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(a-c) Patterns generated by the CA used by Shutts (2005) with, from left to right, NLIVESZ10, 50 and 100. The grey scale intensity is a measure of the number of lives remaining with NLIVES being white and zero being black. The resulting animated pattern is reminiscent of convective cloud organization in a cold air outbreak over the sea.
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Some speculative proposals are made for extending current stochastic sub-gridscale parametrization methods using the techniques adopted from the field of computer graphics and flow visualization. The idea is to emulate sub-filter-scale physical process organization and time evolution on a fine grid and couple the implied coarse-grained tendencies w...
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... Recently, there has been increasing interest in stochastic parameterizations, in which the tendencies include random contributions (e.g., Berner et al., 2012;Buizza et al., 1999;Keane et al., 2016;Plant & Craig, 2008;Shutts et al., 2008). Stochastic parameterizations are intended to generate individual realizations of convective activity, which are samples chosen from the ensemble of possible realizations, like individual cards drawn from a deck. ...
Abstract We have investigated the predictability of precipitation using a new configuration of the superparameterized Community Atmosphere Model (SP‐CAM). The new configuration, called the multiple‐instance SP‐CAM, or MP‐CAM, uses the average heating and drying rates from 10 independent two‐dimensional cloud‐permitting models (CPMs) in each grid column of the global model, instead of a single CPM. The 10 CPMs start from slightly different initial conditions and simulate alternative realizations of the convective cloud systems. By analyzing the ensemble of possible realizations, we can study the predictability of the cloud systems and identify the weather regimes and physical mechanisms associated with chaotic convection. We explore alternative methods for quantifying the predictability of precipitation. Our results show that unpredictable precipitation occurs when the simulated atmospheric state is close to critical points as defined by Peters and Neelin (2006, https://doi.org/10.1038/nphys314). The predictability of precipitation is also influenced by the convective available potential energy and the degree of mesoscale organization. It is strongly controlled by the large‐scale circulation. A companion paper compares the global atmospheric circulations simulated by SP‐CAM and MP‐CAM.
... al. 2013, Weisheimer et al. 2014). The most common stochastic parameterization schemes employed are the stochastically perturbed parameterization tendencies (Buizza et al., 1999; Palmer et al., 2009;, Weisheimer et al. 2014 ) and the stochastic kineticenergy backscatter scheme (Shutts, 2005; Berner et al., 2008 Berner et al., , 2009 Berner et al., , 2011 Tennant et al. 2011, Romine et al., 2015). In these applications, underdispersive ensemble systems tend to produce over-confident and, thus, unreliable forecasts. ...
... Originally developed in the context of Large-Eddy- Simulations (Mason and Thomson, 1992), and applied to models of intermediate complexity (Frederiksen and Davies, 1997), it was adapted by Shutts (2005) for Numerical Weather Prediction (NWP). Its beneficial impact on weather and climate forecasts are reported e.g., in Berner et al. (2008 Berner et al. ( , 2009 Berner et al. ( , 2011 Berner et al. ( , 2012), Bowler et al. (2008 Bowler et al. ( , 2009); Palmer et al. (2009);; Charron et al. (2010); Hacker et al. (2011); Tennant et al. (2011); Weisheimer et al. (2011 Weisheimer et al. ( ,2014). A variant of SKEBS perturbs convective processes only (Shutts, 2015, Sanchez et al. 2015). ...
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groups which show that the stochastic representation of unresolved processes in
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the use of mathematically stringent methods for derivation of stochastic
dynamic equations will lead to substantial improvements in our ability to
accurately simulate weather and climate at all scales. Recent work in
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for the climate problem demonstrated as well as future research directions
outlined.
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Stochastic methods are a crucial area in contemporary climate research and are increasingly being used in comprehensive weather and climate prediction models as well as reduced order climate models. Stochastic methods are used as subgrid‐scale parameterizations (SSPs) as well as for model error representation, uncertainty quantification, data assimilation, and ensemble prediction. The need to use stochastic approaches in weather and climate models arises because we still cannot resolve all necessary processes and scales in comprehensive numerical weather and climate prediction models. In many practical applications one is mainly interested in the largest and potentially predictable scales and not necessarily in the small and fast scales. For instance, reduced order models can simulate and predict large‐scale modes. Statistical mechanics and dynamical systems theory suggest that in reduced order models the impact of unresolved degrees of freedom can be represented by suitable combinations of deterministic and stochastic components and non‐Markovian (memory) terms. Stochastic approaches in numerical weather and climate prediction models also lead to the reduction of model biases. Hence, there is a clear need for systematic stochastic approaches in weather and climate modeling. In this review, we present evidence for stochastic effects in laboratory experiments. Then we provide an overview of stochastic climate theory from an applied mathematics perspective. We also survey the current use of stochastic methods in comprehensive weather and climate prediction models and show that stochastic parameterizations have the potential to remedy many of the current biases in these comprehensive models. WIREs Clim Change 2015, 6:63–78. doi: 10.1002/wcc.318
This article is categorized under: Climate Models and Modeling > Model Components
Stochastic methods are a crucial area in contemporary climate research and
are increasingly being used in comprehensive weather and climate prediction
models as well as reduced order climate models. Stochastic methods are used as
subgrid-scale parameterizations as well as for model error representation,
uncertainty quantification, data assimilation and ensemble prediction. The need
to use stochastic approaches in weather and climate models arises because we
still cannot resolve all necessary processes and scales in comprehensive
numerical weather and climate prediction models. In many practical applications
one is mainly interested in the largest and potentially predictable scales and
not necessarily in the small and fast scales. For instance, reduced order
models can simulate and predict large scale modes. Statistical mechanics and
dynamical systems theory suggest that in reduced order models the impact of
unresolved degrees of freedom can be represented by suitable combinations of
deterministic and stochastic components and non-Markovian (memory) terms.
Stochastic approaches in numerical weather and climate prediction models also
lead to the reduction of model biases. Hence, there is a clear need for
systematic stochastic approaches in weather and climate modelling. In this
review we present evidence for stochastic effects in laboratory experiments.
Then we provide an overview of stochastic climate theory from an applied
mathematics perspectives. We also survey the current use of stochastic methods
in comprehensive weather and climate prediction models and show that stochastic
parameterizations have the potential to remedy many of the current biases in
these comprehensive models.
Finite computing resources limit the spatial resolution of state-of-the-art global climate simulations to hundreds of kilometres. In neither the atmosphere nor the ocean are small-scale processes such as convection, clouds and ocean eddies properly represented. Climate simulations are known to depend, sometimes quite strongly, on the resulting bulk-formula representation of unresolved processes. Stochastic physics schemes within weather and climate models have the potential to represent the dynamical effects of unresolved scales in ways which conventional bulk-formula representations are incapable of so doing. The application of stochastic physics to climate modelling is a rapidly advancing, important and innovative topic. The latest research findings are gathered together in the Theme Issue for which this paper serves as the introduction.
