scholarly journals Medium-Range, Monthly, and Seasonal Prediction for Europe and the Use of Forecast Information

2006 ◽  
Vol 19 (23) ◽  
pp. 6025-6046 ◽  
Author(s):  
Mark J. Rodwell ◽  
Francisco J. Doblas-Reyes
Keyword(s):  
Coupled Model ◽  
Seasonal Prediction ◽  
Model Potential ◽  
Mitigation Strategies ◽  
General Level ◽  
Seasonal Forecasts ◽  
Seasonal Predictability ◽  
Potential Predictability ◽  
Medium Range ◽  
The Impact

Abstract Operational probabilistic (ensemble) forecasts made at ECMWF during the European summer heat wave of 2003 indicate significant skill on medium (3–10 day) and monthly (10–30 day) time scales. A more general “unified” analysis of many medium-range, monthly, and seasonal forecasts confirms a high degree of probabilistic forecast skill for European temperatures over the first month. The unified analysis also identifies seasonal predictability for Europe, which is not yet realized in seasonal forecasts. Interestingly, the initial atmospheric state appears to be important even for month 2 of a coupled forecast. Seasonal coupled model forecasts capture the general level of observed European deterministic predictability associated with the persistence of anomalies. A review is made of the possibilities to improve seasonal forecasts. This includes multimodel and probabilistic techniques and the potential for “windows of opportunity” where better representation of the effects of boundary conditions (e.g., sea surface temperature and soil moisture) may improve forecasts. “Perfect coupled model” potential predictability estimates are sensitive to the coupled model used and so it is not yet possible to estimate ultimate levels of seasonal predictability. The impact of forecast information on different users with different mitigation strategies (i.e., ways of coping with a weather or climate event) is investigated. The importance of using forecast information to reduce volatility as well as reducing the expected expense is highlighted. The possibility that weather forecasts can affect the cost of mitigating actions is considered. The simplified analysis leads to different conclusions about the usefulness of forecasts that could guide decisions about the development of “end-to-end” (forecast-to-user decision) systems.

scholarly journals Randomly correcting model errors in the ARPEGE-Climate v6.1 component of CNRM-CM: applications for seasonal forecasts

2016 ◽  
Vol 9 (6) ◽  
pp. 2055-2076 ◽  
Author(s):  
Lauriane Batté ◽  
Michel Déqué
Keyword(s):  
Stochastic Dynamics ◽  
Stochastic Perturbation ◽  
Coupled Model ◽  
Seasonal Forecasting ◽  
Stochastic Methods ◽  
Boreal Winter ◽  
Model Errors ◽  
Seasonal Forecasts ◽  
Error Statistics ◽  
The Impact

Abstract. Stochastic methods are increasingly used in global coupled model climate forecasting systems to account for model uncertainties. In this paper, we describe in more detail the stochastic dynamics technique introduced by Batté and Déqué (2012) in the ARPEGE-Climate atmospheric model. We present new results with an updated version of CNRM-CM using ARPEGE-Climate v6.1, and show that the technique can be used both as a means of analyzing model error statistics and accounting for model inadequacies in a seasonal forecasting framework.The perturbations are designed as corrections of model drift errors estimated from a preliminary weakly nudged re-forecast run over an extended reference period of 34 boreal winter seasons. A detailed statistical analysis of these corrections is provided, and shows that they are mainly made of intra-month variance, thereby justifying their use as in-run perturbations of the model in seasonal forecasts. However, the interannual and systematic error correction terms cannot be neglected. Time correlation of the errors is limited, but some consistency is found between the errors of up to 3 consecutive days.These findings encourage us to test several settings of the random draws of perturbations in seasonal forecast mode. Perturbations are drawn randomly but consistently for all three prognostic variables perturbed. We explore the impact of using monthly mean perturbations throughout a given forecast month in a first ensemble re-forecast (SMM, for stochastic monthly means), and test the use of 5-day sequences of perturbations in a second ensemble re-forecast (S5D, for stochastic 5-day sequences). Both experiments are compared in the light of a REF reference ensemble with initial perturbations only. Results in terms of forecast quality are contrasted depending on the region and variable of interest, but very few areas exhibit a clear degradation of forecasting skill with the introduction of stochastic dynamics. We highlight some positive impacts of the method, mainly on Northern Hemisphere extra-tropics. The 500 hPa geopotential height bias is reduced, and improvements project onto the representation of North Atlantic weather regimes. A modest impact on ensemble spread is found over most regions, which suggests that this method could be complemented by other stochastic perturbation techniques in seasonal forecasting mode.

scholarly journals The Impact of Air–Sea Interactions on the Predictability of the Tropical Intraseasonal Oscillation

2008 ◽  
Vol 21 (22) ◽  
pp. 5870-5886 ◽  
Author(s):  
Kathy Pegion ◽  
Ben P. Kirtman
Keyword(s):  
Zonal Wind ◽  
Climate Model ◽  
Intraseasonal Oscillation ◽  
Coupled Model ◽  
Longwave Radiation ◽  
Boreal Winter ◽  
Potential Predictability ◽  
Key Factor ◽  
The Impact ◽  
Tropical Intraseasonal Oscillation

Abstract This study investigates whether air–sea interactions contribute to differences in the predictability of the boreal winter tropical intraseasonal oscillation (TISO) using the NCEP operational climate model. A series of coupled and uncoupled, “perfect” model predictability experiments are performed for 10 strong model intraseasonal events. The uncoupled experiments are forced by prescribed SST containing different types of variability. These experiments are specifically designed to be directly comparable to actual forecasts. Predictability estimates are calculated using three metrics, including one that does not require the use of time filtering. The estimates are compared between these experiments to determine the impact of coupled air–sea interactions on the predictability of the tropical intraseasonal oscillation and the sensitivity of the potential predictability estimates to the different SST forcings. Results from all three metrics are surprisingly similar. They indicate that predictability estimates are longest for precipitation and outgoing longwave radiation (OLR) when the ensemble mean from the coupled model is used. Most importantly, the experiments that contain intraseasonally varying SST consistently predict the control events better than those that do not for precipitation, OLR, 200-hPa zonal wind, and 850-hPa zonal wind after the first 10 days. The uncoupled model is able to predict the TISO with similar skill to that of the coupled model, provided that an SST forecast that includes these intraseasonal variations is used to force the model. This indicates that the intraseasonally varying SSTs are a key factor for increased predictability and presumably better prediction of the TISO.

Impact of a quasi-stochastic cellular automaton backscatter scheme on the systematic error and seasonal prediction skill of a global climate model

2008 ◽  
Vol 366 (1875) ◽  
pp. 2559-2577 ◽  
Author(s):  
J Berner ◽  
F.J Doblas-Reyes ◽  
T.N Palmer ◽  
G Shutts ◽  
A Weisheimer
Keyword(s):  
Cellular Automaton ◽  
Systematic Error ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Seasonal Prediction ◽  
Global Climate Models ◽  
Seasonal Forecasts ◽  
The Impact

The impact of a nonlinear dynamic cellular automaton (CA) model, as a representation of the partially stochastic aspects of unresolved scales in global climate models, is studied in the European Centre for Medium Range Weather Forecasts coupled ocean–atmosphere model. Two separate aspects are discussed: impact on the systematic error of the model, and impact on the skill of seasonal forecasts. Significant reductions of systematic error are found both in the tropics and in the extratropics. Such reductions can be understood in terms of the inherently nonlinear nature of climate, in particular how energy injected by the CA at the near-grid scale can backscatter nonlinearly to larger scales. In addition, significant improvements in the probabilistic skill of seasonal forecasts are found in terms of a number of different variables such as temperature, precipitation and sea-level pressure. Such increases in skill can be understood both in terms of the reduction of systematic error as mentioned above, and in terms of the impact on ensemble spread of the CA's representation of inherent model uncertainty.

The GMAO High‐Resolution Coupled Model and Assimilation System for Seasonal Prediction

2021 ◽  
Author(s):  
Andrea Molod ◽  
Keyword(s):  
Coupled Model ◽  
Seasonal Prediction ◽  
Earth System ◽  
Aerosol Model ◽  
Earth Observing System ◽  
The North ◽  
The Earth ◽  
Weakly Coupled ◽  
The North Atlantic Oscillation ◽  
The Impact

<p>The Global Modeling and Assimilation Office (GMAO) is about to release a new version of the Goddard Earth Observing System (GEOS) Subseasonal to Seasonal prediction (S2S) system, GEOS‐S2S‐3, that represents an improvement in performance and infrastructure over the  previous system, GEOS-S2S-2. The system will be described briefly, highlighting some features unique to GEOS-S2S, such as the coupled interactive aerosol model and ensemble  perturbation strategy and size. Results are presented from forecasts and from climate  equillibrium simulations. GEOS-S2S-3 will be used to produce a long term weakly coupled reanalysis called MERRA-2 Ocean.</p><p>The climate or equillibrium state of the atmosphere and ocean shows a reduction in systematic error relative to GEOS‐S2S‐2, attributed in part to an increase in ocean resolution and to the upgrade in the glacier runoff scheme.  The forecast skill shows improved prediction  of the North Atlantic Oscillation, attributed to the increase in forecast ensemble members.  </p><p>With the release of GEOS-S2S-3 and MERRA-2 Ocean, GMAO will continue its tradition of maintaining a state‐of‐the‐art seasonal prediction system for use in evaluating the impact on seasonal and decadal forecasts of assimilating newly available satellite observations, as well as evaluating additional sources of predictability in the Earth system through the expanded coupling of the Earth system model and assimilation components.</p>

scholarly journals Seasonal Forecasting in the Pacific Using the Coupled Model POAMA-2

2013 ◽  
Vol 28 (3) ◽  
pp. 668-680 ◽  
Author(s):  
Andrew Cottrill ◽  
Harry H. Hendon ◽  
Eun-Pa Lim ◽  
Sally Langford ◽  
Kay Shelton ◽  
...  
Keyword(s):  
El Niño ◽  
El Nino ◽  
Regional Climate ◽  
Coupled Model ◽  
Forecast Model ◽  
Seasonal Prediction ◽  
La Niña ◽  
Spatial And Temporal Variability ◽  
Seasonal Forecasts ◽  
La Nina

Abstract The development of a dynamical model seasonal prediction service for island nations in the tropical South Pacific is described. The forecast model is the Australian Bureau of Meteorology's Predictive Ocean–Atmosphere Model for Australia (POAMA), a dynamical seasonal forecast system. Using a hindcast set for the period 1982–2006, POAMA is shown to provide skillful forecasts of El Niño and La Niña many months in advance and, because the model faithfully simulates the spatial and temporal variability of rainfall associated with displacements of the southern Pacific convergence zone (SPCZ) and ITCZ during La Niña and El Niño, it also provides good predictions of rainfall throughout the tropical Pacific region. The availability of seasonal forecasts from POAMA should be beneficial to Pacific island countries for the production of regional climate outlooks across the region.

scholarly journals The Impact of a Low Bias in Snow Water Equivalent Initialization on CFS Seasonal Forecasts

2017 ◽  
Vol 30 (21) ◽  
pp. 8657-8671 ◽  
Author(s):  
Patrick D. Broxton ◽  
Xubin Zeng ◽  
Nicholas Dawson
Keyword(s):  
Lead Time ◽  
Snow Water Equivalent ◽  
Seasonal Prediction ◽  
Seasonal Forecasts ◽  
Near Surface ◽  
Air Temperatures ◽  
Forecast Lead Time ◽  
Snow Water ◽  
The Impact

Across much of the Northern Hemisphere, Climate Forecast System forecasts made earlier in the winter (e.g., on 1 January) are found to have more snow water equivalent (SWE) in April–June than forecasts made later (e.g., on 1 April); furthermore, later forecasts tend to predict earlier snowmelt than earlier forecasts. As a result, other forecasted model quantities (e.g., soil moisture in April–June) show systematic differences dependent on the forecast lead time. Notably, earlier forecasts predict much colder near-surface air temperatures in April–June than later forecasts. Although the later forecasts of temperature are more accurate, earlier forecasts of SWE are more realistic, suggesting that the improvement in temperature forecasts occurs for the wrong reasons. Thus, this study highlights the need to improve atmospheric processes in the model (e.g., radiative transfer, turbulence) that would cause cold biases when a more realistic amount of snow is on the ground. Furthermore, SWE differences in earlier versus later forecasts are found to much more strongly affect April–June temperature forecasts than the sea surface temperature differences over different regions, suggesting the major role of snowpack in seasonal prediction during the spring–summer transition over snowy regions.

scholarly journals Impact of Atmosphere and Land Surface Initial Conditions on Seasonal Forecasts of Global Surface Temperature

2014 ◽  
Vol 27 (24) ◽  
pp. 9253-9271 ◽  
Author(s):  
Stefano Materia ◽  
Andrea Borrelli ◽  
Alessio Bellucci ◽  
Andrea Alessandri ◽  
Pierluigi Di Pietro ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Initial Conditions ◽  
Seasonal Prediction ◽  
Forecast Skill ◽  
Seasonal Forecasts ◽  
Initial State ◽  
Predictive Skill ◽  
The Impact ◽  
Initialization Strategy

Abstract The impact of land surface and atmosphere initialization on the forecast skill of a seasonal prediction system is investigated, and an effort to disentangle the role played by the individual components to the global predictability is done, via a hierarchy of seasonal forecast experiments performed under different initialization strategies. A realistic atmospheric initial state allows an improved equilibrium between the ocean and overlying atmosphere, increasing the model predictive skill in the ocean. In fact, in regions characterized by strong air–sea coupling, the atmosphere initial condition affects forecast skill for several months. In particular, the ENSO region, eastern tropical Atlantic, and North Pacific benefit significantly from the atmosphere initialization. On the mainland, the effect of atmospheric initial conditions is detected in the early phase of the forecast, while the quality of land surface initialization affects forecast skill in the following seasons. Winter forecasts in the high-latitude plains benefit from the snow initialization, while the impact of soil moisture initial state is particularly effective in the Mediterranean region and central Asia. However, the initialization strategy based on the full value technique may not be the best choice for land surface, since soil moisture is a strongly model-dependent variable: in fact, initialization through land surface reanalysis does not systematically guarantee a skill improvement. Nonetheless, using a different initialization strategy for land, as opposed to atmosphere and ocean, may generate inconsistencies. Overall, the introduction of a realistic initialization for land and atmosphere substantially increases skill and accuracy. However, further developments in the procedure for land surface initialization are required for more accurate seasonal forecasts.

scholarly journals Evaluation of Coupled Model Forecasts of Ethiopian Highlands Summer Climate

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Mark R. Jury
Keyword(s):  
Crop Yields ◽  
Coupled Model ◽  
Maximum Temperature ◽  
Seasonal Forecasts ◽  
Early Season ◽  
Ethiopian Highlands ◽  
Climate Forecasting ◽  
Medium Range Forecast ◽  
Medium Range ◽  
Better Than

This study evaluates seasonal forecasts of rainfall and maximum temperature across the Ethiopian highlands from coupled ensemble models in the period 1981–2006, by comparison with gridded observational products (NMA + GPCC/CRU3). Early season forecasts from the coupled forecast system (CFS) are steadier than European community medium range forecast (ECMWF). CFS and ECMWF April forecasts of June–August (JJA) rainfall achieve significant fit (r2=0.27, 0.25, resp.), but ECMWF forecasts tend to have a narrow range with drought underpredicted. Early season forecasts of JJA maximum temperature are weak in both models; hence ability to predict water resource gains may be better than losses. One aim of seasonal climate forecasting is to ensure that crop yields keep pace with Ethiopia’s growing population. Farmers using prediction technology are better informed to avoid risk in dry years and generate surplus in wet years.

scholarly journals Predictability and Prediction of Southern California Rains during Strong El Niño Events: A Focus on the Failed 2016 Winter Rains

2018 ◽  
Vol 31 (2) ◽  
pp. 555-574 ◽  
Author(s):  
Tao Zhang ◽  
Martin P. Hoerling ◽  
Klaus Wolter ◽  
Jon Eischeid ◽  
Linyin Cheng ◽  
...  
Keyword(s):  
El Niño ◽  
Southern California ◽  
El Nino ◽  
Coupled Model ◽  
Atmospheric Model ◽  
Seasonal Forecasts ◽  
Potential Predictability ◽  
Model Simulations ◽  
El Niño Events ◽  
El Nino Events

The failed Southern California (SCAL) winter rains during the 2015/16 strong El Niño came as a surprise and a disappointment. Similarities were drawn to very wet winters during several historical strong El Niño events, leading to heightened expectations that SCAL’s multiyear drought would abate in 2016. Ensembles of atmospheric model simulations and coupled model seasonal forecasts are diagnosed to determine both the potential predictability and actual prediction skill of the failed rains, with a focus on understanding the striking contrast of SCAL precipitation between the 2016 and 1998 strong El Niño events. The ensemble mean of simulations indicates that the December–February 2016 winter dryness was not a response to global boundary forcings, which instead generated a wet SCAL signal. Nor was the extreme magnitude of observed 1998 wetness entirely reconcilable with a boundary-forced signal, indicating it was not a particularly precise analog for 2016. Furthermore, model simulations indicate the SCAL 2016 wet signal was 20%–50% less intense than its simulated 1998 counterpart. Such a weaker signal was captured in November 2015 initialized seasonal forecasts, indicating dynamical model skill in predicting a less prolific 2016 rainy season and a capability to forewarn that 2016 would not likely experience the flooding rains of 1998. Analysis of ensemble spread indicates that 2016 dryness was an extreme climate event having less than 5% likelihood in the presence of 2016 global forcings, even though its probability of occurrence was 3–4 times greater in 2016 compared to 1998. Therefore, the failed seasonal rains themselves are argued to be primarily a symptom of subseasonal variability unrelated to boundary forcings whose predictability remains to be explored.

scholarly journals The Subseasonal to Seasonal (S2S) Prediction Project Database

2017 ◽  
Vol 98 (1) ◽  
pp. 163-173 ◽  
Author(s):  
F. Vitart ◽  
C. Ardilouze ◽  
A. Bonet ◽  
A. Brookshaw ◽  
M. Chen ◽  
...  
Keyword(s):  
Model Simulation ◽  
Research Programme ◽  
Time Range ◽  
Madden Julian Oscillation ◽  
Seasonal Forecasts ◽  
Seasonal Predictability ◽  
Atlantic Sector ◽  
Medium Range ◽  
Long Time ◽  
Preliminary Study

Abstract Demands are growing rapidly in the operational prediction and applications communities for forecasts that fill the gap between medium-range weather and long-range or seasonal forecasts. Based on the potential for improved forecast skill at the subseasonal to seasonal time range, the Subseasonal to Seasonal (S2S) Prediction research project has been established by the World Weather Research Programme/World Climate Research Programme. A main deliverable of this project is the establishment of an extensive database containing subseasonal (up to 60 days) forecasts, 3 weeks behind real time, and reforecasts from 11 operational centers, modeled in part on the The Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble (TIGGE) database for medium-range forecasts (up to 15 days). The S2S database, available to the research community since May 2015, represents an important tool to advance our understanding of the subseasonal to seasonal time range that has been considered for a long time as a “desert of predictability.” In particular, this database will help identify common successes and shortcomings in the model simulation and prediction of sources of subseasonal to seasonal predictability. For instance, a preliminary study suggests that the S2S models significantly underestimate the amplitude of the Madden–Julian oscillation (MJO) teleconnections over the Euro-Atlantic sector. The S2S database also represents an important tool for case studies of extreme events. For instance, a multimodel combination of S2S models displays higher probability of a landfall over the islands of Vanuatu 2–3 weeks before Tropical Cyclone Pam devastated the islands in March 2015.

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