-9326/10/4/044005)
• Shi, W. et al., 2015. Impact of hindcast length on estimates of seasonal climate predictability. Geophysical Research Letters 42 (5), pp.1554-1559. (10.1002/2014GL062829)

2014

• MacLeod, D. A. and Morse, A. P. 2014. Visualizing the uncertainty in the relationship between seasonal average climate and malaria risk. Scientific Reports 4 7264. (10.1038/srep07264)
• Lauderdale, J. M. et al., 2014. Towards seasonal forecasting of malaria in India. Malaria Journal 13 (1) 310. (10.1186/1475-2875-13-310)
• Caminade, C. et al., 2014. Rift Valley fever outbreaks in Mauritania and related environmental conditions. International Journal of Environmental Research and Public Health 11 (1), pp.903-918. (10.3390/ijerph110100903)

2013

• MacLeod, D. 2013. Quantifying uncertainty in climate-driven disease risk predictions. PhD Thesis , University of Liverpool.

2012

• MacLeod, D. A. , Caminade, C. and Morse, A. P. 2012. Useful decadal climate prediction at regional scales? A look at the ENSEMBLES stream 2 decadal hindcasts. Environmental Research Letters 7 (4) 044012. (10.1088/1748-9326/7/4/044012)