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Authors: J Cohen; X Zhang; J Francis; T Jung; R Kwok; J Overland; T Ballinger; R Blackport; U S Bhatt; H Chen; D Coumou; S Feldstein; D Handorf; M Hell; G Henderson; M Ionita; M Kretschmer; F Laliberte; S Lee; H Linderholm; W Maslowski; I Rigor; C Routson; J Screen; T Semmler; D Singh; D Smith; J Stroeve; P C Taylor; T Vihma; M Wang; S Wang; Y Wu; M Wendisch; J Yoon
Journal: US CLIVAR Rep Date: 2018-03

6. Machine learning for weather and climate are worlds apart.

Authors: D Watson-Parris
Journal: Philos Trans A Math Phys Eng Sci Date: 2021-02-15 Impact factor: 4.226

7. More reliable forecasts with less precise computations: a fast-track route to cloud-resolved weather and climate simulators?

Authors: T N Palmer
Journal: Philos Trans A Math Phys Eng Sci Date: 2014-06-28 Impact factor: 4.226

Climate change. What are climate models missing?

1. Metrological challenges for measurements of key climatological observables, Part 4: Atmospheric relative humidity.

2. Aerosols: Uncertain then, irrelevant now.

3. Climate forecasting: build high-resolution global climate models.

4. Short-term tests validate long-term estimates of climate change.

5. ARCTIC CHANGE AND POSSIBLE INFLUENCE ON MID-LATITUDE CLIMATE AND WEATHER: A US CLIVAR White Paper.

6. Machine learning for weather and climate are worlds apart.

7. More reliable forecasts with less precise computations: a fast-track route to cloud-resolved weather and climate simulators?

8. A personal perspective on modelling the climate system.

9. Diurnal cloud cycle biases in climate models.

10. Quantifying the circulation induced by convective clouds in kilometer-scale simulations.