Predictability of weather and climate: publications

HadGEM2-CC model output prepared for CMIP5 RCP8.5, served by ESGF

Authors:

SM Osprey, SC Hardiman, N Butchart, T Hinton, L Gray, C Jones, J Hughes

Abstract:

Project: IPCC Assessment Report 5 and Coupled Model Intercomparison Project data sets - These data belong to two projects:1) to the Assessment Report No 5 of the International Panel on Climate Change (IPCC-AR5) and2) to the Coupled Model Intercomparison Project No 5 (CMIP5).CMIP5 is executed by the Program for Climate Model Diagnosis and Intercomparison (PCMDI) on behalf of the World Climate Research Programme (WCRP). Most of the data is replicated between the three data nodes at the World Data Centre for Climate (WDCC), the British Atmospheric Data Centre (BADC), and the PCMDI.The project embraces the simulations with about 30 climate models of about 20 institutes worldwide.

High frequency variability of the Atlantic meridional overturning circulation

Authors:

B Balan Sarojini, JM Gregory, R Tailleux, GR Bigg, AT Blaker, D Cameron, NR Edwards, AP Megann, LC Shaffrey, B Sinha

Human Creativity and Consciousness: Unintended Consequences of the Brain’s Extraordinary Energy Efficiency?

Impact of Eurasian autumn snow on the winter North Atlantic Oscillation in seasonal forecasts of the 20^th century

Authors:

Martin Wegmann, Yvan Orsolini, Antje Weisheimer, Bart van den Hurk, Gerrit Lohmann

Introducing the Probabilistic Earth-System Model: Examining The Impact of Stochasticity in EC-Earth v3.2

Geoscientific Model Development European Geosciences Union

Authors:

Kristian Strommen, Hannah M Christensen, David MacLeod, Stephan Juricke, Tim N Palmer

Abstract:

<p><strong>Abstract.</strong> We introduce and study the impact of three stochastic schemes in the EC-Earth climate model, two atmospheric schemes and one stochastic land scheme. These form the basis for a probabilistic earth-system model in atmosphere-only mode. Stochastic parametrisations have become standard in several operational weather-forecasting models, in particular due to their beneficial impact on model spread. In recent years, stochastic schemes in the atmospheric component of a model have been shown to improve aspects important for the models long-term climate, such as ENSO, North Atlantic weather regimes and the Indian monsoon. Stochasticity in the land-component has been shown to improve variability of soil processes and improve the representation of heatwaves over Europe. However, the raw impact of such schemes on the model mean is less well studied, It is shown that the inclusion all three schemes notably change the model mean state. While many of the impacts are beneficial, some are too large in amplitude, leading to large changes in the model's energy budget. This implies that in order to keep the benefits of stochastic physics without shifting the mean state too far from observations, a full re-tuning of the model will typically be required.</p>

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