Climate Analytics Lab

Improving climate projections with machine learning

2025

  1. Talvinen, S., Kim, P., Tovazzi, E., Holopainen, E., Cremer, R., Kühn, T., Kokkola, H., Kipling, Z., Neubauer, D., Teixeira, J. C., Sellar, A., Watson-Parris, D., Yang, Y., Zhu, J., Krishnan, S., Virtanen, A., and Partridge, D. G.: Towards an improved understanding of the impact of clouds and precipitation on the representation of aerosols over the Boreal Forest in GCMs, Atmospheric Chemistry and Physics, 25, 14449–14478, 2025. [doi]
  2. Jordan, G., Malavelle, F., Haywood, J., Chen, Y., Johnson, B., Partridge, D., Peace, A., Duncan, E., Watson-Parris, D., Neubauer, D., Laakso, A., Michou, M., and Nabat, P.: How well are aerosol–cloud interactions represented in climate models? – Part 2: Isolating the aerosol impact on clouds following the 2014–2015 Holuhraun eruption, Atmospheric Chemistry and Physics, 25, 13393–13428, 2025. [doi]
  3. Baño-Medina, J., Sengupta, A., Michaelis, A., Monache, L. D., Kalansky, J., and Watson-Parris, D.: Harnessing AI Data-Driven Global Weather Models for Climate Attribution: An Analysis of the 2017 Oroville Dam Extreme Atmospheric River, 2025. [doi]
  4. Herbert, R. J., Williams, A. I. L., Weiss, P., Watson-Parris, D., Dingley, E., Klocke, D., and Stier, P.: Regional variability of aerosol impacts on clouds and radiation in global kilometer-scale simulations, Atmospheric Chemistry and Physics, 25, 7789–7814, 2025. [doi]
  5. Wang, K., Varambally, S., Watson-Parris, D., Ma, Y., and Yu, R.: Discovering Latent Causal Graphs from Spatiotemporal Data, in: , 2025. [link]
  6. Lyu, B., Cao, Y., Watson-Parris, D., Bergen, L., Berg-Kirkpatrick, T., and Yu, R.: Adapting While Learning: Grounding LLMs for Scientific Problems with Tool Usage Adaptation, in: , 2025. [link]
  7. Watson-Parris, D., Wilcox, L. J., Stjern, C. W., Allen, R. J., Persad, G., Bollasina, M. A., Ekman, A. M. L., Iles, C. E., Joshi, M., Lund, M. T., McCoy, D., Westervelt, D. M., Williams, A. I. L., and Samset, B. H.: Surface temperature effects of recent reductions in shipping SO_\textrm2 emissions are within internal variability, Atmospheric Chemistry and Physics, 25, 4443–4454, 2025. [doi]
    Highlighted in Science
  8. Baño-Medina, J., Sengupta, A., Doyle, J. D., Reynolds, C. A., Watson-Parris, D., and Monache, L. D.: Are AI weather models learning atmospheric physics? A sensitivity analysis of cyclone Xynthia, npj Climate and Atmospheric Science, 8, 92, 2025. [doi]
  9. Nowack, P. and Watson-Parris, D.: Opinion: Why all emergent constraints are wrong but some are useful – a machine learning perspective, Atmospheric Chemistry and Physics, 25, 2365–2384, 2025. [doi]
  10. Petrenko, M., Kahn, R., Chin, M., Bauer, S. E., Bergman, T., Bian, H., Curci, G., Johnson, B., Kaiser, J. W., Kipling, Z., Kokkola, H., Liu, X., Mezuman, K., Mielonen, T., Myhre, G., Pan, X., Protonotariou, A., Remy, S., Skeie, R. B., Stier, P., Takemura, T., Tsigaridis, K., Wang, H., Watson-Parris, D., and Zhang, K.: Biomass burning emission analysis based on MODIS aerosol optical depth and AeroCom multi-model simulations: implications for model constraints and emission inventories, Atmospheric Chemistry and Physics, 25, 1545–1567, 2025. [doi]
  11. Russo, M. R., Bartholomew, S. L., Hassell, D., Mason, A. M., Neininger, E., Perman, A. J., Sproson, D. A. J., Watson-Parris, D., and Abraham, N. L.: Virtual Integration of Satellite and In-situ Observation Networks (VISION) v1.0: In-Situ Observations Simulator (ISO_simulator), Geoscientific Model Development, 18, 181–191, 2025. [doi]
  12. Myhre, G., Samset, B. H., Stjern, C. W., Hodnebrog, Ø., Kramer, R., Smith, C., Andrews, T., Boucher, O., Faluvegi, G., Forster, P. M., Iversen, T., Kirkevåg, A., Olivié, D., Shindell, D., Stier, P., and Watson-Parris, D.: The warming effect of black carbon must be reassessed in light of observational constraints, Cell Reports Sustainability, 100428, 2025. [doi]
  13. Watson-Parris, D.: Integrating Top-Down Energetic Constraints With Bottom-Up Process-Based Constraints for More Accurate Projections of Future Warming, Geophysical Research Letters, 52, e2024GL114269, 2025. [doi]
  14. Lütjens, B., Ferrari, R., Watson-Parris, D., and Selin, N. E.: The Impact of Internal Variability on Benchmarking Deep Learning Climate Emulators, Journal of Advances in Modeling Earth Systems, 17, e2024MS004619, 2025. [doi]
  15. Baño-Medina, J., Sengupta, A., Watson-Parris, D., Hu, W., and Delle Monache, L.: Toward Calibrated Ensembles of Neural Weather Model Forecasts, Journal of Advances in Modeling Earth Systems, 17, e2024MS004734, 2025. [doi]
  16. Dewey, M., Hansson, H.-C., Watson-Parris, D., Samset, B. H., Wilcox, L. J., Lewinschal, A., Sand, M., Seland, Ø., Krishnan, S., and Ekman, A. M. L.: AeroGP: Machine Learning How Aerosols Impact Regional Climate, Journal of Geophysical Research: Machine Learning and Computation, 2, e2025JH000741, 2025. [doi]

2024

  1. Manivannan, V. V., Jafari, Y., Eranky, S., Ho, S., Yu, R., Watson-Parris, D., Ma, Y., Bergen, L., and Berg-Kirkpatrick, T.: ClimaQA: An Automated Evaluation Framework for Climate Question Answering Models, in: , 2024. [link]
  2. Niu, R., Wu, D., Kim, K., Ma, Y., Watson-Parris, D., and Yu, R.: Multi-Fidelity Residual Neural Processes for Scalable Surrogate Modeling, in: , 2024. [link]
  3. Fiedler, S., Naik, V., O’Connor, F. M., Smith, C. J., Griffiths, P., Kramer, R. J., Takemura, T., Allen, R. J., Im, U., Kasoar, M., Modak, A., Turnock, S., Voulgarakis, A., Watson-Parris, D., Westervelt, D. M., Wilcox, L. J., Zhao, A., Collins, W. J., Schulz, M., Myhre, G., and Forster, P. M.: Interactions between atmospheric composition and climate change – progress in understanding and future opportunities from AerChemMIP, PDRMIP, and RFMIP, Geoscientific Model Development, 17, 2387–2417, 2024. [doi]
  4. Jordan, G., Malavelle, F., Chen, Y., Peace, A., Duncan, E., Partridge, D. G., Kim, P., Watson-Parris, D., Takemura, T., Neubauer, D., Myhre, G., Skeie, R., Laakso, A., and Haywood, J.: How well are aerosol–cloud interactions represented in climate models? – Part 1: Understanding the sulfate aerosol production from the 2014–15 Holuhraun eruption, Atmospheric Chemistry and Physics, 24, 1939–1960, 2024. [doi]
  5. Bouabid, S., Watson-Parris, D., Stefanović, S., Nenes, A., and Sejdinovic, D.: Aerosol optical depth disaggregation: toward global aerosol vertical profiles, Environmental Data Science, 3, 2024. [doi]
  6. Toll, V., Rahu, J., Keernik, H., Trofimov, H., Voormansik, T., Manshausen, P., Hung, E., Michelson, D., Christensen, M. W., Post, P., Junninen, H., Murray, B. J., Lohmann, U., Watson-Parris, D., Stier, P., Donaldson, N., Storelvmo, T., Kulmala, M., and Bellouin, N.: Glaciation of liquid clouds, snowfall, and reduced cloud cover at industrial aerosol hot spots., Science (New York, N.Y.), 386, 756–762, 2024. [doi]
  7. Eidhammer, T., Gettelman, A., Thayer-Calder, K., Watson-Parris, D., Elsaesser, G., Morrison, H., Lier-Walqui, M. van, Song, C., and McCoy, D.: An extensible perturbed parameter ensemble for the Community Atmosphere Model version 6, Geoscientific Model Development, 17, 7835–7853, 2024. [doi]
  8. Gettelman, A., Eidhammer, T., Duffy, M. L., McCoy, D. T., Song, C., and Watson-Parris, D.: The Interaction Between Climate Forcing and Feedbacks, Journal of Geophysical Research: Atmospheres, 129, e2024JD040857, 2024. [doi]
  9. Gettelman, A., Christensen, M. W., Diamond, M. S., Gryspeerdt, E., Manshausen, P., Stier, P., Watson-Parris, D., Yang, M., Yoshioka, M., and Yuan, T.: Has Reducing Ship Emissions Brought Forward Global Warming?, Geophysical Research Letters, 51, e2024GL109077, 2024. [doi]
  10. Bouabid, S., Sejdinovic, D., and Watson-Parris, D.: FaIRGP: A Bayesian Energy Balance Model for Surface Temperatures Emulation, Journal of Advances in Modeling Earth Systems, 16, e2023MS003926, 2024. [doi]
  11. Song, C., McCoy, D. T., Eidhammer, T., Gettelman, A., McCoy, I. L., Watson-Parris, D., Wall, C. J., Elsaesser, G., and Wood, R.: Buffering of Aerosol-Cloud Adjustments by Coupling Between Radiative Susceptibility and Precipitation Efficiency, Geophysical Research Letters, 51, e2024GL108663, 2024. [doi]

2023

  1. Regayre, L. A., Deaconu, L., Grosvenor, D. P., Sexton, D. M. H., Symonds, C., Langton, T., Watson-Paris, D., Mulcahy, J. P., Pringle, K. J., Richardson, M., Johnson, J. S., Rostron, J. W., Gordon, H., Lister, G., Stier, P., and Carslaw, K. S.: Identifying climate model structural inconsistencies allows for tight constraint of aerosol radiative forcing, Atmospheric Chemistry and Physics, 23, 8749–8768, 2023. [doi]
  2. Yik, W., Silva, S. J., Geiss, A., and Watson-Parris, D.: Exploring Randomly Wired Neural Networks for Climate Model Emulation, Artificial Intelligence for the Earth Systems, 1–34, 2023. [doi]
  3. Manshausen, P., Watson-Parris, D., Christensen, M. W., Jalkanen, J.-P., and Stier, P.: Rapid saturation of cloud water adjustments to shipping emissions, Atmospheric Chemistry and Physics, 23, 12545–12555, 2023. [doi]
  4. Williams, A. I. L., Watson-Parris, D., Dagan, G., and Stier, P.: Dependence of fast changes in global and local precipitation on the geographical location of absorbing aerosol, Journal of Climate, 1–38, 2023. [doi]
  5. Manshausen, P., Watson-Parris, D., Wagner, L., Maier, P., Muller, S. J., Ramminger, G., and Stier, P.: Pollution tracker: Finding industrial sources of aerosol emission in satellite imagery, Environmental Data Science, 2, 2023. [doi]

2022

  1. Harder, P., Watson-Parris, D., Stier, P., Strassel, D., Gauger, N. R., and Keuper, J.: Physics-informed learning of aerosol microphysics, Environmental Data Science, 1, e20, 2022. [doi]
  2. Williams, A. I. L., Stier, P., Dagan, G., and Watson-Parris, D.: Strong control of effective radiative forcing by the spatial pattern of absorbing aerosol, Nature Climate Change, 1–8, 2022. [doi]
  3. Watson-Parris, D., Christensen, M. W., Laurenson, A., Clewley, D., Gryspeerdt, E., and Stier, P.: Shipping regulations lead to large reduction in cloud perturbations, Proceedings of the National Academy of Sciences, 119, e2206885119, 2022. [doi]
    Highlight: “Air pollution cools climate more than expected – this makes cutting carbon emissions more urgent.” The Conversation
  4. Watson-Parris, D. and Smith, C. J.: Large uncertainty in future warming due to aerosol forcing, Nature Climate Change, 1–3, 2022. [doi]
  5. Kasim, M. F., Watson-Parris, D., Deaconu, L., Oliver, S., Hatfield, P., Froula, D. H., Gregori, G., Jarvis, M., Khatiwala, S., Korenaga, J., Topp-Mugglestone, J., Viezzer, E., and Vinko, S. M.: Building high accuracy emulators for scientific simulations with deep neural architecture search, Machine Learning: Science and Technology, 3, 015013, 2022. [doi]
    Highlight: “From models of galaxies to atoms, simple AI shortcuts speed up simulations by billions of times.” Science
  6. Salas-Porras, E. D., Tazi, K., Braude, A., Okoh, D., Lamb, K. D., Watson-Parris, D., Harder, P., and Meinert, N.: Identifying the Causes of Pyrocumulonimbus (PyroCb), arXiv, 2022. [doi]
  7. Christensen, M. W., Gettelman, A., Cermak, J., Dagan, G., Diamond, M., Douglas, A., Feingold, G., Glassmeier, F., Goren, T., Grosvenor, D. P., Gryspeerdt, E., Kahn, R., Li, Z., Ma, P.-L., Malavelle, F., McCoy, I. L., McCoy, D. T., McFarquhar, G., Mülmenstädt, J., Pal, S., Possner, A., Povey, A., Quaas, J., Rosenfeld, D., Schmidt, A., Schrödner, R., Sorooshian, A., Stier, P., Toll, V., Watson-Parris, D., Wood, R., Yang, M., and Yuan, T.: Opportunistic experiments to constrain aerosol effective radiative forcing, Atmospheric Chemistry and Physics, 22, 641–674, 2022. [doi]
  8. Tazi, K., Salas-Porras, E. D., Braude, A., Okoh, D., Lamb, K. D., Watson-Parris, D., Harder, P., and Meinert, N.: Pyrocast: a Machine Learning Pipeline to Forecast Pyrocumulonimbus (PyroCb) Clouds, arXiv, 2022. [doi]
  9. Manshausen, P., Watson-Parris, D., Christensen, M. W., Jalkanen, J.-P., and Stier, P.: Invisible ship tracks show large cloud sensitivity to aerosol, Nature, 610, 101–106, 2022. [doi]
    Nature Briefing: “Finding the invisible traces of shipping in marine clouds”
  10. Che, H., Stier, P., Watson-Parris, D., Gordon, H., and Deaconu, L.: Source attribution of cloud condensation nuclei and their impact on stratocumulus clouds and radiation in the south-eastern Atlantic, Atmospheric Chemistry and Physics, 22, 10789–10807, 2022. [doi]
  11. Whaley, C. H., Mahmood, R., Salzen, K. von, Winter, B., Eckhardt, S., Arnold, S., Beagley, S., Becagli, S., Chien, R.-Y., Christensen, J., Damani, S. M., Dong, X., Eleftheriadis, K., Evangeliou, N., Faluvegi, G., Flanner, M., Fu, J. S., Gauss, M., Giardi, F., Gong, W., Hjorth, J. L., Huang, L., Im, U., Kanaya, Y., Krishnan, S., Klimont, Z., Kühn, T., Langner, J., Law, K. S., Marelle, L., Massling, A., Olivié, D., Onishi, T., Oshima, N., Peng, Y., Plummer, D. A., Popovicheva, O., Pozzoli, L., Raut, J.-C., Sand, M., Saunders, L. N., Schmale, J., Sharma, S., Skeie, R. B., Skov, H., Taketani, F., Thomas, M. A., Traversi, R., Tsigaridis, K., Tsyro, S., Turnock, S., Vitale, V., Walker, K. A., Wang, M., Watson-Parris, D., and Weiss-Gibbons, T.: Model evaluation of short-lived climate forcers for the Arctic Monitoring and Assessment Programme: a multi-species, multi-model study, Atmospheric Chemistry and Physics, 22, 5775–5828, 2022. [doi]

2021

  1. Sand, M., Samset, B. H., Myhre, G., Gliß, J., Bauer, S. E., Bian, H., Chin, M., Checa-Garcia, R., Ginoux, P., Kipling, Z., Kirkevåg, A., Kokkola, H., Le Sager, P., Lund, M. T., Matsui, H., Noije, T. van, Olivié, D. J. L., Remy, S., Schulz, M., Stier, P., Stjern, C. W., Takemura, T., Tsigaridis, K., Tsyro, S. G., and Watson-Parris, D.: Aerosol absorption in global models from AeroCom phase III, Atmospheric Chemistry and Physics, 21, 15929–15947, 2021. [doi]
  2. Watson-Parris, D.: Machine learning for weather and climate are worlds apart, Philosophical Transactions of the Royal Society A, 379, 20200098, 2021. [doi]
  3. Allan, J. and Watson-Parris, D.: Measurements of ambient aerosol properties, in: Aerosols and Climate, Elsevier, 2021. [link]
  4. Witt, C. S. de, Tong, C., Zantedeschi, V., Martini, D. D., Kalaitzis, A., Chantry, M., Watson-Parris, D., and Bilinski, P.: RainBench: Towards Data-Driven Global Precipitation Forecasting from Satellite Imagery, Proceedings of the AAAI Conference on Artificial Intelligence, 35, 14902–14910, 2021. [doi]
  5. Zhang, S., Stier, P., and Watson-Parris, D.: On the contribution of fast and slow responses to precipitation changes caused by aerosol perturbations, Atmospheric Chemistry and Physics, 21, 10179–10197, 2021. [doi]
  6. Watson-Parris, D., Williams, A., Deaconu, L., and Stier, P.: Model calibration using ESEm v1.1.0 – an open, scalable Earth system emulator, Geoscientific Model Development, 14, 7659–7672, 2021. [doi]

2020

  1. Harder, P., Jones, W., Lguensat, R., Bouabid, S., Fulton, J., Quesada-Chacón, D., Marcolongo, A., Stefanović, S., Rao, Y., Manshausen, P., and Watson-Parris, D.: NightVision: Generating Nighttime Satellite Imagery from Infra-Red Observations, arXiv, 2020. [link]
  2. Watson-Parris, D. and Deaconu, L.: Example Perturbed Parameter Ensemble (Black Carbon), , 2020. [doi]
  3. Che, H., Stier, P., Gordon, H., Watson-Parris, D., and Deaconu, L.: Cloud adjustments dominate the overall negative aerosol radiative effects of biomass burning aerosols in UKESM1 climate model simulations over the south-eastern Atlantic, Atmospheric Chemistry and Physics, 21, 17–33, 2020. [doi]
  4. McCoy, I. L., McCoy, D. T., Wood, R., Regayre, L., Watson-Parris, D., Grosvenor, D. P., Mulcahy, J. P., Hu, Y., Bender, F. A.-M., Field, P. R., Carslaw, K. S., and Gordon, H.: The hemispheric contrast in cloud microphysical properties constrains aerosol forcing, Proceedings of the National Academy of Sciences of the United States of America, 117, 18998–19006, 2020. [doi]
  5. Haywood, J. M., Abel, S. J., Barrett, P. A., Bellouin, N., Blyth, A., Bower, K. N., Brooks, M., Carslaw, K., Che, H., Coe, H., Cotterell, M. I., Crawford, I., Cui, Z., Davies, N., Dingley, B., Field, P., Formenti, P., Gordon, H., Graaf, M. de, Herbert, R., Johnson, B., Jones, A. C., Langridge, J. M., Malavelle, F., Partridge, D. G., Peers, F., Redemann, J., Stier, P., Szpek, K., Taylor, J. W., Watson-Parris, D., Wood, R., Wu, H., and Zuidema, P.: The CLoud–Aerosol–Radiation Interaction and Forcing: Year 2017 (CLARIFY-2017) measurement campaign, Atmospheric Chemistry and Physics, 21, 1049–1084, 2020. [doi]

2019

  1. Hodnebrog, Ø., Myhre, G., Samset, B. H., Alterskjær, K., Andrews, T., Boucher, O., Faluvegi, G., Fläschner, D., Forster, P. M., Kasoar, M., Kirkevåg, A., Lamarque, J.-F., Olivié, D., Richardson, T. B., Shawki, D., Shindell, D., Shine, K. P., Stier, P., Takemura, T., Voulgarakis, A., and Watson-Parris, D.: Water vapour adjustments and responses differ between climate drivers, Atmospheric Chemistry and Physics, 19, 12887–12899, 2019. [doi]
  2. Zantedeschi, V., Falasca, F., Douglas, A., Strange, R., Kusner, M. J., and Watson-Parris, D.: Cumulo: A Dataset for Learning Cloud Classes, arXiv, 2019. [link]
    Best Paper Award - NeurIPS 2019 Climate Change AI Workshop
  3. Watson-Parris, D., Sutherland, S., Christensen, M., Caterini, A., Sejdinovic, D., and Stier, P.: Detecting anthropogenic cloud perturbations with deep learning, arXiv, 2019. [link]
    Best Paper Award - ICML 2019 Climate Change AI Workshop
  4. Fanourgakis, G. S., Kanakidou, M., Nenes, A., Bauer, S. E., Bergman, T., Carslaw, K. S., Grini, A., Hamilton, D. S., Johnson, J. S., Karydis, V. A., Kirkevåg, A., Kodros, J. K., Lohmann, U., Luo, G., Makkonen, R., Matsui, H., Neubauer, D., Pierce, J. R., Schmale, J., Stier, P., Tsigaridis, K., Noije, T. van, Wang, H., Watson-Parris, D., Westervelt, D. M., Yang, Y., Yoshioka, M., Daskalakis, N., Decesari, S., Gysel-Beer, M., Kalivitis, N., Liu, X., Mahowald, N. M., Myriokefalitakis, S., Schrödner, R., Sfakianaki, M., Tsimpidi, A. P., Wu, M., and Yu, F.: Evaluation of global simulations of aerosol particle and cloud condensation nuclei number, with implications for cloud droplet formation, Atmospheric Chemistry and Physics, 19, 8591–8617, 2019. [doi]
  5. Watson-Parris, D., Schutgens, N., Reddington, C., Pringle, K. J., Liu, D., Allan, J. D., Coe, H., Carslaw, K. S., and Stier, P.: In situ constraints on the vertical distribution of global aerosol, Atmospheric Chemistry and Physics, 19, 11765–11790, 2019. [doi]
  6. Tegen, I., Neubauer, D., Ferrachat, S., Drian, C. S.-L., Bey, I., Schutgens, N., Stier, P., Watson-Parris, D., Stanelle, T., Schmidt, H., Rast, S., Kokkola, H., Schultz, M., Schroeder, S., Daskalakis, N., Barthel, S., Heinold, B., and Lohmann, U.: The global aerosol–climate model ECHAM6.3–HAM2.3 – Part 1: Aerosol evaluation, Geoscientific Model Development, 12, 1643–1677, 2019. [doi]

2018

  1. Tegen, I., Lohmann, U., Neubauer, D., Drian, C. S.-L., Ferrachat, S., Heinhold, B., Stier, P., Watson-Parris, D., Schultz, M. G., Schutgens, N. A. J., Rast, S., and Kokkola, H.: The aerosol-climate model ECHAM6.3-HAM2.3: Aerosol evaluation, 2018.
  2. Lund, M. T., Samset, B. H., Skeie, R. B., Watson-Parris, D., Katich, J. M., Schwarz, J. P., and Weinzierl, B.: Short Black Carbon lifetime inferred from a global set of aircraft observations, npj Climate and Atmospheric Science, 1, 31, 2018. [doi]

2016

  1. Watson-Parris, D., Schutgens, N., Cook, N., Kipling, Z., Kershaw, P., Gryspeerdt, E., Lawrence, B., and Stier, P.: Community Intercomparison Suite (CIS) v1.4.0: a tool for intercomparing models and observations, Geoscientific Model Development, 9, 3093–3110, 2016. [doi]

2013

  1. Badcock, T. J., Hammersley, S., Watson-Parris, D., Dawson, P., Godfrey, M. J., Kappers, M. J., McAleese, C., Oliver, R. A., and Humphreys, C. J.: Carrier density dependent localization and consequences for efficiency droop in InGaN/GaN quantum well structures, Japanese Journal of Applied Physics, 52, 2013.

2012

  1. Hammersley, S., Watson-Parris, D., Dawson, P., Godfrey, M. J., Badcock, T. J., Kappers, M. J., McAleese, C., Oliver, R. A., and Humphreys, C. J.: The consequences of high injected carrier densities on carrier localization and efficiency droop in InGaN/GaN quantum well structures, Journal of Applied Physics, 111, 083512, 2012. [doi]

2011

  1. Watson-Parris, D., Godfrey, M. J., Dawson, P., Oliver, R. A., Galtrey, M. J., Kappers, M. J., and Humphreys, C. J.: Carrier localization mechanisms in InxGa1-xN/GaN quantum wells, Physical Review B - Condensed Matter and Materials Physics, 83, 2011.
  2. Hammersley, S., Badcock, T. J., Watson-Parris, D., Godfrey, M. J., Dawson, P., Kappers, M. J., and Humphreys, C. J.: Study of efficiency droop and carrier localisation in an InGaN/GaN quantum well structure, Physica Status Solidi (C), 2196, n/a–n/a, 2011. [doi]

2010

  1. Watson-Parris, D., Godfrey, M. J., Oliver, R. A., Dawson, P., Galtrey, M. J., Kappers, M. J., and Humphreys, C. J.: Energy landscape and carrier wave-functions in InGaN/GaN quantum wells, in: Physica Status Solidi (C) Current Topics in Solid State Physics, 2255–2258, 2010.