scholarly journals Design and implementation of the infrastructure of HadGEM3: the next-generation Met Office climate modelling system

2011 ◽  
Vol 4 (2) ◽  
pp. 223-253 ◽  
Author(s):  
H. T. Hewitt ◽  
D. Copsey ◽  
I. D. Culverwell ◽  
C. M. Harris ◽  
R. S. R. Hill ◽  
...  
Keyword(s):  
Climate Model ◽  
Model Performance ◽  
Model Atmosphere ◽  
Ocean Model ◽  
Climate Modelling ◽  
Model Components ◽  
The Individual ◽  
The Uk ◽  
Technical Solutions ◽  
Modelling System

Abstract. This paper describes the development of a technically robust climate modelling system, HadGEM3, which couples the Met Office Unified Model atmosphere component, the NEMO ocean model and the Los Alamos sea ice model (CICE) using the OASIS coupler. Details of the coupling and technical solutions of the physical model (HadGEM3-AO) are documented, in addition to a description of the configurations of the individual submodels. The paper demonstrates that the implementation of the model has resulted in accurate conservation of heat and freshwater across the model components. The model performance in early versions of this climate model is briefly described to demonstrate that the results are scientifically credible. HadGEM3-AO is the basis for a number of modelling efforts outside of the Met Office, both within the UK and internationally. This documentation of the HadGEM3-AO system provides a detailed reference for developers of HadGEM3-based climate configurations.

scholarly journals Design and implementation of the infrastructure of HadGEM3: the next-generation Met Office climate modelling system

2010 ◽  
Vol 3 (4) ◽  
pp. 1861-1937 ◽  
Author(s):  
H. T. Hewitt ◽  
D. Copsey ◽  
I. D. Culverwell ◽  
C. M. Harris ◽  
R. S. R. Hill ◽  
...  
Keyword(s):  
Climate Model ◽  
Model Performance ◽  
Model Atmosphere ◽  
Ocean Model ◽  
Climate Modelling ◽  
Model Components ◽  
The Individual ◽  
The Uk ◽  
Technical Solutions ◽  
Modelling System

Abstract. This paper describes the development of a technically robust climate modelling system, HadGEM3, which couples the Met Office Unified Model atmosphere component, the NEMO ocean model and the Los Alamos sea ice model (CICE) using the OASIS coupler. Details of the coupling and technical solutions are documented in the paper in addition to a description of the configurations of the individual submodels. The paper demonstrates that the implementation of the model has resulted in accurate conservation of heat and freshwater across the model components. The model performance in early versions of this climate model is briefly described to demonstrate that the results are scientifically credible. HadGEM3 is the basis for a number of modelling efforts outside of the Met Office, both within the UK and internationally. This documentation of the HadGEM3 system provides a detailed reference for developers of HadGEM3-based climate configurations.

scholarly journals An Intermediate Complexity Climate Model (ICCM) based on the GFDL Flexible Modelling System

2009 ◽  
Vol 2 (1) ◽  
pp. 341-383
Author(s):  
R. Farneti ◽  
G. K. Vallis
Keyword(s):  
Climate Model ◽  
Ocean Model ◽  
Primitive Equations ◽  
Climate Modelling ◽  
Geophysical Fluid Dynamics Laboratory ◽  
Intermediate Complexity ◽  
Wide Range ◽  
Ocean Atmosphere ◽  
Ice Model ◽  
Modelling System

Abstract. An intermediate complexity coupled ocean-atmosphere-land-ice model, based on the Geophysical Fluid Dynamics Laboratory (GFDL) Flexible Modelling System (FMS), has been developed to study mechanisms of ocean-atmosphere interactions and natural climate variability at interannual to interdecadal and longer time scales. The model uses the three-dimensional primitive equations for both ocean and atmosphere, but is simplified from a "state of the art" coupled model in two respects: it uses simplified physics and parameterisation schemes, especially in the atmosphere, and idealised geometry and geography. These simplifications provide considerable savings in computational expense and, perhaps more importantly, allow mechanisms to be investigated more cleanly and thoroughly than with a more elaborate model. For example, the model allows integrations of several millennia as well as broad parameter studies. For the ocean, the model uses the free surface primitive equations Modular Ocean Model (MOM) and the GFDL/FMS sea-ice model (SIS) is coupled to the oceanic grid. The atmospheric component consists of the FMS B-grid moist primitive equations atmospheric dynamical core with highly simplified physical parameterisations. A simple bucket model is implemented for our idealised land following the GFDL/FMS Land model. Here we describe the model components and present a climatology of coupled simulations achieved with two different geometrical configurations. Throughout the paper, we give a flavour of the potential for this model to be a powerful tool for the climate modelling community by mentioning a wide range of studies that are currently being explored.

scholarly journals An Intermediate Complexity Climate Model (ICCMp1) based on the GFDL flexible modelling system

2009 ◽  
Vol 2 (2) ◽  
pp. 73-88 ◽  
Author(s):  
R. Farneti ◽  
G. K. Vallis
Keyword(s):  
Climate Model ◽  
Ocean Model ◽  
Primitive Equations ◽  
Climate Modelling ◽  
Geophysical Fluid Dynamics Laboratory ◽  
Intermediate Complexity ◽  
Wide Range ◽  
Ocean Atmosphere ◽  
Ice Model ◽  
Modelling System

Abstract. An intermediate complexity coupled ocean-atmosphere-land-ice model, based on the Geophysical Fluid Dynamics Laboratory (GFDL) Flexible Modelling System (FMS), has been developed to study mechanisms of ocean-atmosphere interactions and natural climate variability at interannual to interdecadal and longer time scales. The model uses the three-dimensional primitive equations for both ocean and atmosphere but is simplified from a "state of the art" coupled model by using simplified atmospheric physics and parameterisation schemes. These simplifications provide considerable savings in computational expense and, perhaps more importantly, allow mechanisms to be investigated more cleanly and thoroughly than with a more elaborate model. For example, the model allows integrations of several millennia as well as broad parameter studies. For the ocean, the model uses the free surface primitive equations Modular Ocean Model (MOM) and the GFDL/FMS sea-ice model (SIS) is coupled to the oceanic grid. The atmospheric component consists of the FMS B-grid moist primitive equations atmospheric dynamical core with highly simplified physical parameterisations. A simple bucket model is implemented for our idealised land following the GFDL/FMS Land model. The model is supported within the standard MOM releases as one of its many test cases and the source code is thus freely available. Here we describe the model components and present a climatology of coupled simulations achieved with two different geometrical configurations. Throughout the paper, we give a flavour of the potential for this model to be a powerful tool for the climate modelling community by mentioning a wide range of studies that are currently being explored.

scholarly journals Projecting ozone hole recovery using an ensemble of chemistry–climate models weighted by model performance and independence

2020 ◽  
Vol 20 (16) ◽  
pp. 9961-9977 ◽  
Author(s):  
Matt Amos ◽  
Paul J. Young ◽  
J. Scott Hosking ◽  
Jean-François Lamarque ◽  
N. Luke Abraham ◽  
...  
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Model Performance ◽  
Climate Modelling ◽  
Ensemble Averaging ◽  
Weighted Mean ◽  
Out Of Sample ◽  
Potential Applications ◽  
Model Independence ◽  
Model Components

Abstract. Calculating a multi-model mean, a commonly used method for ensemble averaging, assumes model independence and equal model skill. Sharing of model components amongst families of models and research centres, conflated by growing ensemble size, means model independence cannot be assumed and is hard to quantify. We present a methodology to produce a weighted-model ensemble projection, accounting for model performance and model independence. Model weights are calculated by comparing model hindcasts to a selection of metrics chosen for their physical relevance to the process or phenomena of interest. This weighting methodology is applied to the Chemistry–Climate Model Initiative (CCMI) ensemble to investigate Antarctic ozone depletion and subsequent recovery. The weighted mean projects an ozone recovery to 1980 levels, by 2056 with a 95 % confidence interval (2052–2060), 4 years earlier than the most recent study. Perfect-model testing and out-of-sample testing validate the results and show a greater projective skill than a standard multi-model mean. Interestingly, the construction of a weighted mean also provides insight into model performance and dependence between the models. This weighting methodology is robust to both model and metric choices and therefore has potential applications throughout the climate and chemistry–climate modelling communities.

scholarly journals Tuning without over-tuning: parametric uncertainty quantification for the NEMO ocean model

2017 ◽  
Vol 10 (4) ◽  
pp. 1789-1816 ◽  
Author(s):  
Daniel B. Williamson ◽  
Adam T. Blaker ◽  
Bablu Sinha
Keyword(s):  
History Matching ◽  
Climate Model ◽  
Model Performance ◽  
Parametric Uncertainty ◽  
Ocean Model ◽  
Automatic Tuning ◽  
Global Ocean ◽  
Science Literature ◽  
Global Mean Temperature ◽  
Model Tuning

Abstract. In this paper we discuss climate model tuning and present an iterative automatic tuning method from the statistical science literature. The method, which we refer to here as iterative refocussing (though also known as history matching), avoids many of the common pitfalls of automatic tuning procedures that are based on optimisation of a cost function, principally the over-tuning of a climate model due to using only partial observations. This avoidance comes by seeking to rule out parameter choices that we are confident could not reproduce the observations, rather than seeking the model that is closest to them (a procedure that risks over-tuning). We comment on the state of climate model tuning and illustrate our approach through three waves of iterative refocussing of the NEMO (Nucleus for European Modelling of the Ocean) ORCA2 global ocean model run at 2° resolution. We show how at certain depths the anomalies of global mean temperature and salinity in a standard configuration of the model exceeds 10 standard deviations away from observations and show the extent to which this can be alleviated by iterative refocussing without compromising model performance spatially. We show how model improvements can be achieved by simultaneously perturbing multiple parameters, and illustrate the potential of using low-resolution ensembles to tune NEMO ORCA configurations at higher resolutions.

scholarly journals <TT>sympl</TT> (v. 0.3.2) and <TT>climt</TT> (v. 0.11.0) – Towards a flexible framework for building model hierarchies in Python

2018 ◽  
Author(s):  
Joy Merwin Monteiro ◽  
Jeremy McGibbon ◽  
Rodrigo Caballero
Keyword(s):  
Data Analysis ◽  
Model Building ◽  
System Modelling ◽  
Climate Modelling ◽  
Earth System ◽  
Online Data ◽  
Wide Audience ◽  
Building Models ◽  
Model Components ◽  
The Individual

Abstract. sympl (System for Modelling Planets) and climt (Climate Modelling and diagnostics Toolkit) represent an attempt to rethink climate modelling frameworks from the ground up. The aim is to use expressive data structures available in the scientific Python ecosystem along with best practices in software design to build models that are self-documenting, highly inter-operable and that provide fine grained control over model components and behaviour. We believe that such an approach towards building models is essential to allow scientists to easily and reliably combine model components to represent the climate system at a desired level of complexity, and to enable users to fully understand what the model is doing. sympl is a framework which formulates the model in terms of a "state" which gets evolved forward in time by TimeStepper and Implicit components, and which can be modified by Diagnostic components. TimeStepper components in turn rely on Prognostic components to compute tendencies. Components contain all the information about the kinds of inputs they expect and outputs that they provide. Components can be used interchangeably, even when they rely on different units or array configurations. sympl provides basic functions and objects which could be used by any type of Earth system model. climt is an Earth system modelling toolkit that contains scientific components built over the sympl base objects. Components can be written in any language accessible from Python, and Fortran/C libraries are accessed via Cython. climt aims to provide different user APIs which trade-off simplicity of use against flexibility of model building, thus appealing to a wide audience. Model building, configuration and execution is through a Python script (or Jupyter Notebook), enabling researchers to build an end-to-end Python based pipeline along with popular Python based data analysis tools. Because of the modularity of the individual components, using online data analysis, visualisation or assimilation algorithms and tools with sympl/climt components is extremely simple.

scholarly journals Estimating unprecedented extremes in UK summer daily rainfall

2021 ◽  
Author(s):  
Chris Kent ◽  
Nick J. Dunstone ◽  
Simon Tucker ◽  
Adam A. Scaife ◽  
Simon Brown ◽  
...  
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Daily Rainfall ◽  
Extreme Rainfall ◽  
Water Flooding ◽  
Statistical Characteristics ◽  
Climate Modelling ◽  
Climate Model Simulations ◽  
Climate Analysis ◽  
The Uk

Abstract The UNSEEN (UNprecedented Simulated Extremes using ENsembles) method involves using a large ensemble of climate model simulations to increase the sample size of rare events. Here we extend UNSEEN to focus on intense summertime daily rainfall, estimating plausible rainfall extremes in the current climate. To address modelling limitations simulations from two climate models were used; an initialised 25km global model that uses parametrized convection, and a dynamically downscaled 2.2km model that uses explicit convection. In terms of the statistical characteristics that govern very rare return periods, the models are not significantly different from the observations across much of the UK. Our analysis provides more precise estimates of 1000-year return levels for extreme daily rainfall, reducing sampling uncertainty by 70-90% compared to using observations alone. This framework enables observed daily storm profiles to be adjusted to more statistically robust estimates of extreme rainfall. For a damaging storm in July 2007 which led to surface water flooding, we estimate physically plausible increases in the total daily rainfall of 50 – 100%. For much of the UK the annual chance of record-breaking daily summertime rainfall is estimated to be around 1% per year in the present-day climate. Analysis of the dynamical states in our UNSEEN events indicates that heavy daily rainfall is associated with a southward displaced and meandering North Atlantic jet stream, increasing the advection of warm moist air from across Southern Europe and the Mediterranean, and intensifying extratropical storms. This work represents an advancement in the use of climate modelling for estimating present-day climate hazards and outlines a framework for applying UNSEEN at higher spatial and temporal resolutions.

scholarly journals weather@home 2: validation of an improved global–regional climate modelling system

2017 ◽  
Vol 10 (5) ◽  
pp. 1849-1872 ◽  
Author(s):  
Benoit P. Guillod ◽  
Richard G. Jones ◽  
Andy Bowery ◽  
Karsten Haustein ◽  
Neil R. Massey ◽  
...  
Keyword(s):  
Climate Change ◽  
Extreme Events ◽  
Land Surface ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate Model ◽  
Regional Climate ◽  
Extreme Weather Events ◽  
Climate Modelling ◽  
Modelling System

Abstract. Extreme weather events can have large impacts on society and, in many regions, are expected to change in frequency and intensity with climate change. Owing to the relatively short observational record, climate models are useful tools as they allow for generation of a larger sample of extreme events, to attribute recent events to anthropogenic climate change, and to project changes in such events into the future. The modelling system known as weather@home, consisting of a global climate model (GCM) with a nested regional climate model (RCM) and driven by sea surface temperatures, allows one to generate a very large ensemble with the help of volunteer distributed computing. This is a key tool to understanding many aspects of extreme events. Here, a new version of the weather@home system (weather@home 2) with a higher-resolution RCM over Europe is documented and a broad validation of the climate is performed. The new model includes a more recent land-surface scheme in both GCM and RCM, where subgrid-scale land-surface heterogeneity is newly represented using tiles, and an increase in RCM resolution from 50 to 25 km. The GCM performs similarly to the previous version, with some improvements in the representation of mean climate. The European RCM temperature biases are overall reduced, in particular the warm bias over eastern Europe, but large biases remain. Precipitation is improved over the Alps in summer, with mixed changes in other regions and seasons. The model is shown to represent the main classes of regional extreme events reasonably well and shows a good sensitivity to its drivers. In particular, given the improvements in this version of the weather@home system, it is likely that more reliable statements can be made with regards to impact statements, especially at more localized scales.

scholarly journals Projecting ozone hole recovery using an ensemble of chemistry-climate models weighted by model performance and independence

2020 ◽  
Author(s):  
Matt Amos ◽  
Paul J. Young ◽  
J. Scott Hosking ◽  
Jean-François Lamarque ◽  
N. Luke Abraham ◽  
...  
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Model Performance ◽  
Current Method ◽  
Climate Modelling ◽  
Weighted Mean ◽  
Out Of Sample ◽  
Potential Applications ◽  
Model Independence ◽  
Model Ensembles

Abstract. The current method for averaging model ensembles, which is to calculate a multi model mean, assumes model independence and equal model skill. Sharing of model components amongst families of models and research centres, conflated by growing ensemble size, means model independence cannot be assumed and is hard to quantify. We present a methodology to produce a weighted model ensemble projection, accounting for model performance and model independence. Model weights are calculated by comparing model hindcasts to a selection of metrics chosen for their physical relevance to the process or phenomena of interest. This weighting methodology is applied to the Chemistry-Climate Model Initiative (CCMI) ensemble, to investigate Antarctic ozone depletion and subsequent recovery. The weighted mean projects an ozone recovery to 1980 levels, by 2056 with a 95 % confidence interval (2052–2060), 4 years earlier than the most recent study. Perfect model testing and out-of-sample testing validate the results and show a greater projective skill than a standard multi model mean. Interestingly, the construction of a weighted mean also provides insight into model performance and dependence between the models. This weighting methodology is robust to both model and metric choices and therefore has potential applications throughout the climate and chemistry-climate modelling communities.

scholarly journals Tuning without over-tuning: parametric uncertainty quantification for the NEMO ocean model

2016 ◽  
Author(s):  
Daniel Williamson ◽  
Adam T. Blaker ◽  
Bablu Sinha
Keyword(s):  
History Matching ◽  
Climate Model ◽  
Model Performance ◽  
Parametric Uncertainty ◽  
Ocean Model ◽  
Automatic Tuning ◽  
Global Ocean ◽  
Science Literature ◽  
Global Mean Temperature ◽  
Model Tuning

Abstract. In this paper we discuss climate model tuning and present an iterative automatic tuning method from the statistical science literature. The method, which we refer to here as iterative refocussing (though also known as history matching), avoids many of the common pitfalls of automatic tuning procedures that are based on optimisation of a cost function; principally the over-tuning of a climate model due to using only partial observations. This avoidance comes by seeking to rule out parameter choices that we are confident could not reproduce the observations, rather than seeking the model that is closest to them (a procedure that risks over-tuning). We comment on the state of climate model tuning and illustrate our approach through 3 waves of iterative refocussing of the NEMO ORCA2 global ocean model run at 2° resolution. We show how at certain depths the anomalies of global mean temperature and salinity in a standard configuration of the model exceeds 10 standard deviations away from observations and show the extent to which this can be alleviated by iterative refocussing without compromising model performance spatially. We show how model improvements can be achieved by simultaneously perturbing multiple parameters, and illustrate the potential of using low resolution ensembles to tune NEMO ORCA configurations at higher resolutions.

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