The Met Office Global Coupled model 2.0 (GC2) configuration

Abstract. The latest coupled configuration of the Met Office Unified Model (Global Coupled configuration 2, GC2) is presented. This paper documents the model components which make up the configuration (although the scientific description of these components is detailed elsewhere) and provides a description of the coupling between the components. The performance of GC2 in terms of its systematic errors is assessed using a variety of diagnostic techniques. The configuration is intended to be used by the Met Office and collaborating institutes across a range of timescales, with the seasonal forecast system (GloSea5) and climate projection system (HadGEM) being the initial users. In this paper GC2 is compared against the model currently used operationally in those two systems. Overall GC2 is shown to be an improvement on the configurations used currently, particularly in terms of modes of variability (e.g. mid-latitude and tropical cyclone intensities, the Madden–Julian Oscillation and El Niño Southern Oscillation). A number of outstanding errors are identified with the most significant being a considerable warm bias over the Southern Ocean and a dry precipitation bias in the Indian and West African summer monsoons. Research to address these is ongoing.

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Stochastic atmospheric perturbations in the EC-Earth3 global coupled model: impact of SPPT on seasonal forecast quality

Climate Dynamics ◽

10.1007/s00382-015-2548-7 ◽

2015 ◽

Vol 45 (11-12) ◽

pp. 3419-3439 ◽

Cited By ~ 14

Author(s):

Lauriane Batté ◽

Francisco J. Doblas-Reyes

Keyword(s):

Coupled Model ◽

Seasonal Forecast ◽

Atmospheric Perturbations ◽

Forecast Quality ◽

Global Coupled Model

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Re-emergence of North Atlantic subsurface ocean temperature anomalies in a seasonal forecast system

Climate Dynamics ◽

10.1007/s00382-019-04826-w ◽

2019 ◽

Vol 53 (7-8) ◽

pp. 4799-4820 ◽

Cited By ~ 2

Author(s):

Jeremy P. Grist ◽

Bablu Sinha ◽

Helene. T. Hewitt ◽

Aurélie Duchez ◽

Craig MacLachlan ◽

...

Keyword(s):

North Atlantic ◽

Seasonal Forecast ◽

Ocean Temperature ◽

Forecast System ◽

Temperature Anomalies ◽

Subsurface Ocean

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Evaluation of an Integrated Seasonal Forecast System for Agricultural Water Management in Mediterranean Regions

Lecture Notes in Computer Science - Numerical Computations: Theory and Algorithms ◽

10.1007/978-3-030-39081-5_51 ◽

2020 ◽

pp. 596-603

Author(s):

Alfonso Senatore ◽

Domenico Fuoco ◽

Antonella Sanna ◽

Andrea Borrelli ◽

Giuseppe Mendicino ◽

...

Keyword(s):

Water Management ◽

Seasonal Forecast ◽

Agricultural Water ◽

Forecast System ◽

Agricultural Water Management ◽

Mediterranean Regions

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Development of a Coupled Subseasonal-to-Seasonal Prediction Model Using Community-based Unified Forecast System for NCEP Operations

10.5194/egusphere-egu21-5722 ◽

2021 ◽

Author(s):

Yan Xue ◽

Dorothy Koch ◽

Vijay Tallapragada ◽

Avichal Mehra ◽

Fanglin Yang ◽

...

Keyword(s):

Data Assimilation ◽

Land Surface ◽

Online Survey ◽

Ocean Waves ◽

Atmospheric Model ◽

Seasonal Forecasts ◽

Forecast System ◽

Community Based ◽

Medium Range Weather Forecast ◽

Model Components

The Unified Forecast System (UFS) is a community-based coupled Earth modeling system, designed to support the Weather Enterprise and also be the source system for NOAA&#8217;s operations. NOAA&#8217;s Unified Forecast System Research to Operations Project (UFS-R2O) aims to develop the next generation coupled Global Forecast System (GFS v17)/Global Ensemble Forecast System (GEFS v13) targeting operational implementation in FY24. The Project is part of the larger UFS community and includes scientists from NOAA Labs and Centers, NCAR, UCAR, NRL and several U.S. universities.The UFS is targeted to be a six-way coupled Earth prediction system, consisting of the FV3 dynamical core with the Common Community Physics Package (CCPP) for the atmosphere,&#160; MOM6 for the ocean, CICE6 for the sea ice, WW3 for ocean waves, Noah-MP for the land surface and GOCART for aerosols.&#160; Currently, four of the six model components have been coupled using the Community Mediator for Earth Prediction Systems (CMEPS). All the components of the coupled system will be initialized with a weakly coupled data assimilation system based on the Joint Effort for Data Assimilation Integration (JEDI) framework. A 30-year coupled reanalysis and reforecast will be conducted for model calibration and post-processing forecast products. The UFS is the basis for the future updates of the deterministic GFS medium-range weather forecast up to 16 days, the ensemble GEFS subseasonal forecast up to 45 days, and the seasonal forecasts up to one year using the new Seasonal Forecast System (SFS) planned to replace the operational Climate Forecast System (CFSv2).Several prototypes of a four-way coupled atmosphere-ocean-ice-wave model have been built and tested with a C384 horizontal grid (~25km) and 64 vertical levels for the atmospheric model, and a &#188; degree tripolar grid for the ocean and ice model components. The presentation will highlight the results of these prototype runs. The UFS-R2O Project has made the latest UFS prototype (S2Sp5) output available on Amazon Web Services (AWS). Researchers interested in the S2S prediction and model development are invited to evaluate the UFS S2Sp5 data. Analysis of the data may include process-based evaluations, diagnostic measures that reveal coupled feedback processes, model biases and S2S forecast skill estimations. To identify and prioritize key metrics in evaluating the UFS applications, the UFS-R2O Project is soliciting community inputs through a online survey and UFS Evaluation Metric Workshop in Feb 2021. The metrics will be incorporated into the METplus verification tools for both research and operation.&#160;A few more prototypes are planned beyond S2Sp5 which include increasing the vertical resolution of the atmospheric model to 127 vertical levels, the transition of land model from Noah to Noah-MP, inclusion of aerosol component, advanced physics suites as well as stochastic physics parameterizations to account for uncertainties in each model component. Coarser and higher resolution configurations along with coupled ensemble prototypes are also being built in order to evaluate the resolution-dependence of forecast biases and to assess the benefit vs cost of higher resolution. The development code is available on Github, and the UFS community contributes to the development through a R2O process.

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SEAS5: The new ECMWF seasonal forecast system

10.5194/gmd-2018-228 ◽

2018 ◽

Cited By ~ 2

Author(s):

Stephanie J. Johnson ◽

Timothy N. Stockdale ◽

Laura Ferranti ◽

Magdalena Alonso Balmaseda ◽

Franco Molteni ◽

...

Keyword(s):

Sea Ice ◽

Decadal Variability ◽

Seasonal Forecast ◽

Arctic Sea Ice ◽

Prediction Skill ◽

The Arctic ◽

Western Boundary ◽

Forecast System ◽

Enso Prediction ◽

Ice Model

Abstract. In this paper we describe SEAS5, ECMWF’s fifth generation seasonal forecast system, which became operational in November 2017. Compared to its predecessor, System 4, SEAS5 is a substantially changed forecast system. It includes upgraded versions of the atmosphere and ocean models at higher resolutions, and adds a prognostic sea ice model. Here, we describe the configuration of SEAS5 and summarise the most noticeable results from a set of diagnostics including biases, variability, teleconnections and forecast skill. An important improvement in SEAS5 is the reduction of the Equatorial Pacific cold tongue bias, which is accompanied by a more realistic ENSO amplitude and an improvement in ENSO prediction skill over the central-west Pacific. Improvements in two-metre temperature skill are also clear over the tropical Pacific. SST biases in the northern extratropics change due to increased ocean resolution, especially in regions associated with western boundary currents. The increased ocean resolution exposes a new problem in the northwest Atlantic, where SEAS5 fails to capture decadal variability of the North Atlantic subpolar gyre, resulting in a degradation of DJF two-metre temperature prediction skill in this region. The prognostic sea ice model improves seasonal predictions of sea ice cover, although some regions and seasons suffer from biases introduced by employing a fully dynamical model rather than the simple, empirical scheme used in System 4. There are also improvements in two-metre temperature skill in the vicinity of the Arctic sea-ice edge. Cold temperature biases in the troposphere improve, but increase at the tropopause. Biases in the extratropical jets are larger than in System 4: extratropical jets are too strong, and displaced northwards in summer. In summary, development and added complexity since System 4 has ensured SEAS5 is a state-of-the-art seasonal forecast system which continues to display a particular strength in ENSO prediction.

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Seasonal Forecasting of Tropical Cyclone Landfall Using ECMWF’s System 4

Weather and Forecasting ◽

10.1175/waf-d-18-0032.1 ◽

2019 ◽

Vol 34 (5) ◽

pp. 1239-1255 ◽

Cited By ~ 1

Author(s):

Dan L. Bergman ◽

Linus Magnusson ◽

Johan Nilsson ◽

Frederic Vitart

Keyword(s):

Tropical Cyclone ◽

North American ◽

Seasonal Forecast ◽

Seasonal Forecasting ◽

Forecast System ◽

Forecast Period ◽

The North ◽

Hurricane Season ◽

Tropical Cyclone Landfall

Abstract A method has been developed to forecast seasonal landfall risk using ensembles of cyclone tracks generated by ECMWF’s seasonal forecast system 4. The method has been applied to analyze and retrospectively forecast the landfall risk along the North American coast. The main result is that the method can be used to forecast landfall for some parts of the coast, but the skill is lower than for basinwide forecasts of activity. The rank correlations between forecasts issued on 1 May and observations are 0.6 for basinwide tropical cyclone number and 0.5 for landfall anywhere along the coast. When the forecast period is limited to the peak of the hurricane season, the landfall correlation increases to 0.6. Moreover, when the forecast issue date is pushed forward to 1 August, basinwide tropical cyclone and hurricane correlations increase to 0.7 and 0.8, respectively, whereas landfall correlations improve less. The quality of the forecasts is in line with that obtained by others.

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The Impact of Air–Sea Interactions on the Simulation of Tropical Intraseasonal Variability

Journal of Climate ◽

10.1175/2008jcli2180.1 ◽

2008 ◽

Vol 21 (24) ◽

pp. 6616-6635 ◽

Cited By ~ 49

Author(s):

Kathy Pegion ◽

Ben P. Kirtman

Keyword(s):

Intraseasonal Variability ◽

Intraseasonal Oscillation ◽

Coupled Model ◽

Coupled Simulation ◽

Maritime Continent ◽

Forecast System ◽

Climate Forecast System ◽

Environmental Prediction ◽

The Impact ◽

The Western Pacific

Abstract The impact of coupled air–sea feedbacks on the simulation of tropical intraseasonal variability is investigated in this study using the National Centers for Environmental Prediction Climate Forecast System. The simulation of tropical intraseasonal variability in a freely coupled simulation is compared with two simulations of the atmospheric component of the model. In one experiment, the uncoupled model is forced with the daily sea surface temperature (SST) from the coupled run. In the other, the uncoupled model is forced with climatological SST from the coupled run. Results indicate that the overall intraseasonal variability of precipitation is reduced in the coupled simulation compared to the uncoupled simulation forced by daily SST. Additionally, air–sea coupling is responsible for differences in the simulation of the tropical intraseasonal oscillation between the coupled and uncoupled models, specifically in terms of organization and propagation in the western Pacific. The differences between the coupled and uncoupled simulations are due to the fact that the relationships between precipitation and SST and latent heat flux and SST are much stronger in the coupled model than in the uncoupled model. Additionally, these relationships are delayed by about 5 days in the uncoupled model compared to the coupled model. As demonstrated by the uncoupled simulation forced with climatological SST, some of the intraseasonal oscillation can be simulated by internal atmospheric dynamics. However, the intraseasonally varying SST appears to be important to the amplitude and propagation of the oscillation beyond the Maritime Continent.

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