Zobrazeno 1 - 10
of 72
pro vyhledávání: '"Sansom, Philip G"'
Autor:
Sansom, Philip G.1,2 (AUTHOR), Catto, Jennifer L.2 (AUTHOR) j.catto@exeter.ac.uk
Publikováno v:
Geoscientific Model Development. 2024, Vol. 17 Issue 16, p6137-6151. 15p.
Autor:
Manning, Colin, Kendon, Elizabeth J., Fowler, Hayley J., Catto, Jennifer L., Chan, Steven C., Sansom, Philip G.
Publikováno v:
In Weather and Climate Extremes June 2024 44
Quantifying the risk of global warming exceeding critical targets such as 2.0 K requires reliable projections of uncertainty as well as best estimates of Global Mean Surface Temperature (GMST). However, uncertainty bands on GMST projections are often
Externí odkaz:
http://arxiv.org/abs/2101.08198
The use of emergent constraints to quantify uncertainty for key policy relevant quantities such as Equilibrium Climate Sensitivity (ECS) has become increasingly widespread in recent years. Many researchers, however, claim that emergent constraints ar
Externí odkaz:
http://arxiv.org/abs/1905.01241
Existing methods for diagnosing predictability in climate indices often make a number of unjustified assumptions about the climate system that can lead to misleading conclusions. We present a flexible family of state-space models capable of separatin
Externí odkaz:
http://arxiv.org/abs/1807.02671
We develop Bayesian state space methods for modelling changes to the mean level or temporal correlation structure of an observed time series due to intermittent coupling with an unobserved process. Novel intervention methods are proposed to model the
Externí odkaz:
http://arxiv.org/abs/1711.04135
Numerical climate models are used to project future climate change due to both anthropogenic and natural causes. Differences between projections from different climate models are a major source of uncertainty about future climate. Emergent relationsh
Externí odkaz:
http://arxiv.org/abs/1711.04139
Ensemble forecasts of weather and climate are subject to systematic biases in the ensemble mean and variance, leading to inaccurate estimates of the forecast mean and variance. To address these biases, ensemble forecasts are post-processed using stat
Externí odkaz:
http://arxiv.org/abs/1509.07102
Autor:
Siegert, Stefan, Stephenson, David B., Sansom, Philip G., Scaife, Adam A., Eade, Rosie, Arribas, Alberto
Predictability estimates of ensemble prediction systems are uncertain due to limited numbers of past forecasts and observations. To account for such uncertainty, this paper proposes a Bayesian inferential framework that provides a simple 6-parameter
Externí odkaz:
http://arxiv.org/abs/1504.01933
Publikováno v:
Journal of the Royal Statistical Society. Series C (Applied Statistics), 2019 Jan 01. 68(5), 1259-1280.
Externí odkaz:
https://www.jstor.org/stable/26820913