Unified Modeling and Prediction of Weather and Climate: A 25-Year Journey

330 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

The Met Office Global Coupled Model 3.0 and 3.1 (GC3.0 and GC3.1) Configurations (2017, background): “This seamless approach has yielded numerous benefits including improved ability to investigate model systematic errors, greater scientific robustness of the model, and a more efficient use of resources (scientists, high performance computing, etc.; Brown et al., 2012).”
The Transpose-AMIP II Experiment and Its Application to the Understanding of Southern Ocean Cloud Biases in Climate Models (2012, background): “Indeed, the benefits are leading these centers to unify the model physics and dynamics in their prediction systems to a greater extent than ever before (Brown et al. 2012).”
Climate variability and vulnerability to climate change: a review (2014, background): “There are surely synergies to be explored between these two communities of practice, particularly given rapid developments in the field of seamless prediction of weather and climate (Brown et al., 2012; Meehl et al., 2013).”
On the Correspondence between Mean Forecast Errors and Climate Errors in CMIP5 Models (2013, methodology): “…running climate simulations and explore whether the correspondence between short- and long-term systematic errors previously reported in individual models (Brown et al. 2012; Xie et al. 2012; Ma et al. 2013) is robust across other climate models based on simulations from the TAMIP and CMIP5/AMIP.”